Cao Zhi, Zhang Jing, Lu Zuolin, Chen Han, Min Jiahao, Hou Yabing, Wang Xiaohe, Xu Chenjie
School of Public Health, Hangzhou Normal University, China (Z.C., J.Z., H.C., J.M., X.W., C.X.).
School of Public Health, Zhejiang University, Hangzhou, China (Z.C.).
Stroke. 2024 May;55(5):1278-1287. doi: 10.1161/STROKEAHA.123.044322. Epub 2024 Mar 27.
Cumulative evidence suggests a correlation between physical or mental activity and the risk of stroke. However, the combined impact of these activities on stroke onset remains unexplored. This study identified physical and mental activity patterns using principal component analysis and investigated their associations with risk of incident stroke in the general population.
Our study was sourced from the UK Biobank cohort between 2006 and 2010. Information on physical and mental-related activities were obtained through a touch-screen questionnaire. The incident stroke was diagnosed by physicians and subsequently verified through linkage to Hospital Episode Statistics. Principal component analysis was used to identify potential physical and mental activity patterns. Cox proportional hazard regression models were performed to calculate hazard ratios (HRs) and 95% CIs of incident stroke, adjusting for potential confounders.
The initial UK Biobank cohort originally consisted of 502 411 individuals, of whom a total of 386 902 participants (aged 38-79 years) without any history of stroke at baseline were included in our study. During a median follow-up of 7.7 years, 6983 (1.8%) cases of stroke were documented. The mean age of the included participants was 55.9 years, and the proportion of women was 55.1%. We found that multiple individual items related to physical and mental activity showed significant associations with risk of stroke. We identified 4 patterns of physical activity and 3 patterns of mental activity using principal component analysis. The adherence to activity patterns of vigorous exercise, housework, and walking predominant patterns were associated with a lower risk of stroke by 17% (HR, 0.83 [95% CI, 0.78-0.89]; 20% (HR, 0.80 [95% CI, 0.75-0.85]; and 20% (HR, 0.80 [95% CI, 0.75-0.86), respectively. Additionally, the transportation predominant pattern (HR, 1.36 [95% CI, 1.28-1.45) and watching TV pattern (HR, 1.43 [95% CI, 1.33-1.53) were found to be significantly associated with a higher risk of stroke. These associations remained consistent across all subtypes of stroke.
Activity patterns mainly related to frequent vigorous exercise, housework, and walking were associated with lower risks of stroke and all its subtypes. Our findings provide new insights for promoting suitable patterns of physical and mental activity for primary prevention of stroke.
越来越多的证据表明，身体或精神活动与中风风险之间存在关联。然而，这些活动对中风发作的综合影响仍未得到探索。本研究使用主成分分析确定身体和精神活动模式，并调查它们与普通人群中风发病风险的关联。
我们的研究来源于2006年至2010年的英国生物银行队列。通过触摸屏问卷获取有关身体和精神相关活动的信息。中风事件由医生诊断，并随后通过与医院病历统计数据的关联进行核实。使用主成分分析来识别潜在的身体和精神活动模式。进行Cox比例风险回归模型以计算中风事件的风险比（HR）和95%置信区间，并对潜在混杂因素进行调整。
最初的英国生物银行队列最初由502411人组成，其中共有386902名参与者（年龄在38 - 79岁之间）在基线时无任何中风病史被纳入我们的研究。在中位随访7.7年期间，记录了6983例（1.8%）中风病例。纳入参与者的平均年龄为55.9岁，女性比例为55.1%。我们发现，多个与身体和精神活动相关的个体项目与中风风险存在显著关联。通过主成分分析，我们确定了4种身体活动模式和3种精神活动模式。坚持剧烈运动、家务劳动和以步行为主的活动模式与中风风险降低17%（HR，0.83 [95% CI，0.78 - 0.89]）、20%（HR，0.80 [95% CI，0.75 - 0.85]）和20%（HR，0.80 [95% CI，0.75 - 0.86]）相关，分别对应。此外，以交通出行为主的模式（HR，1.36 [95% CI，1.28 - 1.45]）和看电视模式（HR，1.43 [95% CI，1.33 - 1.53]）被发现与中风风险较高显著相关。这些关联在所有中风亚型中均保持一致。
主要与频繁剧烈运动、家务劳动和步行相关的活动模式与中风及其所有亚型的较低风险相关。我们的研究结果为促进适合的身体和精神活动模式以进行中风一级预防提供了新的见解。
