ObjectiveTo develop a new geometrical index that combines height, waist circumference (WC), and hip circumference (HC) and relate this index to total and visceral body fat.Design and MethodsSubject data were pooled from three databases that contained demographic, anthropometric, dual energy X-ray absorptiometry (DXA) measured fat mass, and magnetic resonance imaging measured visceral adipose tissue (VAT) volume. Two elliptical models of the human body were developed. Body roundness was calculated from the model using a well-established constant arising from the theory. Regression models based on eccentricity and other variables were used to predict % body fat and % VAT.ResultsA body roundness index (BRI) was derived to quantify the individual body shape in a height-independent manner. Body roundness slightly improved predictions of % body fat and % VAT compared to the traditional metrics of body mass index (BMI), WC, or HC. On this basis, healthy body roundness ranges were established. An automated graphical program simulating study results was placed at http://www.pbrc.edu/bodyroundness.ConclusionsBody roundness index, a new shape measure, is a predictor of % body fat and % VAT and can be applied as a visual tool for health status evaluations.

Our energy-balance model provides plausible predictions of GWG that results from changes in energy intakes. Because the model was implemented as a Web-based applet, it can be widely used by pregnant women and their health care providers.

Despite theoretical evidence that the model commonly referred to as the 3500 kcal rule grossly overestimates actual weight loss, widespread application of the 3500 kcal formula continues to appear in textbooks, on respected government and health related websites, and scientific research publications. Here we demonstrate the risk of applying the 3500 kcal rule even as a convenient estimate by comparing predicted against actual weight loss in seven weight loss experiments conducted in confinement under total supervision or objectively measured energy intake. We offer three newly developed, downloadable applications housed in Microsoft® Excel and Java which simulates a rigorously validated, dynamic model of weight change. The first two tools available at www.pbrc.edu/sswcp, provide a convenient alternative method for providing patients with projected weight loss/gain estimates in response to changes in dietary intake. The second tool which can be downloaded from the URL, www.pbrc.edu/mswcp, projects estimated weight loss simultaneously for multiple subjects. This tool was developed to inform weight change experimental design and analysis. While complex dynamic models may not be directly tractable, the newly developed tools offer the opportunity to deliver dynamic model predictions as a convenient and significantly more accurate alternative to the 3500 kcal rule.

Objective Obesity prevalence in the United States (US) appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. Design and Methods A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and non-social influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. Results The dynamic model predicts that: obesity prevalence is a function of birth rate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of obesity, overweight, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9%, respectively. Conclusions The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence.

SummaryObjectiveProviding effective dietary counselling so that pregnancy weight gain remains within the 2009 Institute of Medicine (IOM) guidelines requires accurate maternal energy intake measures. Current practice is based on self‐reported intake that has been demonstrated unreliable. This study applies an objective calculation of energy intake from a validated mathematical model to identify characteristics of individuals more likely to misreport during pregnancy.MethodsA validated maternal energy balance equation was used to calculate energy intake from gestational weight gain in 1,368 subjects. The difference between self‐reported and model‐predicted energy intake was tested for demographics, economic status, education level and maternal health status.ResultsA weight gain of 15.2 kg resulted in model‐predicted intake during pregnancy of 2,882.97 ±  135.71 kcal day−1, which differed from self‐reported intake of 2,180.5 ± 856.0 kcal day−1. The achieved weight gain exceeded the IOM guidelines; however, the model predicted weight gain from self‐reported energy intake was below IOM guidelines. Higher income (p = 0.004), education (p = 0.003), birth weight (p = 0.017), gestational diabetes (p = 0.008) and pre‐existing diabetes (p < 0.001) were associated with under‐reported energy intake. More children living at home (p = 0.001) were associated with more accurate self‐reported intake.ConclusionsWhen assessing self‐reported energy intake in pregnancy studies, birth weight, gestational diabetes status, pre‐existing diabetes, higher income and education predict higher under‐reporting. Clinicians providing dietary treatment recommendations during pregnancy should be aware that individuals with pre‐existing diabetes and gestational diabetes mellitus are more likely to misreport their intake. Additionally, the systems model approach can be applied early in intervention to objectively monitor dietary compliance to treatment recommendations.