Metabolic adaptation is not a major barrier to weight-loss maintenance

Changes in Trunk, but Not Limb, Lean Body Mass Contribute to Variability in Metabolic Adaptation Following Weight Loss

Background

Metabolic adaptation (MA) in response to weight loss is highly variable. Several methodological issues are likely to contribute to the large inter-individual variability in MA, namely the lack of adjustment for changes in the composition of fat-free mass.

Objective

The aim of this analysis was to investigate the contribution of changes in trunk versus limb lean body mass (LBM) to the variability in MA, at the level of resting metabolic rate (RMR).

Methods

116 premenopausal women with overweight (body mass index (BMI): 28.2 ± 1.2 kg/m2; age: 34.4 ± 6.4 years) enrolled in a weight loss program. Body weight/composition (dual energy x-ray absorptiometry), RMR (indirect calorimetry) and insulin sensitivity (SI) (intravenous glucose tolerance test) were measured after 4 weeks of weight stability at baseline and after weight loss. Multiple linear regression was used to determine the contribution of changes in trunk versus limb LBM to MA variability, after adjusting for relevant confounders.

Results

A large variation in MA (-206 to +233 kcal/day) was found after an average of 12.1 ± 2.4 kg weight loss. After adjusting for RMR at baseline and changes in SI, changes in trunk (but not limb) LBM were a significant contributor to MA variability.

Conclusion

In premenopausal women with overweight and loss of trunk, but not limbs, LBM contributes to MA variability, suggesting that loss of organ mass might be more important than loss of skeletal mass in modulating the magnitude of MA. Trial Registration: ClinicalTrial.gov Identifier (JULIET study): NCT00067873, URL: https://clinicaltrials.gov/ct2/show/NCT0006787.

Adaptive thermogenesis, at the level of resting energy expenditure, after diet alone or diet plus bariatric surgery

OBJECTIVE

The objective of this study was to compare the magnitude of adaptive thermogenesis (AT), at the level of resting energy expenditure (REE), after a very low-energy diet alone or combined with Roux-en-Y gastric bypass or sleeve gastrectomy, as well as to investigate the association between AT and changes in appetite.

METHODS

A total of 44 participants with severe obesity underwent 10 weeks of a very low-energy diet alone or combined with Roux-en-Y gastric bypass or sleeve gastrectomy. Body weight and composition, REE, subjective appetite feelings, and plasma concentrations of gastrointestinal hormones were measured at baseline and week 11. AT, at the level of REE, was defined as a significantly lower measured versus predicted (using a regression model with baseline data) REE.

RESULTS

Participants lost 18.4 ± 3.9 kg of body weight and experienced AT, at the level of REE (-121 ± 188 kcal/day; p < 0.001), with no differences among groups. The larger the AT, at the level of REE, the greater the reduction in fasting ghrelin concentrations and the smaller the reduction in feelings of hunger and desire to eat in the postprandial state.

CONCLUSIONS

Weight-loss modality does not seem to modulate the magnitude of AT, at the level of REE. The greater the AT, at the level of REE, the greater the drive to eat following weight loss.

What could be the reasons for not losing weight even after following a weight loss program?

INTRODUCTION

Approximately four million people worldwide die annually because of obesity. Weight loss is commonly recommended as a first-line therapy in overweight and obese patients. Although many individuals attempt to lose weight, not everyone achieves optimal success. Few studies point out that weight loss eventually slows down, stagnates or reverses in 85% of the cases.

RESEARCH QUESTION

What could be the reasons for not losing weight even after following a weight loss program?

METHODS

A scoping review of the literature was performed using weight loss-related search terms such as 'Obesity,' 'Overweight,' 'Lifestyle,' 'weight loss,' 'Basal Metabolism,' 'physical activity,' 'adherence,' 'energy balance,' 'Sleep' and 'adaptations. The search involved reference tracking and database and web searches (PUBMED, Science Direct, Elsevier, Web of Science and Google Scholar). Original articles and review papers on weight loss involving human participants and adults aged > 18 years were selected. Approximately 231 articles were reviewed, and 185 were included based on the inclusion criteria.

DESIGN

Scoping review.

RESULTS

In this review, the factors associated with not losing weight have broadly been divided into five categories. Studies highlighting each subfactor were critically reviewed and discussed. A wide degree of interindividual variability in weight loss is common in studies even after controlling for variables such as adherence, sex, physical activity and baseline weight. In addition to these variables, variations in factors such as previous weight loss attempts, sleep habits, meal timings and medications can play a crucial role in upregulating or downregulating the association between energy deficit and weight loss results.

CONCLUSION

This review identifies and clarifies the role of several factors that may hinder weight loss after the exploration of existing evidence. Judging the effectiveness of respective lifestyle interventions by simply observing the 'general behavior of the groups' is not always applicable in clinical practice. Each individual must be monitored and advised as per their requirements and challenges.

Metabolic adaptation is associated with a greater increase in appetite following weight loss: a longitudinal study

BACKGROUND

Weight loss is associated with a disproportionate reduction in energy expenditure, along with increases in hunger feelings and ghrelin concentrations. These changes are presumed to be homeostatic mechanisms to counteract the energy deficit. The possibility that these 2 components of the energy balance equation are mechanistically linked has never been examined.

OBJECTIVE

This study aimed to determine if the disproportionate reduction in resting metabolic rate (RMR) seen with weight loss is associated with changes in the plasma concentration of gastrointestinal hormones involved in appetite regulation and subjective appetite ratings.

METHODS

This was a longitudinal study with repeated measurements. Fifty-six individuals with obesity (body mass index [BMI]: 34.5±0.5 kg/m2; age: 47±1 y; 26 males) underwent an 8 wk low-energy diet, followed by 4 wk of refeeding and weight stabilization. The RMR, respiratory quotient (RQ), body composition, plasma concentrations of ghrelin, glucagon-like peptide 1, peptide YY, cholecystokinin, insulin, and appetite ratings in the fasting and postprandial states were measured at baseline, Wk9 and 13. Metabolic adaptation was defined as significantly lower when measured versus the predicted RMR (pRMR) (from own regression model using baseline data).

RESULTS

A 14.2±0.6 kg weight loss was seen at Wk9 and maintained at Wk13. RQ was significantly reduced at Wk9 (0.82±0.06 vs. 0.76±0.05, P< 0.001) but returned to baseline at Wk13. Metabolic adaptation was seen at Wk9, but not Wk13 (-341±58, P <0.001 and -75±72 kJ/d, P = 0.305, respectively). The larger the difference between measured and predicted RMR at both timepoints, the greater the increase in hunger, desire to eat, and composite appetite score (fasting and postprandial at Wk9, postprandial only at Wk13), even after adjusting for weight loss and RQ.

CONCLUSION

A larger metabolic adaptation during weight loss is accompanied by a greater drive to eat. This might help explain the interindividual differences in weight loss outcomes to dietary interventions.

Longitudinal estimates of resting energy expenditure using predictive equations in individuals with excess weight after weight loss: A systematic review with meta-analysis

BACKGROUND & AIMS

To determine which resting energy expenditure (REE) predictive equation has the lowest bias in the aggregate level in individuals with excess weight during weight loss interventions.

METHODS

Searches were performed in MEDLINE, Web of Science, Scopus, CENTRAL and gray literature databases. Longitudinal studies on weight loss interventions which evaluated REE by predictive equations compared to that measured by indirect calorimetry in adults with excess weight at different follow-up times were included. Meta-analyses were performed with the differences between biases of predictive equations of the REE at the different follow-up times of weight loss.

RESULTS

Of the total of 2178 occurrences found in the databases, only eight studies were included. The Harris-Benedict (1919) equation showed the smallest differences between bias up to the third month (MD = 103.33 kcal; 95%CI = -39.01; 245.67), in the sixth month (MD = 59.16 kcal; 95%CI = 8.74; 109.57) and at the 12th month (MD = -71.41 kcal; 95%CI = -150.38; 7.55) of weight loss follow-up. Weight loss does not seem to have an effect on bias at different follow-up times.

CONCLUSION

Harris-Benedict (1919) equation seems to be the most adequate to assess the REE of individuals with excess weight during weight loss. However, the finding of large estimated predictive intervals may indicate that predictive equations may not be handy tools for individuals losing and regaining weight due to changes other than body weight.

The BREAK study protocol: Effects of intermittent energy restriction on adaptive thermogenesis during weight loss and its maintenance

BACKGROUND

Adaptive thermogenesis, defined as the decrease in the energy expenditure components beyond what can be predicted by changes in body mass stores, has been studied as a possible barrier to weight loss and weight maintenance. Intermittent energy restriction (IER), using energy balance refeeds, has been pointed out as a viable strategy to reduce adaptive thermogenesis and improve weight loss efficiency (greater weight loss per unit of energy deficit), as an alternative to a continuous energy restriction (CER). Following a randomized clinical trial design, the BREAK Study aims to compare the effects of IER versus CER on body composition and in adaptive thermogenesis, and understand whether participants will successfully maintain their weight loss after 12 months.

METHODS

Seventy-four women with obesity and inactive (20-45 y) will be randomized to 16 weeks of CER or IER (8x2 weeks of energy restriction interspersed with 7x1 week in energy balance). Both groups will start with 2 weeks in energy balance before energy restriction, followed by 16 weeks in energy restriction, then 8 weeks in energy balance and finally a 12-month weight maintenance phase. Primary outcomes are changes in fat-mass and adaptive thermogenesis after weight loss and weight maintenance. Secondary outcomes include weight loss, fat-free mass preservation, alterations in energy expenditure components, and changes in hormones (thyroid function, insulin, leptin, and cortisol).

DISCUSSION

We anticipate that The BREAK Study will allow us to better understand adaptive thermogenesis during weight loss and weight maintenance, in women with obesity. These findings will enable evidence-based decisions for obesity treatment.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT05184361.

Obesity-induced and weight-loss-induced physiological factors affecting weight regain

Weight regain after successful weight loss resulting from lifestyle interventions is a major challenge in the management of overweight and obesity. Knowledge of the causal mechanisms for weight regain can help researchers and clinicians to find effective strategies to tackle weight regain and reduce obesity-associated metabolic and cardiovascular complications. This Review summarizes the current understanding of a number of potential physiological mechanisms underlying weight regain after weight loss, including: the role of adipose tissue immune cells; hormonal and neuronal factors affecting hunger, satiety and reward; resting energy expenditure and adaptive thermogenesis; and lipid metabolism (lipolysis and lipid oxidation). We describe and discuss obesity-associated changes in these mechanisms, their persistence during weight loss and weight regain and their association with weight regain. Interventions to prevent or limit weight regain based on these factors, such as diet, exercise, pharmacotherapy and biomedical strategies, and current knowledge on the effectiveness of these interventions are also reviewed.

Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission

OBJECTIVE

To determine the extent to which changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials.

RESEARCH DESIGN AND METHODS

We applied SomaSignal predictive tests (each derived from ∼5,000 plasma protein measurements using aptamer-based proteomics assay) to baseline and 1-year samples of trial intervention (Diabetes Remission Clinical Trial [DiRECT], n = 118, and Diabetes Intervention Accentuating Diet and Enhancing Metabolism [DIADEM-I], n = 66) and control (DiRECT, n = 144, DIADEM-I, n = 76) group participants.

RESULTS

Mean (SD) weight loss in DiRECT (U.K.) and DIADEM-I (Qatar) was 10.2 (7.4) kg and 12.1 (9.5) kg, respectively, vs. 1.0 (3.7) kg and 4.0 (5.4) kg in control groups. Cardiometabolic SomaSignal test results showed significant improvement (Bonferroni-adjusted P < 0.05) in DiRECT and DIADEM-I (expressed as relative difference, intervention minus control) as follows, respectively: liver fat (-26.4%, -37.3%), glucose tolerance (-36.6%, -37.4%), body fat percentage (-8.6%, -8.7%), resting energy rate (-8.0%, -5.1%), visceral fat (-34.3%, -26.1%), and cardiorespiratory fitness (9.5%, 10.3%). Cardiovascular risk (measured with SomaSignal tests) also improved in intervention groups relative to control, but this was significant only in DiRECT (DiRECT, -44.2%, and DIADEM-I, -9.2%). However, weight loss >10 kg predicted significant reductions in cardiovascular risk, -19.1% (95% CI -33.4 to -4.91) in DiRECT and -33.4% (95% CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months.

CONCLUSIONS

Intentional weight loss in recent-onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with greater (>10 kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.

Differential mechanisms affecting weight loss and weight loss maintenance

In most lifestyle, pharmacological and surgical interventions, weight loss occurs over an approximately 6- to 9-month period and is followed by a weight plateau and then weight regain. Overall, only about 15% of individuals can sustain a 10% or greater non-surgical, non-pharmacological, weight loss. A key question is the degree to which the genotypes, phenotypes and environmental correlates of success in weight loss and weight loss maintenance are continuous or dichotomous. This Perspective is a comparison of the interactions of weight loss and maintenance with genetic, behavioural, physiological and environmental homeostatic systems and a discussion of the implications of these findings for research in, and treatment of, obesity. Data suggest that weight loss and weight loss maintenance are physiologically and psychologically different in many ways. Consequently, individuals may require different interventions designed for temporarily sustaining a negative energy balance during weight loss versus permanently maintaining energy balance after weight loss.

The Influence of Energy Balance and Availability on Resting Metabolic Rate: Implications for Assessment and Future Research Directions

Resting metabolic rate (RMR) is a significant contributor to an individual's total energy expenditure. As such, RMR plays an important role in body weight regulation across populations ranging from inactive individuals to athletes. In addition, RMR may also be used to screen for low energy availability and energy deficiency in athletes, and thus may be useful in identifying individuals at risk for the deleterious consequences of chronic energy deficiency. Given its importance in both clinical and research settings within the fields of exercise physiology, dietetics, and sports medicine, the valid assessment of RMR is critical. However, factors including varying states of energy balance (both short- and long-term energy deficit or surplus), energy availability, and prior food intake or exercise may influence resulting RMR measures, potentially introducing error into observed values. The purpose of this review is to summarize the relationships between short- and long-term changes in energetic status and resulting RMR measures, consider these findings in the context of relevant recommendations for RMR assessment, and provide suggestions for future research.

Factors that predict weight loss success differ by diet intervention type

Background

Many types of diet intervention can achieve negative energy balance and successful weight loss in persons with obesity. However, within any dietary strategy, there is large inter-individual variation in the weight loss response. The aim of this study is to determine factors that predict weight loss success for diet interventions that vary by macronutrient and caloric composition.

Methods

Participants with BMI 30.0 to 49.9 kg/m2 self-selected one of three diet intervention trials for weight loss: low carbohydrate (LOW CHO), low fat (LOW FAT), or low calorie (LOW KCAL). Multivariable regression models were developed to determine the significance of predictor demographic, body composition, metabolic, clinical, and dietary variables for each diet type.

Results

Weight loss over 12-16 weeks averaging -5.1 ± 4.0 kg from baseline weight, p < 0.001, was not significantly different among diet types. Several different factors were identified that account for the inter-individual variance in weight loss success. Regardless of diet type, the most robust predictor of weight loss success was completion of the intervention, accounting for 20-30% of the variance. Factors predicting diet intervention completion were age, physical activity level, blood leptin level, blood pressure, and the amount of weight loss occurring. Differences by diet type in cardiometabolic risk factor reduction were identified with LOW CHO decreasing glycemia/insulinemia factors, LOW FAT decreasing lipidemia factors, and LOW KCAL decreasing inflammatory factors.

Conclusion

These data provide evidence to inform more precise and personalized approaches to diet intervention for weight loss and cardiometabolic health.

An analysis of nutritional risk factors in older adults with gastrointestinal tumours

INTRODUCTION

The aim of this study was to investigate risk factors for nutritional risk in older adults with gastrointestinal tumours.

MATERIALS AND METHODS

A total of 170 eligible hospitalised older adults with gastrointestinal tumours were included. Their clinical characteristics were collected, their nutritional risk was screened by NRS 2002, and then patients were divided into a nutritional risk group and a non-nutritional risk group. The observation indicators included body mass index (BMI), muscle mass, muscle strength, and calf circumference. The third lumbar skeletal muscle index (L3 SMI) was calculated using abdominal computed tomography (CT) scan results, and grip strength/muscle strength, 6-m walking speed and calf circumference were measured. Sarcopenia was diagnosed according to the criteria of the Asian Sarcopenia Working Group (AWGS). Finally, we analysed the relationship between nutritional risk and sarcopenia and other related factors (BMI, calf circumference, L3 SMI, grip strength/muscle strength, 6-m walking speed) in older adults with gastrointestinal tumours by multivariate logistic regression analysis.

RESULTS

Older adults with gastrointestinal tumours who were at nutritional risk accounted for 51.8% of patients in this study. The differences between sex, tumour stage, age, BMI, calf circumference, L3 SMI, grip strength/muscle strength, 6-m walking speed, and prevalence of sarcopenia were statistically significant in two groups (all P < 0.05). Multivariate logistic regression analysis showed that age, BMI, grip strength/muscle strength, and sarcopenia were risk factors of nutritional risk in older adults with gastrointestinal tumours (all P < 0.05).

DISCUSSION

Older adults with gastrointestinal cancer had a higher proportion of nutritional risk, and L3 SMI, grip strength/muscle strength were independent risk factors for nutritional risk. In clinical practice, attention to nutritional risk screening and sarcopenia development in older adults with gastrointestinal cancer is warranted.

Resting energy expenditure depends on energy intake during weight loss in people with obesity: a retrospective cohort study

Objective

Resting energy expenditure (REE) decreases if there is reduced energy intake and body weight (BW). The decrease in REE could make it difficult for patients with obesity to maintain decreased BW. This study aimed to investigate the correlation among changes in REE, energy intake, and BW during the weight loss process in patients with obesity.

Materials and methods

We conducted a retrospective cohort study of patients hospitalized for the treatment of obesity in Japan. Patients received fully controlled diet during hospitalization and performed exercises if able. REE was measured once a week using a hand-held indirect calorimetry. Energy intake was determined by actual dietary intake.

Results

Of 44 inpatients with obesity, 17 were included in the analysis. Their BW decreased significantly after 1 week (-4.7 ± 2.0 kg, P < 0.001) and 2 weeks (-5.7 ± 2.2 kg, P < 0.001). The change in REE after 1 and 2 weeks was positively correlated with the energy intake/energy expenditure ratio (r = 0.66, P = 0.004 at 1 week, r = 0.71, P = 0.002 at 2 weeks). Using a regression equation (y = 0.5257x - 43.579), if the energy intake/energy expenditure ratio within the second week was 82.9%, the REE after 2 weeks was similar to the baseline level. There was no significant correlation between the change in REE and BW.

Conclusion

Our data suggest that changes in REE depend on energy intake/energy expenditure ratio and that the decrease in REE can be minimized by matching energy intake to energy expenditure, even during the weight loss process.

Effects of a 4-month active weight loss phase followed by weight loss maintenance on adaptive thermogenesis in resting energy expenditure in former elite athletes

PURPOSE

Despite adaptive thermogenesis (AT) being studied as a barrier to weight loss (WL), few studies assessed AT in the resting energy expenditure (REE) compartment after WL maintenance. The aim of this study was twofold: (1) to understand if AT occurs after a moderate WL and if AT persists after a period of WL maintenance; and (2) if AT is associated with changes in body composition, hormones and energy intake (EI).

METHODS

Ninety-four participants [mean (SD); BMI, 31.1(4.3)kg/m2; 43.0(9.4)y; 34% female] were randomized to intervention (IG, n = 49) or control groups (CG, n = 45). Subjects underwent a 1-year lifestyle intervention, divided in 4 months of an active WL followed by 8 months of WL maintenance. Fat mass (FM) and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry and REE by indirect calorimetry. Predicted REE (pREE) was estimated through a model using FM, FFM. EI was measured by the "intake-balance" method.

RESULTS

For the IG, the weight and FM losses were - 4.8 (4.9) and - 11.3 (10.8)%, respectively (p < 0.001). A time-group interaction was found between groups for AT. After WL, the IG showed an AT of -85(29) kcal.d-1 (p < 0.001), and remained significant after 1 year [AT = - 72(31)kcal.d-1, p = 0.031]. Participants with higher degrees of restriction were those with an increased energy conservation (R = - 0.325, p = 0.036 and R = - 0.308, p = 0.047, respectively). No associations were found between diet adherence and AT. Following a sub-analysis in the IG, the group with a higher energy conservation showed a lower WL and fat loss and a higher initial EI.

CONCLUSION

AT in REE occurred after a moderate WL and remained significant after WL maintenance. More studies are needed to better clarify the mechanisms underlying the large variability observed in AT and providing an accurate methodological approach to avoid overstatements. Future studies on AT should consider not only changes in FM and FFM but also the FFM composition.

Precision nutrition in diabetes: when population-based dietary advice gets personal

Diet plays a fundamental role in maintaining long-term health, with healthful diets being endorsed by current dietary guidelines for the prevention and management of type 2 diabetes. However, the response to dietary interventions varies widely, highlighting the need for refinement and personalisation beyond population-based 'one size fits all'. This article reviews the clinical evidence supporting precision nutrition as a fundamental approach for dietary advice in diabetes. Further, it proposes a framework for the eventual implementation of precision nutrition and discusses key challenges for the application of this approach in the prevention of diabetes. One implication of this approach is that precision nutrition would not exclude the parallel goal of population-based healthy dietary advice. Nevertheless, the shift in prioritising precision nutrition is needed to reflect the dynamic nature of responses to dietary interventions that vary among individuals and change over the life course.

Metabolic adaptation after combined resistance and aerobic exercise training in older women

OBJECTIVE

This study investigated whether combined aerobic and resistance training in older women leads to metabolic adaptation.

METHODS

A total of 80 women (64 White individuals; BMI: 30.0 [4.4] kg/m² ; age: 64.8 [3.5] years) followed 32 weeks of aerobic and resistance training. Body weight/composition (dual-energy X-ray absorptiometry) and resting metabolic rate (RMR; indirect calorimetry) were measured at baseline, week 16, and week 32. Metabolic adaptation was defined as significantly lower measured versus predicted RMR. A regression model to predict metabolic adaptation was developed that included race, age, baseline fat-free mass, RMR and respiratory quotient, and changes in net submaximal oxygen consumption after different tasks.

RESULTS

There was significant metabolic adaptation at week 16 (-59 [136] kcal/d, p = 0.002), following a 640-kcal/wk energy loss (-0.7 [2.6] kg of weight loss). In 53 women with complete data, metabolic adaptation was seen both at week 16 (-64 [129] kcal/d, p = 0.001) and at week 32 (-94 [127] kcal/d, p < 0.001). Metabolic adaptation at week 16 was predicted by race, age, baseline fat-free mass, RMR and respiratory quotient, and change in net oxygen consumption of walking (R² adjusted = 0.90, p < 0.001). Similar results were seen at week 32.

CONCLUSIONS

In older women with overweight and obesity, a minimal energy deficit induced by aerobic and resistance exercise is associated with metabolic adaptation at the level of RMR.

Managing weight and glycaemic targets in people with type 2 diabetes—How far have we come?

Introduction

As the vast majority of people with type 2 diabetes (T2D) are also overweight or obese, healthcare professionals (HCP) are faced with the task of addressing both weight management and glucose control. In this narrative review, we aim to identify the challenges of reaching and maintaining body weight targets in people with T2D and highlight current and future treatment interventions.

Methods

A search of the PubMed database was conducted using the search terms “diabetes” and “weight loss.”

Results

According to emerging evidence, treating obesity may be antecedent to the development and progression of T2D. While clinical benefits typically set in upon achieving a weight loss of 3–5%, these benefits are progressive leading to further health improvements, and weight loss of >15% can have a disease‐modifying effect in people with T2D, an outcome that up to recently could not be achieved with any blood glucose‐lowering pharmacotherapy. However, advanced treatment options with weight‐loss effects currently in development including the dual GIP/GLP‐1 receptor agonists may enable simultaneous achievement of individual glycemic and weight goals.

Conclusion

Despite considerable therapeutic progress, there is still a large unmet medical need in patients with T2D who miss their individualized glycemic and weight‐loss targets. Nonetheless, it is to be expected that development of future therapies and their use will favourably change the scenario of weight and glucose control in T2D.

Adaptive thermogenesis after moderate weight loss: magnitude and methodological issues

PURPOSE

The aim of this study was (1) to assess AT through 13 different mathematical approaches and to compare their results; and (2) to understand if AT occurs after moderate WL.

METHODS

Ninety-four participants [mean (SD); BMI, 31.1 (4.3) kg/m2; age, 43.0 (9.4) years; 34% females] underwent a 1-year lifestyle intervention (clinicaltrials.gov ID: NCT03031951) and were randomized to intervention (IG, n = 49) or control groups (CG, n = 45), and all measurements were made at baseline and after 4 months. Fat mass (FM) and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry and REE by indirect calorimetry. AT was assessed through 13 different approaches, varying in how REE was predicted and/or how AT was assessed.

RESULTS

IG underwent a mean negative energy balance (EB) of 270 (289) kcal/day, p < 0.001), resulting in a WL of - 4.8 (4.9)% and an FM loss of - 11.3 (10.8)%. Regardless of approach, AT occurred in the IG, ranging from ~ - 65 to ~ - 230 kcal/day and three approaches showed significant AT in the CG.

CONCLUSIONS

Regardless of approach, AT occurred after moderate WL in the IG. AT assessment should be standardized and comparisons among studies with different methodologies to assess AT must be avoided.

Does adaptive thermogenesis occur after weight loss in adults? A systematic review

Adaptive thermogenesis (AT) has been proposed to be a compensatory response that may resist weight loss (WL) and promote weight regain. This systematic review examined the existence of AT in adults after a period of negative energy balance (EB) with or without a weight stabilisation phase. Studies published until 15 May 2020 were identified from PubMed, Cochrane Library, EMBASE, MEDLINE, SCOPUS and Web of Science. Inclusion criteria included statistically significant WL, observational with follow-up or experimental studies, age > 18y, sample size ≥10 participants, intervention period ≥ 1week, published in English, objective measures of total daily energy expenditure (EE) (TDEE), resting EE (REE) and sleeping EE(SEE). The systematic review was registered at PROSPERO (2020 CRD42020165348). A total of thirty-three studies comprising 2528 participants were included. AT was observed in twenty-seven studies. Twenty-three studies showed significant values for AT for REE (82·8 %), four for TDEE (80·0 %) and two for SEE (100 %). A large heterogeneity in the methods used to quantify AT and between subjects and among studies regarding the magnitude of WL and/or of AT was reported. Well-designed studies reported lower or non-significant values for AT. These findings suggest that although WL may lead to AT in some of the EE components, these values may be small or non-statistically significant when higher-quality methodological designs are used. Furthermore, AT seems to be attenuated, or non-existent, after periods of weight stabilisation/neutral EB. More high-quality studies are warranted not only to disclose the existence of AT but also to understand its clinical implications on weight management outcomes.

Metabolic adaptation delays time to reach weight loss goals

OBJECTIVE

The aim of this study was to determine whether metabolic adaptation, at the level of resting metabolic rate, was associated with time to reach weight loss goals, after adjusting for confounders.

METHODS

A total of 65 premenopausal women with overweight (BMI: 28.6 ± 1.5 kg/m2 ; age: 36.4 ± 5.9 years; 36 were White, and 29 were Black) followed an 800-kcal/d diet until BMI ≤25 kg/m2 . Body weight and composition were measured at baseline and after weight loss. Dietary adherence was calculated from total energy expenditure, determined by double labeled water, and body composition changes. Metabolic adaptation was defined as a significantly lower measured versus predicted resting metabolic rate (from own regression model). A regression model to predict time to reach weight loss goals was developed including target weight loss, energy deficit, dietary adherence, and metabolic adaptation as predictors.

RESULTS

Participants lost on average 12.5 ± 3.1 kg (16.1% ± 3.4%) over 155.1 ± 49.2 days. Average dietary adherence was 63.6% ± 31.0%. There was significant metabolic adaptation after weight loss (-46 ± 113 kcal/d, p = 0.002) and this variable was a significant predictor of time to reach weight loss goals (β = -0.1, p = 0.041), even after adjusting for confounders (R2 adjusted = 0.63, p < 0.001).

CONCLUSION

In premenopausal women with overweight, metabolic adaptation after a 16% weight loss increases the length of time necessary to achieve weight loss goals.

Physical Activity, Obesity and Weight Loss Maintenance

Regular physical activity has an impact on all human organ systems and mediates multiple beneficial effects on overall health. Physical activity alone is a poor strategy for weight loss; however, physical activity is of crucial importance for weight loss maintenance. The role of exercise in maintaining a stable body weight is not clear but might be related to better appetite regulation and food preference. In relation to exercise, muscle secretes myokines and other factors that can influence the metabolism in other organs, not least fat and brain tissues. Thereby, physical activity reduces the risk of obesity-associated diseases, such as type 2 diabetes and cardiovascular diseases, independently of weight loss and BMI. Therefore, physical activity should always be included in weight loss strategies and as a tool to maintain a healthy weight, despite its modest effect on energy expenditure and overall body weight.

Weight Loss Strategies

Lifestyle interventions for weight loss combine support for changing diet and physical activity with weight management education and are considered the first line treatment for obesity. A variety of diet-focused interventions including time-restricted eating are also increasingly being promoted for weight management. This chapter reviews different types of interventions for weight management, their underlying health behavior change models, and effectiveness to date in randomized trials. The results justify increasing efforts to improve program effectiveness generally, and to personalize interventions to support long-term adherence. The high prevalence of obesity worldwide, combined with the known increase in risk of non-communicable diseases with duration of excess weight, provides a compelling justification for routine delivery of effective weight management interventions in the community and in clinical care.

Association of energy availability with resting metabolic rates in competitive female teenage runners: a cross-sectional study

Background

Resting metabolic rate (RMR) has been examined as a proxy for low energy availability (EA). Previous studies have been limited to adult athletes, despite the serious health consequences of low EA, particularly during adolescence. This study aimed to explore the relationship between RMR and EA in competitive teenage girl runners.

Methods

Eighteen girl runners (mean ± standard-deviation; age, 16.8 ± 0.9 years; body mass, 45.6 ± 5.2 kg, %fat, 13.5 ± 4.2 %) in the same competitive high-school team were evaluated. Each runner was asked to report dietary records with photos and training logs for seven days. Energy intake (EI) was assessed by Registered Dietitian Nutritionists. The runners were evaluated on a treadmill with an indirect calorimeter to yield individual prediction equations for oxygen consumption using running velocity and heart rate (HR). Exercise energy expenditure (EEE) was calculated by the equations based on training logs and HR. Daily EA was calculated by subtracting EEE from EI. The daily means of these variables were calculated. RMR was measured early in the morning by whole-room calorimetry after overnight sleep on concluding the final day of the seven-day assessment. The ratio of measured RMR to predicted RMR (RMR ratio) was calculated by race, age, sex-specific formulae, and Cunningham’s equation. Body composition was measured using dual-energy X-ray absorptiometry. Bivariate correlation analyses were used to examine the relationship between variables.

Results

RMR, EI, EEE, and EA were 26.9 ± 2.4, 56.8 ± 15.2, 21.7 ± 5.9, and 35.0 ± 15.0 kcal⋅kg⁻¹ FFM⋅d⁻¹, respectively. RMR reduced linearly with statistical significance, while EA decreased to a threshold level (30 kcal⋅kg⁻¹ FFM⋅d⁻¹) (r= 0.58, p= 0.048). Further reduction in RMR was not observed when EA fell below the threshold. There was no significant correlation between RMR ratios and EA, irrespective of the prediction formulae used.

Conclusions

These results suggest that RMR does not reduce with a decrease in EA among highly competitive and lean teenage girl runners. RMR remains disproportionally higher than expected in low EA states. Free-living teenage girl runners with low EA should be cautiously identified using RMR as a proxy for EA change.

Does Modern Lifestyle Favor Neuroimmunometabolic Changes? A Path to Obesity

Factors linked to modern lifestyles, such as physical inactivity, Western diet, and poor sleep quality have been identified as key contributors to the positive energy balance (PEB). PEB rises adipose tissue hypertrophy and dysfunction over the years, affecting cells and tissues that are metabolically critical for energy homeostasis regulation, especially skeletal muscle, hypothalamic-pituitary-adrenal axis, and gut microbiota. It is known that the interaction among lifestyle factors and tissue metabolic dysfunction increases low-grade chronic systemic inflammation, leading to insulin resistance and other adverse metabolic disorders. Although immunometabolic mechanisms are widely discussed in obesity, neuroimmunoendocrine pathways have gained notoriety, as a link to neuroinflammation and central nervous system disorders. Hypothalamic inflammation has been associated with food intake dysregulation, which comprises homeostatic and non-homeostatic mechanisms, promoting eating behavior changes related to the obesity prevalence. The purpose of this review is to provide an updated and integrated perspective on the effects of Western diet, sleep debt, and physical exercise on the regulation of energy homeostasis and low-grade chronic systemic inflammation. Subsequently, we discuss the intersection between systemic inflammation and neuroinflammation and how it can contribute to energy imbalance, favoring obesity. Finally, we propose a model of interactions between systemic inflammation and neuroinflammation, providing new insights into preventive and therapeutic targets for obesity.

Dynamic changes in energy expenditure in response to underfeeding: a review

The observation that 64% of English adults are overweight or obese despite a rising prevalence in weight-loss attempts suggests our understanding of energy balance is fundamentally flawed. Weight-loss is induced through a negative energy balance; however, we typically view weight change as a static function, in that energy intake and energy expenditure are independent variables, resulting in a fixed rate of weight-loss assuming a constant energy deficit. Such static modelling provides the basis for the clinical assumption that a 14644 kJ (3500 kcal) deficit translates to a 1 lb weight-loss. However, this '3500 kcal (14644 kJ) rule' is consistently shown to significantly overestimate weight-loss. Static modelling disregards obligatory changes in energy expenditure associated with the loss of metabolically active tissue, i.e. skeletal muscle. Additionally, it disregards the presence of adaptive thermogenesis, the underfeeding-associated fall in resting energy expenditure beyond that caused by loss of fat-free mass. This metabolic manipulation of energy expenditure is observed from the onset of energy restriction to maintain weight at a genetically pre-determined set point. As a result, the observed magnitude of weight-loss is disproportionally less, followed by earlier weight plateau, despite strict compliance to a dietary intervention. By simulating dynamic changes in energy expenditure associated with underfeeding, mathematical modelling may provide a more accurate method of weight-loss prediction. However, accuracy at an individual level is limited due to difficulty estimating energy requirements, physical activity and dietary intake in free-living individuals. In the present paper, we aim to outline the contribution of dynamic changes in energy expenditure to weight-loss resistance and weight plateau.

Are metabolic adaptations to weight changes an artefact?

BACKGROUND

Adaptive thermogenesis (AT) is currently defined as the fat-free mass (FFM)-independent change in resting energy expenditure (REE) in response to caloric restriction (CR) or overfeeding (OF). So far, the impact of changes in the anatomical and molecular composition of FFM on AT has not been addressed.

OBJECTIVES

To assess the impact of changes in FFM composition on AT.

METHODS

FFM was assessed in 32 healthy young men during controlled 21-d CR and 14 d of subsequent OF. Anatomical (i.e., the organ/tissue level) and molecular (i.e., water, mineral, and protein content and thus body density) composition of FFM were characterized. REE was measured by indirect calorimetry.

RESULTS

With CR, body weight and REE decreased by 4.2 ± 0.9 kg and 173 ± 107 kcal/d, respectively, with corresponding increases of 3.5 ± 1.2 kg and 194 ± 110 kcal/d during OF (P < 0.001 for all changes). Changes in FFM explained 56.7% and 66.7% of weight loss and weight gain, respectively. Weight changes were associated with changes in various anatomical (i.e., masses of skeletal muscle, liver, kidneys, and brain) and molecular components (total body water, protein, and bone minerals) of FFM. After adjustments for changes in FFM only, AT was 116 ± 127 (P < 0.001) and 27 ± 115 kcal/d (NS) with CR and OF, respectively. Adjustments for FFM and its anatomical and molecular composition reduced AT in response to CR to 83 ± 116 and 122 ± 123 kcal/d (P < 0.05 and P < 0.001) whereas during OF, AT became significant at 87 ± 146 kcal/d (anatomical; P < 0.05) and 86 ± 118 kcal/d (molecular; P < 0.001).

CONCLUSIONS

Adjusting changes in REE with under- and overfeeding for the corresponding changes in the anatomical and molecular composition of FFM decreased AT after CR and increased AT after OF, but overall adjusted AT was likely not large enough in magnitude to be able to prevent weight loss or resist weight gain.

Weight and body composition changes affect resting energy expenditure predictive equations during a 12-month weight-loss intervention

OBJECTIVE

Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight-loss interventions. Although such equations are known to introduce group- and individual-level error into REE prediction, their validity has largely been assessed in weight-stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12-month weight-loss intervention.

METHODS

Changes in predictive error of four models (Mifflin-St-Jeor, Harris-Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1-, 6-, and 12-month time points in adults (n = 66, 76% female, aged 18-55 years, BMI = 27-45 kg/m2 ) enrolled in a randomized clinical weight-loss trial.

RESULTS

All equations experienced significant negative shifts in bias (measured - predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat-free mass (p ≤ 0.01).

CONCLUSIONS

Changes in body composition and mass during a 12-month weight-loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.

Association between ketosis and metabolic adaptation at the level of resting metabolic rate

BACKGROUND

The ketone body β-hydroxybutyrate (βHB) has been shown to act as a signaling molecule that regulates metabolism and energy homeostasis during starvation in animal models. A potential association between βHB and metabolic adaptation (a reduction in energy expenditure below predicted levels) in humans has never been explored.

OBJECTIVE

To determine if metabolic adaptation at the level of resting metabolic rate (RMR) was associated with the magnitude of ketosis induced by a very-low energy diet (VLED). A secondary aim was to investigate if the association was modulated by sex.

METHODS

Sixty-four individuals with obesity (BMI: 34.5 ± 3.4 kg/m²; age: 45.7 ± 8.0 years; 31 males) enrolled in a 1000 kcal/day diet for 8 weeks. Body weight/composition, RMR and βHB (as a measure of ketosis) were determined at baseline and week 9 (W9). Metabolic adaptation was defined as a significantly lower measured versus predicted RMR (from own regression model).

RESULTS

Participants lost on average 14.0 ± 3.9 kg and were ketotic (βHB: 0.76 ± 0.51 mM) at W9. A significant metabolic adaptation was seen (-84 ± 106 kcal/day, P < 0.001), with no significant differences between sexes. [βHB] was positively correlated with the magnitude of metabolic adaptation in females (r = 0.432, P = 0.012, n = 33), but not in males (r = 0.089, P = 0.634, n = 31).

CONCLUSION

In females with obesity, but not males, the larger the [βHB] under VLED, the greater the metabolic adaptation at the level of RMR. More studies are needed to confirm these findings and to explore the mechanisms behind the sex difference in the association between ketosis and metabolic adaptation.

TRIAL REGISTRATION NAME

Clinicaltrials.gov.

STUDY REGISTRATION ID

NCT02944253. URL: https://clinicaltrials.gov/ct2/show/NCT02944253.

Metabolic adaptation is associated with less weight and fat mass loss in response to low-energy diets

BACKGROUND

The practical relevance of metabolic adaptation remains a controversial issue. To the best of our knowledge, no study has properly evaluated the role of metabolic adaptation in modulating weight loss outcomes. Therefore, the aim of this study was to determine the association between metabolic adaptation, at the level of resting metabolic rate (RMR), and weight and fat mass (FM) loss after low-energy diets (LED), after adjusting for dietary adherence and other confounders.

METHODS

71 individuals with obesity (BMI: 34.6 ± 3.4 kg/m2; age: 45.4 ± 8.2 years; 33 males) were randomized to one of three 1000 kcal/day diets for 8 weeks. Body weight, FM and fat-free mass (FFM) (air displacement plethysmography), RMR (indirect calorimetry) and physical activity level (PAL) (armbands) were measured at baseline and at week 9. Metabolic adaptation at week 9 was defined as measured RMR minus predicted RMR at week 9. An equation to predict RMR was derived from baseline data of all participants that were part of this analysis and included age, sex, FM and FFM as predictors. Dietary adherence was calculated from RMR, PAL and body composition changes. Linear regression was used to assess the potential role of metabolic adaptation in predicting weight and FM loss after adjusting for dietary adherence, average PAL, sex, baseline FM and FFM and randomization group.

RESULTS

Participants lost on average 14 ± 4 kg of body weight (13 ± 3%) and presented with metabolic adaptation (-92 ± 110 kcal/day, P < 0.001). Metabolic adaptation was a significant predictor of both weight (β = -0.009, P < 0.001) and FM loss (β = -0.008, P < 0.001), even after adjusting for confounders (R2 = 0.88, 0.93, respectively, P < 0.001 for both). On average, an increase in metabolic adaptation of 50 kcal/day was associated with a 0.5 kg lower weight and FM loss in response to the LED.

CONCLUSION

In individuals with obesity, metabolic adaptation at the level of RMR is associated with less weight and FM loss in response to LED. Trial registration ID: NCT02944253.

Weight Regain and Breast Cancer-Related Biomarkers Following an Exercise Intervention in Postmenopausal Women

BACKGROUND

Epidemiologic studies have reported associations between weight fluctuations and postmenopausal breast cancer risk; however, the biological markers involved in this association are unknown. This study aimed to explore the associations between breast cancer-related biomarkers and weight regain following exercise-induced weight loss.

METHODS

From the 400 participants included in the Breast Cancer and Exercise Trial in Alberta, a total of 214 lost weight during the intervention and had follow-up blood samples, body composition, and covariate measurements. Outcomes were measured at baseline, 12 months (end of the study), and 24 months (follow-up).

RESULTS

During follow-up, weight regain was 1.80 kg [95% confidence interval (CI): -0.40-3.90], and was significantly associated with increases in estradiol [treatment effect ratio (TER) = 1.03; 95% CI, 1.01-1.04], estrone (TER = 1.02; 95% CI, 1.01-1.03), free estradiol (TER = 1.04; 95% CI, 1.02-1.05), the homeostatic model assessment for insulin resistance (TER = 1.03; 95% CI, 1.02-1.05), and insulin (TER = 1.03; 95% CI, 1.01-1.04), and decreases in sex hormone-binding globulin (SHBG; TER = 0.98; 95% CI, 0.97-0.99) levels. Nonstatistically significant associations were found for glucose and C-reactive protein. Furthermore, a statistically significant linear trend of increasing levels for all biomarkers, and decreasing SHBG, across weight regain categories was found.

CONCLUSIONS

These results suggest that weight regain following exercise-induced weight loss is associated with breast cancer-related biomarker changes in postmenopausal women.

IMPACT

These findings provide evidence to support the importance of developing effective strategies to prevent weight regain and, consequently, decrease postmenopausal breast cancer risk via changes in adiposity-related biomarkers.

Metabolic Adaptations to Weight Loss: A Brief Review

ABSTRACT

Martínez-Gómez, MG and Roberts, BM. Metabolic adaptations to weight loss: A brief review. J Strength Cond Res XX(X): 000-000, 2021-As the scientific literature has continuously shown, body mass loss attempts do not always follow a linear fashion nor always go as expected even when the intervention is calculated with precise tools. One of the main reasons why this tends to happen relies on our body's biological drive to regain the body mass we lose to survive. This phenomenon has been referred to as "metabolic adaptation" many times in the literature and plays a very relevant role in the management of obesity and human weight loss. This review will provide insights into some of the theoretical models for the etiology of metabolic adaptation as well as a quick look into the physiological and endocrine mechanisms that underlie it. Nutritional strategies and dietetic tools are thus necessary to confront these so-called adaptations to body mass loss. Among some of these strategies, we can highlight increasing protein needs, opting for high-fiber foods or programming-controlled diet refeeds, and diet breaks over a large body mass loss phase. Outside the nutritional aspects, it might be wise to increase the physical activity and thus the energy flux of an individual when possible to maintain diet-induced body mass loss in the long term. This review will examine these protocols and their viability in the context of adherence and sustainability for the individual toward successful body mass loss.

Resting Energy Expenditure: From Cellular to Whole-Body Level, a Mechanistic Historical Perspective

The basis of heat generated by the human body has been a source of speculation and research for more than 2,000 years. Basal heat production, now usually referred to as resting energy expenditure (REE), is currently recognized as deriving from biochemical reactions at subcellular and cellular levels that are expressed in the energy expended by the body's 78 organs and tissues. These organs and tissues, and the 11 systems to which they belong, influence body size and shape. Connecting these subcellular-/cellular-level reactions to organs and tissues, and then on to body size and shape, provides a comprehensive understanding of individual differences in REE, a contemporary topic of interest in obesity research and clinical practice. This review critically examines these linkages, their association with widely used statistical and physiological REE prediction formulas, and often-unappreciated aspects of measuring basal heat production in humans.

Body Composition Changes after a Weight Loss Intervention: A 3-Year Follow-Up Study

Studies comparing different types of exercise-based interventions have not shown a consistent effect of training on long-term weight maintenance. The aim of this study was to compare the effects of exercise modalities combined with diet intervention on body composition immediately after intervention and at 3 years’ follow-up in overweight and obese adults. Two-hundred thirty-nine people (107 men) participated in a 6-month diet and exercise-based intervention, split into four randomly assigned groups: strength group (S), endurance group (E), combined strength and endurance group (SE), and control group (C). The body composition measurements took place on the first week before the start of training and after 22 weeks of training. In addition, a third measurement took place 3 years after the intervention period. A significant interaction effect (group × time) (p = 0.017) was observed for the fat mass percentage. It significantly decreased by 5.48 ± 0.65%, 5.30 ± 0.65%, 7.04 ± 0.72%, and 4.86 ± 0.65% at post-intervention for S, E, SE, and C, respectively. Three years after the intervention, the fat mass percentage returned to values similar to the baseline, except for the combined strength and endurance group, where it remained lower than the value at pre-intervention (p < 0.05). However, no significant interaction was discovered for the rest of the studied outcomes, neither at post-intervention nor 3 years later. The combined strength and endurance group was the only group that achieved lower levels of fat mass (%) at both post-intervention and 3 years after intervention, in comparison with the other groups.

Approach to the Patient: Management of the Post-Bariatric Surgery Patient With Weight Regain

CONTEXT

Weight regain (WR) after bariatric surgery is emerging as a common clinical problem due to the increase in the number of procedures performed. Early interventions are necessary to curtail the potential recurrence of comorbid conditions. However, it is often difficult to recognize WR early enough to introduce mitigating measures because there are no current guidelines for timely diagnosis and assessment of the severity of this condition.

OBJECTIVE

We present a practical approach for the early recognition of WR, based on 11-year follow-up data from our multiethnic bariatric surgery patient population.

METHODS

We classify WR according to the rate of increase in weight relative to nadir weight, normalized per 30-day interval. We also review pertinent literature about the etiologic factors contributing to WR after bariatric surgery.

RESULTS

According to our algorithm, mild, moderate, and rapid WR are defined as weight increases of 0.2% to <0.5%, 0.5% to 1.0%, and more than 1.0% of nadir weight per 30 days, respectively. Treatment options, including dietary counseling, use of antiobesity medication, and consideration of surgical revision, are described. A case is presented to illustrate the utility of timely identification of WR and the importance of collaboration between bariatric surgeons, obesity medicine specialists, and dietitians.

CONCLUSION

Our approach emphasizes the importance of regular long-term follow-up for all bariatric surgery patients.

Revisiting the Compensatory Theory as an explanatory model for relapse in obesity management

Weight regain remains the main challenge in obesity management, and its etiology remains elusive. The aim of the present review was to revise the available evidence regarding the "Compensatory Theory," which is an explanatory model of relapse in obesity treatment, and to propose alternative mechanisms that can contribute to weight regain. It has been proposed, and generally accepted as true, that when a person loses weight the body fights back, with physiological adaptations on both sides of the energy balance equation that try to bring body weight back to its original state: this is the Compensatory Theory. This theory proposes that the increased orexigenic drive to eat and the reduced energy expenditure that follow weight loss are the main drivers of relapse. However, evidence showing a link between these physiological adaptations to weight loss and weight regain is lacking. Here, we propose that the physiological adaptations to weight loss, both at the level of the homeostatic appetite control system and energy expenditure, are in fact a normalization to a lower body weight and not drivers of weight regain. In light of this we explore other potential mechanisms, both physiological and behavioral, that can contribute to the high incidence of relapse in obesity management. More research is needed to clearly ascertain whether the changes in energy expenditure and homeostatic appetite markers seen in reduced-obese individuals are a compensatory mechanism that drives relapse or a normalization towards a lower body weight, and to explore alternative hypotheses that explain relapse in obesity management.