research paper on body weight

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Recent advances in understanding body weight homeostasis in humans

Manfred j. müller.

1 Institute of Human Nutrition and Food Science, Christian-Albrechts-Universität zu Kiel, Kiel, Germany

Corinna Geisler

Steven b. heymsfield.

2 Pennington Biomedical Research Center, Baton Rouge, LA, USA

Anja Bosy-Westphal

Presently, control of body weight is assumed to exist, but there is no consensus framework of body weight homeostasis. Three different models have been proposed, with a “set point” suggesting (i) a more or less tight and (ii) symmetric or asymmetric biological control of body weight resulting from feedback loops from peripheral organs and tissues (e.g. leptin secreted from adipose tissue) to a central control system within the hypothalamus. Alternatively, a “settling point” rather than a set point reflects metabolic adaptations to energy imbalance without any need for feedback control. Finally, the “dual intervention point” model combines both paradigms with two set points and a settling point between them. In humans, observational studies on large populations do not provide consistent evidence for a biological control of body weight, which, if it exists, may be overridden by the influences of the obesogenic environment and culture on personal behavior and experiences. To re-address the issue of body weight homeostasis, there is a need for targeted protocols based on sound concepts, e.g. lean rather than overweight subjects should be investigated before, during, and after weight loss and weight regain. In addition, improved methods and a multi-level–multi-systemic approach are needed to address the associations (i) between masses of individual body components and (ii) between masses and metabolic functions in the contexts of neurohumoral control and systemic effects. In the future, simplifications and the use of crude and non-biological phenotypes (i.e. body mass index and waist circumference) should be avoided. Since changes in body weight follow the mismatch between tightly controlled energy expenditure at loosely controlled energy intake, control (or even a set point) is more likely to be about energy expenditure rather than about body weight itself.

Introduction

For decades of research, body weight control has been taken as given and scientists have looked at different aspects of a proposed biological control system including genes, neuropeptides, hormones, proteins, and metabolites. However, without being negative at all, our present knowledge and concepts of body weight control explain neither weight gain in individual subjects nor the obesity epidemic in populations. Thus, our aim here is to show that we might do better with a new way of thinking and a different approach.

Conceptual framework

The conceptual framework of a biological control of body weight is mainly based on animal studies (for a very recent and excellent review, see 1 ). For example, when compared with feeding a standard chow (or mixed) diet, feeding rats with an energy-dense diet (rich in fat and sugar) resulted in overeating and a disproportionate weight gain 2 . After withdrawing that diet and introducing a mixed diet again, rats then spontaneously returned to the weight of continuously mixed-diet-fed control rats 2 . Vice versa , after caloric restriction and weight loss, rats regained body weight with re-feeding, reaching their previous track of weight gain again 3 . These findings are taken as evidence for an inherited body weight (or in rats as an inherited weight gain with age) and served as examples of a so-called “set point”.

The set point theory assumes a strong genetic and humoral control of body weight characterized by a proportional feedback system designed to control body weight (or body energy and/or fat and/or protein and/or glycogen) to a constant “body-inherent” weight (or “body-inherent” energy, fat, protein, or glycogen content, respectively). The control systems (or thermo-stats, lipo-stats, proteo-stats, or gluco-stats, respectively) adjust food intake and/or energy expenditure (EE) in proportion to the difference between the current and the set point weight (for a previous F1000 Faculty Review, see 4 ). Although its molecular nature is still unknown, the set point paradigm is popular among molecular biologists, and today it is textbook knowledge. It is assumed that set points are defended by biological mechanisms within the brain stem and the hypothalamus. This is part of the homeostatic system controlling energy intake (EI), EE, energy stores (ES), and thus energy balance (EB) involving (i) afferent signals from the periphery, like leptin-signaling ES in adipose tissue to control EI, and (ii) efferent signals, like the sympathetic nervous system (SNS) activity to control EE. Accordingly, a defect in the lipo-static control system characterized by leptin resistance is considered to result in hyperphagia and relative or absolute hypometabolism and thus to explain obesity 1 .

Homeostatic control of body weight keeping to a set point is thought to be under genetic influences. The genetic component also integrates multiple ancestral influences like growth and pubertal development 5 . Developmental influences add to the differences in body weight (and body composition) between individuals by affecting the tightness of the homeostatic processes involved in body weight control 6 . Growth patterns in early life also add to susceptibility of certain diseases and mortality and thus the cardio-metabolic risk. Trans-generational effects on body weight are also reflected by the observation that in primates the trend in birth weight lags generations behind the trend in maternal weight 7 .

During the last few decades, research activities mainly focused on the biology of the feedback loop between adipose tissue and the hypothalamic melanocortin neuronal system mediated by leptin controlling EI and EE 8 , 9 . The melanocortin system in the basomedial hypothalamus is highly sensitive to nutrient availability, including the leptin signal. Leptin is secreted from adipocytes in proportion to fat mass (FM) and adiposity. In addition, with increasing adiposity, hyperinsulinemia and insulin resistance develop. Both leptin and insulin sensitivity moderate the strength of the association between FM and the body weight control system. Central and peripheral resistances to leptin and/or insulin (as seen in obese patients) are considered to reduce their effects on EI 8 , 9 .

Leptin as well as insulin bind to specific receptors in the brain. These receptors are found not only in the hypothalamus and brain stem but also in pre-frontal regions, the hippocampus, and the amygdala, all together explaining the multifaceted effects of leptin and insulin not only on EB but also on learning, memory, and rewards 9 . Obviously, this feedback system goes beyond a homeostatic control of body weight (based on the body’s needs and and/or its deficits of energy and specific nutrients) and refers to non-homeostatic factors (i.e. environment, hedonics, palatability, opportunity, cognition, learning, and social factors) too. Thus, EI (and also appetite, hunger, and satiety) is explained by both homeostatic and non-homeostatic factors.

As a modification of the classical set point concept, an asymmetric (or threshold) body weight control system has been proposed 8 , 10 . The idea is that the anabolic response to leptin becomes evident only when plasma leptin levels have fallen under a certain threshold level, which may resemble a low set point related to starvation and the risk of death 8 , 10 . In this model, no biological control is assumed to exist with overfeeding.

When compared with the set point paradigm, an alternative model to explain changes in body weight involves multiple “body weight steady states”. With overfeeding and underfeeding, weight changes result from the difference between EI and EE. During weight gain or weight loss, the differences between EI and EE diminish more and more owing to increases or decreases in fat free mass (FFM) and EE and metabolic adaptations (in response to weight loss), and a new stable lower or higher body weight is finally reached, reflecting a zero EB (i.e. there is no difference between EI and EE anymore). This new steady state in body weight is called the “settling point” 1 , 4 . In this model, there is no need for feedback control of either EI or EE. It is worthwhile to mention that the settling point model does not take into account metabolic adaptations to changes in EI and body weight (see below; 1 ).

The settling point paradigm also relates to body composition. This is because weight changes follow changes in body composition and the energy density characteristics of individual body components 11 – 14 . Any energy imbalance is partitioned between stored or mobilized fat in FM and protein and glycogen in FFM 12 . In a healthy subject, about 70–85% and 15–30% of body weight changes are due to FM and FFM, respectively. These numbers differ throughout the course of weight loss with a greater loss in FFM in the early phase while the loss of FM exceeds decreases in FFM during ongoing weight loss 13 . Partitioning of FM and FFM with weight changes is affected by age and exercise. However, even during controlled overfeeding and underfeeding of young healthy subjects, there is a considerable inter-individual variance in the fraction of energy imbalance from or to FM and FFM, respectively 13 , 14 .

The variance in partitioning adds to the inter-individual variance in weight change at a given EB. This is due to the differences in the energy content (or energy densities) of FM (9.4 kcal/g) and FFM (1.8 kcal/g) 12 . For example, at an energy imbalance of 940 kcal/day, it will take about 10 days to lose or gain 1 kg of FM at a 100% fraction to or from FM, whereas theoretically one may gain or lose 500 g of FFM per day at a 100% fraction of energy imbalance to or from FFM.

With ongoing weight loss, the proportion of FM exceeds that of FFM, resulting in an increased proportion of FFM relative to FM after weight loss 11 , 13 , 14 . This impacts EI and EE and thus EB and body weight 15 – 18 . The drive to eat is related to the energy demand of FFM, but putative energy-demanding signals from skeletal muscle and from high-metabolic-rate organs like the liver, kidneys, heart, and brain still remain to be characterized. FFM is closely related to resting EE (REE; 17 ), and both REE and FFM are determinants of EI, hunger, and self-selected meal size 15 , 16 , 18 . With weight loss, REE and FFM decrease at a concomitant change in FFM composition with a disproportional loss in skeletal muscle mass compared with visceral organs like the liver and kidneys 13 , 14 . Although the neuroendocrine link between FFM and/or FFM composition and EI has not been characterized up to now, it is tempting to speculate that this may affect appetite and hunger feelings. With weight regain, FFM is increased together with a disproportional increase in FM (i.e. FM and FFM cannot change independently from each other 19 ). The increase of FFM then aims to increase REE and thus to finally match EI and EE. In fact, hyperphagia related to FFM depletion persists until full recovery of FFM, and thus a new steady state is reached 15 , 19 . However, this will also increase EI again and so may end in some kind of a roundabout with no escape, which argues against the theory. Although signals generated in FFM affecting EI have to be identified in the future, the general idea of FFM as a determinant of EI brings FFM into the center of “body weight control” with the faster recovery of FM as a so-called “collateral fattening” phenomenon 19 . Then putting FFM within the center of the discussion 15 , 16 , 18 , 19 is an alternative or additive concept when compared with most of the recent research activities on body weight control with a primary focus on FM 8 , 9 .

Both the set point and the settling point paradigms do not address possible “gene-by-environment” interactions and metabolic adaptations. Therefore, the set point paradigm has been further elaborated by an alternative concept proposing a so-called “dual intervention model” 1 , 20 . In this model, there is no single set point and body weight may change in response to environmental factors within upper and lower “intervention points” (or upper and lower boundaries) where the boundaries themselves and/or the distance between them may be biologically (e.g. genetically) determined 1 , 20 . In this model, the lower boundary is considered to reflect the risks of starvation, wasting diseases, and survival, whereas the upper boundary is related to the risk of predation 1 , 20 . The model relates to the original concept of Castro and Plunkett 21 , where EI is controlled by uncompensated (primarily environmental) as well as compensated (i.e. biological) factors. While the former factors are unaffected by EI, only the latter have negative feedback loops 1 , 20 , 21 . Interestingly, uncompensated or environmental factors may override biological control, which thus seems to be loose in response to overfeeding but is tight in response to weight loss. The dual intervention model combines the set point (feedback control of body weight at the two boundaries only) with the settling point paradigm (explaining flexible weight changes between the boundaries 1 , 20 ).

It is likely (but not proven) that the two intervention points are regulated separately 1 . However, other authors 22 have recently proposed that the upper and lower boundaries are linked together, switching between the resting state and feeding. It is then tempting to speculate that at the population level fatness may change symmetrically. This idea does not fit with the asymmetrical distribution of body mass index (BMI) seen in Western populations 1 . Anyhow, if one assumes an asymmetric control of body weight 8 , 10 , the meaning of the two boundaries may differ, with the lower boundary explaining resistance to weight loss in both normal-weight and obese subjects and the upper boundary being variable or even weak to defend body weight against overfeeding. It has been speculated that the level at which control mechanisms become activated is genetically controlled and thus may show a considerable inter-individual variance 1 , 20 .

To summarize, at present there is no consensus framework to explain body weight control in humans. Presently, there are three different models which have been developed from animal data. First, a set point suggests (i) a more or less tight and (ii) symmetric or asymmetric biological control of body weight. Alternatively, a settling point rather than a set point has been proposed with adaptations of body weight to energy imbalance without any need for feedback control. Finally, the “dual intervention point” model combines both paradigms. All models presently serve as a possible conceptual framework in research on physiology and cellular biology of body weight control. However, body weight homeostasis may be overridden by the influences of the obesogenic environment and culture, which have a considerable impact on personal behavior and experiences and thus are considered as the major drivers of the obesity epidemic 23 . However, these concepts suggest that in humans living in affluent societies, biological control of body weight is unlikely to become apparent.

What is the human evidence of body weight control?

In humans, the proof of the matter mainly refers to observational data obtained in large populations as well as to interventions in normal-weight subjects and in obese patients. By contrast, rigorously well-controlled experiments addressing body weight homeostasis in humans rarely exist. As far as observational data in free-living subjects are concerned, it was and still is impossible to control for all variables, weakening any conclusion. In addition, most observations are based on cross-sectional study designs which do not allow far-reaching conclusions. Then, the interpretation of the data is down to the intelligence of the scientists and to what they want to be true. This is a critical point of the present discussion.

Monogenetic forms of obesity, heritability estimates, and genome-wide association studies supporting the idea of a biological control of body weight

During the last 30 years, specific study designs (e.g. twin, family, and adoption studies) have been used to calculate the total genetic influence on body weight (for a recent review, see 24 ). More recently, genome-wide association studies (GWAS) on BMI, waist circumference (WC), and FM have been undertaken with the goal to identify human genes that biologically cause overweight. While familial correlation in BMI is high in monozygotic twins, so far GWAS on obesity at the population level explained a minor proportion of the variation in adult BMI only.

Up to now, a total number of 19 rare monogenetic defects associated with severe obesity are impressive manifestations of disturbed control of body weight 25 . These rare cases are in favor of the idea that specific genes influence EI and/or EE and thus EB and body weight. However, these findings do not explain population-wide obesity. Although specific variants in individual genes (e.g. the FM- and obesity-associated gene, FTO ) are considered suitable candidates to explain the individual variability in (i) the predisposition to become obese or (ii) individual responses to weight loss strategies in obese patients, the proposed genetic basis of obesity is still uncertain 24 .

Using specific study designs (e.g. twin, family, and adoption studies), heritability 1 estimates (which are considered synonymous with genes) of BMI, FM, and visceral adipose tissue (VAT) have been calculated in numerous studies, which gave evidence for a biological influence on body weight 26 – 41 . The familial correlations in BMI were between 0.20 and 0.23 in parent–offspring pairs and 0.20 and 0.34 in dizygotic twins and reached 0.58 to 0.88 in monozygotic twins. In general, additive genetic factors explaining the proportion of variation in BMI varied between 0.31 and 0.85. Molecular mechanisms of heritability may not be limited to DNA sequence differences, since epigenetic factors also contribute to the phenotype. In fact, analyzing DNA-methylation profiles in pairs of monozygotic and dizygotic twins may be due to epigenomic differences in the zygotes adding to heritability estimates 42 .

When different age groups of twin pairs were compared, the proportion of BMI explained by genetic and epigenetic factors increased until late adolescence with no or only minor effects of the shared environment 40 . By contrast, shared environmental factors related to education and/or culture seemed to have a stronger influence during mid-puberty. Furthermore, in pooled cohorts of a total of 140,379 complete twin pairs from different regions of the world, the heritability estimates of BMI decreased from 0.77 in young adults to 0.59 in adults aged 70 to 80 years, which was independent of the obesity prevalence in the populations studied 41 . However, heritability estimates cannot explain steep increases in the prevalence of obesity, which are due to non-biological and thus environmental changes as the driving factors. Furthermore, heritability does not take into account the complexity of the genotype–phenotype relationship. In addition, additive and non-additive genetic effects cannot be addressed separately. Finally, incomplete adjustments for co-variates like growth spurts during puberty or regional diversities in the environments affect heritability estimates.

In addition to cross-sectional data from population studies, differences in the response to overfeeding studied in pairs of monozygotic twins showed that inter-pair variances in gains of weight, FM, and VAT were found to be three to six times higher than the respective intra-pair variance 26 , 27 . This was taken as evidence for a “genotype-overfeeding interaction” that determines weight and fat gain as well as fat distribution. Vice versa with negative EB (due to a controlled exercise program 31 ), the intra-pair variances in changes in weight, FM, and VAT were also lower than the inter-pair variances, suggesting a “genotype-underfeeding interaction” as well. However, these data have to be compared with considerable intra-individual variances in changes of body weight, which have not been taken into account in the studies cited 24 .

The high heritability estimates were not supported by the results of recent GWAS on BMI, FM, or VAT in greater populations 24 . Up to now, 115 genetic loci have been identified where sequence variation was statistically associated with the BMI, explaining 2 to 3% of the variation in adult BMI only 43 . In addition, longitudinally, no significant associations were found between any lead single nucleotide polymorphisms (SNPs) and weight changes 44 . Obesity thus has a polygenic architecture, with small effects of each associated gene 5 . The polygenic basis of adiposity may provide small sensitivities to environmental influences. More recent data suggested that obesogenic environments accentuate the genetic risk of obesity 45 : using BMI as an outcome and a 10-variant genetic risk score in a socially deprived population, researchers found that genetic risk was associated with 3.8 kg extra weight in a normal subject. These data were compared with 2.9 kg extra weight in the least-deprived group 45 . This finding may be taken as evidence for a moderate gene–obesogenic environment interaction.

Methodological limits do not allow detailed insights into body weight control

It has been stated already that it is impossible to directly assess a set point in humans 46 . Even in controlled experiments (e.g. using overfeeding or underfeeding protocols), EI and EE cannot be tightly controlled, owing to compensations on both sides of the EB and the components of EB being dynamically inter-related. This issue became obvious in a controlled underfeeding and overfeeding protocol in healthy normal-weight subjects 13 . Comparing the differences between (i) the EB data calculated from the difference between EI and EE and (ii) EB data based on changes in accurate measurements of FM and FFM (assuming their constants for energy equivalents) as an alternative approach resulted in considerable discrepancies between the two measures of EB of about 800 to 1,000 kcal/day 47 . These discrepancies cannot be explained by the limited precision of the methods used to assess either EI or EE or body composition only 47 .

Although EI is considered as the major driver of individual weight gain and the population-wide obesity issue 23 , it cannot be measured with confidence 48 . There are large errors involved in the methods used under everyday living conditions to assess EI at the individual as well as the population level 48 , 49 ; thus, true variations and between-group differences cannot be differentiated from measurement errors. Today, the measurement of 24-hour EE (by DLW) together with direct assessments of FM and FFM are considered the most precise and valid way to investigate EI during periods of more than 3 to 4 weeks 48 , 50 , 51 . However, this approach does not provide detailed data on food and nutrient intakes. Hence, the great dilemma of nutrition research is obvious.

It is obvious that limitations in both our present concepts of biological control and methods used to assess the individual components of EB limit the direct assessment of a biological control of body weight in humans.

Observational data questioning a biological control of body weight in humans

Globally, about 20 to 25% of adult populations are presently obese 52 , 53 . When compared to the previous generation, there was a more than twofold increase in the prevalence of obesity in affluent societies. This is indirect evidence for the idea that a tight control of body weight is unlikely to exist in humans living in an obesogenic environment. Alternatively, the obesity epidemic is presumably due to environmental, societal, and economic “drivers” rather than the proposed biological determinants of body weight 20 , 23 .

A tight biological control of body weight is also questioned by repeated measurements of body weight, which show a considerable intra-individual variance in its spontaneous day-to-day changes 13 , 24 . Even during controlled underfeeding and overfeeding, there are high intra-individual day-to-day variances in weight loss and weight gain which resemble inter-individual variances in weight changes 13 , 24 , 54 . However, when compared with weight loss, gaining body weight is slow, suggesting again that body weight is, to a certain degree (almost not perfectly), defended. It has been proposed that this defense may constrain weight changes 55 .

As far as weight-reduced obese patients are concerned, only 20% maintain at least 10% weight loss over a period of 1 year, suggesting that in free-living trials weight loss maintenance is difficult to hold 56 . This is also true in lifestyle intervention trials, such as the Diabetes Prevention Program 57 and the Look AHEAD Study 58 . In the "Biggest Loser Competition", an extreme weight loss was observed with 58 kg at the end of the competition 59 . However, the regain was 41 kg after 6 years of follow up 59 . This regain was taken as evidence for the set point paradigm. In addition, long-term adaptation and a stable body weight after weight loss following bariatric surgery has been proposed to reflect a permanent re-setting of the body weight set point, restoring “normal” leptin signaling (or its downstream signals) in the hypothalamus 60 , 61 . Accordingly, after bariatric surgery, the proposed re-programming of the body weight defense mechanisms at a lower body weight was not associated with increased hunger feelings or reduced EE 61 . By contrast, animal data suggest no increase in hypothalamic leptin sensitivity after weight loss due to bariatric surgery, questioning the idea of re-programming a set point 60 .

Thus, observational studies on greater populations living in affluent societies and also clinical data on obese patients do not provide consistent evidence for a biological control of body weight.

Aspects of body weight homeostasis to be addressed in future studies

The present issues related to biological control of body weight in humans are due to preliminary and simplifying concepts of biological control of body weight, weak study designs (i.e. cross-sectional observational studies indicating associations only), methodological issues associated with the assessment of EI, EE, and EB, and/or inappropriate phenotypes studied so far (i.e. the BMI). Alternatively, biological control of body weight may exist but may not become apparent in subjects living in an obesogenic environment supporting a lifestyle characterized by a high EI at low physical activity. Accordingly, several points should be addressed in future studies.

Need of studying normal weight instead of overweight subjects

Following the framework of the “dual intervention model” 1 , 20 , biological control of body weight may be overridden by strong environmental and economic drivers (i.e. uncompensated factors) of overweight. Thus, studying overweight subjects in affluent societies is unlikely to address biological control (if it exists). Alternatively, body weight homeostasis should be investigated in lean subjects undergoing weight gain, weight loss, and weight maintenance.

Crude phenotypes should not be addressed in research anymore

Investigating crude phenotypes like BMI and WC (i.e. phenotypes which have been most frequently used in studies on heritability estimates as well as in GWAS) is spurious and cannot provide any deep insights. This is because BMI and WC are merely surrogate measures of nutritional status. BMI is calculated from weight and height squared and has no biological meaning. Both BMI and WC have practical value in daily clinical practice. By contrast, they are weak outcomes in research on body weight homeostasis 62 – 64 . Furthermore, there are ethnic differences in the associations between these crude measures and FM, FFM, or VAT which lead us to question their value as a phenotype to be used in multicenter studies on subjects with different ethnic backgrounds 65 . It is worthwhile to remember that the concept of BMI dates back to a period of underdeveloped scientific methodologies and simplistic theories 63 , 64 . By contrast, at the time of modern biomedical science, still keeping to BMI and/or WC is unacceptable.

Alternatively, addressing a suitable phenotype, a framework is needed to assess the structures of the body 66 , 67 . To do so, we should start with a simple question: what do we really want to know? In the case of obesity, we are interested in excess FM, which can be easily measured by, for example, bioelectrical impedance analysis (BIA) in population studies and/or by either densitometry (as assessed by air displacement plethysmography [ADP]) or dual energy absorptiometry (DXA) in clinical studies. Obesity-related cardio-metabolic risks are characterized by hyperinsulinemia (i.e. basal plasma insulin levels >7–10 μU/mL), hypertriglyceridemia, elevated biomarkers of inflammation, and high blood pressure. Alternatively, an estimate of liver fat (as measured qualitatively using ultrasound [US] or MRS, Magnetic Resonance Spectroscopy or by biochemical estimates, e.g. liver enzymes and fetuin A) or VAT (as measured by magnetic resonance imaging [MRI]) can be used as a risk estimate. As far as malnutrition is concerned, this is characterized by recent weight loss (in the case of wasting diseases) and/or a low muscle mass (i.e. sarcopenia, which is characterized by either DXA or MRI or multifrequency BIA validated against those two reference methods). Fluid overload is again measured with confidence using multifrequency BIA or D 2 O dilution. Finally, in the case of risk of osteoporosis, bone mineral density plus skeletal muscle mass are assessed by DXA measurements.

A “multi-level–multi-systemic approach” should be used

Presently, it is unclear whether body weight control is about control of (i) the static masses of the body, including masses of individual organs and tissues (which add up to body weight), and/or (ii) the association between FM and FFM and their concerted changes when body weight changes. In any case, focusing on body mass or masses of organs and tissues alone does not take into account different levels and systems of control 24 . Thus, to go on a more systemic approach to body weight homeostasis, we need to take into account different levels and systems of control 67 .

Up to now, studies on body weight control in humans have addressed the structural level only (level 1 in Figure 1 ). According to a more advanced model 67 , control is about relationships within and between structures, their related functions and systemic outcomes, and thus not about body weight or its individual components only. The masses of organs and tissues and their inter-relationships (level 1) have to be addressed in the contexts of neurohumoral control (level 2) together with metabolic (e.g. EE, i.e. level 3) and systemic outcomes (e.g. heart rate, blood pressure, respiration, excretion, and body temperature, i.e. level 4) ( Figure 1 ).

Individual body components and their inter-relationships are seen in the context of metabolism, endocrine determinants, and systemic outcomes, e.g. body temperature, heart rate, etc. The model thus addresses relationships between organ and tissue masses (rather than their isolated masses only) in the context of age- and sex-specific metabolic or functional traits (e.g. energy expenditure, insulin sensitivity, muscle strength, and physical performance) together with the systemic response of the body. The model is supported by the findings that (i) changes in weight (during either weight loss or weight gain) are associated with concomitant changes in body composition, which are not independent of each other (e.g. FM and FFM both decrease with weight loss, while muscle mass decreases, whereas FM increases in the case of age-related sarcopenia) and (ii) body weight control hinges on the relationship between organs and tissues and their functional correlates. See text and 67 for further details. ANP, atrial natriuretic peptide; BP, blood pressure; DNL, de novo lipogenesis; ECW, extracellular water; FatOx, lipid oxidation; FFM, fat free mass; FM, fat mass; GFR, glomerular filtration rate; GlucOx; glucose oxidation; GNG, gluconeogenesis; HR, heart rate; ICW, intracellular water; ProtOX, protein oxidation; RAAS, renin-angiotensin-aldosterone system; SAT, subcutaneous adipose tissue; SNS, sympathetic nervous system; T3, 3,5,3'-triiodothyronine; Temp, body temperature; VAT, visceral adipose tissue.

Following that “multi-level–multi-systemic” model, body weight control (if it exists) is likely to happen between, but not at, a single mass and level (i.e. control is about operating within and between levels). Then changes in body weight follow the control of the associations within levels (e.g. between FM and FFM or between individual organs and tissues) as well as between different levels (e.g. between structures and metabolic functions). Thus, strictly speaking, the issue of body weight control is about associations within and between different levels and systems which add up to maintain a stable body weight.

Consequently, phenotypes worthwhile of study are related to body mass–body function relationships rather than to body weight itself 67 ( Figure 1 ). The concept of functional body composition 67 , 68 refers to, for example, the association between FFM (and its anatomical and physical characteristics) and REE in the context of neurohumoral control (e.g. thyroid state and SNS activity) and related systemic outcomes (e.g. heart rate and body temperature). Similarly, the association between FM (or its distribution) and plasma leptin levels has to be seen in the contexts of insulin resistance, O2 consumption and CO 2 production, as a measure of lipid oxidation and respiration. To go on with that idea, the structure–function associations and their changes have to be studied separately in different situations, e.g. before, during, and after weight change as well as during weight maintenance. As far as weight loss is concerned, it has been shown recently that control systems involved in metabolic adaptation differ between weight loss and weight maintenance 69 .

Metabolic adaptations and compensations may provide a suitable phenotype to study

Adaptive thermogenesis (AT) refers to changes in EE which are independent from changes in FFM and FFM composition 69 – 73 . Since there is a considerable inter-individual variance, AT may provide a suitable phenotype to investigate in future studies. AT is asymmetric (i.e. during weight loss it adds to energy sparing, whereas no energy dissipation is observed with overfeeding 70 , 71 ). AT is also observed after massive weight loss following bariatric surgery in severely obese patients 72 . During early starvation, AT is related to hepatic glycogen depletion and the fall in insulin secretion 13 , 69 . Thus, AT is considered a metabolic adaptation to meet glucose oxidation in the brain (i.e. the brain's energy metabolism requires 80–100 g glucose per day 69 ). AT is associated with systemic outcomes, i.e. decreases in body temperature, heart rate, and glomerular filtration rate 13 . These data suggest that AT is part of the concerted physiological response to weight loss, with the fall in insulin secretion as its major characteristic 69 . In the long term, AT adds to weight loss maintenance 69 , 73 – 75 where AT is related to the low plasma leptin levels sparing triglycerides stored in adipose tissue where a low FM limits biological functions like reproduction 69 . Obviously, the meaning of AT varies with the phase of caloric restriction and weight loss.

AT has been related to the set point and settling point paradigms. Recently, three models for AT have been proposed 74 . First, a “mechanical model” related to the settling of body weight; second, a “threshold model”, where AT is related to the decrease in FM below a minimum (i.e. a set point); and, third, a so-called “spring-loading model” with an effect on the dynamics of weight loss. No model fully explained AT. However, during weight maintenance, decreases in REE were consistent with the threshold model and thus a low set point related to ES in adipose tissue and plasma leptin levels.

As far as metabolic adaptation during early weight loss is concerned, another threshold (or a low set point) related to the depletion of hepatic glycogen stores has been proposed 69 . This finding is in line with the idea that energy allocation to the brain controls EE (i.e. the brain is assumed to have a hierarchical position in whole-body energy metabolism 75 ). The brain has a high metabolic rate 14 , 76 and is the only organ which does not lose weight with weight loss 13 . When compared with muscle-specific metabolic rates, the brain and skeletal muscle differ by a factor of 18 76 . Since the brain demands a constant energy budget, it has “pole position” in a competitive situation of whole-body energy allocation and thus control of whole-body EE ( Figure 2 ).

FFM and FM both exert feedback controls on different levels of the brain control systems of energy balance. It is assumed that FFM is the major determinant of energy balance. See text for further details. The right part of the figure (i.e. the partitioning model) is based on the original work of Jonathan Wells (see 83 ).

Sedentary behavior and physical activity level also impact the control of appetite, satiety, and body weight. Becoming sedentary does not downregulate EI, i.e. at low physical activity there is a weak coupling between EE and EI 16 , 77 , 78 . By contrast, an increase in physical activity improves satiety signaling 16 , 77 and also increases activity EE (AEE). Then both REE and AEE drive EI 77 . In fact, following the spectrum from low to high physical activity, there are “regulated” and “non-regulated” areas of EI 77 . The association between physical activity and EI is J-shaped 16 . However, in clinical practice, physical activity (or even exercise) does not add much to the treatment of obese patients 79 , 80 . This paradox is explained by compensations in the individual components of EE. EE increases with low and moderate physical activity but plateaus at high activities to maintain EE within the target range (that is, EE is constrained with respect to physical activity 81 , 82 ). In fact, with a high level of exercise, the compensation is explained by a reduced REE, which resembles a decrease in basal biological functions, e.g. reproduction.

A concept that needs reconsideration: is there a control of body weight or a control of energy expenditure?

The finding of a tight control of EE gives rise to speculation that body weight control (if it exists) is about control of EE 14 . This idea provides an alternative paradigm, which is a putative set point or dual intervention point model of EE. The latter framework is characterized by a lower boundary (or lower set point of EE) given by the lowest metabolic rate needed for survival, whereas the upper set point of EE is explained by maximum mitochondrial capacity of cells of the body. If this EE–set point paradigm holds true, this would put recent concepts of body weight homeostasis into perspective. Then, any change in body weight follows the variance of EI at a tightly controlled EE, i.e. body weight itself is not controlled but results from the balance between a tight control of EE at loosely controlled EI.

Need to go beyond the adipocentric view

During the last 25 years, most research on body weight control has been adipocentric. However, since FM accounts for only 10 to 40% of body weight, any control of FM can represent only a similarly sized part of the body weight control. In addition, FM (as far as it can be assessed today) is constant throughout a day, and it starts to decline within 72 hours in response to starvation 13 . Thus, FM by itself (as far as it’s mass can be measured by todays methods) has no association with daily meal frequency and short-term calorie restriction 1 . However, since decreases in plasma leptin concentrations observed with caloric restriction precede and exceed the changes in FM 13 , the adipocentric view of body weight control refers to both the size and the secretory activity of FM.

It is only recently that, besides FM, FFM came into the center of research on body weight control 16 , 77 , 78 , 84 , 85 . Obviously, there is need for a broader view on the control of EB and body weight taking into account biological inputs from sensory systems (i.e. taste and olfactory signals), the gut and feedback related to FFM (controlling protein and glycogen content) and FM (controlling fatness), and the role of hedonism and rewards in the contexts of environmental and behavioral pressures 76 . Comparing different inputs, tonic afferences from FFM (signaling energy demands and metabolic requirements) and FM (signaling ES) have to be differentiated from episodic or dynamic feedback from the gut (signaling nutrient availability and meal and macronutrient intake by neural and enteroendocrine signals) 16 .

Need to address homeostatic as well as hedonic aspects ( Figure 2 )

EI is regulated by interactions between homeostatic and non-homeostatic mechanisms; it is thus influenced by experiences, learning, and culture 86 . This helps to explain why individual factors (like leptin and insulin) may have limited effects on EI. Using functional MRI (fMRI) after intra-nasal insulin application, selective insulin resistance in the prefrontal cortex (responsible for cognitive control and decision making) and in the hypothalamus was characterized as being associated with reduced inhibition of EI, food craving, and thus overeating in obese patients 87 . In addition to the “cognitive brain”, hedonic and incentive signals related to brain reward systems of the “emotional brain” (related to the mesoaccumbal dopamine system) may further add to overeating (i.e. eating is pleasurable and rewarding). In line with this idea, two set points, a homoeostatic and a hedonic set point, have been proposed, with obesity affecting the balance between the two and one inducing shift in the other 88 . Going on with that idea, “metabolic obesity” (with a genetically determined set point) and “hedonic obesity” (due to hedonic overeating overriding the homoeostatic set point) has been defined, and the two types of obesity may serve as a future stratification in the treatment of obese patients 89 . However, the quantitative effects of non-homeostatic influences on the set point model are unknown.

A self-critical view at the end

The ideas presented in this paper also point to the need for a self-critical view: two generations of scientists might have gone the wrong way when they (i) followed a hypothetical concept (i.e. there is biological control of body weight), (ii) had to accept the limited promise of methodologies to assess EB, and (iii) focused too much on statistical associations (e.g. calculating heritability estimates of BMI and, in the case of GWAS, studying associations between allele frequencies and crude anthropometric phenotypes) without addressing detailed and sound concepts and targeted analyses of structures and different levels of body weight control. It is also worthwhile to keep in mind that our present thinking is based not only on objective data but also on the interpretation of scientists, which adds a subjective factor to the discussion related to intelligence and the sovereignty of interpretation 55 .

Faced with the present lack of direct evidence for the biological control of body weight in humans, there is need of (i) conceptual thinking, (ii) better methods to be developed in integrative physiology, and (iii) controlled (instead of merely observational) studies. It is a principal matter of science that we should also be open to the alternative idea, i.e. there is no feedback control of body weight and thus a set point does not exist with multiple settling points to explain weight changes. Since no model can perfectly explain weight changes in humans, this may suggest the possibility of some misconception of past and present research activities on body weight homeostasis.

It is obvious from the present state of the art that observational and poorly controlled studies are not a sufficient basis to form reliable knowledge and guidelines regarding body weight control 55 . Back to the starting line again, one may also take an evolutionary point of view (which is frequently taken as justification when discussing body weight homeostasis) and ask a simple question: what should be the advantage of a tight control of body weight? As far as the body weight–mortality association is concerned, the normal range of body weight is broad, i.e. a variance of 20 kg does not affect cardio-metabolic risk. Thus, except for extreme body weights (as seen in severely obese patients or vice versa with underweight and malnutrition), an advantage of a tight control of body weight or FM within a broad normal range is unlikely to exist. Biologically, a normal range (if it exists) is difficult to define. Accepting a lower boundary, a one-intervention point model with multiple settling points (or equilibria) above a low and critical body weight (or low body fat and/or low-protein and/or low-glycogen content in the liver associated with the risk of hypoglycemia) which is related to an increased risk of impaired body functions, infectious diseases, and ultimately death, may provide an alternative model to be discussed in future.

To summarize, presently, there are three different models of body weight control. Although striking at first view, all models have limitations and cannot fully explain weight fluctuations in humans. In the short term, there is no auto-correlation between EI and EE, which might argue against a tight control system. Long-term control of body weight may suit the settling point model. The present evidence suggests that biological control (if it exists) is more likely to become apparent in normal-weight subjects and during caloric restriction and weight loss. However, there is obvious need of (i) an open discussion between scientists about shortcomings in past and present research and (ii) some food for thought about better concepts, methods, and research on body weight homeostasis in the future.

Abbreviations

AEE, activity energy expenditure; AT, adaptive thermogenesis; BIA, bioelectrical impedance analysis; BMI, body mass index; DIT, diet-induced thermogenesis; DXA, dual energy absorptiometry; EB, energy balance; EE, energy expenditure; EI, energy intake; ES, energy stores; FFM, fat free mass; FM, fat mass; FTO, fat mass and obesity-associated gene; GWAS, genome-wide association studies; REE, resting energy expenditure; SNS, sympathetic nervous system; VAT, visceral adipose tissue; WC, waist circumference.

[version 1; referees: 4 approved]

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

Editorial Note on the Review Process

F1000 Faculty Reviews are commissioned from members of the prestigious F1000 Faculty and are edited as a service to readers. In order to make these reviews as comprehensive and accessible as possible, the referees provide input before publication and only the final, revised version is published. The referees who approved the final version are listed with their names and affiliations but without their reports on earlier versions (any comments will already have been addressed in the published version).

The referees who approved this article are:

• Margriet S. Westerterp-Plantenga , Department of Human Biology, School for Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, Netherlands No competing interests were disclosed.
• Herman Pontzer , Department of Anthropology, Hunter College, City University of New York, New York, NY, USA; New York Consortium for Evolutionary Primatology, New York, NY, USA No competing interests were disclosed.
• Hubert Preissl , Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, German Center for Diabetes Research, Tübingen, Germany No competing interests were disclosed.
• Jonathan C. K. Wells , Childhood Nutrition Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK No competing interests were disclosed.

1 Heritability is a statistical concept that draws upon correlations between relatives to quantify how much of the overall variability of a phenotype at the population level is due to genetic variation. For example, a heritability of 0.5 for body weight would imply that half of the weight difference between two unrelated individuals is directly or indirectly attributable to genetic differences between them 24 .

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.

• - Google Chrome

Intended for healthcare professionals

• Access provided by Google Indexer
• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Effectiveness of...

Effectiveness of weight management interventions for adults delivered in primary care: systematic review and meta-analysis of randomised controlled trials

• Related content
• Peer review
• Claire D Madigan , senior research associate 1 ,
• Henrietta E Graham , doctoral candidate 1 ,
• Elizabeth Sturgiss , NHMRC investigator 2 ,
• Victoria E Kettle , research associate 1 ,
• Kajal Gokal , senior research associate 1 ,
• Greg Biddle , research associate 1 ,
• Gemma M J Taylor , reader 3 ,
• Amanda J Daley , professor of behavioural medicine 1
• 1 Centre for Lifestyle Medicine and Behaviour (CLiMB), The School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
• 2 School of Primary and Allied Health Care, Monash University, Melbourne, Australia
• 3 Department of Psychology, Addiction and Mental Health Group, University of Bath, Bath, UK
• Correspondence to: C D Madigan c.madigan{at}lboro.ac.uk (or @claire_wm and @lboroclimb on Twitter)
• Accepted 26 April 2022

Objective To examine the effectiveness of behavioural weight management interventions for adults with obesity delivered in primary care.

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

Eligibility criteria for selection of studies Randomised controlled trials of behavioural weight management interventions for adults with a body mass index ≥25 delivered in primary care compared with no treatment, attention control, or minimal intervention and weight change at ≥12 months follow-up.

Data sources Trials from a previous systematic review were extracted and the search completed using the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021.

Data extraction and synthesis Two reviewers independently identified eligible studies, extracted data, and assessed risk of bias using the Cochrane risk of bias tool. Meta-analyses were conducted with random effects models, and a pooled mean difference for both weight (kg) and waist circumference (cm) were calculated.

Main outcome measures Primary outcome was weight change from baseline to 12 months. Secondary outcome was weight change from baseline to ≥24 months. Change in waist circumference was assessed at 12 months.

Results 34 trials were included: 14 were additional, from a previous review. 27 trials (n=8000) were included in the primary outcome of weight change at 12 month follow-up. The mean difference between the intervention and comparator groups at 12 months was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, P<0.001), favouring the intervention group. At ≥24 months (13 trials, n=5011) the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, P<0.001) favouring the intervention. The mean difference in waist circumference (18 trials, n=5288) was −2.5 cm (−3.2 to −1.8 cm, I 2 =69%, P<0.001) in favour of the intervention at 12 months.

Conclusions Behavioural weight management interventions for adults with obesity delivered in primary care are effective for weight loss and could be offered to members of the public.

Systematic review registration PROSPERO CRD42021275529.

Introduction

Obesity is associated with an increased risk of diseases such as cancer, type 2 diabetes, and heart disease, leading to early mortality. 1 2 3 More recently, obesity is a risk factor for worse outcomes with covid-19. 4 5 Because of this increased risk, health agencies and governments worldwide are focused on finding effective ways to help people lose weight. 6

Primary care is an ideal setting for delivering weight management services, and international guidelines recommend that doctors should opportunistically screen and encourage patients to lose weight. 7 8 On average, most people consult a primary care doctor four times yearly, providing opportunities for weight management interventions. 9 10 A systematic review of randomised controlled trials by LeBlanc et al identified behavioural interventions that could potentially be delivered in primary care, or involved referral of patients by primary care professionals, were effective for weight loss at 12-18 months follow-up (−2.4 kg, 95% confidence interval −2.9 to−1.9 kg). 11 However, this review included trials with interventions that the review authors considered directly transferrable to primary care, but not all interventions involved primary care practitioners. The review included interventions that were entirely delivered by university research employees, meaning implementation of these interventions might differ if offered in primary care, as has been the case in other implementation research of weight management interventions, where effects were smaller. 12 As many similar trials have been published after this review, an updated review would be useful to guide health policy.

We examined the effectiveness of weight loss interventions delivered in primary care on measures of body composition (weight and waist circumference). We also identified characteristics of effective weight management programmes for policy makers to consider.

This systematic review was registered on PROSPERO and is reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. 13 14

Eligibility criteria

We considered studies to be eligible for inclusion if they were randomised controlled trials, comprised adult participants (≥18 years), and evaluated behavioural weight management interventions delivered in primary care that focused on weight loss. A primary care setting was broadly defined as the first point of contact with the healthcare system, providing accessible, continued, comprehensive, and coordinated care, focused on long term health. 15 Delivery in primary care was defined as the majority of the intervention being delivered by medical and non-medical clinicians within the primary care setting. Table 1 lists the inclusion and exclusion criteria.

Study inclusion and exclusion criteria

• View inline

We extracted studies from the systematic review by LeBlanc et al that met our inclusion criteria. 11 We also searched the exclusions in this review because the researchers excluded interventions specifically for diabetes management, low quality trials, and only included studies from an Organisation for Economic Co-operation and Development country, limiting the scope of the findings.

We searched for studies in the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021 (see supplementary file 1). Reference lists of previous reviews 16 17 18 19 20 21 and included trials were hand searched.

Data extraction

Results were uploaded to Covidence, 22 a software platform used for screening, and duplicates removed. Two independent reviewers screened study titles, abstracts, and full texts. Disagreements were discussed and resolved by a third reviewer. All decisions were recorded in Covidence, and reviewers were blinded to each other’s decisions. Covidence calculates proportionate agreement as a measure of inter-rater reliability, and data are reported separately by title or abstract screening and full text screening. One reviewer extracted data on study characteristics (see supplementary table 1) and two authors independently extracted data on weight outcomes. We contacted the authors of four included trials (from the updated search) for further information. 23 24 25 26

Outcomes, summary measures, and synthesis of results

The primary outcome was weight change from baseline to 12 months. Secondary outcomes were weight change from baseline to ≥24 months and from baseline to last follow-up (to include as many trials as possible), and waist circumference from baseline to 12 months. Supplementary file 2 details the prespecified subgroup analysis that we were unable to complete. The prespecified subgroup analyses that could be completed were type of healthcare professional who delivered the intervention, country, intensity of the intervention, and risk of bias rating.

Healthcare professional delivering intervention —From the data we were able to compare subgroups by type of healthcare professional: nurses, 24 26 27 28 general practitioners, 23 29 30 31 and non-medical practitioners (eg, health coaches). 32 33 34 35 36 37 38 39 Some of the interventions delivered by non-medical practitioners were supported, but not predominantly delivered, by GPs. Other interventions were delivered by a combination of several different practitioners—for example, it was not possible to determine whether a nurse or dietitian delivered the intervention. In the subgroup analysis of practitioner delivery, we refer to this group as “other.”

Country —We explored the effectiveness of interventions by country. Only countries with three or more trials were included in subgroup analyses (United Kingdom, United States, and Spain).

Intensity of interventions —As the median number of contacts was 12, we categorised intervention groups according to whether ≤11 or ≥12 contacts were required.

Risk of bias rating —Studies were classified as being at low, unclear, and high risk of bias. Risk of bias was explored as a potential influence on the results.

Meta-analyses

Meta-analyses were conducted using Review Manager 5.4. 40 As we expected the treatment effects to differ because of the diversity of intervention components and comparator conditions, we used random effects models. A pooled mean difference was calculated for each analysis, and variance in heterogeneity between studies was compared using the I 2 and τ 2 statistics. We generated funnel plots to evaluate small study effects. If more than two intervention groups existed, we divided the number of participants in the comparator group by the number of intervention groups and analysed each individually. Nine trials were cluster randomised controlled trials. The trials had adjusted their results for clustering, or adjustment had been made in the previous systematic review by LeBlanc et al. 11 One trial did not report change in weight by group. 26 We calculated the mean weight change and standard deviation using a standard formula, which imputes a correlation for the baseline and follow-up weights. 41 42 In a non-prespecified analysis, we conducted univariate and multivariable metaregression (in Stata) using a random effects model to examine the association between number of sessions and type of interventionalist on study effect estimates.

Risk of bias

Two authors independently assessed the risk of bias using the Cochrane risk of bias tool v2. 43 For incomplete outcome data we defined a high risk of bias as ≥20% attrition. Disagreements were resolved by discussion or consultation with a third author.

Patient and public involvement

The study idea was discussed with patients and members of the public. They were not, however, included in discussions about the design or conduct of the study.

The search identified 11 609 unique study titles or abstracts after duplicates were removed ( fig 1 ). After screening, 97 full text articles were assessed for eligibility. The proportionate agreement ranged from 0.94 to 1.0 for screening of titles or abstracts and was 0.84 for full text screening. Fourteen new trials met the inclusion criteria. Twenty one studies from the review by LeBlanc et al met our eligibility criteria and one study from another systematic review was considered eligible and included. 44 Some studies had follow-up studies (ie, two publications) that were found in both the second and the first search; hence the total number of trials was 34 and not 36. Of the 34 trials, 27 (n=8000 participants) were included in the primary outcome meta-analysis of weight change from baseline to 12 months, 13 (n=5011) in the secondary outcome from baseline to ≥24 months, and 30 (n=8938) in the secondary outcome for weight change from baseline to last follow-up. Baseline weight was accounted for in 18 of these trials, but in 14 24 26 29 30 31 32 44 45 46 47 48 49 50 51 it was unclear or the trials did not consider baseline weight. Eighteen trials (n=5288) were included in the analysis of change in waist circumference at 12 months.

Studies included in systematic review of effectiveness of behavioural weight management interventions in primary care. *Studies were merged in Covidence if they were from same trial

• Download figure
• Open in new tab
• Download powerpoint

Study characteristics

Included trials (see supplementary table 1) were individual randomised controlled trials (n=25) 24 25 26 27 28 29 32 33 34 35 38 39 41 44 45 46 47 50 51 52 53 54 55 56 59 or cluster randomised controlled trials (n=9). 23 30 31 36 37 48 49 57 58 Most were conducted in the US (n=14), 29 30 31 32 33 34 35 36 37 45 48 51 54 55 UK (n=7), 27 28 38 41 47 57 58 and Spain (n=4). 25 44 46 49 The median number of participants was 276 (range 50-864).

Four trials included only women (average 65.9% of women). 31 48 51 59 The mean BMI at baseline was 35.2 (SD 4.2) and mean age was 48 (SD 9.7) years. The interventions lasted between one session (with participants subsequently following the programme unassisted for three months) and several sessions over three years (median 12 months). The follow-up period ranged from 12 months to three years (median 12 months). Most trials excluded participants who had lost weight in the past six months and were taking drugs that affected weight.

Meta-analysis

Overall, 27 trials were included in the primary meta-analysis of weight change from baseline to 12 months. Three trials could not be included in the primary analysis as data on weight were only available at two and three years and not 12 months follow-up, but we included these trials in the secondary analyses of last follow-up and ≥24 months follow-up. 26 44 50 Four trials could not be included in the meta-analysis as they did not present data in a way that could be synthesised (ie, measures of dispersion). 25 52 53 58 The mean difference was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, τ 2 =3.38; P<0.001) in favour of the intervention group ( fig 2 ). We found no evidence of publication bias (see supplementary fig 1). Absolute weight change was −3.7 (SD 6.1) kg in the intervention group and −1.4 (SD 5.5) kg in the comparator group.

Mean difference in weight at 12 months by weight management programme in primary care (intervention) or no treatment, different content, or minimal intervention (control). SD=standard deviation

Supplementary file 2 provides a summary of the main subgroup analyses.

Weight change

The mean difference in weight change at the last follow-up was −1.9 kg (95% confidence interval −2.5 to −1.3 kg, I 2 =81%, τ 2 =2.15; P<0.001). Absolute weight change was −3.2 (SD 6.4) kg in the intervention group and −1.2 (SD 6.0) kg in the comparator group (see supplementary figs 2 and 3).

At the 24 month follow-up the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, τ 2 =3.13; P<0.001) (see supplementary fig 4). As the weight change data did not differ between the last follow-up and ≥24 months, we used the weight data from the last follow-up in subgroup analyses.

In subgroup analyses of type of interventionalist, differences were significant (P=0.005) between non-medical practitioners, GPs, nurses, and other people who delivered interventions (see supplementary fig 2).

Participants who had ≥12 contacts during interventions lost significantly more weight than those with fewer contacts (see supplementary fig 6). The association remained after adjustment for type of interventionalist.

Waist circumference

The mean difference in waist circumference was −2.5 cm (95% confidence interval −3.2 to −1.8 cm, I 2 =69%, τ 2 =1.73; P<0.001) in favour of the intervention at 12 months ( fig 3 ). Absolute changes were −3.7 cm (SD 7.8 cm) in the intervention group and −1.3 cm (SD 7.3) in the comparator group.

Mean difference in waist circumference at 12 months. SD=standard deviation

Risk of bias was considered to be low in nine trials, 24 33 34 35 39 41 47 55 56 unclear in 12 trials, 25 27 28 29 32 45 46 50 51 52 54 59 and high in 13 trials 23 26 30 31 36 37 38 44 48 49 53 57 58 ( fig 4 ). No significant (P=0.65) differences were found in subgroup analyses according to level of risk of bias from baseline to 12 months (see supplementary fig 7).

Risk of bias in included studies

Worldwide, governments are trying to find the most effective services to help people lose weight to improve the health of populations. We found weight management interventions delivered by primary care practitioners result in effective weight loss and reduction in waist circumference and these interventions should be considered part of the services offered to help people manage their weight. A greater number of contacts between patients and healthcare professionals led to more weight loss, and interventions should be designed to include at least 12 contacts (face-to-face or by telephone, or both). Evidence suggests that interventions delivered by non-medical practitioners were as effective as those delivered by GPs (both showed statistically significant weight loss). It is also possible that more contacts were made with non-medical interventionalists, which might partially explain this result, although the metaregression analysis suggested the effect remained after adjustment for type of interventionalist. Because most comparator groups had fewer contacts than intervention groups, it is not known whether the effects of the interventions are related to contact with interventionalists or to the content of the intervention itself.

Although we did not determine the costs of the programme, it is likely that interventions delivered by non-medical practitioners would be cheaper than GP and nurse led programmes. 41 Most of the interventions delivered by non-medical practitioners involved endorsement and supervision from GPs (ie, a recommendation or checking in to see how patients were progressing), and these should be considered when implementing these types of weight management interventions in primary care settings. Our findings suggest that a combination of practitioners would be most effective because GPs might not have the time for 12 consultations to support weight management.

Although the 2.3 kg greater weight loss in the intervention group may seem modest, just 2-5% in weight loss is associated with improvements in systolic blood pressure and glucose and triglyceride levels. 60 The confidence intervals suggest a potential range of weight loss and that these interventions might not provide as much benefit to those with a higher BMI. Patients might not find an average weight loss of 3.7 kg attractive, as many would prefer to lose more weight; explaining to patients the benefits of small weight losses to health would be important.

Strengths and limitations of this review

Our conclusions are based on a large sample of about 8000 participants, and 12 of these trials were published since 2018. It was occasionally difficult to distinguish who delivered the interventions and how they were implemented. We therefore made some assumptions at the screening stage about whether the interventionalists were primary care practitioners or if most of the interventions were delivered in primary care. These discussions were resolved by consensus. All included trials measured weight, and we excluded those that used self-reported data. Dropout rates are important in weight management interventions as those who do less well are less likely to be followed-up. We found that participants in trials with an attrition rate of 20% or more lost less weight and we are confident that those with high attrition rates have not inflated the results. Trials were mainly conducted in socially economic developed countries, so our findings might not be applicable to all countries. The meta-analyses showed statistically significant heterogeneity, and our prespecified subgroups analysis explained some, but not all, of the variance.

Comparison with other studies

The mean difference of −2.3 kg in favour of the intervention group at 12 months is similar to the findings in the review by LeBlanc et al, who reported a reduction of −2.4 kg in participants who received a weight management intervention in a range of settings, including primary care, universities, and the community. 11 61 This is important because the review by LeBlanc et al included interventions that were not exclusively conducted in primary care or by primary care practitioners. Trials conducted in university or hospital settings are not typically representative of primary care populations and are often more intensive than trials conducted in primary care as a result of less constraints on time. Thus, our review provides encouraging findings for the implementation of weight management interventions delivered in primary care. The findings are of a similar magnitude to those found in a trial by Ahern et al that tested primary care referral to a commercial programme, with a difference of −2.7 kg (95% confidence interval −3.9 to −1.5 kg) reported at 12 month follow-up. 62 The trial by Ahern et al also found a difference in waist circumference of −4.1 cm (95% confidence interval −5.5 to −2.3 cm) in favour of the intervention group at 12 months. Our finding was smaller at −2.5 cm (95% confidence interval −3.2 to −1.8 cm). Some evidence suggests clinical benefits from a reduction of 3 cm in waist circumference, particularly in decreased glucose levels, and the intervention groups showed a 3.7 cm absolute change in waist circumference. 63

Policy implications and conclusions

Weight management interventions delivered in primary care are effective and should be part of services offered to members of the public to help them manage weight. As about 39% of the world’s population is living with obesity, helping people to manage their weight is an enormous task. 64 Primary care offers good reach into the community as the first point of contact in the healthcare system and the remit to provide whole person care across the life course. 65 When developing weight management interventions, it is important to reflect on resource availability within primary care settings to ensure patients’ needs can be met within existing healthcare systems. 66

We did not examine the equity of interventions, but primary care interventions may offer an additional service and potentially help those who would not attend a programme delivered outside of primary care. Interventions should consist of 12 or more contacts, and these findings are based on a mixture of telephone and face-to-face sessions. Previous evidence suggests that GPs find it difficult to raise the issue of weight with patients and are pessimistic about the success of weight loss interventions. 67 Therefore, interventions should be implemented with appropriate training for primary care practitioners so that they feel confident about helping patients to manage their weight. 68

Unanswered questions and future research

A range of effective interventions are available in primary care settings to help people manage their weight, but we found substantial heterogeneity. It was beyond the scope of this systematic review to examine the specific components of the interventions that may be associated with greater weight loss, but this could be investigated by future research. We do not know whether these interventions are universally suitable and will decrease or increase health inequalities. As the data are most likely collected in trials, an individual patient meta-analysis is now needed to explore characteristics or factors that might explain the variance. Most of the interventions excluded people prescribed drugs that affect weight gain, such as antipsychotics, glucocorticoids, and some antidepressants. This population might benefit from help with managing their weight owing to the side effects of these drug classes on weight gain, although we do not know whether the weight management interventions we investigated would be effective in this population. 69

What is already known on this topic

Referral by primary care to behavioural weight management programmes is effective, but the effectiveness of weight management interventions delivered by primary care is not known

Systematic reviews have provided evidence for weight management interventions, but the latest review of primary care delivered interventions was published in 2014

Factors such as intensity and delivery mechanisms have not been investigated and could influence the effectiveness of weight management interventions delivered by primary care

What this study adds

Weight management interventions delivered by primary care are effective and can help patients to better manage their weight

At least 12 contacts (telephone or face to face) are needed to deliver weight management programmes in primary care

Some evidence suggests that weight loss after weight management interventions delivered by non-medical practitioners in primary care (often endorsed and supervised by doctors) is similar to that delivered by clinician led programmes

Ethics statements

Ethical approval.

Not required.

Data availability statement

Additional data are available in the supplementary files.

Contributors: CDM and AJD conceived the study, with support from ES. CDM conducted the search with support from HEG. CDM, AJD, ES, HEG, KG, GB, and VEK completed the screening and full text identification. CDM and VEK completed the risk of bias assessment. CDM extracted data for the primary outcome and study characteristics. HEJ, GB, and KG extracted primary outcome data. CDM completed the analysis in RevMan, and GMJT completed the metaregression analysis in Stata. CDM drafted the paper with AJD. All authors provided comments on the paper. CDM acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: AJD is supported by a National Institute for Health and Care Research (NIHR) research professorship award. This research was supported by the NIHR Leicester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. ES’s salary is supported by an investigator grant (National Health and Medical Research Council, Australia). GT is supported by a Cancer Research UK fellowship. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: This research was supported by the National Institute for Health and Care Research Leicester Biomedical Research Centre; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

The lead author (CDM) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported, and that no important aspects of the study have been omitted.

Dissemination to participants and related patient and public communities: We plan to disseminate these research findings to a wider community through press releases, featuring on the Centre for Lifestyle Medicine and Behaviour website ( www.lboro.ac.uk/research/climb/ ) via our policy networks, through social media platforms, and presentation at conferences.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

• Renehan AG ,
• Heller RF ,
• Bansback N ,
• Birmingham CL ,
• Abdullah A ,
• Peeters A ,
• de Courten M ,
• Stoelwinder J
• Aghili SMM ,
• Ebrahimpur M ,
• Arjmand B ,
• KETLAK Study Group
• ↵ Department of Health and Social Care. New specialised support to help those living with obesity to lose weight UK2021. www.gov.uk/government/news/new-specialised-support-to-help-those-living-with-obesity-to-lose-weight [accessed 08/02/2021].
• U.S. Preventive Services Task Force
• ↵ National Institute for Health and Care Excellence. Maintaining a Healthy Weight and Preventing Excess Weight Gain in Children and Adults. Cost Effectiveness Considerations from a Population Modelling Viewpoint. 2014, NICE. www.nice.org.uk/guidance/ng7/evidence/evidence-review-2-qualitative-evidence-review-of-the-most-acceptable-ways-to-communicate-information-about-individually-modifiable-behaviours-to-help-maintain-a-healthy-weight-or-prevent-excess-weigh-8733713.
• ↵ The Health Foundation. Use of primary care during the COVID-19 pandemic. 17/09/2020: The Health Foundation, 2020.
• ↵ Australian Bureau of Statistics. Patient Experiences in Australia: Summary of Findings, 2017-18. 2019 ed. Canberra, Australia, 2018. www.abs.gov.au/AUSSTATS/[email protected]/Lookup/4839.0Main+Features12017-18?OpenDocument.
• LeBlanc ES ,
• Patnode CD ,
• Webber EM ,
• Redmond N ,
• Rushkin M ,
• O’Connor EA
• Damschroder LJ ,
• Liberati A ,
• Tetzlaff J ,
• Altman DG ,
• PRISMA Group
• McKenzie JE ,
• Bossuyt PM ,
• ↵ WHO. Main terminology: World Health Organization; 2004. www.euro.who.int/en/health-topics/Health-systems/primary-health-care/main-terminology [accessed 09.12.21].
• Aceves-Martins M ,
• Robertson C ,
• REBALANCE team
• Glasziou P ,
• Isenring E ,
• Chisholm A ,
• Wakayama LN ,
• Kettle VE ,
• Madigan CD ,
• ↵ Covidence [program]. Melbourne, 2021.
• Welzel FD ,
• Carrington MJ ,
• Fernández-Ruiz VE ,
• Ramos-Morcillo AJ ,
• Solé-Agustí M ,
• Paniagua-Urbano JA ,
• Armero-Barranco D
• Bräutigam-Ewe M ,
• Hildingh C ,
• Yardley L ,
• Christian JG ,
• Bessesen DH ,
• Christian KK ,
• Goldstein MG ,
• Martin PD ,
• Dutton GR ,
• Horswell RL ,
• Brantley PJ
• Wadden TA ,
• Rogers MA ,
• Berkowitz RI ,
• Kumanyika SK ,
• Morales KH ,
• Allison KC ,
• Rozenblum R ,
• De La Cruz BA ,
• Katzmarzyk PT ,
• Martin CK ,
• Newton RL Jr . ,
• Nanchahal K ,
• Holdsworth E ,
• ↵ RevMan [program]. 5.4 version: Copenhagen, 2014.
• Sterne JAC ,
• Savović J ,
• Gomez-Huelgas R ,
• Jansen-Chaparro S ,
• Baca-Osorio AJ ,
• Mancera-Romero J ,
• Tinahones FJ ,
• Bernal-López MR
• Delahanty LM ,
• Tárraga Marcos ML ,
• Panisello Royo JM ,
• Carbayo Herencia JA ,
• Beeken RJ ,
• Leurent B ,
• Vickerstaff V ,
• Hagobian T ,
• Brannen A ,
• Rodriguez-Cristobal JJ ,
• Alonso-Villaverde C ,
• Panisello JM ,
• Conroy MB ,
• Spadaro KC ,
• Takasawa N ,
• Mashiyama Y ,
• Pritchard DA ,
• Hyndman J ,
• Jarjoura D ,
• Smucker W ,
• Baughman K ,
• Bennett GG ,
• Steinberg D ,
• Zaghloul H ,
• Chagoury O ,
• Leslie WS ,
• Barnes AC ,
• Summerbell CD ,
• Greenwood DC ,
• Huseinovic E ,
• Leu Agelii M ,
• Hellebö Johansson E ,
• Winkvist A ,
• Look AHEAD Research Group
• LeBlanc EL ,
• Wheeler GM ,
• Aveyard P ,
• de Koning L ,
• Chiuve SE ,
• Willett WC ,
• ↵ World Health Organization. Obesity and Overweight, 2021, www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
• Starfield B ,
• Sturgiss E ,
• Dewhurst A ,
• Devereux-Fitzgerald A ,
• Haesler E ,
• van Weel C ,
• Gulliford MC
• Fassbender JE ,
• Sarwer DB ,
• Brekke HK ,

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 17 October 2022

Food intake and body weight in rodent studies: the devil is in the details

• Camille Allard   ORCID: orcid.org/0000-0002-2198-6011 1 ,
• Philippe Zizzari   ORCID: orcid.org/0000-0001-8838-0762 1 ,
• Carmelo Quarta   ORCID: orcid.org/0000-0002-1352-4239 1 &
• Daniela Cota   ORCID: orcid.org/0000-0002-6909-2156 1  

Nature Metabolism volume  4 ,  pages 1424–1426 ( 2022 ) Cite this article

2249 Accesses

1 Citations

33 Altmetric

Metrics details

• Feeding behaviour
• Homeostasis

In metabolic studies using rodents, body weight and food intake measurements seem easy to obtain, but several potential pitfalls can lead to erroneous data generation and interpretation. This Comment raises awareness of key conceptual and technical aspects that can increase the quality and reproducibility of this type of data.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Pickel, L. & Sung, H.-K. Front. Nutr. 7 , 39 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Ellacott, K. L. J., Morton, G. J., Woods, S. C., Tso, P. & Schwartz, M. W. Cell Metab. 12 , 10–17 (2010).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Robbers, Y., Tersteeg, M. M. H., Meijer, J. H. & Coomans, C. P. R. Soc. Open Sci. 8 , 201985 (2021).

Olsson, I. A. S. & Dahlborn, K. Lab. Anim. 36 , 243–270 (2002).

Article   CAS   PubMed   Google Scholar  

Castelhano-Carlos, M. J. & Baumans, V. Lab. Anim. 43 , 311–327 (2009).

Butler-Struben, H. M., Kentner, A. C. & Trainor, B. C. Neuropsychopharmacol. 47 , 1285–1291 (2022).

Article   Google Scholar  

Gouveia, K. & Hurst, J. L. Sci. Rep. 9 , 20305 (2019).

Tu, Y.-K., Law, G. R., Ellison, G. T. H. & Gilthorpe, M. S. Pharm. Stat. 9 , 77–83 (2010).

Article   PubMed   Google Scholar  

Speakman, J. R., Fletcher, Q. & Vaanholt, L. Dis. Model. Mech. 6 , 293–301 (2013).

Tschöp, M. H. et al. Nat. Methods 9 , 57–63 (2011).

Müller, T. D., Klingenspor, M. & Tschöp, M. H. Nat. Metab. 3 , 1134–1136 (2021).

Curran-Everett, D. Adv. Physiol. Educ. 37 , 213–219 (2013).

Pak, H. H. et al. Nat. Metab. 3 , 1327–1341 (2021).

Download references

Acknowledgements

The authors receive support from INSERM (D.C., P.Z., C.Q.), Agence Nationale de la Recherche (ANR-18-CE14-0029, ANR-21-CE14-0018 to D.C.; ANR-20-CE14-0046 to C.Q.), University of Bordeaux’s IdEx ‘Investments for the Future’ program/GPR BRAIN_2030 (D.C.) and the Fondation pour la Recherche Médicale (to C.A., FRM-ARF201809006962). C.Q. is also supported by the Société Francophone du Diabète, Société Française d’Endocrinologie, Société Française de Nutrition, Institut Benjamin Delessert and the Fyssen Foundation.

Author information

Authors and affiliations.

University of Bordeaux, INSERM, Neurocentre Magendie, U1215, F-33000, Bordeaux, France

Camille Allard, Philippe Zizzari, Carmelo Quarta & Daniela Cota

You can also search for this author in PubMed   Google Scholar

Contributions

C.A., P.Z., C.Q. and D.C. wrote the manuscript. C.A. and P.Z. generated the figure and the table.

Corresponding author

Correspondence to Daniela Cota .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Allard, C., Zizzari, P., Quarta, C. et al. Food intake and body weight in rodent studies: the devil is in the details. Nat Metab 4 , 1424–1426 (2022). https://doi.org/10.1038/s42255-022-00658-x

Download citation

Published : 17 October 2022

Issue Date : November 2022

DOI : https://doi.org/10.1038/s42255-022-00658-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• 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

7297 Accesses

1 Citations

6 Altmetric

Metrics details

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%).

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 ).

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

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.

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.

Minnick C, Raffoul A, Hammond D, Kirkpatrick SI. Intentional weight gain efforts among young Canadian adults aged 17–32 years. Eat Behav. 2020;38(June):101407.

Nagata JM, Murray SB, Bibbins-Domingo K, Garber AK, Mitchison D, Griffiths S. Predictors of muscularity-oriented disordered eating behaviors in U.S. young adults: a prospective cohort study. Int J Eat Disord. 2019;52(12):1380-1388.

Nagata JM, Ganson KT, Griffiths S, Mitchison D, Garber AK, Vittinghoff E, et al. Prevalence and correlates of muscle-enhancing behaviors among adolescents and young adults in the United States. Int J Adolesc Med Health. 2020 Jun 5; Available from: https://www.degruyter.com/document/doi/ https://doi.org/10.1515/ijamh-2020-0001/html

Solmi F, Sharpe, Phd H, Gage SH, Maddock J, Lewis G, Patalay P. Changes in the prevalence and correlates of weight-control behaviors and weight perception in adolescents in the UK, 1986–2015. JAMA Pediatr. 2020;175(3):1–10.

Ganson KT, Nagata JM, Lavender JM, Rodgers RF, Cunningham M, Murray SB, et al. Prevalence and correlates of weight gain attempts across five countries. Int J Eat Disord. 2021 Aug 20;(August):eat.23595. Available from: https://onlinelibrary.wiley.com/doi/ https://doi.org/10.1002/eat.23595

Eisenberg ME, Wall M, Neumark-Sztainer D. Muscle-enhancing behaviors among adolescent girls and boys. Pediatrics. 2012;130(6):1019–26.

Article   Google Scholar  

Nagata JM, Brown TA, Lavender JM, Murray SB. Emerging trends in eating disorders among adolescent boys: muscles, macronutrients, and biohacking. Lancet Child Adolesc Heal. 2019;4642(19):19–20. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352464219301476 .

Google Scholar  

Cunningham ML, Nagata JM, Murray SB. Muscularity-Oriented Disordered Eating in Boys and Men. Eat Disord Boys Men. 2021;21–35.

Health Canada. Canada’s Dietary Guidelines for Health Professionals and Policy Makers. Health Canada. 2019. Available from: https://food-guide.canada.ca/static/assets/pdf/CDG-EN-2018.pdf

U.S. Department of Agriculture, U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2020–2025. 2020. Available from: https://www.dietaryguidelines.gov/

Bonvecchio-Arenas A, Fernández-Gaxiola AC, Belausteguigoitia MP, Kaufer-Horwitz M, Pérez Lizaur AB, Rivera Dommarco JÁ. Documento de postura: Guías alimentarias y de actividad física en contexto de soprebeso y obesidad en la población mexicana. Guías alimentarias y de actividad física. 2015. Available from: https://www.anmm.org.mx/publicaciones/CAnivANM150/L29_ANM_Guias_alimentarias.pdf

National Health and Medical Research Council. Eat for Health: Australian Dietary Guidelines. 2013. Available from: www.nhmrc.gov.au/guidelines-publications/n55

Public Health England. Government Dietary Recommendations: Government recommendations for energy and nutrients for males and females aged 1–18 and 19+ years. 2016. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/618167/government_dietary_recommendations.pdf

International Food Policy Study. Technical Report 2019 Survey (Wave 3). 2021 [cited 2021 Jan 3]. Available from: http://foodpolicystudy.com/wp-content/uploads/2021/03/2019-IFPS-Technical-Report_20210301.pdf

Nagata JM, Bibbins-Domingo K, Garber AK, Griffiths S, Vittinghoff E, Murray SB. Boys, bulk, and body ideals: sex differences in weight-gain attempts among adolescents in the United States. J Adolesc Heal. 2019;64(4):450–3.

Vanderlee L, White CM, Kirkpatrick SI, Rynard VL, Jáuregui A, Adams J, et al. Nonalcoholic and alcoholic beverage intakes by adults across 5 Upper-Middle- and high-income countries. J Nutr. 2021;151(1):140–51.

Forde H, White M, Levy L, Greaves F, Hammond D, Vanderlee L, et al. The relationship between self-reported exposure to sugar-sweetened beverage promotions and intake: Cross-sectional analysis of the 2017 international food policy study. Nutrients. 2019;11(12):3047.

Kwon J, Cameron AJ, Hammond D, White CM, Vanderlee L, Bhawra J, et al. A multi-country survey of public support for food policies to promote healthy diets: Findings from the International Food Policy Study. BMC Public Health. 2019;19(1):1–10.

Article   CAS   Google Scholar  

Centers for Disease Control and Prevention. Assessing Your Weight. 2020.

Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–6.

Calzo JP, Corliss HL, Blood EA, Field AE, Austin SB. Development of muscularity and weight concerns in heterosexual and sexual minority males. Heal Psychol. 2013;32(1):42–51.

Murray SB, Griffiths S, Mond JM. Evolving eating disorder psychopathology: conceptualising muscularity-oriented disordered eating. Br J Psychiatry. 2016;208(5):414–5.

Lavender JM, Brown TA, Murray SB. Men, muscles, and eating disorders: an overview of traditional and muscularity-oriented disordered eating. Curr Psychiatry Rep. 2017;19(6):1–7.

Murray SB, Nagata JM, Griffiths S, Calzo JP, Brown TA, Mitchison D, et al. The enigma of male eating disorders: a critical review and synthesis. Clin Psychol Rev. 2017 Nov;57(August):1–11. Available from: https://linkinghub.elsevier.com/retrieve/pii/S027273581730137X .

Baghurst T, Hollander DB, Nardella B, Haff GG. Change in sociocultural ideal male physique: an examination of past and present action figures. Body Image. 2006;3(1):87–91.

Pope HG, Khalsa JH, Bhasin S. Body image disorders and abuse of anabolic-androgenic steroids among men. JAMA - J Am Med Assoc. 2017;317(1):23–4.

Rodgers RF, Franko DL, Lovering ME, Luk S, Pernal W, Matsumoto A. Development and validation of the female muscularity scale. Sex Roles. 2018;78(1–2):18–26.

Girard M, Rodgers RF, Chabrol H. Prospective predictors of body dissatisfaction, drive for thinness, and muscularity concerns among young women in France: a sociocultural model. Body Image. 2018;26:103–10. Available from: https://doi.org/10.1016/j.bodyim.2018.07.001 .

Freire R. Scientific evidence of diets for weight loss: different macronutrient composition, intermittent fasting, and popular diets. Nutrition. 2020;69:110549. Available from: https://doi.org/10.1016/j.nut.2019.07.001

Cafri G, Thompson JK, Ricciardelli L, McCabe M, Smolak L, Yesalis C. Pursuit of the muscular ideal: Physical and psychological consequences and putative risk factors. Clin Psychol Rev. 2005;25(2):215–39.

Sharp RL. Role of sodium in fluid homeostasis with exercise. J Am Coll Nutr. 2006;2006(25):231S-239S.

Bie P. Blood volume, blood pressure and total body sodium: Internal signalling and output control. Acta Physiol. 2009;195(1):187–96.

Burke LM, Van Loon LJC, Hawley JA. Postexercise muscle glycogen resynthesis in humans. J Appl Physiol. 2017;122(5):1055–67.

Gonzalez JT, Fuchs CJ, Betts JA, van Loon LJC. Glucose plus fructose ingestion for post-exercise recovery—greater than the sum of its parts? Nutrients. 2017;9(4):1–15.

Pila E, Mond JM, Griffiths S, Mitchison D, Murray SB. A thematic content analysis of #cheatmeal images on social media: Characterizing an emerging dietary trend. Int J Eat Disord. 2017;50(6):698–706. https://doi.org/10.1002/eat.22671 .

Article   PubMed   Google Scholar  

Murray SB, Griffiths S, Hazery L, Shen T, Wooldridge T, Mond JM. Go big or go home: A thematic content analysis of pro-muscularity websites. Body Image. 2016;16:17–20. https://doi.org/10.1016/j.bodyim.2015.10.002 .

Bowen KJ, Sullivan VK, Kris-Etherton PM, Petersen KS. Nutrition and cardiovascular disease—an update. Curr Atheroscler Rep. 2018;20(2):8. https://doi.org/10.1007/s11883-018-0704-3 .

Article   CAS   PubMed   Google Scholar  

Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States. JAMA - J Am Med Assoc. 2017;317(9):912–24.

Popkin BM, Barquera S, Corvalan C, Hofman KJ, Monteiro C, Ng SW, et al. Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol. 2021;9(7):462–70. https://doi.org/10.1016/S2213-8587(21)00078-4 .

Article   PubMed   PubMed Central   Google Scholar  

Frederick DA, Buchanan GM, Sadehgi-Azar L, Peplau LA, Haselton MG, Berezovskaya A, et al. Desiring the muscular ideal: Men’s body satisfaction in the United States, Ukraine, and Ghana. Psychol Men Masculinity. 2007;8(2):103–17.

Download references

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.

Author information

Authors and affiliations.

Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada

Kyle T. Ganson

Department of Pediatrics, Division of Adolescent and Young Adult Medicine, University of California, San Francisco, 550 16th Street., Box 0110, San Francisco, CA, 94158, USA

Jason M. Nagata

École de Nutrition, Centre de Nutrition, Santé Et Société (NUTRISS), Université Laval, Quebec City, QC, Canada

Lana Vanderlee

APPEAR, Department of Applied Psychology, Northeastern University, Boston, MA, USA

Rachel F. Rodgers

Department of Psychiatric Emergency & Acute Care, Lapeyronie Hospital, Montpellier, France

Department of Medicine, Uniformed Services University, Bethesda, MD, USA

Jason M. Lavender

Military Cardiovascular Outcomes Research (MiCOR) Program, Bethesda, MD, USA

The Metis Foundation, San Antonio, TX, USA

Sanford Center for Bio-Behavioral Research, Sanford Health, Fargo, ND, USA

Vivienne M. Hazzard

Department of Psychiatry and the Behavioral Sciences, University of Southern California, Los Angeles, CA, USA

Stuart B. Murray

School of Psychology, The University of Sydney, NSW, Sydney, Australia

Mitchell Cunningham

School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada

David Hammond

You can also search for this author in PubMed   Google Scholar

Contributions

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.

Corresponding author

Correspondence to Jason M. Nagata .

Ethics declarations

Ethics approval and consent to participate.

The International Food Policy Study was approved by the University of Waterloo Research Ethics Committee (ORE#30829). This study is exempt from research ethics approval given the data is publicly available and unidentifiable. Informed consent was obtained from all individual participants included in the study.

Consent for publication

Competing interests.

All authors declare no conflicts of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 19 November 2021

Accepted : 02 May 2022

Published : 13 May 2022

DOI : https://doi.org/10.1186/s12937-022-00784-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Weight gain attempts
• Diet modification
• Muscularity

Nutrition Journal

ISSN: 1475-2891

• General enquiries: [email protected]