Validation of an inexpensive and accurate mathematical method to measure long-term changes in free-living energy intake

Impact of estrogens on resting energy expenditure: A systematic review

The fear of weight gain is one of the main reasons for women not to initiate or to early discontinue hormonal contraception or menopausal hormone therapy. Resting energy expenditure is by far the largest component and the most important determinant of total energy expenditure. Given that low resting energy expenditure is a confirmed predictive factor for weight gain and consecutively for the development of obesity, research into the influence of sex steroids on resting energy expenditure is a particularly exciting area. The objective of this systematic review was to evaluate the effects of medication with natural and synthetic estrogens on resting energy expenditure in healthy normal weight and overweight women. Through complex systematic literature searches, a total of 10 studies were identified that investigated the effects of medication with estrogens on resting energy expenditure. Our results demonstrate that estrogen administration increases resting energy expenditure by up to +208 kcal per day in the context of contraception and by up to +222 kcal per day in the context of menopausal hormone therapy, suggesting a preventive effect of circulating estrogen levels and estrogen administration on weight gain and obesity development.

Total energy expenditure as assessed by doubly labeled water and all-cause mortality in a cohort of postmenopausal women

BACKGROUND

The association of TEE with all-cause mortality is uncertain, as is the dependence of this association on age.

OBJECTIVES

To examine the association between TEE and all-cause mortality, and its age interaction, in a Women's Health Initiative (WHI) cohort of postmenopausal United States women (1992-present).

METHODS

A cohort of 1131 WHI participants having DLW TEE assessment of ∼10.0 y (median) following WHI enrollment with ∼13.7 y (median) of subsequent follow-up, was used to study the EE associations with all-cause mortality. To enhance the comparability of TEE and total EI, key analyses excluded participants having >5% weight change between WHI enrollment and DLW assessment. The influence of participant age on mortality associations was examined, as was the ability of concurrent and earlier weight and height measurements to explain the results.

RESULTS

There were 308 deaths following the TEE assessment through 2021. TEE was unrelated to overall mortality (P = 0.83) in this cohort of generally healthy, older (mean 71 y at TEE assessment) United States women. However, this potential association varied with age (P = 0.003). Higher TEE was associated with a higher mortality rate at the age of 60 y and a lower mortality rate at the age of 80 y. Within the weight-stable subset (532 participants, 129 deaths), TEE was weakly positively related to overall mortality (P = 0.08). This association also varied with age (P = 0.03), with mortality HRs (95% CIs) for a 20% increment in TEE of 2.33 (1.24, 4.36) at the age of 60 y, 1.49 (1.10, 2.02) at 70 y of age, and 0.96 (0.66, 1.38) at 80 y of age. This pattern remained, although was somewhat attenuated, following control for baseline weight and weight changes between WHI enrollment and TEE assessment.

CONCLUSIONS

Higher EE is associated with higher all-cause mortality among younger postmenopausal women, only partially explained by weight and weight change. This study is registered with clinicaltrials.gov identifier: NCT00000611.

Brown Adipose Tissue-A Translational Perspective

Brown adipose tissue (BAT) displays the unique capacity to generate heat through uncoupled oxidative phosphorylation that makes it a very attractive therapeutic target for cardiometabolic diseases. Here, we review BAT cellular metabolism, its regulation by the central nervous and endocrine systems and circulating metabolites, the plausible roles of this tissue in human thermoregulation, energy balance, and cardiometabolic disorders, and the current knowledge on its pharmacological stimulation in humans. The current definition and measurement of BAT in human studies relies almost exclusively on BAT glucose uptake from positron emission tomography with 18F-fluorodeoxiglucose, which can be dissociated from BAT thermogenic activity, as for example in insulin-resistant states. The most important energy substrate for BAT thermogenesis is its intracellular fatty acid content mobilized from sympathetic stimulation of intracellular triglyceride lipolysis. This lipolytic BAT response is intertwined with that of white adipose (WAT) and other metabolic tissues, and cannot be independently stimulated with the drugs tested thus far. BAT is an interesting and biologically plausible target that has yet to be fully and selectively activated to increase the body's thermogenic response and shift energy balance. The field of human BAT research is in need of methods able to directly, specifically, and reliably measure BAT thermogenic capacity while also tracking the related thermogenic responses in WAT and other tissues. Until this is achieved, uncertainty will remain about the role played by this fascinating tissue in human cardiometabolic diseases.

Glucagon-like peptide-1/glucagon receptor agonism associates with reduced metabolic adaptation and higher fat oxidation: A randomized trial

OBJECTIVE

This study tested the hypothesis that treatment with the glucagon-like peptide-1/glucagon receptor agonist SAR425899 would lead to a smaller decrease in sleeping metabolic rate (SMR; kilocalories/day) than expected from the loss of lean and fat mass (metabolic adaptation).

METHODS

This Phase 1b, double-blind, randomized, placebo-controlled study was conducted at two centers in inpatient metabolic wards. Thirty-five healthy males and females with overweight and obesity (age = 36.5 ± 7.1 years) were randomized to a calorie-reduced diet (-1000 kcal/d) and escalating doses (0.06-0.2 mg/d) of SAR425899 (n = 17) or placebo (n = 18) for 19 days. SMR was measured by whole-room calorimetry.

RESULTS

Both groups lost weight (-3.68 ± 1.37 kg placebo; -4.83 ± 1.44 kg SAR425899). Those treated with SAR425899 lost more weight, fat mass, and fat free mass (p < 0.05) owing to a greater achieved energy deficit than planned. The SAR425899 group had a smaller reduction in body composition-adjusted SMR (p = 0.002) as compared with placebo, but not 24-hour energy expenditure. Fat oxidation and ketogenesis increased in both groups, with significantly greater increases with SAR425899 (p < 0.05).

CONCLUSIONS

SAR425899 led to reduced selective metabolic adaptation and increased lipid oxidation, which are believed to be beneficial for weight loss and weight-loss maintenance.

Do eating behavior traits predict energy intake and body mass index? A systematic review and meta-analysis

At present, it is unclear whether eating behavior traits (EBT) predict objectively measured short-term energy intake (EI) and longer-term energy balance as estimated by body mass index (BMI). This systematic review examined the impact of EBT on BMI and laboratory-based measures of EI in adults ( ≥ 18 years) in any BMI category, excluding self-report measures of EI. Articles were searched up until 28th October 2021 using MEDLINE, PsycINFO, EMBASE and Web of Science. Sixteen EBT were identified and the association between 10 EBT, EI and BMI were assessed using a random-effects meta-analysis. Other EBT outcomes were synthesized qualitatively. Risk of bias was assessed with the mixed methods appraisal tool. A total of 83 studies were included (mean BMI = 25.20 kg/m² , mean age = 27 years and mean sample size = 70). Study quality was rated moderately high overall, with some concerns in sampling strategy and statistical analyses. Susceptibility to hunger (n = 6) and binge eating (n = 7) were the strongest predictors of EI. Disinhibition (n = 8) was the strongest predictor of BMI. Overall, EBT may be useful as phenotypic markers of susceptibility to overconsume or develop obesity (PROSPERO: CRD42021288694).

Predicting energy intake in adults who are dieting and exercising

BACKGROUND

When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question.

OBJECTIVE

The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE.

DESIGN

The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE.

RESULTS

Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was -84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was -155.70 ± 205.70 kcal/d.

CONCLUSION

The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

Immune and non-immune functions of adipose tissue leukocytes

Adipose tissue is a complex dynamic organ with whole-body immunometabolic influence. Much of the work into understanding the role of immune cells in adipose tissue has been in the context of obesity. These investigations have also uncovered a range of typical (immune) and non-typical functions exerted by adipose tissue leukocytes. Here we provide an overview of the adipose tissue immune system, including its role as an immune reservoir in the whole-body response to infection and as a site of parasitic and viral infections. We also describe the functional roles of specialized immunological structures found within adipose tissue. However, our main focus is on the recently discovered 'non-immune' functions of adipose tissue immune cells, which include the regulation of adipocyte homeostasis, as well as responses to changing nutrient status and body temperature. In doing so, we outline the therapeutic potential of the adipose tissue immune system in health and disease.

Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults

BACKGROUND

The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI.

OBJECTIVES

The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population.

METHODS

Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error.

RESULTS

There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing.

CONCLUSIONS

Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

"I didn't want to do it on my own": A qualitative study of women's perceptions of facilitating and risk factors for weight control on a UK commercial community program

Overweight and obesity remain serious public health concerns. Outcomes from community based commercial weight management programmes vary, relapse is common and drop out is high. Outcomes could be improved by better understanding experiences on these programmes. The aim of our study was to generate accounts of people's experience on a commercial weight-management program to identify what experiences were perceived as facilitating, and what posed risks, to programme effectiveness and compliance. We conducted individual, semi-structured interviews with eighteen Caucasian women (mean age 45.4y) who were members of nation-wide UK commercial, fee-paying, community weight management programme. Interview data was analysed via framework analysis. Participants' experiences indicated that the programme helped by triggering several intra- and interpersonal processes that catalysed change across psychological, physiological, dietary and behavioural areas of their life. Risks to program adherence and effectiveness spanned well-known risks such as self-regulation fatigue and the difficulty of recovering from negative self-criticism, as well as new factors such as the confusing nature of weight change, the relatively powerful impact of everyday events, and the difficulty in getting the balance right between personalised support vs. intrusion. The complexity of reported experiences challenges the linear, predictive pathways of change proposed by many health behaviour models of weight management. To improve effectiveness, programmes need to go well beyond behavioural and dietary support. It is recommended that community, commercial programmes educate people about the physiological and psychological tensions they will encounter, why people lose weight at different rates, the likelihood of weight relapse and strategies to manage these, including evidence-based support for managing self-criticism.

Effects of caloric restriction on human physiological, psychological, and behavioral outcomes: highlights from CALERIE phase 2

Caloric restriction (CR) is a strategy that attenuates aging in multiple nonhuman species. The Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) trials are part of a research program aiming to test the effects of CR on aging and longevity biomarkers in humans. Building on CALERIE phase 1, CALERIE phase 2 (CALERIE 2) was the largest study to date to assess sustained CR in healthy humans without obesity. In a 24-month randomized controlled trial comprising 218 participants at baseline, CALERIE 2 showed that moderate CR, 11.9% on average, induced improvements in aging-related biomarkers without adversely affecting psychological or behavioral outcomes. The objectives of this report are to summarize and review the highlights of CALERIE 2 and report previously unpublished results on eating disorder symptoms and cognitive function. This article specifically summarizes the physiological, psychological, aging, behavioral, and safety results of the trial. Also provided are research directions beyond CALERIE 2 that highlight important opportunities to investigate the role of CR in aging, longevity, and health span in humans.

Evidence-Based Digital Tools for Weight Loss Maintenance: The NoHoW Project

BACKGROUND

Effective interventions and commercial programmes for weight loss (WL) are widely available, but most people regain weight. Few effective WL maintenance (WLM) solutions exist. The most promising evidence-based behaviour change techniques for WLM are self-monitoring, goal setting, action planning and control, building self-efficacy, and techniques that promote autonomous motivation (e.g., provide choice). Stress management and emotion regulation techniques show potential for prevention of relapse and weight regain. Digital technologies (including networked-wireless tracking technologies, online tools and smartphone apps, multimedia resources, and internet-based support) offer attractive tools for teaching and supporting long-term behaviour change techniques. However, many digital offerings for weight management tend not to include evidence-based content and the evidence base is still limited. The Project: First, the project examined why, when, and how many European citizens make WL and WLM attempts and how successful they are. Second, the project employed the most up-to-date behavioural science research to develop a digital toolkit for WLM based on 2 key conditions, i.e., self-management (self-regulation and motivation) of behaviour and self-management of emotional responses for WLM. Then, the NoHoW trial tested the efficacy of this digital toolkit in adults who achieved clinically significant (≥5%) WL in the previous 12 months (initial BMI ≥25). The primary outcome was change in weight (kg) at 12 months from baseline. Secondary outcomes included biological, psychological, and behavioural moderators and mediators of long-term energy balance (EB) behaviours, and user experience, acceptability, and cost-effectiveness.

IMPACT

The project will directly feed results from studies on European consumer behaviour, design and evaluation of digital toolkits self-management of EB behaviours into development of new products and services for WLM and digital health. The project has developed a framework and digital architecture for interventions in the context of EB tracking and will generate results that will help inform the next generation of personalised interventions for effective self-management of weight and health.

An objective measure of energy intake using the principle of energy balance

BACKGROUND

The measurement of energy intake is central to the understanding of energy balance and predicting changes in body weight. Until recently, the most commonly used methods of assessing intake were self-reported diet recalls, diet diaries, or food-frequency questionnaires. These methods, however, are subject to systematic biases and are often inaccurate.

AIM

Review the validations and applications of an expenditure/balance method for measuring energy intake.

METHODS

Review the literature regarding the theory and practice of objectively measuring energy intake based on the principle of energy balance i.e., energy intake is calculated from the measured total energy expenditure plus the change in body energy stores (ES). The attainable precision is modeled and compared with the accuracy and precision of validations against known energy intake.

RESULTS

Measurement of energy intake, calculated in this way, is accurate to within 2% and has a precision of 4-37% depending on the expenditure and body composition methods used and the time interval between measures. Applications of this expenditure/balance (EB) method have provided novel data on the compliance to dietary restriction and its association with physical activity interventions, and the effects of bariatric surgery on energy intake and weight gain. Practical limitations to this method, however, include cost and limited access to the analyses required by the DLW method.

CONCLUSION

The EB method of objectively measuring energy intake is objective, accurate, and reasonably precise. It is practical for moderate-sized studies.

From famine to therapeutic weight loss: Hunger, psychological responses, and energy balance-related behaviors

Understanding physiological and behavioral responses to energy imbalances is important for the management of overweight/obesity and undernutrition. Changes in body composition and physiological functions associated with energy imbalances provide the structural and functional context in which to consider psychological and behavioral responses. Compensatory changes in physiology and behavior are more pronounced in response to negative than positive energy balances. The physiological and psychological impact of weight loss (WL) occur on a continuum determined by (i) the degree of energy deficit (ED), (ii) its duration, (iii) body composition at the onset of the energy deficit, and (iv) the psychosocial environment in which it occurs. Therapeutic WL and famine/semistarvation both involve prolonged EDs, which are sometimes similar in magnitude. The key differences are that (i) the body mass index (BMI) of most famine victims is lower at the onset of the ED, (ii) therapeutic WL is intentional and (iii) famines are typically longer in duration (partly due to the voluntary nature of therapeutic WL and disengagement with WL interventions). The changes in psychological outcomes, motivation to eat, and energy intake in therapeutic WL are often modest (bearing in mind the nature of the measures used) and can be difficult to detect but are quantitatively significant over time. As WL progresses, these changes become more marked. It appears that extensive WL beyond 10%-20% in lean individuals has profound effects on body composition and physiological function. At this level of WL, there is a marked erosion of psychological functioning, which appears to run in parallel to WL. Psychological resources dwindle and become increasingly focused on alleviating escalating hunger and food seeking behavior. Functional changes in fat-free mass, characterized by catabolism of skeletal muscle and organs may be involved in the drive to eat associated with semistarvation. Higher levels of body fat mass may act as a buffer to protect fat-free mass, functional integrity and limit compensatory changes in energy balance behaviors. The increase in appetite that accompanies therapeutic WL appears to be very different to the intense and all-consuming drive to eat that occurs during prolonged semistarvation. The mechanisms may also differ but are not well understood, and longitudinal comparisons of the relationship between body structure, function, and behavior in response to differing EDs in those with higher and lower BMIs are currently lacking.

Comparison and calibration of 24-hour physical activity recall in adult population

AbstractThis study compared and calibrated metabolic equivalents (METs) per day from 24-hour physical activity recall (24hPAR) with accelerometry. A sub-sample of 74 adults of both sexes, residents of Brasília, Brazil, from a larger study had same day measurements of accelerometry and 24hPAR data. METs values were assessed by accelerometry (7 consecutive days of use) and by 24hPAR (minimum of one and maximum of 2 per person). A script was written in the R statistical software to analyse the recall and accelerometer data. The script ran a simple linear regression to visualize the relationship between total METs/day for the two methods and to execute the recall measurement error correction. Most of participants were female (54.1%), with at least university graduate (94.6%) and mean age of 34.8 years (±11.83). The correlation coefficient obtained between 24hPAR and accelerometer was r = 0.55, considered moderate and significant (p < 0.001). A majority of the participants (77%) underestimated METs values compared to accelerometry when answering the questionnaire. Calibration of 24hPAR allowed us to approximate MET values to the accelerometer. The calibration equation to correct total METs/day for measurement error is (total 24hPAR METs/day - 10.6)/0.619. The 24hPAR is a decent tool to assess PA level in large adults' samples. However, compared with accelerometer, it underestimates METs values, which can be corrected with the use of the calibration equation provided in this study.

Dietary interventions for obesity: clinical and mechanistic findings

Dietary modification is central to obesity treatment. Weight loss diets are available that include various permutations of energy restriction, macronutrients, foods, and dietary intake patterns. Caloric restriction is the common pathway for weight reduction, but different diets may induce weight loss by varied additional mechanisms, including by facilitating dietary adherence. This narrative Review of meta-analyses and select clinical trials found that lower-calorie diets, compared with higher-calorie regimens, reliably induced larger short-term (<6 months) weight losses, with deterioration of this benefit over the long term (>12 months). Few significant long-term differences in weight loss were observed for diets of varying macronutrient composition, although some regimens were found to have short-term advantages (e.g., low carbohydrate versus low fat). Progress in improving dietary adherence, which is critical to both short- and long-term weight loss, could result from greater efforts to identify behavioral and metabolic phenotypes among dieters.

The H2020 "NoHoW Project": A Position Statement on Behavioural Approaches to Longer-Term Weight Management

There is substantial evidence documenting the effects of behavioural interventions on weight loss (WL). However, behavioural approaches to initial WL are followed by some degree of longer-term weight regain, and large trials focusing on evidence-based approaches to weight loss maintenance (WLM) have generally only demonstrated small beneficial effects. The current state-of-the-art in behavioural interventions for WL and WLM raises questions of (i) how we define the relationship between WL and WLM, (ii) how energy balance (EB) systems respond to WL and influence behaviours that primarily drive weight regain, (iii) how intervention content, mode of delivery and intensity should be targeted to keep weight off, (iv) which mechanisms of action in complex interventions may prevent weight regain and (v) how to design studies and interventions to maximise effective longer-term weight management. In considering these issues a writing team within the NoHoW Consortium was convened to elaborate a position statement, and behaviour change and obesity experts were invited to discuss these positions and to refine them. At present the evidence suggests that developing the skills to self-manage EB behaviours leads to more effective WLM. However, the effects of behaviour change interventions for WL and WLM are still relatively modest and our understanding of the factors that disrupt and undermine self-management of eating and physical activity is limited. These factors include physiological resistance to weight loss, gradual compensatory changes in eating and physical activity and reactive processes related to stress, emotions, rewards and desires that meet psychological needs. Better matching of evidence-based intervention content to quantitatively tracked EB behaviours and the specific needs of individuals may improve outcomes. Improving objective longitudinal tracking of energy intake and energy expenditure over time would provide a quantitative framework in which to understand the dynamics of behaviour change, mechanisms of action of behaviour change interventions and user engagement with intervention components to potentially improve weight management intervention design and evaluation.

Effects of Time-Restricted Eating on Weight Loss and Other Metabolic Parameters in Women and Men With Overweight and Obesity: The TREAT Randomized Clinical Trial

IMPORTANCE

The efficacy and safety of time-restricted eating have not been explored in large randomized clinical trials.

OBJECTIVE

To determine the effect of 16:8-hour time-restricted eating on weight loss and metabolic risk markers.

INTERVENTIONS

Participants were randomized such that the consistent meal timing (CMT) group was instructed to eat 3 structured meals per day, and the time-restricted eating (TRE) group was instructed to eat ad libitum from 12:00 pm until 8:00 pm and completely abstain from caloric intake from 8:00 pm until 12:00 pm the following day.

DESIGN, SETTING, AND PARTICIPANTS

This 12-week randomized clinical trial including men and women aged 18 to 64 years with a body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) of 27 to 43 was conducted on a custom mobile study application. Participants received a Bluetooth scale. Participants lived anywhere in the United States, with a subset of 50 participants living near San Francisco, California, who underwent in-person testing.

MAIN OUTCOMES AND MEASURES

The primary outcome was weight loss. Secondary outcomes from the in-person cohort included changes in weight, fat mass, lean mass, fasting insulin, fasting glucose, hemoglobin A1c levels, estimated energy intake, total energy expenditure, and resting energy expenditure.

RESULTS

Overall, 116 participants (mean [SD] age, 46.5 [10.5] years; 70 [60.3%] men) were included in the study. There was a significant decrease in weight in the TRE (-0.94 kg; 95% CI, -1.68 to -0.20; P = .01), but no significant change in the CMT group (-0.68 kg; 95% CI, -1.41 to 0.05, P = .07) or between groups (-0.26 kg; 95% CI, -1.30 to 0.78; P = .63). In the in-person cohort (n = 25 TRE, n = 25 CMT), there was a significant within-group decrease in weight in the TRE group (-1.70 kg; 95% CI, -2.56 to -0.83; P < .001). There was also a significant difference in appendicular lean mass index between groups (-0.16 kg/m2; 95% CI, -0.27 to -0.05; P = .005). There were no significant changes in any of the other secondary outcomes within or between groups. There were no differences in estimated energy intake between groups.

CONCLUSIONS AND RELEVANCE

Time-restricted eating, in the absence of other interventions, is not more effective in weight loss than eating throughout the day.

TRIAL REGISTRATION

ClinicalTrials.gov Identifiers: NCT03393195 and NCT03637855.

Developing evidence-based behavioural strategies to overcome physiological resistance to weight loss in the general population

Physiological and behavioural systems are tolerant of excess energy intake and responsive to energy deficits. Weight loss (WL) changes body structure, physiological function and energy balance (EB) behaviours, which resist further WL and promote subsequent weight regain. Measuring and understanding the response of EB systems to energy deficits is important for developing evidence-based behaviour change interventions for longer-term weight management. Currently, behaviour change approaches for longer-term WL show modest effect sizes. Self-regulation of EB behaviours (e.g. goal setting, action plans, self-monitoring, relapse prevention plans) and aspects of motivation are important for WL maintenance. Stress management, emotion regulation and food hedonics may also be important for relapse prevention, but the evidence is less concrete. Although much is known about the effects of WL on physiological and psychological function, little is known about the way these dynamic changes affect human EB behaviours. Key areas of future importance include (i) improved methods for detailed tracking of energy expenditure, balance and by subtraction intake, using digital technologies, (ii) how WL impacts body structure, function and subsequent EB behaviours, (iii) how behaviour change approaches can overcome physiological resistance to WL and (iv) who is likely to maintain WL or relapse. Modelling physiological and psychological moderators and mediators of EB-related behaviours is central to understanding and improving longer-term weight and health outcomes in the general population.

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

Wearable devices are increasingly prevalent in research environments for the estimation of energy expenditure (EE) and heart rate (HR). The aim of this study was to validate the HR and EE estimates of the Fitbit charge 2 (FC2), and the EE estimates of the Sensewear armband mini (SWA). We recruited 59 healthy adults to participate in walking, running, cycling, sedentary and household tasks. Estimates of HR from the FC2 were compared to a HR chest strap (Polar) and EE to a stationary metabolic cart (Vyntus CPX). The SWA overestimated overall EE by 0.03 kcal/min−1 and was statistically equivalent to the criterion measure, with a mean absolute percentage error (MAPE) of 29%. In contrast, the FC2 was not equivalent overall (MAPE = 44%). In household tasks, MAPE values of 93% and 83% were observed for the FC2 and SWA, respectively. The FC2 HR estimates were equivalent to the criterion measure overall. The SWA is more accurate than the commercial-grade FC2. Neither device is consistently accurate across the range of activities used in this study. The HR data obtained from the FC2 is more accurate than its EE estimates and future research may focus more on this variable.

Control-theory models of body-weight regulation and body-weight-regulatory appetite

Human body weight (BW), or some variable related to it, is physiologically regulated. That is, negative feedback from changes in BW elicits compensatory influences on appetite, which may be called BW-regulatory appetite, and a component of energy expenditure (EE) called adaptive thermogenesis (AdEE). BW-regulatory appetite is of general significance because it appears to be related to a variety of aspects of human appetite beyond just energy intake. BW regulation, BW-regulatory appetite and AdEE are frequently discussed using concepts derived from control theory, which is the mathematical description of dynamic systems involving negative feedback. The aim of this review is to critically assess these discussions. Two general types of negative-feedback control have been invoked to describe BW regulation, set-point control and simple negative-feedback control, often called settling-point control in the BW literature. The distinguishing feature of set-point systems is the existence of an externally controlled target level of regulation, the set point. The performance of almost any negative-feedback regulatory system, however, can be modeled on the basis of feedback gain without including a set point. In both set-point and simple negative-feedback models of BW regulation, the precision of regulation is usually determined mainly by feedback gain, which refers to the transformations of feedback into compensatory changes in BW-regulatory appetite and AdEE. Stable BW most probably represents equilibria shaped by feedback gain and tonic open-loop challenges, especially obesogenic environments. Data indicate that simple negative-feedback control accurately models human BW regulation and that the set-point concept is superfluous unless its neuroendocrine representation is found in the brain. Additional research aimed at testing control-theory models in humans and non-human animals is warranted.

Validity and reliability of an online self-report 24-h dietary recall method (Intake24): a doubly labelled water study and repeated-measures analysis

Online self-reported 24-h dietary recall systems promise increased feasibility of dietary assessment. Comparison against interviewer-led recalls established their convergent validity; however, reliability and criterion-validity information is lacking. The validity of energy intakes (EI) reported using Intake24, an online 24-h recall system, was assessed against concurrent measurement of total energy expenditure (TEE) using doubly labelled water in ninety-eight UK adults (40-65 years). Accuracy and precision of EI were assessed using correlation and Bland-Altman analysis. Test-retest reliability of energy and nutrient intakes was assessed using data from three further UK studies where participants (11-88 years) completed Intake24 at least four times; reliability was assessed using intra-class correlations (ICC). Compared with TEE, participants under-reported EI by 25 % (95 % limits of agreement -73 % to +68 %) in the first recall, 22 % (-61 % to +41 %) for average of first two, and 25 % (-60 % to +28 %) for first three recalls. Correlations between EI and TEE were 0·31 (first), 0·47 (first two) and 0·39 (first three recalls), respectively. ICC for a single recall was 0·35 for EI and ranged from 0·31 for Fe to 0·43 for non-milk extrinsic sugars (NMES). Considering pairs of recalls (first two v. third and fourth recalls), ICC was 0·52 for EI and ranged from 0·37 for fat to 0·63 for NMES. EI reported with Intake24 was moderately correlated with objectively measured TEE and underestimated on average to the same extent as seen with interviewer-led 24-h recalls and estimated weight food diaries. Online 24-h recall systems may offer low-cost, low-burden alternatives for collecting dietary information.

Weight loss variability with SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes mellitus and obesity: Mechanistic possibilities

We are facing a global epidemic of obesity and type 2 diabetes. Weight loss, in the context of obesity and type 2 diabetes, may improve glycaemic control and weight-related comorbidities, and in some cases, induce diabetes remission. Although lifestyle-based weight loss strategies may be initially successful, most are not effective long-term. There is an increasing need to consider pharmacological approaches to assist weight loss in diabetes-obesity. Older glucose-lowering agents may cause weight gain, whereas the newer drug classes, sodium-glucose co-transporter 2 inhibitors (SGLT2i) and glucagon-like peptide receptor agonists (GLP-1 RAs), concomitantly target weight loss and glycaemic control. Clinical trial data suggest that both SGLT2i and GLP1 RAs cause a mean weight loss of approximately 2 to 3 kg but real-world evidence and clinical experience suggests a significant heterogeneity in the magnitude of the weight loss (GLP-1 RAs) or the magnitude of the actual weight loss is significantly less than anticipated (SGLT2i). Why do some individuals lose more weight than others in response to these pharmacological treatments? This review will first explore mechanisms by which body weight is regulated through control of energy balance and its dysregulation in obesity, and then consider how these mechanisms may be modulated therapeutically with SGLT2i and GLP1 RAs.

Determination of a pharmacokinetic model for [11C]-acetate in brown adipose tissue

Background

[¹¹C]-acetate positron emission tomography is used to assess oxidative metabolism in various tissues including the heart, tumor, and brown adipose tissue. For brown adipose tissue, a monoexponential decay model is commonly employed. However, no systematic assessment of kinetic models has been performed to validate this model or others.

The monoexponential decay model and various compartmental models were applied to data obtained before and during brown adipose tissue activation by cold exposure in healthy men. Quality of fit was assessed visually and by analysis of residuals, including the Akaike information criterion. Stability and accuracy of compartmental models were further assessed through simulations, along with sensitivity and identifiability of kinetic parameters.

Results

Differences were noted in the arterial input function between the warm and cold conditions. These differences are not taken into account by the monoexponential decay model. They are accounted for by compartmental models, but most models proved too complex to be stable. Two and three-tissue models with no more than four distinct kinetic parameters, including blood volume fraction, provided the best compromise between fit quality and stability/accuracy.

Conclusion

For healthy men, a three-tissue model with four kinetic parameters, similar to a heart [¹¹C]-palmitate model seems the most appropriate based on model stability and its ability to describe the main [¹¹C]-acetate pathways in BAT cells. Further studies are required to validate this model in women and people with metabolic disorders.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0497-6) contains supplementary material, which is available to authorized users.

Objective versus Self-Reported Energy Intake Changes During Low-Carbohydrate and Low-Fat Diets

OBJECTIVE

This study aimed to compare self-reported with objective measurements of energy intake changes (∆EI) during a 1-year weight-loss intervention with subjects randomized to low-carbohydrate versus low-fat diets.

METHODS

Repeated body weight measurements were used as inputs to an objective mathematical model to calculate ∆EI_Model and to compare with self-reported energy intake changes assessed by repeated 24-hour recalls (∆EI_Recall ).

RESULTS

∆EI_Recall indicated a relatively persistent state of calorie restriction of ~500 to 600 kcal/d at 3, 6, and 12 months with no significant differences between the diets. ∆EI_Model demonstrated large early decreases in calorie intake > 800 kcal/d followed by an exponential return to ~100 kcal/d below baseline at the end of the year. Accounting for self-reported physical activities did not materially affect the results. Discrepancies between ∆EI_Model and ∆EI_Recall became progressively greater over time. The low-carbohydrate diet resulted in ∆EI_Model that was 162 ± 53 kcal/d lower than the low-fat diet over the first 3 months (P  =  0.002), but no significant diet differences were found thereafter.

CONCLUSIONS

Self-reported ∆EI measurements were inaccurate. Model-based calculations of ∆EI found that instructions to follow the low-carbohydrate diet resulted in greater calorie restriction than the low-fat diet in the early phases of the intervention, but these diet differences were not sustained.

Modeling energy balance while correcting for measurement error via free knot splines

Measurements of energy balance components (energy intake, energy expenditure, changes in energy stores) are often plagued with measurement error. Doubly-labeled water can measure energy intake (EI) with negligible error, but is expensive and cumbersome. An alternative approach that is gaining popularity is to use the energy balance principle, by measuring energy expenditure (EE) and change in energy stores (ES) and then back-calculate EI. Gold standard methods for EE and ES exist and are known to give accurate measurements, albeit at a high cost. We propose a joint statistical model to assess the measurement error in cheaper, non-intrusive measures of EE and ES. We let the unknown true EE and ES for individuals be latent variables, and model them using a bivariate distribution. We try both a bivariate Normal as well as a Dirichlet Process Mixture Model, and compare the results via simulation. Our approach, is the first to account for the dependencies that exist in individuals' daily EE and ES. We employ semiparametric regression with free knot splines for measurements with error, and linear components for error free covariates. We adopt a Bayesian approach to estimation and inference and use Reversible Jump Markov Chain Monte Carlo to generate draws from the posterior distribution. Based on the semiparameteric regression, we develop a calibration equation that adjusts a cheaper, less reliable estimate, closer to the true value. Along with this calibrated value, our method also gives credible intervals to assess uncertainty. A simulation study shows our calibration helps produce a more accurate estimate. Our approach compares favorably in terms of prediction to other commonly used models.

Narrative Review of New Methods for Assessing Food and Energy Intake

Dietary self-report instruments are essential to nutritional analysis in dietetics practice and their use in research settings has facilitated numerous important discoveries related to nutrition, health and chronic diseases. An important example is obesity, for which measuring changes in energy intake is critical for assessing efficacy of dietary interventions. However, current methods, including counting calories, estimating portion size and using food labels to estimate human energy intake have considerable constraints; consequently, research on new methodologies/technologies has been encouraged to mitigate the present weaknesses. The use of technologies has prompted innovation in dietary analysis. In this review, the strengths and limitations of new approaches have been analyzed based on ease of use, practical limitations, and statistical evaluation of reliability and validity. Their utility is discussed through the lens of the 4Ms of Obesity Assessment and Management, which has been used to evaluate root causes of obesity and help select treatment options.

Brown Adipose Tissue Energy Metabolism in Humans

The demonstration of metabolically active brown adipose tissue (BAT) in humans primarily using positron emission tomography coupled to computed tomography (PET/CT) with the glucose tracer 18-fluorodeoxyglucose (18FDG) has renewed the interest of the scientific and medical community in the possible role of BAT as a target for the prevention and treatment of obesity and type 2 diabetes (T2D). Here, we offer a comprehensive review of BAT energy metabolism in humans. Considerable advances in methods to measure BAT energy metabolism, including nonesterified fatty acids (NEFA), chylomicron-triglycerides (TG), oxygen, Krebs cycle rate, and intracellular TG have led to very good quantification of energy substrate metabolism per volume of active BAT in vivo. These studies have also shown that intracellular TG are likely the primary energy source of BAT upon activation by cold. Current estimates of BAT's contribution to energy expenditure range at the lower end of what would be potentially clinically relevant if chronically sustained. Yet, 18FDG PET/CT remains the gold-standard defining method to quantify total BAT volume of activity, used to calculate BAT's total energy expenditure. Unfortunately, BAT glucose metabolism better reflects BAT's insulin sensitivity and blood flow. It is now clear that most glucose taken up by BAT does not fuel mitochondrial oxidative metabolism and that BAT glucose uptake can therefore be disconnected from thermogenesis. Furthermore, BAT thermogenesis is efficiently recruited upon repeated cold exposure, doubling to tripling its total oxidative capacity, with reciprocal reduction of muscle thermogenesis. Recent data suggest that total BAT volume may be much larger than the typically observed 50-150 ml with 18FDG PET/CT. Therefore, the current estimates of total BAT thermogenesis, largely relying on total BAT volume using 18FDG PET/CT, may underestimate the true contribution of BAT to total energy expenditure. Quantification of the contribution of BAT to energy expenditure begs for the development of more integrated whole body in vivo methods.

Low-carbohydrate diets for the treatment of obesity and type 2 diabetes

PURPOSE OF REVIEW

Summarize the physiological effects of low-carbohydrate diets as they relate to weight loss, glycemic control, and metabolic health.

RECENT FINDINGS

Low-carbohydrate diets are at least as effective for weight loss as other diets, but claims about increased energy expenditure and preferential loss of body fat are unsubstantiated. Glycemic control and hyperinsulinemia are improved by low-carbohydrate diets, but insulin sensitivity and glucose-stimulated insulin secretion may be impaired, especially in the absence of weight loss. Fasting lipid parameters are generally improved, but such improvements may depend on the quality of dietary fat and the carbohydrates they replaced. Postprandial hyperlipemia is a potential concern given the high fat content typical of low-carbohydrate diets.

SUMMARY

Low-carbohydrate diets have several potential benefits for treatment of obesity and type 2 diabetes, but more research is required to better understand their long-term consequences as well as the variable effects on the endocrine control of glucose, lipids, and metabolism.

Individually Tailored, Adaptive Intervention to Manage Gestational Weight Gain: Protocol for a Randomized Controlled Trial in Women With Overweight and Obesity

Background

High gestational weight gain is a major public health concern as it independently predicts adverse maternal and infant outcomes. Past interventions have had only limited success in effectively managing pregnancy weight gain, especially among women with overweight and obesity. Well-designed interventions are needed that take an individualized approach and target unique barriers to promote healthy weight gain.

Objective

The primary aim of the study is to describe the study protocol for Healthy Mom Zone, an individually tailored, adaptive intervention for managing weight in pregnant women with overweight and obesity.

Methods

The Healthy Mom Zone Intervention, based on theories of planned behavior and self-regulation and a model of energy balance, includes components (eg, education, self-monitoring, physical activity/healthy eating behaviors) that are adapted over the intervention (ie, increase in intensity) to better regulate weight gain. Decision rules inform when to adapt the intervention. In this randomized controlled trial, women are randomized to the intervention or standard care control group. The intervention is delivered from approximately 8-36 weeks gestation and includes step-ups in dosages (ie, Step-up 1 = education + physical activity + healthy eating active learning [cooking/recipes]; Step-up 2 = Step-up 1 + portion size, physical activity; Step-up 3 = Step-up 1 + 2 + grocery store feedback, physical activity); 5 maximum adaptations. Study measures are obtained at pre- and postintervention as well as daily (eg, weight), weekly (eg, energy intake/expenditure), and monthly (eg, psychological) over the study period. Analyses will include linear mixed-effects models, generalized estimating equations, and dynamical modeling to understand between-group and within-individual effects of the intervention on weight gain.

Results

Recruitment of 31 pregnant women with overweight and obesity has occurred from January 2016 through July 2017. Baseline data have been collected for all participants. To date, 24 participants have completed the intervention and postintervention follow-up assessments, 3 are currently in progress, 1 dropped out, and 3 women had early miscarriages and are no longer active in the study. Of the 24 participants, 13 women have completed the intervention to date, of which 1 (8%, 1/13) received only the baseline intervention, 3 (23%, 3/13) received baseline + step-up 1, 6 (46%, 6/13) received baseline + step-up 1 + step-up 2, and 3 (23%, 3/13) received baseline + step-up 1 + step-up 2 +step-up 3. Data analysis is still ongoing through spring 2018.

Conclusions

This is one of the first intervention studies to use an individually tailored, adaptive design to manage weight gain in pregnancy. Results from this study will be useful in designing a larger randomized trial to examine efficacy of this intervention and developing strategies for clinical application.

Registered Report Identifier

RR1-10.2196/9220

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain

BACKGROUND

The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment.

OBJECTIVES

The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research.

SIGNIFICANCE

The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments.

Simulating long-term human weight-loss dynamics in response to calorie restriction

Background

Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term.

Objective

We compared mathematical models of human BW dynamics underlying 2 popular web-based weight-loss prediction tools, the National Institutes of Health Body Weight Planner (NIH BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC WLP), with data from the 2-year Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study.

Design

Mathematical models were initialized using baseline CALERIE data, and changes in body weight (ΔBW), fat mass (ΔFM), and energy expenditure (ΔEE) were simulated in response to time-varying changes in energy intake (ΔEI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values.

Results

The PBRC WLP model simulated an exaggerated early decrease in EE in response to calorie restriction, resulting in substantial underestimation of the observed mean (95% CI) BW losses by 3.8 (3.5, 4.2) kg. The NIH WLP simulations were much closer to the data, with an overall mean ΔBW bias of -0.47 (-0.92, -0.015) kg. Linearized model analysis revealed that the main reason for the PBRC WLP model bias was a parameter value defining how spontaneous physical activity expenditure decreased with caloric restriction. Both models exhibited substantial variability in their ability to simulate individual results in response to calorie restriction. Monte Carlo simulations demonstrated that ΔEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations.

Conclusions

The NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction. However, the substantial variability in the NIH BWP model predictions at the individual level suggests cautious interpretation of individual-level simulations. This trial was registered at clinicaltrials.gov as NCT00427193.

Energy Intake Derived from an Energy Balance Equation, Validated Activity Monitors, and Dual X-Ray Absorptiometry Can Provide Acceptable Caloric Intake Data among Young Adults

Background

Assessments of energy intake (EI) are frequently affected by measurement error. Recently, a simple equation was developed and validated to estimate EI on the basis of the energy balance equation [EI = changed body energy stores + energy expenditure (EE)].

Objective

The purpose of this study was to compare multiple estimates of EI, including 2 calculated from the energy balance equation by using doubly labeled water (DLW) or activity monitors, in free-living adults.

Methods

The body composition of participants (n = 195; mean age: 27.9 y; 46% women) was measured at the beginning and end of a 2-wk assessment period with the use of dual-energy X-ray absorptiometry. Resting metabolic rate (RMR) was calculated through indirect calorimetry. EE was assessed with the use of the DLW technique and an arm-based activity monitor [Sensewear Mini Armband (SWA); BodyMedia, Inc.]. Self-reported EI was calculated by using dietitian-administered 24-h dietary recalls. Two estimates of EI were calculated with the use of a validated equation: quantity of energy stores estimated from the changes in fat mass and fat-free mass occurring over the assessment period plus EE from either DLW or the SWA. To compare estimates of EI, reporting bias (estimated EI/EE from DLW × 100) and Goldberg ratios (estimated EI/RMR) were calculated.

Results

Mean ± SD EEs from DLW and SWA were 2731 ± 494 and 2729 ± 559 kcal/d, respectively. Self-reported EI was 2113 ± 638 kcal/d, EI derived from DLW was 2723 ± 469 kcal/d, and EI derived from the SWA was 2720 ± 730 kcal/d. Reporting biases for self-reported EI, DLW-derived EI, and SWA-derived EI are as follows: -21.5% ± 22.2%, -0.7% ± 18.5%, and 0.2% ± 20.8%, respectively. Goldberg cutoffs for self-reported EI, DLW EI, and SWA EI are as follows: 1.39 ± 0.39, 1.77 ± 0.38, and 1.77 ± 0.38 kcal/d, respectively.

Conclusions

These results indicate that estimates of EI based on the energy balance equation can provide reasonable estimates of group mean EI in young adults. The findings suggest that, when EE derived from DLW is not feasible, an activity monitor that provides a valid estimate of EE can be substituted for EE from DLW.

Energy Balance over One Athletic Season

INTRODUCTION

Magnitude and variation in energy balance (EB) components over an athletic season are largely unknown.

PURPOSE

We investigated the longitudinal changes in EB over one season and explored the association between EB variation and change in the main fat-free mass (FFM) components in highly trained athletes.

METHODS

Eighty athletes (54 males; handball, volleyball, basketball, triathlete, and swimming) were evaluated from the beginning of the season to the main competition stage. Resting and total energy expenditure (REE and TEE, respectively) were assessed by indirect calorimetry and doubly labeled water, respectively. Physical activity energy expenditure was calculated as TEE - 0.1 TEE - REE. Fat mass (FM), FFM, and bone mineral were evaluated with dual-energy x-ray absorptiometry; changed body energy stores were calculated as 1.0(ΔFFM/Δtime) + 9.5(ΔFM/Δtime). Total-body water (TBW) and its compartments were assessed through dilution techniques, and total-body protein was calculated from a four-compartment model, with body volume assessed by air displacement plethysmography.

RESULTS

Although a negative EB of -17.4 ± 72.7 kcal·d was observed (P < 0.05), EB varied widely among sports and across sex groups resulting in a net weight increase (0.7 ± 2.3 kg) that is attributable to significant changes in FFM (1.2 ± 1.6 kg) and FM (-0.7 ± 1.5 kg) (P < 0.05). EB was related with TBW and intracellular water (r = 0.354, r = 0.257, P < 0.05, respectively), regardless of sex, sports, and age.

CONCLUSIONS

The mean negative EB observed over the season resulted from the rate of FM use and FFM accretion, but with a large variation by sex and sports. TBW, but not total-body protein or mineral balance, explained the magnitude of EB, which means that athletes under a positive or a negative EB showed a TBW expansion or shrinkage, respectively, specifically within the cells, over one athletic season.

Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients

The treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we compared, in a large cohort of morbidly obese patients, the accuracy of REE predictive equations recommended by current obesity guidelines [Harris-Benedict, WHO/FAO/ONU and Mifflin-St Jeor (MJ)] and/or developed for obese patients (Muller, Muller BC, Lazzer, Lazzer BC), focusing on the effect of comorbidities on the accuracy of the equations. Data on REE measured by indirect calorimetry and body composition were collected in 4,247 obese patients (69% women, mean age 48 ± 19 years, mean BMI 44 ± 7 Kg/m²) admitted to the Istituto Auxologico Italiano from 1999 to 2014. The performance of the equations was assessed in the whole cohort, in 4 groups with 0, 1, 2, or ≥ 3 comorbidities and in a subgroup of 1,598 patients with 1 comorbidity (47.1% hypertension, 16.7% psychiatric disorders, 13.3% binge eating disorders, 6.1% endocrine disorders, 6.4% type 2 diabetes, 3.5% sleep apnoea, 3.1% dyslipidemia, 2.5% coronary disease). In the whole cohort of obese patients, as well as in each stratum of comorbidity number, the MJ equation had the highest performance for agreement measures and bias. The MJ equation had the best performance in obese patients with ≥3 comorbidities (accuracy of 61.1%, bias of -89.87) and in patients with type 2 diabetes and sleep apnoea (accuracy/bias 69%/-19.17 and 66%/-21.67 respectively), who also have the highest levels of measured REE. In conclusion, MJ equation should be preferred to other equations to estimate the energy needs of Caucasian morbidly obese patients when measurement of the REE cannot be performed. As even MJ equation does not precisely predict REE, it should be better to plan the diet intervention by measuring rather than estimating REE. Future studies focusing on the clinical differences that determine the high inter-individual variability of the precision of the REE predictive equations (e.g., on the organ-tissue metabolic rate), could help to develop predictive equations with a better performance.

Maintenance of Lost Weight and Long-Term Management of Obesity

Weight loss can be achieved through a variety of modalities, but long-term maintenance of lost weight is much more challenging. Obesity interventions typically result in early weight loss followed by a weight plateau and progressive regain. This review describes current understanding of the biological, behavioral, and environmental factors driving this near-ubiquitous body weight trajectory and the implications for long-term weight management. Treatment of obesity requires ongoing clinical attention and weight maintenance-specific counseling to support sustainable healthful behaviors and positive weight regulation.

Associations of Alcoholic Beverage Consumption with Dietary Intake, Waist Circumference, and Body Mass Index in US Adults: National Health and Nutrition Examination Survey 2003-2012

BACKGROUND

Findings from studies of alcohol and obesity measures (eg, waist circumference [WC] and body mass index [BMI; calculated as kg/m²]) are conflicting. Residual confounding by dietary intake, inconsistent definitions of alcohol consumption across studies, and the inclusion of former drinkers in the nondrinking comparison group can contribute to the mixed literature.

OBJECTIVE

This study examines associations of alcoholic beverage consumption with dietary intake, WC, and BMI.

DESIGN

Cross-sectional data from the 2003-2012 National Health and Nutrition Examination Survey were analyzed.

PARTICIPANTS/SETTING

Adults 20 to 79 years of age (n=7,436 men; n=6,939 women) were studied.

MAIN OUTCOME MEASURES

Associations of alcoholic beverage consumption with energy (kcal), macronutrient and sugar intakes (% kcal), WC, and BMI were determined.

STATISTICAL ANALYSES PERFORMED

Multivariable linear regression models were used to determine associations of average daily volume and drinking quantity (ie, drinks per drinking day) with dietary intake and obesity measures. Former and never drinkers were analyzed as distinct categories; associations of drinking with WC and BMI were examined with and without adjustment for dietary intake variables.

RESULTS

Heavier-drinking men (≥3 drinks/day) and women (≥2 drinks/day) consumed less nonalcoholic energy (β -252 kcal/day, 95% CI -346 to -159 kcal/day and β -159 kcal/day, 95% CI -245 to -73 kcal/day, respectively) than moderate drinkers (1 to 2 drinks/day in men and 1 drink/day in women). By average daily drinking volume, differences in WC and BMI between former and moderate drinkers were +1.78 cm (95% CI 0.51 to 3.05 cm) and +0.65 (95% CI 0.12 to 1.18) in men and +4.67 cm (95% CI 2.95 to 6.39 cm) and +2.49 (95% CI 1.64 to 3.34) in women. Compared with moderate drinking, heavier drinking volume was not associated with WC or BMI among men or women. In men, drinking ≥5 drinks/drinking day was associated with higher WC (β 3.48 cm, 95% CI 1.97 to 5.00 cm) and BMI (β 1.39, 95% CI 0.79 to 2.00) compared with men who consumed 1 to 2 drinks/drinking day. In women, WC and BMI were not significantly different for women drinking ≥4 drinks/drinking day compared with 1 drink/drinking day.

CONCLUSIONS

Differences in dietary intake across drinking subgroups and separation of former drinkers from nondrinkers should be considered in studies of alcohol intake in relation to WC and BMI.

Compensatory Changes in Energy Balance Regulation over One Athletic Season

PURPOSE

Mechanisms in energy balance (EB) regulation may include compensatory changes in energy intake (EI) and metabolic adaption (MA), but information is unavailable in athletes who often change EB components. We aim to investigate EB regulation compensatory mechanisms over one athletic season.

METHODS

Fifty-seven athletes (39 males/18 females; handball, volleyball, basketball, triathlon, and swimming) were evaluated from the beginning to the competitive phase of the season. Resting and total energy expenditure (REE and TEE, respectively) were assessed by indirect calorimetry and doubly labeled water, respectively, and physical activity energy expenditure was determined as TEE - 0.1(TEE) - REE. Fat mass (FM) and fat-free mass (FFM) were evaluated by dual-energy x-ray absorptiometry and changed body energy stores was determined by 1.0(ΔFFM/Δtime) + 9.5(ΔFM/Δtime). EI was derived as TEE + EB. REE was predicted from baseline FFM, FM, sex, and sports. %MA was calculated as 100(measured REE/predicted REE-1) and MA (kcal) as %MA/100 multiplied by baseline measured REE. Average EI minus average physical activity energy expenditure was computed as a proxy of average energy availability, assuming that a constant nonexercise EE occurred over the season.

RESULTS

Body mass increased by 0.8 ± 2.5 kg (P < 0.05), but a large individual variability was found ranging from -6.1 to 5.2 kg. The TEE raise (16.8% ± 11.7%) was compensated by an increase EI change (16.3% ± 12.0%) for the whole group (P < 0.05). MA was found in triathletes, sparing 128 ± 168 kcal·d, and basketball players, dissipating 168 ± 205 kcal·d (P < 0.05). MA was associated (P < 0.05) with EB and energy availability (r = 0.356 and r = 0.0644, respectively).

CONCLUSION

TEE increased over the season without relevant mean changes in weight, suggesting that EI compensation likely occurred. The thrifty or spendthrift phenotypes observed among sports and the demanding workloads these athletes are exposed to highlight the need for sport-specific energy requirements.

Proportional Feedback Control of Energy Intake During Obesity Pharmacotherapy

OBJECTIVE

Obesity pharmacotherapies result in an exponential time course for energy intake whereby large early decreases dissipate over time. This pattern of declining drug efficacy to decrease energy intake results in a weight loss plateau within approximately 1 year. This study aimed to elucidate the physiology underlying the exponential decay of drug effects on energy intake.

METHODS

Placebo-subtracted energy intake time courses were examined during long-term obesity pharmacotherapy trials for 14 different drugs or drug combinations within the theoretical framework of a proportional feedback control system regulating human body weight.

RESULTS

Assuming each obesity drug had a relatively constant effect on average energy intake and did not affect other model parameters, our model correctly predicted that long-term placebo-subtracted energy intake was linearly related to early reductions in energy intake according to a prespecified equation with no free parameters. The simple model explained about 70% of the variance between drug studies with respect to the long-term effects on energy intake, although a significant proportional bias was evident.

CONCLUSIONS

The exponential decay over time of obesity pharmacotherapies to suppress energy intake can be interpreted as a relatively constant effect of each drug superimposed on a physiological feedback control system regulating body weight.

Obesity Energetics: Body Weight Regulation and the Effects of Diet Composition

Weight changes are accompanied by imbalances between calorie intake and expenditure. This fact is often misinterpreted to suggest that obesity is caused by gluttony and sloth and can be treated by simply advising people to eat less and move more. Rather various components of energy balance are dynamically interrelated and weight loss is resisted by counterbalancing physiological processes. While low-carbohydrate diets have been suggested to partially subvert these processes by increasing energy expenditure and promoting fat loss, our meta-analysis of 32 controlled feeding studies with isocaloric substitution of carbohydrate for fat found that both energy expenditure (26 kcal/d; P <.0001) and fat loss (16 g/d; P <.0001) were greater with lower fat diets. We review the components of energy balance and the mechanisms acting to resist weight loss in the context of static, settling point, and set-point models of body weight regulation, with the set-point model being most commensurate with current data.

From the past to future: from energy expenditure to energy intake to energy expenditure

Although most recent research on energy balance focusses on energy intake (EI) there is still need to think about both sides of the energy balance. Current research on energy expenditure (EE) relates to metabolic adaptation to negative energy balance, mitochondrial metabolism associated with aging, obesity and type 2 diabetes mellitus, the role of EE in hunger and appetite control, non-shivering thermogenesis and brown adipose tissue activity, cellular bioenergetics as a target of obesity treatment and the evolutionary and ecological determinants of EE in humans and other primates. As far as regulation of energy balance is concerned there is recent evidence that EE rather than body weight is under tight control. Biologically, EE is maintained within a narrow physiological range. An EE-set point has been proposed as the width between the upper and lower boundaries of the individual EE range. Regulation of EE may fail in very obese patients with an EI above their upper boundary and after drastic weight loss when patients may go far below their lower EE boundary and thus are loosing control. In population studies, fat-free mass (FFM) and its composition (that is, the proportion of high to low metabolic rate organs) are major determinants of EE. It is tempting to speculate that tight biologic control of EE is related to brain energy need, which is preserved at the cost of peripheral metabolism. There is a moderate heritability of EE, which is independent of the heritability of FFM. In future, metabolic phenotyping should focus on the EE-FFM relationship rather than on EE-values alone.

How Strongly Does Appetite Counter Weight Loss? Quantification of the Feedback Control of Human Energy Intake

OBJECTIVE

To quantify the feedback control of energy intake in response to long-term covert manipulation of energy balance in free-living humans.

METHODS

A validated mathematical method was used to calculate energy intake changes during a 52-week placebo-controlled trial in 153 patients treated with canagliflozin, a sodium glucose co-transporter inhibitor that increases urinary glucose excretion, thereby resulting in weight loss without patients being directly aware of the energy deficit. The relationship between the body weight time course and the calculated energy intake changes was analyzed using principles from engineering control theory.

RESULTS

It was discovered that weight loss leads to a proportional increase in appetite resulting in eating above baseline by ∼100 kcal/day per kilogram of lost weight-an amount more than threefold larger than the corresponding energy expenditure adaptations.

CONCLUSIONS

While energy expenditure adaptations have often been considered the main reason for slowing of weight loss and subsequent regain, feedback control of energy intake plays an even larger role and helps explain why long-term maintenance of a reduced body weight is so difficult.

Analysis of energy metabolism in humans: A review of methodologies

Background

Obesity is a consequence of chronic energy imbalance. We need accurate and precise measurements of energy intake and expenditure, as well as the related behaviors, to fully understand how energy homeostasis is regulated in order to develop interventions and evaluate their effectiveness to combat the global obesity epidemic.

Scope of review

We provide an in-depth review of the methodologies currently used to measure energy intake and expenditure in humans, including their principles, advantages, and limitations in the clinical research setting. The aim is to provide researchers with a comprehensive guide to conduct obesity research of the highest possible quality.

Major conclusions

An array of methodologies is available to measure various aspects of energy metabolism and none is perfect under all circumstances. The choice of methods should be specific to particular research questions with practicality and quality of data the priorities for consideration. A combination of complementary measurements may be preferable. There is an imperative need to develop new methodologies to improve the accuracy and precision of energy intake assessments.

•

Image-based technology is a significant step to improve energy intake measurement.

•

Physical activity informs patterns but not absolute energy expenditure.

•

Combining complementary measurements overcomes shortfalls of individual methods.

Impact of Masked Replacement of Sugar-Sweetened with Sugar-Free Beverages on Body Weight Increases with Initial BMI: Secondary Analysis of Data from an 18 Month Double-Blind Trial in Children

Background

Substituting sugar-free for sugar-sweetened beverages reduces weight gain. This effect may be more pronounced in children with a high body mass index (BMI) because their sensing of kilocalories might be compromised. We investigated the impact of sugar-free versus sugary drinks separately in children with a higher and a lower initial BMI z score, and predicted caloric intakes and degree of compensation in the two groups.

Methods and Findings

This is a secondary, explorative analysis of our double-blind randomized controlled trial (RCT) which showed that replacement of one 250-mL sugary drink per day by a sugar—free drink for 18 months significantly reduced weight gain. In the 477 children who completed the trial, mean initial weights were close to the Dutch average. Only 16% were overweight and 3% obese. Weight changes were expressed as BMI z-score, i.e. as standard deviations of the BMI distribution per age and sex group. We designated the 239 children with an initial BMI z-score below the median as ‘lower BMI’ and the 238 children above the median as ‘higher BMI’. The difference in caloric intake from experimental beverages between treatments was 86 kcal/day both in the lower and in the higher BMI group. We used a multiple linear regression and the coefficient of the interaction term (initial BMI group times treatment), indicated whether children with a lower BMI responded differently from children with a higher BMI. Statistical significance was defined as p ≤ 0.05. Relative to the sugar sweetened beverage, consumption of the sugar—free beverage for 18 months reduced the BMI z-score by 0.05 SD units within the lower BMI group and by 0.21 SD within the higher BMI group. Body weight gain was reduced by 0.62 kg in the lower BMI group and by 1.53 kg in the higher BMI group. Thus the treatment reduced the BMI z-score by 0.16 SD units more in the higher BMI group than in the lower BMI group (p = 0.04; 95% CI -0.31 to -0.01). The impact of the intervention on body weight gain differed by 0.90 kg between BMI groups (p = 0.09; 95% CI -1.95 to 0.14). In addition, we used a physiologically-based model of growth and energy balance to estimate the degree to which children had compensated for the covertly removed sugar kilocalories by increasing their intake of other foods. The model predicts that children with a lower BMI had compensated 65% (95% CI 28 to 102) of the covertly removed sugar kilocalories, whereas children with a higher BMI compensated only 13% (95% CI -37 to 63).

Conclusions

The children with a BMI above the median might have a reduced tendency to compensate for changes in caloric intake. Differences in these subconscious compensatory mechanisms may be an important cause of differences in the tendency to gain weight. If further research bears this out, cutting down on the intake of sugar-sweetened drinks may benefit a large proportion of children, especially those who show a tendency to become overweight.

Trial Registration

ClinicalTrials.gov NCT00893529

Comparison between Human and Bite-Based Methods of Estimating Caloric Intake

BACKGROUND

Current methods of self-monitoring kilocalorie intake outside of laboratory/clinical settings suffer from a systematic underreporting bias. Recent efforts to make kilocalorie information available have improved these methods somewhat, but it may be possible to derive an objective and more accurate measure of kilocalorie intake from bite count.

OBJECTIVE

This study sought to develop and examine the accuracy of an individualized bite-based measure of kilocalorie intake and to compare that measure to participant estimates of kilocalorie intake. It was hypothesized that kilocalorie information would improve human estimates of kilocalorie intake over those with no information, but a bite-based estimate of kilocalorie intake would still outperform human estimates.

PARTICIPANTS/SETTINGS

Two-hundred eighty participants were allowed to eat ad libitum in a cafeteria setting. Their bite count and kilocalorie intake were measured. After completion of the meal, participants estimated how many kilocalories they consumed, some with the aid of a menu containing kilocalorie information and some without. Using a train and test method for predictive model development, participants were randomly divided into one of two groups: one for model development (training group) and one for model validation (test group).

STATISTICAL ANALYSIS

Multiple regression was used to determine whether height, weight, age, sex, and waist-to-hip ratio could predict an individual's mean kilocalories per bite for the training sample. The model was then validated with the test group, and the model-predicted kilocalorie intake was compared with human-estimated kilocalorie intake.

RESULTS

Only age and sex significantly predicted mean kilocalories per bite, but all variables were retained for the test group. The bite-based measure of kilocalorie intake outperformed human estimates with and without kilocalorie information.

CONCLUSIONS

Bite count might serve as an easily measured, objective proxy for kilocalorie intake. A tool that can monitor bite count may be a powerful assistant to self-monitoring.