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Sleep profiles of different psychiatric traits.

ABSTRACT: Disturbed sleep comes in many forms. While the key role of sleep in mental health is undisputed, our understanding of the type of sleeping problems that manifest in the early stages of psychiatric disorders is limited. A sample without psychiatric diagnoses (N = 440, 341 women, 97 men, 2 non-binaries; M_age = 32.1, SD = 9.4, range 18-77) underwent a comprehensive assessment, evaluating eight sleep features and 13 questionnaires on common psychiatric complaints. Results revealed that traits of affect disorders, generalized anxiety, and ADHD had the worst sleep profiles, while autism disorder, eating disorder, and impulsivity traits showed milder sleep issues. Mania was the only trait associated with an overall better sleep profile. Across traits, insomnia and fatigue dominated and sleep variability was least prominent. These findings provide support for both transdiagnostic and disorder-specific targets for prevention and treatment.

SUBMITTER: Axelsson J

PROVIDER: S-EPMC11245526 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Sleep profiles of different psychiatric traits.

Axelsson John J van Someren Eus J W EJW Balter Leonie J T LJT

Translational psychiatry 20240712 1

Disturbed sleep comes in many forms. While the key role of sleep in mental health is undisputed, our understanding of the type of sleeping problems that manifest in the early stages of psychiatric disorders is limited. A sample without psychiatric diagnoses (N = 440, 341 women, 97 men, 2 non-binaries; M<sub>age</sub> = 32.1, SD = 9.4, range 18-77) underwent a comprehensive assessment, evaluating eight sleep features and 13 questionnaires on common psychiatric complaints. Results revealed that tr ...[more]

PMID: 38997280

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Project description:DNA methylation has become increasingly recognized in the etiology of psychiatric disorders. Because brain tissue is not accessible in living humans, epigenetic studies are most often conducted in blood. Saliva is often collected for genotyping studies but is rarely used to examine DNA methylation because the proportion of epithelial cells and leukocytes varies extensively between individuals. The goal of this study was to evaluate whether saliva DNA is informative for studies of psychiatric disorders. Saliva and blood methylation was clearly distinguishable though there was positive correlation overall. There was little correlation in CpG sites within relevant candidate genes. Correlated CpG sites were more likely to occur in areas of low CpG density (i.e. CpG shores and open seas). There was more variability in CpG sites from saliva than blood, which may reflect its heterogeneity. Thus, this study provides a framework for using DNA methylation from saliva and suggests that DNA methylation of saliva may offer distinct opportunities for epidemiological and longitudinal studies of psychiatric traits. DNA methylation was assessed in saliva and blood samples from 64 adult African Americans. Both saliva and blood samples were collected from each participant. Saliva was stored in Oragene DNA sample collection kits (DNA Genotek), and blood was collected in EDTA vacuum tubes. DNA was extracted using the Puregene Genomic DNA kit (Invitrogen). DNA methylation was interrogated for each sample using the HumanMethylation450 BeadChip (Illumina). Analyses for tissue-specific DNA patterns were conducted using linear regression adjusted for appropriate covariates, including estimated cellular proportions. The estimated proportion of epithelial cells in saliva DNA ranged from 3-99% (median 26%). In blood, the estimated proportion of lymphocytes ranged from 25-70% (median 48%) and neutrophils ranged from 42-84% (median 58%). Methylation of 68.8% of all CpG sites differed relative to epithelial cell proportion (FDR<.05). Our results provide a framework for methylation studies in DNA extracted from saliva and highlight the importance of controlling for the proportion of epithelial and leukocyte cells. This presents an attractive opportunity for investigators that have already collected salivary DNA for genetic studies of psychiatric traits.

Project description:BackgroundPolygenic risk scores (PRSs) operationalize genetic propensity toward a particular mental disorder and hold promise as early predictors of psychopathology, but before a PRS can be used clinically, explanatory power must be increased and the specificity for a psychiatric domain established. To enable early detection, it is crucial to study these psychometric properties in childhood. We examined whether PRSs associate more with general or with specific psychopathology in school-aged children. Additionally, we tested whether psychiatric PRSs can be combined into a multi-PRS score for improved performance.MethodsWe computed 16 PRSs based on GWASs of psychiatric phenotypes, but also neuroticism and cognitive ability, in mostly adult populations. Study participants were 9,247 school-aged children from three population-based cohorts of the DREAM-BIG consortium: ALSPAC (UK), The Generation R Study (Netherlands), and MAVAN (Canada). We associated each PRS with general and specific psychopathology factors, derived from a bifactor model based on self-report and parental, teacher, and observer reports. After fitting each PRS in separate models, we also tested a multi-PRS model, in which all PRSs are entered simultaneously as predictors of the general psychopathology factor.ResultsSeven PRSs were associated with the general psychopathology factor after multiple testing adjustment, two with specific externalizing and five with specific internalizing psychopathology. PRSs predicted general psychopathology independently of each other, with the exception of depression and depressive symptom PRSs. Most PRSs associated with a specific psychopathology domain, were also associated with general child psychopathology.ConclusionsThe results suggest that PRSs based on current GWASs of psychiatric phenotypes tend to be associated with general psychopathology, or both general and specific psychiatric domains, but not with one specific psychopathology domain only. Furthermore, PRSs can be combined to improve predictive ability. PRS users should therefore be conscious of nonspecificity and consider using multiple PRSs simultaneously, when predicting psychiatric disorders.

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Sleep profiles of different psychiatric traits.

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Sleep profiles of different psychiatric traits.

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