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A range of risk factors have been reported to influence the occurrence and development of cardiovascular diseases (CVDs) in the previous decades. This cluster analysis was carried out in a registered study to investigate any potential risk factors for CVDs in patients undergoing polysomnography monitoring.
In this prospective cohort study (ClinicalTrials.gov Identifier: NCT00005275), clinical data, questionnaires of sleep and life habits, and polysomnography data were acquired from 5042 community subjects (2349 males and 2693 females). CVDs included coronary artery disease, stroke and heart failure. The importance of the variables was ranked according to their influence on CVDs with decision tree analysis. Then a cluster analysis was performed with the most important 100 variables to search the underlying risk factor for CVDs. Chi-squared test and analysis of variance (ANOVA) were performed to check the significance of variables among different clusters.
Four clusters have been identified. Cluster 1 (C1, n=2213, 43.89%) had the largest number of subjects. The ratio of male was the highest (51.20%) while the cholesterol (205.41±37.68mg/dL) and triglycerides level were the lowest. Importantly, subjects in C1 took the most naps every week (2.89±3.72) and were most likely to suffer from CVDs (25.8%). C2 (n=676, 13.40%) had the highest cholesterol (210.77±38.93mg/dL) and triglycerides level (161.08±98.54mg/dL), while the total sleep time was the shortest (589.44±107.57minutes). C3 (n=177, 3.51%), the smallest group, had the eldest population with an average of 65.95±12.09 years old and was least likely to be male (31.64%). They had the highest HDL level (53.43±16.86mg/dL) but the lowest forced expiratory volume in one second (FEV1, 2.42±0.81L). In contrast to C1,C4 (n=1976, 39.19%) showed the least possibility to suffer CVDs (21.71%). This group owed the longest total sleep time (603.36±100.87 minutes), the lowest frequency of naps (2.49±3.33/week) and the highest FEV1 (2.71±0.79L).
Cluster analysis of diverse variables identified four different clinical phenotypes and highlight the significant importance of total sleep time, number of naps every week and FEV1 for CVDs. However, logistic regression analysis and external validation in Chinese population are still required in the future research.