Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Center for Disease Modeling, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.
J Diabetes Investig. 2020 Mar;11(2):466-474. doi: 10.1111/jdi.13133. Epub 2019 Sep 21.
AIMS/INTRODUCTION: This longitudinal study aimed to explore whether distinct developmental trajectories of body mass index (BMI) would be predictive of diabetes risk in general Chinese adults.
A total of 4,519 participants aged >18 years who were free of diabetes in 2011 (baseline of the current analysis) were enrolled in this study. All participants completed a medical examination every year during 2011-2016, and BMI levels were measured two to six (average 5.6) times. Group-based trajectory modeling was applied to identify BMI trajectories over time. New-onset diabetes was confirmed in 2016.
During 2011-2016, four distinct BMI trajectories were identified according to BMI range and changing pattern over time: "low" (19.6%), "moderate" (33.4%), "moderate-high" (33.4%) and "high" (13.6%). A total of 168 (3.7%) new-onset diabetes cases were confirmed in 2016. Compared with the "low" BMI trajectory, participants in the "high" BMI trajectory were at significantly higher risk for new-onset diabetes (adjusted relative risk 3.24, 95% confidence interval 1.27-8.24). Notably, BMI trajectories based on the first four or three annual BMI tests yielded similar results. By contrast, no significant correlation was found between categories of baseline BMI and new-onset diabetes in 2016 after multivariate adjustment.
The present results show that distinct BMI trajectories, even identified using just four or three annual BMI tests, are significantly associated with new-onset diabetes. Monitoring BMI trajectories over time might provide an important approach to identify subpopulations at higher risk for developing diabetes.
目的/引言:本纵向研究旨在探讨中国成年人的体质指数(BMI)不同发展轨迹是否可预测糖尿病风险。
本研究共纳入 4519 名年龄>18 岁、2011 年(本次分析的基线)无糖尿病的参与者。所有参与者在 2011-2016 年期间每年都接受一次体检,BMI 水平测量 2-6 次(平均 5.6 次)。采用基于群组的轨迹建模来识别随时间变化的 BMI 轨迹。2016 年确诊新发糖尿病。
2011-2016 年,根据 BMI 范围和随时间变化的模式,共确定了 4 种不同的 BMI 轨迹:“低”(19.6%)、“中”(33.4%)、“中高”(33.4%)和“高”(13.6%)。2016 年共确诊 168 例新发糖尿病病例(3.7%)。与“低”BMI 轨迹相比,“高”BMI 轨迹的参与者发生新发糖尿病的风险显著更高(校正后的相对风险 3.24,95%置信区间 1.27-8.24)。值得注意的是,使用前 4 次或前 3 次年度 BMI 检测结果确定的 BMI 轨迹得出了相似的结果。相比之下,在进行多变量调整后,基线 BMI 类别与 2016 年新发糖尿病之间无显著相关性。
本研究结果表明,不同的 BMI 轨迹,即使仅通过 4 次或 3 次年度 BMI 检测即可确定,与新发糖尿病显著相关。监测随时间变化的 BMI 轨迹可能是识别糖尿病发病风险较高的亚人群的重要方法。