Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, USA.
Int J Obes (Lond). 2019 May;43(5):1113-1119. doi: 10.1038/s41366-018-0194-y. Epub 2018 Sep 11.
A single measure that distills complex body mass index (BMI) trajectories into one value could facilitate otherwise complicated analyses. This study creates and assesses the validity of such a measure: average excess BMI.
We use data from Waves I-IV of the National Longitudinal Study of Adolescent to Adult Health (n = 17,669). We calculate average excess BMI by integrating to find the area above a healthy BMI trajectory and below each subject-specific trajectory and divide this value by total study time. To assess validity and utility, we (1) evaluate relationships between average excess BMI from adolescence to adulthood and adult chronic conditions, (2) compare associations and fit to models using subject-specific BMI trajectory parameter estimates as predictors, and (3) compare associations to models using BMI trajectory parameter estimates as outcomes.
Average excess BMI from adolescence to adulthood is associated with increased odds of hypertension (OR = 1.56; 95% CI: 1.47, 1.67), hyperlipidemia (OR = 1.36; 95% CI: 1.26, 1.47), and diabetes (OR = 1.57; 95% CI: 1.47, 1.67). The odds associated with average excess BMI are higher than the odds associated with the BMI intercept, linear, or quadratic slope. Correlations between observed and predicted health outcomes are slightly lower for some models using average excess BMI as the focal predictor compared to those using BMI intercept, linear, and quadratic slope. When using trajectory parameters as outcomes, some co-variates associate with the intercept, linear, and quadratic slope in contradicting directions.
This study supports the utility of average excess BMI as an outcome. The higher an individual's average excess BMI from adolescence to adulthood, the greater their odds of chronic conditions. Future studies investigating longitudinal BMI as an outcome should consider using average excess BMI, whereas studies that conceptualize longitudinal BMI as the predictor should continue using traditional latent growth methods.
将复杂的体重指数 (BMI) 轨迹简化为一个单一的指标，可以方便进行复杂的分析。本研究创建并评估了这样一个指标的有效性：平均超重 BMI。
我们使用国家青少年至成人健康纵向研究（n=17669）的 I-IV 波数据。我们通过积分来计算平均超重 BMI，找到健康 BMI 轨迹上方和每个个体轨迹下方的区域，并将这个值除以总研究时间。为了评估有效性和实用性，我们 (1) 评估从青春期到成年的平均超重 BMI 与成人慢性病之间的关系，(2) 比较使用个体 BMI 轨迹参数估计值作为预测因子的模型的关联和拟合，以及 (3) 将关联与使用 BMI 轨迹参数估计值作为结果的模型进行比较。
从青春期到成年的平均超重 BMI 与高血压（OR=1.56；95%CI：1.47，1.67）、高血脂（OR=1.36；95%CI：1.26，1.47）和糖尿病（OR=1.57；95%CI：1.47，1.67）的患病风险增加有关。与平均超重 BMI 相关的几率高于与 BMI 截距、线性或二次斜率相关的几率。与使用 BMI 截距、线性和二次斜率作为焦点预测因子的模型相比，使用平均超重 BMI 作为焦点预测因子的模型的观察到的和预测到的健康结果之间的相关性略低。当使用轨迹参数作为结果时，一些协变量与截距、线性和二次斜率呈相反方向的关联。
本研究支持将平均超重 BMI 作为结果的有效性。个体从青春期到成年的平均超重 BMI 越高，其患慢性病的几率就越大。未来研究将 BMI 作为结果进行纵向研究时，应考虑使用平均超重 BMI，而将 BMI 作为预测因子的研究应继续使用传统的潜在增长方法。
