Independent and synergistic associations of sleep quality and frailty with cardiometabolic multimorbidity: a cross-sectional analysis in Chinese elderly population

BACKGROUND: Cardiometabolic multimorbidity (CMM) poses a substantial public health burden in aging populations. However, limited evidence exists regarding the clustering of cardiometabolic conditions or the interplay between sleep and frailty in CMM. This study aimed to explore the independent and synergistic associations of these factors with CMM, which may inform targeted preventive strategies. METHODS: This cross-sectional study analyzed data from the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Self-reported sleep quality was categorized as good, fair, or bad. Frailty was quantified using a 35-item deficit accumulation frailty index (FI), dichotomized as non-frail (FI ≤ 0.25) or frail (FI > 0.25). Multivariable logistic regression models assessed associations between sleep quality, frailty, and CMM (≥ 2 concurrent cardiometabolic conditions). Multiplicative interaction was tested via likelihood ratio tests, while additive interaction was quantified using the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: Among 8,592 participants (mean age 83.17 ± 11.00 years; 48.0% male), CMM prevalence was 21.6% (n = 1,854). Females exhibited higher CMM prevalence than males (54.7% vs. 45.3%), with significant sex- and age-specific disparities (e.g., 65-74 years: females 28.1% vs. males 21.3%; 75-84 years: females 28.5% vs. males 23.5%; all P < 0.05). In adjusted models, bad sleep quality was independently associated with higher odds of CMM (bad vs. good: adjusted odds ratio [aOR] = 1.84, 95% CI: 1.55-2.18; fair vs. good: aOR = 1.22, 95% CI: 1.06-1.40), with a dose-response relationship (P < 0.001). Frailty demonstrated the strongest association (frail vs. non-frail: aOR = 6.87, 95% CI: 5.70-8.28). Notably, frail individuals with bad sleep quality had 9.18-fold higher odds of CMM (95% CI: 6.63-12.72) compared to non-frail good sleepers, with 21% of CMM cases attributable to this interaction (RERI = 1.09, 95% CI: 0.08-2.10; AP = 0.21, 95% CI: 0.06-0.36; SI = 1.35, 95% CI: 1.03-1.67). CONCLUSIONS: Sleep quality and frailty are associated with CMM, suggesting that their combined assessment may enhance predictive accuracy for CMM risk. These findings underscore the need to prioritize high-risk elderly individuals for integrated interventions addressing sleep disturbances and frailty, thereby facilitating the transition from reactive to proactive cardiometabolic care models.