Depression and anxiety · 2026
Plasma Proteomic Landscape of Multidimensional Sleep Health and Its Association With Depression Risk Under Genetic Susceptibility.
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Chen Feng, Hong Yanggang, Zhu Yue, Wang Yukai, Chen Qi, Li Jiajun, Lu Yangguang, Guan Haoxiong, Sheng Huixiang, Xie Ruijie, Ye Fan
Аннотация
BACKGROUND: Multidimensional sleep health, encompassing duration, snoring, chronotype, insomnia symptoms, and daytime sleepiness, is associated with various chronic diseases. However, its relationship with depression risk, especially the underlying molecular mechanisms across different levels of genetic susceptibility, remains unclear.
METHODS: In this prospective cohort study, we analyzed data from 36,852 UK Biobank participants, integrating multidimensional sleep scores, plasma proteomic data covering 2,911 proteins, and polygenic risk scores (PRS) for depression. Sleep-related proteins were identified via elastic net regression, and their associations with incident depression were assessed using Cox proportional hazards models. Mediation and interaction analyses were conducted to explore the role of proteomic features and their effect modification by genetic risk.
RESULTS: Seventy-three plasma proteins were selected by elastic net as sleep-related proteomic features, mainly involving immune regulation, neural signaling, metabolic homeostasis, and extracellular matrix remodeling. The proteomic score derived from these features was positively correlated with sleep health (r = 0.234, p < 1×10-100). Healthy sleepers had a lower risk of depression (HR = 0.553, 95% CI: 0.454-0.674, p < 0.001), as did those with higher proteomic scores (HR = 0.660, 95% CI: 0.566-0.770, p < 0.001). Mediation analysis suggested that the proteomic signature statistically accounted for a modest proportion of the observed association between sleep health and incident depression. A significant additive interaction was observed between unhealthy sleep and high genetic risk (RERI = 0.280, 95% CI: 0.022-0.538).
CONCLUSION: This study characterizes the plasma proteomic profile of multidimensional sleep health and identifies sleep-associated proteomic signatures related to incident depression across levels of genetic susceptibility. These findings highlight potential biological pathways for further investigation and require independent replication and causal validation before clinical interpretation.
METHODS: In this prospective cohort study, we analyzed data from 36,852 UK Biobank participants, integrating multidimensional sleep scores, plasma proteomic data covering 2,911 proteins, and polygenic risk scores (PRS) for depression. Sleep-related proteins were identified via elastic net regression, and their associations with incident depression were assessed using Cox proportional hazards models. Mediation and interaction analyses were conducted to explore the role of proteomic features and their effect modification by genetic risk.
RESULTS: Seventy-three plasma proteins were selected by elastic net as sleep-related proteomic features, mainly involving immune regulation, neural signaling, metabolic homeostasis, and extracellular matrix remodeling. The proteomic score derived from these features was positively correlated with sleep health (r = 0.234, p < 1×10-100). Healthy sleepers had a lower risk of depression (HR = 0.553, 95% CI: 0.454-0.674, p < 0.001), as did those with higher proteomic scores (HR = 0.660, 95% CI: 0.566-0.770, p < 0.001). Mediation analysis suggested that the proteomic signature statistically accounted for a modest proportion of the observed association between sleep health and incident depression. A significant additive interaction was observed between unhealthy sleep and high genetic risk (RERI = 0.280, 95% CI: 0.022-0.538).
CONCLUSION: This study characterizes the plasma proteomic profile of multidimensional sleep health and identifies sleep-associated proteomic signatures related to incident depression across levels of genetic susceptibility. These findings highlight potential biological pathways for further investigation and require independent replication and causal validation before clinical interpretation.