Plasma Proteomic Profile of Sleep Measures and Its Implication for Health
Poster No:
692
Submission Type:
Abstract Submission
Authors:
yuzhu li1, Linbo Wang1, Wei Cheng2, JianFeng Feng3
Institutions:
1Fudan University, Shanghai, Shanghai, 2Institute of Science and Technology for Brain-Inspired Intelligence, Shanghi, Shanghai, 3Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
First Author:
Co-Author(s):
JianFeng Feng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Introduction:
Sleep is a fundamental aspect of human health and is notably critical for the brain's clearance of metabolites and proteins, immune function, synaptic homeostasis, and restorative function [1]. Animal studies have shown that sleep disruptions can induce profound pathological consequences in the brain and peripheral system [2]. In human studies, however, little is known about how blood circulating proteins change molecularly with different sleep phenotypes [3]. Considering proteins as the end products of gene expression, they may offer a novel insight into the molecular mechanisms of different sleep phenotypes and their relationship with overall health [4].
Methods:
A linear regression model was utilized to measure the associations between 2922 proteins and eight sleep-related phenotypes, including short/long sleep duration, insomnia, chronotype, daytime dozing, snoring, napping during the day and getting up in the morning. Covariates adjusted in the model included age, sex, body mass index, Townsend deprivation index etc. Partial correlations were utilized to measure the associations between 331 sleep-related proteins and brain structure; the imaging scanning site and total intracranial volume were further added to the model as covariates for the brain structure. Correlation coefficients and two-sided p-values were acquired from the model to measure the correlation between proteins and brain structure. FDR corrections were performed for the multiple comparisons (FDR corrected p<0.05).
Results:
Significant linear associations were identified between sleep phenotypes and proteins after adjusting for age, sex, Townsend Deprivation Index (TDI), BMI, education, smoking, drinking, batch, and the interval between blood sampling and protein measurement. A total of 1,018 proteins were significantly associated with eight sleep phenotypes (Bonferroni corrected p < 0.05, Fig. 1). Among these, long sleep duration and daytime napping showed the strongest associations, with 589 and 547 significant proteins, respectively, and 401 overlapping proteins (Fig. 1b). Daytime dozing was linked to 320 proteins, 215 of which overlapped with those associated with napping and long sleep (Fig. 1d).
Enrichment analysis revealed proteins positively associated with naps were enriched in immune pathways (e.g., cytokine-cytokine receptor interactions) and extracellular region components (FDR corrected p < 0.05). Conversely, proteins negatively associated with naps were enriched in lipoprotein metabolism pathways, including plasma lipoprotein particles and lipid transport processes.
Partial correlations between 331 sleep-related proteins and brain structure revealed significant associations with subcortical and cortical volumes, adjusted for total intracranial volume, imaging sites, and covariates. Subcortical regions with the most associations included the thalamus, caudate nucleus, pallidum, and hippocampus (123 proteins, Fig. 2a), while cortical regions included the insula, medial orbitofrontal cortex, middle temporal cortex, and lateral orbitofrontal cortex (101 proteins, Fig. 2c). These findings align with previous studies on sleep duration and disturbances. For structural connectivity, 56 proteins were associated with at least one tract based on fractional anisotropy (FA), and 72 proteins were linked via mean diffusivity (MD) (FDR corrected p < 0.05, Fig. 2b). MD showed the strongest associations in the superior longitudinal fasciculus, uncinate fasciculus, and anterior thalamic radiation, while FA associations were highest in the posterior thalamic radiation, inferior longitudinal fasciculus, and medial lemniscus.
Enrichment analysis revealed proteins positively associated with naps were enriched in immune pathways (e.g., cytokine-cytokine receptor interactions) and extracellular region components (FDR corrected p < 0.05). Conversely, proteins negatively associated with naps were enriched in lipoprotein metabolism pathways, including plasma lipoprotein particles and lipid transport processes.
Partial correlations between 331 sleep-related proteins and brain structure revealed significant associations with subcortical and cortical volumes, adjusted for total intracranial volume, imaging sites, and covariates. Subcortical regions with the most associations included the thalamus, caudate nucleus, pallidum, and hippocampus (123 proteins, Fig. 2a), while cortical regions included the insula, medial orbitofrontal cortex, middle temporal cortex, and lateral orbitofrontal cortex (101 proteins, Fig. 2c). These findings align with previous studies on sleep duration and disturbances. For structural connectivity, 56 proteins were associated with at least one tract based on fractional anisotropy (FA), and 72 proteins were linked via mean diffusivity (MD) (FDR corrected p < 0.05, Fig. 2b). MD showed the strongest associations in the superior longitudinal fasciculus, uncinate fasciculus, and anterior thalamic radiation, while FA associations were highest in the posterior thalamic radiation, inferior longitudinal fasciculus, and medial lemniscus.
Conclusions:
These findings shed light on the potential molecular mechanisms that underlie the complex relationships among sleep, brain disorders, and metabolic diseases, thereby providing new therapeutic targets for treating sleep and comorbid health conditions.
Genetics:
Genetics Other 1
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 2
Keywords:
Blood
Psychiatric
Sleep
1|2Indicates the priority used for review
·The associations between plasma proteins and eight sleep phenotypes
·The associations between sleep-related proteins and brain structure
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[2] Irwin, M. R. Sleep and inflammation: partners in sickness and in health. Nat. Rev. Immunol. 19, 702–715 (2019)
[3] Vanrobaeys, Y. et al. Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation. Nat. Commun. 14, 1–15 (2023).
[4] Walker, K. A. et al. Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk. Nat. Aging 1, 473–489 (2021).