Modeling in clinical nutrition: does it add to patient care?

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

Obesity Tissue: Composition, Energy Expenditure, and Energy Content in Adult Humans

OBJECTIVE

Chronic positive energy balance leads to obesity, and the "excess" weight is usually described as consisting solely of adipose tissue (AT) or its two components, fat and fat-free mass (nonfat cell mass, extracellular fluid). This study aimed to clarify the nature of "obesity" tissue.

METHODS

A total of 333 adults had AT, skin, skeletal muscle, bone, heart, liver, kidney, spleen, brain, and residual mass measured or derived using magnetic resonance imaging and dual-energy x-ray absorptiometry. First, associations between these components and AT were examined by developing multiple regression models. Next, obesity-tissue composition was developed by deriving mean component mass differences between participant groups with normal weight (BMI < 25 kg/m² ) and those with obesity (BMI > 29.9 kg/m² ); respective resting energy expenditures and metabolizable energy and protein contents were calculated.

RESULTS

AT significantly predicted organ-tissue mass in 17 of 18 multiple regression models. In addition to AT and skeletal muscle, the following associations were found: skin, liver, and bone were main contributors to obesity-tissue composition; liver, kidneys, and heart to resting energy expenditure; and skin, liver, and bone to metabolizable energy and protein contents. A pronounced sexual dimorphism was present in all three models.

CONCLUSIONS

Obesity is characterized not only by excess AT but by increases in the masses of other "companion" organs and tissues and their related metabolic properties.

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.

Why are there race/ethnic differences in adult body mass index-adiposity relationships? A quantitative critical review

Body mass index (BMI) is now the most widely used measure of adiposity on a global scale. Nevertheless, intense discussion centers on the appropriateness of BMI as a phenotypic marker of adiposity across populations differing in race and ethnicity. BMI-adiposity relations appear to vary significantly across race/ethnic groups, but a collective critical analysis of these effects establishing their magnitude and underlying body shape/composition basis is lacking. Accordingly, we systematically review the magnitude of these race-ethnic differences across non-Hispanic (NH) white, NH black and Mexican American adults, their anatomic body composition basis and potential biologically linked mechanisms, using both earlier publications and new analyses from the US National Health and Nutrition Examination Survey. Our collective observations provide a new framework for critically evaluating the quantitative relations between BMI and adiposity across groups differing in race and ethnicity; reveal new insights into BMI as a measure of adiposity across the adult age-span; identify knowledge gaps that can form the basis of future research and create a quantitative foundation for developing BMI-related public health recommendations.

Dispatch from the field: is mathematical modeling applicable to obesity treatment in the real world?

Mathematical models of human weight dynamics have been validated in research settings, but are they applicable in the real world of clinical obesity treatment? We compared model calculations to weight loss data from 49 patients in a medically-supervised, outpatient weight loss program. A range of expected weight losses was defined for each patient based on uncertainties in their baseline energy requirements and physical activity changes. Assuming that energy intake was within the prescribed range, the observed and model-calculated weight losses were highly correlated (r=0.9, p<0.0001) and the mean calculated loss of 14.0±9.1 kg (mean±SD) achieved over 13.2±9.4 weeks was not significantly different from the data (13.2±8.9 kg; p=0.14). However, the model identified 14 patients whose weight losses were less than expected, suggesting the possibility of non-compliance. Therefore, mathematical models can be clinically useful tools for prospective goal-setting and assessment of compliance, both of which are important considerations for treating obesity.