Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends - PubMed (original) (raw)
Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends
Diana M Thomas et al. Obesity (Silver Spring). 2014 Feb.
Abstract
Objective: Obesity prevalence in the United States 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 nonsocial 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 birthrate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of overweight, obesity, 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.
Copyright © 2013 The Obesity Society.
Conflict of interest statement
CONFLICT OF INTEREST
Bouchard: Claude Bouchard is a consultant for Weight Watchers International and a member of the Scientific Advisory Board of Pathway Genomics.
Dhurandhar: The following Patents are granted or have been applied for: Patent number 6,127,113: Viral obesity methods and compositions. Patent number 6,664,050: Viral obesity methods and compositions. Patent number US 8,008,436B2, dated August 30, 2011: Adenovirus 36 E4orf1 gene and protein and their uses. Provisional patent filed: Adenovirus Ad36 E4orf1 protein for prevention and treatment of non-alcoholic fatty liver disease, July 2010. Provisional patent filed: Enhanced glycemic control using Ad36E4orf1 and AKT1 Inhibitor. January 2012.
Thomas: Diana Thomas is a consultant for Jenny Craig.
Figures
Figure 1
Diagram describing flow from each compartment formulated in the dynamic model.
Figure 2
The method of shooting employed to determine parameter values to force solutions through the initial prevalence data (1988–2000). Panel A depicts model solutions for an initial guess of parameter values. This initial guess led to an overestimated rate of increase in the overweight category (blue) and an underestimated rate of increase in the obese and extremely obese category. Parameters that influence these rates were adjusted so that the rate of overweight increase was lowered and the rate of increased prevalence of obese and extremely obese were slightly higher. Panel B shows a good fit through prevalence data from 1988–2000. The resulting curves continue to follow prevalence data after 2000 which validates their projections and the parameter estimates.
Figure 1
Diagram describing flow from each compartment formulated in the dynamic model. All compartments include a population wide differential death term.
Figure 2
Comparison of model predictions with actual trends. Parameters and baseline conditions applied in model simulations appear in Table 1. Panel A depicts model predicted trends (solid curves) in overweight, obese, and extremely obese in US adults from years 1988 to 2030. Solid circles depict the Centers for Disease Control reported trends in overweight, obese, and extremely obese in US adults from years 1988–2008 (20). Panel B depicts model predicted trends (solid curves) in overweight, obese, and extremely obese in adults in the UK from years 1993 to 2033. Solid circles depict the Health Survey for England reported trends in overweight, obese, and extremely obese in US adults from years 1993–2008. In comparison to the US, parameter values for social influence and recovery rates are almost identical. The spontaneous rate of transition is significantly lower. The portion of the simulations that were fit to data is depicted by solid curves. The dashed curves represents the simulation which did not rely on curve fitting and represents model validation. The dotted portion of the simulations represent the portion of the curve that is a forecast beyond available data.
Figure 3
The dependency of the plateau on birth rate can be observed by varying the birth rate parameter. In Panel A, the percent of the obese population was plotted for four birth rates; μ = 0.001, 0.0144, 0.02, and 0.05, which reflect rates of 1, 14.4, 20, and 50 births per 1,000 individuals. The curves show that the percent at which obesity plateaus decreases as a function of increasing birth rate. Similarly, Panel B depicts three simulations for different probabilities of being born into obesogenic environment; p = 0.0, p = 0.55, p = 0.95. As p increases, the value at which obesity plateaus increases and the time to plateau increases. Panel C depicts three simulations for different obese and extremely obese population death rates _D_0 = 0.0144, _D_0 = 0.0150, _D_0 = 0.02. As _D_0 increases the value at which obesity plateaus decreases.
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