Association of Sleep Duration, Metabolic Markers, and Risk of All-Cause and Cause-Specific Mortality in China and the UK

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Background: Sleep duration is associated with various health outcomes like metabolic disturbances and mortality, but the underlying mechanisms remain unclear. Objectives: To investigate the link between sleep duration, metabolic markers, and mortality using data from the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). Methods: Sleep duration was self-reported, and metabolic markers were quantified using nuclear magnetic resonance spectroscopy. The outcomes of interest were all-cause and cause-specific mortality. Linear regression was conducted to assess the association between sleep duration and metabolic markers, which were further compared with the association between metabolic markers and all-cause and cause-specific mortality using Cox models. Results: In both cohorts, sleep duration showed positive associations with all-cause and cause-specific mortality after adjustment. A total of 17 metabolic markers—encompassing lipoprotein subclasses in high-density lipoprotein cholesterol, relative lipoprotein lipid concentrations in very-low-density lipoproteins, cholesterol, and ketone bodies—were significantly associated with sleep duration and mortality. In general, the associations of sleep duration with metabolic markers and of these markers with mortality showed similar patterns for lipoprotein subclasses and relative lipoprotein lipid concentrations, whereas contrary patterns were observed for cholesterol and ketone bodies in the CKB. Furthermore, significant associations were also observed in the UKB cohort, with metabolic markers presenting generally consistent association patterns with sleep duration and mortality. Gender-specific analyses revealed significant links in females in the CKB and in both genders in the UKB. Sensitivity analyses confirmed the stability of these associations. Conclusions: Our findings suggest that lipid metabolism may partially explain the effect of sleep duration on mortality. These insights highlight the potential of targeted metabolic interventions to mitigate the risks associated with abnormal sleep patterns, especially when modifying sleep duration itself is challenging, and the projected health burden associated with sleep disorders could be reduced through metabolic-focused strategies. Lay Summary • What question did this study address, and why does it matter? This study examined whether sleep duration is associated with metabolic markers and whether these markers, in turn, are associated with mortality risk, using data from two large population-based cohorts in China and the UK. Sleep problems are becoming increasingly common worldwide, and identifying metabolic pathways that may link sleep to health outcomes could inform future prevention strategies. • Where was the study done and what were the main results? The study used data from the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). In both cohorts, sleep duration was associated with changes in specific metabolic markers, including lipoprotein subclasses, cholesterol, and ketone bodies. These metabolic changes were further associated with all-cause and cause-specific mortality. While lipoprotein subclasses showed broadly consistent patterns across the two cohorts, cholesterol and ketone bodies exhibited contrary associations with mortality in the CKB compared to the UKB. Sex-specific differences were also observed, particularly in the CKB cohort, where significant associations were found only in women. • What do these findings mean in practice? These findings suggest that lipid metabolism may play a role in the relationship between sleep duration and mortality. This raises the possibility that metabolic monitoring or targeted interventions—such as lifestyle or dietary modifications—could potentially help reduce the health risks associated with abnormal sleep duration, particularly in settings where sleep modification itself is challenging. However, further research is needed to determine whether these associations are causal and to explore their underlying mechanisms.

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