纵向研究
认知
医学
认知障碍
比例危险模型
探索性研究
认知功能衰退
入射(几何)
物理医学与康复
睡眠剥夺对认知功能的影响
内科学
心理学
心脏病学
基线(sea)
听力学
前瞻性队列研究
物理疗法
累积发病率
预测值
队列研究
痴呆
认知障碍
试验预测值
急诊科
认知测验
年轻人
纵向数据
探索性分析
作者
Peijian Wang,Lijun Ge,Beiyao Gao,Shan Jiang,Siyuan Wang
标识
DOI:10.1177/13872877261494159
摘要
BackgroundPrevious studies, including CHARLS analyses, have linked lower peak expiratory flow (PEF) with cognitive decline. Evidence regarding incident score-based cognitive impairment, longitudinal PEF patterns, and incremental prediction remains limited.ObjectiveTo examine associations of baseline PEF and exploratory PEF trajectories with incident score-based cognitive impairment and evaluate the incremental predictive value of PEF.MethodsThe primary analysis included 2604 CHARLS participants aged ≥60 years without score-based cognitive impairment at baseline. Cognitive impairment was defined as a global cognitive score ≥1 SD below the wave- and age-specific mean and was not a clinical diagnosis of mild cognitive impairment or dementia. Cox models, sensitivity analyses, prediction analyses, and exploratory trajectory modeling were performed.ResultsOver a median follow-up of 9.0 years, 636 participants developed score-based cognitive impairment. Each 1-SD increase in PEF was associated with lower risk in fully adjusted models (HR = 0.801, 95% CI 0.728-0.880) and after adjustment for baseline cognition (HR = 0.846, 95% CI 0.768-0.930). Nine-year cumulative incidence decreased from 35.6% to 16.6% across PEF quartiles. Adding PEF modestly improved 9-year AUC (ΔAUC = 0.011, p = 0.042). In the exploratory trajectory analysis, the moderate PEF group was not significantly associated with risk versus the high group (HR = 1.294, 95% CI 0.860-1.948), whereas the low PEF group had higher risk (HR = 1.817, 95% CI 1.172-2.817), with no clear graded pattern.ConclusionsLower baseline PEF was associated with wave-specific score-based cognitive impairment, but not under stricter definitions requiring subsequent confirmation with wave-specific or fixed thresholds. PEF trajectories were exploratory, and incremental predictive value was limited.
科研通智能强力驱动
Strongly Powered by AbleSci AI