Reges Orna, Dicker Dror, Haase Christiane L, Finer Nick, Karpati Tomas, Leibowitz Morton, Satylganova Altynai, Feldman Becca
Clalit Research Institute Clalit Health Services Ramat Gan Israel.
Department of Preventive Medicine Feinberg School of Medicine Northwestern University Chicago Illinois USA.
Obes Sci Pract. 2020 Dec 23;7(2):148-158. doi: 10.1002/osp4.475. eCollection 2021 Apr.
Previous studies using longitudinal weight data to characterize obesity are based on populations of limited size and mostly include individuals of all body mass index (BMI) levels, without focusing on weight changes among people with obesity. This study aimed to identify BMI trajectories over 5 years in a large population with obesity, and to determine the trajectories' association with mortality.
For inclusion, individuals aged 30-74 years at index date (1 January 2013) with continuous membership in Clalit Health Services from 2008 to 2012 were required to have ≥1 BMI measurement per year in ≥3 calendar years during this period, of which at least one was ≥30 kg/m. Latent class analysis was used to generate BMI trajectories over 5 years (2008-2012). Cox proportional hazards models were used to assess the association between BMI trajectories and all-cause mortality during follow-up (2013-2017).
In total, 367,141 individuals met all inclusion criteria. Mean age was 57.2 years; 41% were men. The optimal model was a quadratic model with four classes of BMI clusters. Most individuals (90.0%) had stable high BMI over time. Individuals in this cluster had significantly lower mortality than individuals in the other trajectory clusters ( < 0.01), including clusters of people with dynamic weight trajectories.
The results of the current study show that people with stable high weight had the lowest mortality of all four BMI trajectories identified. These findings help to expand the scientific understanding of the impact that weight trajectories have on health outcomes, while demonstrating the challenges of discerning the cumulative effects of obesity and weight change, and suggest that dynamic historical measures of BMI should be considered when assessing patients' future risk of obesity-related morbidity and mortality, and when choosing a treatment strategy.
以往利用纵向体重数据来描述肥胖特征的研究基于规模有限的人群,且大多纳入了所有体重指数(BMI)水平的个体,未聚焦肥胖人群的体重变化。本研究旨在确定一大群肥胖人群5年期间的BMI轨迹,并确定这些轨迹与死亡率的关联。
纳入标准为,在索引日期(2013年1月1日)年龄为30 - 74岁,在2008年至2012年期间持续为克拉利特健康服务机构会员,在此期间≥3个日历年中每年至少有1次BMI测量值,其中至少有1次≥30kg/m²。采用潜在类别分析生成5年(2008 - 2012年)期间的BMI轨迹。使用Cox比例风险模型评估BMI轨迹与随访期间(2013 - 2017年)全因死亡率之间的关联。
共有367141名个体符合所有纳入标准。平均年龄为57.2岁;41%为男性。最佳模型是一个具有四类BMI聚类的二次模型。大多数个体(90.0%)随着时间推移BMI保持稳定且较高。该聚类中的个体死亡率显著低于其他轨迹聚类中的个体(P<0.01),包括体重轨迹动态变化的聚类。
本研究结果表明,在所有确定的四种BMI轨迹中,体重稳定且较高的人群死亡率最低。这些发现有助于扩展对体重轨迹对健康结果影响的科学认识,同时展示了辨别肥胖和体重变化累积效应的挑战,并表明在评估患者未来肥胖相关发病和死亡风险以及选择治疗策略时,应考虑BMI的动态历史测量值。