Comparative cohort analysis of sleep-disordered breathing in German and South Korean populations

ABSTRACT Background Sleep-disordered breathing (SDB) is common and is closely associated with obesity, hypertension, diabetes, dyslipidaemia, and cardiovascular risk. However, most evidence has been generated in predominantly Western populations, and it remains unclear whether the relationship between SDB, adiposity, and cardiometabolic health is comparable across populations with different anthropometric and disease-risk profiles. Harmonised comparisons between geographically and ethnically distinct population-based cohorts are therefore needed to determine whether a similar level of SDB carries the same clinical implications across populations. Methods Data were drawn from the Study of Health in Pomerania (SHIP-TREND; Germany) and the Korean Genome and Epidemiology Study (KoGES; South Korea). Clinical, laboratory, and polysomnographic measures were harmonised. Participants aged ≥45 years with usable sleep recordings and ≥4 hours of total sleep time were included. Inverse probability weighting balanced age and sex distributions between cohorts. Regression models examined associations of body mass index (BMI) and waist-to-height ratio (WHtR) with Apnea–Hypopnea Index (AHI), and associations of AHI with diabetes, hypertension, lipid markers, and Framingham 10-year cardiovascular risk scores. Results Analyses included 768 SHIP-TREND and 2,867 KoGES participants. SDB severity increased with age and was higher in men. Higher adiposity was associated with higher AHI in both cohorts, but the BMI–AHI association was stronger in KoGES. After WHtR adjustment, hypertension remained associated with AHI in KoGES but not SHIP-TREND; diabetes, lipids, and cardiovascular risk associations were broadly comparable although there were population differences in absolute burden. Conclusions SDB carries distinct cardiometabolic implications across German and South Korean populations, reflecting differences in adiposity related susceptibility and underlying disease burden. AHI should therefore be interpreted alongside population appropriate measures of adiposity and cardiometabolic risk.