Journal
DIABETES RESEARCH AND CLINICAL PRACTICE
Volume 204, Issue -, Pages -Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.diabres.2023.110894
Keywords
Sleep; Hypertension; Diabetes; Dyslipidemia; Metabolic syndrome; Cardiovascular disease
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This study aimed to establish the optimal cutoffs of sleep timing and duration to assess various cardiovascular disease risk factors. The findings showed that bedtime between 9:00 PM to 0:30 AM for men and 10:00 PM to 11:00 PM for women is appropriate. The cutoff range was slightly earlier for participants aged 65 years and older. Early MSFsc between 12:00 AM to 3:00 AM and sleep durations around 6 hours were associated with the optimal cutoffs for assessing CVD risk factors.
Aim: We aimed to establish the optimal cutoffs of sleep timing and duration to assess obesity, hypertension (HTN), diabetes mellitus (DM), dyslipidemia (DL), and metabolic syndrome (MetS) using data from the Korea National Health and Nutrition Examination Surveys.Methods: In this cross-sectional study, data from 18,677 participants (8,107 men and 10,570 women) aged 19 or over were used. A receiver operating characteristic (ROC) curve adjusted for potential confounding variables was constructed to calculate the cutoff of sleep-related variables (bedtime, mid-sleep on free days corrected for sleep debt on workdays (MSFsc), and sleep duration) for assessing cardiovascular disease (CVD) risk factors according to sex.Results: Bedtime between 9:00 PM to 0:30 AM for men and 10:00 PM to 11:00 PM for women is appropriate for assessing obesity, HTN, DM, DL, and MetS. The cutoff range was 9:00 PM to 11:00 PM for men =65 years and 9:00 PM to 12:00 AM for women =65 years, which was slightly earlier than that for participants <65 years. The optimal MSFsc cutoff points were established between 12:00 AM to 3:00 AM and sleep durations around 6 h were associated with the optimal cutoffs for assessing CVD risk factors.Conclusions: Bedtime between 10:00 PM to 11:00 PM, early MSFsc, and short sleep durations were appropriate for assessing CVD risk factors.
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