Future body mass index modelling based on macronutrient profiles and physical activity

David K Cundiff,Nikunj Raghuvanshi

doi:10.1186/1742-4682-9-43

David K Cundiff, Nikunj Raghuvanshi

Open Access

https://doi.org/10.1186/1742-4682-9-43

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Abstract

BackgroundAn accurate system of determining the relationship of macronutrient profiles of foods and beverages to the long-term weight impacts of foods is necessary for evidence-based, unbiased front-of-the-package food labels.MethodsData sets on diet, physical activity, and BMI came from the Food and Agriculture Organization (FAO), the World Health Organization (WHO), the Diabetes Control and Complications Trial (DCCT), and Epidemiology Diabetes Intervention and Complications (EDIC). To predict future BMI of individuals, multiple regression derived FAO/WHO and DCCT/EDIC formulas related macronutrient profiles and physical activity (independent variables) to BMI change/year (dependent variable). Similar formulas without physical activity related macronutrient profiles of individual foods and beverages to four-year weight impacts of those items and compared those forecasts to published food group profiling estimates from three large prospective studies by Harvard nutritional epidemiologists.ResultsFAO/WHO food and beverage formula: four-year weight impact (pounds)=(0.07710 alcohol g+11.95 (381.7+carbohydrates g per serving)*4/(2,613+kilocalories per serving)–304.9 (30.38+dietary fiber g per serving)/(2,613+kilocalories per serving)+19.73 (84.44+total fat g)*9/(2,613+kilocalories per serving)–68.57 (20.45+PUFA g per serving)*9/(2,613+kilocalories per serving))*2.941–12.78 (n=334, R2=0.29, P < 0.0001). DCCT/EDIC formula for four-year weight impact (pounds)=(0.898 (102.2+protein g per serving)*4/(2,297+kilocalories per serving)+1.063 (264.2+carbohydrates g per serving)*4/(2,297+ kilocalories per serving)–13.19 (24.29+dietary fiber g per serving)/ (2,297+kilocalories per serving)+ 0.973 (74.59+(total fat g per serving–PUFA g per serving)*9/(2,297+kilocalories per serving))*85.82–68.11 (n=1,055, R2=0.03, P < 0.0001). (FAO/WHO+ DCCT/EDIC formula forecasts averaged correlated strongly with published food group profiling findings except for potatoes and dairy foods (n=12, r=0.85, P = 0.0004). Formula predictions did not correlate with food group profiling findings for potatoes and dairy products (n=10, r= −0.33 P=0.36). A formula based diet and exercise analysis tool is available to researchers and individuals: http://thehealtheconomy.com/healthTool/.ConclusionsTwo multiple regression derived formulas from dissimilar databases produced markedly similar estimates of future BMI for 1,055 individuals with type 1 diabetes and female and male cohorts from 167 countries. These formulas predicted the long-term weight impacts of foods and beverages, closely corresponding with most food group profiling estimates from three other databases. If discrepancies with potatoes and dairy products can be resolved, these formulas present a potential basis for a front-of-the-package weight impact rating system.

Highlights

Previous mathematical modelling approaches to predict weight change have largely looked at short term results of changes of energy intake and expenditure [1,2]
Many researchers have characterized the etiology of the worldwide obesity epidemic as much more complex that an imbalance of calories in versus calories out
Food and Agriculture Organization (FAO) and World Health Organization (WHO) data Of 200 countries in the Global Health Observatory Data Repository of the WHO and the FAO databases, 112 countries have complete data on plant and animal food commodity availability per capita [14], physical activity [16], and mean Body mass index (BMI) of adults aged 25+ in 2008 [17]

Summary

Introduction

Previous mathematical modelling approaches to predict weight change have largely looked at short term results of changes of energy intake and expenditure [1,2]. Many researchers have characterized the etiology of the worldwide obesity epidemic as much more complex that an imbalance of calories in versus calories out There are both complex economic causes and consequences of obesity [4]. To defend profits from sales of unhealthy foods, the food industry has adapted techniques long used by the tobacco industry to defend cigarettes They seek to instill doubt in the public about scientific evidence linking certain foods and eating patterns to obesity, emphasize personal responsibility, hire scientists to counteract obesity research, make self-regulatory pledges, lobby to stop government public health anti-obesity programs, and, heavily advertise unhealthy foods [6]. An accurate system of determining the relationship of macronutrient profiles of foods and beverages to the long-term weight impacts of foods is necessary for evidence-based, unbiased front-of-the-package food labels

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