Liu Yalan, Zhang Li, Jin Zhaofeng, Zhang Lin, Song Yan, He Li
Nanan District Center for Disease Control and Prevention, Chongqing, 401336, China.
Qianxi People's Hospital, Guizhou, 551500, Qianxi, China.
Geroscience. 2025 May 1. doi: 10.1007/s11357-025-01681-y.
To examine the association between body mass index (BMI) trajectories, early and recent BMI changes, and phenotypic age acceleration (PhenoAgeAccel), addressing inconsistent findings in previous studies on weight change and aging. Data from the National Health and Nutrition Examination Survey from 2005 to 2018 were used, selecting participants aged 50 years and older. A growth mixture model was employed to identify BMI trajectories. The association between different BMI trajectories and PhenoAgeAccel was assessed using linear and multinomial logistic regression models. The nonlinear effects of BMI changes were identified through threshold effect analysis. Among 5404 participants, the four BMI trajectories identified were as follows: stable weight (29.07%), midlife weight gain (24.31%), late-life weight gain (32.22%), and chronic obesity (14.41%). The chronic obesity group exhibited the most significant elevations in PhenoAgeAccel, indicating they were phenotypically older compared to other groups (β = 4.34, 95% confidence interval 3.67-5.02). Early BMI changes of less than 6% were associated with being phenotypically younger (β = - 5.06, P = 0.029), whereas increases exceeding 6% were linked to being phenotypically older (β = 2.83, P < 0.001). The key threshold for recent BMI changes was 2%; changes below this level were associated with being phenotypically younger, while those exceeding this threshold were linked to being phenotypically older (P < 0.001). This cross-sectional study suggests that individuals with long-term chronic obesity tend to be phenotypically older, whereas those with stable body weight are more likely to be phenotypically younger.
为了研究体重指数(BMI)轨迹、早期和近期BMI变化与表型年龄加速(PhenoAgeAccel)之间的关联,以解决先前关于体重变化与衰老的研究中不一致的结果。使用了2005年至2018年美国国家健康与营养检查调查的数据,选取了50岁及以上的参与者。采用生长混合模型来识别BMI轨迹。使用线性和多项逻辑回归模型评估不同BMI轨迹与PhenoAgeAccel之间的关联。通过阈值效应分析确定BMI变化的非线性效应。在5404名参与者中,识别出的四种BMI轨迹如下:体重稳定(29.07%)、中年体重增加(24.31%)、晚年体重增加(32.22%)和慢性肥胖(14.41%)。慢性肥胖组的PhenoAgeAccel升高最为显著,表明与其他组相比,他们的表型年龄更大(β = 4.34,95%置信区间3.67 - 5.02)。早期BMI变化小于6%与表型年龄较小相关(β = -5.06,P = 0.029),而超过6%的增加与表型年龄较大相关(β = 2.83,P < 0.001)。近期BMI变化的关键阈值为2%;低于此水平的变化与表型年龄较小相关,而超过此阈值的变化与表型年龄较大相关(P < 0.001)。这项横断面研究表明,长期慢性肥胖的个体往往表型年龄较大,而体重稳定的个体更可能表型年龄较小。