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A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures.

ABSTRACT: Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

SUBMITTER: Perrault AA

PROVIDER: S-EPMC10979931 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures.

Perrault Aurore A AA Kebets Valeria V Kuek Nicole M Y NMY Cross Nathan E NE Tesfaye Rackeb R Pomares Florence B FB Li Jingwei J Chee Michael W L MWL Dang-Vu Thien Thanh TT Yeo B T Thomas BTT

bioRxiv : the preprint server for biology 20240721

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five prof ...[more]

PMID: 38559143

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Project description:BackgroundStratified or personalised medicine targets treatments for groups of individuals with a disorder based on individual heterogeneity and shared factors that influence the likelihood of response. Psychiatry has traditionally defined diagnoses by constellations of co-occurring signs and symptoms that are assigned a categorical label (e.g. schizophrenia). Trial methodology in psychiatry has evaluated interventions targeted at these categorical entities, with diagnoses being equated to disorders. Recent insights into both the nosology and neurobiology of psychiatric disorder reveal that traditional categorical diagnoses cannot be equated with disorders. We argue that current quantitative methodology (1) inherits these categorical assumptions, (2) allows only for the discovery of average treatment response, (3) relies on composite outcome measures and (4) sacrifices valuable predictive information for stratified and personalised treatment in psychiatry.Methods and findingsTo achieve a truly 'stratified psychiatry' we propose and then operationalise two necessary steps: first, a formal multi-dimensional representation of disorder definition and clinical state, and second, the similar redefinition of outcomes as multidimensional constructs that can expose within- and between-patient differences in response. We use the categorical diagnosis of schizophrenia-conceptualised as a label for heterogeneous disorders-as a means of introducing operational definitions of stratified psychiatry using principles from multivariate analysis. We demonstrate this framework by application to the Clinical Antipsychotic Trials of Intervention Effectiveness dataset, showing heterogeneity in both patient clinical states and their trajectories after treatment that are lost in the traditional categorical approach with composite outcomes. We then systematically review a decade of registered clinical trials for cognitive deficits in schizophrenia highlighting existing assumptions of categorical diagnoses and aggregate outcomes while identifying a small number of trials that could be reanalysed using our proposal.ConclusionWe describe quantitative methods for the development of a multi-dimensional model of clinical state, disorders and trajectories which practically realises stratified psychiatry. We highlight the potential for recovering existing trial data, the implications for stratified psychiatry in trial design and clinical treatment and finally, describe different kinds of probabilistic reasoning tools necessary to implement stratification.

Project description:BackgroundAlzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.MethodsDifferentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.ResultsDEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.ConclusionAD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.

Project description:Background/objectivesSleep health has been associated with diet quality, but the relationship between chrononutrition patterns and multidimensional sleep health is unclear. This study identifies chrononutrition patterns among U.S. adults and examines their associations with multidimensional sleep health.MethodsThis cross-sectional analysis used data from the 2017-2020 National Health and Nutrition Examination Survey. Chrononutrition behaviors were assessed using two 24 h dietary recalls. Latent profile analysis was used to identify chrononutrition profiles. Multivariable survey regression models determined the associations between chrononutrition patterns and sleep health dimensions.ResultsThe sample included 5228 subjects with a median age of 49 years. Of the sample, 52% of the participants were female, and 65% were White. In adjusted models, each additional hour between wake time and first instance of eating was associated with a 19% increase in the odds of poor timing (sleep midpoint < 2:00 a.m. or >4:00 a.m.; 95% CI: 1.07-1.33) and a 21% increase in poor duration (<7 or >9 h/night; 95% CI: 1.09-1.33). Each additional hour between last eating and bedtime was associated with 9% higher odds of poor duration (95% CI: 1.03-1.16). A one-hour longer eating window was associated with 10% lower odds of poor timing (95% CI: 0.84-0.98). We identified five chrononutrition profiles: Typical Eating (reference), Early Finished Eating, Later Heavy Eating, Extended Window Eating, and Restricted Window Eating. The Later Heavy Eating profile exhibited 96% higher odds of poor timing (95% CI: 1.09-3.51) and the Restricted Window Eating profile had 94% higher odds of poor duration (95% CI: 1.10-3.43).ConclusionsThese findings highlight the importance of unique chrononutrition patterns in relation to multidimensional sleep health. We provide a framework for future studies to identify personalized chrononutrition interventions and their role in improving sleep health.

Dataset Information

A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures.

Publications

A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures.

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