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• Published: 20 July 2020

Diet and exercise in the prevention and treatment of type 2 diabetes mellitus

• Faidon Magkos   ORCID: orcid.org/0000-0002-1312-7364 1 ,
• Mads F. Hjorth   ORCID: orcid.org/0000-0001-9440-2737 1 &
• Arne Astrup   ORCID: orcid.org/0000-0001-8968-8996 1  

Nature Reviews Endocrinology volume  16 ,  pages 545–555 ( 2020 ) Cite this article

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• Type 2 diabetes

Evidence from observational studies and randomized trials suggests that prediabetes and type 2 diabetes mellitus (T2DM) can develop in genetically susceptible individuals in parallel with weight (that is, fat) gain. Accordingly, studies show that weight loss can produce remission of T2DM in a dose-dependent manner. A weight loss of ~15 kg, achieved by calorie restriction as part of an intensive management programme, can lead to remission of T2DM in ~80% of patients with obesity and T2DM. However, long-term weight loss maintenance is challenging. Obesity and T2DM are associated with diminished glucose uptake in the brain that impairs the satiating effect of dietary carbohydrate; therefore, carbohydrate restriction might help maintain weight loss and maximize metabolic benefits. Likewise, increases in physical activity and fitness are an important contributor to T2DM remission when combined with calorie restriction and weight loss. Preliminary studies suggest that a precision dietary management approach that uses pretreatment glycaemic status to stratify patients can help optimize dietary recommendations with respect to carbohydrate, fat and dietary fibre. This approach might lead to improved weight loss maintenance and glycaemic control. Future research should focus on better understanding the individual response to dietary treatment and translating these findings into clinical practice.

Studies show that weight loss can produce remission of type 2 diabetes mellitus (T2DM) in a dose-dependent manner.

In patients with T2DM and obesity, weight loss of ~15 kg, achieved by an intensive management programme involving calorie restriction, can lead to remission of T2DM in ~80% of individuals.

Long-term maintenance of weight loss and metabolic health in people who have undergone intensive lifestyle intervention is challenging.

Carbohydrate restriction might help maintain weight loss and maximize metabolic benefits.

When combined with calorie restriction and weight loss, increases in physical activity and fitness are an important contributor to T2DM remission.

Preliminary work suggests that pretreatment glycaemic status could be used to stratify patients in order to optimize dietary recommendations.

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M.F.H. and A.A. are co-inventors on a pending provisional patent application on the use of biomarkers for prediction of weight loss responses and co-founders/owners of the University of Copenhagen spin-out company Personalized Weight Management Research Consortium ApS (Gluco-diet.dk). A.A. is a consultant or advisory board member for Basic Research, USA, Beachbody, USA, BioCare Copenhagen, Denmark, Gelesis, USA, Groupe Éthique et Santé, France, McCain Foods Limited, USA, Nestlé Research Center, Switzerland, and Weight Watchers, USA. A.A. and M.F.H. are co-authors of a number of diet/cookery books, including personalized nutrition for weight loss, published in several languages. F.M. declares no competing interests.

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An intermediate condition between normoglycaemia and type 2 diabetes mellitus, characterized by moderately elevated fasting or postprandial blood glucose or HbA 1c .

A relative ranking of foods according to their ability to increase blood glucose levels relative to a reference food (glucose or white bread) for the same amount of bioavailable carbohydrate.

An extension of the glycaemic index that takes into account the actual amount of available carbohydrate present in one serving of a food or in the whole diet.

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Magkos, F., Hjorth, M.F. & Astrup, A. Diet and exercise in the prevention and treatment of type 2 diabetes mellitus. Nat Rev Endocrinol 16 , 545–555 (2020). https://doi.org/10.1038/s41574-020-0381-5

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DOI : https://doi.org/10.1038/s41574-020-0381-5

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• Role of diet in type 2...

Role of diet in type 2 diabetes incidence: umbrella review of meta-analyses of prospective observational studies

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• Peer review
• Manuela Neuenschwander , doctoral researcher 1 ,
• Aurélie Ballon , masters student 1 ,
• Katharina S Weber , postdoctoral researcher 2 3 ,
• Teresa Norat , principal research fellow 5 ,
• Dagfinn Aune , postdoctoral researcher and associate professor 4 5 6 ,
• Lukas Schwingshackl , postdoctoral researcher 7 8 ,
• Sabrina Schlesinger , senior research associate 1 3
• 1 Institute for Biometrics and Epidemiology, German Diabetes Centre, Leibniz Centre for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, D-40225 Düsseldorf, Germany
• 2 Institute for Clinical Diabetology, German Diabetes Centre, Leibniz Centre for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
• 3 German Centre for Diabetes Research, München-Neuherberg, Germany
• 4 Department of Epidemiology and Biostatistics, Imperial College London, London, UK
• 5 Department of Nutrition, Bjørknes University College, Oslo, Norway
• 6 Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
• 7 Institute for Evidence in Medicine, Faculty of Medicine and Medical Centre-University of Freiburg, Freiburg, Germany
• 8 Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
• Correspondence to: S Schlesinger sabrina.schlesinger{at}ddz.de
• Accepted 8 May 2019

Objective To summarise the evidence of associations between dietary factors and incidence of type 2 diabetes and to evaluate the strength and validity of these associations.

Design Umbrella review of systematic reviews with meta-analyses of prospective observational studies.

Data sources PubMed, Web of Science, and Embase, searched up to August 2018.

Eligibility criteria Systematic reviews with meta-analyses reporting summary risk estimates for the associations between incidence of type 2 diabetes and dietary behaviours or diet quality indices, food groups, foods, beverages, alcoholic beverages, macronutrients, and micronutrients.

Results 53 publications were included, with 153 adjusted summary hazard ratios on dietary behaviours or diet quality indices (n=12), food groups and foods (n=56), beverages (n=10), alcoholic beverages (n=12), macronutrients (n=32), and micronutrients (n=31), regarding incidence of type 2 diabetes. Methodological quality was high for 75% (n=115) of meta-analyses, moderate for 23% (n=35), and low for 2% (n=3). Quality of evidence was rated high for an inverse association for type 2 diabetes incidence with increased intake of whole grains (for an increment of 30 g/day, adjusted summary hazard ratio 0.87 (95% confidence interval 0.82 to 0.93)) and cereal fibre (for an increment of 10 g/day, 0.75 (0.65 to 0.86)), as well as for moderate intake of total alcohol (for an intake of 12-24 g/day v no consumption, 0.75 (0.67 to 0.83)). Quality of evidence was also high for the association for increased incidence of type 2 diabetes with higher intake of red meat (for an increment of 100 g/day, 1.17 (1.08 to 1.26)), processed meat (for an increment of 50 g/day, 1.37 (1.22 to 1.54)), bacon (per two slices/day, 2.07 (1.40 to 3.05)), and sugar sweetened beverages (for an increase of one serving/day, 1.26 (1.11 to 1.43)).

Conclusions Overall, the association between dietary factors and type 2 diabetes has been extensively studied, but few of the associations were graded as high quality of evidence. Further factors are likely to be important in type 2 diabetes prevention; thus, more well conducted research, with more detailed assessment of diet, is needed.

Systematic review registration PROSPERO CRD42018088106.

Introduction

Diabetes mellitus is a global health problem, with a prevalence of 8.8%. Both the incidence and prevalence of the disorder are projected to rise. An estimated 425 million adults are living with diabetes mellitus worldwide. 1 Patients with diabetes mellitus are at increased risk for many other health problems, which are associated with high healthcare costs. 2 3 According to the International Diabetes Federation, associated healthcare costs in 2017 were an estimated US$727bn (£574bn; €652bn) worldwide, which is an 8% rise compared with 2015. 1 Thus, the prevention and management of this disease is of major importance to public health interest. Type 2 diabetes is the most common type of diabetes mellitus and accounts for 90% of all cases of diabetes. 1 Although unmodifiable factors such as family history and age partly have a role in the causal pathway of type 2 diabetes, modifiable factors such as lifestyle factors (including diet) contribute to the onset of the disorder as well. 4 Changes in these lifestyle factors could reduce the risk of type 2 diabetes and influence the progression of this disease. 5 6 7

A large body of research is available on the association between dietary factors and the incidence of type 2 diabetes. In the past few decades, many published systematic reviews and meta-analyses have summarised evidence on the associations between dietary behaviours or diet quality indices, food groups, single foods and beverages, alcohol, specific macronutrients and micronutrients, and the incidence of type 2 diabetes. These findings could be of importance for the prevention of the disease. However, the strength, precision, and influence of potential bias regarding these associations need to be clarified.

Umbrella reviews are useful tools that provide a comprehensive overview of evidence of published systematic reviews and meta-analyses on a specific topic. They can elucidate the strength of evidence and the precision of the estimates, and evaluate risk of bias of the published reports. 8 Recent reports summarised evidence for selected dietary factors regarding prevention of type 2 diabetes. 9 10 11 Strong evidence was observed for a decreased incidence of type 2 diabetes with higher consumption of whole grains 10 11 and higher adherence to a healthy dietary pattern, 10 as well as an increased incidence of the disease for a higher intake of total red meat, 11 processed meat, 10 11 and sugar sweetened beverages. 10 11 Micha and colleagues summarised findings with probable or convincing evidence and found a higher incidence of type 2 diabetes with a low intake of whole grain, yogurt, nuts or seeds, and dietary fibre as well as with high consumption of unprocessed red meat, processed meat, foods with a high glycaemic load, and sugar sweetened beverages. 9 However, none of these studies focused on any existing evidence between dietary factors (such as dietary behaviours or diet quality indices, food groups, foods and beverages, alcoholic beverages, macronutrients, and micronutrients) and incidence of type 2 diabetes. Furthermore, the methodological quality of the meta-analyses and quality of evidence remain to be assessed by validated tools. Thus, this study aimed to conduct an umbrella review of meta-analyses to gain a systematic, comprehensive overview of the existing evidence of prospective observational studies on dietary factors (including those mentioned above) and incidence of type 2 diabetes in adults and to assess its strength and validity.

Our protocol has been registered in PROSPERO (CRD42018088106). The systematic literature search was conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. 12

Literature search

The systematic literature search was conducted in PubMed, Web of Science, and Embase until August 2018 for meta-analyses of observational studies investigating the association between diet and type 2 diabetes, using a predefined search strategy (supplementary table 1). We did not apply any restrictions or filters. We also screened the reference lists of relevant reviews and meta-analyses. The literature search was conducted by two authors (MN, SS). Disagreements were resolved by consensus.

Selection of meta-analyses

Studies were included if they met the following criteria: (1) included meta-analysis of observational prospective cohort studies in adults with multivariable adjusted summary risk estimates and corresponding 95% confidence intervals, (2) considered the incidence of type 2 diabetes as the outcome, (3) investigated the association of different dietary factors assessed by established dietary assessment instruments (eg, food frequency questionnaires, diet history, 24 hour dietary recalls, and dietary records) with incidence of type 2 diabetes. Eligible dietary factors included:

Dietary behaviours or diet quality indices, including dietary patterns as combinations of nutrients, foods, and beverages. Examples are breakfast skipping for dietary behaviours; glycaemic index, glycaemic load, or potential renal acid load for dietary quality indices; the Healthy Eating Index (HEI), Dietary Approaches to Stop Hypertension (DASH), Mediterranean diet, or vegetarian diet for a priori dietary patterns; and the application of principal component analysis, factor analysis, or reduced rank regression for exploratory-derived dietary patterns

Food groups, foods, and beverages, including dairy products, eggs, meat, fish, fats (eg, butter) and oils, potatoes, whole grain, grains, cereals, rice, legumes, nuts, vegetables, fruit, tea, coffee, sugar sweetened beverages, and alcoholic beverages

Macronutrients (carbohydrates, fats, protein), micronutrients (vitamins, minerals), fibre, and polyphenols.

Studies were excluded if they were primary studies, if no summary estimate was reported (eg, systematic reviews without meta-analysis), if they were pooled analyses of cohorts with individual patient data, or if the meta-analyses considered type 1 diabetes or gestational diabetes as outcome. We also excluded publications reporting on exposure of plasma levels or biomarkers rather than dietary intake. If more than one published meta-analysis on the same association was identified, we chose only one meta-analysis for each exposure to avoid the inclusion of duplicate studies. In that case, we included the one with the largest number of primary studies. If more than one published meta-analysis included the same number of studies, the one with the largest number of people with type 2 diabetes was chosen. If more than one published meta-analysis fulfilled both criteria, the one with more available information (eg, dose-response meta-analysis) was selected.

Data extraction

Data was extracted by one author (MN) and double-checked by a second author (AB). For each published meta-analysis, we extracted the following data: name of the first author, publication year, exposure (including dose of exposure), number of included studies, study design of the primary studies, total number of cases and participants, type of comparison (high v low meta-analysis or dose-response meta-analysis), quality score of primary studies (mean) if reported, publication bias, information on funding, and conflict of interest.

For each primary study included in the published meta-analysis, we extracted the first author’s name, year of publication, exposure (including dose of exposure), number of total cases, number of participants, and hazard ratios that adjusted for the most confounders, along with their 95% confidence intervals, as well as adjustment factors included in the model to check if relevant confounders were accounted for. Based on the literature, the most important potential confounders in the investigation between dietary factors and incidence of type 2 diabetes include age, sex, smoking, physical activity, overweight, and other dietary factors, including total energy intake, alcohol intake, and family history of diabetes.

Assessment of methodological quality

The methodological quality of each included published meta-analysis was evaluated by the validated AMSTAR tool (a measurement tool to assess the methodological quality of systematic reviews). 13 14 15 It includes 11 items about the conduct of a meta-analysis, including the literature search, study selection and data extraction, reporting of included and excluded studies, quality assessment of the included studies, statistical methods for the meta-analysis, publication bias, and conflict of interest. Each question can be answered with “yes,” “no,” “can’t answer,” and “not applicable.” A “yes” scores one point, whereas the other answers score 0 points. 15 An overall score of at least 8 points was defined as the cutoff value for high quality, 4-7 points as moderate quality, and 3 points or less as low quality. 16

Evaluation of quality of evidence

The quality of evidence was evaluated by using a modified version of NutriGrade 17 (modifications described in supplementary table 5). It is a numerical scoring system (maximum 10 points), which includes eight items:

Risk of bias, study quality, or study limitations (mean of all primary studies included in the published meta-analysis; 0-2 points)

Precision of the estimate (0-1 points)

Heterogeneity (0-1 point)

Directness (eg, whether there were differences in the study populations or interventions; 0-1 point)

Publication bias (0-1 point)

Funding bias (0-1 point)

Effect size (0-2 points)

Dose-response association (0-1 point).

An overall score of at least 8 points was assigned to high quality of evidence, which means that there is high confidence in the effect estimate and that further research probably will not change that confidence in the effect estimate. An overall score of 6 to <8 points was assigned to moderate confidence in the effect estimate, where further research could add evidence on the confidence and could change the effect estimate. An overall score of 4 to <6 points meant that there was low confidence in the effect estimate, and that further research would provide important evidence on the confidence and would likely change the effect estimate. Scores less than 4 points was assigned to very low quality of evidence, which meant that the quality of evidence was very limited and uncertain. 17 Therefore, the quality of evidence for an association could be high despite possible shortcomings (eg, high heterogeneity and indication for publication bias) if the meta-analysis scores the maximum amount of points for the other items.

Data analysis

Assessment of summary effects.

For each exposure, we recalculated the chosen meta-analysis using hazard ratios of the primary studies included in the published meta-analyses that adjusted for the most confounders. We recalculated the adjusted summary hazard ratios and corresponding 95% confidence intervals by using the random effects model by DerSimonian and Laird, which takes into account heterogeneity both within and between studies. 18 We used this approach to ensure that all adjusted summary hazard ratios were calculated by a random effects model and to receive further information for the evaluation of the quality of evidence (including τ 2 , 95% prediction intervals, I 2 , and publication bias). Since this method has been used in previous meta-analyses, we chose this approach to ensure comparability with the published meta-analyses.

When the published meta-analysis presented hazard ratios from the same cohort separately by sex or race, we first combined the hazard ratios per cohort using fixed effect methods, before conducting the overall meta-analysis. If the published meta-analysis included retrospective case-control studies or cross sectional studies as well as prospective cohort studies, we only included results from the prospective cohort studies in our meta-analysis. If the published meta-analysis included a primary study only reporting unadjusted estimates, this study was excluded from the reanalysis. We recalculated dose-response meta-analyses if the dose-response estimate for each primary study was presented separately. If this information was missing, we could not recalculate the dose-response meta-analysis, but extracted the adjusted summary hazard ratios from the published meta-analysis. If the 95% confidence interval of an adjusted summary hazard ratio included the null value and the quality of evidence was moderate, low, or very low, this was interpreted as no clear association. If the quality of evidence was high, the adjusted summary hazard ratio of 1.0 (or close to 1.0, respectively), and the 95% confidence interval of the adjusted summary hazard ratios was very narrow and included the null value, we assumed that an association was unlikely. All calculations were conducted with Stata 14.1.

Assessment of heterogeneity

In each meta-analysis, we evaluated heterogeneity by using the I 2 statistic. The I 2 value ranges from 0% to 100% and represents the percentage of the total variation across studies that can be explained by heterogeneity. 19 However, I 2 is dependent on the study size (it increases with increasing study size). Therefore, we also calculated τ 2 , which is independent of study size and describes variability between studies, in relation to the risk estimates. 20 Finally, we calculated 95% prediction intervals, which also account for heterogeneity and show the range in which the underlying true effect size of future studies will lie with 95% certainty. 20

Assessment of publication bias and small study effect

Publication bias and small study effects were assessed for each meta-analysis by graphical and statistical tests, namely the funnel plot and Egger’s test. 21 22 Therefore, the primary studies from the meta-analyses included in our umbrella review, were plotted. A P value less than 0.10 was taken as statistical evidence of the presence of small study effects (potential publication bias). 22

Patient and public involvement

This research was done without patient involvement. Patients were not invited to comment on the study design and were not consulted to develop patient relevant outcomes or interpret the results. Patients were not invited to contribute to the writing or editing of this document for readability or accuracy. The results of this study will be disseminated to the public through a press release, published at the website of the German Diabetes Centre, the web based information platform of the National Diabetes Information Centre ( https://diabetesinformationsdienst.de ) and our partners, including the German Centre for Diabetes Research (Deutsches Zentrum für Diabetesforschung: DZD) and the Leibniz Association. In addition, findings will be spread via social media.

Of the 11 413 publications initially identified, we finally selected 53 published meta-analyses including 153 adjusted summary hazard ratios (supplementary figure 1) on dietary behaviours or diet quality indices (n=12), food groups and foods (n=56), beverages (n=10), alcoholic beverages (n=12), macronutrients (n=32), and micronutrients (n=31), regarding incidence of type 2 diabetes. These 153 adjusted summary hazard ratios correspond to one meta-analysis per exposure. If a high versus low meta-analysis as well as a dose-response meta-analysis was available for one exposure, we presented the dose-response meta-analysis. A list of excluded studies can also be found in supplementary table 2.

We found meta-analyses on the following exposures: healthy dietary pattern, 23 unhealthy dietary pattern, 24 HEI, 25 alternative HEI (AHEI), 25 DASH, 25 Mediterranean diet, 26 vegetarian diet, 27 low carbohydrate diet, 28 breakfast skipping, 29 high glycaemic index, 30 high glycaemic load, 30 dietary acid load, 31 dairy 11 and dairy products, 32 33 34 chocolate, 35 eggs, 11 meat 36 and specific types of meat, 36 37 38 39 total fish or seafood 11 40 and types of fish, 40 butter, 41 olive oil, 42 potatoes and types of potatoes, 43 whole grain 11 and whole grain products, 44 refined grain, 11 rice, 45 white rice, 44 brown rice, 44 soy products, 46 legumes, 11 nuts, 11 fruit and vegetables, 47 total fruit 11 and specific types of fruit, 48 49 50 total vegetables 11 and specific types of vegetables, 51 52 tea, 53 coffee, 54 55 sugar sweetened beverages, 56 artificially sweetened beverages, 56 total fruit juice 56 and types of fruit juice, 57 total protein and types of protein, 58 animal protein-to-potassium ratio, 31 total fat, 59 types of fat 59 and fatty acids, 59 60 61 62 dietary cholesterol, 63 carbohydrates 59 and types of carbohydrates, 59 64 total fibre 65 and types of fibre, 65 vitamin D, 66 niacin, 67 iron, 68 magnesium, 69 calcium, 70 selenium, 71 polyphenols and subgroups of polyphenols 72 and antioxidants, 73 as well as total alcohol, 74 wine, beer, and spirits. 75

Description of published meta-analyses

For most exposures, we identified more than one published meta-analysis on the same topic in our search. These published meta-analyses were in agreement regarding the direction and magnitude of the adjusted summary hazard ratios (data not shown, but available on request), because they usually included the same primary studies with an update of one or two additional primary studies. Exceptions were two published meta-analyses on egg intake and incidence of type 2 diabetes from 2013, which found a moderate association for an increased incidence of the disease in a high versus low meta-analysis and for an increase of four eggs/week, 76 77 by contrast with more recently published meta-analyses that did not find an association. 11 78 79 All published meta-analyses included primary studies from the United States, Europe, and Asia or Australia. All included primary studies (n=277) conducted multivariable adjustment with 22% (n=62) using multivariable logistic regression and 78% (n=215) using the Cox proportional hazard regression model. In 49% (n=106) of these studies, the proportional hazard assumption was evaluated and no violations were observed, while 51% (n=109) of the studies did not provide any information about the evaluation of the assumption.

Almost all of the primary studies (90%) adjusted for age (n=254) and sex (n=249), 88% (n=243) for smoking, 86% for body mass index (n=239) and for physical activity (n=238), 67% (n=184) for total energy intake, 65% (n=181) for alcohol intake, 60% (n=167) for other dietary factors and cardiovascular risk factors (eg, hypertension), and 52% (n=143) for family history of diabetes. Three primary studies only reported unadjusted estimates and were therefore excluded from the meta-analyses on milk, 80 total coffee, 80 and total alcohol, 81 82 which did not affect the results. Information on linearity of the dose-response relations (eg, P for non-linearity) were available for 72% (n=67) of the dose-response meta-analyses (n=93). A third of these dose-response relations indicated non-linearity (potential renal acid load, yogurt, ice cream, chocolate, processed meat, olive oil, whole grain, total grains, whole grain bread, whole grain cereals, wheat bran, brown rice, total fruit, apples and pears, total vegetables, cereal fibre, fruit fibre, vegetable fibre, magnesium, and anthocyanins).

Methodological quality

Overall scores of AMSTAR for each published meta-analysis are shown in supplementary table 3, with the single items summarised in supplementary table 4. The conduct of the meta-analyses was rated as high (≥8 points) for 75% (n=115) of the published meta-analyses, moderate (4-7 points) for 23% (n=35), and low (0-3 points) for 2% (n=3). 36 In the report with the low methodological quality, the methods section was completely missing and thus the methodological quality was rated as low. 36 In general, main flaws were that grey literature was not accounted for in the literature search, no list of excluded studies was provided, study quality was not assessed or the influence of the quality of the individual studies on the results was not discussed, and publication bias was not assessed.

Associations and quality of evidence between dietary factors and incidence of type 2 diabetes

Adjusted summary hazard ratios and the quality of evidence for each exposure are summarised in figure 1 , figure 2 , figure 3 , figure 4 , figure 5 , and figure 6 and are reported in supplementary table 3. The grading of every item of NutriGrade is shown in supplementary table 5. In total, the evidence was graded as high for 5% (n=7) of the associations. Moderate, low, and very low quality of evidence was found for 22% (n=33), 60% (n=92), and 14% (n=21) of the associations, respectively.

Adjusted summary hazard ratios (SHR) with 95% confidence intervals and quality of evidence for association between dietary behaviours or diet quality indices and incidence of type 2 diabetes. Data are based on results from 53 published meta-analyses selected for umbrella review. AHEI=alternative healthy eating index; DASH=dietary approach to stop hypertension; HEI=healthy eating index; NA=not available. Cases refer to individuals with type 2 diabetes. *Includes Mediterranean diet, DASH, AHEI, and healthy dietary patterns derived from principal component analysis. †Summary hazard ratio extracted from published meta-analysis, no reanalysis possible

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Adjusted summary hazard ratios (SHR) with 95% confidence intervals and quality of evidence for association between food groups and foods and incidence of type 2 diabetes. Data are based on results from 53 published meta-analyses selected for umbrella review. S=serving; NA=not available. *Summary hazard ratio extracted from published meta-analysis, no reanalysis possible

Adjusted summary hazard ratios (SHR) with 95% confidence intervals and quality of evidence for association between beverages and incidence of type 2 diabetes. Data are based on results from 53 published meta-analyses selected for umbrella review. *Summary hazard ratio extracted from published meta-analysis, no re-analysis possible. †Total fruit juice=fruit juices with added sugar and without added sugar; 100% fruit juice=fruit juice without added sugar; sugar sweetened fruit juice=fruit juice with added sugar; fruit juice, not specified=type of fruit juice (with or without added sugar) was not specified in these studies; s=serving; G=group (based on the range divided by quintiles)

Adjusted summary hazard ratios (SHR) with 95% confidence intervals and quality of evidence for association between total alcohol and alcoholic beverages and incidence of type 2 diabetes. Data are based on results from 53 published meta-analyses selected for umbrella review

Adjusted summary hazard ratios (SHR) with 95% confidence intervals and quality of evidence for association between macronutrients and incidence of type 2 diabetes. Data are based on results from 53 published meta-analyses selected for umbrella review. *Summary hazard ratio extracted from published meta-analysis, no re-analysis possible. EPA=eicosapentaenoic acid, DHA=docosahexaenoic acid, G=group (based on the range divided by quintiles); NA=not available

Adjusted summary hazard ratios (SHR) with 95% confidence intervals and quality of evidence for association between micronutrients and incidence of type 2 diabetes. Data are based on results from 53 published meta-analyses selected for umbrella review. *Summary hazard ratio extracted from published meta-analysis, no reanalysis possible. IU=international units; NA=not available

Dietary behaviours or diet quality indices

Figure 1 shows the adjusted summary hazard ratios with their corresponding 95% confidence intervals and the quality of evidence for the associations between dietary behaviours or diet quality indices and the incidence of type 2 diabetes. No association was rated with high quality of evidence. Higher versus lower adherence to a healthy dietary pattern and the AHEI was associated with a decreased incidence of type 2 diabetes with moderate quality of evidence. The associations between diets with a high potential renal acid load and high glycaemic load, as well as an unhealthy dietary pattern, which was derived from reduced rank regression, and increased incidence of type 2 diabetes were also rated with moderate quality of evidence. The quality of evidence for the associations between the other dietary patterns (HEI, DASH, Mediterranean diet, vegetarian diet, consumption of foods with a high glycaemic index, breakfast skipping, and a low carbohydrate diet) and incidence of type 2 diabetes were rated as low to very low.

Food groups, foods, and beverages

Figure 2 shows the adjusted summary hazard ratios with their corresponding 95% confidence intervals and the quality of evidence for the associations between food groups and foods and the incidence of type 2 diabetes. High quality of evidence was found for an inverse association of type 2 diabetes, whereby an increased intake of whole grain was associated with a reduced incidence of the disease (for an increment of 30 g/day, adjusted summary hazard ratio 0.87 (95% confidence interval 0.82 to 0.93)). We also found an increased incidence of type 2 diabetes for higher intake of red meat (for an increment of 100 g/day, 1.17 (1.08 to 1.26)), processed meat (for an increment of 50 g/day, 1.37 (1.22 to 1.54)), and bacon (per two slices/day, 2.07 (1.40 to 3.05)), from evidence rated as high quality. The inverse association between chocolate, wheat bran, yogurt, and total dairy and the incidence of type 2 diabetes for dose-response meta-analyses were rated with moderate quality of evidence. The association between increased incidence of the disease and total meat, white rice, processed red meat, french fries, and hot dogs for dose-response meta-analyses were also found from evidence rated as moderate quality.

We observed no clear association with moderate quality of evidence between total vegetables, total fruit, and refined grain and the incidence of type 2 diabetes. Low quality of evidence was found between yellow vegetables and oily fish and incidence of type 2 diabetes (comparing high v low intakes). Furthermore, low quality evidence was found for dose-response meta-analyses of whole grain cereals, whole grain bread, total grains, brown rice, olive oil, ice cream, berry fruits, and apples and pears, which were associated with decreased incidence of the disease. Low quality evidence was also found for dose-response meta-analyses of boiled/baked/mashed potatoes, hamburgers, and total potatoes, which were associated with increased incidence. No clear association with low to very low quality of evidence was shown for the association for incidence of type 2 diabetes with nuts, cruciferous vegetables, fermented dairy products, butter, low fat dairy products, fruits and vegetables, green leafy vegetables, high fat dairy products, wheat germ, cream, high fat milk, milk, sherbet, cheese, legumes, low fat milk, fish, lean fish, shellfish, poultry, eggs, total fish or seafood, unprocessed red meat, rice, soy products, cottage cheese, and citrus fruits.

For beverages, quality of evidence was high for an increased incidence of type 2 diabetes with higher intake of sugar sweetened beverages (for an increase of one serving/day, adjusted summary hazard ratio 1.26 (95% confidence interval 1.11 to 1.43); fig 3 ). The inverse association in dose-response meta-analyses between caffeinated coffee, decaffeinated coffee, and total coffee and the incidence of type 2 diabetes, as well as the increased incidence of type 2 diabetes with higher intake of artificially sweetened beverages in dose-response meta-analyses, were rated with moderate quality of evidence. Total fruit juice and sugar sweetened fruit juice were associated with an increased incidence of type 2 diabetes with low quality of evidence in dose-response meta-analyses and in meta-analyses comparing high versus low intake, respectively. No clear associations were found for 100% fruit juice and unspecified fruit juice, and rated with very low quality of evidence.

Alcoholic beverages

Figure 4 shows the adjusted summary hazard ratios with their corresponding 95% confidence intervals and the quality of evidence for the associations between alcoholic beverages and incidence of type 2 diabetes. Moderate intake of total alcohol was inversely associated with incidence of the disease with high quality of evidence (for 12-24 g/day v no alcohol consumption, adjusted summary hazard ratio 0.75 (95% confidence interval 0.67 to 0.83)). The associations between light intake of total alcohol and moderate consumption of beer and increased incidence of type 2 diabetes were rated with moderate quality of evidence. We saw no clear association between heavy intake of total alcohol and incidence of the disease, with moderate quality of evidence. Low quality of evidence was found for the inverse association of any intake of wine with incidence of type 2 diabetes; and for no clear association of light and heavy beer intake, and of any intake of spirits with type 2 diabetes incidence.

Macronutrients and micronutrients

Figure 5 shows the adjusted summary hazard ratios with their corresponding 95% confidence intervals and the quality of evidence for the associations between macronutrients and the incidence of type 2 diabetes. The inverse association between cereal fibre and incidence of the disorder (for an increment of 10 g/day, adjusted summary hazard ratio 0.75 (95% confidence interval 0.65 to 0.86)) was rated with high quality of evidence. Moderate quality of evidence was found for vegetable fat and total fibre, which were associated with a decreased incidence of type 2 diabetes in meta-analyses comparing high versus low intake and in dose-response meta-analyses, respectively; and for total protein, animal protein-to-potassium ratio, and animal protein, which showed an association with increased incidence of disease. Low quality evidence was found for an inverse association between incidence of type 2 diabetes for ruminant trans-fatty acids in meta-analyses comparing high versus low intake, and insoluble fibre and sucrose in dose-response meta-analyses, as well as for cholesterol, which was associated with increased incidence of the disease. No clear association (with low to very quality of evidence) was shown for soluble fibre, plant protein, polyunsaturated fatty acids, alpha linolenic acid, total fat, monounsaturated fatty acids, animal fat, total omega-3 fatty acids, eicosapentaenoic acid and docosahexaenoic acid (alone and in combination), total carbohydrates, total sugars, maltose, total omega-6 fatty acids, saturated fat, lactose, fructose, trans-fatty acids, and glucose.

For the micronutrients ( fig 6 ), no association was rated with high quality of evidence. Moderate quality of evidence was observed for the inverse association between magnesium intake and incidence of type 2 diabetes in dose-response meta-analyses. Total polyphenols, calcium, and catechin were associated with decreased incidence of type 2 diabetes in meta-analysis comparing high versus low intake. In dose-response meta-analyses, phenolic acids, caffeine, and anthocyanins were also associated with decreased incidence of type 2 diabetes, but these associations were rated with low quality of evidence. Evidence on the association of haem iron with increased incidence the disease was also of low quality. No clear association (with low to very low quality of evidence) was found for stilbenes, flavanols, proanthocyanids, myricetin, flavonoles, flanovoids, flavan-3-ols, kaempferol, daidzein, isoflavones, quercetin, genistein, total iron, flavanones, flavones and lignans, vitamin D, lycopene, dihydrochalcones, luteolin, and other polyphenols. Vitamins C and E were inversely associated with incidence of type 2 diabetes but with very low quality of evidence.

Heterogeneity between primary studies

I 2 , τ 2 , and 95% prediction intervals are reported in supplementary table 3. For 24% (n=36) and 29% (n=44) of the meta-analyses, τ 2 and the 95% prediction intervals could not be recalculated, respectively. As for the 95% prediction intervals, only 5% (n=8) of the meta-analyses excluded the null value—that is, high versus low adherence meta-analyses of healthy dietary patterns, unhealthy dietary patterns, and breakfast skipping and the dose-response meta-analyses of apples and pears, total coffee, artificially sweetened beverages, any wine intake, and magnesium. This result indicates that in future studies, the true effect size on these exposures is expected to point to the same direction. However, for most of the findings, the true effect size of future studies could be null or small in some populations.

Publication bias and small study effects

Our results indicated the presence of small study effects (potential publication bias) according to Egger’s test (P<0.10) for rice, soy products, monounsaturated fatty acids, total carbohydrates, vitamin D, and flavanones in meta-analyses comparing high versus low intake. The presence of these effects were also indicated for total dairy, low fat milk, total coffee, beer (light consumption), and cereal fibre from dose-response meta-analyses (supplementary table 3).

More than 10 primary studies were available for 20% (n=30) of the funnel plots, between five and 10 were available for 40% (n=61), and fewer than five were available for 40% (n=62). The funnel plots (supplementary figures 2-19) indicated small study effects for eight associations, including the dose-response relation between incidence for type 2 diabetes and low fat dairy (supplementary figure 3b), ice cream (supplementary figure 3l), and wine (light consumption; supplementary figure 13d); and the high versus low adherence meta-analyses of AHEI (supplementary figure 2d), DASH (supplementary figure 2e), breakfast skipping (supplementary figure 2i), soy products (supplementary figure 9e), and vitamin D (supplementary figure 18a).

Principal findings

The influence of dietary behaviours or diet quality indices, food groups, foods, beverages, alcoholic beverages, macronutrients, and micronutrients on the incidence of type 2 diabetes has been examined in many published meta-analyses. In this umbrella review, we provided a broad overview of the existing evidence and evaluated the methodological quality of the meta-analyses and quality of evidence for all these associations.

We included 53 published meta-analyses, which comprised 153 adjusted summary hazard ratios for different dietary factors and incidence of type 2 diabetes. The methodological quality was high for most of the published meta-analyses. The quality of evidence was graded as high only for whole grain, cereal fibre, and moderate consumption of total alcohol, which decreased the incidence for type 2 diabetes; and for red meat, processed meat, bacon, and sugar sweetened beverages, which increased the incidence for the disease. For the other associations, the quality of evidence was moderate, low, or very low, which might be explained by the high proportion of meta-analyses that included fewer than five studies, had high heterogeneity, or had moderate effect sizes.

Comparison with other studies

Our umbrella review supports existing guidelines, and adds evidence in several aspects. Recommendations for higher intake of whole grain products, high fibre intake, and avoiding products with a high glycaemic index are included in the guidelines and reviews. 6 7 83 84 85 This information accords with our results that higher intake of whole grain products and total fibre intake was associated with a decreased incidence of type 2 diabetes, for which we found high and moderate quality of evidence, respectively. Although intake of foods with a high glycaemic index was associated with an increased incidence of type 2 diabetes in our umbrella review, quality of evidence was only low and further investigation is needed. Furthermore, the source of fibre seems to be important; we found high quality evidence that cereal fibre is associated with a decreased incidence of type 2 diabetes, while fruit fibre and vegetable fibre were not significantly associated with incidence of the disease.

Our findings confirm recommendations for higher intake of yogurt, 84 coffee, 6 7 84 tea 7 84 and vegetable fat, 6 and we observed a decreased incidence of type 2 diabetes with moderate quality of evidence for these associations. The American Diabetes Association recommends a higher intake of berry fruits, 84 for which we also found an inverse association with incidence of type 2 diabetes, but the quality of evidence was low, indicating that more studies are needed. Our results also confirmed the adverse association of red meat, processed meat products, 7 84 and sugar sweetened beverages 7 84 with incidence of type 2 diabetes, with a high quality of evidence. Moreover, we found high quality of evidence for an association between bacon, a type of processed meat, with increased incidence of type 2 diabetes. Our overview also showed associations between dietary patterns and incidence of type 2 diabetes, which are consistent with the results for some of the individual nutrients, foods, and food groups.

However, our findings did not indicate a beneficial association of incidence of type 2 diabetes with higher intake of low fat dairy products, 86 fruit and vegetables, 6 7 84 green leafy vegetables, 7 nuts, 6 7 84 and single unsaturated fatty acids (eg, omega-3 fatty acids), 6 84 or an adverse association with saturated fatty acids 7 or trans-fatty acids. 6 Nevertheless, we observed a decreased disease incidence with high intake of vegetable fat and a decreased incidence with healthy dietary patterns, which were partly characterised by a high ratio of unsaturated fatty acids to saturated fatty acids.

Only one of the guidelines mentions alcohol in their recommendations, which states that there is evidence that moderate consumption of alcohol lowers incidence of type 2 diabetes. 6 We found high quality of evidence for an inverse association between moderate total alcohol consumption and incidence of type 2 diabetes. Although we also observed a reduced incidence of type 2 diabetes with moderate consumption of wine and beer, quality of evidence for these associations was low and moderate, respectively.

In a previous review, Ley and colleagues summarised findings from primary studies and meta-analyses on dietary patterns, food groups and foods, beverages, and macronutrients and micronutrients regarding incidence of type 2 diabetes. However, this report was not a systematic review but only a narrative review, and the authors did not evaluate the validity and quality of evidence of the included meta-analyses. 83 We came to similar conclusions, with some exceptions, concerning the beneficial association between omega-6 fatty acids and nuts and the incidence of type 2 diabetes. 83 In their review, Micha and colleagues found probable or convincing evidence for an association with incidence of type 2 diabetes for low consumption of nuts, whole grain, and dietary fibre, as well as high consumption of unprocessed red meat, processed red meat, and foods with a high glycaemic load. 9 These results mostly accord with our findings, apart from the beneficial association of nuts and the harmful association of unprocessed red meat with incidence of type 2 diabetes. Conflicts could be explained by the inclusion of different primary studies. While Micha and colleagues included a meta-analysis with both randomised controlled trials and observational studies, our report only focused on observational studies. 87 In addition, the Micha meta-analysis missed one primary study that reported an increased incidence of type 2 diabetes with higher intake of nut intake, 88 which resulted in a decreased but not statistically significant summary estimate in our report.

In their umbrella review, Bellou and colleagues summarised findings from meta-analyses on the association between different risk factors (including selected dietary factors) and incidence of type 2 diabetes. However, they conducted their literature search up to February 2016, 10 and our umbrella review included more recent meta-analyses (eg, nuts, processed meat, red meat, whole grain). Furthermore, we included more exposures (153 v 53). While Bellou and collagues also evaluated epidemiological credibility, they used a different tool for their evaluation. 10 Therefore, our results differed from their evaluation in several aspects (eg, for processed meat and red meat), but were similar in relation to whole grain and sugar sweetened beverages.

Possible explanations

Individuals who have unhealthy dietary behaviours (such as low intake of whole grains and fibre and high intake of red and processed meat) are likely to have an unhealthier lifestyle, such as higher rates of obesity, smoking, and physical inactivity. 89 90 91 However, 87% of the primary studies included in the present review had adjusted for smoking and 86% for body mass index and for physical activity in multivariable regression models, and the associations persisted. Nevertheless, residual confounding cannot be ruled out, perhaps particularly in the analysis of artificially sweetened beverages, where obese individuals could have switched from sugar sweetened beverages to artificially sweetened beverages to lose weight. This behavioural change might explain the association observed before adjustment for body mass index and the attenuation of the association with body mass index adjustment. 56

For both whole grain and cereal fibre, high quality of evidence indicated an inverse association with incidence of type 2 diabetes. This beneficial effect could be partly explained by their high content in phytochemicals, vitamins, and minerals, which are lost in the production process of refining grains. 44 65 92 93 High intake of whole grain and cereal fibre has been associated with greater insulin sensitivity, lower fasting insulin concentrations, 93 94 95 and lower concentrations of inflammatory markers such as C reactive protein, 96 97 98 99 100 which could increase incidence for type 2 diabetes 101 ; and higher concentrations of the cytokine adiponectin, which is associated with a reduced incidence. 101 102

A recent meta-analysis of randomised controlled trials showed acute beneficial effects for an intervention with increased whole grain consumption compared with control meals (including mainly white wheat bread) on postprandial glucose and insulin response, 103 which reduces pancreas exhaustion. 104 105 However, in medium and long term randomised controlled trials, the intervention of increased whole grain consumption had no effect on fasting glucose, fasting insulin, and insulin resistance compared with the control diet. Nevertheless, when randomised controlled trials with people at increased risk for type 2 diabetes were excluded, fasting glucose was lower in the intervention group than in the control group. 103 High intake of whole grains has been associated with reduced weight gain over time. 106 In a previous meta-analysis, adjustment for body mass index attenuated the association between whole grain intake and type 2 diabetes risk by 33-50%, 44 suggesting that reduced adiposity might be an important mechanism by which whole grain consumption reduces type 2 diabetes risk. Therefore, randomised controlled trials 103 could have found weak effects of whole grain intake and intermediate risk factors, because the trials could have been too short to observe an effect of whole grain intake on weight loss.

High quality of evidence was also observed for the positive association between incidence of type 2 diabetes and red meat, processed meat, and bacon. In a pooled analysis of 14 studies, the consumption of processed meat and unprocessed red meat was associated with higher fasting glucose and fasting insulin levels, 107 and some studies 96 108 109 but not all 107 have reported similar results as well as associations with C reactive protein, ferritin, glycated haemoglobin, and gamma-glutamyl transferase. 96 109 In some of these studies, associations were attenuated when adjusted for body mass index, 96 108 109 which is consistent with the much stronger associations reported between type 2 diabetes and intake of unprocessed and processed red meat in analyses unadjusted for body mass index than when adjusted for body mass index. 39 110 111 Given that both unprocessed and processed red meat has been associated with weight gain over time, 106 112 increased weight gain could be an important mechanism by which meat intake increases incidence of type 2 diabetes. Although the association with unprocessed red meat was not statistically significant in a meta-analysis from 2013 36 , this finding needs to be interpreted with caution. Of cohort studies that have since been published, 113 114 115 116 117 118 most with the larger cohorts found an increased risk also with unprocessed red meat. 113 114 115 116 Processed meat contains high amounts of sodium that could cause microvascular dysfunction and increase incidence of type 2 diabetes. 119 120 121 Processed meat also contains nitrates, nitrites, and their by-products (eg, peroxynitrite), which could have a role in the pathogenesis of type 2 diabetes. 122 Another possible explanation is the high content of haem iron in meat and meat products, which has been shown to be associated with an increased incidence of type 2 diabetes. 68 Iron has strong pro-oxidative capacities, which might damage pancreatic cells. 123 Furthermore, meat contains high amounts of advanced glycation end products, 124 which can be absorbed into the body and have been shown to increase inflammatory markers. 125 126

Additionally, we found high quality evidence for an increased incidence of type 2 diabetes with sugar sweetened beverages. Although these beverages are a major risk factor for weight gain and obesity, 127 other mechanisms other than the influence of overweight and obesity on incidence of type 2 diabetes are physiologically plausible. Sugar sweetened beverages, such as sugar containing lemonades, can have a high glycaemic index, 128 which is related to an increase in blood sugar levels and associated with increased incidence of type 2 diabetes. 104 105 However, beverages that are high in fructose or isomaltulose, a slowly absorbable disaccharide used in sports drinks, have a lower glycaemix index. 129 A randomised controlled trial compared two intervention groups consuming 20% of their energy requirement in form of beverages sweetened with isomaltulose (low glycaemic index) and maltodextrin (high glycaemic index). The insulin response was lower and insulin sensitivity was better preserved in the group consuming beverages with a low glycaemic index than in the group consuming beverages with a high glycaemic index. 129 However, fructose that might be contained in these beverages increases hepatic lipogenesis and insulin resistance. 130 Another randomised controlled trial compared interventions of four servings/day of sugar sweetened, fructose sweetened, and aspartame sweetened beverages for eight days. Ad libitum energy intake was significantly increased in the sugar and fructose groups compared with the aspartame group, with no difference between the first two groups. However, since all groups received the same standard diet, the excess calories in the sugar sweetened and fructose sweetened beverages possibly contributed to the increased calorie intake in those groups. 131

Nevertheless, high intake of sugar in liquid form seems to have a role in the association of sugar sweetened beverages with incidence of type 2 diabetes. Sugar containing liquids have been shown to negatively affect the regulation of hunger and satiety as opposed to sugar containing solid foods. 132 However, in our umbrella review, a surprising result was found for sucrose, which was associated with a decreased incidence of type 2 diabetes, based on evidence that was of moderate quality. The reason for this relation is not yet clear, although it might be because sweetened beverages are not the only source of sucrose. Sucrose is also a component of grains and dairy products, which are associated with a decreased incidence of type 2 diabetes. 64 Furthermore, the reason could lie in the influence of energy misreporting, as shown by Gottschald and colleagues. 133 The researchers found an inverse association between intake of high sucrose foods (cakes and cookies) and cardiometabolic disease risk factors in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. However, the association diminished after the researchers adjusted for energy misreporting. 133

We found high quality evidence for an inverse association between moderate alcohol consumption and incidence of type 2 diabetes. Results from observational studies suggested that light or moderate alcohol consumption increases insulin sensitivity. 134 135 136 However, a meta-analysis of randomised controlled trials comparing moderate alcohol consumption (including wine, beer, and spirits) with no alcohol consumption only confirmed this result for women. No effect on insulin sensitivity was found for men. Nevertheless, moderate alcohol intake decreased fasting insulin concentrations and glycated haemoglobin. 137 The sex specific effect was also confirmed by the meta-analysis by Li and colleagues. which showed a stronger inverse association with incidence of type 2 diabetes for women than for men, although both associations were statistically significant. 74 Results from a randomised controlled trial comparing the effect of red wine, dealcoholised red wine, and gin on glucose homeostasis suggested that the non-alcoholic fraction of red wine (mainly the polyphenols) contributes to possible beneficial effects of red wine on plasma insulin and the homeostatic model assessment for insulin resistance. 138 This possible effect is supported by our findings that especially wine was inversely associated with incidence of type 2 diabetes. However, alcohol causes adverse health effects such as liver cirrhosis, which increases incidence of type 2 diabetes. 139 Given the U shaped association of total alcohol and alcoholic beverages with incidence of the disease, the risk for alcohol abuse, and other adverse health effects of alcohol (eg, increased risk for certain types of cancer), 140 translation of these results into recommendations have to be considered carefully.

In terms of internal consistency, we observed that related exposures showed the same direction of the association with incidence of type 2 diabetes. For example, a healthy dietary pattern (characterised, amongst others, by a high intake of whole grain products and low intake of red and processed meat) and high consumption of whole grain products, fibre, and magnesium were all associated with a reduced incidence of the disease. Accordingly, an unhealthy dietary pattern and high consumption of red meat, processed meat (eg, bacon), animal protein, and haem iron were related to an increased disease incidence. However, as explained above, the role of unprocessed red meat regarding incidence of type 2 diabetes needs further investigation.

The results on caffeinated and decaffeinated coffee and caffeine warrant further discussion. Both caffeinated and decaffeinated coffee were associated with a decreased incidence of type 2 diabetes, suggesting that caffeine does not have a major role in the health effect of coffee on the disease. Nevertheless, caffeine was also observed to decrease type 2 diabetes incidence. All associations were graded as moderate quality of evidence. Although caffeine has been discussed to have beneficial properties (eg, increase insulin sensitivity 141 ), the results are difficult to interpret because of the strong correlation with coffee consumption. 55 Therefore, caffeine might act as a marker for coffee intake, which contains several beneficial compounds (eg, chlorogenic acid and antioxidants) that contribute to the reduction of type 2 diabetes incidence. 55 Since decaffeinated coffee showed a similar association with incidence of type 2 diabetes as caffeinated coffee, it seems plausible that these other bioactive compounds in coffee mainly contribute to the reduction of type 2 diabetes incidence with coffee consumption.

In general, diet is a complex combination of foods and nutrients that act synergistically. 142 In this umbrella review, dietary patterns were all associated with the incidence of type 2 diabetes, but the quality of evidence for dietary patterns was moderate. To account for the full spectrum of the association between diet and the disease, future studies could investigate a dietary score, including all important aspects of a healthy diet that have been identified to have a role in the risk of type 2 diabetes. This approach might be more predictive of disease risk than the investigation of single foods and nutrients. 142 For example, a strong reduced risk of the disease (reduction by 52%, 95% confidence interval 14% to 73%) was identified for the adherence to the Mediterranean diet (supplemented with either extra virgin olive oil or mixed nuts) compared with a control diet (advice to reduce only dietary fat) in the PREDIMED trial. 143 Thus, to give accurate recommendations regarding diabetes prevention, it is important to identify the optimal diet(s).

Strengths and limitations

Our umbrella review had several strengths. It provides a systematic, comprehensive overview of the evidence from all published meta-analyses regarding the role of dietary factors in the prevention of type 2 diabetes. We also evaluated the methodological quality and quality of evidence by using validated tools. 13 14 15 17 In this context, most of the included meta-analyses were of high methodological quality. Furthermore, internal inconsistencies were uncovered and relevant research directions could therefore be identified.

This umbrella review also had several limitations. For 36 of the 96 dose-response meta-analyses, recalculation was not possible, because no single effect estimates were reported. But authors stated that the dose-response associations were estimated by random effects meta-analyses. 31 35 37 38 44 48 53 54 60 61 72 However, we could not calculate τ 2 and 95% prediction intervals for these associations because of missing values. We also resumed the doses defined in the published meta-analyses; therefore, they are not standardised and the doses have to be considered when interpreting the results. For example, the serving sizes defined for total sugars, sucrose, and fructose were large and the summary risk estimate might be smaller with a smaller serving size. Additionally, information on linearity (eg, P for non-linearity) of the dose-response relations were available for 72% of the dose-response meta-analyses. A third of these dose-response relations indicated non-linearity. To derive recommendations, further investigation is needed to set optimal cutoff points. Furthermore, to ensure comparability with previous meta-analyses, we used the random effects approach by DerSimonian and Laird 18 for the calculation of the adjusted summary hazard ratios and their corresponding 95% confidence intervals. However, future meta-analyses should use the Hartung-Knapp approach, which gives a better reflection of the uncertainty in the variance between studies, expressed by wider confidence intervals. 144

The included primary studies were based on an observational prospective study design, which is prone to biases such as confounding. Nevertheless, the most important confounders were adjusted for in most of the primary studies (90% for age and sex, 87% for smoking, and 86% for body mass index and for physical activity). However, residual confounding cannot be completely ruled out. For example, only 52% of the studies adjusted for family history of diabetes, which should be included in the adjustment model of future studies. Additionally, only 49% of the primary studies that used Cox proportional hazard regression evaluated the proportional hazard assumption and found no violation, while the other 51% provided no information about the evaluation of the assumption. Thus, it remained unclear whether the assumption was fulfilled or violated in those studies. Future studies should report on the evaluation of the assumption of the according statistical method. As the included primary studies were prospective studies, risk of recall bias was avoided and selection bias was reduced. Randomised controlled trials, which reduce risk of biases such as confounding, are scarce in research on diet-disease associations because large sample size trials with long follow-up periods are expensive to conduct, 6 and long term adherence can be a challenge in trials of major dietary changes.

We did not explore subgroup analysis (eg, by sex, geographical locations, or adjustment factors such as body mass index) or sensitivity analysis (eg, exclusion of studies at high risk of bias). For example, regarding total omega-3 fatty acids and incidence of type 2 diabetes, differences between US, Australian/Asian, and European populations have been shown, with an increased incidence of type 2 diabetes in US populations, no association for European countries, and an inverse association in Asian/Australian populations. 145 Additionally, body mass index could be an influencing factor in the association between nut intake and incidence of type 2 diabetes, with an inverse association before and a null association after adjustment for body mass index. In a subgroup analysis, a reduced incidence of type 2 diabetes was observed for participants with body mass index of at least 25, and no association for those with a body mass index of less than 25. 146

Although publication bias was indicated for only 7% of the meta-analyses according to the Egger’s test and for only 5% according to the funnel plots, 40% included five to 10 studies and 40% included even fewer than five studies, which indicates that the results are not reliable. 17 Since some of the meta-analyses included only a few primary studies, some data are missing (for example, unpublished null associations). Thus, more research is needed to investigate these associations, which were based on small numbers of included studies.

Furthermore, primary studies that were not included in any published meta-analyses might have been missing, and additional studies could have come out after publication of each meta-analysis and could have influenced the results. We did not include pooled analyses in our umbrella meta-analyses and it was also beyond the scope of this umbrella review to include exposures of biomarkers. However, the measurement of certain exposures (eg, fatty acids) could lead to bias, 60 and more specific information on long term intake might be obtained from biomarkers. 147 148

Dietary changes over time were also not assessed in this umbrella review. Only two of the included meta-analyses repeated measures of dietary intake diet. One meta-analysis on sugar sweetened beverages, artificially sweetened beverages, and total fruit juice showed that the association with increased incidence of type 2 diabetes was diminished for total fruit juice when diet was measured repeatedly. 56 Another meta-analysis found stronger associations between red and processed meat intake and type 2 diabetes incidence in subgroups of studies that used repeated dietary assessments compared with only baseline dietary assessments. 37 In the three Harvard cohorts, associations between red and processed meat and incidence of type 2 diabetes were stronger after repeated dietary assessments than after only baseline dietary intake. 39 Increases and reductions in red and processed meat intake over time were also found to be associated with subsequent increases and decreases in the incidence of type 2 diabetes, respectively. 149

In the NutriGrade tool, all criteria contribute to the overall score with one point, except for bias, study quality, study limitations, and effect size, which contribute with two points and therefore receive more weight. However, bias, study quality, and study limitations include several aspects that might justify a higher weight (eg, exposure, outcome, and confounding). Although NutriGrade is a validated tool, the use of other tools (eg, GRADE 150 ) could have led to different conclusions regarding the quality of the evidence.

Finally, we systematically chose the meta-analysis including the largest number of primary studies for each exposure. Therefore, the chosen meta-analysis might not have had the highest quality of evidence. However, the meta-analysis with the largest number of primary studies was mostly based on the same primary studies as meta-analyses including fewer studies, with an update of one or more studies. Additionally, in our umbrella review, one of the main reasons for a low quality of evidence was due to the small number of primary studies included in the meta-analyses. Therefore, it is unlikely that inclusion of a meta-analysis with even fewer primary studies would have achieved a higher quality of evidence compared with the meta-analysis included in our umbrella review.

Conclusions and future research outlook

The association of dietary behaviours or diet quality indices, food groups, foods, beverages, macronutrients, and micronutrients with incidence of type 2 diabetes has been examined in many published meta-analyses. Evidence indicates that dietary factors have an important role in the primary prevention of the disease. Although the methodological quality of the included meta-analyses was mostly high, the quality of evidence was only high for associations for incidence of type 2 diabetes with whole grains, cereal fibre, moderate alcohol consumption, red meat, processed meat, bacon, and sugar sweetened beverages.

To achieve high quality of evidence for these associations and be able to give strong recommendations, future studies should regard several aspects. Dietary data with high validity should be attained, by improving dietary measurement methods and by assessing and accounting for changes in dietary behaviour over time. Studies should focus on exposures, which are biologically likely to be associated with incidence of type 2 diabetes, but for which quality of evidence is still low. Since recommendations are based on foods and food groups, studies should also focus on answering open questions in terms of internal inconsistencies, such as the role of unprocessed meat versus processed red meat in the association of total meat and red meat with disease incidence. More research is also needed on specific foods for which evidence is still low, such as types of rice (white rice, brown rice), fish (oily or lean fish), or fat (eg, olive oil). To take into account interactions between different foods and nutrients and the influence of mediating factors (eg, body mass index), future studies should also focus on dietary patterns, substitution analysis, mediation analysis, and network meta-analysis. Furthermore, to avoid selective publication bias, existing and future cohorts should publish data in comprehensive analyses, including data on dietary exposures that have not been investigated (or published) so far.

What is already known on this topic

A large body of research is available regarding the association of type 2 diabetes incidence with dietary behaviours or diet quality indices, food groups, single foods and beverages, macronutrients, and micronutrients

These findings could be of importance given the substantial global health burden and healthcare costs of type 2 diabetes

However, the strength, precision, and influence of potential bias regarding these associations need to be clarified

What this study adds

In an umbrella review of meta-analyses including observational studies, existing evidence indicates that dietary factors have a role in the development and prevention of type 2 diabetes

Although the methodological quality of the meta-analyses was mostly high, quality of evidence was only high for whole grains, cereal fibre, and moderate total alcohol intake (associated with a reduced incidence of type 2 diabetes) and for red meat, processed meat, bacon, and sugar sweetened beverages (associated with an increased incidence of type 2 diabetes)

Future studies should attain dietary data with high validity and focus on dietary exposures and specific food groups for which quality of evidence has been low; and publish more comprehensive analyses including data on less frequently investigated and subtypes of dietary exposures

Acknowledgments

We thank Oliver Kuss, director of the Institute for Biometrics and Epidemiology at the German Diabetes Centre, for his statistical advice and support.

Contributors: SS designed the research. MN and SS conducted the literature search and literature screening. MN and AB extracted the data. MN and SS analysed the data and wrote the first draft of the paper. All authors interpreted the data, read the manuscript, and approved the final version. MN and SS are guarantors. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria were omitted.

Funding: The German Diabetes Centre is funded by the German Federal Ministry of Health and the Ministry of Innovation, Science, Research and Technology of the State North Rhine-Westphalia. This study was also supported in part by a grant from the German Federal Ministry of Education and Research to the German Centre for Diabetes Research. The funding source has no role in the decisions about the data collection, analysis, interpretation of the data, preparation, review or approval of the manuscript.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from the German Federal Ministry of Health, the Ministry of Innovation, Science, Research and Technology of the State North Rhine-Westphalia, and the German Federal Ministry of Education and Research to the German Centre for Diabetes Research for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: Data were extracted from published meta-analyses, all of which are available and accessible.

The lead authors affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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The effects of popular diets on type 2 diabetes management

Affiliations.

• 1 Department of Nutrition, Dietetics, and Hospitality Management, Auburn University, Auburn, Alabama.
• 2 Boshell Metabolic Diseases and Diabetes Program, Auburn University, Auburn, Alabama.
• 3 Center for Neuroscience Initiative, Auburn University, Auburn, Alabama.
• PMID: 31121637
• DOI: 10.1002/dmrr.3188

Type 2 diabetes can be managed with the use of diabetes self-management skills. Diet and exercise are essential segments of the lifestyle changes necessary for diabetes management. However, diet recommendations can be complicated in a world full of different diets. This review aims to evaluate the evidence on the effects of three popular diets geared towards diabetes management: low-carbohydrate and ketogenic diet, vegan diet, and the Mediterranean diet. While all three diets have been shown to assist in improving glycaemic control and weight loss, patient adherence, acceptability, and long-term manageability play essential roles in the efficacy of each diet.

Keywords: Mediterranean; diabetes; diet; ketogenic; vegan.

© 2019 John Wiley & Sons, Ltd.

Publication types

• Research Support, Non-U.S. Gov't
• Diabetes Mellitus, Type 2 / diet therapy*
• Diabetes Mellitus, Type 2 / prevention & control
• Diet, Diabetic / methods*
• Diet, Mediterranean / statistics & numerical data*
• Diet, Vegetarian / methods*
• Health Behavior*
• Patient Compliance*
• Weight Loss

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Diabetes Diet: The Best and Worst Foods for Type 2 Diabetes

What Should Type 2 Diabetics Eat?

For people with type 2 diabetes, figuring out a healthy diet and food choices can be an uphill battle. In addition to having to adjust lifelong eating habits, there’s a great deal of conflicting information about what you can eat, what you should eat, and what you might want to eat.

We’ll try to offer a bit of a simplification. For most people with type 2 diabetes, the number one goal is to reverse insulin resistance , as this is the most surefire way to reverse type 2 diabetes and ensure that your pancreas is healthy in the long term.

Add in a secondary goal of maintaining healthy blood glucose (blood sugar levels) along the way, and the evidence points very clearly to one type of diet: a low-fat, plant-based, whole-food diet high in whole carbohydrates.

In this article, we’ll explain our research-backed approach for diabetes management (and reversal), which works well for type 1 diabetes management, prediabetes and type 2 diabetes, along with some of the principles behind how it works.

Then, we’ll touch on how this diet compares to the alternatives, and touch on a recommended meal plan and some tips for healthy eating.

Why Follow a Diabetes Diet?

A nutrient-poor diet is one of the most powerful contributors to diabetes risk , which can lead to comorbidities (associated diseases) like high blood pressure, high cholesterol levels, heart disease, and kidney disease.

Conversely, the low-fat, plant-based, whole-food diet that helps prevent diabetes (including insulin resistance and high blood glucose levels/hyperglycemia), also has other benefits, like weight loss, increased energy, increased immunity, and better physical fitness.

So it’s a win-win.

Type 2 Diabetes Food Breakdown

First and foremost, we recommend that the vast majority of your nutrients come from plants, due to the various risks associated with eating meat .

We also recommend removing artificial sweeteners and synthetic/processed products from your diet as much as possible, due to their proven association with chronic disease .

Finally, we recommend that you get 80+% of your calories from the carbohydrates found in whole-grains, fruits and berries , and vegetables, with the remaining calories coming from healthy fats and proteins.

Sound restrictive? You might think, but countless plant based recipes might make you rethink your food groups.

Carbohydrates

In recent years, carbohydrates have gotten a bad rap, mostly due to the presence of ‘bad’ carbohydrates. However, fruits, grains , and vegetables are rich in whole, natural carbohydrates, which are the foundation of a diabetes diet.

You can read more about the difference between these types of carbohydrates here.

What Are Good Carbs for Type 2 Diabetics to Eat?

Almost all plants, fruits, and vegetables are rich in good carbohydrates that you can eat ad libitum , meaning as much as you want without worrying about portion size.

In addition to being a great source of energy, they’re also high in nutrients like dietary fiber, antioxidants, and key vitamins.

These carbohydrate-rich foods (most of which are high-fiber) include:

• All fruits (exceptions: dates, avocados, and durian)
• All non-starchy vegetables
• All starchy vegetables (potatoes, sweet potatoes, squash, and root vegetables)
• All legumes, including all types of beans , peas, and lentils (exception: soybeans)
• Intact whole grains (minimally processed, like brown rice and quinoa)

What Carbs Should a Person with Diabetes Avoid?

The answer here is “simple”. No, literally, you should avoid simple carbohydrates, which include all processed and artificial sweeteners.

Keep an eye out and avoid:

• Foods with added sugar or refined sugar
• Foods with high fructose corn syrup
• Fruit juice with added sugar
• White bread
• And, in general, almost all processed foods

We recommend a low-fat diet because dietary excess dietary fat is the primary cause of insulin resistance, the underlying condition that causes diabetes. Now, this doesn’t necessarily mean that you should remove all fat from your diet (which, in fact, is actually impossible).

Instead, focus on limiting your consumption of high fat foods.

What Are Good Fats for Type 2 Diabetics to Eat?

Foods like avocados, dates, durian, nuts and seeds, coconut meat, and soy products are all dense with healthy fats. Eaten in moderation, these foods won’t increase your insulin resistance, but can pose a risk in higher quantities.

One type of food that still has a bit of controversy around it is natural oils like olive oil . These oils are processed and high in fat, so you should be wary as to their effect on your overall health, but some research shows that in small, small quantities they can be healthy.

What Fatty Foods Should a Person with Diabetes Avoid?

In general, we recommend avoiding fat-rich foods if they fall into the 2 categories we recommend avoiding (artificial, or derived from meats).

This includes most saturated fats, trans fats , margarine, ghee and so on. They can be tasty, but the cardiovascular and health risks just aren’t worth it.

The research on high-protein diets is relatively clear: in most cases, you just don’t need that much protein, and if you’re not training hard and seeking to build muscle, these diets can actually increase your risk of disease significantly .

And while we may sound like a broken record, we recommend plant-based proteins, while avoiding most meats!

What Are Good Protein Sources for People with Type 2 Diabetes?

Plant-based proteins are the go-to, especially those from legumes and beans .

Some research, particularly on the Mediterranean diet , has begun to show that in small, small quantities some forms of meats like organ meats, as well as some varieties of fish and shellfish (mackerel, tuna, scallops, etc.) may not increase your risk as much as other meats.

However, if you’re looking for the fastest way to reverse insulin resistance and diabetes completely, plants are the way to go.

What Protein Should a Person with Type 2 Diabetes Avoid?

Processed meats and red meats are the big red flags here, with dairy products (even low-fat dairy) and most white meats filling out the list of foods to avoid. Though these foods can often be tasty, their long term risks just aren’t worth it , especially when trying to overcome diabetes.

The Best Type 2 Diabetes Diet

So what does the best type 2 diabetes diet look like? We break foods down into three categories: green light, yellow light, and red light foods.

Green lights you can eat as much as you want, period. Yellow lights are okay, but shouldn’t be daily staples. And we recommend removing red light foods from your diet and your pantry.

To learn more about this diet and how to execute it, you can check out our article on the diabetes diet , or talk to our coaches .

Other Type 2 Diabetes Eating Plans

One of the main obstacles to diabetes care that we discussed before is that there’s a great deal of conflicting information out there about which diet is best for you. We’ll touch on a few here, including the principles behind them and where they may fall flat as a ‘diabetic diet’.

Of course, always work with your dietitian or diabetes health care provider to ensure that your team is on the same page about what you’re planning to eat.

Glycemic Index

The glycemic index is essentially a measure of how ‘energy dense’ specific foods are, based on how much they spike your blood glucose after consuming them. This can be a very helpful resource if you’re having trouble controlling your blood glucose and keeping it in range.

However, where the glycemic index falls a bit short in informing a diabetes diet is that it doesn’t acknowledge the tendency of different foods to cause insulin resistance and diabetes.

Final Word: A useful tool for informing blood glucose control, but not a method to base your eating schedule on.

Carbohydrate Counting

Carbohydrate counting, objectively, is simply counting the amount of carbohydrates in your diet compared to fats and proteins, which is not a bad thing. In fact, it can be a very helpful technique to make sure you’re eating a low-fat diet.

However, carbohydrate counting is often associated with trying to keep carbohydrates down , which is counterproductive to insulin resistance. As we’ve touched on, fats and proteins are some of the main culprits in causing insulin resistance.

Final Word: It’s not necessarily bad to count your carbohydrates, but we prefer focusing on the foods that you can happily eat in high quantities!

Mediterranean Diet

The Mediterranean Diet is modeled after the common breakdowns of plants, vegetables, fruits, seeds, oils, meats, and grains in traditional Mediterranean cultures.

Though exact definitions can vary, most common understandings of this diet place a heavy emphasis on plants and whole foods, with lean or fresh meats and seafoods on occasion.

Extensive studies of this diet have found that, especially compared to the Standard American Diet and diets high in processed foods and sugars, the Mediterranean Diet can be a powerful tool to combat diabetes, high cholesterol, high blood pressure, and risk of heart disease.

Final Word: In many ways, the Mediterranean diet is simply a less strict version of the low-fat, plant-based, whole-food diet, though they prioritize the same emphasis on whole carbohydrates, low-fats, and natural foods. A potential option once you’ve reversed insulin resistance.

Low-Carb Diet

The low-carb diet isn’t the worst possible diet that you can have if you’re suffering from diabetes, but in the long term it’s pretty bad for your overall health.

Though low-carb diets (which by definition are high in fat and protein) often have quick immediate results in terms of weight loss, better blood glucose, and better blood pressure, in the long term these diets are disastrous for your diabetes health .

Final Word: Don’t let the quick results fool you. Low-carb diets like keto , paleo, carnivore , and others are enticing, but don’t bode well for overall health.

Intermittent Fasting

Intermittent fasting is less of a dietary plan and more of an eating schedule, though when combined with a low-fat, plant-based, whole-food diet the results can be astounding.

The key here is a process called autophagy. When your body enters a period of fasting/calorie restriction, it recycles excess fats, proteins, and old cells, which helps with weight loss as well as immunity and overall health. Combined with the right diet, you’re set up for success.

Final Word: By itself, intermittent fasting is a potent tool, but when used in tandem with exercise and the right diet it can be a key to reversing insulin resistance fast .

Type 2 Diabetes Meal Plan

We know that one of the hardest parts of adjusting to a diabetes diet can be the feeling like you’re “giving up” a lot of your favorite foods.

That’s why we’ve put together a database of recipes for breakfast , lunch, dinner, desserts, and snacks that will make you feel like you’re not missing a beat! See how a week might look in our sample meal plan .

You can reach out to our coaches if you’d like help putting together one of your own.

Healthy Eating Tips for People with Type 2 Diabetes

In addition to choosing the right diet, here are a few tips to stay on top of your eating plan, reverse diabetes, and avoid problems like chronic disease and weight gain.

Read Food Labels

A simple start to choosing healthy foods. In general, the less ingredients and the less complicated/scientific sounding the ingredients, the better, though there are always exceptions.

A good rule of thumb is that the food defaults to its lowest-level ingredient. So if you have six green-light ingredients, but one red-light, it’s best to search for alternatives.

Bonus points for natural fruits, plants, and vegetables that are so fresh they don’t even need a label!

Engage in Physical Activity

Another common sense suggestion, but one that can make a major difference! According to the American Diabetes Association (ADA), exercise is a fundamental part of diabetes treatment , and we’re big believers that it’s a fun and fulfilling part of our routine.

Not to mention the many, many, many other health benefits of exercise .

Get Expert Support

You don’t have to go it alone, especially if you’re just starting out. Meal planning is one of the most powerful tools to keep a diabetes diet fresh , fun, and healthy, but if you’re not used to the red-light green-light guidelines it can be hard to come up with recipes.

That’s where you can enlist the help of experts. Whether it’s working with a registered dietitian until you get the hang of a diet, teaming up with friends to swap recipes and cook together, or relying on diabetes educators like the experts at Mastering Diabetes , you can find support and guidance in any way you choose.

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+ references.

Mastering Diabetes has strict guidelines for scientific references in our articles. We rely on peer-reviewed studies, academic research institutions, governmental organizations, and reputable medical organizations. We do our best to avoid using non evidence-based references in all articles. The references in this article are listed below.

CDC. “Benefits of Physical Activity.” Centers for Disease Control and Prevention, December 2, 2020. https://www.cdc.gov/physicalactivity/basics/pa-health/index.htm .

Colberg, Sheri R., Ronald J. Sigal, Jane E. Yardley, Michael C. Riddell, David W. Dunstan, Paddy C. Dempsey, Edward S. Horton, Kristin Castorino, and Deborah F. Tate. “Physical Activity/Exercise and Diabetes: A Position Statement of the American Diabetes Association.” Diabetes Care 39, no. 11 (November 1, 2016): 2065–79. https://doi.org/10.2337/dc16-1728 . Lăcătușu, Cristina-Mihaela, Elena-Daniela Grigorescu, Mariana Floria, Alina Onofriescu, and Bogdan-Mircea Mihai. “The Mediterranean Diet: From an Environment-Driven Food Culture to an Emerging Medical Prescription.” International Journal of Environmental Research and Public Health 16, no. 6 (March 2019). https://doi.org/10.3390/ijerph16060942 . Pase, Matthew P., Jayandra J. Himali, Alexa S. Beiser, Hugo J. Aparicio, Claudia L. Satizabal, Ramachandran S. Vasan, Sudha Seshadri, and Paul F. Jacques. “Sugar- and Artificially-Sweetened Beverages and the Risks of Incident Stroke and Dementia: A Prospective Cohort Study.” Stroke 48, no. 5 (May 2017): 1139–46. https://doi.org/10.1161/STROKEAHA.116.016027 . “Prevalence of Comorbidities High in Type 2 Diabetes.” https://www.healio.com/news/endocrinology/20160425/prevalence-of-comorbidities-high-in-type-2-diabetes . Romagnolo, Donato F., and Ornella I. Selmin. “Mediterranean Diet and Prevention of Chronic Diseases.” Nutrition Today 52, no. 5 (September 2017): 208–22. https://doi.org/10.1097/NT.0000000000000228 . Sami, Waqas, Tahir Ansari, Nadeem Shafique Butt, and Mohd Rashid Ab Hamid. “Effect of Diet on Type 2 Diabetes Mellitus: A Review.” International Journal of Health Sciences 11, no. 2 (2017): 65–71.

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About the author 

Cyrus Khambatta, PhD

Cyrus Khambatta, PhD is a New York Times bestselling co-author of Mastering Diabetes: The Revolutionary Method to Reverse Insulin Resistance Permanently in Type 1, Type 1.5, Type 2, Prediabetes, and Gestational Diabetes. He is the co-founder of Mastering Diabetes and Amla Green , and is an internationally recognized nutrition and fitness coach who has been living with type 1 diabetes since 2002. He co-created the Mastering Diabetes Method to reverse insulin resistance in all forms of diabetes, and has helped more than 10,000 people improve their metabolic health using low-fat, plant-based, whole-food nutrition, intermittent fasting, and exercise. Cyrus earned a Bachelor of Science in Mechanical Engineering from Stanford University in 2003, then earned a PhD in Nutritional Biochemistry from the University of California at Berkeley in 2012. He is the co-author of many peer-reviewed scientific publications. He is the co-host of the annual Mastering Diabetes Online Summit , a featured speaker at the Plant-Based Nutrition and Healthcare Conference (PBNHC), the American College of Lifestyle Medicine Conference (ACLM), Plant Stock , the Torrance Memorial Medical Center , and has been featured on The Doctors, NPR , KQED , Forks Over Knives , Healthline , Fast Company , Diet Fiction , and the wildly popular podcasts the Rich Roll Podcast , Plant Proof , MindBodyGreen , and Nutrition Rounds. Scientific Publications: Sarver, Jordan, Cyrus Khambatta, Robby Barbaro, Bhakti Chavan, and David Drozek. “Retrospective Evaluation of an Online Diabetes Health Coaching Program: A Pilot Study.” American Journal of Lifestyle Medicine, October 15, 2019, 1559827619879106. https://doi.org/10.1177/1559827619879106 Shrivastav, Maneesh, William Gibson, Rajendra Shrivastav, Katie Elzea, Cyrus Khambatta, Rohan Sonawane, Joseph A. Sierra, and Robert Vigersky. “Type 2 Diabetes Management in Primary Care: The Role of Retrospective, Professional Continuous Glucose Monitoring.” Diabetes Spectrum: A Publication of the American Diabetes Association 31, no. 3 (August 2018): 279–87. https://doi.org/10.2337/ds17-0024 Thompson, Airlia C. S., Matthew D. Bruss, John C. Price, Cyrus F. Khambatta, William E. Holmes, Marc Colangelo, Marcy Dalidd, et al. “Reduced in Vivo Hepatic Proteome Replacement Rates but Not Cell Proliferation Rates Predict Maximum Lifespan Extension in Mice.” Aging Cell 15, no. 1 (February 2016): 118–27. https://doi.org/10.1111/acel.12414 Roohk, Donald J., Smita Mascharak, Cyrus Khambatta, Ho Leung, Marc Hellerstein, and Charles Harris. “Dexamethasone-Mediated Changes in Adipose Triacylglycerol Metabolism Are Exaggerated, Not Diminished, in the Absence of a Functional GR Dimerization Domain.” Endocrinology 154, no. 4 (April 2013): 1528–39. https://doi.org/10.1210/en.2011-1047 Price, John C., Cyrus F. Khambatta, Kelvin W. Li, Matthew D. Bruss, Mahalakshmi Shankaran, Marcy Dalidd, Nicholas A. Floreani, et al. “The Effect of Long Term Calorie Restriction on in Vivo Hepatic Proteostatis: A Novel Combination of Dynamic and Quantitative Proteomics.” Molecular & Cellular Proteomics: MCP 11, no. 12 (December 2012): 1801–14. https://doi.org/10.1074/mcp.M112.021204 Bruss, Matthew D., Airlia C. S. Thompson, Ishita Aggarwal, Cyrus F. Khambatta, and Marc K. Hellerstein. “The Effects of Physiological Adaptations to Calorie Restriction on Global Cell Proliferation Rates.” American Journal of Physiology. Endocrinology and Metabolism 300, no. 4 (April 2011): E735-745. https://doi.org/10.1152/ajpendo.00661.2010 Bruss, Matthew D., Cyrus F. Khambatta, Maxwell A. Ruby, Ishita Aggarwal, and Marc K. Hellerstein. “Calorie Restriction Increases Fatty Acid Synthesis and Whole Body Fat Oxidation Rates.” American Journal of Physiology. Endocrinology and Metabolism 298, no. 1 (January 2010): E108-116. https://doi.org/10.1152/ajpendo.00524.2009

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I reversed my type 2 diabetes. Here’s how I did it

Modern medicine makes it seem as if drugs are the only way to deal with diabetes. But what if diet can be a solution?

O ne gray Sunday in the middle of the Covid lockdown, I received an unwelcome call from my family doctor. Until then, for virtually my entire life, I had managed to stay out of a doctor’s office, except for routine checkups. My luck had run out.

“I am sorry to disturb you on a weekend,” she said. “But your tests just came back and your blood sugar levels are alarming. I am pretty sure you have diabetes.”

During the lockdown, I experienced symptoms I now understand to be warning signs for type 2 diabetes, the disease – along with its precursor pre-diabetes – that, according to the US Centers for Disease Control and Prevention, afflicts nearly half of all adult Americans. I was always thirsty, and had taken to drinking pitchers of sweet apple cider. I was urinating more than usual, and my urine had an orange hue. When my doctor gave me a blood test, she found my A1C, a measurement of blood sugar over a three-month period, was 11.8%, a level commonly known as “through the roof”. Anything over 5.7% is considered pre-diabetic. Above, 6.4%, you’re diabetic.

I did my homework. I learned that type 2 diabetes is a condition of high blood sugar that makes me vulnerable to blindness, amputation and kidney and heart disease. I am not a worrier by nature, but I suddenly had the dreaded sensation that my life was about to be shortened. In reality, my education about the dysfunctional state of American diet and nutrition was just beginning.

This was a new world for me, but it was also eerily familiar. For the past several years, as founder of a non-profit criminal justice organization called the Marshall Project, I have focused on the issue of criminal justice reform. I learned that as polarizing as criminal justice and racial equity are in this country, nutrition is even more controversial. If mass incarceration is a national scandal hiding in plain sight, then our twin diabetes and obesity epidemics – which disproportionately afflict the poor and people of color – are similarly sources of deep human suffering that we have simply become inured to.

The Upper East Side diabetes specialist my doctor sent me to tried to put me at ease. “This is not a death sentence,” he said. “It tends to get worse over time, but with the right medication and lifestyle changes, it can be managed.” He wrote prescriptions for insulin and metformin, and offered meticulous instructions on how to prick my fingertip to measure my blood twice daily, how to record my numbers, and how to stick a needle into my belly to inject insulin.

I asked him what, if any, changes I should make to my diet. Two decades earlier, I had been affected by the writer Gary Taubes’s controversial and groundbreaking 2002 article in the New York Times Magazine, “What If It’s All Been a Big Fat Lie?”, which chronicled the growing movement in dietary and diabetes circles to reduce carbohydrates in order to lose weight and lower blood sugar.

Taubes’s forthcoming book , Rethinking Diabetes: What Science Reveals About Diet, Insulin and Successful Treatments, explores a century’s worth of research into diabetes, and attempts to show why so many experts got things so wrong for so long. This is Taubes’s fifth book about nutrition science, and I would argue that his meticulous, science-based work (he is a three-time winner of the National Association of Science Writers’ Science in Society Journalism award) makes him the Bryan Stevenson of nutrition, an early voice in the wilderness for an unorthodox view that is increasingly becoming accepted.

While low-carbohydrate diets might be more accepted today than when Taubes first wrote about them in 2002, in large part because of his journalism and advocacy, they are still far from the standard of care for patients, even for diabetics. In fact, my doctor’s facial expression suggested that this might be the very first time he had considered the question of how diet might factor into diabetes treatment.

“Sure, you should cut down on sugar if you can,” he said meekly. “Basically, if you’re at a birthday party, instead of eating a piece of cake, just have half a piece.” Was this the equivalent of a doctor telling a smoker with lung cancer to smoke fewer cigarettes instead of quitting? Pharmaceutical intervention was clearly going to be the prescribed medicine. Dietary change was hinted at, but not stressed.

On my way out, the doctor next handed me a brochure, Living with Diabetes , published by the American College of Physicians. On its cover was a photo of a cheerful and very overweight couple holding hands. “You can still eat carbs,” it read. “Just make the portion sizes smaller.” Inside were photos of delicious carbohydrate-rich foods such as cake, orange juice, bagels and pasta, followed by pages of instructions on preparing, injecting, storing and traveling with an insulin supply.

“At first, I didn’t want to take shots, but I didn’t realize how much better I could feel,” cooed one happy customer in the brochure. “It made a big difference to me.”

On page 57, in fine print, came the kicker: “The development of the Living with Diabetes: An Everyday Guide for You and Your Family was funded by a grant from Novo Nordisk,” the Danish pharmaceutical giant that has been selling insulin to diabetics since 1924.

Fear can be a powerful motivator, and I happen to have an aversion both to shooting substances into my body and premature death, so I decided to read the literature about what type 2 diabetes is. I discovered a gigantic community of scientists, doctors and patients who had already come to understand that type 2 diabetes is in fact reversible – meaning, blood sugar levels can be reduced below the diabetes range, although this does not guarantee the diabetes has gone permanently – and that the remedy is straightforward: stop eating carbohydrates, the one macronutrient that diabetics like me cannot safely metabolize without the help of drug therapies.

I stopped eating the breads, pastas, sweets and starches I had grown accustomed to. It wasn’t easy; I still miss my pizza and bagels and sushi (white rice is a no-no for me). I had consumed all of it with gusto in the old days.

In effect, I was living in parallel universes. On one hand, I was in close consultation with my doctor, who prescribed cumbersome, painful and expensive drug therapies that were consecrated by the American Diabetes Association. Independently, I was pursuing a cheap, commonsense path that was working better than any drug could. Happily, my blood sugar numbers plummeted. My A1C fell to 5.4%, a healthy level. Within three months of first shooting insulin into my belly, my diabetes appeared to be in remission. I lost 20lb. One way to think about it is that my diabetes manifested itself if I ate carbs. If I didn’t, I was essentially fine.

To his credit, when my doctor saw my blood sugar numbers, he took me off all medication. “You don’t need me any more,” he said. But he also evinced a shocking lack of curiosity about what I did to lower my A1C so dramatically. I now realize my doctor was making an honest attempt to follow the guidelines issued by the American Diabetes Association. I didn’t ask him if he was aware that the top five funders of the ADA are the pharmaceutical companies Abbott, AstraZeneca, Eli Lilly and Co, Novo Nordisk and Regeneron.

Nutrition in America is undoubtedly tricky. Consider the debates medical professionals still engage in – low fat v low carb, carnivores v vegetarians and vegans, the energy balance model (calories in, calories out) v the carbohydrate insulin model (it’s the carbs!). Given the billions spent on research there is a shocking lack of consensus about why we get fat and diabetic, and what we should eat and not eat to avoid it or prevent it.

There is more to this than one patient’s anecdotal story. Diabetes and obesity are costly killers. Diabetes alone is likely to be the sixth-highest cause of death for Americans this year, but since it is also closely linked to coronary and kidney diseases, Alzheimer’s and stroke, it is difficult to know precisely how many Americans die prematurely because of it.

Diabetes is also big business – in 2017, Americans spent $237bn treating the disease, approximately $100bn more than a decade earlier. Obesity – which is either a symptom or a cause of diabetes, depending which medical professional you ask – accounts for billions more dollars. Nearly half a million Americans’ deaths annually can be attributed to excess weight, according to a 2022 article in the Lancet.

I recently asked Gary Taubes how we might create the same sense of national urgency about diet and diabetes that the Marshall Project and other organizations are trying to bring to criminal justice policy. His reply was both measured and responsible. He did not call for immediately banning or taxing sugary substances, toxic as he might think they are, as I might, nor did he demonize the pharmaceutical or food industries or the medical profession, as I would.

“There is significant evidence that replacing carbohydrates with mostly fats is beneficial in treating both obesity and diabetes,” he said. “There have been close to 200 clinical trials that have been done to test health outcomes of these diets.” But, he added, none of those trials were on a scale or duration that provide the kind of evidence needed to move the medical consensus.

What was needed, he said, were massive government-funded nutritional studies that establish once and for all why we get fat, and how we should treat people with diabetes. “I believe the scientific consensus is wrong, but we need more studies to prove it,” he concluded.

This was not the answer I was looking for, but I respected Taubes’s respect for dietary due process. Taubes is a journalist who thinks like a scientist – this is what makes his work so compelling.

I do not think like a scientist; I am a patient. It outrages me that we allow companies to market sugary cereals to children, and sugary beverages to everyone else, or that the American Diabetes Association simultaneously is funded by the pharmaceutical industry and tilts so heavily toward pharmaceutical solutions for my disease. Or that more than 100,000 people will die this year from diabetes, a disease that is often reversible.

It astounds me that there is still such resistance to funding nutritional studies that get to the bottom of whether sugar is addictive or toxic or determine once and for all why 42% of all Americans, according to a recent CDC study, are obese. Finally, it pains me that overweight people are demonized, despite mountains of evidence suggesting obesity is a function of metabolism, poverty, poor diet and bad medical advice.

The obesity and diabetes epidemics are a collective national failure; the sooner we acknowledge this, the sooner we can begin work on fixing them.

This article was amended on 6 December 2023 to state that while type 2 diabetes can be put into remission – or “reversed” – by lifestyle changes, there is no guarantee that this is permanent.

Neil Barsky is a former Wall Street Journal reporter and founder of the Marshall Project, a non-profit newsroom that covers the US criminal justice system

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Diabetes Meal Planning

Counting carbs and the plate method  are two common tools that can help you plan meals.

A meal plan is your guide for when, what, and how much to eat to get the nutrition you need while keeping your blood sugar levels in your target range. A good meal plan will consider your goals, tastes, and lifestyle, as well as any medicines you’re taking.

A good meal plan will also:

• Include more nonstarchy vegetables, such as broccoli, spinach, and green beans.
• Include fewer added sugars and refined grains, such as white bread, rice, and pasta with less than 2 grams of fiber  per serving.
• Focus on whole foods instead of highly processed foods as much as possible.

Carbohydrates in the food you eat raise your blood sugar levels. How fast carbs raise your blood sugar depends on what the food is and what you eat with it. For example, drinking fruit juice raises blood sugar faster than eating whole fruit. Eating carbs with foods that have protein, fat, or fiber slows down how quickly your blood sugar rises.

For more information, see Carb Counting .

You’ll want to plan for regular, balanced meals to avoid high or low blood sugar levels. Eating about the same amount of carbs at each meal can be helpful. Counting carbs and using the plate method are two common tools that can make planning meals easier too.

Counting Carbs

Keeping track of how many carbs you eat and setting a limit for each meal can help keep your blood sugar levels in your target range. Work with your doctor or a registered dietitian to find out how many carbs you can eat each day and at each meal, and then refer to this list of common foods that contain carbs and serving sizes. For more information, see Carb Counting .

The Plate Method

It’s easy to eat more food than you need without realizing it. The plate method is a simple, visual way to make sure you get enough nonstarchy vegetables and lean protein while limiting the amount of higher-carb foods you eat that have the highest impact on your blood sugar.

Start with a 9-inch dinner plate (about the length of a business envelope):

• Fill half with nonstarchy vegetables, such as salad, green beans, broccoli, cauliflower, cabbage, and carrots.
• Fill one quarter with a lean protein, such as chicken, turkey, beans, tofu, or eggs.
• Fill one quarter with carb foods. Foods that are higher in carbs include grains, starchy vegetables (such as potatoes and peas), rice, pasta, beans, fruit, and yogurt. A cup of milk also counts as a carb food.

Then choose water or a low-calorie drink such as unsweetened iced tea to go with your meal.

Did you know? Food portions are much larger now than they were 20 years ago. Test your knowledge of portion distortion here .

About Portion Size

Portion size and serving size aren’t always the same. A portion is the amount of food you choose to eat at one time, while a serving is a specific amount of food, such as one slice of bread or 8 ounces (1 cup) of milk.

These days, portions at restaurants are quite a bit larger than they were several years ago. One entrée can equal 3 or 4 servings! Studies show that people tend to eat more when they’re served more food, so getting portions under control is really important for managing weight and blood sugar.

If you’re eating out , have half of your meal wrapped up to go so you can enjoy it later. At home, measure out snacks; don’t eat straight from the bag or box. At dinnertime, reduce the temptation to go back for seconds by keeping the serving bowls out of reach. And with this “handy” guide, you’ll always have a way to estimate portion size at your fingertips:

• 3 ounces of meat, fish, or poultry Palm of hand (no fingers)
• 1 ounce of meat or cheese Thumb (tip to base)
• 1 cup or 1 medium fruit Fist
• 1–2 ounces of nuts or pretzels Cupped hand
• 1 tablespoon Thumb tip (tip to 1 st joint)
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• Introduction
• Conclusions
• Article Information

HR indicates hazard ratio. Reference group in A and C is 7 to 8 hours of sleep duration; B and D, healthy diet score of 0.

HR indicates hazard ratio. Reference group is 7 to 8 hours of sleep duration.

eTable 1. Cohort Characteristics Split by Sleep Duration Category

eTable 2. Cohort Characteristics Split by Healthy Diet Score

eTable 3. Association of Short Sleep Duration and Adherence to Consumption of Individual Food Groups With Incident Type 2 Diabetes Mellitus

eTable 4. Association Between Short Sleep Duration and Incident Type 2 Diabetes Mellitus Stratified by Adherence to Consumption of Individual Food Groups

eFigure 1. Final Sample Estimation

eFigure 2. Association of Short Sleep Duration (Using 7-9 h of Daily Sleep as the Sleep Duration Reference Category) and Adherence to Healthy Diet With Incident Type 2 Diabetes Mellitus

eFigure 3. Association Between Short Sleep Duration (Using 7-9 h of Daily Sleep as the Sleep Duration Reference Category) and Incident Type 2 Diabetes Mellitus Stratified by Diet Status

eFigure 4. Association of Short Sleep Duration and Adherence to Healthy Diet With Incident Type 2 Diabetes Mellitus (Without First 5 Years T2D Incidence)

eFigure 5. Association Between Short Sleep Duration and Incident Type 2 Diabetes Mellitus Stratified by Diet Status (Without First 5 Years T2D Incidence)

eFigure 6. Association of Short Sleep Duration and Adherence to Healthy Diet With Incident Type 2 Diabetes Mellitus (Without Prediabetic Individuals)

eFigure 7. Association Between Short Sleep Duration and Incident Type 2 Diabetes Mellitus Stratified by Diet Status (Without Prediabetic Individuals)

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• Less Sleep Tied to Increased Risk of Diabetes Despite Healthy Diet JAMA Medical News in Brief April 5, 2024 Emily Harris

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Nôga DA , Meth EDMES , Pacheco AP, et al. Habitual Short Sleep Duration, Diet, and Development of Type 2 Diabetes in Adults. JAMA Netw Open. 2024;7(3):e241147. doi:10.1001/jamanetworkopen.2024.1147

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Habitual Short Sleep Duration, Diet, and Development of Type 2 Diabetes in Adults

• 1 Department of Pharmaceutical Biosciences, Uppsala University, Sweden
• 2 Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China
• 3 Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
• 4 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
• 5 Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden
• 6 Department of Psychiatry and Psychotherapy, Tübingen Centre for Mental Health, Medical Faculty, University of Tübingen, Tübingen, Germany
• Medical News in Brief Less Sleep Tied to Increased Risk of Diabetes Despite Healthy Diet Emily Harris JAMA

Question   Is there an association between adherence to healthy diet, sleep duration, and risk of developing type 2 diabetes (T2D) in adults?

Findings   This cohort study analyzing data from 247 867 adults in the UK Biobank found that individuals sleeping less than 6 hours daily had a notably higher risk of developing T2D compared with those with 7 to 8 hours of sleep. Despite the association between healthier diets and reduced T2D risk, the increased risk associated with short sleep duration persisted even among adults with healthy eating habits.

Meaning   These findings suggest that adopting a healthy diet may not reduce the risk of developing T2D among those with habitual short sleep duration.

Importance   Understanding the interplay between sleep duration, dietary habits, and the risk of developing type 2 diabetes (T2D) is crucial for public health and diabetes prevention strategies.

Objective   To investigate the associations of type of diet and duration of sleep with the development of T2D.

Design, Setting, and Participants   Data derived from the UK Biobank baseline investigation (2006-2010) were analyzed for this cohort study between May 1 and September 30, 2023. The association between sleep duration and healthy dietary patterns with the risk of T2D was investigated during a median (IQR) follow-up of 12.5 (11.8-13.2) years (end of follow-up, September 30, 2021).

Exposure   For the analysis, 247 867 participants were categorized into 4 sleep duration groups: normal (7-8 hours per day), mild short (6 hours per day), moderate short (5 hours per day), and extreme short (3-4 hours per day). Their dietary habits were evaluated based on population-specific consumption of red meat, processed meat, fruits, vegetables, and fish, resulting in a healthy diet score ranging from 0 (unhealthiest) to 5 (healthiest).

Main Outcomes and Measures   Cox proportional hazards regression analysis was used to calculate hazard ratios (HRs) and 95% CIs for the development of T2D across various sleep duration groups and healthy diet scores.

Results   The cohort comprised 247 867 participants with a mean [SD] age of 55.9 [8.1] years, of whom 52.3% were female. During the follow-up, 3.2% of participants were diagnosed with T2D based on hospital registry data. Cox regression analysis, adjusted for confounding variables, indicated a significant increase in the risk of T2D among participants with 5 hours or less of daily sleep. Individuals sleeping 5 hours per day exhibited a 1.16 adjusted HR (95% CI, 1.05-1.28), and individuals sleeping 3 to 4 hours per day exhibited a 1.41 adjusted HR (95% CI, 1.19-1.68) compared with individuals with normal sleep duration. Furthermore, individuals with the healthiest dietary patterns had a reduced risk of T2D (HR, 0.75 [95% CI, 0.63-0.88]). The association between short sleep duration and increased risk of T2D persisted even for individuals following a healthy diet, but there was no multiplicative interaction between sleep duration and healthy diet score.

Conclusions and Relevance   In this cohort study involving UK residents, habitual short sleep duration was associated with increased risk of developing T2D. This association persisted even among participants who maintained a healthy diet. To validate these findings, further longitudinal studies are needed, incorporating repeated measures of sleep (including objective assessments) and dietary habits.

Many people sleep less than 7 hours per day, a condition often termed as short sleep duration. For instance, according to the 2020 Behavioral Risk Factor Surveillance System, 33.2% of US adults were short sleepers. 1 Prolonged periods of insufficient sleep are associated with various health risks, including an increased risk of type 2 diabetes (T2D). A meta-analysis of prospective studies involving 482 502 participants with follow-up periods spanning from 2.5 to 16.0 years demonstrated that each hour of sleep duration below 7 hours per day was associated with a 1.09-fold likelihood of developing T2D. 2 Similar patterns are observed when investigating the association between objectively measured sleep duration and T2D in the UK Biobank. Participants with daily sleep duration below 7 to 8 hours demonstrated a hazard ratio (HR) of 1.21 for the development of T2D. 3 Further support is derived from various experimental studies that demonstrate impaired glucose tolerance test responses and indicators of insulin resistance associated with acute sleep restriction. 4 - 7

Based on current evidence, increasing daily sleep duration to at least 7 hours may reduce the risk of T2D in individuals with insufficient sleep. Nevertheless, challenges in achieving the recommended sleep duration persist, including factors such as work schedules, childcare responsibilities, and economic pressures. Given those constraints, adhering to an otherwise healthy lifestyle may be an alternative approach for mitigating T2D risk among individuals with short sleep duration. For instance, the results of a small experimental study suggest that engaging in high-intensity interval exercise during the daytime may counteract the detrimental effects of sleep restriction on glucose tolerance in humans. 8 Those findings were reaffirmed by a recent analysis of UK Biobank data, which indicated that individuals with short sleep duration who engaged in regular physical activity exhibited a lower risk of developing T2D. 3 While the effectiveness of a healthy dietary pattern in lowering the risk of T2D is well-established, 9 , 10 the extent to which adherence to such a diet can mitigate the elevated risk of T2D associated with chronic short sleep duration is less clear. This area of research is particularly challenging due to the tendency of short sleep to promote unhealthy food choices. 11 - 14

Previous research provides substantial evidence that short sleep duration adversely affects glucose metabolism. 15 In contrast, current literature does not offer strong evidence that extended sleep in individuals with normal sleep patterns significantly disrupts glucose regulation. Thus, the association between habitual long sleep duration (often defined as more than 8 or 9 hours per day) and T2D 2 may not be causally linked. 16 With this evidence in mind, our research, encompassing 247 867 participants from the UK Biobank cohort, explored the association between self-reported short sleep duration and T2D incidence, particularly considering adherence to a healthy diet. We hypothesized that a healthy dietary pattern would lower the risk of T2D among those with short sleep duration.

This cohort study is part of UK Biobank project No. 80513. Data from 247 867 participants 38 to 71 years of age who took part in the baseline visit (scheduled from 2006 to 2010) were available. We applied multiple criteria, including the absence of data on exposure or confounding variables and a T2D diagnosis within 1 year of assessment, to define the final cohort. A detailed summary of this process is available in eFigure 1 in Supplement 1 . The assessment of participants’ daily sleep duration and dietary habits was conducted at baseline as part of a touchscreen questionnaire. The UK Biobank study was approved by the North West Multi-Center Research Ethics Committee 17 ; all participants provided written informed consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cohort studies.

Based on a response to the touchscreen question “About how many hours sleep do you get in every 24 hours? (please include naps)” completed during the baseline visit, participants who reported a daily sleep duration of 7 to 8 hours were categorized as having normal sleep duration. Short sleep duration was classified as mild short sleep (6 hours), moderate short sleep (5 hours), and extreme short sleep (3-4 hours) for considering the dose-response relationship with T2D. 2 In line with previous research, 18 participants with a daily sleep duration of less than 3 hours were not included in the main analysis (eFigure 1 in Supplement 1 ).

Similar to a previous UK Biobank study, 19 participants’ adherence to a healthy diet was determined in the present study via responses to an electronic questionnaire. Healthy eating, based on a population-specific median split, included criteria such as fewer than 2 servings of unprocessed red meat products per week (67.3%), fewer than 2 servings of processed meat products per week (39.2%), 4 or more tablespoons of vegetables per day (64.8%), 2 or more pieces of fruit per day (72.7%), and 2 or more servings of fish products per week (52.3%). Each healthy dietary behavior scored 1 point, resulting in a healthy diet score ranging from 0 (unhealthiest) to 5 (healthiest).

The outcome of this study was incident T2D, which was ascertained from hospital inpatient records ( International Statistical Classification of Diseases, Tenth Revision , codes E110-E119). Records were available until September 30, 2021, and detailed procedures can be found in the UK Biobank online resource. 20

Data were analyzed for this cohort study between May 1 and September 30, 2023. All analyses were conducted using SPSS, version 28.0.1.0 (IBM; SPSS Inc), and R version, 4.3.2 (R Project for Statistical Computing). Cox proportional hazards regression analysis was used to calculate HRs and 95% CIs for the development of T2D across various sleep duration groups and the healthy diet score. Statistical significance was defined as a 2-sided P  < .05. Additionally, we assessed multiplicative and additive interactions between sleep duration and the healthy diet score. For additive interactions, we computed the relative excess risk due to interaction, the attributable proportion due to the interaction, and the synergy index by using the interactionR package (version 0.1.7) in R. The time at risk (measured in days) was calculated from the date of the baseline assessment until the occurrence of T2D diagnosis, death, or the conclusion of the follow-up period (September 30, 2021), whichever came first. Proportional hazards assumptions were verified by assessing Kaplan-Meier survival curves.

To enhance the robustness of the crude associations between sleep duration, adherence to healthy dietary patterns, and incident T2D, the adjusted model considered various participant characteristics from the baseline visit. These characteristics encompassed sleep duration, healthy diet score, age, biological sex (female or male), race and ethnicity (African or Caribbean, Asian, White European, or other [including other ethnic group, any other ethnic background, Black or Black British, other Black background, and other White background] because some racial and ethnic groups reportedly have higher rates of T2D than others 21 ; self-reported based on predefined categories from the UK Biobank, which were then combined to form the options used), smoking status (never smoked, previous smoker, current smoker), frequency of weekly alcohol intake (not current, less than 3 times a week, 3 or more times a week), antidepressant use (self-reported use of selective serotonin reuptake inhibitor, selective noradrenaline reuptake inhibitor, tricyclic antidepressant, atypical antidepressant, or monoamine oxidase inhibitor), assessment center region (England, Scotland, or Wales), body mass index, systolic blood pressure (automated reading taken at baseline), socioeconomic status (measured by the Townsend index), educational level (no qualification, university degree, and any other qualification), insomnia symptoms frequency (never or rarely; sometimes; and usually), and physical activity level (categorized as low, moderate, and high levels as defined by the International Physical Activity Questionnaire 22 ).

To evaluate the potential competing risk of all-cause death, we computed the Fine-Gray subdistribution hazard by using the cmprsk R package (version 2.2-11). Recommended sleep duration by organizations such as the US Sleep Foundation for adults aged 18 and older is 7 to 9 hours daily. 23 Consequently, we conducted an additional sensitivity analysis, designating 7 to 9 hours of daily sleep duration as the reference category (eFigure 1 in Supplement 1 ). Furthermore, we explored the association between short sleep duration and increased T2D risk, considering adherence to individual healthy eating habits. To mitigate bias from inverse causation, we reran the analysis excluding individuals who developed T2D within 5 years after their assessment visit. Finally, the primary analysis was repeated, excluding participants with prediabetes at baseline (hemoglobin A 1c [HbA 1c ] levels, 39-47 mmol/mol or 5.7%-6.5% of total hemoglobin 24 ; to convert from percentage to proportion of total hemoglobin, multiply by 0.01).

The cohort comprised 247 867 participants with a mean (SD) age of 55.9 (8.1) years, of whom 52.3% were females, 47.7% were males, 93.6% identified as White European, 1.7% as Asian, 0.9% as Caribbean or African, and 3.8% as other race or ethnicity. In addition, 75.5% reported normal sleep duration, 19.8% reported mild short sleep duration, 3.9% reported moderate short sleep duration, and 0.8% reported extreme short sleep duration. Additionally, 1.5% attained a healthy diet score of 0, 7.4% scored 1, 17.6% scored 2, 27.5% scored 3, 29.0% scored 4, and 17.0% scored 5 (defined as the healthiest dietary pattern). Additional cohort characteristics can be found in the Table . Cohort characteristics categorized by either sleep duration or healthy diet score are given in eTable 1 and eTable 2 in Supplement 1 .

The total follow-up time for the investigated cohort was 3 029 282 years at risk, and 7905 participants (3.2%) were diagnosed with T2D during a median (IQR) follow-up of 12.5 (11.8-13.2) years. In comparison with participants reporting normal sleep duration (reference group), participants who reported sleep durations of less than 6 hours per night had greater risk of developing T2D (adjusted HRs,1.16 [95% CI, 1.05-1.28], P  = .003 for 5 hours; and 1.41 [95% CI, 1.19-1.68], P  < .001 for 3-4 hours). There was no statistically significant difference between participants who reported normal sleep duration and those who reported 6 hours (adjusted HR, 1.02 [95% CI, 0.97-1.08) ( Figure 1 ). Figure 2 shows Kaplan-Meier curves by sleep duration status.

Participants with a healthy diet score of 4 or 5 exhibited lower risk of developing T2D than those with the least healthy dietary pattern (HR, 0.75 [95% CI, 0.63-0.88], P  < .001 for 5 points; HR, 0.82 [95% CI, 0.70-0.96], P  = .01 for 4 points; HR, 0.89 [95% CI, 0.76-1.04], P  = .13 for 3 points; HR, 0.88 [95% CI, 0.76-1.03], P  = .12 for 2 points; and HR, 0.90 [95% CI, 0.76-1.06], P  = .22 for 1 point) ( Figure 1 ). Figure 2 shows Kaplan-Meier plots by healthy diet scale.

Contrary to our hypothesis, no multiplicative interaction between sleep duration and the healthy diet score was observed, either in the unadjusted (HR [95% CI] range, 0.83-3.02 [0.30-7.20]; P  = .48) or adjusted (HR [95% CI] range, 0.93-3.49 [0.39-8.34]; P  = .38) analysis. Considering that sleeping less than 6 hours per day was associated with higher risk, and a healthy diet score of 4 or higher was associated with lower HRs for T2D, we transformed daily sleep duration and the healthy diet score into binary variables to explore a potential additive interaction. Specifically, we categorized daily sleep duration as 6 to 9 hours vs 3 to 5 hours, and the healthy diet score as 4 to 5 points vs 0 to 3 points. There was no significant additive interaction between daily sleep duration and a healthy diet (relative excess risk due to interaction, 0.05 [95% CI, −0.16 to 0.26]; attributable proportion due to the interaction, 0.04 [95% CI, −0.12, 0.19]; synergy index, 1.17 [95% CI, 0.61-2.26]). Figure 3 illustrates the associations between daily sleep duration and T2D incidence during the follow-up, categorized by a T2D-protective dietary pattern (4-5 points) and T2D-nonprotective dietary pattern (0-3 points).

When considering the possible competitive risk of all-cause death, the main results changed for neither sleep (adjusted subdistribution HRs, 1.02 [95% CI, 0.97-1.08], P  = .48 for mild short sleep duration; 1.16 [95% CI, 1.05-1.28], P  = .004, for moderate short sleep duration; and 1.41 [95% CI, 1.19-1.69], P  < .001 for extreme short sleep duration) nor for the healthy diet score (adjusted subdistribution HRs, 0.75 [95% CI, 0.64-0.89], P  < .001 for 5 points; 0.82 [95% CI, 0.70-0.96], P  = .02 for 4 points; 0.89 [0.76-1.04], P  = .15 for 3 points; 0.88 [95% CI, 0.75-1.04], P  = .13 for 2 points; and 0.90 [95% CI, 0.76-1.07], P  = .22 for 1 point). When using a daily sleep duration of 7 to 9 hours as the reference category, our results were largely supported (eFigure 2 and eFigure 3 in Supplement 1 ). These observations persisted even after excluding individuals who developed T2D within the first 5 years of follow-up (eFigure 4 and eFigure 5 in Supplement 1 ). Examining individual healthy eating habits, we found that a reduced weekly consumption of unprocessed red meat and processed meat was associated with a decreased risk of T2D (eTable 3 in Supplement 1 ). However, irrespective of whether participants reported high or low consumption of unprocessed red meat and processed meat products, the association between shorter sleep duration and higher HRs for developing T2D remained significant (eTable 4 in Supplement 1 ).

When excluding participants with prediabetes at baseline, daily sleep durations between 3 and 5 hours remained significantly associated with higher HRs for T2D (eFigure 6 in Supplement 1 ). None of the healthy diet scores were significantly associated with the risk of T2D (eFigure 6 in Supplement 1 ). However, when combining participants who scored 4 or 5 on the healthy diet scale as 1 group and participants who scored less as the other group, the risk of developing T2D was lower in the first group in both the unadjusted (HR, 0.77 [95% CI, 0.72-0.83], P  < .001) and adjusted (HR, 0.81 [95% CI, 0.76-0.88], P  < .001) analyses. Still, when categorizing participants into those scoring 4 or 5 and those scoring less than 4, short sleep duration remained significantly associated with a higher risk of developing T2D (eFigure 7 in Supplement 1 ).

This cohort study assessing daily sleep duration, dietary habits, and the risk of T2D among individuals in the UK Biobank cohort 38 to 71 years of age found that habitual short sleep duration was associated with increased risk of developing T2D and that this association persisted even among participants who maintained a healthy diet. Many adults struggle to sleep 7 to 8 hours per day. 1 As suggested by laboratory studies, a lack of sleep may contribute to the development of T2D through various mechanisms, such as impaired cellular insulin sensitivity, 6 a skeletal muscle energy metabolism shifted toward nonglucose oxidation, 25 increased activity of the sympathetic nervous system, 26 and altered gut microbiota composition. 4 , 27 Consequently, the high prevalence of individuals with short sleep duration may contribute to the projected global escalation of T2D prevalence. 28 Supporting this notion, prospective associations have been observed between short sleep duration and increased risk of T2D. For instance, in the Nurses’ Health Study II and the Whitehall II Study, persistent short sleep duration, defined as either 5.5 hours per day 29 or 5.5 hours or less per day, 30 was found to be correlated with a heightened risk of T2D during follow-up.

Recognizing that extending sleep duration may not be a feasible goal for a substantial proportion of individuals with short sleep duration, exploring alternative strategies to mitigate the risk of T2D among them becomes essential. Notably, as suggested by findings from a clinical trial, engaging in high-intensity exercise may mitigate impaired blood glucose control following short sleep. 8 Consistent with those findings, an analysis of the UK Biobank revealed that individuals with habitual short sleep duration were less likely to develop T2D when regularly engaging in physical activity. 3 While diets such as the Mediterranean diet, characterized by a high intake of plant-based foods, have been associated with a reduced risk of T2D, habitual eating patterns marked by a high consumption of processed foods, including meat, may have the opposite effect. 31 - 34 However, whether healthy eating habits have the potential to lower T2D risk among habitual short sleepers remains an underexplored research area.

Thus, in the present study, we used data from the UK Biobank baseline assessment, focusing on participants’ weekly consumption of red meat, processed meat, and fish, as well as the daily consumption of vegetables and fruits. This information enabled us to categorize participants into 2 distinct dietary groups: those whose dietary patterns were associated with a lower risk of developing T2D and those whose dietary patterns did not modify the risk of developing T2D. Our findings revealed an elevated T2D risk associated with shorter sleep durations across both dietary groups. These findings, further confirmed in several sensitivity analyses, suggest that healthy dietary habits may not necessarily offset the risk of T2D incurred by habitual short sleep duration.

While our research indeed established a higher risk of T2D associated with short sleep durations, aligning with previous epidemiological and experimental evidence, 2 - 7 it remains crucial to consider the underlying causes of short sleep duration. For instance, obstructive sleep apnea can lead to premature awakening and insufficient sleep duration. 35 Notably, a recent analysis indicates that nearly 1 billion individuals worldwide experience sleep-disordered breathing, 36 with as many as approximately 80% of them likely being unaware of their condition. 37 Obstructive sleep apnea is known to heighten the risk of insulin resistance and T2D 38 - 41 and may, in part, explain the observed association between short sleep duration and elevated T2D risk. Given this possibility, the efficacy of healthy dietary patterns in mitigating the adverse effects of short sleep on glucose metabolism may be limited if obstructive sleep apnea is coexistent.

Despite the robust nature of our findings—as they remain significant even after adjusting for multiple confounding variables such as participants’ body mass index, age, and weekly physical activity level—a nuanced interpretation is necessary concerning their generalizability. We adopted a method to assess participants’ healthy eating habits similar to a previous UK Biobank study. 19 However, whether other types of dietary patterns, such as time-restricted eating or the Mediterranean diet, can modify the risk of T2D among individuals with short sleep duration remains unclear. Emerging evidence indicates that such dietary patterns are associated with enhanced blood glucose control and reduced T2D risk. 9 , 42 , 43 Additionally, there may be specific macronutrients or micronutrients, not explored in this study, that could more effectively counteract the adverse metabolic effects induced by sleep loss. Those nutrients may be particularly beneficial for individuals at higher risk of developing conditions such as T2D. 44 Another limitation of our study is the absence of updated data on follow-up unavailability from the UK Biobank since May 2017. It should also be noted that daily sleep duration and dietary habits were self-reported and only assessed at baseline. This raises concerns about recall bias and the potential variability of those behaviors during the follow-up period. Therefore, to substantiate our findings, additional longitudinal studies are warranted. Those studies should include repeated and objective assessments of sleep and eating habits. Despite our efforts to adjust for a comprehensive range of known confounders, including hypertension, obesity, high HbA 1c , depression, and various lifestyle factors, the influence of unmeasured variables not captured in our dataset may still play a role in the associations observed between sleep duration, diet, and the risk of T2D. Finally, the majority of our participants were of White ancestry, which may limit the applicability of our results to more diverse populations.

This cohort study did not yield compelling evidence to support the notion that maintaining a diet characterized by a low consumption of red meat and processed meat products and a high intake of fruits, vegetables, and fish can sufficiently mitigate the risk of developing T2D associated with habitual short sleep duration. However, given the constraints of the current analysis, further research is necessary to explore whether specific dietary patterns, such as time-restricted eating, can counteract or alleviate the adverse metabolic consequences associated with short sleep duration. Future studies exploring the associations among adherence to a healthy diet, sleep duration, and the risk of developing T2D would benefit substantially from including repeated and objective measures of both sleep and dietary habits. Such an approach is essential to unravel the dynamic interplay between these factors in the context of T2D, providing a more comprehensive understanding of their combined association with T2D risk.

Accepted for Publication: January 14, 2024.

Published: March 5, 2024. doi:10.1001/jamanetworkopen.2024.1147

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Nôga DA et al. JAMA Network Open .

Corresponding Author: Christian Benedict, PhD, Department of Pharmaceutical Biosciences, Uppsala University, Husargatan 3, Box 593, 751 24 Uppsala, Sweden ( [email protected] ).

Author Contributions: Dr Nôga had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Nôga and Ms Meth contributed equally.

Concept and design: Meth, Pacheco, Tan, van Egmond, Xue, Benedict.

Acquisition, analysis, or interpretation of data: Nôga, Cedernaes.

Drafting of the manuscript: Nôga.

Critical review of the manuscript for important intellectual content: Meth, Pacheco, Tan, Cedernaes, van Egmond, Xue, Benedict.

Statistical analysis: Nôga, Cedernaes.

Obtained funding: Xue, Benedict.

Administrative, technical, or material support: Cedernaes.

Supervision: Benedict.

Conflict of Interest Disclosures: Dr Nôga reported receiving grants from Fredrik och Ingrid Thurgins Stiftelse outside the submitted work. Dr Cedernaes reported receiving grants from the Swedish Cancer Foundation, Swedish Research Council, Swedish Brain Foundation, Selander Foundation, and Swedish Society for Medical Research during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was supported by Åke Wiberg Foundation grant M22-0081 and an institutional grant from the Department of Pharmaceutical Biosciences at Uppsala University to Dr Xue and grants from the Novo Nordisk Foundation (NNF23OC0081873) and the Swedish Brain Research Foundation (FO2023-0292) to Dr Benedict.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes

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• Published: 23 August 2023
• Volume 12 , pages 679–694, ( 2023 )

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• Janet Antwi   ORCID: orcid.org/0000-0002-8270-7717 1  

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Purpose of Review

Existing dietary and lifestyle interventions and recommendations, to improve the risk factors of obesity and type 2 diabetes with the target to mitigate this double global epidemic, have produced inconsistent results due to interpersonal variabilities in response to these conventional approaches, and inaccuracies in dietary assessment methods. Precision nutrition, an emerging strategy, tailors an individual’s key characteristics such as diet, phenotype, genotype, metabolic biomarkers, and gut microbiome for personalized dietary recommendations to optimize dietary response and health. Precision nutrition is suggested to be an alternative and potentially more effective strategy to improve dietary intake and prevention of obesity and chronic diseases. The purpose of this narrative review is to synthesize the current research and examine the state of the science regarding the effect of precision nutrition in improving the risk factors of obesity and type 2 diabetes.

Recent Findings

The results of the research review indicate to a large extent significant evidence supporting the effectiveness of precision nutrition in improving the risk factors of obesity and type 2 diabetes. Deeper insights and further rigorous research into the diet-phenotype-genotype and interactions of other components of precision nutrition may enable this innovative approach to be adapted in health care and public health to the special needs of individuals.

Precision nutrition provides the strategy to make individualized dietary recommendations by integrating genetic, phenotypic, nutritional, lifestyle, medical, social, and other pertinent characteristics about individuals, as a means to address the challenges of generalized dietary recommendations. The evidence presented in this review shows that precision nutrition markedly improves risk factors of obesity and type 2 diabetes, particularly behavior change.

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Introduction

Obesity and diabetes have emerged as enormous public health problems not only in the USA but also globally. Diabetes is a significant global challenge to the health and well-being of individuals and societies [ 1 ]. With a continued global increase in diabetes, the current prevalence of 537 million adults living with diabetes is projected to rise to 643 million by 2030 [ 1 ]. In the USA, an estimated 37.3 million people have diabetes, of which 90–95% of cases, including children, adolescents, and young adults are attributed to type 2 diabetes [ 2 , 3 , 4 ]. Diabetes data and trends for 2019 available at the Centers for Disease Control and Prevention indicated that diabetes is the sixth leading cause of death, and number one cause of kidney failure and lower limb amputation [ 3 , 4 ]. Obesity is the strongest risk factor for the development of type 2 diabetes [ 5 , 6 , 7 ]. Thus, the burden of type 2 diabetes is increasing in parallel to increasing cases of obesity [ 8 ]. Clinical data show that of the people diagnosed with type 2 diabetes, about 80–90% are highly likely to be diagnosed as obese [ 9 , 10 , 11 , 12 ]. The associated medical expenses of obesity and type 2 diabetes are steep. Obesity costs the US health care system nearly $173 billion a year [ 13 , 14 ], while the total estimated economic burden of type 2 diabetes was $327 billion in medical costs and lost productivity [ 15 ].

Both obesity and type 2 diabetes have related multifactorial etiology, making them highly complex diseases and investment in their effective prevention and management has become necessary to tackle this global epidemic. While obesity and type 2 diabetes have traditionally been studied to be diseases of energy imbalance, other risk factors such as high body weight and fat, dyslipidemia, high blood glucose, and insulin resistance are also involved in the etiology [ 16 , 17 , 18 ]. Unhealthy diet characterized by foods high in fat, sugars, and calories, but low in plant-based sources, and lack of physical activity are now considered top risk factors for the development and progression of obesity and type 2 diabetes [ 19 ]. Thus, improving dietary intake and physical activity is a global priority [ 20 ].

Dietary recommendations and public health campaigns for tackling risk factors of obesity and type 2 diabetes have focused on using population averages, have been based on generalized advice, or have been poorly adhered to [ 21 , 22 , 23 , 24 ]. Moreover, there have been great challenges with the validity, consistency, and reproducibility of dietary assessments [ 25 ]. Because obesity and type 2 diabetes are heterogeneous diseases from the pathophysiological, genetic, and clinical perspectives, and there is dramatic inter-individual variability in response to any therapeutic diet or physical activity regime, there is a need to shift to or complement the population perspective with patient-centric interventions [ 26 , 27 , 28 ]. These variabilities are attributed to differences in genetics, biomarkers of metabolic pathways, gut microbiome, environmental, physiological, behavioral, social, and economic factors. Given the substantial burden of obesity and its related comorbidities, research and practice efforts should adopt a holistic approach for sustainable solutions in preventing and treating the obesity and type 2 diabetes epidemic [ 9 ].

Precision nutrition (or personalized nutrition) has emerged as a new area of lifestyle intervention that allows dietary recommendations to be tailored at the individual level through integration of demographic information, lifestyle-based information (e.g., dietary intake, and physical activity), phenotype-based information (e.g., anthropometrics, and standard clinical biomarkers of disease risk), and gene- and omics-based information (e.g., genetic testing of single nucleotide polymorphisms, and gut microbiome) (Fig.  1 ) [ 29 , 30 ]. The current use of nutrigenetics, metabolomics, and metagenomics in precision nutrition enables the holistic interrogation of dietary and lifestyle factors to objectively assess risk factors of obesity and type 2 diabetes. The identification of various genes and polymorphisms has been determined as the basis for the interpersonal variability in metabolic response to specific diets [ 31 , 32 , 33 ]. Metabolomics investigates, among other things, the effect of food-derived biomarkers metabotypes variation among individuals in metabolizing the same diets in health and disease states for customized dietary interventions through metabolic patterns [ 34 ]. The identification of metabolites of food intake to serve as target of nutrition intervention makes metabolomics have potential to improve the accuracy of dietary assessment [ 35 ]. Metagenomics is vital in precision nutrition because it can be used to comprehensively analyze the diet-microbiome interaction to identify various metabotypes that characterize metabolic risk and tailor dietary intervention approaches for improved health [ 36 ].

Components of the precision nutrition approach. The individual characteristics of demographic, phenotype, lifestyle, genetic, and omics information are incorporated into the precision nutrition intervention to address the interpersonal variabilities in response to general nutrition intervention and recommendations to improve the risk factors of obesity and type 2 diabetes

It is suggested that precision nutrition interventions could result in greater weight loss and blood glucose control than non-personalized strategies [ 37 , 38 ]. In personalizing nutritional advice, there is evidence that people are more motivated to make appropriate behavioral changes [ 39 , 40 ]. The interest in precision nutrition has not only significantly increased in the scientific community [ 41 ], but is already becoming more accessible to consumers, largely through self-administered test-kits coupled with diet plans and subscription programs [ 41 , 42 , 43 ]. Thus, precision nutrition has been identified as the individualized solution to prevent and manage obesity and type 2 diabetes in lieu of the population-based dietary interventions, whose effectiveness in reducing the risks of these conditions using the “one-way diet” approach for all individuals is questionable [ 44 ].

The purpose of this review is to examine the current state of the science regarding precision nutrition in improving the risk factors of obesity and type 2 diabetes with emphasis on studies that included more than one component of precision nutrition and not only genetic testing to provide individualized/personalized dietary advice. While progress has been made on the quantity of research focused on precision nutrition, reviews discussing particularly behavior change and changes in nutrient/diet quality and physical activity as part of a comprehensive analysis of the utility of precision nutrition intervention and its outcomes are lacking.

• Nutrigenetics

Nutrigenetics is considered the foundation of precision nutrition (Table 1 ) [ 45 , 46 ]. Genetic variation in the form of single nucleotide polymorphisms (SNPs) is considered to account for the heterogeneity in individual dietary response and risk for obesity and type 2 diabetes [ 47 , 48 ]. Nutrigenetic research has investigated the interactions between SNPs influencing body composition, insulin signaling, and dietary factors in relation to adiposity and glucose homeostasis in obesity and type 2 diabetes. In an observational study, a genetic risk score-diet interaction used to provide precision nutrition based on 16 SNPs related to obesity or lipid metabolism demonstrated its value in obesity prediction. Specifically, in individuals carrying > 7 risk alleles, there was higher body mass index (BMI), body fat mass, waist circumference, and waist-to-hip ratio more than the individuals with ≤ 7 risk alleles [ 49 ]. Additionally, there was a significant interaction between genetic risk score and the macronutrient intake used in personalized intervention. Similarly, a systematic review and meta-analyses and two observational studies reported genetic interactions with specific macronutrients, that is, carbohydrate [ 50 ], fat [ 51 ], and protein intakes, respectively [ 52 ]. SNPs in the apolipoprotein A1 and C3 ( APOA1 and APOC3 ) genes and cluster of differentiation 36 ( CD36 ) gene led to increased risk of metabolic syndrome in subjects with Western dietary pattern and dyslipidemia in individuals who consumed high amounts of fat, respectively. Two randomized controlled trials (RCT) showed that personalized prescription of energy-restricted diets (low-fat and moderately high-protein) based on 95 different genetic variants related to energy homeostasis, phenotypic, and environmental factors was associated with differential adiposity outcomes, with waist circumference and total body fat loss particularly among obese subjects who carried the Peroxisome Proliferator Activator Receptor Gamma Coactivator 1 ( PPARGC1A Gly482Gly) genotype [ 53 ••, 54 ]. In an observational prospective cohort design from the RCT, Prevención con Dieta Mediterránea (PREDIMED), the investigators concluded that genetic predisposition to type 2 diabetes associated with the Transcription Factor 7-Like 2 Gene [ TCF7L2 gene (rs790314 TT)] homozygosity could be counteracted through precision nutrition interventions with the Mediterranean diet [ 55 ]. While precision nutrition effectively addresses the genetic variability in nutrient metabolism, and other physiological processes among individuals, it was found in a parallel-group, pragmatic, RCT that providing nutrigenetic information and advice for management could help reduce body fat percentage up to 6 months, and reductions in body fat were similar to the standard weight loss intervention after 12 months. The clinical implications of this study are that the genetic-based precision nutrition approach should be considered for use for clinical cases which require short- to long-term body fat loss, particularly for individuals needing that to undergo surgery or transplant [ 56 ]. The Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) RCT was conducted to determine the impact of precision nutrition on fasting glucose, fasting insulin, hemoglobin A1C (HbA1C), insulin resistance, and β cell function. The precision nutrition diet varied in macronutrient composition and was investigated with type 2 diabetes genetic risk scores on these parameters of glucose metabolism. At 2 years of intervention, low-protein diet responses significantly interacted with lower genetic risk score and greater decreases in fasting insulin, HbA1C, insulin resistance, and a lesser increase in β cell function, compared to those with a higher genetic risk score [ 57 ]. A post hoc analysis of the POUNDS LOST RCT showed that in response to high-fat diets, participants with the highest genetic risk score showed increased fasting glucose, insulin resistance, and decreased insulin sensitivity at 6-month follow-up than those with low-fat diets [ 58 ]. The influence of genetic factors and nutrient-gene interactions in precision nutrition applications has been indicated by twin studies. In the Personalized Responses to Dietary Composition Trial (PREDICT) RCT [ 59 ••], a large inter-individual variability in postprandial blood glucose and insulin responses was observed following the same meals among 1002 twins and unrelated healthy adults in the UK. Genetic variants had modest impact on predictions of glucose, triglycerides, and C-peptide. These results were independently validated among 100 US adults. In addition, a machine learning algorithm predicted these variabilities to precision nutrition. An observational retrospective pre/post comparison of digital twin-enabled precision nutrition therapy was used to examine diabetes reversal [ 60 ••]. The authors reported diabetes reversal (that is, achieving HbA1C < 6.5% at least 3 months after stopping antidiabetic medications) during 90 days of precision nutrition therapy at varying rates of subgroups of obese and non-obese type 2 diabetes patients. Baseline data showed that only 9.5% of patients were in reversal stage 4 or better; however, over the first 90 days, 82.1% achieved advanced stages of reversal with improved clinical outcomes and fewer pharmacotherapy. Furthermore, a retrospective study reported that there was a decrease in HbA1C, body weight, fasting blood glucose, and insulin resistance at 90-day follow-up assessment [ 61 ]. In contrast, a prospective RCT [ 62 ] that randomized overweight or obese individuals to receive a nutrigenetic-based precision nutrition diet or standard balanced diet reported no difference in weight loss between the two groups. However, the results highlight the need for larger macronutrient differences between groups and adherence to the recommended intervention diet plan. Further research should be conducted to provide new data and make the use of genetic-based precision nutrition management in the clinical setting more effective [ 62 ]. Studies on diet-gene interactions among non-Caucasians are limited. In a prospective cohort study of Hispanics of Caribbean origin who were genotyped for the Perilipin SNP [ PLIN 11482G  >  A (rs894160)] to determine whether dietary macronutrients modulated the associations of the SNP with obesity (measured as BMI, waist and hip circumference), the investigators found that the minor allele was protective against obesity for subjects who consumed higher complex carbohydrate, whereas among those with lower complex carbohydrate intake, the minor allele was linked with increased risk of obesity [ 63 ].

• Metabolomics

Metabolomics, an emerging technology which encompasses comprehensive analysis of metabolites, holds promise to inform precision nutrition recommendations (Table 1 ) [ 64 ]. The various metabolites produced from metabolism of dietary factors have been used to characterize metabolic phenotypes or biomarkers that can be used for individual stratification. This metabolic specificity enables precision nutrition to resolve metabolic derangements that underlie obesity and type 2 diabetes [ 34 ]. Additionally, metabotyping which stratifies individuals with metabolic similarity into metabotype subgroups using their metabolic and phenotype patterns could be used for population stratification to customize dietary interventions [ 65 ]. Earlier studies that paved the way for the use of metabolomics in precision nutrition showed that dietary intake patterns were revealed in metabolomic profiles [ 66 ], and were associated with biomarkers such as high levels of lipid metabolites, amino acids, and ferritin that mediated red meat consumption and risk of type 2 diabetes [ 67 ]. Recently, a study analyzed blood metabolites using metabolomics among normoglycemic healthy adults to predict the risk of developing type 2 diabetes. A web-based platform interventional study was used to deliver precision nutrition intervention based on the blood metabolites health risk score to lower the blood metabolites to normal levels for 40 participants. A follow-up assessment of the blood metabolites showed significant reductions in the health risks associated with the development of type 2 diabetes, insulin resistance, and related comorbidities [ 68 •]. A replication of the study through observational longitudinal analysis in a larger cohort of 1000 US adults demonstrated similar positive results with the precision nutrition intervention given based on biomarkers measured through metabolomics [ 69 ]. Bouwman et al. [ 70 ] in a double-blind placebo-controlled cross-over design used a health space model to visualize the effect of personalized nutrition intervention on metabolic stress profile including inflammatory and oxidative processes associated with obesity and type 2 diabetes. After following the recommendations for 5 weeks, the 145 metabolites and 79 proteins measured prior and before treatment were able to distinguish modulation of metabolic stress and specific oxidative and inflammatory response to treatment. Fiamoncini et al. [ 71 ] in an experimental design identified 2 metabotype clusters and tested their responses to a personalized nutrition intervention over a 12-week weight loss program. The researchers reported that only the study participants with higher disease-linked metabotype demonstrated improvements in glucose and insulin levels when fed a low caloric diet. They concluded that through the application of metabolomics in precision nutrition advice, a responsive and non-responsive metabotype was revealed. In the DIRECT (Dietary Intervention Randomized Controlled Trial) trial, personalized weight-loss diets decreased circulating amino acid metabolites that were associated with risk of type 2 diabetes, and improved insulin resistance. In addition, the reduction in the level of circulating amino acid metabolites which is indicative of an increase in insulin sensitivity was independent of weight loss [ 72 ]. Walford and colleagues performed plasma metabolite profiling to elucidate new pathways of type 2 diabetes incidence and the role of personalized nutrition interventions in a nested case–control design [ 73 ]. Dietary and lifestyle modifications based on the metabolites effectively raised betaine concentration from baseline to 2-year follow-up, which predicted lower risk of type 2 diabetes. Interestingly, a 10-week RCT that allocated 100 overweight and obese adults to a personalized diet and control diet based on their metabolomic and genetic information did not show significant difference between groups in fat mass; however, the individual diets produced significant improvements in insulin resistance and lipid profile, which was not significantly different between groups. The soundness of various precision nutrition approaches is required to translate such findings into clinical relevance [ 74 •].

• Metagenomics

Metagenomics is the comprehensive study of host microbial and their genetic material (Table 1 ) [ 75 ]. The role of the gut microbiota in obesity and type 2 diabetes has been underscored, and this has been an area of immense research [ 76 ]. It is believed that the metabolism of dietary compounds into other metabolites by the gut microbiota, which is associated with disease risk, mediates the impact of the gut microbiota on human health [ 77 , 78 , 79 ]. For example, the metabolism of dietary fibers and resistant starches into bacterial metabolites of short-chain fatty acids such as acetate, propionate, and butyrate presents a mechanism that modulates the pathways involved in obesity, insulin resistance, and type 2 diabetes [ 80 ]. Studies show that the diet-gut microbiota interactions vary in composition and functionality among individuals [ 81 ], and this appears to be a determinant to integrate metagenomics into precision nutrition [ 36 ]. Pioneering work by Zeevi et al. [ 82 ] in an observational study and blinded randomized controlled dietary intervention showed that postprandial glucose responses have high interpersonal variability even when individuals consumed identical standardized diets. The authors further used a machine learning algorithm that integrated dietary habits, blood parameters, anthropometrics, physical activity, and gut microbiota features for precision nutrition recommendations in the 800 person cohort. The precision nutrition recommendations accurately predicted personalized postprandial glucose response to the recommendations and resulted in significantly lower glucose levels and consistent alterations in gut microbiome. In modifying and extending the model created by Zeevi and colleagues, two cohort studies that evaluated the utility of such precision nutrition approaches to predict postprandial glucose responses found that across the cohort of non-diabetic adults that were examined, a personalized model was more predictive than current models of carbohydrate content [ 83 , 84 ]. Similarly, Kovatcheva-Datchary et al. [ 85 ] in a cross-over study demonstrated that among 39 healthy Swedes, improved postprandial glucose metabolism was in those with statistically significant higher ratio of Prevotella/Bacteroides spp., following an intervention of 3-day consumption of barley kernel bread diet. Another RCT demonstrated through metagenomic analysis and a dietary weight loss intervention that compared to individuals with a low bacterial ratio, subjects with a high Prevotella/Bacteroides genera ratio lost more weight and body fat in response to high-fiber diets [ 86 ]. In a sub-study of a larger RCT, researchers examined whether the baseline composition and diversity of gut microbiota was associated with weight loss in a sample of 49 participants. Findings from the study showed that baseline gut microbiota composition was not associated with weight loss; however, there were substantial changes in gut microbiota in response to each diet, 3 months after initiating the intervention. The changes were attributed specifically to the healthy low-carbohydrate diet used in the intervention, although the changes were attenuated after 12 months [ 87 ]. Another important step in the use of metagenomics in precision nutrition was the work conducted by Vangay et al. [ 88 ] in an observational study that provided valuable insight into differences in population groups that requires racial considerations and sociocultural influences when employing precision nutrition approaches. In this study, Karen and Hmong natives residing in Thailand and the USA as well as European Americans born in the USA were assessed for the impact of migration to the USA on the gut microbiota in development of metabolic diseases such as obesity. After metagenomic DNA sequencing, the investigators found that US immigration rapidly depleted gut microbiota diversity and function and was replaced by US-associated strains and functions, and was exacerbated by obesity. These results were confirmed in a prospective cohort study that used similar metagenomic approaches of 16S and deep shotgun DNA sequencing among 144 Chinese individuals in Shanghai. A long-term healthy diet intervention was associated with greater diversity of Tenericutes , Firmicutes , and Actinobacteria , with or without adjustment for BMI [ 89 ]. Data from an RCT of an integrative model using gut microbiota and genetic information to personalize weight loss prescription among 190 Spanish overweight and obese participants suggested that the mixed models’ microbiota scores facilitated the selection of the optimal diet in 84% of men and 72% of women for weight loss [ 90 ••].

Behavioral (Dietary Patterns, and Physical Activity) Aspects of Precision Nutrition

Healthy behaviors (e.g., consuming a healthy diet and engaging in regular physical activity) are associated with the incidence of morbidity and mortality of chronic diseases including obesity and type 2 diabetes [ 91 ]. Behavior change components that may be beneficial to improve adoption of healthier options are goal setting, social interactions, and customized messages [ 92 , 93 ]. Diet and physical activity behaviors are the strongest risk factors for obesity and type 2 diabetes prevention and outcomes [ 94 ]. Given this crucial role of behavior in preventing and treating chronic diseases, it is important to assess behavior change in dietary patterns and physical activity for improvement. The 2019 global burden of disease study reported that among the 3 largest increases in risk exposure for disability-adjusted life years (DALYs) lost across the world, 2 were high BMI and high fasting plasma glucose, and 6 of the top 10 causes of DALYs are due to poor health behaviors, including unhealthy dietary patterns and low physical activity levels [ 95 ]. Diet quality which represents the nutritional adequacy of a diet with varied nutrient composition, measured by how closely dietary patterns are within core nutrient-dense food groups, is a higher priority than the quantity of dietary intake [ 96 , 97 , 98 , 99 ]. In a systematic review of prospective cohort studies, a strong association was found between poor diet quality and greater weight gain, irrespective of gender [ 100 ]. In addition, higher diet quality is demonstrated in several studies to be associated with chronic disease risk, cause-specific mortality, and all-cause mortality [ 101 , 102 , 103 ]. Diet quality in the USA remains far from optimal and for all Americans, the average diet quality measured by the Healthy Eating Index (HEI) score is 58, which is far from the maximum of 100 points [ 104 ]. The top dietary risk factors in the USA are diets low in fruits, vegetables, whole grains, nuts, and legumes, and high in refined grains, red or processed meats, sodium, saturated and trans fats, and sugar-sweetened beverages [ 21 , 105 , 106 , 107 ]. The transition from heavy labor to sedentary livelihoods, increased screen time, decrease in school physical education, and improved transportation has been implicated in the decline in physical activity levels [ 18 , 107 ]. Studies show that moderate to vigorous-intensity physical activity such as walking or running is necessary for optimal health. A systematic review and meta-analysis of prospective cohort studies [ 108 ] reported that individuals who engaged in the minimum recommended amount of physical activity had potentially significant benefits to reduce the risk for type 2 diabetes by 26%, compared with inactive individuals. Thus, improvement in diet and physical activity signifies a huge potential for obesity and type 2 diabetes reduction either directly or indirectly through improvements in weight gain and blood glucose levels. It has been suggested that conventional dietary advice does not have as big of an impact on improving dietary health as expected [ 109 , 110 ].

Precision nutrition interventions have demonstrated encouraging changes in dietary behaviors (Table 1 ). Precision nutrition studies that reported on behavior changes observed as healthy dietary patterns found that optimizing dietary patterns through individualized care improves management of obesity and type 2 diabetes [ 111 , 112 , 113 ]. For example, a randomized controlled trial that provided personalized nutrition advice using individualized information on diet and lifestyle, phenotype and/or genotype, produced larger, more appropriate, and sustained changes in dietary behavior to healthier diet as food groups compared to a conventional approach. Study participants in the precision nutrition group consumed less red meat, salt, and saturated fat, increased folate intake, and had higher HEI scores [ 114 ]. In line with these results, another RCT [ 115 ] that considered application of a dietary pattern technique instead of individual food items in isolation has reported that the use of precision nutrition enhanced dietary behavior changes associated with higher Mediterranean-style diet scores. The Mediterranean diet, characterized by high intakes of fruit and vegetables and low intakes of sugar-sweetened beverages and snacks, has been consistently linked with a beneficial effect on health, including obesity and type 2 diabetes [ 116 , 117 , 118 ]. Thus, it is strongly suggested that changing dietary intakes so as to align more appropriately with the Mediterranean diet would yield extensive public health benefit [ 119 ]. Through post hoc analyses, findings of the study further supported the importance of personalized nutritional advice which, when done with increased frequency, promoted sustained changes in dietary behavior and larger improvements in overall diet quality [ 120 ]. The changes in behavior of dietary patterns through the implementation of precision nutrition recommendations have also been associated with reduced intake of calories, carbohydrates, sugar, total fat, and saturated fat which correlated with significant weight loss, reduced waist circumference, and increased high density lipoprotein (HDL), decreased total cholesterol and low density lipoprotein (LDL) with improved glucose levels through observational studies, single-arm, multi-phase, open-label exploratory trial, and retrospective analysis of an RCT [ 121 ,  122 ••,  123 , 124 ]. A pretest–posttest pilot study that organized a personalized dietary advice in a real-life setting found that dietary quality measured by the Dutch Healthy Diet Index was significantly improved compared with baseline. In addition, this research revealed that personalized dietary advice resulted in positive effects in self-perceived health in motivated pre-metabolic syndrome adults. Because the study was performed in the real-life setting (do-it-yourself), it highlighted the potential of at-home health behavior improvement through dietary changes [ 125 ]. The EatWellUK is another RCT that attests to the advancement of precision nutrition research beyond the USA. The authors of this research reported that an automated precision nutrition advice via a mobile web app was effective to elicit beneficial dietary change, improve diet quality, and increase engagement in healthy dietary behaviors in UK adults, relative to general population-based dietary guidelines [ 126 ••]. Similarly, other precision nutrition interventions found behavior change in dietary intake which favored healthier choices and increase in diet quality irrespective of the setting and/or platform used for delivery of the intervention, as well as measure used to assess diet quality score [ 127 , 128 ••]. Short-term dietary behavior changes are usually very short lived, thus long-term compliance to dietary behavior change should not be compromised because it is crucial in maintaining body weight and blood glucose levels [ 129 ]. Generally, long-term dietary changes are difficult when it comes to consistency; however with the application of precision nutrition, there is a potential to optimize dietary behavior change by motivating greater adherence and change in dietary intake for the long-term for improved weight and glucose management [ 130 , 131 , 132 ]. The nutrigenomics overweight/obesity and weight management (NOW) trial was an RCT that shed more light on long-term dietary behavior change and adherence. More specifically, the investigators described that the use of precision nutrition increased motivation to long-term reduction in total fat intake, and long-term adherence to total fat and saturated fat advice [ 133 ].

Evidence shows that fixed step goals that are not personalized can discourage individuals, leading to unchanged behavior or even reduced physical activity levels [ 134 , 135 , 136 ]. There are findings, however, that show that the effect of precision nutrition to promote behavior change in physical inactivity and improve physical activity levels is not as consistent as observed for behavior changes in dietary patterns and diet quality. The findings of an RCT that included 1279 participants in 7 European countries to determine the effects of personalized advice on physical activity showed that while self-report-based physical activity levels increased to a greater extent with more personalized nutrition advice, there was no difference between the effect of personalized advice to promote changes in physical activity levels and conventional guidelines when physical activity was objectively measured. The authors concluded that it is vital to measure physical activity objectively in any physical activity intervention study [ 137 ]. Studies that analyzed objective measurement of physical activity levels in personalized advice support this theory as they found association between personalized and adaptive goal-setting intervention and steady daily steps, but not with constant steps in the control group, thus promoting behavior change in physical activity [ 138 ]. These data are in contrast with the results of an RCT that reported no changes in physical activity behavior after a precision nutrition intervention using objectively measured physical activity [ 139 ]. Nevertheless, an observational study found that precision nutrition significantly increased strength exercise frequency which was attributed to direct motivation of their personal genetic testing results to make behavior changes [ 140 ]. However, genetic results were not consistently associated with physical activity changes. Together these studies provide important insights into the precision nutrition effects on physical activity behavior changes, which highlights the need for further research.

The current review provides evidence that although the application of precision nutrition is emerging, it is to a large extent associated with obesity and type 2 diabetes and may be effective approach in improving the risks factors including dietary patterns, physical activity, body weight and fat, blood lipids, blood glucose, and insulin resistance. This advancement has been enabled through the use of cutting-edge omics technologies which provide genetic, biomarkers, and microbiome insights into variabilities in individual metabolic pathways in response to dietary intakes that may impact health. It is worth noting as presented in this review that the evidence for precision nutrition is stronger for behavior change than for actual hard endpoints but maintaining the behavior changes in the long term is important for the hard endpoints to change, and this is challenging. The choosing of genetic and phenotypic parameters as a rational basis for individual-level, precision nutrition advice is a key factor that motivates people to make appropriate behavioral changes. However, individual health aspirations, food preferences, and barriers/facilitators to behavior change need to be considered and integrated more using a biopsychosocial model in developing precision nutrition approaches to maintain long-term behavior change and promote sustainability for better health outcomes [ 141 ]. In addition, there are still methodological challenges in the design and application of precision nutrition in clinical settings and scale up to the population level in addressing obesity and type 2 diabetes. While sensitivity and specificity issues of the omics technologies exist, some studies do not incorporate all the sources of individual variability in their assessment, and others do not have relevant behavior change techniques, are of short duration in their intervention, low diet quality, and of small sample sizes to observe an effect. More rigorous and well-executed RCTs are required to reinforce the evidence base for precision nutrition to be widely and effectively used in clinical setting and the public health domain. Moreover, increasing the reliability and reducing the cost of cutting-edge omics technologies and new frontiers in machine learning will undoubtedly pave the way for comprehensive and integrated framework of big data to combine multi-omics approaches with lifestyle and behavioral, phenotype, sociocultural, and demographic factors. This will help apprise the optimal design of precision nutrition interventions in clinical settings, and improve population diets at scale in improving the risk factors of obesity and type 2 diabetes. The vast majority of present knowledge and research on precision nutrition has been derived from developed countries [ 142 ]. It is crucial to conduct original research in other populations with different dietary habits, disease susceptibility, genetic makeup, socioeconomic characteristics, and health-related lifestyles. Extending precision nutrition research and application by examining and understanding a wider array of multi-race population health, technological and digital landscape, and political will are needed to ensure that there is equity prior to implementation of such approaches.

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Antwi, J. Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes. Curr Nutr Rep 12 , 679–694 (2023). https://doi.org/10.1007/s13668-023-00491-y

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.

• Patient Care & Health Information
• Diseases & Conditions
• Type 2 diabetes

Type 2 diabetes is usually diagnosed using the glycated hemoglobin (A1C) test. This blood test indicates your average blood sugar level for the past two to three months. Results are interpreted as follows:

• Below 5.7% is normal.
• 5.7% to 6.4% is diagnosed as prediabetes.
• 6.5% or higher on two separate tests indicates diabetes.

If the A1C test isn't available, or if you have certain conditions that interfere with an A1C test, your health care provider may use the following tests to diagnose diabetes:

Random blood sugar test. Blood sugar values are expressed in milligrams of sugar per deciliter ( mg/dL ) or millimoles of sugar per liter ( mmol/L ) of blood. Regardless of when you last ate, a level of 200 mg/dL (11.1 mmol/L ) or higher suggests diabetes, especially if you also have symptoms of diabetes, such as frequent urination and extreme thirst.

Fasting blood sugar test. A blood sample is taken after you haven't eaten overnight. Results are interpreted as follows:

• Less than 100 mg/dL (5.6 mmol/L ) is considered healthy.
• 100 to 125 mg/dL (5.6 to 6.9 mmol/L ) is diagnosed as prediabetes.
• 126 mg/dL (7 mmol/L ) or higher on two separate tests is diagnosed as diabetes.

Oral glucose tolerance test. This test is less commonly used than the others, except during pregnancy. You'll need to not eat for a certain amount of time and then drink a sugary liquid at your health care provider's office. Blood sugar levels then are tested periodically for two hours. Results are interpreted as follows:

• Less than 140 mg/dL (7.8 mmol/L ) after two hours is considered healthy.
• 140 to 199 mg/dL (7.8 mmol/L and 11.0 mmol/L ) is diagnosed as prediabetes.
• 200 mg/dL (11.1 mmol/L ) or higher after two hours suggests diabetes.

Screening. The American Diabetes Association recommends routine screening with diagnostic tests for type 2 diabetes in all adults age 35 or older and in the following groups:

• People younger than 35 who are overweight or obese and have one or more risk factors associated with diabetes.
• Women who have had gestational diabetes.
• People who have been diagnosed with prediabetes.
• Children who are overweight or obese and who have a family history of type 2 diabetes or other risk factors.

After a diagnosis

If you're diagnosed with diabetes, your health care provider may do other tests to distinguish between type 1 and type 2 diabetes because the two conditions often require different treatments.

Your health care provider will test A1C levels at least two times a year and when there are any changes in treatment. Target A1C goals vary depending on age and other factors. For most people, the American Diabetes Association recommends an A1C level below 7%.

You also receive tests to screen for complications of diabetes and other medical conditions.

More Information

• Glucose tolerance test

Management of type 2 diabetes includes:

• Healthy eating.
• Regular exercise.
• Weight loss.
• Possibly, diabetes medication or insulin therapy.
• Blood sugar monitoring.

These steps make it more likely that blood sugar will stay in a healthy range. And they may help to delay or prevent complications.

Healthy eating

There's no specific diabetes diet. However, it's important to center your diet around:

• A regular schedule for meals and healthy snacks.
• Smaller portion sizes.
• More high-fiber foods, such as fruits, nonstarchy vegetables and whole grains.
• Fewer refined grains, starchy vegetables and sweets.
• Modest servings of low-fat dairy, low-fat meats and fish.
• Healthy cooking oils, such as olive oil or canola oil.
• Fewer calories.

Your health care provider may recommend seeing a registered dietitian, who can help you:

• Identify healthy food choices.
• Plan well-balanced, nutritional meals.
• Develop new habits and address barriers to changing habits.
• Monitor carbohydrate intake to keep your blood sugar levels more stable.

Physical activity

Exercise is important for losing weight or maintaining a healthy weight. It also helps with managing blood sugar. Talk to your health care provider before starting or changing your exercise program to ensure that activities are safe for you.

• Aerobic exercise. Choose an aerobic exercise that you enjoy, such as walking, swimming, biking or running. Adults should aim for 30 minutes or more of moderate aerobic exercise on most days of the week, or at least 150 minutes a week.
• Resistance exercise. Resistance exercise increases your strength, balance and ability to perform activities of daily living more easily. Resistance training includes weightlifting, yoga and calisthenics. Adults living with type 2 diabetes should aim for 2 to 3 sessions of resistance exercise each week.
• Limit inactivity. Breaking up long periods of inactivity, such as sitting at the computer, can help control blood sugar levels. Take a few minutes to stand, walk around or do some light activity every 30 minutes.

Weight loss

Weight loss results in better control of blood sugar levels, cholesterol, triglycerides and blood pressure. If you're overweight, you may begin to see improvements in these factors after losing as little as 5% of your body weight. However, the more weight you lose, the greater the benefit to your health. In some cases, losing up to 15% of body weight may be recommended.

Your health care provider or dietitian can help you set appropriate weight-loss goals and encourage lifestyle changes to help you achieve them.

Monitoring your blood sugar

Your health care provider will advise you on how often to check your blood sugar level to make sure you remain within your target range. You may, for example, need to check it once a day and before or after exercise. If you take insulin, you may need to check your blood sugar multiple times a day.

Monitoring is usually done with a small, at-home device called a blood glucose meter, which measures the amount of sugar in a drop of blood. Keep a record of your measurements to share with your health care team.

Continuous glucose monitoring is an electronic system that records glucose levels every few minutes from a sensor placed under the skin. Information can be transmitted to a mobile device such as a phone, and the system can send alerts when levels are too high or too low.

Diabetes medications

If you can't maintain your target blood sugar level with diet and exercise, your health care provider may prescribe diabetes medications that help lower glucose levels, or your provider may suggest insulin therapy. Medicines for type 2 diabetes include the following.

Metformin (Fortamet, Glumetza, others) is generally the first medicine prescribed for type 2 diabetes. It works mainly by lowering glucose production in the liver and improving the body's sensitivity to insulin so it uses insulin more effectively.

Some people experience B-12 deficiency and may need to take supplements. Other possible side effects, which may improve over time, include:

• Abdominal pain.

Sulfonylureas help the body secrete more insulin. Examples include glyburide (DiaBeta, Glynase), glipizide (Glucotrol XL) and glimepiride (Amaryl). Possible side effects include:

• Low blood sugar.
• Weight gain.

Glinides stimulate the pancreas to secrete more insulin. They're faster acting than sulfonylureas. But their effect in the body is shorter. Examples include repaglinide and nateglinide. Possible side effects include:

Thiazolidinediones make the body's tissues more sensitive to insulin. An example of this medicine is pioglitazone (Actos). Possible side effects include:

• Risk of congestive heart failure.
• Risk of bladder cancer (pioglitazone).
• Risk of bone fractures.

DPP-4 inhibitors help reduce blood sugar levels but tend to have a very modest effect. Examples include sitagliptin (Januvia), saxagliptin (Onglyza) and linagliptin (Tradjenta). Possible side effects include:

• Risk of pancreatitis.
• Joint pain.

GLP-1 receptor agonists are injectable medications that slow digestion and help lower blood sugar levels. Their use is often associated with weight loss, and some may reduce the risk of heart attack and stroke. Examples include exenatide (Byetta, Bydureon Bcise), liraglutide (Saxenda, Victoza) and semaglutide (Rybelsus, Ozempic, Wegovy). Possible side effects include:

SGLT2 inhibitors affect the blood-filtering functions in the kidneys by blocking the return of glucose to the bloodstream. As a result, glucose is removed in the urine. These medicines may reduce the risk of heart attack and stroke in people with a high risk of those conditions. Examples include canagliflozin (Invokana), dapagliflozin (Farxiga) and empagliflozin (Jardiance). Possible side effects include:

• Vaginal yeast infections.
• Urinary tract infections.
• Low blood pressure.
• High cholesterol.
• Risk of gangrene.
• Risk of bone fractures (canagliflozin).
• Risk of amputation (canagliflozin).

Other medicines your health care provider might prescribe in addition to diabetes medications include blood pressure and cholesterol-lowering medicines, as well as low-dose aspirin, to help prevent heart and blood vessel disease.

Insulin therapy

Some people who have type 2 diabetes need insulin therapy. In the past, insulin therapy was used as a last resort, but today it may be prescribed sooner if blood sugar targets aren't met with lifestyle changes and other medicines.

Different types of insulin vary on how quickly they begin to work and how long they have an effect. Long-acting insulin, for example, is designed to work overnight or throughout the day to keep blood sugar levels stable. Short-acting insulin generally is used at mealtime.

Your health care provider will determine what type of insulin is right for you and when you should take it. Your insulin type, dosage and schedule may change depending on how stable your blood sugar levels are. Most types of insulin are taken by injection.

Side effects of insulin include the risk of low blood sugar — a condition called hypoglycemia — diabetic ketoacidosis and high triglycerides.

Weight-loss surgery

Weight-loss surgery changes the shape and function of the digestive system. This surgery may help you lose weight and manage type 2 diabetes and other conditions related to obesity. There are several surgical procedures. All of them help people lose weight by limiting how much food they can eat. Some procedures also limit the amount of nutrients the body can absorb.

Weight-loss surgery is only one part of an overall treatment plan. Treatment also includes diet and nutritional supplement guidelines, exercise and mental health care.

Generally, weight-loss surgery may be an option for adults living with type 2 diabetes who have a body mass index (BMI) of 35 or higher. BMI is a formula that uses weight and height to estimate body fat. Depending on the severity of diabetes or the presence of other medical conditions, surgery may be an option for someone with a BMI lower than 35.

Weight-loss surgery requires a lifelong commitment to lifestyle changes. Long-term side effects may include nutritional deficiencies and osteoporosis.

People living with type 2 diabetes often need to change their treatment plan during pregnancy and follow a diet that controls carbohydrates. Many people need insulin therapy during pregnancy. They also may need to stop other treatments, such as blood pressure medicines.

There is an increased risk during pregnancy of developing a condition that affects the eyes called diabetic retinopathy. In some cases, this condition may get worse during pregnancy. If you are pregnant, visit an ophthalmologist during each trimester of your pregnancy and one year after you give birth. Or as often as your health care provider suggests.

Signs of trouble

Regularly monitoring your blood sugar levels is important to avoid severe complications. Also, be aware of symptoms that may suggest irregular blood sugar levels and the need for immediate care:

High blood sugar. This condition also is called hyperglycemia. Eating certain foods or too much food, being sick, or not taking medications at the right time can cause high blood sugar. Symptoms include:

• Frequent urination.
• Increased thirst.
• Blurred vision.

Hyperglycemic hyperosmolar nonketotic syndrome (HHNS). This life-threatening condition includes a blood sugar reading higher than 600 mg/dL (33.3 mmol/L ). HHNS may be more likely if you have an infection, are not taking medicines as prescribed, or take certain steroids or drugs that cause frequent urination. Symptoms include:

• Extreme thirst.
• Drowsiness.
• Dark urine.

Diabetic ketoacidosis. Diabetic ketoacidosis occurs when a lack of insulin results in the body breaking down fat for fuel rather than sugar. This results in a buildup of acids called ketones in the bloodstream. Triggers of diabetic ketoacidosis include certain illnesses, pregnancy, trauma and medicines — including the diabetes medicines called SGLT2 inhibitors.

The toxicity of the acids made by diabetic ketoacidosis can be life-threatening. In addition to the symptoms of hyperglycemia, such as frequent urination and increased thirst, ketoacidosis may cause:

• Shortness of breath.
• Fruity-smelling breath.

Low blood sugar. If your blood sugar level drops below your target range, it's known as low blood sugar. This condition also is called hypoglycemia. Your blood sugar level can drop for many reasons, including skipping a meal, unintentionally taking more medication than usual or being more physically active than usual. Symptoms include:

• Irritability.
• Heart palpitations.
• Slurred speech.

If you have symptoms of low blood sugar, drink or eat something that will quickly raise your blood sugar level. Examples include fruit juice, glucose tablets, hard candy or another source of sugar. Retest your blood in 15 minutes. If levels are not at your target, eat or drink another source of sugar. Eat a meal after your blood sugar level returns to normal.

If you lose consciousness, you need to be given an emergency injection of glucagon, a hormone that stimulates the release of sugar into the blood.

• Medications for type 2 diabetes
• GLP-1 agonists: Diabetes drugs and weight loss
• Bariatric surgery
• Endoscopic sleeve gastroplasty
• Gastric bypass (Roux-en-Y)

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Clinical trials

Explore Mayo Clinic studies testing new treatments, interventions and tests as a means to prevent, detect, treat or manage this condition.

Lifestyle and home remedies

Careful management of type 2 diabetes can reduce the risk of serious — even life-threatening — complications. Consider these tips:

• Commit to managing your diabetes. Learn all you can about type 2 diabetes. Make healthy eating and physical activity part of your daily routine.
• Work with your team. Establish a relationship with a certified diabetes education specialist, and ask your diabetes treatment team for help when you need it.
• Identify yourself. Wear a necklace or bracelet that says you are living with diabetes, especially if you take insulin or other blood sugar-lowering medicine.
• Schedule a yearly physical exam and regular eye exams. Your diabetes checkups aren't meant to replace regular physicals or routine eye exams.
• Keep your vaccinations up to date. High blood sugar can weaken your immune system. Get a flu shot every year. Your health care provider also may recommend the pneumonia vaccine. The Centers for Disease Control and Prevention (CDC) also recommends the hepatitis B vaccination if you haven't previously received this vaccine and you're 19 to 59 years old. Talk to your health care provider about other vaccinations you may need.
• Take care of your teeth. Diabetes may leave you prone to more-serious gum infections. Brush and floss your teeth regularly and schedule recommended dental exams. Contact your dentist right away if your gums bleed or look red or swollen.
• Pay attention to your feet. Wash your feet daily in lukewarm water, dry them gently, especially between the toes, and moisturize them with lotion. Check your feet every day for blisters, cuts, sores, redness and swelling. Contact your health care provider if you have a sore or other foot problem that isn't healing.
• Keep your blood pressure and cholesterol under control. Eating healthy foods and exercising regularly can go a long way toward controlling high blood pressure and cholesterol. Take medication as prescribed.
• If you smoke or use other types of tobacco, ask your health care provider to help you quit. Smoking increases your risk of diabetes complications. Talk to your health care provider about ways to stop using tobacco.
• Use alcohol sparingly. Depending on the type of drink, alcohol may lower or raise blood sugar levels. If you choose to drink alcohol, only do so with a meal. The recommendation is no more than one drink daily for women and no more than two drinks daily for men. Check your blood sugar frequently after drinking alcohol.
• Make healthy sleep a priority. Many people with type 2 diabetes have sleep problems. And not getting enough sleep may make it harder to keep blood sugar levels in a healthy range. If you have trouble sleeping, talk to your health care provider about treatment options.
• Caffeine: Does it affect blood sugar?

Alternative medicine

Many alternative medicine treatments claim to help people living with diabetes. According to the National Center for Complementary and Integrative Health, studies haven't provided enough evidence to recommend any alternative therapies for blood sugar management. Research has shown the following results about popular supplements for type 2 diabetes:

• Chromium supplements have been shown to have few or no benefits. Large doses can result in kidney damage, muscle problems and skin reactions.
• Magnesium supplements have shown benefits for blood sugar control in some but not all studies. Side effects include diarrhea and cramping. Very large doses — more than 5,000 mg a day — can be fatal.
• Cinnamon, in some studies, has lowered fasting glucose levels but not A1C levels. Therefore, there's no evidence of overall improved glucose management.

Talk to your health care provider before starting a dietary supplement or natural remedy. Do not replace your prescribed diabetes medicines with alternative medicines.

Coping and support

Type 2 diabetes is a serious disease, and following your diabetes treatment plan takes commitment. To effectively manage diabetes, you may need a good support network.

Anxiety and depression are common in people living with diabetes. Talking to a counselor or therapist may help you cope with the lifestyle changes and stress that come with a type 2 diabetes diagnosis.

Support groups can be good sources of diabetes education, emotional support and helpful information, such as how to find local resources or where to find carbohydrate counts for a favorite restaurant. If you're interested, your health care provider may be able to recommend a group in your area.

You can visit the American Diabetes Association website to check out local activities and support groups for people living with type 2 diabetes. The American Diabetes Association also offers online information and online forums where you can chat with others who are living with diabetes. You also can call the organization at 800-DIABETES ( 800-342-2383 ).

Preparing for your appointment

At your annual wellness visit, your health care provider can screen for diabetes and monitor and treat conditions that increase your risk of diabetes, such as high blood pressure, high cholesterol or a high BMI .

If you are seeing your health care provider because of symptoms that may be related to diabetes, you can prepare for your appointment by being ready to answer the following questions:

• When did your symptoms begin?
• Does anything improve the symptoms or worsen the symptoms?
• What medicines do you take regularly, including dietary supplements and herbal remedies?
• What are your typical daily meals? Do you eat between meals or before bedtime?
• How much alcohol do you drink?
• How much daily exercise do you get?
• Is there a history of diabetes in your family?

If you are diagnosed with diabetes, your health care provider may begin a treatment plan. Or you may be referred to a doctor who specializes in hormonal disorders, called an endocrinologist. Your care team also may include the following specialists:

• Certified diabetes education specialist.
• Foot doctor, also called a podiatrist.
• Doctor who specializes in eye care, called an ophthalmologist.

Talk to your health care provider about referrals to other specialists who may be providing care.

Questions for ongoing appointments

Before any appointment with a member of your treatment team, make sure you know whether there are any restrictions, such as not eating or drinking before taking a test. Questions that you should regularly talk about with your health care provider or other members of the team include:

• How often do I need to monitor my blood sugar, and what is my target range?
• What changes in my diet would help me better manage my blood sugar?
• What is the right dosage for prescribed medications?
• When do I take the medications? Do I take them with food?
• How does management of diabetes affect treatment for other conditions? How can I better coordinate treatments or care?
• When do I need to make a follow-up appointment?
• Under what conditions should I call you or seek emergency care?
• Are there brochures or online sources you recommend?
• Are there resources available if I'm having trouble paying for diabetes supplies?

What to expect from your doctor

Your health care provider is likely to ask you questions at your appointments. Those questions may include:

• Do you understand your treatment plan and feel confident you can follow it?
• How are you coping with diabetes?
• Have you had any low blood sugar?
• Do you know what to do if your blood sugar is too low or too high?
• What's a typical day's diet like?
• Are you exercising? If so, what type of exercise? How often?
• Do you sit for long periods of time?
• What challenges are you experiencing in managing your diabetes?
• Professional Practice Committee: Standards of Medical Care in Diabetes — 2020. Diabetes Care. 2020; doi:10.2337/dc20-Sppc.
• Diabetes mellitus. Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/diabetes-mellitus-dm. Accessed Dec. 7, 2020.
• Melmed S, et al. Williams Textbook of Endocrinology. 14th ed. Elsevier; 2020. https://www.clinicalkey.com. Accessed Dec. 3, 2020.
• Diabetes overview. National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/diabetes/overview/all-content. Accessed Dec. 4, 2020.
• AskMayoExpert. Type 2 diabetes. Mayo Clinic; 2018.
• Feldman M, et al., eds. Surgical and endoscopic treatment of obesity. In: Sleisenger and Fordtran's Gastrointestinal and Liver Disease: Pathophysiology, Diagnosis, Management. 11th ed. Elsevier; 2021. https://www.clinicalkey.com. Accessed Oct. 20, 2020.
• Hypersmolar hyperglycemic state (HHS). Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/hyperosmolar-hyperglycemic-state-hhs. Accessed Dec. 11, 2020.
• Diabetic ketoacidosis (DKA). Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/diabetic-ketoacidosis-dka. Accessed Dec. 11, 2020.
• Hypoglycemia. Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/hypoglycemia. Accessed Dec. 11, 2020.
• 6 things to know about diabetes and dietary supplements. National Center for Complementary and Integrative Health. https://www.nccih.nih.gov/health/tips/things-to-know-about-type-diabetes-and-dietary-supplements. Accessed Dec. 11, 2020.
• Type 2 diabetes and dietary supplements: What the science says. National Center for Complementary and Integrative Health. https://www.nccih.nih.gov/health/providers/digest/type-2-diabetes-and-dietary-supplements-science. Accessed Dec. 11, 2020.
• Preventing diabetes problems. National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/diabetes/overview/preventing-problems/all-content. Accessed Dec. 3, 2020.
• Schillie S, et al. Prevention of hepatitis B virus infection in the United States: Recommendations of the Advisory Committee on Immunization Practices. MMWR Recommendations and Reports. 2018; doi:10.15585/mmwr.rr6701a1.
• Diabetes prevention: 5 tips for taking control
• Hyperinsulinemia: Is it diabetes?

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What to eat to prevent spikes in your blood sugar

Over the past three decades, the prevalence of Type 2 diabetes has risen dramatically worldwide.

According to the International Federation of Diabetes, by 2045 one in eight adults will be living with diabetes, an increase of 46 per cent.

A recent study of dietary and health data from 184 countries found that poor carbohydrate quality – eating too many refined grains and too few whole grains – was the leading dietary driver of Type 2 diabetes cases.

Now, a global study conducted in 20 countries adds to existing evidence that carbohydrate quality matters when it comes to staving off the disease.

The findings strongly suggest that eating foods with a low glycemic index – ones that don’t spike blood glucose and insulin after eating – is protective.

Here’s a breakdown of the latest research, plus what to eat to prevent blood sugar spikes.

Glycemic index and glycemic load defined

The glycemic index (GI), developed by University of Toronto researchers Dr. David Jenkins and Dr. Thomas Wolever in 1981, assigns carbohydrate-containing foods a score of 0 to 100 based on how rapidly they raise blood glucose compared to pure glucose.

A surge in blood glucose triggers an outpouring of the hormone insulin; over time these events can lead to glucose intolerance and Type 2 diabetes.

Foods with a high GI (70 or more) cause a sharp increase in blood glucose that declines rapidly. Examples include white bread, whole wheat bread, soda crackers, rice cakes, jasmine rice, instant rice, baked russet potato, instant oats, refined breakfast cereals, croissants, doughnuts, cakes and raisins.

Foods with a low GI (55 or less) lead to a slower and lower rise in blood glucose that declines gradually. Low GI foods include dense multigrain breads, sourdough bread, 100-per-cent bran cereals, steel-cut and rolled oats, barley, quinoa, brown rice, al dente pasta, beans and lentils, sweet potato, winter squash, most fruit and yogurt.

The glycemic load (GL) gives a more accurate picture of how foods affect your blood glucose. It considers not only the food’s glycemic index but also how much carbohydrate it contains per serving.

For example, if you eat a high glycemic food that contains only a small amount of carbohydrate, it won’t have much impact on blood glucose and its GL will be low.

The new research findings

The study, published April 5 in the journal Lancet Diabetes & Endocrinology, included 127,954 adults ages 35-70, enrolled in the PURE study (Prospective Urban and Rural Epidemiology).

Participants were from 20 low-income, middle-income and high-income countries and, at the study’s outset, did not have Type 2 diabetes.

Diet information was collected and used to calculate dietary glycemic index and glycemic load.

After 12 years, 7,326 participants had developed Type 2 diabetes.

Compared to those whose diets had the lowest GI and GL, those with the highest scores had a significantly greater risk of Type 2 diabetes. The increased risk was more pronounced in people with a high body mass index.

The researchers accounted for other factors that could influence diabetes risk such as family history, smoking, physical activity and intake of calories, fibre and whole grains.

The study’s strengths are its long duration of follow-up, large sample size and the inclusion of participants from low- to high-income countries.

Limitations include the fact that diet was measured only at the beginning of the study; dietary habits could have changed over time. Dietary information was also self-reported which is prone to error.

The study was observational; it doesn’t prove that a low glycemic diet prevents Type 2 diabetes.

How a high glycemic diet can harm metabolic health

This isn’t the first study to link high GI diets to an increased risk of Type 2 diabetes.

A review of large studies published earlier this year turned up similar findings.

High GI diets have been tied to reduced insulin sensitivity, impaired insulin secretion and poor blood glucose control.

Large spikes in blood glucose after eating have been shown to increase inflammation and oxidative stress, factors thought to promote the development of Type 2 diabetes.

Diet strategies to balance blood sugar

To lower the glycemic load of meals, choose unprocessed or minimally processed carbohydrates (e.g., whole grains, sweet potato, winter squash, beans and lentils, whole fruit) over refined carbohydrates.

These foods deliver fibre, which delays the rate that carbohydrates are digested and absorbed into the bloodstream.

Balance meals with protein and healthy fats, macronutrients that also slow down carbohydrate digestion.

Adding vinegar to meals (e.g., vinaigrette dressing) can also blunt the rise in postmeal glucose by slowing digestion and increasing glucose uptake by cells.

Consider food order too. Studies have found that eating vegetables, protein or fat first and eating refined carbohydrates last (e.g., white rice, pasta or bread) helps minimize blood sugar spikes.

Leslie Beck, a Toronto-based private practice dietitian, is director of food and nutrition at Medcan. Follow her on Twitter @LeslieBeckRD

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Novo Nordisk Foundation Center for Basic Metabolic Research

• Professor Juleen Ziera...

Professor Juleen Zierath awarded prestigious grant to study how circadian rhythms control type 2 diabetes

The €2.5 million Advanced Grant from the European Research Council will support research into the mechanisms that underpin the relationship between the circadian clock, diet and exercise, and metabolism, and their dysfunction in type 2 diabetes.

When is the right time to exercise? To eat? To take different drugs? Scientists already know that our bodies respond differently to food, exercise and medication depending on the time of day. That is because our bodies keep time through a circadian clock, which ensures that the body’s biological processes are optimally synchronized with the environment.

The risk of metabolic diseases such as type 2 diabetes increases when circadian rhythms are disrupted. While there are many treatments available, the majority target biological pathways that follow a circadian rhythm. This means that the time of day could play an important role in optimizing treatment. The same goes for diet and exercise, the first line of treatment for people living with type 2 diabetes, whose health impacts are also modulated by the body’s circadian rhythms.

The problem is that scientists still don’t understand how circadian rhythms affect the impact of exercise, and the uptake of food and drugs, on a molecular level. This is the goal of a new research project by Professor Juleen R. Zierath , from the University of Copenhagen and Karolinska Institute. The project, CIRCAMET, was awarded a five-year €2.5 million (DKK 19 million) Advanced Grant from the European Research Council, which are available to leading researchers with a proven track record of significant achievements.

I am delighted to receive my second ERC Advanced Grant, this time to support important research into the circadian control of type 2 diabetes. Professor Juleen R. Zierath

The overarching hypothesis of CIRCAMET is that synchronizing diet and exercise to the molecular circadian clock may maximize the health benefits on metabolism.

"I believe that this research holds enormous potential for discoveries that can lead to new therapeutic and preventative measures for type 2 diabetes, which globally is an epidemic, with prevalence now in excess of 500 million cases," says Professor Juleen R. Zierath.

A step toward chronomedications Studying the influence of circadian rhythms on type 2 diabetes is complicated, due to the interplay of factors. Currently, scientists tend to study each factor individually. Traditional studies into the effects of diet and exercise on metabolism tend to study each factor individually. The data they capture also tend to represent snapshots in time, which may not depict the full picture.

CIRCAMET will harness the potential of new ‘omics’ technologies to allow scientists to study the impact of diet and exercise on metabolism at the same time. And instead of capturing snapshots, they hope to study the same cells and organs over an extended period, rather than simply capturing moments in time.

The research may help to develop so-called “chrono medicines”, which may help to treat type 2 diabetes by resetting the disrupted circadian rhythms that cause it. The research may also inform new interventions that that promote primary lifestyle modifications with the body’s daily rhythm to improve energy homeostasis.

“Integrating many different types of data over time involves a high degree of risk, but if successful it will offer high reward,” says Professor Juleen R. Zierath.

ERC Advanced Grants

The European Research Council (ERC) has announced the names of 255 outstanding research leaders in Europe set to be awarded ERC Advanced Grants. The funding is amongst the EU’s most prestigious and competitive, providing leading senior researchers with the opportunity to pursue ambitious, curiosity-driven projects that could lead to major scientific breakthroughs. The new grants, worth in total nearly €652 million, are part of the EU’s Horizon Europe programme.

This competition attracted 1,829 proposals, which were reviewed by panels of internationally renowned researchers. Nearly fourteen percent of proposals were selected for funding. Estimates show that the grants will create 2,480 jobs in teams of new grantees.  

The ERC Advanced Grants target established, leading researchers with a proven track record of significant achievements. In recent years, there has been a steady rise in mid-career researchers (12-17 years post-PhD), who have been successful in the Advanced Grants competitions, with 18% securing grants in this latest round.

Study: Mediterranean Diet Could Help Reduce Type 2 Diabetes Risk

• A new study found the Mediterranean diet may reduce the risk of type 2 diabetes.
• The diet focuses on fiber and heart-healthy fats, nutrients needed for type 2 diabetes prevention.
• Experts note that while a Mediterranean diet isn’t guaranteed to ward off type 2 diabetes, the eating pattern is a nutritious option with many health benefits.

New research found the Mediterranean diet could help reduce the risk of type 2 diabetes.

The Mediterranean diet quickly gained popularity due to its well-known health benefits. The eating pattern is a favorite amongst doctors and registered dietitians, not only for its emphasis on a variety of nutritious foods but also for its flexible and sustainable characteristics.

A new study examined the link between the Mediterranean diet and type 2 diabetes. The findings suggest that the Mediterranean diet may help reduce type 2 diabetes even better than originally thought.

What Is the Mediterranean Diet?

Inspired by the typical foods and eating habits of southern Italy, southern Spain, and Greece, the Mediterranean diet is rich in fruits, vegetables, whole grains, legumes, nuts, seeds, lean proteins and fish, and heart-healthy fats.

While called a “diet,” the Mediterranean eating style does not adhere to strict rules or regulations and includes most foods, making this a highly sustainable way of eating.

As this eating pattern has gained popularity, researchers have found that individuals from the originating countries and those who emulate their eating habits have a lower risk of chronic disease.

Related: What Is the Green Mediterranean Diet—And Should You Try It?

The Mediterranean Diet and Type 2 Diabetes

The Mediterranean diet is a commonly recommended eating pattern for the prevention of type 2 diabetes and is associated with a decreased risk.

Nita Forouhi, MBBS, PhD , one of the authors of this study, noted the previous research on the Mediterranean diet and type 2 diabetes was based on subjective reporting. In other words, it relied on asking study participants about their diet—human memory can be prone to reporting errors.

“We wanted to improve the assessment of dietary intakes by using objective markers of foods that can be measured in the blood,” Dr. Forouhi explained.

The team of researchers collected data on 340,234 individuals living in eight European countries. They used blood carotenoids and fatty acids in order to assess adherence to the Mediterranean diet.

“We focused on these lipid-soluble nutritional biomarkers because they can reflect dietary exposures over a period of weeks or months prior to the blood draw”, Dr. Forouhi clarified.

In other words, these biomarkers allowed the researchers to ascertain if the Mediterranean diet was habitual for the study participants.

The study results showed the Mediterranean eating pattern was positively associated with a lower risk of new-onset type 2 diabetes. The results also indicated a stronger relationship with the use of nutritional biomarkers than with self-report questionnaires.

Based on these findings, following a Mediterranean style of eating may be even more beneficial for the primary prevention of type 2 diabetes than originally thought and reinforces the evidence for this recommendation. Additionally, the study found that even moderate adherence to the eating pattern results in health benefits.

“The Mediterranean diet is high in whole grains, fruits and veggies, nuts and seeds, lean proteins, and heart-healthy fats. These foods specifically are high in nutrients that are great for type 2 diabetes like fiber and whole grains, fruits and vegetables, and heart-healthy fats like avocados, olive oil, nuts and seeds,” agreed Caroline Thomason, RD, CDCES , a dietitian and diabetes educator in northern Virginia. 

Yet no study is without limitations. As is common in nutrition research, this study was observational and did not control for all factors that are related to the risk of disease. The authors took great care to isolate the association of the biomarker score with disease risk, however, it is almost impossible to account for all other lifestyle and medical factors.

“The study does not allow us to draw conclusions as strong as to say that the Mediterranean diet is a causal factor in lowering the risk of type 2 diabetes. That said, our results are consistent with other lines of evidence and reinforce the Mediterranean diet as a dietary strategy for the prevention of type 2 diabetes,” explained Dr. Forouhi.

The Mediterranean Diet and Gut Health

Another rapidly growing area of research is the relationship between type 2 diabetes and the gut microbiome.

“The diet encourages several plant-based foods that provide fiber, which changes gut bacteria and may aid in blood sugar management,” Lisa Andrews, MEd, RD, LD , owner of Sound Bites Nutrition, told Health .

Andrews added, “These plant-based foods provide antioxidants that may have anti-inflammatory properties that aid in diabetes prevention.”

Not only can the Mediterranean diet give your gut health a boost, but it’s also known to be associated with many other health benefits as well. Andrews noted a “reduced risk of cardiovascular disease, hypertension, and neurodegenerative diseases such as Alzheimer’s. It may also aid in reducing the risk of certain cancers, including breast cancer.”

There are many ways to adopt and follow a Mediterranean eating pattern. Consider starting out with one or two changes at a time. Thomason suggested setting small, achievable goals like “incorporating one fruit or vegetable per day or swapping refined grains for whole grains at least half the time.”

Lastly, make the Mediterranean diet your own to fit into your lifestyle.

“There are actually 23 total countries in the Mediterranean ranging from Italy and Greece to middle eastern countries and northern parts of Africa,” Thomas concluded. “You can pull from any of these cuisines to create Mediterranean habits!”

Related: Best Mediterranean Diet Meal Delivery Services

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• Diet & Nutrition

What Experts Really Think About Diet Soda

G rowing up, Olivia Dreizen Howell, 39, “lived on” diet soda. So did her family. At a family reunion in 1996, everyone sported T-shirts with their shared surname in Diet Coke-can font. “We drank Diet Coke, Diet ginger ale, and Diet Sprite like water—there was no difference in our household,” she says.

Like many, Howell believed that sugar-free soda was a benign choice. But the latest research casts doubt on that assumption, linking diet drinks to mood disorders, fatty liver development, autoimmune diseases, and cancer, to name a few. 

Before you pour your diet soda down the drain (a step one health expert does, in fact, recommend), know this about diet-soda research: the vast majority of it is observational—drawn from public-health records and long-term population studies—as opposed to the scientific gold standard of double-blind placebo-controlled studies. 

Here’s what we know so far about what diet soda might be doing to your health.

Diet soda is linked to a higher diabetes risk 

“Type 2 diabetes seems to be the strongest link” when it comes to diet soda and health risks, says Susan E. Swithers, a professor of neuroscience at Purdue University who researches diet soda's effects on metabolic health.“That seems to be a fairly consistent finding.” A 2023 study of nearly 106,000 people found that people who consumed more artificial sweeteners had a higher risk of Type 2 diabetes than people who didn’t eat or drink any.

Read More : Why Your Diet Needs More Fermented Pickles

Earlier work by Swithers found that people who drink a lot of diet soda face increased risks for excessive weight gain, Type 2 diabetes, cardiovascular disease, and metabolic syndrome, a constellation of conditions which include excess body fat (especially in the middle), elevated blood sugar and blood pressure, and higher triglycerides — “all of which are risks for the development of Type 2 diabetes and heart disease,” says Dr. Barry Schuval, an endocrinologist at Northwell Health.

It's linked to worse heart health

Several studies have linked artificially sweetened drinks like diet soda to heart issues, particularly increased risks of stroke , coronary heart disease , and heart attacks . Most recently, a March 2024 study found that people who drank more than two liters of artificially sweetened beverages per week had a 20% higher risk of atrial fibrillation than people who didn’t consume sweetened drinks. “It’s important not to assume that low-calorie [diet drinks] are inherently healthy,” says Dr. Ningjian Wang, lead author and professor of endocrinology and metabolism at Shanghai Ninth People's Hospital in China.

Melissa Prest, a dietitian and spokesperson for the Academy of Nutrition and Dietetics (who was not involved in the study), emphasized that the observational nature of the study means we don’t know why this link occurred. Before leaping to any conclusions about whether diet drinks increase the risk for atrial fibrillation, we need more research “to understand all potential variables, like health conditions, body weight, physical activity, and other dietary habits,” says Prest.

Diet soda is linked to cancer

In July 2023, after reviewing research on humans and animals, the World Health Organization (WHO) added aspartame, a common ingredient in diet soda, to a list of ingredients that are “possibly carcinogenic in humans.” That might sound worse in theory than it does in practice: the WHO concluded that a person who weighs about 150 pounds can safely drink about eight cans of aspartame-sweetened diet soda per day.

Read More : Why Are So Many Young People Getting Cancer?

Even with this designation, aspartame isn’t necessarily carcinogenic, says Schuval. “We must keep in mind that correlation does not necessarily imply causation,” he says, and the existing research isn’t conclusive. 

Other research has found potential links from diet soda to cancers including colon, uterine, kidney, and pancreatic. But instead of diet soda being the culprit, weight gain may be, says Schuval. 

Diet soda is linked to weight gain

Artificial sweeteners like aspartame, sucralose, and saccharin are much sweeter than sugar and may alter sweet-taste receptors in your body. Some experts think that this can cause changes to your body’s hunger and satiety hormones, leading you to eat and drink more than you otherwise would. The theory isn’t a slam dunk, however. “While this change has been commonly reported in animal studies, human-based studies have had inconsistent results,” says Prest. 

Another possibility is that both sugars and artificial sweeteners can disrupt the healthy balance of gut bacteria in the GI tract, which may lead to the development of insulin resistance and Type 2 diabetes, says Prest. This, too, is hard to prove in studies, and ones that point to this pathway are often small and inconclusive, says Leah Reitmayer, a dietitian in Sanford, N.C.

Read More : Ozempic Gets the Oprah Treatment in a New Special

As is the case with much nutrition research, the associations found between diet soda and weight gain (and obesity) may be red herrings. “The research shows that more obese individuals drink diet soda than regular—but also eat more food than healthy weight adults,” says Reitmayer. More research is necessary to determine if diet soda is making people gain weight, or if the relationship is complicated by other factors. 

What to make of all this research

Overall, the findings are mixed, leading to bewilderment among consumers about whether diet soda is a safe beverage. 

Swithers believes we still have more questions than answers. While she says she feels persuaded by a true link between diet soda and Type 2 diabetes,  the evidence for artificial sweeteners contributing to cancer and heart disease is less clear, she says. “It just comes down to what explains that relationship,” says Swithers. Are people who choose to drink diet soda already at higher risk for certain health conditions? Are all artificial sweeteners the same? Is there another variable scientists aren’t looking at? 

“That’s where it gets really muddy,” she says. Unfortunately, we’ll have to wait to get a fuller picture of diet soda’s health effects.

Is diet soda at least better for you than regular soda?

If you routinely drink sugary sodas, all experts would rather you switch to water (naturally). But barring that, many would prefer you drink diet. “Some people find that artificially sweetened beverages help them have better control of their blood sugar,” says Prest.

Another reason is we have much more persuasive evidence of the harms of excess sugar than we do for artificial sweeteners. Over many years, research has linked sugar to conditions like obesity, inflammation, heart disease, metabolic syndrome, insulin resistance, Type 2 diabetes or worsening of prediabetes, weight gain, and tooth decay, and some studies have even indicated that reducing added sugar in the U.S. food supply could save money and lives .

It’s also important to consider what else your diet soda might be replacing. Dan DeBaun, a 32-year-old public relations manager in Minnetonka, Minn., uses diet soda as a tool to cut back on alcohol. “I never drank much alcohol previously, but I wanted to cut back even more after more studies emerged about the negative health impacts ,” he says. After a successful “dry October,” where he abstained from alcohol completely, he realized he still liked having something to drink when he was out with friends or at a sporting event or concert. So he’d order a Diet Coke or Diet Pepsi.

Read More : How to Be a Healthier Drinker

“Diet soda doesn't necessarily make me feel great while I'm drinking it, but I consider it a net positive compared to alcoholic beverages,” says DeBaun. “I'd only drink one, but I found having it was a good substitute.”

And dentally speaking, diet soda does clearly trump regular. “One benefit of artificial sugars is their role in reducing dental caries,” says Prest. “When sugar-sweetened beverages are exchanged for artificially sweetened beverages, the risk of developing dental caries or cavities is reduced,” she says, and this is due to the reduction in the growth of bacteria that cause them.

How to limit your diet-soda intake—or quit it altogether

Few among us can give up a hard-core diet-soda habit cold turkey, but there’s still plenty you can do to cut back, Swithers says. 

Dump it down the drain (really)

People typically think they need more of a food or drink to feel satisfied than they actually do. If you simply crave the taste of diet soda, open a can or bottle, take a few sips, and dump the rest down the sink. You might find your craving is satisfied after only a few gulps. “Drinking just a little bit, then stopping and thinking about whether you even want more soda, could be a helpful step to reducing consumption,” Swithers says.

Treat your diet soda like candy

Instead of thinking about your diet soda as a drink, think about it as candy, suggests Swithers. That way, it might start to seem ludicrous to have one with meals. “Most adults wouldn’t open a bag of candy and pour it onto their dinner plate,” she says. “Just because it’s in a glass doesn’t make it magic in some way. Would you pour a bag of jelly beans as a side dish with your meal?”

Disguise your water 

A lot of people drink diet soda because they don’t like the taste of water. To give a glass of water an appealing flavor, drop in some frozen fruit (especially the kind that releases juice, like pineapple, strawberries, and mango). Using seltzer instead of still water will make it feel even more like the bubbly treat you love.

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Dietary Interventions for Type 2 Diabetes: How Millet Comes to Help

1 Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Gogerddan, Aberystwyth, UK

Swati Puranik

Hanna r. manwaring, sandra pierre, rakesh k. srivastava.

2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India

Rattan S. Yadav

Diabetes has become a highly problematic and increasingly prevalent disease world-wide. It has contributed toward 1.5 million deaths in 2012. Management techniques for diabetes prevention in high-risk as well as in affected individuals, beside medication, are mainly through changes in lifestyle and dietary regulation. Particularly, diet can have a great influence on life quality for those that suffer from, as well as those at risk of, diabetes. As such, considerations on nutritional aspects are required to be made to include in dietary intervention. This review aims to give an overview on the general consensus of current dietary and nutritional recommendation for diabetics. In light of such recommendation, the use of plant breeding, conventional as well as more recently developed molecular marker-based breeding and biofortification, are discussed in designing crops with desired characteristics. While there are various recommendations available, dietary choices are restricted by availability due to geo-, political-, or economical- considerations. This particularly holds true for countries such as India, where 65 million people (up from 50 million in 2010) are currently diabetic and their numbers are rising at an alarming rate. Millets are one of the most abundant crops grown in India as well as in Africa, providing a staple food source for many poorest of the poor communities in these countries. The potentials of millets as a dietary component to combat the increasing prevalence of global diabetes are highlighted in this review.

Type 2 Diabetes Overview and Associated Complications

Diabetes is a chronic disease that is characterized by high level of blood glucose also known as hyperglycaemia. According to WHO 2015 published figure 1 , 9% of the world population aged 18 and above has contracted diabetes and an estimated 1.5 million deaths per year are attributed to diabetes directly. It is well known that glucose level of a diabetic patient increases dramatically beyond the normal range after a meal. It is also true that their blood glucose level would soon drop as the body failed to store the excess glucose for later use.

Diabetes is classified into Types 1 and 2. Type 1 diabetes is also known as juvenile diabetes or insulin-dependent diabetes as the patients’ pancreas cannot produce or produces little insulin and often presents itself from childhood ( Diabetes.co.uk, 2016c ). Type 2 diabetes (T2D), however, often first appears in adults when the body becomes resistant to insulin or fails to make sufficient amounts of insulin ( Martin et al., 1992 ; Weyer et al., 2001 ). T2D comprises 90% of people with diabetes around the world ( NHS choice, 2014 ). This can largely be the result of excess body weight and physical inactivity. Added complication to T2D is that it presents less marked symptoms than Type 1 diabetes and is often diagnosed only when complications have already arisen.

Major complications caused by hyperglycaemia include atherosclerosis that hardens and narrows the blood vessels. Other diabetes-related complications are heart disease, stroke, retinopathy, and kidney failure ( Bitzur et al., 2009 ; Sone et al., 2011 ). Diabetic retinopathy leads to blindness by causing cumulative damage to the small blood vessels in the retina and contributes to 1% blindness globally. Similarly, kidney failure due to prolonged restricted blood flow is a very common complication. Elevated blood glucose can also cause nerve damage ( Boulton et al., 2005 ) that may lead to the need of limb amputation ( Brownlee, 2001 ). Such ailments reduce the patients’ quality of life, and potentially relationship with others around them.

Other additional complications also include increased bone fracture risks in both Types 1 and 2 diabetics ( Saito et al., 2006 ; Vestergaard, 2006 ; Oei et al., 2015 ). Interestingly, however, Types 1 and 2 diabetics have lower and higher bone mineral density than healthy subjects, respectively, even though both are at elevated risk of fracturing ( Ma et al., 2012 ; Oei et al., 2013 ; Strotmeyer, 2013 ; NIH Osteoporosis and Related Bone Disease National Resource Center, 2015 ). Fracturing risks associated with different bone mineral densities can be explained by other diabetes related factors. Firstly, T2D subjects are often found to have higher body mass index and less physical exercise making any fall carrying a higher risk of fracture ( Vestergaard, 2006 ; Ma et al., 2012 ). Also, other complications such as retinopathy, as well as often-associated habits such as higher alcohol consumption interfering with the sense of balance, making fall more frequent adding to the fracture risk ( Rubin, 2015 ). Physiologically, the rise of glucose level in the body interferes with glycation that subsequently reduces collagen cross-linking and results in more brittle bone despite the higher bone mineral density ( Hein et al., 2006 ; Saito et al., 2006 ; Vestergaard, 2006 ). In addition, lower bone turnover rate causes poor fracture healing in diabetic patients, through interference from alternated glycaemia with a key bone remodeling regulator, the parathyroid hormone ( Rubin, 2015 ). Therefore, in many converging ways, T2D contributes toward a higher fracture risk. Subsequently, fractures further restrict the mobility of patients, worsening the condition of diabetes.

Another symptom that a T2D patient may have to endure involves muscle fatigability due to poor glycaemic control ( Halvatsiotis et al., 2002 ). This in turn causes tiredness and lack of energy often demotivate patients from engaging in physical exercises. Also, patients loose muscle mass as the body draws energy from breaking down muscles. Such abnormal anabolism of muscle makes muscle mass loss one of the many dangers that a diabetic patient have to face ( Møller and Nair, 2008 ; Bassil and Gougeon, 2013 ). The subsequent loss of motor function adds further physical as well as psychological complications to the patients.

Why Diet Is an Important Intervention

Diabetic patients experience fluctuation of blood glucose causing various health complications ( Coppell et al., 2010 ; Jali et al., 2012 ). One of the interventions is to control this fluctuation using dietary regulation with or without exercise and medications. With the number of people suffering from diabetes on the rise globally ( WHO, 2016 ), it is imperative to develop preventative measures involving intervention of diet and lifestyle, which would greatly reduce the risk of developing diabetes ( Diabetes Prevention Program Research Group, 2002 ; Lindstrom et al., 2006 ). In addition, the risk of subsequent health complication can be reduced with the right treatment ( Gaede et al., 1999 ). Indeed, decline of diabetes-related complication such as retinopathy has been shown to be positively correlated with earlier intervention ( Vallance et al., 2008 ). This results in the reduction of financial burden on the health services as well as improving the well-being for the patients.

A study by the Diabetes Prevention Program Research Group (2002) concluded that lifestyle intervention resulted in 39% lower incidence of diabetes than another group using only metformin, an interventive medication for people who are at risk of diabetes. The same study found that both lifestyle intervention and metformin were effective in restoring normal fasting glucose values. Indeed, lifestyle intervention, including dietary habit reform, was found to be more effective in restoring normal glucose values after ingestion. Though this experiment was not designed to investigate dietary change or increased physical activities individually, it was undeniable that both contributed significantly toward lowering the risk of developing diabetes.

It is notable that there are different prescribed diets across continents and countries ( Ajala et al., 2013 ). Based on extensive literature search, Ajala et al. (2013) concluded that lower carbohydrate and Mediterranean diet lowers HbA1c count (HbA1c is a test of blood glucose level over a period of time). In some cases, vegetarian ( Kahleova et al., 2011 ) and low-glycemic index (GI) ( Ben-Avraham et al., 2009 ) diets were recommended to help reduce the use of diabetic medication.

Interestingly, two studies comparing high-protein diet and high-carbohydrate diet showed no significant differences in weight loss between the two diet groups ( Brinkworth et al., 2004 ; Larsen et al., 2011 ). In fact Larsen et al. (2011) showed that the two different diets did not produce any difference in the level of HbA1c. That may explain as to why there are variations in accepted dietary recommendations for T2D patients. However, Brinkworth et al. (2004) did observe better improvement on blood pressure for those that were on high protein diet and concluded that it may have a long term favorable effect on cardiovascular risk profile. Both papers noted that the two different diets improved the general health for T2D patient.

An interesting conclusion was drawn in a study with more than 37 000 participants, of which 915 incidences of diabetes were reported over 10 years ( Sluijs et al., 2010 ). This study has confirmed that a positive correlation exists between higher GI food and diabetes and that fiber intake inversely correlated with diabetes. Interestingly, only starch in the carbohydrate sub-types was found to be related to diabetes risk. They concluded that diet constituents play a major part in controlling diabetes. These are just a few examples of how dietary intervention can improve diabetic condition.

Current Recommended Diets for Diabetics

Diets play an important role in controlling the on-set of diabetes, as there is a positive correlation between dietary glycaemic load and increased diabetic risk ( Sluijs, 2011 ; Greenwood et al., 2013 ). Eating the ‘wrong’ thing can accelerate the onset of diabetes. On the bright side, study has shown that dietitians can select appropriate intervention diets based on the client’s lifestyle ( Franz et al., 2010 ). Such options reduce the requirement to impact on the patients’ lifestyle, thus increasing the likelihood of the treatment to be successful.

Problems with dietary intervention, however, also arise from different standards from different countries. For example, the United Kingdom has a very different standard than the rest of Europe ( Ben-Avraham et al., 2009 ; Ajala et al., 2013 ). Also the American Diabetes Association (ADA) and the Canadian Diabetes Association (CDA) show differences among themselves, as well as with their European counterpart ( Ben-Avraham et al., 2009 ). More differences can be observed when Japan, South Africa, and India are added into the collective. This may mainly be due to the lifestyle differences between different cultures, but also to different dietary requirements in their local climate. Also, there seems to be many variations in terms of the length of studies on dietary intervention on T2D ( Salas-Salvado et al., 2011 ). These studies ranged from less than 4 and up to 23 years with variable dietary comparison as well as sample sizes. Further, some benefits from life style intervention requires longer observation period with higher sample size to draw a more accurate conclusion ( The Look Ahead Research Group, 2010 ).

A quick scan on recent publications of various nutrition recommendations for diabetics is summarized in Table ​ Table1 1 ( Ben-Avraham et al., 2009 ; Salas-Salvado et al., 2011 ; Ajala et al., 2013 ). All these recommendations include carbohydrate, fiber, protein and fat. Most countries would recommend for carbohydrate intake to range from 40 to 60%, with the exception of India (>65%). Fat intake varies between different diets while protein intakes varied between 10 and 35%. Most recommendations would also include advice for fiber intake except the US. Yet the defined unit for fiber differed from one another. So there is a lack of uniform recommendation of diet globally. One, however, may question the sensibility of having a uniform recommendation as people from different regions have very different lifestyles as well as physiologies.

Various recommendations for diabetic diet summarized in publications ( Ben-Avraham et al., 2009 ; Salas-Salvado et al., 2011 ; Ajala et al., 2013 ).

Salas-Salvado et al. (2011) suggested a dietary regime of plant-based food with a lower intake of meat, sweets, high-fat dairy and refined grains, which is commonly known as Mediterranean diet, for lowering the risk of diabetes. This recommendation has attracted attention ( American Diabetes Association, 2016 ; Diabetes.co.uk, 2016b ) and research ( Trichopoulou et al., 1995 , 2003 ). Note that Mediterranean diet has a diversity of definition depending on geographical location. The Mediterranean diet here is referring to that of Greece and southern Italy in the early 1960s ( Willett et al., 1995 ). Mediterranean diet is characterized by a high intake of vegetables, legumes, fruits, nuts, cereals, and a high intake of olive oil but a low intake of saturated lipids, a moderately high intake of fish (depending on the geographical location), a low-to-moderate intake of dairy products, a low intake of meat and poultry, and a regular but moderate intake of wine during meals. Total fat in this diet may be higher (40–42%, Table ​ Table1 1 ) but the mono-unsaturated:saturated fat ratio is above two. However, Salas-Salvado et al. (2011) admitted that this diet alone may not suffice in controlling incidence of diabetes.

The limited information in literature on the role of micro-nutrients seems to indicate that they can have significant influence on diabetes ( O’Connell, 2001 ). The latter part of this review will attempt to investigate the relationship of micro-nutrients with health benefits against diabetes.

Carbohydrates and Fiber

Starch is a carbohydrate that provides much needed energy for day-to-day activities. It is essentially composed of linear amylose involving α-1,4 linked D -glucopyranosyl units and branched amylopectin which is also interconnected by α-1,6 glycosidic linkages ( Zhang et al., 2006a , b ; Lehmann and Robin, 2007 ). The chain length and branching pattern, as well as the amylose to amylopectin ratio, all play a role in digestion efficiency ( Zhang et al., 2006a , b ).

Starch is generally divided into three different digestibility types: rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS) ( Englyst et al., 1992 ). RS is characterized by the fact that it is unable to be broken down in the small intestine and is therefore passed onto the large intestine. RS is further divided into three different types: physically inaccessible starch (RS 1 ), resistant starch granules (RS 2 ), and retrograded amylose (RS 3 ) ( Sajilata et al., 2006 ). RDS is referred to the starch fraction that is transformed into glucose soon upon ingestion (20 min). RDS is digested and absorbed in the duodenum and proximal regions of the small intestine ( Englyst et al., 1992 ). The rapidly digestible nature of the RDS causes a rapid rise of blood glucose followed by a subsequent hypoglycaemia. On the other hand, SDS is referred to starch that breaks down into glucose over a longer duration (20–120 min; Lehmann and Robin, 2007 ; Zhang and Hamaker, 2009 ). The slow release of SDS improves overall blood glucose control as well as providing stable energy to patients with T2D.

While RS is not digested and absorbed by human as energy, it has a positive role against diabetes. Johnston et al. (2010) has discovered that consumption of resistant starch improves insulin sensitivity. It does not affect body weight, fat storage in muscle, liver or visceral depots significantly. Also, it helps managing meal-associated hyperglycaemia ( Lehmann and Robin, 2007 ). This is particularly important for people who are at risk of, or suffering from, T2D.

Slavin (2005) defined dietary fiber as non-digestible carbohydrates and lignin that are derived from plant. It can be further classified into soluble and non-soluble fiber ( Slavin, 2003 ). It has already been known for a while that fiber plays a positive role in glycaemic control ( Jenkins et al., 1978 ; Chandalia et al., 2000 ). It is recommended that an increase of dietary fiber, particularly the soluble type, should be taken by T2D patients ( Wood et al., 1990 ; Slavin, 2005 ). It is reasoned that soluble fiber reduces enzyme access to its substrates through viscosity effect. Slavin (2005) has concluded that fiber intake and obesity are inversely correlated, indicating that fiber is an important instrument in starch and cholesterol control.

Starch granule structure is organized in concentric layers with amylopectin as branching polymer and amylose as liner polymer ( Gallant et al., 1992 ; Zhang et al., 2006a ). These layers are organized in crystalline structures that form different patterns, resulting in variable enzymatic digestion susceptibility. Note that starch physical structure alone may not necessarily reflect glucose response in the gastrointestinal tract. For an example, physical mixture of starch and protein can influence digestion time ( Zhang and Hamaker, 2009 ). Therefore, partially adding non-starch filler such as protein or fiber, potentially can produce a lower glycaemic response. Like many other nutrients, starch can be modified by other additives ( Agama-Acevedo et al., 2012 ). By adding unripen banana flour into cookies, Agama-Acevedo et al. (2012) have reduced RDS and increased SDS contents. Hence, even if the intrinsic RDS, SDS, and RS ratios of a type of food may give indication of GI responses, it can be influenced by other nutrients present in the meal.

Another carbohydrate to consider is sugar intake. It can often provide rapid energy, in case of emergency of glucose depletion, for the patient ( American Diabetes Association, 2015b ; NHS choice, 2015 ). On the other hand, most sweet things (e.g., drinks/cakes/sweets etc.) which people enjoy often contain too much sugar, thus causing hyperglycaemia ( Diabetes.co.uk, 2016a ). Sugar-sweetened foods, particularly beverages taken in large quantity, are generally not recommended for anyone and particularly those who have or are at risk of diabetes. Although reducing sugar intake may seem to be a logical step to take in reducing blood sugar level, eliminating it completely from diet would be a mistake ( American Diabetes Association, 2015a ; Diabetes U. K., 2016 ). There are, however, alternatives to those who have a sweet tooth and find sweets irresistible. There are different sugar substitutes such as sucromalt which shows delayed glucose and insulin fall ( Grysman et al., 2008 ). Another sugar substitute that is often used, fructose, has the ability to blunt glycaemic and insulin responses. The latter may seem to be advantageous on glycaemic control, but the corresponding physiological response on human may indicate otherwise ( Havel, 2005 ). Problems arise as fructose is not controlled by the glucose homeostasis system and high consumption subsequently leads to dysregulation of energy homeostasis. This may result in hyperlipidaemia and obesity that further put strain on the person already suffering from diabetes ( Elliott et al., 2002 ). When it comes to sugar, perhaps moderation would be the consensus between many dietary recommendations. A monitored diet maybe required due to sugar craving caused by previous excess consumption ( Avena et al., 2008 ).

Protein/amino acid (leucine)

Protein has been repeatedly identified as an important component for dietary strategies for diabetics ( Ezeogu et al., 2008 ; Ben-Avraham et al., 2009 ; Singh et al., 2010 ; Ajala et al., 2013 ). There are many findings of proteins or specific amino acids such as leucine that have some positive influence on the condition of diabetic patients. These include improved glycaemic control and muscle loss prevention ( Manders et al., 2006 ; Zhang et al., 2007 ; Melnik, 2012 ; Norton et al., 2012 ).

An advantage of having protein in the food matrix is that it can influence the rate of starch digestion ( Singh et al., 2010 ). A study on Sorghum concluded that proteins with disulfide bonds interfere with starch digestibility ( Wong et al., 2009 ). Such influence includes delayed starch digestion and control of postprandial hyperglycaemia. Another study on wheat starch-protein on glycaemic response and in vitro digestion has also concluded that the removal of protein such as gluten in wheat flour causes a rise of postprandial blood-glucose level ( Jenkins et al., 1987 ). These evidences support the notion that protein integrated in the food matrix can improve postprandial glycaemia.

T2D shows a positive correlation with patients’ muscle mass loss ( Bassil and Gougeon, 2013 ; Leenders et al., 2013 ). Therefore, muscle mass maintenance is part of the issue that needs to be considered. Since muscle is made up of protein, intake of amino acid is vital. In particular, the amino acid leucine has been known to induce muscle growth ( Dardevet et al., 2000 ; Crozier et al., 2005 ; Katsanos et al., 2006 ). It has been found to be most effective through ingestion. In fact, much of the body building products such as protein shake contains leucine for muscle building as a significant ingredient ( Hennessy, 2013 ). Protein supplement such as whey has a substantial level of leucine added to the product ( Jakubowicz and Froy, 2013 ). It is often mentioned that consuming protein supplement such as whey has insulinotropic as well as glucose lowering, effects. This is why leucine is often in the spotlight of many diabetes research ( Melnik, 2012 ).

The relationship between leucine and glycaemic control is rather complex. In one way, it appears to help with the condition by stimulation of insulin production ( Zhang et al., 2007 ). However, prolonged usage of excess leucine may accelerate the deterioration of the health condition of the patient ( Melnik, 2012 ). Melnik (2012) surmised that excessive intake of leucine from meat (often red meat) leads to hyper activation of mTORC1 (mammalian target of rapamycin complex 1), a nutrient-sensitive kinase. mTORC1 when activated, leads to insulin resistance in the long term. While in the short term it will increase insulin production, prolonged supplement of leucine will cause pancreatic β-cells hyperfunction leading to their early senescence and apoptosis ( Figure ​ Figure1 1 ). The subsequent lack of insulin production will cause the increase of blood glucose as well as other side effects. In parallel, the primarily increased insulin production will also increase insulin resistance, which further complicates the condition.

Leucine influence on T2D ( Melnik, 2012 ) .

However, Melnik (2012) were investigating how excess intake of leucine may contribute to T2D, whereas moderate intake of leucine may be beneficial toward T2D patients. Zhang et al. (2007) suggested that dietary supplement of leucine improves glucose and cholesterol metabolism, particularly decreasing hyperglycaemia and hypercholesterolemia in mice. Although the paper in question was mainly interested in obesity, there may be some indication that dietary leucine would help with diabetes. With high fat diet mice, having leucine in their water supply lowered the glucose content in their plasma sample after fasting. Although concentration of insulin was lower in high fat plus leucine fed mice, the paper claimed that these mice were more glucose tolerant and insulin sensitive. However, since these tests were done on healthy mice, more works are needed to test on diabetic mice.

Another study found that a mixture of protein hydrolysate and leucine increased insulin level and helped reduce glucose level in blood plasma ( Manders et al., 2006 ). Their research indicated that while supplementing with additional leucine had a distinct advantage on insulin response from healthy subjects, distinction between the effects of protein supplement with or without leucine was lost in T2D subjects. It may be explained by T2D patients already having a decreased insulin sensitivity. In addition, postprandial glucose response indicated that both protein supplement with or without leucine supplement could effectively reduce glucose response in both healthy and T2D subjects. Therefore, the authors concluded that protein hydrolysate augments endogenous insulin secretion with or without additional leucine. However, the authors admitted that there are still more work to be done to be able to conclusively indicate that the role of leucine and its role with diabetes. They pointed out that healthy and diabetic subjects’ plasma glucose responses occur on different scales (i.e., diabetic subjects have higher plasma glucose responses). Also, they showed that glucose responses were inversely correlated with the accompanying insulin responses in patients with T2D. However, this was expected, for the insulin response in the T2D patient did not behave like that of control subjects, suggesting that glucose content measurement should be undertaken on diabetic patients in parallel to healthy subjects. However, the author maintained that incorporation of leucine should still be considered as beneficial toward improving the condition of T2D.

Although leucine’s role in blood glucose is somewhat ambiguous, its role in the positive regulation of mTORC1 is often mentioned ( Manders et al., 2006 ; Melnik, 2012 ; Norton et al., 2012 ). As mentioned early on in this section, the induction of mTOCR1 induces insulin production and hence plasma glucose reduction. It seems to positively correlate with muscle growth, thus benefiting diabetic patients in terms of retaining muscle mass ( Manders et al., 2006 ). Although Norton et al. (2012) suggest that the addition of extra protein from whey or added leucine increases plasma insulin, one has to keep in mind that this particular experiment has been conducted on healthy male mice with a short time scale. So the effect of persistent leucine supplement on diabetic subject is inconclusive. Yet it would be prudent to consider Melnik (2012) warning of long term detrimental effect of excessive leucine intake on pancreatic β-cells.

One may also need to consider the potential effect of leucine on different age groups. One study on advanced age male found that prolonged leucine supplement has no effect on glycaemic control or muscle mass augmentation ( Leenders et al., 2011 ). Meanwhile in another study, a high proportion of leucine was required to stimulate the rate of muscle protein synthesis, particularly in elderly patients as compared to younger patients ( Katsanos et al., 2006 ).

Over all, much work is required to be done on the subject of protein supplementation for patients with T2D. While protein and amino acids, particularly leucine, have many advantages in improving the condition of T2D, one has to consider its implication on pancreatic β-cell senescence subsequently increasing the dependency of externally supplied insulin. Therefore, when advising on dietary treatments, considerations of the present condition of the subject is important to keep in mind.

Fat/Cholesterol

Fat is an essential part of a healthy diet. Many biological functions are dependent on fat but as with any other nutrients, problem arise when it is consumed in excess. In particular, it increases the risk of blocking blood vessels when these lipids enter into the blood stream, and the problem gets further aggravated when coupled with elevated level of glucose ( American Heart Association, 2016 ). If not controlled carefully, over accumulation of fat and cholesterol will lead to an increasing risk of cardiovascular disease (CVD), one of the complications very commonly observed in diabetics. There are three well known cholesterol and fat, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG).

Low-density lipoprotein cholesterol has been associated with higher risk for CVD ( Marz et al., 2011 ). It can be caused by a diet high in trans fats often associated with industrially hydrogenated vegetable fat as well as from fat of grazing animals ( de Souza et al., 2015 ; Ganguly and Pierce, 2015 ). The primary concern with LDL-C in terms of T2D is that it induces apoptosis in pancreatic β-cells in vitro ( Abderrahmani et al., 2007 ). Also, Drew et al. (2009) has discovered in their experiment that higher oxidated LDL-C reduces insulin secretion in cultured mouse pancreatic β-cells. Studies show that it will require more than a year of sustained lifestyle intervention to improve the LDL-C profile ( The Look Ahead Research Group, 2010 ; Wing et al., 2011 ). Therefore, lowering LDL-C is often considered as a target in management strategy.

High-density lipoprotein cholesterol has been associated with lowering the risk for CVD. Lowered HDL-C will put one at a higher risk of CVD, particularly people with T2D ( The Look Ahead Research Group, 2010 ; Wing et al., 2011 ). It is particularly responsive to lifestyle intervention. There are evidences of HDL-C being able to reduce plasma glucose by intravenous reconstituted HDL-C (rHDL) to T2D patients ( Drew et al., 2009 ). The results demonstrated that infusing rHDL into the patient’s blood stream led to an increase of β-cell function and subsequently plasma insulin level. In addition, a recent article pointed out that HDL-C may have a positive role in improving β-cell function as well as offering protection against stress-induced apoptosis ( Kruit et al., 2010 ). While HDL-C level is generally a good health indicator, there is a scenario where HDL-C benefits may be inhibited. If the HDL-C is enriched with triglycerides and depletion in cholesteryl ester with conformational alternation of apolipoprotein A-1, this will render HDL-C more likely to be immobilized on arterial wall ( Kontush and Chapman, 2008 ). Therefore, management strategy have to consider beyond HDL-C profile alone.

Triglycerides (TG) is known as the most common type of fat in the body. It is also known that high level of TG is not necessarily the cause of diabetes but an indicator of an individual at risk of diabetes ( Dansigner, 2015 ). For an example, reducing TG has been found to reduce the risk or delay in the onset of T2D ( Kruit et al., 2010 ). High level of TG have also been linked with higher incidence of cardiovascular complication, in particular coronary heart disease in an ethnically Japanese study ( Sone et al., 2011 ). Therefore, health risk is often associated with high TG level.

Lipid improvement may not have a direct positive effect on glycaemic index, however, it has the potential to help in reducing the subsequent complications. As part of a review, Bitzur et al. (2009) concluded that omega-3 has been shown with the ability to regulate the balance of LDL, HDL, and TG. Therefore, it has been successful in correcting dyslipidemia, a condition that reflects an imbalance between LDL, HDL, and TG, and which is part of the T2D symptoms that can cause cardiovascular disease events. These are but a few examples on how diet may improve the condition of those that have T2D.

Micro- and Anti-Nutrients

Micro-nutrients are nutrients such as minerals and vitamins that are required in small quantity while being essential for health ( Karunasinghe et al., 2016 ; Maiti et al., 2016 ). Anti-nutrients are often referred to as those that decrease the digestibility of nutrients ( Pal et al., 2016 ). Little quantification has been made when it comes to dietary recommendation on micro-nutrients and anti-nutrients and their effects on T2D.

Several metabolic pathways and cellular reactions in the body require minerals and vitamins to act as coenzymes and cofactors. Unlike the previously established notion that their deficiencies are related to specific diseases, with the progress in nutritional biology research, it has increasingly become clear that these micro-nutrients also have the potential to impact on other chronic ailments such as Types 1 and Type 2 diabetes ( Mooradian et al., 1994 ; Franz and Bantle, 1999 ). Indeed, reports confirm that micro-nutrients play a part in improving the diabetic condition ( Thorne et al., 1983 ; Boivin et al., 1988 ; O’Connell, 2001 ; McDougall et al., 2005 ; He et al., 2007 ).

Meanwhile, anti-nutrients research has shown that α-amylase can be inhibited by various plant-derived molecules like luteolin, polyphenols, as well as amylase inhibitors ( Boivin et al., 1988 ; McDougall et al., 2005 ; He et al., 2007 ). In particular, Boivin et al. (1988) showed that α-amylase inhibitor can significantly reduce postprandial glucose peak in both healthy and T2D subjects. To deter predation, plant α-amylase inhibitor causes the reduction of starch digestion by acting against animal α-amylase activity ( Singh et al., 2010 ). However, these inhibitors can be inactivated/reduced by heat during cooking ( Rehman and Shah, 2005 ). Polyphenols from tea can also help with reducing starch, lipid, and protein bioavailability, thus reducing excessive amount of nutrients to be absorbed by the body ( He et al., 2007 ). Others like anthocyanin which is often found in soft fruits inhibit α-glucosidase activities ( McDougall et al., 2005 ). This can be an effective tool to combat T2D, as diabetes is often caused by overindulgence in food.

In terms of minerals, the best established beneficial effector for diabetics is supplemental chromium (Cr) ( Anderson, 1998 ; Anderson, 2000 ; Sharma et al., 2011 ; Yin and Phung, 2015 ). Diabetics may not show any specific Cr deficiency ( Anderson, 1998 ), but Cr basically acts by creating a better mechanism for insulin action through improved receptor numbers, binding ability, phosphorylation and activation ( O’Connell, 2001 ). The indirect nature of Cr’s benefit toward T2D may be the cause of some contradictory results among studies ( Gunton et al., 2005 ). In its naturally occurring form, Cr is the active component of glucose tolerance factor (GTF), which renders Cr into the most bioavailable and it is safer than its other supplemental forms like chromium picolinate ( Vincent, 2007 ). Consuming 100 microgram of GTF Cr by consuming whole grains, beans, nuts/seeds, and mushrooms has been proposed to significantly alleviate diabetes. Apart from Cr, minerals like zinc, magnesium, manganese, potassium, and vanadium have also been found to be essential for T2D patients by controlling glucose and insulin homeostasis ( O’Connell, 2001 ; Diabetes.co.uk, 2016d ). Mineral deficiency interferes with the functioning of insulin, thus affecting the glucose metabolism, and deregulating blood sugar content.

Besides minerals, vitamins also regulate the activity of insulin and thus have been promoted as role players in diabetes management ( Pittas et al., 2006 , 2007 ; Pflipsen et al., 2009 ). In its most bioavailable and natural form, vitamin E (d-α-tocopherol) was found to significantly improve glycaemic control without changing insulin secretion ( Paolisso et al., 1993 ). Vitamin B12 deficiency has been found to be prevalent (22%) in T2D populations ( Pflipsen et al., 2009 ). Furthermore, patients who have diabetes-related CVD also develop the mortality risk factor, hyperhomocysteinemia (Hhcys), characterized by very high total homocysteine present in the blood plasma leading to death. Vitamin B6, folic acid (vitamin B9), and vitamin B12 have been used to decrease levels of plasma homocysteine and the risk of CVD in Type 2 diabetics ( Thornalley and Rabbani, 2010 ). Molecules such as inositol, coenzyme Q10 and carnitine are regulators of various carbohydrate, fatty acid, and protein metabolism pathways. Although their direct role in diabetes is still unclear, they may be useful to prevent or help in several diabetes-associated effects like diabetic ketoacidosis, diabetic retinopathy, and diabetic neuropathy ( Diabetes.co.uk, 2016d ). Thus, regular consumption of micro-nutrients in the form of natural or fortified food or any other intervention strategies supplementing micro-nutrients should be part of diabetes management.

Research on Food Processing/Cooking Methods

Another aspect to consider in dietary intervention is that most foods consumed are processed or cooked. For example, the cooking methods as well as the duration and level of heat used in cooking may change the ratio of different starch fractions ( Snow and O’Dea, 1981 ; Anderson and Guraya, 2006 ; Roopa and Premavalli, 2008 ; Chung and Liu, 2009 ; Zhang et al., 2009 ). In addition, the subsequent storage/refrigeration of food has also been shown to have effects on reduced RDS content ( Mishra et al., 2008 ). There are some inconsistencies though in such findings. For an example, a report stated that heating with microwaves have no significant effect on starch digestibility ( Anderson and Guraya, 2006 ). While Zhang et al. (2009) showed a different conclusion where heating has been related with increased RS. Therefore subsequently, cooking and processing will have an impact to GI value ( Frei et al., 2003 ; Hu et al., 2004 ). It is also important to know that dietary fiber content, tannin and in vitro protein digestibility of processed grain, all affected by cooking and processing, can affect GI ( Pushparaj and Urooj, 2011 ).

For an example, grain such as pearl millet is never eaten raw or as a whole grain. It is milled with the seed coat (rich in dietary fiber and micro-nutrients) to prepare whole meal flour utilized in preparation of foods. Commonly used traditional methods of processing and cooking pearl millet are: milling, roasting, boiling, pressure cooking, sprouting/germination. Processes such as fermentation and germination also are known to decrease the phytate (an anti-nutrient) content by 60% and improve bio-availability of minerals ( Suma and Urooj, 2014 ). Processing such as dehulling has been found to cause a significant reduction in protein, polyphenol and phytic acid content ( Pawar and Parlikar, 1990 ). Fermentation after dehulling can also cause a significant increase in the in vitro protein digestibility (IVPD) from 3 to 14% ( El Hag et al., 2002 ). However, cooking had little effects on the total dietary fiber (TDF; Pushparaj and Urooj, 2011 ). When put together, all these processes will cause changes in the availability of various nutrients content and should be considered as part of the management program.

There is much advice available on diabetics diet, but little advice in terms of scientific research on methods of processing/cooking that are most beneficial for diabetics. Some suggestions such as a research on pressure cooked legume have found the increase of SDS content at the expense of RDS and RS ( Kasote et al., 2014 ). While the authors have suggested that it would benefit people who are suffering from diabetes, one problem as they have pointed out, that the predicted GI (pGI) is higher after pressure cooking, which make it less desirable for diabetics. However, while the SDS, RS and RDS analysis was well defined in the paper, their pGI was not. Other studies from rice and millet have also shown increased SDS content after heat moisture treatment supporting the potential benefit of cooked grain for diabetics ( Anderson and Guraya, 2006 ; Lehmann and Robin, 2007 ).

Traits Desired in Plants for Diabetic Dietary Requirement

Plant modification (breeding and transgenic) toward targeted phenotype.

Traditional plant breeding has played a key role in improving plant yield and vigor and recently its emphasis is shifting to improving nutritional quality too ( Khoshgoftarmanesh et al., 2010 ; Richards et al., 2010 ). One of the prime example is the breeding of soybean ( Clarke and Wiseman, 2000 ). As Clarke and Wiseman (2000) report, there have been a number of efforts in breeding soybean to reduce its anti-nutrients content. By reducing various anti-nutrients compounds the protein availability in food for human or feed for farm animals has been substantially improved in this crop. This sets an excellent example of improving nutritional quality of the crop through selective breeding.

Efficiency of selective breeding can be further improved using molecular markers ( Yadav et al., 2011 , 2013 ; Varshney et al., 2013 ; Thudi et al., 2014 ; Kale et al., 2015 ). Marker-assisted breeding has been developed recently by identifying genomic regions or even causational genes that led to the development of better crop in terms of resilience, crop value, and nutritional qualities ( Serraj et al., 2005 ; Yadav et al., 2011 ; Varshney et al., 2013 , 2014 ; Thudi et al., 2014 ). With new marker-assisted technologies, breeding becomes less time consuming than traditional breeding and will continue to be a major tool for crop development and improvement.

There are many markers developed to identify crops with abiotic/biotic stress resistance and other agronomic traits including yield ( Fan et al., 2006 ; Jena and Mackill, 2008 ; Miedaner and Korzun, 2012 ; Sharma et al., 2014 ). There are also efforts that identified markers showing implication in nutrition ( Muthamilarasana et al., 2016 ). These markers targets include proteins ( Kumar et al., 2011 ), minerals ( Zhang et al., 2011 ; Kwon et al., 2012 ), sugar/acid ( Francia et al., 2005 ) as well as anti-oxidants and phenolics ( Shao et al., 2010 ; Torres et al., 2010 ) in various crops. Markers that have implication on downstream food processing such as malting for barley and cooking for rice are also available ( Francia et al., 2005 ). Collection of such markers will allow faster developmental time for producing desirable nutritional traits in various crops.

Various diabetic associations have identified micro-nutrients and anti-nutrients that influence the speed of digestion or optimizing nutrition use. Fortifying plants with micro-nutrients have been the focus of much research to improve nutritional quality to combat diabetes as well as malnutrition ( O’Connell, 2001 ; Bouis and Welch, 2010 ). Much has been done to improve micro-nutrients content ( Cakmak et al., 2010 ) but more can be done in targeting marco-nutritional benefits (e.g., carbohydrate, fat, and protein) for diabetics.

Although less welcomed in the public domain, transgenic technology is another mean and tool for biofortification of grain nutritional traits ( Pérez-Massot et al., 2012 ; Galili and Amir, 2013 ). Galili and Amir (2013) argued that traditional breeding methods have failed to raise some aspects of nutrition in crop to a satisfactory level. With continually expending knowledge and understanding of the biochemical pathways with molecular control, as well as with improvement of the transgenic technique, one can argue that genetic modification should be considered as a viable option. On the other hand, even though transgenic technology may have its advantages over traditional breeding, it has its limitations. For an example, one has to consider the acceptance of the general public, as well as other considerations beyond the technology itself ( Sample, 2012 ). Examples would be the legal frame work for this technology to be used on market products as well as the regulatory framework to conduct these processes safely. Currently, the additional cost to secure transgenic trial sites in Europe can exceed the cost of the experiment itself due to the lack of public support ( Cressey, 2012 ; Kuntz, 2012 ; The Telegraph, 2015 ). This in itself has much to desire as there is little evidence to assess the safety of such technology due to the lack of public support to explore and test such technology ( Nature Editorial, 2012 ). Many experiments, including those for safety assessment, have suffered premature termination ( Pilate et al., 2002 ; Kuntz, 2012 ). Perhaps much transparent process, public education, and campaign are needed to win over the trust of the general public if this technology is to be successfully deployed.

Over all, based on the above discussion on nutrition requirements of diabetics, some of the targeted benefits of improving staple crops for nutritional traits are clear. Given that there already exist technologies for crop improvement, we can start looking more into improving nutritional qualities such as lipid, fiber, mineral, amino acid and starch contents in our staple crops.

Millets Benefit for Diabetics

There are many dietary advice and options readily available for diabetics. Some have even provided advice on food groups down to grain type ( Dansinger, 2016 ). Recently, millets are receiving increasing spotlight in combating diabetes as a dietary option ( Henry and Kaur, 2014 ; Nambiar and Patwardhan, 2014 ; Muthamilarasana et al., 2016 ). Indeed, there are evidences to support that millets have many properties making it a good dietary option for diabetics. For an example, an experiment that has used diabetic mice to test different diets has concluded that added millet protein can increase insulin sensitivities, and reduce blood glucose level as well as triglyceride level ( Nishizawa et al., 2009 ). Added benefits such as increased plasma level of adiponectin and high-density lipoprotein cholesterol were also found in their 3 weeks study.

The cereal crop millet is one of the most abundant crops grown in India and African continent, and provides a staple food for many poor communities ( Ravi, 2004 ). Compared to other cereal crops such as wheat and maize, millets are high in nutritional content, gluten free, and have low GI ( Abdalla et al., 1998 ). They provide high energy, high dietary fiber, protein with balanced amino acid profile, many essential minerals, some vitamins, and antioxidants ( FAO, 1995 ; Lestienne et al., 2005 ; Suma and Urooj, 2012 ). These play a substantial role in prevention of many human illnesses such as T2D, cancer, cardiovascular, and neurodegenerative diseases ( Kannan, 2010 ; Shahidi and Chandrasekara, 2013 ). There is great potential for harnessing these positive attributes through selective breeding. Subsequently, combining grain processing/cooking methods and food production technologies in producing food product from such varieties will be directly useful for controlling diabetes through diets.

There are various species of millets (pearl, foxtail, finger, little and kodo, just to name a few) growing in various parts of the world ( Ravi, 2004 ). These millets are known to be able to survive and produce food in regions that are more prone to drought. The added benefit for millets is their potential positive contribution toward controlling the symptoms of diabetes ( Choi et al., 2005 ; Park et al., 2008 ; Shobana et al., 2010 ; Jali et al., 2012 ). They are known to have higher SDS ( Liu et al., 2006 ), mineral ( FAO, 1995 ) as well as leucine ( Ejeta et al., 1987 ; FAO, 1995 ) contents, that are positively attributed toward healthy diet for diabetics. Furthermore well characterized genetic, genomic and breeding methods exist for pearl millet ( Yadav et al., 2011 ; Hash et al., 2003 ), providing a head start for breeding program in this crop.

Amongst millets antidiabetic properties, a study in India reported that patients with T2D fed with foxtail millet for 90 days showed improved glycaemic control as well as other improvements ( Jali et al., 2012 ). The patients were given the diet of a combination of foxtail millet, split black gram and spice mix with a high degree of compliance. The result showed a reduced HbA1c, fasting glucose, insulin, total cholesterol, triglyceride, and LDL concentrations. These were all indications that this diet had a positive impact on T2D patients. The reduction of cholesterol, triglyceride and LDL-C concentration had a positive implication in cardio health ( Marz et al., 2011 ; Sone et al., 2011 ; Wing et al., 2011 ), a complication that many T2D patients suffered from ( Bitzur et al., 2009 ). One could argue that the effect of medication or the more regulated diet could cause an equal positive impact. However, millet’s positive role was consistent throughout these studies. Further, medication has negative side-effects that diet did not introduce.

Similar research on effects of finger millet on T2D rat had also been published ( Shobana et al., 2010 ). The study demonstrated that finger millet may help reduce subcapsular cataract when T2D mice were fed with added finger millet seeds coat. In this experiment, Shobana et al. (2010) had also observed the reversal of hypercholesterolemia and hypertriacylglycerolemia associated with diabetes. Not only did finger millet had an implication in diabetics’ health but health improvement in general, as many obese rat subjects had experienced weight loss.

Two studies from the same group on proso- millet and foxtail millet concluded that diet with mixture of their respective protein faction improved HDL-C concentration as well as reduced insulin and plasma glucose concentration ( Choi et al., 2005 ; Park et al., 2008 ). Though these were positive results in managing diabetes, caution have to be exercised that such results were drawn from protein fraction only and not the whole grain. The more wholesome properties of the grain such as fat and starch portion had yet to be investigated. This did not, however, negate the fact that these millets carried beneficial properties toward managing diabetes.

Thus, while there are many indications of dietary health benefits offered by millets in managing diabetes, more research is required.

Concluding Remarks/Summary

Much is desired toward developing crop products that can offer options to treat diabetes through diet. Even more research is needed for developing crop that can be used as a raw materials for these dietary products. In light of their benefits, millets hold a key to the well-being for those who suffer from, and those that are at risk of, diabetes. More research must be done in establishing the benefit and the method of deployment of these benefits in combating the global rising tide of diabetes.

Author Contributions

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer EP and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.

Acknowledgments

The authors wish to express their thanks to IBERS for the support of the writing of this paper as well as to Innovate UK for the financial support.

Funding. The senior author is supported by Innovate UK Grant 131788.

1 http://www.who.int/diabetes/en/

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