实用医学杂志2026，Vol.42Issue(16)：2908-2917,10.DOI:10.3969/j.issn.1006-5725.2026.16.006
基于加速度计活动行为表型与心血管疾病风险的关联研究:衰弱状态的影响及代谢组学特征
Association between accelerometer-derived activity behavior phenotypes and cardiovascular disease risk:Influence of frailty status and metabolomic characteristics
摘要
Abstract
Objective This study aims to investigate the associations among accelerometer-derived activity phenotypes identified via unsupervised machine learning,frailty,and cardiovascular disease risk.Methods A total of 87 824 participants were enrolled from a large-scale European cohort study.Accelerometer-derived physical activity indicators were extracted,including moderate-to-vigorous physical activity(MVPA),light physical activity(LPA),sedentary time and overall activity intensity.Gaussian mixture modeling was adopted to identify activity behavior phenotypes.Cox proportional hazards regression models were constructed to analyze the associations between the identified phenotypes and cardiovascular disease risk,and LASSO regression was utilized to screen key metabolites associated with the activity phenotypes.Results Three distinct physical activity behavior phenotypes were identified in this study.Over the follow-up period,4 832 cardiovascular disease events were documented.Phenotype A was characterized by relatively low MVPA,prolonged sedentary time,and lower overall activity inten-sity;Phenotype B exhibited an intermediate activity level;and Phenotype C was characterized by relatively high MVPA and LPA,shorter sedentary time,and higher overall activity intensity.Compared with Phenotype A,Pheno-types B and C were associated with a significantly lower risk of cardiovascular disease,with hazard ratios of 0.80 and 0.74 respectively,and both comparisons reached statistical significance(P<0.001).This association remained consistent among non-frail and pre-frail participants,and no significant interaction was detected between physical activity behavior phenotype and frailty status.LASSO regression identified 26 core metabolites,which revealed that Phenotype A was mainly characterized by inflammation-and energy metabolism-related abnormalities,while Pheno-type C presented relatively elevated levels of fatty acids and HDL-related lipid components.Conclusions Accelerometer-derived physical activity behavioral phenotypes identified via unsupervised machine learning are independently associated with cardiovascular disease risk and are characterized by distinct metabolic profiles.These data-driven phenotypes can provide additional information for cardiovascular disease risk stratification beyond the scope of traditional frailty assessments.关键词
机器学习/加速度计/活动行为表型/衰弱/心血管疾病Key words
machine learning/accelerometer/activity behavior phenotype/frailty/cardiovascular disease分类
医药卫生引用本文复制引用
刘杰,何广志,沙卫红..基于加速度计活动行为表型与心血管疾病风险的关联研究:衰弱状态的影响及代谢组学特征[J].实用医学杂志,2026,42(16):2908-2917,10.基金项目
国家自然科学基金(编号:82170561,82570637) （编号:82170561,82570637）
河南省哲学社会科学规划年度项目(编号:2024BTY019) （编号:2024BTY019）
河南省软科学项目(编号:262400410432) （编号:262400410432）
