Body Mass Index: Obesity, BMI, and Health: A Critical Review

Affiliation.

  • 1 is a full professor at the University of Minnesota, Minneapolis, and chief of the Endocrine, Metabolic and Nutrition Section at the Minneapolis VA Medical Center, Minnesota. His PhD degree is in biochemistry. He has more than 250 scientific publications in peer-reviewed journals, and he is the winner of numerous prestigious academic and scientific awards, including the 2014 Physician/Clinician Award of the American Diabetes Association.
  • PMID: 27340299
  • PMCID: PMC4890841
  • DOI: 10.1097/NT.0000000000000092

The body mass index (BMI) is the metric currently in use for defining anthropometric height/weight characteristics in adults and for classifying (categorizing) them into groups. The common interpretation is that it represents an index of an individual's fatness. It also is widely used as a risk factor for the development of or the prevalence of several health issues. In addition, it is widely used in determining public health policies.The BMI has been useful in population-based studies by virtue of its wide acceptance in defining specific categories of body mass as a health issue. However, it is increasingly clear that BMI is a rather poor indicator of percent of body fat. Importantly, the BMI also does not capture information on the mass of fat in different body sites. The latter is related not only to untoward health issues but to social issues as well. Lastly, current evidence indicates there is a wide range of BMIs over which mortality risk is modest, and this is age related. All of these issues are discussed in this brief review.

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Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies

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  • Peer review
  • Lisa Te Morenga , research fellow 1 2 ,
  • Simonette Mallard , research assistant 1 ,
  • Jim Mann , professor 1 2 3
  • 1 Departments of Human Nutrition and Medicine, University of Otago, PO Box 56, Dunedin 9054, New Zealand
  • 2 Riddet Institute, University of Otago
  • 3 Edgar National Centre for Diabetes and Obesity Research, University of Otago
  • Correspondence to: J Mann jim.mann{at}otago.ac.nz
  • Accepted 28 October 2012

Objective To summarise evidence on the association between intake of dietary sugars and body weight in adults and children.

Design Systematic review and meta-analysis of randomised controlled trials and prospective cohort studies.

Data sources OVID Medline, Embase, PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, and Web of Science (up to December 2011).

Review methods Eligible studies reported the intake of total sugars, intake of a component of total sugars, or intake of sugar containing foods or beverages; and at least one measure of body fatness. Minimum duration was two weeks for trials and one year for cohort studies. Trials of weight loss or confounded by additional medical or lifestyle interventions were excluded. Study selection, assessment, validity, data extraction, and analysis were undertaken as specified by the Cochrane Collaboration and the GRADE working group. For trials, we pooled data for weight change using inverse variance models with random effects. We pooled cohort study data where possible to estimate effect sizes, expressed as odds ratios for risk of obesity or β coefficients for change in adiposity per unit of intake.

Results 30 of 7895 trials and 38 of 9445 cohort studies were eligible. In trials of adults with ad libitum diets (that is, with no strict control of food intake), reduced intake of dietary sugars was associated with a decrease in body weight (0.80 kg, 95% confidence interval 0.39 to 1.21; P<0.001); increased sugars intake was associated with a comparable weight increase (0.75 kg, 0.30 to 1.19; P=0.001). Isoenergetic exchange of dietary sugars with other carbohydrates showed no change in body weight (0.04 kg, −0.04 to 0.13). Trials in children, which involved recommendations to reduce intake of sugar sweetened foods and beverages, had low participant compliance to dietary advice; these trials showed no overall change in body weight. However, in relation to intakes of sugar sweetened beverages after one year follow-up in prospective studies, the odds ratio for being overweight or obese increased was 1.55 (1.32 to 1.82) among groups with the highest intake compared with those with the lowest intake. Despite significant heterogeneity in one meta-analysis and potential bias in some trials, sensitivity analyses showed that the trends were consistent and associations remained after these studies were excluded.

Conclusions Among free living people involving ad libitum diets, intake of free sugars or sugar sweetened beverages is a determinant of body weight. The change in body fatness that occurs with modifying intakes seems to be mediated via changes in energy intakes, since isoenergetic exchange of sugars with other carbohydrates was not associated with weight change.

Introduction

Sugar has been a component of human diets since ancient times, with earliest reports of consumption coming from China and India, and much later from Europe after the Crusades in the 11th century. 1 The suggestion that sugar might have adverse health effects has been a recurring theme for decades, with claims that high intake may be associated with an increased risk of conditions as diverse as dental caries, obesity, cardiovascular disease, diabetes, gout, fatty liver disease, some cancers, and hyperactivity. 2 3 4 5 6 However, inadequate study design, differences in assessing dietary intake, inconsistent findings, and varying definitions of “sugars” have precluded definitive conclusions regarding these associations.

The most consistent association has been between a high intake of sugar sweetened beverages and the development of obesity, 7 8 9 10 11 12 but not all published meta-analyses have reported a statistically significant link. 7 11 The expert consultations organised by the World Health Organization and the Food and Agriculture Organization of the United Nations and the scientific updates undertaken by WHO 13 14 15 have adopted a classification of carbohydrates and clarified definitions of various groups of sugars including the category of “free sugars” (table 1 ⇓ ). This classification enables a more standardised approach to examining potential adverse health effects.

 Classification of dietary carbohydrates

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To update the recommendations through the guideline’s development process that was launched in January 2009, WHO commissioned a systematic literature review to answer a series of questions 16 relating to the effects of sugars on excess adiposity. These questions asked whether reducing or increasing intake of dietary sugars influences measures of body fatness in adults and children, and whether the existing evidence provides support for the recommendation to reduce intake of free sugars to less than 10% total energy (box). 15 Body fatness was selected as an outcome in view of the extent to which comorbidities of obesity contribute to the global burden of non-communicable disease.

Questions posed by the WHO Nutrition Guidance Expert Advisory Group-Subgroup on Diet and Health, to develop recommendations regarding sugars intakes

What is the effect of a reduction in free sugars intakes in adults?

What is the effect of an increase in free sugars intakes in adults?

What is the effect of a reduction in free sugars intakes in children?

What is the effect of an increase in free sugars intakes in children?

(Where “free sugars” are defined as all monosaccharides and disaccharides added to foods by the manufacturer, cook, or consumer; plus sugars naturally present in honey, syrups, and fruit juices.)

Since the answers to the questions posed (box) were designed to inform population based dietary guidelines rather than advise individual patients, it was deemed appropriate to include cohort studies and randomised controlled trials of free living people consuming ad libitum diets (that is, with no strict control of food intake). The interventions mainly involved advice to increase or decrease intake of sugars, or of sugar containing foods or beverages, without emphasising the need to achieve weight loss.

We also examined randomised controlled trials comparing higher and lower intakes of sugars, but where energy intake was strictly controlled. Trials specifically designed to achieve weight loss were excluded. We acknowledged that the studies identified by this approach would inevitably be heterogeneous, that it would be difficult to disentangle the effects of a range of dietary changes that might occur after altering the intake of sugars, and that it might be difficult to identify a dose response. However, the findings from such an approach were expected to provide an indication of what might be achieved by population changes in intake of dietary sugars.

In accordance with the WHO guideline’s development process, 17 systematic reviews and meta-analyses were conducted according to the methods of the Cochrane Collaboration. 18 We prepared tables summarising quality assessment, effect size, and importance of findings, from which recommendations may be derived, in the format required by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group. Ethical approval was not required for this research.

Search strategy

Two separate electronic searches were conducted to identify randomised trials and prospective cohort studies relating intake of dietary sugars to measures or changes of body fatness (web appendix 1). OVID Medline, Embase, PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, and Web of Science electronic databases were searched for clinical trials and cohort studies, published up to December 2011, which met the inclusion criteria. In OVID Medline, we used the highly sensitive Cochrane search strategy to limit the first search to clinical trials, meta-analyses, and randomised controlled trials. We hand searched meta-analyses and reviews to identify studies that might have been missed.

Study selection

Two reviewers assessed titles and abstracts of all identified English language studies. Discrepancies in opinion as to whether studies should be selected for full review were resolved by discussion. A similar approach was used to determine which of these studies should be included in the formal analysis. Animal studies, cross sectional studies, and case-control studies were excluded. Studies were required to report intake of total sugars, intake of a component of total sugars (expressed in absolute amounts or as a percentage of total energy), or intake of sugar containing foods or beverages, assessed by continuous or categorical variables; and at least one measure of body fatness.

Participants were adults and children free from acute illness, but those with diabetes or other non-communicable diseases in whom conditions were regarded as stable could be included. Randomised trials were required to be of at least two weeks’ duration, and prospective cohort studies were required to be of at least one year’s duration. We included trials comparing diets differing in sugars intakes and in which the effect of sugars could be separated from the effects of other lifestyle or medical interventions.

Two groups of trials were identified. One group included studies in which participants in the intervention arm were advised to decrease or increase sugars, or foods and drinks containing sugars. Although such advice was generally accompanied by the recommendation to increase or decrease other forms of carbohydrate, there was no strict attempt at weight control. These trials are referred to as ad libitum studies. The other group of trials attempted to achieve isoenergetic replacement of sugars with other forms of carbohydrate. Interventions designed to achieve weight loss were excluded because the ultimate aim of the review was to facilitate the development of population based recommendations rather than nutritional recommendations for the management of obesity.

Data extraction and quality assessment

Data extraction and validity assessment were carried out independently by two reviewers, and any discrepancies resolved by discussion. For both randomised trials and cohort studies, outcomes, data relating to participants, exposure or interventions, potential effect modifiers, and study quality were extracted by use of piloted data extraction forms. In the cohort studies, we aimed to extract the least and most adjusted relative risk, odds ratio, or mean difference when comparing the most exposed group of participants with the least exposed group, or a β coefficient for the continuous effect of a one unit change in sugars intake. We extracted these statistics separately for sugars exposures reported as baseline values or as values for change over time.

Cochrane criteria 18 were used to examine validity of each randomised trial, including sequence generation, allocation concealment, blinding of participants, personnel and outcome assessors, incomplete outcome data, and selective outcome reporting. Additional review specific criteria included similarity, or not, of type and intensity of intervention in both arms, and whether the studies were funded by industries with potentially vested interests. We examined the effect of bias on the pooled effect estimates by excluding studies that had a high risk of bias for two or more validity criteria in sensitivity analyses.

Statistical analysis

Studies were grouped to answer the major questions that had been posed (box). We considered data for adults and children separately. Studies of isoenergetic exchange of sugars with other carbohydrates were examined to help explain possible mechanisms through which sugars might exert their effects.

Randomised trials

The effects of decreasing or increasing dietary sugars in adults were examined principally by meta-analysing the randomised trials in which participants were required to consume different amounts of sugar (sucrose) or other sugars (which would now be classified as “free sugars”). Terminology varied among trials. The term “free sugars” refers to all monosaccharides and disaccharides added to foods by the manufacturer, cook, or consumer, plus sugars naturally present in honey, syrups, and fruit juices (table 1). 14 The term “added sugar” is sometimes used interchangeably with “free sugar” but is considered to include sugars and syrups added to foods during processing, food preparation, or at the table—but does not include honey, syrups, or fruit juice. 19 “Sugar” is generally assumed to be purified sucrose. 14

Data for each group of studies were pooled using Review Manager 5.1 software. 20 In trials involving adult participants, we used generic inverse variance of analysis for mean differences in body weight between intervention and control groups to compare the parallel and crossover experimental designs reporting change in body weight. In the studies involving children and adolescents, we used standardised mean differences because studies reported differences in either body mass index (BMI) or standardised BMI units.

Heterogeneity was assessed with the I 2 test and Q statistics. We considered an I 2 value greater than 50% and P<0.05 as indicative of heterogeneity. 18 We used random effects models because significant heterogeneity was associated with trial design and duration in some analyses.

Estimates for the standard error of the difference in means for treatment groups in crossover studies were derived from reported P values when the standard error of the mean difference was not reported. 18 If P values for the differences were reported simply as non-significant, then P=0.2 was assumed. 18

We did sensitivity analyses to explore the differences between studies in the short term (<eight weeks) and longer term (>eight weeks). We also tested the effects of removing those studies that achieved a difference in sugars intakes of less than 5% of total energy intake between intervention and control groups. Metaregression (using Stata/IC 11.2 software for Mac (StataCorp)) was used to test for a dose-response effect of sugars on weight change, and for associations between weight change and study duration, study design (that is, crossover or parallel), and whether sugars intake changed in the intervention arm.

Publication bias among the randomised controlled trials of adults was examined by visual inspection of a funnel plot and Egger’s test for bias. 21 Publication bias is suspected when the funnel plot is asymmetrical. We combined the 15 ad libitum studies for this analysis because it is generally accepted that asymmetry cannot readily be assessed with 10 or fewer studies. 18 Sensitivity analyses examined the influence of small study effects, by comparing the estimates derived from random and fixed effects models 22 and by using the Duval and Tweedie 23 “trim and fill” method in Stata 12 (Metatrim). There were insufficient studies in children to conduct a meaningful examination of publication bias.

Prospective cohort studies

Cohort studies in adults provided limited additional information. Data from cohort studies in children were necessary to determine the effect of increasing sugars intake on adiposity, owing to a lack of suitable randomised trials. We grouped individual studies for meta-analysis on the basis of the methods used for reporting adiposity outcomes and sugars exposure variables.

We used four main methods of reporting outcomes:

β coefficients for the continuous association between sugars exposure at baseline and adiposity outcome.

Odds ratios for the risk of overweight or obesity comparing participants who had the highest intakes of sugars with those who had the lowest intakes of sugars (groups or frequency of servings).

Mean differences in change in measures of adiposity over time between participants with the highest intakes of sugars and those with the lowest intakes (groups or frequency of servings).

β coefficients for the continuous association between increases in sugars exposure over time and adiposity outcome.

Sugars exposures included sugar sweetened beverages, fruit juice, sweets (including jams, syrups, cakes, and desserts), sucrose, or total sugars. Exposures were reported as servings per time period and were converted to servings per day, volume of beverage consumed per day, percentage of energy intake, frequency of consumption, or grams per day. Where possible, we scaled exposures to comparable units to allow data to be pooled. We assumed that one serving of sugar sweetened beverage was equivalent to 240 mL or 8 fluid ounces, and contained 26 g of sucrose. 24 This portion equated to about 5% of daily total energy intake in adults.

Measures of body fatness included weight change, change in BMI or BMI z score, waist circumference, body fat (%), fat mass, and trunk fat (%). If studies reported more than one measure of sugars intake, we derived an average effect size. We ranked adiposity outcomes in terms of importance for pooling, from highest to lowest: BMI z score, BMI, body weight, waist circumference, percentage body fat, fat mass, and percentage trunk fat. If studies reported outcomes for more than one measure of adiposity, we used the highest ranked adiposity outcome. We generated pooled estimates for the various subgroups using metan commands with random effects in Stata. Two sided P<0.05 was considered significant for all analyses.

GRADE assessment

GRADE assessment 25 was carried out to assess the totality of the evidence by the authors and then refined by the WHO Nutrition Guidance Expert Advisory Group (NUGAG) Subgroup on Diet and Health ( www.who.int/nutrition/topics/advisory_group/en/index.html ) to fulfil the required process for developing WHO guidelines. 17 GRADE assessment took into account study design limitations, consistency of results across the available studies, precision of the results, directness, and likelihood of publication bias when assessing the quality of the evidence from the randomised trials. 17 25 Further criteria were considered for the cohort studies. These criteria included magnitude of the effect, evidence of a dose-response gradient, and the direction of plausible biases. The quality of the evidence was categorised as high, moderate, low, or very low. Web appendix 2 shows the relevant GRADE tables.

Figures 1 ⇓ and 2 ⇓ show the process by which the included studies were identified. We identified 7895 potential randomised trials from the electronic search and a further 10 studies through hand searches of relevant review articles and on recommendation from NUGAG panel members. Removing duplicates left 6634 articles, of which 6557 were assessed to be irrelevant. Abstracts and full text articles for the remaining 77 studies were judged as requiring full review and were reviewed by three independent reviewers. Of these remaining studies, 19 met the inclusion criteria for ad libitum studies 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 and 11 were identified for the comparative analysis of isoenergetic studies. 48 49 50 51 52 53 54 55 56 57 58 For cohort studies, we identified 9445 potential studies from the electronic search and an additional 10 studies through hand searches of relevant review articles. Of 69 studies selected for full review, 38 were considered to meet the inclusion criteria. 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 The 47 excluded randomised trials and 31 excluded cohort studies are described in web appendices 3 and 4.

Fig 1 PRISMA flow diagram for randomised controlled trials

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Fig 2 PRISMA flow diagram for cohort studies

Assessment of study quality

Risk of bias varied among the randomised trials (web figs 1 and 2, web appendix 5). Failure to conceal treatment allocation (almost impossible to achieve in dietary trials involving free living participants) was the major potential source of bias (performance bias). In many trials, it was unclear as to whether outcome measures had been assessed by observers unaware of treatment allocation (detection bias) and whether there had been selection bias. Three trials, in which there was evidence of differences between dropouts and completers, reported data only for those who completed the intervention. 28 34 39

Our analysis included 38 prospective studies lasting at least 12 months, and in which data relating to an association between sugars and a measure of adiposity could be extracted; none was excluded on the basis of study quality. Of these 38 studies, 15 used self reported estimates of adiposity outcomes 59 64 65 66 67 68 70 71 73 74 75 76 77 78 79 80 ; seven collected exposure data from questionnaires where the validity for assessing sugars intake was not stated or not assessed 60 61 67 79 81 82 ; 19 involved convenience sampling 59 61 62 67 71 73 78 83 84 85 86 87 88 89 90 91 92 93 ; and 18 provided estimates that were adjusted for total energy intake. 59 60 64 66 69 72 75 76 86 88 90 91 92 93 94 95 96 97 There was a lack of consistency in the covariates used to adjust analyses and a wide range of methods of assessing sugars exposures and adiposity outcomes, which made pooling studies difficult.

Effect of reducing dietary sugars on measures of body fatness in adults

Table 2 ⇓ describes the five studies identified for this analysis, 28 30 31 33 41 49 and figure 3 ⇓ shows the quantitative meta-analysis (forest plot). Reduction in dietary sugars intake was associated with significantly reduced weight (−0.80 kg (95% confidence interval −1.21 to −0.39); P<0.001) at the end of the intervention period by comparison with no reduction or an increase in sugars intake. The trials all involved a reduction in intake of sugars (classified as free sugars) in the intervention arm compared with the control arm. 28 31 33 39 41 Study durations ranged from 10 weeks to eight months. In four studies, participants were advised to limit sugar containing foods, 31 33 39 41 and in one study, participants were asked to substitute usual sugar rich foods with low sugar alternatives. 28 Three of the five trials reported data for completers only. 28 39 41 However, only two of these studies considered this to be a potential source of bias. 28 41 Exclusion of these two studies from the meta-analysis slightly attenuated the effect, although the effect estimate remained significant (−0.81 kg, −1.41 to −0.21). After excluding three studies 28 39 41 that had a high risk of bias for two or more validity criteria, the effect estimate was no longer significant although the difference in weight was similar (−0.81 kg, −1.69 to 0.07).

 Characteristics of trials examining the effect of reducing intake of free sugars on measures of body fatness in adults

Fig 3 Effect of reducing intake of free sugars on measures of body fatness in adults. Pooled effects for difference in body weight (kg) shown for studies comparing reduced intakes (lower sugars) with usual or increased intakes (higher sugars). Overall effect shows increased body weight after intervention in the higher sugars groups. Data are expressed as weighted mean difference (95% confidence interval), using generic inverse variance models with random effects

Differences in sugar intakes between intervention and control groups ranged from less than 1% 33 to 14% of total energy intake. 39 Two studies achieved a difference in reported sugars intake of less than 5% of total energy intake at the end of the intervention. 28 33 Paineau and colleagues 33 reported a difference in sugars intake between groups of 2.2 g/day, and Gatenby and colleagues 28 reported a difference of about 3% of energy intake (15 g/day). Exclusion of these studies from the meta-analysis strengthened the overall effect of lowered sugar intakes on body weight change (−1.22 kg, 95% confidence interval −1.81 to −0.63). We saw no evidence of heterogeneity (I 2 =17%, P=0.3), and the test for overall effect showing an association between sugar reduction and increased weight loss was highly significant.

Effects of increasing dietary sugars on measures of body fatness in adults

Table 3 ⇓ describes the 10 studies identified for this analysis, and figure 4 ⇓ shows the quantitative meta-analysis (forest plot). 26 32 34 36 37 38 43 44 45 47 Because there was statistical evidence for significant heterogeneity among the studies (I 2 =82%, P<0.001), we used a random effects model to derive the pooled estimates. Increased intake in dietary sugars was associated with significantly greater weight (0.75 kg (95% confidence interval 0.30 to 1.19); P=0.001) at the end of the intervention period by comparison with no increase in sugars intake. The studies involved an increase in dietary sugars; mostly sugar sweetened beverages, in the intervention arm of the randomised trial. Only two studies lasted longer than eight weeks. 34 36 Subgroup analysis for these two longer term studies resulted in a significantly greater effect size (2.73 kg, 1.68 to 3.78) than the pooled effect for the shorter term studies (0.52 kg, 0.14 to 0.89). The difference between these subgroups was highly significant (P<0.001).

 Characteristics of randomised trials examining the effect of increasing intake of free sugars on measures of body fatness in adults

Fig 4 Effect of increasing free sugars on measures of body fatness in adults. Pooled effects for difference in body weight (kg) shown for studies comparing increased intake (higher sugars) with usual intake (lower sugars). Overall effect shows increased body weight after intervention in the higher sugars groups. Data are expressed as weighted mean difference (95% confidence interval), using generic inverse variance models with random effects

One trial reported a higher rate of participant dropout in the high sugars group than in the low sugars group and presented results for only participants who completed the whole study. 37 Exclusion of this study from the meta-analysis increased the overall effect size slightly (0.83 kg, 95% confidence interval 0.31 to 1.35). The association also remained significant after excluding from the meta-analysis five studies 26 32 34 37 43 that had a high risk of bias for two or more validity criteria (0.96 kg, 0.06 to 1.85).

Isoenergetic exchanges of dietary sugars with other carbohydrates or other macronutrient sources

We identified 12 studies that involved isoenergetic exchange of dietary sugars with other macronutrients (table 4 ⇓ ). 48 49 50 51 52 53 54 55 56 57 58 Interventions ranged from two weeks to six months, and sugars were in the form of either sucrose or fructose used to sweeten foods or liquids. We saw no evidence of difference in weight change as a result of differences in sugars intakes when energy intakes were equivalent (0.04 kg (95% confidence interval −0.04 to 0.13); fig 5 ⇓ ).

 Characteristics of trials comparing the effect on body weight change in adults of isocaloric diets high in free sugars with diets relatively low in free sugars

Fig 5 Isoenergetic exchanges of free sugars with other carbohydrates or other macronutrient sources. Pooled effects for difference in body weight (kg) for studies comparing isoenergetic exchange of free sugars (higher sugars) with other carbohydrates (lower sugars). Data are expressed as weighted mean difference (95% confidence interval), using generic inverse variance models with random effects

Findings of cohort studies

Table 5 ⇓ describes 16 cohort studies in adults that provided analyses of the relation between sugars exposures and measures of adiposity. 59 60 61 62 64 65 66 67 68 69 70 71 72 73 74 76 With a vote counting approach, 11 studies reported one or more significantly positive associations between a sugars exposure and a measure of adiposity, 59 60 61 62 64 65 68 69 70 71 73 74 and one study reported a significantly negative association. 73 Two studies reporting changes in intake of sugar sweetened beverages during follow-up showed a significantly greater increase in weight change among participants with the highest intake than in those with the lowest intake. 71 74 Web table 1 summarises pooled estimates for the relation between sugars intakes and various measures of adiposity from all other prospective studies in adults that met the inclusion criteria. Forest plots for these comparisons are provided in web figures 3-5 (web appendix 5).

 Summary of prospective cohort studies examining association between free sugars exposures and adiposity in adults

Effects of reducing dietary sugars on measures of body fatness in children

Table 6 ⇓ describes the five intervention trials identified for this analysis, and figure 6 ⇓ shows the forest plot. 27 29 33 40 46 Interventions generally included advice to reduce sugar sweetened beverages and other foods containing (free) sugars. We saw no association between such advice to reduce intake of dietary sugars and change in standardised BMI or BMI z score in children (0.09, 95% confidence interval −0.14 to 0.32). The studies included in this meta-analysis involved advice to reduce the intake of sugar sweetened beverages alone, 27 29 40 or together with a further reduction in other sugar rich foods and an increase in dietary fibre. 33 46 Poor compliance with the intervention advice was reported in three of the five studies, 29 33 46 and the effect of the intervention was a reduction of 51 mL/day in another study. 40 Significant heterogeneity was observed and a random effects model was used for the meta-analysis. Excluding the study by Davis and colleagues, 46 which had a high risk of bias for two or more validity criteria, did not alter the effect estimate.

Fig 6 Effect of reducing free sugars on measures of body fatness in children. Pooled effects for standardised mean difference in body mass index for studies comparing advice to reduce intake of free sugars with no advice regarding free sugars. Data are expressed as weighted, standardised mean difference (95% confidence interval), using generic inverse variance models with random effects

 Characteristics of intervention studies measuring the effect of advice to reduce intakes free sugars on change in BMI in children and adolescents

Effects of increasing dietary sugars on measures of body fatness in children

There were no randomised trials available in children, thus we used data from 21 cohort studies in children (reported in 22 articles) to assess the effect of increasing sugars intakes on body fatness (table 7 ⇓ ). Most studies related to intake of sugar sweetened beverages. A quantitative meta-analysis (fig 7 ⇓ ) was based on five cohort studies, with seven comparisons. These studies reported data for the odds of being overweight at follow-up in children consuming about one daily serving of sugar sweetened beverages at baseline compared with children consuming none or very little. 80 94 95 96 97 Comparison of the higher intakes with lower intakes suggested a significantly increased risk of being overweight associated with higher intakes (odds ratio 1.55, 95% confidence interval 1.32 to 1.82). We saw no evidence of heterogeneity, and all the studies reported a positive association. When assessing the 23 cohort studies in children using a “vote counting” approach, 15 reported a positive association between increased sugars intake and a measure of adiposity. 75 79 80 81 82 86 88 89 91 92 94 95 96 97 98 Fourteen of these 15 studies reported the sugars exposure as a sugar sweetened beverage. By contrast, only four studies reported a negative association, 87 90 93 98 of which two reported fruit juice as the sugars exposure. 90 98

Fig 7 Association between free sugars intakes and measures of body fatness in children. Pooled estimates for odd ratios for incident overweight or obesity in children consuming one or more servings of sugar sweetened beverages per day at baseline compared with children who consumed none or very little at baseline. Overall estimate shows higher odds of overweight or obesity at follow-up in those who consumed one or more servings of sugar sweetened beverages at baseline. Data are expressed as odds ratio (95% confidence interval), using generic inverse variance models with random effects

 Summary of prospective cohort studies examining associations between free sugars exposures and adiposity in children

Web table 2 summarises pooled and unpooled estimates for the association between sugars intakes and measures of adiposity from all other prospective studies in children that met the inclusion criteria. Because of the wide variation in how the study effects were reported, it was not always possible to pool studies reporting similar outcomes, and there was no evidence of association between increased sugars and adiposity. Web figures 6 and 7 (web appendix 5) show forest plots.

Sensitivity analyses

The overall meta-regression of randomised trials examining the effect of sugars on adiposity in adults showed no evidence of a dose-response association between sugar as a percentage of total energy intake and body weight (0.02 kg (95% confidence interval −0.03 to 0.08); P=0.392). The difference in weight changes associated with differing intakes of sugars was unrelated to study design (crossover or parallel design trials; 0.30 kg (−0.44 to 1.05); P=0.393), study duration (0.01 kg per week (−0.02 to 0.05); P=0.460), or whether sugars intakes were reduced or increased in the intervention arm relative to the control arm (0.12 kg (−0.73 to 0.96); P=0.817).

Publication bias

The funnel plot of all 15 randomised ad libitum trials conducted in adults was asymmetrical and the Egger’s test for bias was significant (P=0.001), which suggested possible publication bias (fig 8 ⇓ ). The pooled effect size for all 15 trials was 0.78 kg (95% confidence interval 0.43 to 1.12), based on a random effects model which accounted for significant heterogeneity (I 2 =77%, P<0.001) seen between the relatively short term crossover trials with small variances and the longer term parallel trials with larger variances. Use of fixed effects models attenuated the overall effect (0.42 kg, 0.28 to 0.56), but it remained significant. Excluding the studies with the largest study variances 34 41 from the analysis had little effect (0.72, 0.37 to 1.06). Trim and fill analysis showed a somewhat attenuated but significant effect size (0.50, 0.18 to 0.21). Visual inspection of the funnel plot and the Egger’s test for bias (P=0.248) did not suggest publication bias among the isoenergetic trials.

Fig 8 Funnel plot of randomised ad libitum trials in adults

The meta-analyses based on controlled trials provide consistent evidence that increasing or decreasing intake of dietary sugars from current levels of intake is associated with corresponding changes in body weight in adults. Although some evidence of potential publication bias existed, this did not seem to have an important effect on the findings. Results from cohort studies were generally comparable with the trial findings. The reviewed studies largely related to the manipulation or observation of intake of sugars which, using current terminology, would be described as “free sugars.” Two six month trials, 99 100 published subsequent to the census date for this systematic review, involved different intakes of sugar sweetened beverages in adults. The trials also showed a trend towards increased body weight in participants with raised intake, but the difference between groups was not significant, perhaps owing to small number of participants.

Poor compliance with dietary advice could explain why the data from trials in children were equivocal. This was confirmed by two controlled trials published after our systematic review’s census date. 101 102 De Ruyter and colleagues 101 showed a smaller increase in BMI z score after 18 months among trial completers who were provided with sugar free, artificially sweetened beverages, compared with participants who received equal quantities of sugar sweetened beverages. Ebbeling and colleagues 102 showed the potential of an intervention designed to decrease the consumption of sugar sweetened beverages in overweight and obese adolescents. BMI and body weight were significantly reduced after one year in the intervention group compared with the control group. However, after a further year’s follow-up with no further intervention, the difference between the groups was no longer significant.

Cohort studies in children confirmed a link between intake of sugar sweetened beverages and the risk of becoming overweight, but showed no consistent associations between other measures of sugars intake and adiposity. Although comparison of groups with the highest versus lowest intakes in cohort studies was compatible with a recommendation to restrict intake to below 10% total energy, currently available data did not allow formal dose-response analysis.

Strengths and limitation

An important strength of this in depth review of the literature lay in the overall quality and consistency of the data, especially those derived from adult populations. Although the trials were published over a long timeframe and used different experimental approaches, the results were consistent. Evidence was derived principally from randomised trials, but data from cohort studies that compared higher and lower groups of intake were also confirmatory. Criteria from both GRADE 25 and the World Cancer Research Fund 103 for judging strength of evidence of association specify randomised controlled trials as the highest level of evidence, but evidence from another study type is recognised as providing important confirmation.

We found less consistent findings from the trials conducted in children, which can be attributed to several factors. These trials tended to last longer than adult trials, and where compliance was assessed, it was clear that adherence to dietary advice (typically advice to reduce sugar sweetened beverages) was poor. For example, in a trial by Davis and colleagues, 46 children receiving nutrition education to improve carbohydrate quality achieved a reduction in added sugars intake of only 8 g/day, compared with control children. However, in children (as in adults), comparison of the highest intakes with the lowest intakes (usually of sugar sweetened beverages) suggested that those participants consuming the largest quantities had a higher body weight or other measure of adiposity.

The limitations of these findings are those inherent to the primary research on which they are based, notably inadequacy of dietary intake data, and variation in the nature and quality of the dietary intervention. Most cohort studies and some trials reported effects largely or solely related to the consumption of sugar sweetened beverages. Most trials involved different levels of intake of sugar (sucrose) and other monosaccharides and disaccharides in the control and intervention arms. These compounds have been described as “free sugars,” as defined by WHO (all monosaccharides and disaccharides added to foods by the manufacturer, cook, or consumer, plus sugars naturally present in honey, syrups, and fruit juices). 14 We had originally intended to report separately on the effects of total sugars as well as the various subcategories of sugars, but presentation of data in the studies precluded such analyses.

Assessment of dietary intake of sugars, whether by some method of recall as used in the trials, or by food frequency questionnaires as in cohort studies, was associated with a considerable degree of measurement error even when using validated methods. This is probably one explanation why a dose-response effect could not be shown between change in dietary intake and magnitude of weight change. Nevertheless, even crude estimates of intake provided assistance in interpreting potentially inconsistent findings. The studies of long term intervention in children 27 29 33 40 46 and two studies of interventions reducing dietary sugars in adults 28 33 found little difference in intakes between intervention and control groups, and no meaningful change in weight.

The heterogeneity of the studies, especially in terms of the consequences of altering intake of sugars in ad libitum diets, resulted in difficulties in fully explaining the effects of different dietary changes. Nevertheless, the changes in weight observed in studies of adults provided some indication of what might be achieved by the implementation of a dietary guideline relating to sugar, and conversely what might occur if consumption continued to increase.

The potential problem of residual confounding to explain some or all of an effect is inherent to all cohort studies. However, the overall consistency of our findings, regardless of study type, is reassuring. The only potential major source of bias identified in the trials was that four trials in adults reported data for completers. These data could have overestimated the effect, but we saw no meaningful difference in the magnitude of the effect between these trials and the other studies. Both participants and researchers in many of the trials were not blinded to intervention allocation. Studies providing beverages as a means of manipulating sugars intakes were blinded, but blinding was clearly not possible in studies relying on the provision of dietary advice to manipulate sugars intake. However, we do not believe that a lack of blinding altered our findings substantially. Measurement of body weight did not involve judgment that was subject to bias.

Potential mechanisms

The most obvious mechanism by which increasing sugars might promote weight gain is by increasing energy consumption to an extent that exceeds energy output and distorts energy balance. For sugar sweetened beverages, it has been suggested that energy in liquid form could be less satiating than when derived from solid foods, resulting in increased consumption. 104 Solid foods containing sugars are typically (although not invariably) energy dense, and frequent and substantial consumption of energy dense foods is associated with excessive weight gain and other measures of excess adiposity. We observed that isoenergetic replacement of dietary sugars with other macronutrients resulted in no change in weight (fig 5). This finding strongly suggested that energy imbalance is a major determinant of the potential for dietary sugars to influence measures of body fatness. However, other less direct mechanisms independent of energy balance have been proposed.

Sugars (particularly table sugar, sucrose, and high fructose corn syrup) contribute to the intake of fructose, which in turn can, at least in some people, increase levels of uric acid and hyperinsulinaemia. 105 Hyperuricaemia has been identified as a potentially important and independent predictor of obesity and the metabolic syndrome. 2 Sugar sweetened beverages and other sources of dietary fructose have been suggested to promote the deposition of liver, skeletal, and visceral fat and an increase in serum lipids independently of an effect on body weight. 106 Although this issue is relevant to any overarching discussion regarding the health consequences of dietary sugars and the extent to which they should be restricted, it is beyond the scope of this review.

Results in the context of existing knowledge

Most of the relevant published studies, reviews, and meta-analyses related to the association between intake of sugar sweetened beverages and body weight, weight gain, or other measures of adiposity. Widely discrepant conclusions have emerged, ranging from strong or convincing evidence for an association 8 107 to evidence described as inconclusive or equivocal. 3 7 11 108 109 110 This variance is hardly surprising, owing to the poor compliance in most intervention trials, the insensitive instruments used for assessing dietary intakes in cohort studies, and that in such studies, intakes might have changed between initial dietary assessment and measurement of outcome. One meta-analysis combined data for adults and children. 11 We found no evidence for an association between intake and weight in children when considering the intervention trials, nor were the data sufficient to examine for a dose-response effect when considering β coefficients for the continuous association between baseline sugars exposure and adiposity outcome. Nevertheless, we were able to show a consistent effect when comparing groups with the highest intakes of sugars with those with the lowest intakes.

There have been fewer reviews and meta-analyses relating to sugars or sugar rather than sugar sweetened beverages. In a systematic review and meta-analysis, Sievenpiper and colleagues concluded that isoenergetic substitution of fructose for other carbohydrates was not associated with weight gain. 110 However, free fructose at high doses that provided excess calories modestly increased body weight to an extent probably due to the extra calories rather than any particular metabolic attributes of fructose. Dolan and colleagues 111 drew similar conclusions when reviewing studies in which fructose was fed at “normal levels of intake.” Van Baak and Astrup 3 and Ruxton 104 recently concluded that there was insufficient evidence to indicate that replacing sugars with other carbohydrates resulted in a reduction in body weight. However, by limiting analyses to ad libitum trials, and considering studies in adults and children separately, our systematic review showed a clear positive association between higher intake of sugars and body fatness in adults, and provided an explanation as to why the findings in children were less conclusive.

Conclusions

This series of meta-analyses provides evidence that intake of sugars is a determinant of body weight in free living people consuming ad libitum diets. The data suggest that the change in body fatness that occurs with modifying intake of sugars results from an alteration in energy balance rather than a physiological or metabolic consequence of monosaccharides or disaccharides. Owing to the multifactorial causes of obesity, it is unsurprising that the effect of reducing intake is relatively small. The extent to which population based advice to reduce sugars might reduce risk of obesity cannot be extrapolated from the present findings, because few data from the studies lasted longer than ten weeks. However, when considering the rapid weight gain that occurs after an increased intake of sugars, it seems reasonable to conclude that advice relating to sugars intake is a relevant component of a strategy to reduce the high risk of overweight and obesity in most countries.

What is already known on this topic

Excessive intakes of dietary sugars have been linked to obesity, and a higher risk of chronic diseases, but the link with obesity is tenuous

The most consistent association has been between a high intake of sugar sweetened beverages and the development of obesity

No upper safe limit of intake has been agreed universally, but WHO has suggested that intakes of free sugars should be less than 10% of the total energy intake

What this study adds

Among free living people, advice to reduce free sugars was associated with an average 0.80 kg reduction in weight; advice to increase intake was associated with a corresponding 0.75 kg increase

This parallel effect seems to be due to an altered energy intake; isoenergetic replacement of sugars with other carbohydrates did not result in any change in body weight

Evidence was less consistent in children than in adults

Cite this as: BMJ 2012;345:e7492

We thank Carolyn Summerbell and Bernard Venn for their help on the initial development of this research; Melissa Butt and Sarah Harvey, who contributed to the data search for the randomised controlled trials; Marcus Du, who contributed to the data search and extraction for the cohort studies; and the members of the WHO NUGAG Subgroup on Diet and Health for their contribution to this work.

WHO agreed to the publication of this systematic review in a scientific journal, because it serves as the background evidence review for updating WHO guidelines on total sugars intake and should therefore, be available widely.

Contributors: The questions for the review were discussed and developed by the WHO NUGAG Subgroup on Diet and Health in February 2010, and the protocol was approved by the NUGAG Subgroup on Diet and Health. LT and SM supervised study searches. LT, SM, and JIM assessed inclusion, extracted data, and assessed validity. LT did the meta-analyses. LT and JM wrote the manuscript. The NUGAG Subgroup on Diet and Health reviewed the first draft of the report and contributed to the GRADE assessment. All authors read and approved the final draft of the report.

Funding: The authors were supported by the University of Otago and the Riddet Institute, a New Zealand National Centre of Research Excellence. The research was supported by the University of Otago, Riddet Institute, and WHO. The authors undertook the submitted work for WHO for the purposes of updating WHO guidelines on sugars intake, and WHO provided some funding to the University of Otago towards the cost of carrying out the review.

Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from the University of Otago, Riddet Institute, and WHO; no other financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; and no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: No additional data available.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

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research paper on body weight

  • Open access
  • Published: 13 May 2022

Weight gain attempts and diet modification efforts among adults in five countries: a cross-sectional study

  • Kyle T. Ganson   ORCID: orcid.org/0000-0003-3889-3716 1 ,
  • Jason M. Nagata   ORCID: orcid.org/0000-0002-6541-0604 2 ,
  • Lana Vanderlee   ORCID: orcid.org/0000-0001-5384-1821 3 ,
  • Rachel F. Rodgers 4 , 5 ,
  • Jason M. Lavender   ORCID: orcid.org/0000-0001-9853-2280 6 , 7 , 8 ,
  • Vivienne M. Hazzard   ORCID: orcid.org/0000-0003-3933-1766 9 ,
  • Stuart B. Murray   ORCID: orcid.org/0000-0002-5588-2915 10 ,
  • Mitchell Cunningham 11 &
  • David Hammond 12  

Nutrition Journal volume  21 , Article number:  30 ( 2022 ) Cite this article

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Recent research has emphasized a growing trend of weight gain attempts, particularly among adolescents and boys and young men. Little research has investigated these efforts among adults, as well as the specific diet modifications individuals who are trying to gain weight engage in. Therefore, the aims of this study were to characterize the diet modification efforts used by adults across five countries who reported engaging in weight gain attempts and to determine the associations between weight gain attempts and concerted diet modification efforts.

Cross-sectional data from the 2018 and 2019 International Food Policy Study, including participants from Australia, Canada, Mexico, the United Kingdom, and the United States ( N  = 42,108), were analyzed. In reference to the past 12 months, participants reported on weight gain attempts and diet modification efforts related to increased consumption of calories, protein, fiber, fruits and vegetables, whole grains, dairy products, all meats, red meat only, fats, sugar/added sugar, salt/sodium, and processed foods. Unadjusted (chi-square tests) and adjusted (modified Poisson regressions) analyses were conducted to examine associations between weight gain attempts and diet modification efforts.

Weight gain attempts were significantly associated with higher likelihood of each of the 12 forms of diet modification efforts among male participants, and 10 of the diet modification efforts among female participants. Notably, this included higher likelihood of efforts to consume more calories (males: adjusted prevalence ratio [aPR] 3.25, 95% confidence interval [CI] 2.94–3.59; females: aPR 4.05, 95% CI 3.50–4.70) and fats (males: aPR 2.71, 95% CI 2.42–3.03; females: aPR 3.03, 95% CI 2.58–3.55).

Conclusions

Overall, the patterns of association between weight gain attempts and diet modification efforts may be indicative of the phenomenon of muscularity-oriented eating behaviors. Findings further highlight the types of foods and nutrients adults from five countries may try to consume in attempts to gain weight.

Peer Review reports

Research in Canada, the United States (U.S.), and the United Kingdom (U.K.) has shown that targeted weight gain attempts are common among the general population, particularly in boys and young men. Among young adult men and women ages 17 to 32 years in Canada, the prevalence of weight gain attempts is 23% and 6%, respectively [ 1 ]. Among the U.S. population, nearly one third of adolescent boys (30%) and over one quarter of young adult men ages 18 to 26 years (27%) report concerted weight gain attempts, in stark contrast to only 6% of adolescent girls and 5% of young adult women ages 18 to 26 years reporting weight gain attempts [ 2 , 3 ]. Similarly, among adolescents in the U.K., prevalence of weight gain attempts is higher among adolescent boys (13%) than girls (4%) [ 4 ]. Recent research, however, has underscored the relatively common occurrence of weight gain attempts among an international sample of adults [ 5 ], indicating the global relevance of such weight-change efforts.

Among the most commonly reported methods utilized to gain weight among both adolescents and young adults is the adoption of specific diets or modifying food intake. For example, among young adults in Canada reporting weight gain attempts, 72% of men and over 50% of women reported consuming a greater volume of protein, while roughly 20% of both men and women reported eating more fat and 20% of men and 15% of women reported eating more carbohydrates [ 1 ]. This compares to 7% of young adult men and 2% of young adult women in the U.S. reporting eating different foods than usual to gain weight [ 3 ]. Among adolescents in the U. S., roughly two thirds of both boys and girls reported changing their eating to enhance their muscle size or tone [ 6 ], while 4% of adolescent boys and 1% of adolescent girls reported dieting to gain weight [ 3 ]. These data highlight weight gain as a motivating factor for trying to alter to one’s diet and food intake. However, aside from the study by Minnick et al. [ 1 ], the types of diet modification efforts (i.e., efforts to consume more or less of a particular food) among individuals reporting weight gain attempts remains poorly characterized.

This study therefore aimed to address several gaps in the literature. First, to date, much of the research on weight gain attempts has focused on adolescents and young adults, with a dearth of knowledge on the nature of weight gain attempts among adults reflecting the broader lifespan. Second, while studies on weight gain attempts among the general population have been conducted in multiple high-income countries (e.g., Canada, U.S., U.K.), the methodologies of these studies have differed, limiting the ability to conduct meaningful cross-cultural comparisons. Furthermore, there is little information from middle-income countries, such as Mexico, where food environments and dietary patters may differ from high-income countries. Lastly, research has provided a broad overview of the behavioral and diet modification efforts utilized to gain weight; however, these studies often lack specificity and a nuanced assessment of precisely which unique efforts to change diet and food intake were undertaken. Indeed, individuals—particularly boys and men—who are attempting to gain weight often attend closely to their intake of calories and specific macro and micro nutrients [ 7 , 8 ]; investigation of specific diet modification efforts is therefore warranted to provide a clearer understanding of such weight gain behaviors. This is specifically needed in order to evaluate diet modifications in comparison to dietary guidelines proposed across countries that often emphasize “healthful” eating (e.g., increased consumption of whole grains, fruits, and vegetables, decreased consumption of saturated fats and processed foods) [ 9 , 10 , 11 , 12 , 13 ]. Given these gaps, the aims of this study were, first, to describe the types of diet modification efforts most commonly reported among adults endorsing weight gain attempts from five countries, and second, to determine the associations between weight gain attempts and specific types of diet modification efforts.

Data from two survey years (2018; 2019) of the International Food Policy Study (IFPS) were analyzed for the current study. IFPS is an annual repeated cross-sectional survey conducted in Australia, Canada, Mexico, the United Kingdom, and the United States. Participants were recruited via Nielsen Consumer Insights Global Panel and their partners’ panels. Email invitations with unique survey links were sent to a random sample of panelists within each country after targeting for demographic groups. Data were collected via web-based surveys with adults aged 18 years and older. Potential respondents were screened for eligibility, age, and sex quota requirements. Respondents provided informed consent and received remuneration in accordance with their panel’s typical incentive structure (e.g., points-based or monetary rewards, chances to win prizes). Surveys were conducted in English in Australia and the U.K.; Spanish in Mexico; English or French in Canada; and English or Spanish in the U.S. The study was reviewed and received ethics clearance through a University of Waterloo Research Ethics Committee (ORE#30,829). A full description of the study methods can be found elsewhere [ 14 ].

A total of 28,684 participants completed the 2018 survey and 29,290 participants completed the 2019 survey. Respondents were excluded for the following reasons: region was missing, ineligible or had an inadequate sample size (i.e., Canadian territories); invalid response to a data quality question; survey completion time under 15 min; and/or invalid responses to at least three of 20 open-ended measures (2018: N  = 5,860; 2019: N  = 8,322). The majority of missing data for both survey years was due to region missing or ineligible (2018: 81%; 2019: 87.0%). The final samples for the 2018 and 2019 survey years were 22,824 and 20,968, respectively. Responses from participants ( n  = 1,684) who were surveyed both years had their data retained from the 2018 survey year, resulting in a total sample of 42,108 unique participants.

Weight gain attempts were assessed using the question, “During the past 12 months have you tried to… gain weight”. This measure aligns with prior research investigating weight gain attempts [ 1 , 4 , 5 , 15 ].

Diet modification efforts were assessed using the question, “Have you made an effort to consume more or less of the following in the past year?” Categories included: calories, protein, fiber, fruits and vegetables, whole grains, dairy products, all meats, red meat (e.g., beef) only, fats, sugar/added sugar, salt/sodium, and processed foods. These categories largely align with eating behaviors and nutrient groups outlined in dietary guidelines across the five countries [ 9 , 10 , 11 , 12 , 13 ]. Response options for each category included, “consume more,” “consume less,” “no effort made,” and “don’t know.” For the purposes of this study, responses were dichotomized to 0 = “consume less; no effort made; don't know’” and 1 = “consume more”. Self-rated diet quality was assessed using the question, “In general, how healthy is your overall diet?” Potential response options included, “poor,” “fair,” “good,” “very good,” “excellent,” and “don’t know.”

Sociodemographics were assessed via self-report. Specifically, sex at birth was assessed using the question, “What sex were you assigned at birth, meaning on your original birth certificate?” Response options included “male” and female”. Race/ethnicity was categorized into “majority,” “minority,” and “not stated” groups, in line with census questions asked in each country. Education was categorized as “low”, “medium”, or “high” according to country-specific criteria of the highest level of formal education attained. These categorizations of race/ethnicity and education are consistent with prior IFPS research, and enable comparisons across countries while permitting IFPS country-specific data to be compared to national census estimates [ 16 , 17 , 18 ]. Body mass index (BMI) was calculated based on self-reported height and weight measurements according to each country’s measurement unit (e.g., pounds, feet and inches; kg/m 2 ). BMI was categorized into four classes: ≤ 18.49 (“underweight”); ≥ 18.50 to ≤ 24.99; (“normal weight”); ≥ 25.00 to ≤ 29.99 (“overweight”); and ≥ 30.00 (“obesity”) based on Centers for Disease Control and Prevention guidelines [ 19 ].

Statistical analysis

Descriptive statistics were calculated to provide an overview of the sample characteristics. Chi-square tests and independent samples t- tests were used to examine sex differences. Unadjusted prevalence of diet modification efforts by weight gain attempts and sex, and weight gain attempts and country, were estimated. Chi-square tests were used to examine diet modification efforts by sex and country. Unadjusted prevalence of weight gain attempts by diet quality was estimated with chi-square tests used to determine differences between diet quality rating. Multiple modified Poisson regression analyses with robust error variance [ 20 ] were conducted to estimate the associations (reported as prevalence ratios) between weight gain attempts (independent variable) and all 12 diet modification effort types (dependent variables) while adjusting for age, race/ethnicity, education, BMI category, country, and survey year. We tested for effect modification by sex and found statistically significant interactions for all diet modification efforts ( p ’s < 0.05). Therefore, regression analyses were conducted in the overall sample and also stratified by sex. This aligns with prior research showing differing prevalence of weight gain attempts among males and females [ 1 , 3 , 4 , 15 , 21 ]. All analyses included post-stratification sample weights that are constructed using a raking algorithm with population estimates from the census in each country based on age group, sex, region, ethnicity (except in Canada) and education (except in Mexico). Therefore, percentages reported are inclusive of sample weights and may not correspond with observed n’s. Analyses were conducted in 2022 using Stata 17.1.

Among the sample of 42,108 participants, 51.0% were female (Table 1 ). The mean age of the overall sample was 45.5 years, and 78.5% of participants identified with a majority racial or ethnic group within their country. Overall, 10.4% ( n  = 1,900) of male participants endorsed weight gain attempts over the past 12 months, compared to 5.4% ( n  = 1,082) of female participants.

Unadjusted prevalence of diet modification efforts by sex among participants who reported weight gain attempts in the past 12 months are displayed in Fig.  1 . Of the specific diet modification efforts assessed, efforts to consume more fruits and vegetables was most prevalent among both male and female participants who reported weight gain attempts (males: 60.9%; females: 64.6%), while efforts to consume more salt/sodium had the lowest prevalence (males: 18.6%; females: 14.7%). Significant sex differences emerged in the prevalence of diet modification efforts among male and female participants who reported weight gain attempts in the past 12 months, with all types of modifications reported more frequently by male versus female participants. This included attempts to consume more calories (males: 38.9%; females: 30.0%), dairy products (males: 36.8%; females: 30.5%), all meats (males: 44.6%; females: 34.5%), red meat only (males: 36.3%; females: 27.7%), fats (males: 29.4%; females: 22.4%), sugar/added sugar (males: 19.8%; females: 15.1%), and processed foods (males: 21.6%; females: 15.9%).

figure 1

Prevalence of Diet Modification Efforts to Consume More in the Past 12 Months among Male and Female Participants who Reported Weight Gain Attempts from Five Countries in the 2018 and 2019 International Food Policy Study. Note: Chi-square tests for sex differences (* p  < .05 ** p  < .01 *** p  < .001). Analyses included sample weights

Unadjusted prevalence of several diet modification efforts differed significantly across the five countries. Among male participants who reported weight gain attempts in the past 12 months, efforts to consume more protein, dairy products, all meats, salt/sodium, fats, sugar/added sugar, processed foods, and fiber, fruits and vegetables, whole grains, and red meat only significantly differed across the five countries (Fig.  2 ). Among female participants who reported weight gain attempts in the past 12 months, efforts to consume more protein, whole grains, fats, and salt/sodium significantly differed across the five countries (Fig.  3 ).

figure 2

Prevalence of Diet Modification Efforts to Consume More in the Past 12 Months among Male Participants who Reported Weight Gain Attempts, by Country, in the 2018 and 2019 International Food Policy Study by Country. Note: Chi-square tests for country differences (* p  < .05 ** p  < .01 *** p  < .001). Analyses included sample weights

figure 3

Prevalence of Diet Modification Efforts to Consume More in the Past 12 Months among Female Participants who Reported Weight Gain Attempts, by Country, in the 2018 and 2019 International Food Policy Study by Country. Note: Chi-square tests for country differences (* p  < .05 ** p  < .01 *** p  < .001). Analyses included sample weights

The unadjusted prevalence of weight gain attempts by self-rated diet quality among male and female participants is displayed in Fig.  4 . Among male participants, weight gain attempts were most common among participants who rated their diet as “excellent” (16.7%). There were no significant differences between weight gain attempts and diet quality among female participants.

figure 4

Prevalence of Weight Gain Attempts by Self-Rated Diet Quality among Male and Female Participants in the 2018 and 2019 International Food Policy Study. Note: Chi-square tests for differences in self-rated diet (*** p  < .001). Analyses included sample weights

Modified Poisson regression analyses revealed significant associations between weight gain attempts and diet modification efforts in the overall sample and when analyses were stratified by sex, while adjusting for potential confounders (Table 2 ). In the overall sample, weight gain attempts were significantly associated with higher likelihood of efforts to consume more of all 12 types of dietary categories, with efforts to consume more calories (adjusted prevalence ratio [aPR] 3.51, 95% confidence interval [CI] 3.23–3.81) and fats (aPR 2.83, 95% CI 2.58–3.10) having the strongest effect sizes. Among male participants, weight gain attempts were significantly associated with higher likelihood of efforts to consume more of all 12 types of dietary categories, with efforts to consume more calories (aPR 3.25, 95% CI 2.94–3.59) and fats (aPR 2.71, 95% CI 2.42–3.03) having the strongest effect sizes. Among female participants, weight gain attempts were significantly associated with higher likelihood of 10 diet modification efforts, with efforts to consume more calories (aPR 4.05, 95% CI 3.05–4.70), fats (aPR 3.03, 95% CI 2.58–3.55), and sugar/added sugar (aPR 2.71, 95% CI 2.18–3.36) having the strongest effect sizes.

This study is the first to characterize the diet modification efforts among adults reporting weight gain attempts across five middle- and high-income countries. Broadly, descriptive analyses indicated that among adults who reported weight gain attempts, the most commonly reported diet modification efforts were to consume more fruits and vegetables, protein, fiber, and whole grains. This was the case for both men and women across all five countries. However, in regression analyses, efforts to consume more fruits and vegetables, protein, fiber, and whole grains had among the weakest effect sizes in the overall sample and for both males and females, including no significant relationship between weight gain attempts and efforts to consume more fruits and vegetables and whole grains among females. Thus, while these diet modification efforts were common in adults reporting weight gain attempts, they were also common in the full sample irrespective of weight gain attempts, rather than unique to those trying to gain weight. This may be in part due to overall nutrition guidance and education for the population that focuses on increased consumption of healthful foods such as fruits and vegetables, whole grains, and fiber [ 9 , 10 , 11 , 12 , 13 ]. In contrast, efforts to consume more calories were somewhat less common among adults who reported weight gain attempts yet had the strongest effect size in adjusted analyses, including over three-fold higher prevalence among men and four-fold higher prevalence among women who reported weight gain attempts. This was followed by efforts to consume more fats, with roughly three-fold higher prevalence among both males and females who reported weight gain attempts. These findings highlight the high-calorie and high-fat dietary intake efforts of participants reporting weight gain attempts despite existing data suggesting that intentional efforts to gain weight centrally implicate an upregulation in protein consumption [ 22 ] and a downregulation of foods that are less calorically dense and may have little benefit to increasing weight and muscularity (i.e., fruits and vegetables, whole grains, and fiber). This finding is in unique contrast to participants’ self-rated diet quality, where those who reported weight gain attempts, males in particular, were more likely to rate their diet as “excellent.” Taken together, these findings emphasize that both males and females engage in a vast array of diet modification efforts alongside attempts to gain weight, some of which may not support overall healthier dietary patterns, as suggested by governmental and public health guidance from all five countries [ 9 , 10 , 11 , 12 , 13 ], and may subsequently be damaging to their health, despite positive self-ratings of their diets.

The findings from this study may underscore muscularity-oriented eating behaviors, which largely encompass dietary practices (e.g., increased protein intake) that are intended to increase muscle-mass, muscularity, and tone, and decrease body fat [ 7 , 22 , 23 ]. These body characteristics align with the predominant ideal body for men [ 24 , 25 , 26 ] and are becoming more emblematic of the ideal body for women [ 27 , 28 ]. Evidence of muscularity-oriented eating behaviors include, first, 39% higher prevalence of efforts to consume more protein, 61% higher prevalence of efforts to consume more dairy products, 76% higher prevalence of efforts to consume more of all meats, 87% higher prevalence of efforts to consume more red meat only, and nearly three-fold higher prevalence of efforts to consume more fats for males who reported weight gain attempts compared to males in the general population, with similar results among females. Second, these dietary efforts may be characteristics of high protein, high fat, and ketogenic diets [ 29 ], which are claimed to catalyze fat loss along with the maintenance, or even increase, of muscle mass [ 30 ]. Third, muscularity-oriented eating behaviors also include the consumption of dietary supplements, such as protein powders and bars that are marketed for those engaging in weight training, muscle-building, and athletic activities. These products are often considered processed foods, which may in part be driving the prevalence of reported efforts to consume more processed food in this population. Fourth, while it may seem counterintuitive that participants who reported weight gain attempts also reported efforts to consume more salt/sodium, there is evidence that salt/sodium is an important factor in post-workout recovery [ 31 ], including adequate sodium levels playing a role in ensuring sufficient blood volume to transport nutrients to muscles [ 32 ]. This may also align with efforts to consume more sugar/added sugar given that sugars can help with the muscle glycogen resynthesis process post exercise [ 33 , 34 ]. Finally, the higher prevalence of efforts to consume more of all 12 dietary categories among men, and 10 among women, may be related to “cheat meals” or “cheat days,” and similarly, the “bulking” phase of “bulk” and “cut” cycles that are contextualized within a muscularity-oriented tradition. These behaviors consist of cyclical patterns of the consumption of a high quantity of calorie dense foods for a specific period of time before returning to restrictive/restrained diet practices with the intention of conferring the benefits for muscle enhancement [ 7 , 22 , 23 , 35 , 36 ]. Taken together, these findings may provide initial evidence of the dietary practices intended for muscularity, leanness, and tone among adults who report weight gain attempts.

Regarding country-specific differences in diet modification efforts, there are several findings worth highlighting. While efforts to increase caloric intake were commonly reported in all countries, the dietary approaches to increasing calorie content appeared to differ between countries. For example, men in the U.S. who reported weight gain attempts also reported significantly higher prevalence of efforts to consume more red meat, fats, sugar/added sugar, salt/sodium, and processed foods, all of which may increase risk of adverse health outcomes (e.g., cardiovascular disease) if consumed in excess [ 37 , 38 , 39 ]. This is contrasted with men in Mexico who reported weight gain attempts also exhibiting a higher prevalence of efforts to consume more protein, fiber, fruits and vegetables, and all meats, which may indicate the attempt to gain weight through increased intake of foods often considered as “healthier.” Second, among women, fewer explainable patterns across countries emerged signifying differences in prevalence of diet modification efforts among those who reported weight gain attempts. Future research is needed to further describe unique diet modification efforts among women who report weight gain attempts.

Strengths and limitations

This study includes several strengths. First, the IFPS includes a large and international sample of adult participants representing diverse racial/ethnic and age groups. Second, this study analysed multiple survey years with two different participant cohorts, which provides greater assurance that the findings are not unique to one point in time and instead may represent a descriptive pattern of behavior. Lastly, our analysis included an array of specific diet modification efforts, providing more detailed insights in the specific form of dietary intake changes and their associations with weight gain attempts.

Despite these strengths, limitations should be noted. First, given the sampling method used, the findings do not provide nationally representative estimates. However, the data and analyses were weighted using preconstructed sample weights based on country-specific census data in an attempt to maximize external validity. Second, all responses are based on self-report, which may increase recall and social desirability bias. Third, the diet modification effort question did not specify the purpose of the effort; therefore, we are only able to theorize, based on the associations found, that these efforts for increased dietary intake may be motivated at least in part or for some by a desire to gain weight. Fourth, a single item was used to assess weight gain attempts, and no information was collected on the frequency or type of behaviors specifically engaged in for this purpose, or the motivations for weight gain. As such, interpretations of these behaviors in relation to specific motivations (e.g., increased muscularity) are speculative; however, a large proportion of men report wanting to enhance their muscularity [ 40 ] and engage in muscle-enhancing behaviors [ 3 ], which provides evidence for our interpretation of the data. Nevertheless, future research focused on the motivations for weight gain will be needed. Lastly, data were cross-sectional, thus limiting the ability to infer causal relationships between the variables examined.

This study aimed to identify the diet modification efforts used among adults from five countries who report weight gain attempts. Results showed that both male and female adults who reported weight gain attempts had significantly higher likelihood of reporting efforts to modify their diet by consuming more calories, protein, fiber, dairy products, meats, fats, sugar, salt, and processed foods. These findings add to a growing literature on individuals who endorse attempts to gain weight and begin to describe the specific types of dietary intake behaviors those individuals seek to alter. Healthcare professionals should assess for weight gain attempts to provide appropriate clinical oversight and guidance and evaluate whether or not the dietary behaviors undertaken by these individuals may potentially undermine their health. Public health professionals should ensure that prevention and intervention programming aimed towards those attempting to gain weight consider the unique diet modification efforts reported in this study. This programming should be aimed at ensuring individuals are ascribing to balanced eating patterns and reducing the use of potentially detrimental dietary practices.

Availability of data and materials

The International Food Policy Study is available to researchers. Please visit http://foodpolicystudy.com/ for more information.

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Acknowledgements

We would like to thank Nicole E. Lisi for providing research assistance and Samuel Benabou for providing editorial assistance.

The opinions and assertions expressed herein are those of the authors and are not to be construed as reflecting the views of the Uniformed Services University or the U.S. Department of Defense.

Funding for the International Food Policy Study was provided by a Canadian Institutes of Health Research (CIHR) Project Grant, with additional support from an International Health Grant, the Public Health Agency of Canada (PHAC), and a CIHR – PHAC Applied Public Health Chair (Hammond). JMN is supported by the National Institutes of Health (K08HL159350) and the American Heart Association (CDA34760281). No direct funding was used to support this study.

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Kyle T. Ganson

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Jason M. Nagata

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Rachel F. Rodgers

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Mitchell Cunningham

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Kyle T. Ganson conceptualized the study, conducted the statistical analyses, and drafted an initial manuscript. Jason M. Nagata and Lana Vanderlee aided the conceptualization. David Hammond is the principal investigator of the International Food Policy Study. All authors reviewed and edited an initial manuscript and have agreed to the final manuscript as submitted.

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Ganson, K.T., Nagata, J.M., Vanderlee, L. et al. Weight gain attempts and diet modification efforts among adults in five countries: a cross-sectional study. Nutr J 21 , 30 (2022). https://doi.org/10.1186/s12937-022-00784-y

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The Benefits of Physical Activity for People with Obesity, Independent of Weight Loss: A Systematic Review

Rachele pojednic.

1 Department of Health and Human Performance, Norwich University, Northfield, VT 05663, USA

2 Institute of Lifestyle Medicine, Harvard Medical School, Boston, MA 02115, USA

Emma D’Arpino

3 Harvard Extension School, Cambridge, MA 02138, USA; [email protected] (E.D.); ude.dravrah.g@369hai (I.H.)

4 MGH Institute of Health Professions, Charlestown, Boston, MA 02129, USA

Ian Halliday

Amy bantham.

5 Move to Live More, LLC, Somerville, MA 02144, USA; moc.eromevilotevom@mahtnaba

Associated Data

Not applicable.

Purposeful weight loss continues to be the primary focus for treating obesity. However, this strategy appears to be inadequate as obesity rates continue to rise and a myriad of benefits of physical activity that affect multiple health outcomes related to obesity and associated comorbidities are not integrated into treatment strategies. There are emerging correlational data in individuals with obesity that demonstrate physical activity can be beneficial to many critical health markers, independent of weight loss or changes in BMI. This systematic review investigates interventional studies that examine health markers, independent of weight loss, in individuals with obesity. Fourteen studies were identified that utilized a variety of physical activity interventions with primary endpoints that included cellular, metabolic, systemic and brain health outcomes. The review of the literature demonstrates that for individuals with obesity, there are both small-scale and large-scale physiologic benefits that occur with increased physical activity of various modalities. Focusing on these benefits, rather than a narrow focus of weight loss alone, may increase physical activity behavior and health for individuals with obesity.

1. Introduction

Purposeful weight loss continues to be the primary focus for treating obesity. Behavioral recommendations for weight loss include lifestyle modifications that reduce caloric intake from diet and increase caloric output with increased physical activity [ 1 , 2 ]. However, this strategy appears to be inadequate as obesity rates continue to rise and the myriad of benefits of physical activity that affect multiple health outcomes related to obesity and associated comorbidities are not integrated into treatment strategies.

Independent of weight loss or changes in body mass index (BMI), there are emerging correlational data in individuals with obesity that demonstrate physical activity can be beneficial to many critical health markers [ 3 ]. Epidemiologic studies noted specific relationships between physical activity and health outcomes that include increased cardiorespiratory and muscle fitness [ 4 , 5 ] as well as decreased risk of all-cause mortality and cardiovascular disease [ 6 ]. In a prospective epidemiological study, metabolically healthy but obese individuals had a lower risk of all-cause mortality, non-fatal and fatal cardiovascular disease and cancer mortality than their metabolically unhealthy obese peers, while no significant differences were observed between metabolically healthy but obese and metabolically healthy normal-fat individuals [ 7 ]. Furthermore, data indicate that physical activity can improve other markers of health in normal-weight and overweight individuals, such as overall quality of life [ 8 ], brain health [ 9 ], cognition [ 10 ], memory [ 11 ], sleep [ 12 ] and anxiety [ 13 ]. Yet, there are limited intervention studies specifically examining individuals with obesity that investigate health markers independent of weight loss as primary outcomes [ 3 ]. This is a major gap in the literature, and there have been recent calls to characterize outcomes in people with obesity, particularly in obesity subtypes [ 14 ].

By not investigating the benefits of physical activity independent of weight loss, there is a missed opportunity to enhance the physical and mental health of individuals with obesity, perhaps even leading to incorrect courses of treatment by exclusively recommending pounds of weight lost as the primary outcome [ 3 ]. This systematic review was designed to examine interventions with outcomes related to the effects of physical activity on health markers, independent of weight loss, in individuals with obesity. The intention was to highlight potential gaps in the literature and to understand which health outcomes are causally affected by physical activity in this understudied population. Based upon the evidence available, we identified four specific physical activity outcomes that affect individuals with obesity, independent of weight loss and include cellular, metabolic (i.e., fuel utilization), systemic (i.e., cardiovascular) and brain health outcomes.

2. Materials and Methods

A systematic review method that complied with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) was used [ 15 ]. The search engines Pubmed, Embase and PsychInfo were primarily used to yield relevant studies regarding the benefits of physical activity, independent of weight loss, for adults with obesity (BMI > 30 kg/m 2 ). The following terms were included in all database searches: obese *, physical activity, fitness and exercise. The terms found in Table 1 were used to refine each search.

All search terms.

Inclusion criteria consisted of articles originally published in English between the years 2011 and 2021 with average participant BMI reported over 30 kg/m 2 . Studies that included participants with an average BMI < 30 kg/m 2 were excluded. Studies that included BMI change or weight loss as the main outcome were excluded, along with studies that did not control for weight loss. Studies with dietary and/or supplemental interventions were also excluded.

In order to increase inter-rater reliability, upon completion of abstract review, all four members of the research team assessed remaining manuscripts for final eligibility. This included ensuring that the study design included a physical activity intervention, and that weight loss was not a primary end point in the study. The studies were also screened to ensure that the inclusion criteria of BMI of the study participants was >30 kg/m 2 as to only include people specifically with obesity. Studies remained included if the design included a priori intent to recruit subjects with a BMI > 30 kg/m 2 to compare with a normal BMI cohort ( Figure 1 ). Studies were not excluded for any noted limitations by the authors.

An external file that holds a picture, illustration, etc.
Object name is ijerph-19-04981-g001.jpg

Flowchart depicting the choice of studies.

3.1. Included Articles

There were 14 studies that were included in the final review ( Table 2 ). The included studies examined a wide range of physical activity interventions that incorporated sports, aerobic and resistance training combinations, sprint cycling, walking, high intensity interval training and moderate continuous training that were either individually monitored or overseen by a trainer. Based on primary outcomes, studies were divided for analysis into four major categories of the effects of the various physical activity modalities: cellular, metabolic and cardiovascular, systemic and brain health.

Included articles.

a Abbreviations: CON—Control; INT—Intervention; MICT—Moderate Intensity Continuous Training; HIIT—High Intensity Interval Training; SIT—Sprint Interval Training; MAO—metabolically abnormally obese; MHO—metabolically healthy but obese (MHO); HDL—High Density Lipoprotein.

3.2. Cellular Outcomes

Three studies reported that physical activity, independent of weight loss, resulted in positive changes of cellular biomarkers in people with obesity, including increases in telomere length [ 17 ], improvement in anti-oxidative and anti-thrombotic enzymes associated with HDL cholesterol [ 29 ] and skeletal muscle microvascular enzymes that affect capillary density and vasoreactivity [ 18 ]. Physical activity interventions in these studies included combined aerobic and resistance training protocols [ 17 , 29 ], as well as sprint or moderate continuous cycling [ 18 ].

Brandao and colleagues [ 17 ] reported that physical activity increased telomere length in people with obesity regardless of weight loss. Telomeres are protective protein complexes at the end of chromosomes, which limit the chromosome shortening caused by replication. Telomere shortening has been correlated with metabolic disorders and telomere maintenance and lengthening with longevity. In this study, a convenience sample of 13 pre-menopausal women who had a BMI of 30–40 kg/m 2 underwent an 8-week physical activity intervention program that included 55 min of combined aerobic exercise and resistance training three times per week. Upon completion of the training program, the researchers found no change to body weight or BMI ( p < 0.05), but a 6% increase in fat free mass (kg) (6%) ( p < 0.05) and 8% increase in VO2 max (ml/kg/min) ( p < 0.05), as well as a 2% decrease in weight circumference (cm) ( p < 0.05). Researchers also found a significant increase in telomere length (1.03 ± 0.04 to 1.07 ± 0.04 T/S ratio p = 0.001). Telomere length, which is denoted as T/S ratio, was determined by telomere to single gene copy ratio ΔCt (Ct(telomeres)/Ct(single-gene)). Although this paper focused on effects independent of weight loss, this study also identified an inverse relationship between telomere length and waist circumference before (r = −0.536, p = 0.017, r 2 = 0.117) and after the exercise regimen (r = −0.655 p = 0.015, r 2 = 0.321). Limitations of this study included recruitment by convenience and lack of a control group.

Wouldberg and colleagues [ 29 ] reported the role of exercise on lipid profile, HDL functionality and inflammation in 32 South African women with obesity, who were block randomized to either exercise intervention or no change groups. The exercise intervention consisted of 12 weeks of aerobic and resistance exercise training of moderate-vigorous intensity (75–80% peak heart rate) for 40 to 60 min, 4 days per week supervised by a trained exercise physiologist. Cardiovascular exercises included aerobic dance, running, skipping and stepping. Resistance training included body weight and equipment-based exercises (e.g., bands and free weights) that incorporated squats, lunges, bicep curls, push-ups and shoulder press with a prescribed intensity of 60% to 70% heart rate peak. The lipid profile and HDL functionality were measured by evaluating the levels of cellular cholesterol efflux capacity, reduction in endothelial vascular cell adhesion expression and paraxonase (PON) and platelet activating factor acetylhydrolase (PAF-AH). PON and PAF-AH are enzymes that are associated with HDL and have anti-oxidative and anti-thrombotic effects, respectively. Statistical models were corrected for changes in BMI where appropriate. At the end of the study, the researchers reported significant decreases in the exercise group while the control group had significant increases for the following outcomes: BMI (−1.0 ± 0.5% vs. + 1.2 ± 0.6%, p = 0.010), PON activity (−8.7 +/− 2.4%, +1.1 +/− 3.0%, p = 0.021), PAF-AH serum expression (−22.1 ± 8.0% vs. + 16.9 ± 9.8, p = 0.002) and distribution of small HDL subclasses(−10.1 ± 5.4% vs. + 15.7 ± 6.6%, p = 0.004). Limitations of this study include a small sample size overall with only a subset of subjects undergoing some biological assays.

Cocks et al. [ 18 ] aimed to determine the effects that 4 weeks of constant workload through either sprint interval training (SIT) or moderate intensity continuous training (MICT) would have on skeletal muscle microvascular density and microvascular filtration capacity in previously sedentary young men with obesity. Additionally, they evaluated the effects these training programs would have on skeletal muscle microvascular enzymes (NADPH oxidase 2 and endothelial nitric oxide synthase) responsible for nitric oxide bioavailability as well as arterial stiffness and blood pressure. A total of 16 men (age 25 ± 1 years), with obesity were randomly assigned to either SIT or MICT groups, in a matched fashion based on age, BMI and VO2 peak. Groups participated in 4 weeks of MICT (40–60 min cycling at 65% VO2 peak, 5 times per week) or constant load SIT (4–7 constant workload intervals of 200% Wmax 3 times per week). Results demonstrated SIT and MICT have equal benefits on aerobic capacity, insulin sensitivity, muscle capillarization and endothelial eNOS/NAD(P)H-oxidase protein ratio in men with obesity.

3.3. Metabolic and Cardiovascular Outcomes

Six studies reported that physical activity, independent of weight loss, resulted in positive changes in whole body metabolic and cardiovascular outcomes and related biomarkers in people with obesity, including reductions in serum triglycerides and decreased arterial stiffness [ 23 , 27 , 28 ], increased mitochondrial respiration [ 25 ], increased fat oxidation and insulin sensitivity [ 19 ] and decreases in both liver fat and HbA1C [ 26 ]. Physical activity interventions in these studies included walking [ 23 ], high intensity [ 19 , 27 ] and moderate intensity cycling [ 26 , 27 , 28 ] as well as combined aerobic and resistance training [ 25 ].

In individuals with obesity who also have impaired glucose tolerance (IGT), McNeilly et al. [ 23 ] investigated the effects of a 12-week walking intervention on pulse wave velocity, blood pressure, fasting glucose, glycosylated hemoglobin, insulin, blood lipids and indices of oxidative stress and inflammation. Pulse wave velocity is a measure of arterial stiffness, as the existing literature shows that individuals with IGT tend to have greater arterial stiffness when compared to individuals without IGT. A total of 11 adults with obesity participated in 30 min walking exercise on a treadmill to reach 65% of their age-predicted maximum heart rate 5 times a week for 12 weeks and completed a journal for compliance. After completion of the intervention, investigators found a significant improvement in upper limb pulse wave velocity (9.08 + 1.27 m·s −1 vs. 8.39 + 1.21 m·s −1 ; p < 0.05) with a corresponding decrease in systolic blood pressure ( p < 0.05). Subjects also experienced significant reductions in serum triglycerides ((1.52 + 0.53 mmol/L vs. 1.31 + 0.54 mmol/L) and a 34% decrease in lipid hydroperoxides compared to baseline ( p < 0.05). No other metabolic biomarkers resulted in significant improvements. The limitations of this study included a small sample size and lack of a control group.

In order to understand how high intensity interval training (HIIT) or MICT affected endothelial dysfunction in individuals with obesity, a known precursor to atherosclerosis, Sawyer and colleagues [ 27 ] examined eighteen participants with a body mass index of 36.0 ± 5.0 kg/m 2 over eight weeks. Brachial artery flow-mediated dilation and resting artery diameter were primary outcomes and are highly related to structural and functional adaptations that play a role in the improvements in vascular function. Brachial artery flow-mediated dilation increased after HIIT (5.13 ± 2.80% vs. 8.98 ± 2.86%, p = 0.02) and resting artery diameter increased after MICT (3.68 ± 0.58 mm vs. 3.86 ± 0.58 mm, p = 0.02). VO2 max increased ( p < 0.01) similarly after HIIT (2.19 ± 0.65 L/min vs. 2.64 ± 0.88 L/min) and MICT (2.24 ± 0.48 L/min vs. 2.55 ± 0.61 L/min). Interestingly, HIIT required 27.5% less total exercise time and ∼25% less energy expenditure than MICT. This study was limited by a lack of dietary monitoring and the absence of a true sedentary control group.

Sabag et al. [ 26 ], examined the role of low-volume aerobic exercise on liver fat in 35 inactive adults with obesity living with type 2 diabetes. Subjects were randomized into one of the three groups by equally distributed, pregenerated lists of permuted blocks for a 12-week program of either HIIT, MICT, or placebo. Subjects that were in the MICT group exercised at 60% VO2 peak for 45 min, 3 days/week and subjects in the HIIT group exercised at 90% VO2 peak for 4 min, 3 days/week. After the intervention, researchers measured liver fat, HbA1C and cardiorespiratory fitness across all three groups. Researchers found liver fat decreased in both the MICT group (−0.9 +/− 0.7%) and HIIT group (1.7 +/− 1.1%), while increased in the placebo group (1.2 +/− 0.5%) ( p = 0.046). HbA1C decreased in MICT (−0.3 +/− 0.03%) and HIIT (−0.3 +/− 0.03%) groups, while no improvement was observed in the placebo group (0.5 +/− 0.2%) ( p = 0.014). Cardiorespiratory fitness also improved in both the MICT (2.3 +/− 1.2 mL/kg/min) and HIIT groups (1.1 +/− 0.5 mL/kg/min), but not in the placebo group (−1.5 +/− 0.9 mL/kg/min) ( p = 0.006). Limitations of this study included a lack dietary monitoring and changes in energy expenditure outside of training as well as a small sample size.

In a secondary analysis of the data collected by Sabag and colleagues [ 26 ], Way et al. [ 28 ] evaluated the effect of exercise on cardiovascular health outcomes (central arterial stiffness and hemodynamic responses) in individuals with obesity and type 2 diabetes. With regard to arterial stiffness, there was a significant reduction in pulse wave velocity for both HIIT and MICT compared to placebo. Additional post-hoc analysis demonstrated no difference between the HIIT and MICT groups. There was a significant intervention effect for changes in VO2 peak ( p = 0.01), glycosylated hemoglobin ( p = 0.03), systolic blood pressure ( p =0.01) and waist circumference ( p = 0.03), which all improved following MICT or HIIT but not placebo; without differences between MICT and HIIT.

Mendham and colleagues [ 25 ] examined the role of exercise training on mitochondrial respiration and the association with altered intramuscular phospholipids in women with obesity. Over 12 weeks, 35 women were block randomized to an intervention or control group. The intervention group received supervised aerobic and resistance training at a moderate-vigorous intensity for 40–60 min, 4 days/week by a trained facilitator. After the 12 weeks, the intervention group was found to have a significant increase in mitochondrial respiration and content in response to exercise training. The metabolite and lipid signature at baseline were significantly associated with mitochondrial respiratory capacity ( p < 0.05) but were not associated with whole-body insulin sensitivity or insulin regulated glucose transporter (GLUT4) protein content. Exercise training significantly altered the skeletal muscle lipid profile and these changes were associated with content-driven increases in mitochondrial respiration ( p < 0.05), but not with the increase in whole-body insulin sensitivity or GLUT4 protein content.

The effects of SIT on substrate oxidation at rest and at submaximal exercise in people living with and without obesity were measured by Colpitts et al. [ 19 ]. Sixteen adults with obesity and eighteen adults without obesity took part in four weeks of interval training which consisted of sets of 30 s Wingate cycling with 4 min of active recovery three times a week with the number of sets increasing each week. At the completion of the intervention, researchers collected substrate oxidation estimations for resting state and during moderate-intensity exercise for both groups, as well as insulin sensitivity. Substrate oxidation was estimated using the respiratory exchange ratio from a resting metabolic rate test. Where appropriate, statistical analysis was corrected for fat free mass. While there was no change in weight or BMI in the experimental group, submaximal substrate oxidation improved significantly (reduction in RER) following four weeks of SIT in individuals living with obesity (baseline = 0.95 ± 0.08, post = 0.92 ± 0.06; p < 0.05) a change that was significantly different ( p < 0.05) from individuals without obesity. Individuals living with obesity had an increase of 30.1% in insulin sensitivity, although this change did not reach statistical significance (baseline = 5.9 ± 3.4, post: 7.5 ± 7.0; p > 0.05). However, individuals without obesity had a lower, but significant, reduction in insulin sensitivity by 25.3% (baseline = 29.2 ± 18.4, post: 18.7 ± 14.2; p < 0.05) and significant differences were observed for absolute and percent changes in insulin sensitivity between groups ( p < 0.05). Limitations of this study included a small sample size and no control group.

3.4. Systemic Outcomes

Three studies investigated whether physical activity improved systemic health, independent of weight loss, for those with obesity. Outcome markers included metabolic phenotype [ 20 ], measures of cardiorespiratory and muscle fitness [ 16 ] and quality of life with sustained exercise behavior change [ 22 ]. Physical activity modalities included community-based exercise classes [ 20 ], sports games [ 16 ] and resistance training [ 22 ].

Dalleck et al. [ 20 ] demonstrated that physical activity positively impacted the metabolic phenotype of individuals who have obesity. The possible benefits of community-based exercise training in transitioning metabolically abnormal obese (MAO) phenotypes to metabolically healthy but obese (MHO) phenotypes were observed. A total of 332 adults with a BMI > 30 kg/m 2 participated in the study. Individuals who had 2–4 metabolic syndrome components were categorized as MAO. Those who had 1 or no component were considered MHO. Outcome variables assessed included absolute energy expenditure (EE), relative EE, waist circumference, BMI, body mass, systolic blood pressure (BP), diastolic BP, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, glucose, cardiorespiratory fitness and 10-year risk score. Of these outcomes BMI, body mass, total cholesterol, LDL-cholesterol and 10-year risk score did not yield significant mean differences within the MAO to MHO group. After engaging in a community exercise program, 40% of metabolically abnormally obese (MAO) individuals transitioned to a metabolically healthy but obese (MHO) phenotype ( p < 0.05). The major limitation of this retrospective study included not being able to control for the possibility that other factors (e.g., medication use and lifestyle, dietary and behavior changes) contributed to MAO to MHO phenotype transition, irrespective of the community exercise program.

With regard to markers of cardiorespiratory health and muscle strength, Biddle and colleagues [ 16 ] reported improvement in cardiorespiratory fitness and leg strength in Pacific adults who participated in informal sports. Subjects included 20 adults (13 females, 7 males) engaging in 45 min small-sided games of soccer, basketball, volleyball, touch rugby, cricket and other non-conventional games such as ‘chain tag,’ ‘rob the nest’ and ‘bullrush.’ Participants were randomized in a computer-generated 1:1 fashion but were not blinded. The control group was asked not to change their physical activity routine for the next 4 weeks. Primary outcomes of these small-sided sessions included cardiorespiratory fitness (VO2 peak) and leg strength (maximal concentric force of quadriceps at 60 degree/s). Outcomes were both measured at baseline and at 4 weeks. Secondary outcomes included glycemia (fasting glucose, HbA1c), lipid profile (total cholesterol, HDL, triglycerides), blood pressure (BP) and inflammatory markers (C-reactive protein). Results demonstrated that changes in outcomes were greater within the intervention group than in the control group (VO2 peak: 0.9 L/min ( p = 0.003), leg strength: 17.8 N.m ( p = 0.04) and HDL: 0.12 mmol/L ( p = 0.02)). Limitations of this study included a small sample size, unblinded subject pool and a short duration.

Physical activity in the form of resistance training was also found to improve muscular strength and quality of life for adults with obesity and type 2 diabetes [ 22 ]. Over 16 weeks, 48 individuals with obesity and type 2 diabetes were assigned to either resistance training (RT) ( n = 27) or the control group ( n = 21). There were no statistically significant differences between the groups at baseline for any of the study variables. RT included access to a multi-gym with dumbbells and home supervision from a certified personal trainer. Primary outcome was muscular strength (33% increase, p < 0.001) and RT behavior. To assess muscular strength, participants performed one rep max (1RM) tests of seated chest press, seated row and leg press. Chest press, seated row and leg press 1RMs were combined to create a total strength score. The resistance training program was found to have a significant effect on resistance training behavior. During the 16 weeks, participants reported the number and duration of each resistance training session completed. Results showed a significant positive association between changes in RT behavior (C = 110.99, 95%, CI = 78.28 to 143.72, p < 0.001) and changes in RT planning strategies (r = 0.51, p < 0.01) when controlling for the intervention effect. Limitations of this study included a small sample size and self-reported behavioral data.

3.5. Brain Health Outcomes

Two studies investigated whether physical activity improved brain health markers, independent of weight loss, for those with obesity. Outcomes included changes in sleep [ 24 ], depressive symptoms [ 21 , 25 ], quality of life and emotional health [ 21 ]. Physical activity modalities included combined aerobic and resistance training [ 24 ] and tai chi [ 21 ].

Mendham et al. [ 24 ] evaluated whether exercise training had an impact on sleep quality and depressive symptoms in a previously reported cohort [ 25 ] of women with obesity living in low socioeconomic communities. Participants were block randomized to exercise or control groups and no differences were noted between groups at baseline except in VO2 peak. The exercise group completed 12 weeks of combined resistance and aerobic training (40–60 min, 4 d/wk) and the control group maintained habitual diet and activity. The effect of exercise training intervention on sleep and depression, sleep characteristics, peak oxygen consumption and glucose metabolism were assessed over 12 weeks. This intervention indicated improvement in sleep quality ( p < 0.001), sleep efficiency ( p = 0.005) and depressive symptoms ( p = 0.002). These results were further associated with other components such as improved peak oxygen consumption and sedentary time. Depressive symptoms improved with peak oxygen consumption ( p < 0.001) while sleep improvement was correlated with reduced sedentary time ( p = 0.018). This proof-of-concept study was limited by a small sample size, multiple statistical testing and subjectively measured sleep quality.

Liu et al. [ 21 ] evaluated the effect of tai chi on quality of life in adults with central obesity and depression. Over 24 weeks, 213 participants were randomly assigned to either a tai chi group or usual medical care, with no differences noted between groups at baseline. Those engaging in the tai chi group participated in 3 × 1.5 h sessions per week under supervision. Quality of life was assessed prior to the intervention, 12 weeks and post-intervention at 24 weeks using the Medical Outcome Study (MOS) SF-36 survey. Outcome measures included general health, physical functioning, role in physical health, role in emotional health, social functioning, bodily pain, mental health and vitality. Subjects were found to have moderately severe levels of depression at baseline. Results depicted significant improvement within the tai chi group in three of the SF-36 subscale scores—physical functioning ( p < 0.01), role in physical health ( p < 0.01 and role in emotional health ( p < 0.01). Additionally, the following outcomes also demonstrated significant interaction: physical functioning ( p < 0.01), role-physical ( p < 0.01), bodily pain ( p < 0.01) and role-emotional ( p < 0.01), but not in general health, vitality, social functioning and mental health. Limitations of this study included not categorizing patients by specific clinical categorization of depression.

4. Discussion

Physical activity has beneficial physiologic effects from the cellular to systemic level, independent of weight loss, in individuals with obesity. Yet, outcomes have been severely under-studied in this growing population. The current systematic review found only 14 interventional studies of people with obesity that examined health outcomes related to physical activity that were not primarily dependent on weight loss. This is despite the current prevalence of obesity in the United States of 42.4% [ 1 ], with projections to reach over 50% of the population by 2030 and severe obesity likely to become the most common BMI category among women, non-Hispanic black adults and low-income adults [ 30 ]. This novel systematic review highlights how a lack of data on the effects of physical activity for non-weight-related physical and mental health outcomes in individuals with obesity is a critical missed opportunity for the obesity research community, with implications for healthcare counseling strategies and public policy initiatives.

Positive outcomes at the cellular level indicate potential benefit in risk reduction for related comorbidities of obesity and include alterations in telomere length [ 17 ], improvement in anti-oxidative and anti-thrombotic enzymes associated with HDL cholesterol [ 29 ] and skeletal muscle microvascular enzymes that affect capillary density and vasoreactivity [ 18 ]. From a metabolic standpoint, increased physical activity leads to reductions in serum triglycerides and decreased arterial stiffness [ 23 , 27 , 28 ], increased mitochondrial respiration efficiencies [ 25 ], increased fat oxidation and insulin sensitivity [ 19 ] and decreases in both liver fat and HbA1C [ 26 ]. Together, these physiologic markers represent a possible reduction in the development of cardiovascular disease and type 2 diabetes in people that have obesity, leading to reduced risk of mortality and related healthcare costs [ 3 , 4 , 31 , 32 ].

When examining the systemic benefits of physical activity for individuals with obesity, outcome markers included alterations in metabolic phenotype [ 20 ], improved measures of cardiorespiratory and muscle fitness [ 16 ], enhanced quality of life and sustained exercise behavior [ 22 ]. Moreover, physical activity has specific benefits for brain health including improvements in sleep [ 24 ] and depressive symptoms [ 21 , 24 ], as well emotional health and overall quality of life [ 21 ]. The systemic and brain health benefits of physical activity are indeed highlighted in the Physical Activity Guidelines for Americans, yet little to no information was provided about the influence, if any, of weight status on the relationship between physical activity and measures of depressive symptoms or sleep [ 33 ].

Of particular note for the research community is the lack of long-term interventional studies examining consistent and repeatable physical activity modalities that investigate the benefits of physical activity in people with obesity, independent of weight loss. In the current systematic review, studies ranged from 4–16 weeks. Modalities included combination aerobic and resistance training, walking, resistance training alone, cycling, high intensity interval training and tai chi. Conducting extended interventional studies, with consistent and comparable modalities of physical activity, would provide significant opportunities to understand the benefits of physical activity in all individuals with obesity and also between obesity subclasses [ 14 ].

The research gap has implications for how healthcare providers are educated on the benefits of physical activity for individuals with obesity and related counseling strategies and practices. It is critical that data, education and clinical practice patterns be linked in order to affect behavior change in patients. A change in education and counseling strategies, driven by a wider research base, could have critical implications for initiating and sustaining physical activity behaviors in patients with obesity. Indeed, calls have been made to assess physical activity and cardiorespiratory fitness as a vital sign in clinical settings [ 34 ], a practice which could be particularly beneficial for high-risk populations [ 35 ]. Yet, cardiorespiratory fitness is not mentioned in the current guidelines for the management of obesity [ 36 , 37 ] and physical activity is only recommended to the people with obesity as a tool to induce negative energy balance [ 1 , 2 ]. This highlights both a dilemma and also an opportunity for education and improvement in healthcare professional training [ 38 ].

Current communication strategies and skills surrounding physical activity recommendations are known to be lacking and misaligned with behavior change [ 39 , 40 ]. Too narrow a focus—although intended to improve health through weight loss—may instead contribute to the high prevalence of weight gain and weight cycling [ 41 ], creating a “weight loss futile cycle” [ 3 ], which can also be associated with significant health risks. Weight loss by itself is not a behavior and by not examining other outcomes that could make physical activity more relevant and compelling for patients to initiate and sustain [ 39 ], the messaging may be causing more harm than good.

The limitations of this review include examining only studies with subjects with a BMI over 30 kg/m 2 . It is likely the noted benefits are generalizable across subsets of BMI. However, this strict inclusion criteria could also be viewed as a strength as it fills a gap in the literature. Limitations could also include a restriction to interventional study designs, potentially missing beneficial correlational data.

5. Conclusions

The current systematic review demonstrates that for individuals with obesity, there are both small-scale and large-scale physiologic benefits that occur with increased physical activity of various modalities. The positive health outcomes of these changes need to be emphasized to individuals with obesity more than just the general recommendation of weight loss. This will likely require the coordinated efforts of healthcare providers and physical activity practitioners as they work to improve the health of individuals with a BMI over 30. However, as research on best practices is still lacking, more long-term studies that include a variety of stakeholders to understand how to inform and encourage sustainable physical activity behaviors in individuals with obesity are needed. Linking innovative research with education and clinical practice patterns has critical implications for behavior change and overall health in individuals with obesity.

Author Contributions

The authors contributed in the following ways: Conceptualization, R.P. and A.B.; methodology, R.P. and A.B.; data curation, R.P., A.B., I.H. and E.D.; writing—original draft preparation, R.P., A.B., I.H. and E.D.; writing—review and editing, R.P., A.B., I.H. and E.D. All authors have read and agreed to the published version of the manuscript.

There was no funding acquired for this project.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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