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.

Allometric models of adult regional body lengths and circumferences to height: Insights from a three-dimensional body image scanner.

OBJECTIVES: Recent reports on body regional mass scalings to height have advanced understanding differences in adult heights. These studies resulted in conjectures on how regional lengths and circumferences may scale to height. We provide evidence for these conjectures by analyzing a large sample of regional limb, trunk, chest, and head lengths and circumferences in a large sample of US Army basic training recruits. METHODS: Participants consisted of 10 271 males and 2760 females ages 17 to 21 years old who reported for basic training at Fort Jackson, SC. Participants were imaged by a three-dimensional (3D) body scanner for uniform sizing which yielded 159 body measurements of total mass, lengths and circumferences at regional sites of arms, legs, trunk, chest, and head. The allometric model, Body Measur e i = α i H ß i was applied to derive scaling exponents which were applied to estimate regional mass scalings. RESULTS: Body mass scaled to height with powers of ∼2.0 (mean ß ± SE, 1.98 ± 0.04, 1.93 ± 0.06). Arm and leg lengths scaled to exponents larger than 1.0 and head height and circumferences at regional sites scaled to exponents smaller than 1.0. The leg, arm, and trunk mass scaling exponents were all above 2.0. Head mass scaled to powers smaller than 2.0. CONCLUSIONS: The 3D scanner allowed hundreds of anthropometric measurements to be obtained within seconds. The ensuing analysis revealed that greater height yielded disproportional increases in limb lengths, limb mass and trunk mass. These analyses provide evidence that could not be previously measured that further both biomechanical and metabolic conjectures.

Revisiting the United States Army body composition standards: a receiver operating characteristic analysis.

BACKGROUND: The objective for percent body fat standards in the United States Army Body Composition Program (ABCP) is to ensure soldiers maintain optimal well-being and performance under all conditions. However, conducting large-scale experiments within the United States Army to evaluate the efficacy of the thresholds is challenging. METHODS: A receiver operating characteristic (ROC) analysis with corresponding area under the curve (AUC) was performed on body mass index (BMI) and waist circumference to determine optimal gender-specific age cohort thresholds that meet ABCP percent body fat standards in the National Health and Nutrition Examination Survey (NHANES) III. A second dataset consisting of a cohort of basic training recruits (N = 20,896 soldiers, 28% female) with BMI and waist circumference measured using a 3D body image scanner was applied to calculate what percent of basic training recruits meet the ABCP percent body fat standards. Regression models to determine the contribution of different circumference sites to the predictions of percent body fat were developed using a database compiled at the New York Obesity Research Center (N = 500). RESULTS: Optimal BMI thresholds ranged from 23.65 kg/m2 (17-21-year-old cohort) to 26.55 kg/m2 (40 and over age cohort) for males and 21.75 to 24.85 kg/m2 for females. The AUC values were between 0.86 and 0.92. The waist circumference thresholds ranged 81.35 to 97.55 cm for males and 77.05 to 89.35 cm for females with AUC values between 0.90 and 0.91. These BMI thresholds were exceeded by 65% of male and 74% of female basic training recruits and waist circumference thresholds were exceeded by 73% of male and 85% of female recruits. The single circumference that contributed most to prediction of body fat was waist circumference in males and mid-thigh circumference in females. CONCLUSIONS: The ABCP percent body fat thresholds yield BMI thresholds that are below the United States Army BMI standards, especially in females which suggests the ABCP percent body fat standards may be too restrictive. The United States Army percent body fat standards could instead be matched to existing national health guidelines.

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.

Scaling of adult human bone and skeletal muscle mass to height in the US population.

OBJECTIVES: The scaling of structural components to body size is well studied in mammals, although comparable human observations in a large and diverse sample are lacking. The current study aimed to fill this gap by examining the scaling relationships between total body (TB) and regional bone and skeletal muscle (SM) mass with body size, as defined by stature, in a nationally representative sample of the US population. METHODS: Subjects were 17,126 non-Hispanic (NH) white, NH black, and Mexican American men and women, aged ≥18 years, evaluated in the National Health and Nutrition Examination Survey who had TB and regional bone mineral (BMin) and lean soft tissue (LST) mass measured by dual-energy X-ray absorptiometry. BMin and appendicular LST served as surrogate bone and SM mass measures, respectively. The allometric model, BMin or LST = α(height)ß , in a logarithmic form was used to generate scaling exponents. RESULTS: The findings were similar across all gender and race groups: body mass scaled to height with powers of ~2.0 (mean ß ± SE, 1.94 ± 0.08-2.29 ± 0.09) while TB and appendicular BMin and appendicular LST scaled to height with consistently larger powers than those for body mass (eg, all P < .05 in NH white men and women); the largest BMin and LST scaling powers to height were observed in the lower extremities. CONCLUSIONS: Bone and SM mass, notably those of the lower extremities, increase as proportions of body mass with greater adult height. Metabolic and biomechanical implications emerge from these observations, the first of their kind in a representative adult US population sample.

Adult energy requirements predicted from doubly labeled water.

BACKGROUND: Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician's best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative. METHODS: Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases. RESULTS: Sixty-four different linear and nonlinear regression models were developed. The adjusted R2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.

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.

Myths, presumptions, and facts about obesity.

BACKGROUND: Many beliefs about obesity persist in the absence of supporting scientific evidence (presumptions); some persist despite contradicting evidence (myths). The promulgation of unsupported beliefs may yield poorly informed policy decisions, inaccurate clinical and public health recommendations, and an unproductive allocation of research resources and may divert attention away from useful, evidence-based information. METHODS: Using Internet searches of popular media and scientific literature, we identified, reviewed, and classified obesity-related myths and presumptions. We also examined facts that are well supported by evidence, with an emphasis on those that have practical implications for public health, policy, or clinical recommendations. RESULTS: We identified seven obesity-related myths concerning the effects of small sustained increases in energy intake or expenditure, establishment of realistic goals for weight loss, rapid weight loss, weight-loss readiness, physical-education classes, breast-feeding, and energy expended during sexual activity. We also identified six presumptions about the purported effects of regularly eating breakfast, early childhood experiences, eating fruits and vegetables, weight cycling, snacking, and the built (i.e., human-made) environment. Finally, we identified nine evidence-supported facts that are relevant for the formulation of sound public health, policy, or clinical recommendations. CONCLUSIONS: False and scientifically unsupported beliefs about obesity are pervasive in both scientific literature and the popular press. (Funded by the National Institutes of Health.).

First Trimester Detection of Placental Disease: Challenges and Opportunities.

It is generally agreed that placental pathology accounts for the majority of perinatal morbidity and mortality. If a placental prodrome could be diagnosed in vivo, risk for maternal or fetal complications could be estimated and acted upon before clinical symptoms are apparent. This is especially relevant in early diagnoses of gestational diabetes mellitus, which can be controlled through carefully monitored diet and activity changes. To meet this important need, there have been increased efforts to identify early gestation biomarkers of placental dysfunction using innovative imaging technologies. Here we outline innovative quantitative markers of placental shape and their relationship to placental function, clinical implications of these quantifiers, and the most recent mathematical models that utilize placental images to delineate at risk from normal pregnancies. We propose that novel contexts of readily available placental measures and routine collection of in vivo placental images in all pregnancies may be all that are needed to advance the identification of early risk determination of complicated pregnancies from placental images.

The gap between expectations and reality of exercise-induced weight loss is associated with discouragement.

BACKGROUND: Exercise interventions result in modest weight loss, yet exercise is frequently prescribed for weight loss. PURPOSE: To identify individuals who become discouraged when exercise fails to achieve weight loss. METHODS: Representative samples of U.S. adults were recruited using Google Consumer Surveys in August-October 2014. Respondents were asked about beliefs and potential discouragement regarding the role of exercise and weight loss. An analysis of variance was performed to predict individuals that become discouraged if exercise does not lead to weight loss. RESULTS: The belief that exercise is a very effective way to lose weight was common (71% of respondents). Stronger belief that exercise is an effective way to lose weight (p<0.001) in individuals with higher weight status (p=0.04) positively predicted discouragement with exercise. Higher weight status combined with the belief that exercise reduces weight was a significant positive predictor of discouragement (p=0.01). CONCLUSIONS: Individuals with higher weight status that believe that exercise is an effective way to lose weight are more likely to become discouraged when exercise does not lead to weight loss. Prescribing exercise for weight loss might contribute to discouragement. Future studies should evaluate ways to encourage exercise without promoting the belief that exercise will yield weight loss.

Scaling of adult regional body mass and body composition as a whole to height: Relevance to body shape and body mass index.

OBJECTIVES: Adult body mass (MB) empirically scales as height (Ht) squared (MB ∝ Ht(2) ), but does regional body mass and body composition as a whole also scale as Ht(2) ? This question is relevant to a wide range of biological topics, including interpretation of body mass index (BMI). METHODS: Dual-energy X-ray absorptiometry (DXA) was used to quantify regional body mass [head (MH), trunk, arms, and legs] and whole-body composition [fat, lean soft tissue (LST), and bone mineral content (BMC)] in non-Hispanic (NH) white, NH black, Mexican American, and Korean adults participating in the National Health and Nutrition Examination Survey (NHANES; n = 17,126) and Korean NHANES (n = 8,942). Regression models were developed to establish Ht scaling powers for each measured component with adjustments for age and adiposity. RESULTS: Exploratory analyses revealed a consistent scaling pattern across men and women of the four population groups: regional mass powers, head (∼0.8-1) < arms and trunk (∼1.8-2.3) < legs (∼2.3-2.6); and body composition, LST (∼2.0-2.3) < BMC (∼2.1-2.4). Small sex and population differences in scaling powers were also observed. As body mass scaled uniformly across the eight sex and population groups as Ht(∼2) , tall and short subjects differed in body shape (e.g., MH/MB ∝ Ht(-∼1) ) and composition. CONCLUSIONS: Adult human body shape and relative composition are a function of body size as represented by stature, a finding that reveals a previously unrecognized phenotypic heterogeneity as defined by BMI. These observations provide new pathways for exploring mechanisms governing the interrelations between adult stature, body morphology, biomechanics, and metabolism.

The impact of body composition on the degree of misreporting of food diaries.

BACKGROUND/OBJECTIVES: Accurate assessments of energy intake (EI) are needed in lifestyle interventions to guarantee a negative energy balance (EB), thereby losing weight. This study aimed (1) to compare objectively measured and self-reported EI and (2) to determine the predictors of underreporting divided by sex, adiposity and BMI category. METHODS: Seventy-three participants [mean (SD): 43.7 (9.2) years, BMI = 31.5 (4.5) kg/m2, 37% females] of the Champ4Life intervention were included in this study. EI was measured using the "intake-balance method" and self-reported through 3-day food records. Fat mass (FM) and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry. Bland-Altman analysis was performed to compare both EI assessments. RESULTS: Self-reported EI was lower than measured EI during both neutral (-355 kcal/d) and negative EB (-570 kal/day). While no significant trends were observed for EI evaluation in either neutral (p = 0.315) or negative EB (p = 0.611), limits of agreement were wide (-1720 to 1010 and -1920 to 779 kcal/day, respectively). In females, the degree of misreporting (kcal/day and %) was predicted by weight (p = 0.032 and p = 0.039, respectively) and FM (p = 0.029 and p = 0.037, respectively). In males, only BMI (p = 0.036) was a predictor of misreporting (kcal/day). CONCLUSION: Self-reported EI did not agree with measured EI. Our results show that larger body size was associated with higher levels of underestimation for EI (females only). Nevertheless, misreporting EI is a complex issue involving more associations than merely body composition. A deeper understanding could inform counseling for participants filling out food records in other to reduce misreporting and improve validity.

Exercise-induced Changes in Central Adiposity During an RCT: Effect of Exercise Dose and Associations With Compensation.

CONTEXT: Exercise can decrease central adiposity, but the effect of exercise dose and the relationship between central adiposity and exercise-induced compensation is unclear. OBJECTIVE: Test the effect of exercise dose on central adiposity change and the association between central adiposity and exercise-induced weight compensation. METHODS: In this ancillary analysis of a 6-month randomized controlled trial, 170 participants with overweight or obesity (mean ± SD body mass index: 31.5 ± 4.7 kg/m2) were randomized to a control group or exercise groups that reflected exercise recommendations for health (8 kcal/kg/week [KKW]) or weight loss and weight maintenance (20 KKW). Waist circumference was measured, and dual-energy X-ray absorptiometry assessed central adiposity. Predicted weight change was estimated and weight compensation (weight change - predicted weight change) was calculated. RESULTS: Between-group change in waist circumference (control: .0 cm [95% CI, -1.0 to 1.0], 8 KKW: -.7 cm [95% CI, -1.7 to .4], 20 KKW: -1.3 cm [95% CI, -2.4 to -.2]) and visceral adipose tissue (VAT; control: -.02 kg [95% CI, -.07 to .04], 8 KKW: -.01 kg [95% CI, -.07 to .04], 20 KKW: -.04 kg [95% CI, -.10 to .02]) was similar (P ≥ .23). Most exercisers (82.6%) compensated (weight loss less than expected). Exercisers who compensated exhibited a 2.5-cm (95% CI, .8 to 4.2) and .23-kg (95% CI, .14 to .31) increase in waist circumference and VAT, respectively, vs those who did not (P < .01). Desire to eat predicted VAT change during exercise (ß = .21; P = .03). CONCLUSION: In the presence of significant weight compensation, exercise at doses recommended for health and weight loss and weight maintenance leads to negligible changes in central adiposity.

An algorithm to simulate missing data for mixed meal tolerance test response curves.

BACKGROUND: Response curves formed by analyte concentrations measured at sampled time points after consuming a mixed meal are increasingly being used to characterize responses to differing diets. Unfortunately, owing to a variety of reasons, analyte concentrations for some of the time points may be missing. OBJECTIVES: This study aimed to develop an algorithm to estimate the missing values at sampled time points in the analyte response curve to a mixed meal tolerance test (MMTT). METHODS: We developed an algorithm to simulate the missing postprandial concentration values for an MMTT. The algorithm was developed to handle any number of missing values for 2 or less consecutive missing values. The algorithm was tested on MMTT response curve data for glucose and triglyceride measurements in data from 3 different studies with 2119 postprandial MMTT response curves. The algorithm was validated by removing concentration values that were not missing and replacing them with the algorithm simulated values. The AUC error between the actual curve and simulated curves were also calculated. A web-based application was developed to automatically simulate missing values for an uploaded MMTT data set. RESULTS: The algorithm was programmed in Python and the resulting web-based application and a video tutorial were provided. The validation indicated good agreement between actual and simulated values with error increasing for less frequently sampled time points. The study with the mean minimum error of glucose concentrations was 6.2 ± 2.1 mg/dL and study with the mean maximum error of glucose concentrations was 11.3 ± 4.7 mg/dL. Triglycerides had 16.1 ± 6.2 mg/dL mean error. The AUC error was small ranging between 0.01% and 0.28%. CONCLUSIONS: The presented algorithm reconstructs postprandial response curves with estimations of values that are missing.

Transforming Big Data into AI-ready data for nutrition and obesity research.

OBJECTIVE: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions. METHODS: We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed. RESULTS: Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented. CONCLUSIONS: Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Raising the U.S. Army Height-Weight (Body Mass Index) Standards: Quantifying Metabolic Risk.

BACKGROUND & OBJECTIVES: The U.S. Army fell 25% short of its recruitment goal in 2022 and therefore, increasing the eligibility pool for potential recruits is of interest. Raising the body mass index (BMI) standards for eligibility presents a path to increase the recruitable population; however, there may be additional costs incurred due to attendant health risks that may be present in individuals with higher BMI. METHODS: We filtered the 2017-2020 National Health and Nutrition Examination Survey by age (17-25 years) and BMI (up to 30 kg/m2). A k-means cluster analysis was performed on the filtered dataset for the variables used to determine metabolic syndrome. Metabolic syndrome Clusters were characterized through summary statistics and compared over clinical measurements and questionnaire responses. RESULTS: Five distinct clusters were identified and mean BMI in two clusters (Clusters1 and 3) exceeded the current U.S. Army BMI thresholds. Of these two clusters, Cluster 1 members had metabolic syndrome. Cluster 3 members were at higher risk for metabolic syndrome compared to members of Clusters 2, 4, and 5. Mean waist circumference was slightly lower in Cluster 3 compared to Cluster 1. None of the clusters had significant differences in depression scores, poverty index, or frequency of dental visits. CONCLUSIONS: Potential recruits from Cluster 1 have excessive health risk and may incur substantial cost to the U.S. Army if enlisted. However, potential recruits from Cluster 3 appear to add little risk and offer an opportunity to increase the pool for recruiting.