A Simple Model Predicting Individual Weight Change in Humans

Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling

Objective

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings.

Methods

Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.

Results

Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.

Conclusions

3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Predictors of racial differences in weight loss: the PROPEL trial

OBJECTIVE

Studies have consistently shown that African American individuals lose less weight in response to behavioral interventions, but the mechanisms leading to this result have been understudied.

METHODS

Data were derived from the PROmoting Successful Weight Loss in Primary CarE in Louisiana (PROPEL) study, which was a cluster-randomized, two-arm trial conducted in primary care clinics. In the PROPEL trial, African American individuals lost less weight compared with patients who belonged to other racial groups after 24 months. In the current study, counterfactual mediation analyses among 445 patients in the intervention arm of PROPEL were used to determine which variables mediated the relationship between race and weight loss. The mediators included treatment engagement, psychosocial, and lifestyle factors.

RESULTS

At 6 months, daily weighing mediated 33% (p = 0.008) of the racial differences in weight loss. At 24 months, session attendance and daily weighing mediated 35% (p = 0.027) and 66% (p = 0.005) of the racial differences in weight loss, respectively. None of the psychosocial or lifestyle variables mediated the race-weight loss association.

CONCLUSIONS

Strategies specifically targeting engagement, such as improving session attendance and self-weighing behaviors, among African American individuals are needed to support more equitable weight losses over extended time periods.

Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water

Accurate dietary assessment is crucial for nutrition and health research. Traditional methods, such as food records, food frequency questionnaires, and 24-hour dietary recalls (24HR), have limitations, such as the need for trained interviewers, time-consuming procedures, and inaccuracies in estimations. Novel technologies, such as image-based dietary assessment apps, have been developed to overcome these limitations. SNAQ is a novel image-based food-recognition app which, based on computer vision, assesses food type and volume, and provides nutritional information about dietary intake. This cross-sectional observational study aimed to investigate the validity of SNAQ as a dietary assessment tool for measuring energy and macronutrient intake in adult women with normal body weight (n = 30), compared to doubly labeled water (DLW), a reference method for total daily energy expenditure (TDEE). Energy intake was also estimated using a one-day 24HR for direct comparison. Bland-Altman plots, paired difference tests, and Pearson's correlation coefficient were used to assess agreement and relationships between the methods. SNAQ showed a slightly higher agreement (bias = -329.6 kcal/day) with DLW for total daily energy intake (TDEI) compared to 24HR (bias = -543.0 kcal/day). While both SNAQ and 24HR tended to underestimate TDEI, only 24HR significantly differed from DLW in this regard (p < 0.001). There was no significant relationship between estimated TDEI and TDEE using SNAQ (R2 = 27%, p = 0.50) or 24HR (R2 = 34%, p = 0.20) and there were no significant differences in energy and macronutrient intake estimates between SNAQ and 24HR (Δ = 213.4 kcal/day). In conclusion, these results indicate that SNAQ provides a closer representation of energy intake in adult women with normal body weight than 24HR when compared to DLW, but no relationship was found between the energy estimates of DLW and of the two dietary assessment tools. Further research is needed to determine the clinical relevance and support the implementation of SNAQ in research and clinical settings. Clinical trial registration: This study is registered on ClinicalTrials.gov with the unique identifier NCT04600596 (https://clinicaltrials.gov/ct2/show/NCT04600596).

Dietary intake during a pragmatic cluster-randomized weight loss trial in an underserved population in primary care

BACKGROUND

Currently there are limited data as to whether dietary intake can be improved during pragmatic weight loss interventions in primary care in underserved individuals.

METHODS

Patients with obesity were recruited into the PROPEL trial, which randomized 18 clinics to either an intensive lifestyle intervention (ILI) or usual care (UC). At baseline and months 6, 12, and 24, fruit and vegetable (F/V) intake and fat intake was determined. Outcomes were analyzed by repeated-measures linear mixed-effects multilevel models and regression models, which included random cluster (clinic) effects. Secondary analyses examined the effects of race, sex, age, and food security status.

RESULTS

A total of 803 patients were recruited. 84.4% were female, 67.2% African American, 26.1% received Medicaid, and 65.5% made less than $40,000. No differences in F/V intake were seen between the ILI and UC groups at months 6, 12, or 24. The ILI group reduced percent fat at months 6, 12, and 24 compared to UC. Change in F/V intake was negatively correlated with weight change at month 6 whereas change in fat intake was positively associated with weight change at months 6, 12, and 24 for the ILI group.

CONCLUSIONS

The pragmatic weight loss intervention in primary care did not increase F/V intake but did reduce fat intake in an underserved population with obesity. F/V intake was negatively associated with weight loss at month 6 whereas percent fat was positively correlated with weight loss throughout the intervention. Future efforts better targeting both increasing F/V intake and reducing fat intake may promote greater weight loss in similar populations.

TRIAL REGISTRATION

NCT Registration: NCT02561221.

Validity of two weight prediction models for community-living patients participating in a weight loss program

Models predicting individual body weights over time clarify patient expectations in weight loss programs. The accuracy of two commonly used weight prediction models in community living people is unclear. All eligible people entering a weight management program between 1992 and 2015 were included. Patients' diet was 1200 kcal/day for week 0 followed by 900 kcal/day for weeks 1-7 and were excluded from the analysis if they were nonadherent. We generated expected weights using the National Institutes of Health Body Weight Planner (NIH-BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC-WLP). 3703 adherent people were included (mean age 46 years, 72.6% women, mean [SD] weight 262.3 pounds [54.2], mean [SD] BMI 42.4 [7.6]). Mean (SD) relative body weight differences (100*[observed-expected]/expected) for NIH-BWP and PBRC-WLP models was - 1.5% (3.8) and - 2.9% (3.2), respectively. At week 7, mean squared error with NIH-BWP (98.8, 83%CI 89.7-108.8) was significantly lower than that with PBRC-WLP (117.7, 83%CI 112.4-123.4). Notable variation in relative weight difference were seen (for NIH-BWP, 5th-95th percentile was - 6.2%, + 3.7%; Δ 9.9%). During the first 7 weeks of a weight loss program, both weight prediction models returned expected weights that were very close to observed values with the NIH-BWP being more accurate. However, notable variability between expected and observed weights in individual patients were seen. Clinicians can monitor patients in weight loss programs by comparing their progress with these data.

Machine learning approach to predict body weight in adults

Background

Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings.

Methods

We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression.

Results

The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m²) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m²). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (p = 0.323).

Conclusion

The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.

Ketogenic Diet Applied in Weight Reduction of Overweight and Obese Individuals with Progress Prediction by Use of the Modified Wishnofsky Equation

Ketogenic diet is often used as diet therapy for certain diseases, among other things, its positive effect related to weight loss is highlighted. Precisely because of the suggestion that KD can help with weight loss, visceral obesity, and appetite control, 100 respondents joined the weight loss program (of which 31% were men and 69% were women). The aforementioned respondents were interviewed in order to determine their eating habits, the amount of food consumed, and the time when they consume meals. Basic anthropometric data (body height, body mass, chest, waist, hips, biceps, and thigh circumferences) were also collected, in order to be able to monitor their progress during the different phases of the ketogenic diet. Important information is the expected body mass during the time frame of a certain keto diet phase. This information is important for the nutritionist, medical doctor, as well as for the participant in the reduced diet program; therefore, the model was developed that modified the original equation according to Wishnofsky. The results show that women lost an average of 22.7 kg (average number of days in the program 79.5), and for men the average weight loss was slightly higher, 29.7 kg (with an average of 76.8 days in the program). The prediction of expected body mass by the modified Wishnofsky's equation was extremely well aligned with the experimental values, as shown by the Bland-Altman graph (bias for women 0.021 kg and -0.697 kg for men) and the coefficient of determination of 0.9903. The modification of the Wishnofsky equation further shed light on the importance of controlled energy reduction during the dietetic options of the ketogenic diet.

Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity

Background

Weight loss effectively reduces cardiometabolic health risks among people with overweight and obesity, but inter-individual variability in weight loss maintenance is large. Here we studied whether baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss success.

Methods

Within the 8-month multicenter dietary intervention study DiOGenes, we classified a low weight-losers (low-WL) group and a high-WL group based on median weight loss percentage (9.9%) from 281 individuals. Using RNA sequencing, we identified the significantly differentially expressed genes between high-WL and low-WL at baseline and their enriched pathways. We used this information together with support vector machines with linear kernel to build classifier models that predict the weight loss classes.

Results

Prediction models based on a selection of genes that are associated with the discovered pathways 'lipid metabolism' (max AUC = 0.74, 95% CI [0.62-0.86]) and 'response to virus' (max AUC = 0.72, 95% CI [0.61-0.83]) predicted the weight-loss classes high-WL/low-WL significantly better than models based on randomly selected genes (P < 0.01). The performance of the models based on 'response to virus' genes is highly dependent on those genes that are also associated with lipid metabolism. Incorporation of baseline clinical factors into these models did not noticeably enhance the model performance in most of the runs. This study demonstrates that baseline adipose tissue gene expression data, together with supervised machine learning, facilitates the characterization of the determinants of successful weight loss.

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.

Mediators of weight change in underserved patients with obesity: exploratory analyses from the Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL) cluster-randomized trial

BACKGROUND

Intensive lifestyle interventions (ILIs) stimulate weight loss in underserved patients with obesity, but the mediators of weight change are unknown.

OBJECTIVES

We aimed to identify the mediators of weight change during an ILI compared with usual care (UC) in underserved patients with obesity.

METHODS

The PROPEL (Promoting Successful Weight Loss in Primary Care in Louisiana) trial randomly assigned 18 clinics (n = 803) to either an ILI or UC for 24 mo. The ILI group received an intensive lifestyle program; the UC group had routine care. Body weight was measured; further, eating behaviors (restraint, disinhibition), dietary intake (percentage fat intake, fruit and vegetable intake), physical activity, and weight- and health-related quality of life constructs were measured through questionnaires. Mediation analyses assessed whether questionnaire variables explained between-group variations in weight change during 2 periods: baseline to month 12 (n = 779) and month 12 to month 24 (n = 767).

RESULTS

The ILI induced greater weight loss at month 12 compared with UC (between-group difference: -7.19 kg; 95% CI: -8.43, -6.07 kg). Improvements in disinhibition (-0.33 kg; 95% CI: -0.55, -0.10 kg), percentage fat intake (-0.25 kg; 95% CI: -0.50, -0.01 kg), physical activity (-0.26 kg; 95% CI: -0.41, -0.09 kg), and subjective fatigue (-0.28 kg; 95% CI: -0.46, -0.10 kg) at month 6 during the ILI partially explained this between-group difference. Greater weight loss occurred in the ILI at month 24, yet the ILI group gained 2.24 kg (95% CI: 1.32, 3.26 kg) compared with UC from month 12 to month 24. Change in fruit and vegetable intake (0.13 kg; 95% CI: 0.05, 0.21 kg) partially explained this response, and no variables attenuated the weight regain of the ILI group.

CONCLUSIONS

In an underserved sample, weight change induced by an ILI compared with UC was mediated by several psychological and behavioral variables. These findings could help refine weight management regimens in underserved patients with obesity.This trial was registered at clinicaltrials.gov as NCT02561221.

Personalized Metabolic Avatar: A Data Driven Model of Metabolism for Weight Variation Forecasting and Diet Plan Evaluation

Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism. Here, we developed a data-driven model of end-user metabolism, the Personalized Metabolic Avatar (PMA), to estimate its personalized reactions to diets. PMA consists of a gated recurrent unit (GRU) deep learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. The model can perform simulations and evaluation of diet plans, allowing the definition of tailored goals for achieving ideal weight. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim to achieve long-lasting results in pursuing a healthy lifestyle.

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.

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.

Weight Bias 2.0: The Effect of Perceived Weight Change on Performance Evaluation and the Moderating Role of Anti-fat Bias

Overweight employees are viewed as lazy, slow, inactive, and even incapable. Even if such attributes are false, this perspective can seriously undermine others' evaluation of their work performance. The current study explores a broader phenomenon of weight bias that has an effect on weight change. In a longitudinal study with a time lag of 6 months, we surveyed 226 supervisor-employee dyads. We found supervisor perceptions of employee weight change notably altered their evaluation of the employee performance from Time 1, especially following low vs. high Time-1 performance evaluation. Meanwhile, the moderating effects among different levels of supervisor anti-fat bias functioned as boundary conditions for such performance evaluation alteration. In particular, the interaction between the Time-1 performance evaluation and the impact of supervisor perception of employee weight change on the Time-2 performance evaluation was significant only if supervisors held a stronger anti-fat bias.

The Behavioral Intervention with Technology for E-Weight Loss Study (BITES): Incorporating Energy Balance Models and the Bite Counter into an Online Behavioral Weight Loss Program

This study evaluated feasibility and acceptability of adding energy balance modeling displayed on weight graphs combined with a wrist-worn bite counting sensor against a traditional online behavioral weight loss program. Adults with a BMI of 27–45 kg/m2 (83.3% women) were randomized to receive a 12-week online behavioral weight loss program with 12 weeks of continued contact (n = 9; base program), the base program plus a graph of their actual and predicted weight change based on individualized physiological parameters (n = 7), or the base program, graph, and a Bite Counter device for monitoring and limiting eating (n = 8). Participants attended weekly clinic weigh-ins plus baseline, midway (12 weeks), and study culmination (24 weeks) assessments of feasibility, acceptability, weight, and behavioral outcomes. In terms of feasibility, participants completed online lessons (M = 7.04 of 12 possible lessons, SD = 4.02) and attended weigh-ins (M = 16.81 visits, SD = 7.24). Six-month retention appears highest among nomogram participants, and weigh-in attendance and lesson completion appear highest in Bite Counter participants. Acceptability was sufficient across groups. Bite Counter use (days with ≥ 2 eating episodes) was moderate (47.8%) and comparable to other studies. Participants lost 4.6% ± 4.5 of their initial body weight at 12 weeks and 4.5% ± 5.8 at 24 weeks. All conditions increased their total physical activity minutes and use of weight control strategies (behavioral outcomes). Although all groups lost weight and the study procedures were feasible, acceptability can be improved with advances in the technology. Participants were satisfied with the online program and nomograms, and future research on engagement, adherence, and integration with other owned devices is needed. ClinicalTrials.gov Identifier: NCT02857595

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.

Estimating my equilibrium energy intake during lockdown: very introspective study

OBJECTIVE

To estimate the daily dietary energy intake for me to maintain a constant body weight. How hard can it be?

DESIGN

Very introspective study.

SETTING

At home. In lockdown. (Except every Tuesday afternoon and Saturday morning, when I went for a run.) PARTICIPANTS: Me. n=1.

MAIN OUTCOME MEASURES

My weight, measured each day.

RESULTS

Sleeping, I shed about a kilogram each night (1.07 (SD 0.25) kg). Running 5 km, I shed about half a kilogram (0.57 (SD 0.15) kg). My daily equilibrium energy intake is about 10 000 kJ (10 286 (SD 201) kJ). Every kJ above (or below) 10 000 kJ adds (or subtracts) about 40 mg (35.4 (SD 3.2) mg).

CONCLUSIONS

Body weight data show persistent variability, even when the screws of control are tightened and tightened.

Data integration for prediction of weight loss in randomized controlled dietary trials

Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

Energy intake and expenditure in children and adolescents, contributions of biological maturity

OBJECTIVE

The aim of this study was to examine the relationship between the ratio of energy intake (EI) and energy expenditure (EE) and body composition, physical activity and macronutrients intake, considering maturity as a moderator.

METHODS

The study involved 459 adolescents aged 10 to 17. Energy and macronutrients intake were estimated using a valid Food Frequency Questionnaire; basal metabolic rate (BMR) was predicted from Schofield equations and EE was estimated using BMR and physical activity level obtained through a Portuguese validated biosocial questionnaire. Body mass index, body composition, and bone age were objectively measured. Statistical analyses included independent samples t-tests, ANCOVA and Pearson correlations. All analyses were adjusted for chronological age, sex, and EI.

RESULTS

Body mass index, fat and fat-free mass, physical activity and protein intake were negatively correlated with EI/EE (P < .001). The study showed significant interactions between maturity and body mass index, fat-free mass and physical activity level. Maturity attenuated the negative relationships between EI/EE and body mass index, fat-free mass and physical activity, especially among early maturers. All categories of maturity showed implications in body mass index for values lower than 23.8 kg/m² . A significant EI/EE reduction was observed among late maturers with a fat-free mass above 39.8 kg.

CONCLUSIONS

Our findings suggest that maturity moderates the relationship between EI/EE and body mass index, fat-free mass and physical activity, especially evident among late maturers.

Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE) Protocol: A Randomized Clinical Trial

Fire Department of New York (FDNY) rescue and recovery workers exposed to World Trade Center (WTC) particulates suffered loss of forced expiratory volume in 1 s (FEV1). Metabolic Syndrome increased the risk of developing WTC-lung injury (WTC-LI). We aim to attenuate the deleterious effects of WTC exposure through a dietary intervention targeting these clinically relevant disease modifiers. We hypothesize that a calorie-restricted Mediterranean dietary intervention will improve metabolic risk, subclinical indicators of cardiopulmonary disease, quality of life, and lung function in firefighters with WTC-LI. To assess our hypothesis, we developed the Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE), a randomized controlled clinical trial (RCT). Male firefighters with WTC-LI and a BMI > 27 kg/m2 will be included. We will randomize subjects (1:1) to either: (1) Low Calorie Mediterranean (LoCalMed)-an integrative multifactorial, technology-supported approach focused on behavioral modification, nutritional education that will include a self-monitored diet with feedback, physical activity recommendations, and social cognitive theory-based group counseling sessions; or (2) Usual Care. Outcomes include reduction in body mass index (BMI) (primary), improvement in FEV1, fractional exhaled nitric oxide, pulse wave velocity, lipid profiles, targeted metabolic/clinical biomarkers, and quality of life measures (secondary). By implementing a technology-supported LoCalMed diet our FIREHOUSE RCT may help further the treatment of WTC associated pulmonary disease.

Weight Loss in Underserved Patients - A Cluster-Randomized Trial

BACKGROUND

Evidence of the effectiveness of treatment for obesity delivered in primary care settings in underserved populations is lacking.

METHODS

We conducted a cluster-randomized trial to test the effectiveness of a high-intensity, lifestyle-based program for obesity treatment delivered in primary care clinics in which a high percentage of the patients were from low-income populations. We randomly assigned 18 clinics to provide patients with either an intensive lifestyle intervention, which focused on reduced caloric intake and increased physical activity, or usual care. Patients in the intensive-lifestyle group participated in a high-intensity program delivered by health coaches embedded in the clinics. The program consisted of weekly sessions for the first 6 months, followed by monthly sessions for the remaining 18 months. Patients in the usual-care group received standard care from their primary care team. The primary outcome was the percent change from baseline in body weight at 24 months.

RESULTS

All 18 clinics (9 assigned to the intensive program and 9 assigned to usual care) completed 24 months of participation; a median of 40.5 patients were enrolled at each clinic. A total of 803 adults with obesity were enrolled: 452 were assigned to the intensive-lifestyle group, and 351 were assigned to the usual-care group; 67.2% of the patients were Black, and 65.5% had an annual household income of less than $40,000. Of the enrolled patients, 83.4% completed the 24-month trial. The percent weight loss at 24 months was significantly greater in the intensive-lifestyle group (change in body weight, -4.99%; 95% confidence interval [CI], -6.02 to -3.96) than in the usual-care group (-0.48%; 95% CI, -1.57 to 0.61), with a mean between-group difference of -4.51 percentage points (95% CI, -5.93 to -3.10) (P<0.001). There were no significant between-group differences in serious adverse events.

CONCLUSIONS

A high-intensity, lifestyle-based treatment program for obesity delivered in an underserved primary care population resulted in clinically significant weight loss at 24 months. (Funded by the Patient-Centered Outcomes Research Institute and others; PROPEL ClinicalTrials.gov number, NCT02561221.).

A Machine Learning Approach to Short-Term Body Weight Prediction in a Dietary Intervention Program

Weight and obesity management is one of the emerging challenges in current health management. Nutrient-gene interactions in human obesity (NUGENOB) seek to find various solutions to challenges posed by obesity and over-weight. This research was based on utilising a dietary intervention method as a means of addressing the problem of managing obesity and overweight. The dietary intervention program was done for a period of ten weeks. Traditional statistical techniques have been utilised in analyzing the potential gains in weight and diet intervention programs. This work investigates the applicability of machine learning to improve on the prediction of body weight in a dietary intervention program. Models that were utilised include Dynamic model, Machine Learning models (Linear regression, Support vector machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN)). The performance of these estimation models was compared based on evaluation metrics like RMSE, MAE and R2. The results indicate that the Machine learning models (ANN and RF) perform better than the other models in predicting body weight at the end of the dietary intervention program.

Application of mathematical models in the management of obesity during pregnancy and the postpartum period in reproductive age women

Obesity is a complex pandemic, and its effective management involves addressing many different factors. This complexity has given rise to novel analytic methods, integrating intensive computational, engineering, and statistical techniques. Mathematical models are currently applied to inform clinical practice. At the 2017 The Korean Nutrition Society 50th Anniversary International Conference, the development of such models and their application to improve data accuracy and patient care during the pregnancy and postpartum periods were discussed.

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.

A Systematic Scoping Review of Surgically Manipulated Adipose Tissue and the Regulation of Energetics and Body Fat in Animals

OBJECTIVE

Surgical manipulations of adipose tissue by removal, or partial lipectomy, have demonstrated body fat compensation and recovered body weight, suggesting that the body is able to resist changes to body composition. However, the mechanisms underlying these observations are not well understood. The purpose of this scoping review is to provide an update on what is currently known about the regulation of energetics and body fat after surgical manipulations of adipose tissue in small mammals.

METHODS

PubMed and Scopus were searched to identify 64 eligible studies. Outcome measures included body fat, body weight, food intake, and circulating biomarkers.

RESULTS

Surgeries performed included lipectomy (72%) or transplantation (12%) in mice (35%), rats (35%), and other small mammals. Findings suggested that lipectomy did not have consistent long-term effects on reducing body weight and fat because regain occurred within 12 to 14 weeks post surgery. Hence, biological feedback mechanisms act to resist long-term changes of body weight or fat. Furthermore, whether this weight and fat regain occurred because of "passive" and "active" regulation under the "set point" or "settling point" theories cannot fully be discerned because of limitations in study designs and data collected.

CONCLUSIONS

The regulation of energetics and body fat are complex and dynamic processes that require further studies of the interplay of genetic, physiological, and behavioral factors.

Predictive Mathematical Models of Weight Loss

PURPOSE OF REVIEW

Validated thermodynamic energy balance models that predict weight change are ever more in use today. Delivery of model predictions using web-based applets and/or smart phones has transformed these models into viable clinical tools. Here, we provide the general framework for thermodynamic energy balance model derivation and highlight differences between thermodynamic energy balance models using four representatives.

RECENT FINDINGS

Energy balance models have been used to successfully improve dietary adherence, estimate the magnitude of food waste, and predict dropout from clinical weight loss trials. They are also being used to generate hypotheses in nutrition experiments. Applications of thermodynamic energy balance weight change prediction models range from clinical applications to modify behavior to deriving epidemiological conclusions. Novel future applications involve using these models to design experiments and provide support for treatment recommendations.

Is an Energy Surplus Required to Maximize Skeletal Muscle Hypertrophy Associated With Resistance Training

Resistance training is commonly prescribed to enhance strength/power qualities and is achieved via improved neuromuscular recruitment, fiber type transition, and/ or skeletal muscle hypertrophy. The rate and amount of muscle hypertrophy associated with resistance training is influenced by a wide array of variables including the training program, plus training experience, gender, genetic predisposition, and nutritional status of the individual. Various dietary interventions have been proposed to influence muscle hypertrophy, including manipulation of protein intake, specific supplement prescription, and creation of an energy surplus. While recent research has provided significant insight into optimization of dietary protein intake and application of evidence based supplements, the specific energy surplus required to facilitate muscle hypertrophy is unknown. However, there is clear evidence of an anabolic stimulus possible from an energy surplus, even independent of resistance training. Common textbook recommendations are often based solely on the assumed energy stored within the tissue being assimilated. Unfortunately, such guidance likely fails to account for other energetically expensive processes associated with muscle hypertrophy, the acute metabolic adjustments that occur in response to an energy surplus, or individual nuances like training experience and energy status of the individual. Given the ambiguous nature of these calculations, it is not surprising to see broad ranging guidance on energy needs. These estimates have never been validated in a resistance training population to confirm the "sweet spot" for an energy surplus that facilitates optimal rates of muscle gain relative to fat mass. This review not only addresses the influence of an energy surplus on resistance training outcomes, but also explores other pertinent issues, including "how much should energy intake be increased," "where should this extra energy come from," and "when should this extra energy be consumed." Several gaps in the literature are identified, with the hope this will stimulate further research interest in this area. Having a broader appreciation of these issues will assist practitioners in the establishment of dietary strategies that facilitate resistance training adaptations while also addressing other important nutrition related issues such as optimization of fuelling and recovery goals. Practical issues like the management of satiety when attempting to increase energy intake are also addressed.

Influence of Energy Balance on the Rate of Weight Loss Throughout One Year of Roux-en-Y Gastric Bypass: a Doubly Labeled Water Study

OBJECTIVE

To investigate the influence of changes in energy balance and body composition on the rate of weight loss throughout 1 year of Roux-en-Y gastric bypass.

METHODS

Variables were collected pre-, 6, and 12 months (M) post-surgery from 18 women (BMI ≥ 40 and ≤ 50 kg m^-2, 20 to 45 years). Total energy expenditure (TEE_m), fat-free mass (FFM), and fat mass (FM) were measured by doubly labeled water. Self-reported energy intake (EI_sr) was obtained from three non-consecutive food diaries. Metabolic adaptation was assessed via deviations from TEE predictive equation, and the calculated energy intake (EI_c) via the sum of TEE and change in body stores.

RESULTS

BMI significantly decreased (mean ± SD) from 45 ± 2 kg m^-2 to 32 ± 3 kg m^-2 at 6 M, and to 30 ± 3 kg m^-2 at 12 M after surgery. The TEE_m reduced significantly at both time points when compared with pre-surgery (6 M: - 612 ± 317 kcal day^-1; 12 M: - 447 ± 516 kcal day^-1). At 6 M, a metabolic adaptation was observed and the energy balance was - 1151 ± 195 kcal day^-1, while at 12 M it was - 332 ± 158 kcal day^-1. Changes in the values of TEE_m were associated with changes in body weight at 12 M post-surgery. A significant underreporting was observed for EI_sr (1057 ± 385 kcal day^-1) vs. EI_c (2083 ± 309 kcal day^-1) at 12 M post-operative.

CONCLUSION

The higher rate of weight loss at 6 M post-surgery was a response to energy imbalance, which was caused by high restriction in energy intake even with the presence of metabolic adaptation at this time. The EI_sr was not sufficiently accurate to assess the energy consumption of this population.

REGISTRATION OF CLINICAL TRIALS (OBSERVATIONAL STUDY)

Brazilian Clinical Trials Registry: RBR-8k5jsj. Universal Trial Number: U1111-1206-0858.

A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake

Accurate measurement of energy intake (EI) is important for estimation of energy balance, and, correspondingly, body weight dynamics. Traditional measurements of EI rely on self-report, which may be inaccurate and underestimate EI. The imperfections in traditional methodologies such as 24-hour dietary recall, dietary record, and food frequency questionnaire stipulate development of technology-driven methods that rely on wearable sensors and imaging devices to achieve an objective and accurate assessment of EI. The aim of this research was to systematically review and examine peer-reviewed papers that cover the estimation of EI in humans, with the focus on emerging technology-driven methodologies. Five major electronic databases were searched for articles published from January 2005 to August 2017: Pubmed, Science Direct, IEEE Xplore, ACM library, and Google Scholar. Twenty-six eligible studies were retrieved that met the inclusion criteria. The review identified that while the current methods of estimating EI show promise, accurate estimation of EI in free-living individuals presents many challenges and opportunities. The most accurate result identified for EI (kcal) estimation had an average accuracy of 94%. However, collectively, the results were obtained from a limited number of food items (i.e., 19), small sample sizes (i.e., 45 meal images), and primarily controlled conditions. Therefore, new methods that accurately estimate EI over long time periods in free-living conditions are needed.

Mood and quality of life changes in pregnancy and postpartum and the effect of a behavioral intervention targeting excess gestational weight gain in women with overweight and obesity: a parallel-arm randomized controlled pilot trial

BACKGROUND

Intensive lifestyle interventions in pregnancy have shown success in limiting gestational weight gain, but the effects on mood and quality of life in pregnancy and postpartum are less known. The purpose was to quantify changes in mental and physical quality of life and depressive symptoms across pregnancy and the postpartum period, to determine the association between gestational weight gain and change in mood and quality of life, and to assess the effect of a behavioral intervention targeting excess gestational weight gain on these outcomes.

METHODS

A three group parallel-arm randomized controlled pilot trial of 54 pregnant women who were overweight or obese was conducted to test whether the SmartMoms® intervention decreased the proportion of women with excess gestational weight gain. Individuals randomized to Usual Care (n = 17) did not receive any weight management services from interventionists. Individuals randomized to the SmartMoms® intervention (n = 37) were provided with behavioral weight management counseling by interventionists either in clinic (In-Person, n = 18) or remotely through a smartphone application (Phone, n = 19). In a subset of 43 women, mood and mental and physical quality of life were assessed with the Beck Depression Inventory-II and the Rand 12-Item short form, respectively, in early pregnancy, late pregnancy, 1-2 months postpartum, and 12 months postpartum.

RESULTS

The SmartMoms® intervention and Usual Care groups had higher depressive symptoms (p < 0.03 for SmartMoms® intervention, p < 0.01 for Usual Care) and decreased physical health (p < 0.01) from early to late pregnancy. Both groups returned to early pregnancy mood and physical quality of life postpartum. Mental health did not change from early to late pregnancy (p = 0.8), from early pregnancy to 1-2 months (p = 0.5), or from early pregnancy to 12 months postpartum (p = 0.9), respectively. There were no significant intervention effects. Higher gestational weight gain was associated with worsened mood and lower physical quality of life across pregnancy.

CONCLUSION

High depressive symptoms and poor quality of life may be interrelated with the incidence of excess gestational weight gain. The behavioral gestational weight gain intervention did not significantly impact these outcomes, but mood and quality of life should be considered within future interventions and clinical practice to effectively limit excess gestational weight gain.

TRIAL REGISTRATION

NCT01610752 , Expecting Success, Registered 31 May 2012.

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.

What is the effect of diet and/or exercise interventions on behavioural compensation in non-exercise physical activity and related energy expenditure of free-living adults? A systematic review

Non-exercise physical activity (NEPA) and/or non-exercise activity thermogenesis (NEAT) reductions may occur from diet and/or exercise-induced negative energy balance interventions, resulting in less-than-expected weight loss. This systematic review describes the effects of prescribed diet and/or physical activity (PA)/exercise on NEPA and/or NEAT in adults. Studies were identified from PubMed, web-of-knowledge, Embase, SPORTDiscus, ERIC and PsycINFO searches up to 1 March 2017. Eligibility criteria included randomised controlled trials (RCT), randomised trials (RT) and non-randomised trials (NRT); objective measures of PA and energy expenditure; data on NEPA, NEAT and spontaneous PA; ≥10 healthy male/female aged>18 years; and ≥7 d length. The trial is registered at PROSPERO-2017-CRD42017052635. In all, thirty-six articles (RCT-10, RT-9, NRT-17) with a total of seventy intervention arms (diet, exercise, combined diet/exercise), with a total of 1561 participants, were included. Compensation was observed in twenty-six out of seventy intervention arms (fifteen studies out of thirty-six reporting declines in NEAT (eight), NEPA (four) or both (three)) representing 63, 27 and 23 % of diet-only, combined diet/exercise, and exercise-only intervention arms, respectively. Weight loss observed in participants who decreased NEAT was double the weight loss found in those who did not compensate, suggesting that the energy imbalance degree may lead to energy conservation. Although these findings do not support the hypothesis that prescribed diet and/or exercise results in decreased NEAT and NEPA in healthy adults, the underpowered trial design and the lack of state-of-the-art methods may limit these conclusions. Future studies should explore the impact of weight-loss magnitude, energetic restriction degree, exercise dose and participant characteristics on NEAT and/or NEPA.

Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL): Rationale, design and baseline characteristics

Underserved and minority populations suffer from a disproportionately high prevalence of obesity and related comorbidities. Effective obesity treatment programs delivered in primary care that produce significant weight loss are currently lacking. The purpose of this trial is to test the effectiveness of a pragmatic, high intensity lifestyle-based obesity treatment program delivered within primary care among an underserved population. We hypothesize that, relative to patients who receive usual care, patients who receive a high-intensity, health literacy- and culturally-appropriate lifestyle intervention will have greater percent reductions in body weight over 24 months. Eighteen clinics (N = 803 patients) serving low income populations with a high proportion of African Americans in Louisiana were randomized to the intervention or usual car. Patients in the intervention participate in a high-intensity lifestyle program delivered by health coaches employed by an academic health center and embedded in the primary care clinics. The program consists of weekly (16 in-person/6 telephone) sessions in the first six months, followed by sessions held at least monthly for the remaining 18 months. Primary care practitioners in usual care receive information on weight management and the current Centers for Medicare and Medicaid Services reimbursement for obesity treatment. The primary outcome is percent weight loss at 24 months. Secondary outcomes include absolute 24-month changes in body weight, waist circumference, blood pressure, fasting glucose and lipids, health-related quality of life, and weight-related quality of life. The results will provide evidence on the effectiveness of implementing high-intensity lifestyle and obesity counseling in primary care settings among underserved populations.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT02561221.

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.

A long-term mechanistic computational model of physiological factors driving the onset of type 2 diabetes in an individual

A computational model of the physiological mechanisms driving an individual's health towards onset of type 2 diabetes (T2D) is described, calibrated and validated using data from the Diabetes Prevention Program (DPP). The objective of this model is to quantify the factors that can be used for prevention of T2D. The model is energy and mass balanced and continuously simulates trajectories of variables including body weight components, fasting plasma glucose, insulin, and glycosylated hemoglobin among others on the time-scale of years. Modeled mechanisms include dynamic representations of intracellular insulin resistance, pancreatic beta-cell insulin production, oxidation of macronutrients, ketogenesis, effects of inflammation and reactive oxygen species, and conversion between stored and activated metabolic species, with body-weight connected to mass and energy balance. The model was calibrated to 331 placebo and 315 lifestyle-intervention DPP subjects, and one year forecasts of all individuals were generated. Predicted population mean errors were less than or of the same magnitude as clinical measurement error; mean forecast errors for weight and HbA1c were ~5%, supporting predictive capabilities of the model. Validation of lifestyle-intervention prediction is demonstrated by synthetically imposing diet and physical activity changes on DPP placebo subjects. Using subject level parameters, comparisons were made between exogenous and endogenous characteristics of subjects who progressed toward T2D (HbA1c > 6.5) over the course of the DPP study to those who did not. The comparison revealed significant differences in diets and pancreatic sensitivity to hyperglycemia but not in propensity to develop insulin resistance. A computational experiment was performed to explore relative contributions of exogenous versus endogenous factors between these groups. Translational uses to applications in public health and personalized healthcare are discussed.

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.

Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population

Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m² ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics.

A mathematical model of the effects of resistance exercise-induced muscle hypertrophy on body composition

PURPOSE

Current diet and exercise methods used to maintain or improve body composition often have poor long-term outcomes. We hypothesize that resistance exercise (RE) should aid in the maintenance of a healthy body composition by preserving lean mass (LM) and metabolic rate.

METHOD

We extended a previously developed energy balance model of human metabolism to include muscle hypertrophy in response to RE. We first fit model parameters to a hypothetical individual to simulate an RE program and then compared the effects of a hypocaloric diet only to the diet with either cardiovascular exercise (CE) or RE. We then simulated a cohort of individuals with different responses to RE by varying the parameters controlling it using Latin Hypercube Sampling (LHS). Finally, we fit the model to mean data from an elderly population on an RE program.

CONCLUSION

The model is able to reproduce the time course of change in LM in response to RE and can be used to generate a simulated cohort for in silico clinical studies. Simulations suggest that the additional LM generated by RE may shift the body composition to a healthier state.

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.

Prediction Equations Overestimate the Energy Requirements More for Obesity-Susceptible Individuals

Predictive equations to estimate resting metabolic rate (RMR) are often used in dietary counseling and by online apps to set energy intake goals for weight loss. It is critical to know whether such equations are appropriate for those susceptible to obesity. We measured RMR by indirect calorimetry after an overnight fast in 26 obesity susceptible (OSI) and 30 obesity resistant (ORI) individuals, identified using a simple 6-item screening tool. Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations. Absolute measured RMR did not differ significantly between OSI versus ORI (6339 vs. 5893 kJ·d⁻¹, p = 0.313). All three prediction equations over-estimated RMR for both OSI and ORI when measured RMR was ≤5000 kJ·d⁻¹. For measured RMR ≤7000 kJ·d⁻¹ there was statistically significant evidence that the equations overestimate RMR to a greater extent for those classified as obesity susceptible with biases ranging between around 10% to nearly 30% depending on the equation. The use of prediction equations may overestimate RMR and energy requirements particularly in those who self-identify as being susceptible to obesity, which has implications for effective weight management.

Effectiveness of SmartMoms, a Novel eHealth Intervention for Management of Gestational Weight Gain: Randomized Controlled Pilot Trial

BACKGROUND

Two-thirds of pregnant women exceed gestational weight gain (GWG) recommendations. Because excess GWG is associated with adverse outcomes for mother and child, development of scalable and cost-effective approaches to deliver intensive lifestyle programs during pregnancy is urgent.

OBJECTIVE

The aim of this study was to decrease the proportion of women who exceed the Institute of Medicine (IOM) 2009 GWG guidelines.

METHODS

In a parallel-arm randomized controlled trial, 54 pregnant women (age 18-40 years) who were overweight (n=25) or obese (n=29) were enrolled to test whether an intensive lifestyle intervention (called SmartMoms) decreased the proportion of women with excess GWG, defined as exceeding the 2009 IOM guidelines, compared to no intervention (usual care group). The SmartMoms intervention was delivered through mobile phone (remote group) or in a traditional in-person, clinic-based setting (in-person group), and included a personalized dietary intake prescription, self-monitoring weight against a personalized weight graph, activity tracking with a pedometer, receipt of health information, and continuous personalized feedback from counselors.

RESULTS

A significantly smaller proportion of women exceeded the IOM 2009 GWG guidelines in the SmartMoms intervention groups (in-person: 56%, 10/18; remote: 58%, 11/19) compared to usual care (85%, 11/13; P=.02). The remote intervention was a lower cost to participants (mean US $97, SD $6 vs mean US $347, SD $40 per participant; P<.001) and clinics (US $215 vs US $419 per participant) and with increased intervention adherence (76.5% vs 60.8%; P=.049).

CONCLUSIONS

An intensive lifestyle intervention for GWG can be effectively delivered via a mobile phone, which is both cost-effective and scalable.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01610752; https://clinicaltrials.gov/ct2/show/NCT01610752 (Archived by WebCite at http://www.webcitation.org/6sarNB4iW).

Do Dynamic Fat and Fat-Free Mass Changes follow Theoretical Driven Rules in Athletes?

INTRODUCTION

Maximizing fat mass (FM) loss while preserving or increasing fat-free mass (FFM) is a central goal for athletic performance but the composition of body weight (BW) changes over time with training are largely unknown.

PURPOSE

We aimed to analyze FM and FFM contributions to BW changes and to test if these contributions follow established rules and predictions over one athletic season.

METHODS

Seventy athletes (42 men; handball, volleyball, basketball, triathlon, and swimming) were evaluated from the beginning to the competitive stage of the season and were empirically divided into those who lost (n = 20) or gained >1.5% BW (n = 50). FM and FFM were evaluated with a four-compartment model. Energy densities (ED) of 1.0 kcal·g for FFM and 9.5 kcal·g for FM were used to calculate ED/per kilogram BW change.

RESULTS

Athletes that lost >1.5% BW decreased FM by 1.7 ± 1.6 kg (P < 0.05), whereas FFM loss was nonsignificant (-0.7 ± 2.1 kg). Those who gained >1.5% BW increased FFM by 2.3 ± 2.1 kg (P < 0.05) with nonsignificant FM gains (0.4 ± 2.2 kg). The proportion of BW change as FM for those who lost or gained BW was 90% (ED: 8678 ± 2147 kcal·kg) and 5% (ED: 1449 ± 1525 kcal·kg), respectively (P < 0.001). FFM changes from Forbes Curve were inversely related to observed changes (r = -0.64; r = -0.81, respectively for those who lost or gained BW).

CONCLUSIONS

Athletes that lost BW used 90% of the energy from FM while in those gaining BW, 95% was directed to FFM. When BW is lost, dynamic changes in its composition do not follow established rules and predictions used for lean or overweight/obese nonathletic populations.