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The global obesity pandemic: shaped by global drivers and local environments.全球肥胖症大流行：由全球驱动因素和本地环境共同塑造。
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Comparison of different nutrient profiling schemes to a new reference method using dietary surveys.使用膳食调查，将不同营养成分分析方案与一种新的参考方法进行比较。
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基于宏量营养素分布和身体活动的未来体重指数建模

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

作者信息

Cundiff David K, Raghuvanshi Nikunj

出版信息

Theor Biol Med Model. 2012 Oct 29;9:43. doi: 10.1186/1742-4682-9-43.

DOI:10.1186/1742-4682-9-43
PMID:23106911
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3551753/
Abstract

BACKGROUND

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.

METHODS

Data 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.

RESULTS

FAO/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, R(2)=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, R(2)=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/.

CONCLUSIONS

Two 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.

摘要

背景

对于基于证据、无偏见的包装正面食品标签而言，需要一个准确的系统来确定食品和饮料的宏量营养素概况与食品长期体重影响之间的关系。

方法

饮食、身体活动和体重指数（BMI）的数据集来自联合国粮食及农业组织（粮农组织）、世界卫生组织（世卫组织）、糖尿病控制与并发症试验（DCCT）以及糖尿病流行病学干预与并发症研究（EDIC）。为了预测个体未来的BMI，多元回归得出了粮农组织/世卫组织以及DCCT/EDIC公式，将宏量营养素概况和身体活动（自变量）与BMI变化/年（因变量）联系起来。类似的公式在不考虑身体活动的情况下，将单个食品和饮料的宏量营养素概况与这些食品的四年体重影响联系起来，并将这些预测结果与哈佛营养流行病学家进行的三项大型前瞻性研究中公布的食物类别概况估计值进行比较。

结果

粮农组织/世卫组织食品和饮料公式：四年体重影响（磅）=（0.07710×酒精克数 + 11.95×（381.7 + 每份碳水化合物克数）×4÷（2613 + 每份千卡数） - 304.9×（30.38 + 每份膳食纤维克数）÷（2613 + 每份千卡数） + 19.73×（84.44 + 总脂肪克数）×9÷（2613 + 每份千卡数） - 68.57×（20.45 + 每份多不饱和脂肪酸克数）×9÷（2613 + 每份千卡数））×2.941 - 12.78（n = 334，R² = 0.29，P < 0.0001）。DCCT/EDIC四年体重影响公式（磅）=（0.898×（102.2 + 每份蛋白质克数）×4÷（2297 + 每份千卡数） + 1.063×（264.2 + 每份碳水化合物克数）×4÷（2297 + 每份千卡数） - 13.19×（24.29 + 每份膳食纤维克数）÷（2297 + 每份千卡数） + 0.973×（74.59 +（每份总脂肪克数 - 每份多不饱和脂肪酸克数）×9÷（2297 + 每份千卡数））×85.82 - 68.11（n = 1055，R² = 0.03，P < 0.0001）。（粮农组织/世卫组织 + DCCT/EDIC公式预测平均值与公布的食物类别概况结果除土豆和乳制品外强烈相关（n = 12，r = 0.85，P = 0.0004）。公式预测与土豆和乳制品的食物类别概况结果不相关（n = 10，r = -0.33，P = 0.36）。研究人员和个人可使用基于公式的饮食和运动分析工具：http://thehealtheconomy.com/healthTool/。

结论

从不同数据库通过多元回归得出的两个公式对1055名1型糖尿病患者以及来自167个国家的女性和男性队列的未来BMI产生了明显相似的估计值。这些公式预测了食品和饮料的长期体重影响，与其他三个数据库中大多数食物类别概况估计值密切对应。如果能解决与土豆和乳制品的差异问题，这些公式可为包装正面的体重影响评级系统提供潜在基础。