Stokes Andrew, Preston Samuel H
Department of Global Health and Center for Global Health and Development, Boston University School of Public Health, Boston, MA 02118;
Department of Sociology and Population Studies Center, University of Pennsylvania, Philadelphia, PA 19104
Proc Natl Acad Sci U S A. 2016 Jan 19;113(3):572-7. doi: 10.1073/pnas.1515472113. Epub 2016 Jan 4.
Analyses of the relation between obesity and mortality typically evaluate risk with respect to weight recorded at a single point in time. As a consequence, there is generally no distinction made between nonobese individuals who were never obese and nonobese individuals who were formerly obese and lost weight. We introduce additional data on an individual's maximum attained weight and investigate four models that represent different combinations of weight at survey and maximum weight. We use data from the 1988-2010 National Health and Nutrition Examination Survey, linked to death records through 2011, to estimate parameters of these models. We find that the most successful models use data on maximum weight, and the worst-performing model uses only data on weight at survey. We show that the disparity in predictive power between these models is related to exceptionally high mortality among those who have lost weight, with the normal-weight category being particularly susceptible to distortions arising from weight loss. These distortions make overweight and obesity appear less harmful by obscuring the benefits of remaining never obese. Because most previous studies are based on body mass index at survey, it is likely that the effects of excess weight on US mortality have been consistently underestimated.
对肥胖与死亡率之间关系的分析通常是针对某一时刻记录的体重来评估风险。因此,从未肥胖过的非肥胖个体与曾经肥胖但后来体重减轻的非肥胖个体之间通常没有区分。我们引入了关于个体最高体重的额外数据,并研究了四种模型,这些模型代表了调查时的体重和最高体重的不同组合。我们使用1988 - 2010年国家健康与营养检查调查的数据,并通过2011年与死亡记录相链接,来估计这些模型的参数。我们发现最成功的模型使用了最高体重的数据,而表现最差的模型仅使用了调查时的体重数据。我们表明,这些模型在预测能力上的差异与体重减轻者中异常高的死亡率有关,正常体重类别尤其容易受到体重减轻所产生的扭曲影响。这些扭曲通过掩盖一直保持非肥胖状态的益处,使得超重和肥胖看起来危害较小。由于之前的大多数研究是基于调查时的体重指数,很可能超重对美国死亡率的影响一直被低估。