Journal of Shandong University (Health Sciences) ›› 2025, Vol. 63 ›› Issue (9): 11-19.doi: 10.6040/j.issn.1671-7554.0.2024.1421

• Special Issue on “Big DataEnabled, AI Foundation ModelDriven Multimodal Cohort Design and Analysis” • Previous Articles    

Association between modifiable cardiovascular risk factors clustering patterns and stroke in the elderly population

PANG Jinhong1,2, SU Ping3, QIAO Junpeng1,2, CHEN Qiaoqiao1,2, CHEN Xueyu1,2, ZHAO Yingying1,2, SHI Jie4, SUN Xiaoru1,2, LI Qiuchun1,2, HE Ruiyan1,2, FAN Yiou5, CHI Weiwei1,3   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Institute for Medical Dataology, Shandong University, Jinan 250003, Shandong, China;
    3. National Administration of Health Data, Jinan 250002, Shandong, China;
    4. Talent Development Center, Taizhou Medical High-tech Industrial Development Zone, Taizhou 225326, Jiangsu, China;
    5. Centers for Disease Control and Prevention of Shandong, Jinan 250014, Shandong, China
  • Published:2025-09-08

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Abstract: Objective To explore the gender-specific clustering patterns of modifiable cardiovascular risk factors(MCVRFS)and assess their associations with stroke incidence among the elderly population in Shandong Province. Methods Based on the Cheeloo Lifespan Electronic Health Research Data-library(Cheeloo LEAD)database, 58,633 participants aged ≥60 years old with complete health examination records, electronic medical records, and public health archives from June 1 to December 31, 2015 were included. A seven-year follow-up cohort was constructed, with occurence of stroke as the study endpoint. Latent class analysis(LCA)was used to identify MCVRFS clustering patterns, and Cox proportional hazards regression models were applied to evaluate associations between clusters and stroke risk. Results LCA identified four MCVRFS clusters in both genders. Males comprised the low-risk(39.02%), smoking and alcohol consumption(16.41%), overweight/obesity(36.34%), and metabolic syndrome(8.22%)groups. Females comprised the low-risk(41.00%), smoking and alcohol consumption(0.44%), overweight and dyslipidemia(46.76%), and metabolic syndrome(11.80%)groups. During follow-up, 6,764 new stroke cases occurred in males(incidence density: 4,947/100,000 person-years)and 8,141 in females(incidence density: 4,273/100,000 person-years). Adjusted Cox regression showed that males in smoking and alcohol consumption group(HR=1.13, 95%CI=1.05-1.21), overweight obesity group(HR=1.16, 95%CI=1.09-1.23), and metabolic syndrome group(HR=2.20, 95%CI=2.04-2.38)had elevated stroke risks compared to the low-risk group. In females, overweight and dyslipidemia group(HR=1.16, 95%CI=1.10-1.21)and metabolic syndrome group(HR=2.39, 95%CI=2.25-2.54)showed higher stroke risks compared to the low-risk group. Conclusion Four kinds of gender-specific MCVRFS clustering patterns are identified in Shandongs elderly population. Overweight-obesity, smoking-alcohol, and metabolic syndrome clusters increase stroke risk in males, while weight-dyslipidemia and metabolic syndrome clusters elevate risks in females. Targeted intervention strategies tailored to these clusters may reduce stroke incidence and disease burden in the elderly.

Key words: Stroke, Modifiable cardiovascular risk factors, Shandong Province, Elderly, Cohort study, Latent class analysis, Survival analysis

CLC Number: 

  • R195
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