Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways

Associations between post-traumatic stress disorder and neurological disorders: A genetic correlation and Mendelian randomization study

BACKGROUND

Observational studies have reported a close relationship between post-traumatic stress disorder (PTSD) and neurological disorders, but the existence of a causal link remains uncertain. The aim of this study is to investigate these relationships and potential mediators via Mendelian randomization (MR) analysis.

METHODS

We sourced pooled data for genome-wide association study (GWAS) of PTSD (n = 1,222,882) from the psychiatric genomics consortium. Summary-level data for eight neurological traits were derived from large-scale GWASs. Genetic correlations were computed using linkage disequilibrium (LD) score regression. The inverse variance weighted (IVW) method served as the primary analysis method for MR. We employed a range of sensitivity analysis methods to ensure result robustness. A two-step approach was utilized to ascertain the effects and proportions of mediations.

RESULTS

We identified significant genetic associations between PTSD and any dementia, cognitive performance, multiple sclerosis, and migraine. MR analysis revealed a significant association between PTSD and an increased risk of migraine (P = 0.02). This was substantiated by the results of several sensitivity analyses. Notably, the robust association between PTSD and migraine persisted even after adjustment for major depressive disorder and anxiety. Mediation analysis revealed that both alcohol intake frequency and insomnia partially mediated the association between PTSD and migraine.

LIMITATIONS

Participants in the MR analysis were of European descent, and verification in other ethnicities was not possible due to data limitations.

CONCLUSION

Our findings indicate a close association between PTSD and migraine. Alcohol intake frequency and insomnia serve as intermediate factors, partially explaining the relationship between PTSD and migraine.

Insomnia Subtypes Have Differentiating Deviations in Brain Structural Connectivity

BACKGROUND

Insomnia disorder is the most common sleep disorder. A better understanding of insomnia-related deviations in the brain could inspire better treatment. Insufficiently recognized heterogeneity within the insomnia population could obscure detection of involved brain circuits. In the current study, we investigated whether structural brain connectivity deviations differed between recently discovered and validated insomnia subtypes.

METHODS

Structural and diffusion-weighted 3T magnetic resonance imaging data from 4 independent studies were harmonized. The sample consisted of 73 control participants without sleep complaints and 204 participants with insomnia who were grouped into 5 insomnia subtypes based on their fingerprint of mood and personality traits assessed with the Insomnia Type Questionnaire. Linear regression correcting for age and sex was used to evaluate group differences in structural connectivity strength, indicated by fractional anisotropy, streamline volume density, and mean diffusivity and evaluated within 3 different atlases.

RESULTS

Insomnia subtypes showed differentiating profiles of deviating structural connectivity that were concentrated in different functional networks. Permutation testing against randomly drawn heterogeneous subsamples indicated significant specificity of deviation profiles in 4 of the 5 subtypes: highly distressed, moderately distressed reward sensitive, slightly distressed low reactive, and slightly distressed high reactive. Connectivity deviation profile significance ranged from p = .001 to p = .049 for different resolutions of brain parcellation and connectivity weight.

CONCLUSIONS

Our results provide an initial indication that different insomnia subtypes exhibit distinct profiles of deviations in structural brain connectivity. Subtyping insomnia may be essential for a better understanding of brain mechanisms that contribute to insomnia vulnerability.

Association of sleep quality and its change with the risk of depression in middle-aged and elderly people: A 10-year cohort study from England

BACKGROUND

Persistently poor sleep quality in young adults is linked to a higher risk of depression. However, the impact of changes in sleep quality on depression risk in middle-aged and older adults remain unclear. This study investigates the association between sleep quality, its changes, and the risk of depression in middle-aged and elderly people.

METHODS

We included 4007 participants (mean age 63.0 ± 7.6 years, 53.0 % women) from the English Longitudinal Study of Ageing. Sleep quality was assessed using the Jenkins Sleep Problems Scale and a global sleep quality question. Depression was evaluated with the Center for Epidemiological Studies Depression Scale and self-reported doctor-diagnosed depression. Multivariable logistic regression, restricted cubic spline curve, and mediation analysis was employed.

RESULTS

After 10 years of follow-up, 777 individuals developed depression. Sleep quality scores positively correlated with depression risk. Among those with good sleep quality, worsening sleep quality increased depression risk (OR = 1.67, 95 % CI: 1.21-2.31). For those with intermediate sleep quality, improved sleep quality reduced depression risk (OR = 0.70, 95 % CI: 0.50-0.98). Conversely, worsening sleep quality increased depression risk (OR = 2.11, 95 % CI: 1.47-3.02). Pain and functional disability partially mediated the association between intermediate/poor sleep quality and depression (9.8 % and 4.2 %, respectively).

LIMITATIONS

Sleep quality is based on self-reported.

CONCLUSIONS

Intermediate, poor, and worsening sleep quality are linked to higher depression risk. Improving sleep quality mitigates depression risk in those with intermediate sleep quality. Sleep quality may influence depression indirectly through pain and functional disability.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitations in that they measure heterogeneous behavior in a quantitative and unbiased fashion. Here, we analyze wearable and genetic data from the Adolescent Brain Cognitive Development (ABCD) study. Leveraging >250 wearable-derived features as digital phenotypes, we show that an interpretable AI framework can objectively classify adolescents with psychiatric disorders more accurately than previously possible. To relate digital phenotypes to the underlying genetics, we show how they can be employed in univariate and multivariate genome-wide association studies (GWASs). Doing so, we identify 16 significant genetic loci and 37 psychiatric-associated genes, including ELFN1 and ADORA3, demonstrating that continuous, wearable-derived features give greater detection power than traditional case-control GWASs. Overall, we show how wearable technology can help uncover new linkages between behavior and genetics.

Evaluating causal associations of chronotype with pregnancy and perinatal outcomes and its interactions with insomnia and sleep duration: a mendelian randomization study

BACKGROUND

Observational studies suggested chronotype was associated with pregnancy and perinatal outcomes. Whether these associations are causal is unclear. Our aims are to use Mendelian randomization (MR) to explore (1) associations of evening preference with stillbirth, miscarriage, gestational diabetes, hypertensive disorders of pregnancy, perinatal depression, preterm birth and offspring birthweight; and (2) differences in associations of insomnia and sleep duration with those outcomes between chronotype preferences.

METHODS

We conducted two-sample MR using 105 genetic variants reported in a genome-wide association study (N = 248,100) to instrument for lifelong predisposition to evening- versus morning-preference. We generated variant-outcome associations in European ancestry women from UK Biobank (UKB, N = 176,897), Avon Longitudinal Study of Parents and Children (ALSPAC, N = 6826), Born in Bradford (BiB, N = 2940) and the Norwegian Mother, Father and Child Cohort Study (MoBa, N = 57,430), and extracted equivalent associations from FinnGen (N = 190,879). We used inverse variance weighted (IVW) as main analysis, with weighted median and MR-Egger as sensitivity analyses. Relying on the individual participant data from UKB, ALSPAC, BiB and MoBa, we also conducted IVW analyses of insomnia and sleep duration on the pregnancy and perinatal outcomes, stratified by genetically predicted chronotypes.

RESULTS

In IVW and sensitivity analyses, we did not find robust evidence of associations of chronotype with the outcomes. Insomnia was associated with a higher risk of preterm birth among evening preference women (odds ratio 1.61, 95% confidence interval: 1.17, 2.21), but not among morning preference women (odds ratio 0.87, 95% confidence interval: 0.64, 1.18), with an interaction P-value = 0.01. There was no evidence of interactions between insomnia and chronotype on other outcomes, or between sleep duration and chronotype on any outcomes.

CONCLUSIONS

This study raises the possibility of a higher risk of preterm birth among women with insomnia who also have an evening preference. Our findings warrant replications due to imprecise estimates.

A conserved role for ALG10/ALG10B and the N -glycosylation pathway in the sleep-epilepsy axis

Congenital disorders of glycosylation (CDG) comprise a class of inborn errors of metabolism resulting from pathogenic variants in genes coding for enzymes involved in the asparagine-linked glycosylation of proteins. Unexpectedly to date, no CDG has been described for ALG10 , encoding the alpha-1,2-glucosyltransferase catalyzing the final step of lipid-linked oligosaccharide biosynthesis. Genome-wide association studies (GWAS) of human traits in the UK Biobank revealed significant SNP associations with short sleep duration, reduced napping frequency, later sleep timing and evening diurnal preference as well as cardiac traits at a genomic locus containing a pair of paralogous enzymes ALG10 and ALG10B . Modeling Alg10 loss in Drosophila, we identify an essential role for the N -glycosylation pathway in maintaining appropriate neuronal firing activity, healthy sleep, preventing seizures, and cardiovascular homeostasis. We further confirm the broader relevance of neurological findings associated with Alg10 from humans and flies using zebrafish and nematodes and demonstrate conserved biochemical roles for N -glycosylation in Arabidopsis . We report a human subject homozygous for variants in both ALG10 and ALG10B arising from a consanguineous marriage, with epilepsy, brain atrophy, and sleep abnormalities as predicted by the fly phenotype. Quantitative glycoproteomic analysis in our Drosophila model identifies potential key molecular targets for neurological symptoms of CDGs.

Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions

MicroRNAs (miRNAs) play a crucial role in regulating gene expression and influence many biological processes. Despite their importance, understanding of how genetic variation affects miRNA expression in the brain and how this relates to brain disorders remains limited. Here we investigated these questions by identifying microRNA expression quantitative trait loci (miR-QTLs), or genetic variants associated with brain miRNA levels, using genome-wide small RNA sequencing profiles from dorsolateral prefrontal cortex samples of 604 older adult donors of European ancestry. Here we show that nearly half (224 of 470) of the analyzed miRNAs have associated miR-QTLs, many of which fall in regulatory regions such as brain promoters and enhancers. We also demonstrate that intragenic miRNAs often have genetic regulation independent from their host genes. Furthermore, by integrating our findings with 16 genome-wide association studies of psychiatric and neurodegenerative disorders, we identified miRNAs that likely contribute to bipolar disorder, depression, schizophrenia and Parkinson's disease. These findings advance understanding of the genetic regulation of miRNAs and their role in brain health and disease.

Effect of five traditional Chinese medicine exercises on insomnia: A systematic review and network meta-analysis

BACKGROUND

Insomnia has become a significant public health issue. Traditional Chinese medicine (TCM) exercises are used in the treatment of insomnia. The purpose of this study was to compare the effectiveness of five TCM exercise regimens (yijinjing, wuqinxi, liuzijue, baduanjin, and taijiquan) as an intervention for insomnia.

METHOD

We searched six databases-China National Knowledge Infrastructure, Wanfang, PubMed, Embase, Cochrane Library, and Web of Science-for relevant articles, published in English or Chinese, from their inception till April 2024. Data from the included literature were analyzed and evaluated using a network meta-analysis of random effects with a frequency-based framework.

RESULTS

A total of 50 papers were included, comprising 4226 patients with insomnia. The results of the direct comparison of the five TCM exercises (yijinjing, wuqinxi, liuzijue, baduanjin, and taijiquan) with the control group indicated that all five TCM exercises were able to improve insomnia (p < 0.05). In the indirect comparison between the five TCM exercises, there was a significant difference between liuzijue and wuqinxi (p < 0.05), and taijiquan (p < 0.05). We used the areas under the receiver operating curves to rank the effectiveness of the five TCM exercises in treating insomnia as follows: liuzijue (Surface Under the Cumulative Ranking Curve, SUCRA = 96.4%) > yijinjing (SUCRA = 72.6%) > baduanjin (SUCRA = 55.1%) > taijiquan (45%) > wuqinxi (SUCRA = 30.9%).

CONCLUSION

The studied TCM exercises can be used as an effective treatment for insomnia, and liuzijue is the most effective of the studied options.

Associations between insomnia and cardiovascular diseases: a meta-review and meta-analysis of observational and Mendelian randomization studies

STUDY OBJECTIVES

Observational studies suggest associations between insomnia and cardiovascular diseases (CVDs), but the causal mechanism remains unclear. We investigated the potential causal associations between insomnia and CVDs by a combined systematic meta-review and meta-analysis of observational and Mendelian randomization studies.

METHODS

We searched PubMed, Web of Science, and Embase for English-language articles from inception to July 11, 2023. Two reviewers independently screened the articles to minimize potential bias. We summarized the current evidence on the associations of insomnia with coronary artery disease, atrial fibrillation, heart failure, myocardial infarction, hypertension, and stroke risk by combining meta-analyses of observational and Mendelian randomization studies.

RESULTS

Four meta-analyses of observational studies and 9 Mendelian randomization studies were included in the final data analysis. A systematic meta-review of observational studies provided strong evidence that insomnia is an independent risk factor for many CVDs, including atrial fibrillation, myocardial infarction, and hypertension. A meta-analysis of Mendelian randomization studies revealed that insomnia may be potentially causally related to coronary artery disease (odds ratio [OR] = 1.14, 95% confidence interval [CI] = 1.10-1.19, I2 = 97%), atrial fibrillation (OR = 1.02, 95% CI = 1.01-1.04, I2 = 94%), heart failure (OR = 1.04, 95% CI = 1.03-1.06, I2 =97%), hypertension (OR = 1.16, 95% CI = 1.13-1.18, I2 = 28%), large artery stroke (OR = 1.14, 95% CI = 1.05-1.24, I2 = 0%), any ischemic stroke (OR = 1.09, 95% CI = 1.03-1.14, I2 = 60%), and primary intracranial hemorrhage (OR = 1.16, 95% CI = 1.05-1.27, I2 = 0%). No evidence suggested that insomnia is causally associated with cardioembolic or small vessel stroke.

CONCLUSIONS

Our results provide strong evidence supporting a possible causal association between insomnia and CVD risk. Strategies to treat insomnia may be promising targets for preventing CVDs.

CITATION

Zhang X, Sun Y, Ye S, et al. Associations between insomnia and cardiovascular diseases: a meta-review and meta-analysis of observational and Mendelian randomization studies. J Clin Sleep Med. 2024;20(12):1975-1984.

Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases

Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.

Sleep risk factors modify the association between c-reactive protein and type 2 diabetes: A prospective cohort study

OBJECTIVE

To investigate the prospective association between serum CRP levels and T2D incidence and explore whether such association was modified by sleep risk factors.

METHODS

The study included 366 746 participants without diabetes and exhibited CRP measures at baseline from the UK Biobank. Sleep risk factors included sleep duration, insomnia, snoring, chronotype, and daytime sleepiness. Cox proportional hazards model was used to estimate the hazard ratio (HR), and 95 % confidence interval (CI) of T2D associated with CRP levels. Interactions between CRP and sleep risk factors were also tested.

RESULTS

During a median follow-up of 10.4 years, 14 200 T2D cases were identified. The HRs (95 % CIs) of T2D were 1.31 (1.21-1.43), 1.62 (1.50-1.75), 1.98 (1.83-2.13), and 2.38 (2.21-2.57), respectively, in higher quintile groups of CRP levels compared with the lowest group (p-value for trend <0.001). There were interactions of CRP levels with self-reported sleep duration, snoring, and daytime sleepiness (p-value for interaction = 0.002, 0.0002, and 0.0001). The associated risks between T2D and the elevation in CRP were more evident among participants with high-risk sleep factors.

CONCLUSIONS

Our study indicates that the elevation in serum CRP levels is associated with a higher T2D incidence; and such relation is modified by sleep risk factors including sleep duration, snoring, and daytime sleepiness.

Gene expression is associated with brain function of insomnia disorder, rather than brain structure

Previous research has found brain structural and functional abnormalities in patients with insomnia disorder (ID). However, the relationship between brain abnormalities in ID and brain gene expression is unclear. This study explored the relationship between gene expression and brain structural or functional abnormalities in ID, and we validated the reliability of the results with two independent datasets (discover dataset: healthy control (HC) = 129, ID = 264; validation dataset: HC = 160, ID = 115). Brain imaging results show that ID has abnormal resting-state spontaneous activity, regional homogeneity, and widespread gray matter volume reduction compared to HC. The association analysis results with gene expression further revealed that brain function abnormalities in ID were significantly associated with gene expression, but structural abnormalities were not. This study establishes a link between transcriptional changes and brain functional abnormalities in ID, revealing a genetic basis that may involve several biological pathways. Specifically, these pathways include hormonal regulation of the hypothalamic-pituitary-adrenal (HPA) axis, which plays a crucial role in stress response and sleep regulation; ion transport across membranes, vital for neuronal communication; and inhibitory neuronal regulation, essential for maintaining normal brain function. Furthermore, the ID-related genes are enriched for brain tissue and cortical cells, emphasizing their relevance in understanding the biological underpinnings of ID.

[Screening for sleep disorders in internal medicine as potential comorbidities of systemic autoimmune diseases and improving patients' quality of life]

Sleep medicine and internal medicine share a global and transdisciplinary vision of human physiology and illnesses, with an approach guided by the complaint and semiology. In France, approximately 13 to 18 million individuals suffer from a sleep disorder: these disorders therefore represent a public health problem. Their comorbidities with systemic autoimmune diseases are frequent. As such, this article suggests an approach to screening for sleep disorders in daily clinical practice of internal medicine leading, when appropriate, to request specialized diagnostic and/or therapeutic care in sleep medicine to substantially improve patients' quality of life.

An integrative approach prioritizes the orphan GPR61 genomic region in tissue-specific regulation of chronotype

Objectives

Chronotype, a manifestation of circadian rhythms, affects morning or evening preferences and ease of getting-up. This study explores the genetic basis of morning chronotype and ease of getting-up, focusing on the G protein-coupled receptor locus, GPR61.

Methods

We analyzed the genetic correlation between chronotype and ease of getting-up using linkage disequilibrium score regression with summary statistics from the UK Biobank (n=453,379). We prioritized shared signals between chronotype and ease of getting-up using the Human Genetic Evidence (HuGE) score. We assessed the significance of GPR61 and the lead variant rs12044778 through colocalization and in-silico analyses from ENCODE, Genotype-Tissue Expression, Hi-C, and Knockout Mouse Project databases to explore potential regulatory roles of causal genes.

Results

We identified a strong genetic correlation (Rg=0.80, P=4.9 x10 324 ) between chronotype and ease of getting-up. Twenty-three genes, including three circadian core clock components, had high HuGE scores for both traits. Lead variant rs12044778 in GPR61 was prioritized for its high HuGE score (45) and causal pleiotropy (posterior probability=0.98). This morningness variant influenced gene expression in key tissues: decreasing GPR61 in tibial nerve, increasing AMIGO1 in subcutaneous adipose, and increasing ATXN7L2 in the cerebellum. Functional knockout models showed GPR61 knockout increased fat mass and activity, AMIGO1 knockout increased activity, and ATXN7L2 knockout reduced body weight without affecting activity.

Conclusions

Our findings reveal pleiotropic genetic factors influencing chronotype and ease of getting-up, emphasizing GPR61 's rs12044778 and nearby genes like AMIGO1 and ATXN7L2 . These insights advance understanding of circadian preferences and suggest potential therapeutic interventions.

SIGNIFICANCE

This study investigates the genetic underpinnings of chronotype preferences and ease of getting up, with a focus on the orphan G protein-coupled receptor GPR61 and the locus lead variant rs12044778. By combining genomic data with in silico functional analysis, we provide mechanistic insight into a locus for morning chronotype and ease of getting in the morning. We identified the variant rs12044778 as a key regulator of GPR61 and nearby genes AMIGO1 and ATXN7L2 influencing circadian and metabolic traits. Our findings shed light on the intricate genetic networks governing circadian rhythms, suggesting potential therapeutic targets for disorders of the circadian rhythm.

A multi-regional human brain atlas of chromatin accessibility and gene expression facilitates promoter-isoform resolution genetic fine-mapping

Brain region- and cell-specific transcriptomic and epigenomic features are associated with heritability for neuropsychiatric traits, but a systematic view, considering cortical and subcortical regions, is lacking. Here, we provide an atlas of chromatin accessibility and gene expression profiles in neuronal and non-neuronal nuclei across 25 distinct human cortical and subcortical brain regions from 6 neurotypical controls. We identified extensive gene expression and chromatin accessibility differences across brain regions, including variation in alternative promoter-isoform usage and enhancer-promoter interactions. Genes with distinct promoter-isoform usage across brain regions were strongly enriched for neuropsychiatric disease risk variants. Moreover, we built enhancer-promoter interactions at promoter-isoform resolution across different brain regions and highlighted the contribution of brain region-specific and promoter-isoform-specific regulation to neuropsychiatric disorders. Including promoter-isoform resolution uncovers additional distal elements implicated in the heritability of diseases, thereby increasing the power to fine-map risk genes. Our results provide a valuable resource for studying molecular regulation across multiple regions of the human brain and underscore the importance of considering isoform information in gene regulation.

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce Enformer Celltyping, a deep learning model which incorporates distal effects of DNA interactions, up to 100,000 base-pairs away, to predict epigenetic signals in previously unseen cell types. Using DNA and chromatin accessibility data for epigenetic imputation, Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.

Mendelian randomization of individual sleep traits associated with major depressive disorder

BACKGROUND

Observational studies have shown that individual sleep traits habits are potential risk factors for major depression. However, it is not known whether there is a causal relationship between individual sleep traits habits such as continuous sleep duration, short sleep duration, short sleep duration, insomnia, nap during the day, snoring, and major depression. In this study, Mendelian randomization (MR) was used to predict major depressive disorder (MDD) in individuals sleep traits habits.

METHODS

Data were obtained from the genome-wide association study (GWAS). Nine MR analysis methods were used: Inverse Variance Weighted (IVW) [fixed effects/multiplicative random effects], simple mode, simple mode, weighted mode, simple median, weighted median, penalised weighted median, and MR-Egger, MR Egger (bootstrap). IVW was used as the main analysis method for the MR analysis of two samples, and the other methods were used as supplements.

RESULTS

The results obtained through the IVW method supported a causal relationship between sleep duration and decreased risk of MDD (odds ratio, ORivw: 0.998; 95 % CI: 0.996-0.999, P<0.001). Two-Sample MR, results showed that short sleep duration has a causal effect on the increased risk of MDD (odds ratio, ORivw: 1.179; 95 % CI: 1.108-1.255, P<0.001). However, there were no sufficient evidence supported that long sleep duration has a causal effect on the decreased risk of MDD (odds ratio, ORivw: 0.991; 95 % CI: 0.924-1.062, P = 0.793). A significant causal relationship between insomnia and increased risk of MDD was observed (OR: 1.233; 95 % CI: 1.214-1.253, P<0.001). Interestingly, our study also found that daytime napping has a causal effect on the increased risk of MDD (odds ratio, ORivw: 1.519; 95 % CI: 1.376-1.678, P<0.001). The present results did not show a significant causal relationship between snoring and the risk of MDD (ORivw: 1.000; 95 % CI: 0.998-1.002, P = 0.906). Obstructive sleep apnea (odds ratio, ORivw: 1.021; 95 % CI: 0.972-1.072, P = 0.407) and morning person (odds ratio, ORivw: 1.021; 95 % CI: 0.972-1.072, P = 0.407) have no causal effect on the increased risk of MDD.

LIMITATIONS

The study could not ascertain whether there were genetic differences among different ethnicities, nations, and regions, as it only included participants of European ancestry.

CONCLUSIONS

In summary, our research provides genetic evidence for the relationship between individual sleep traits (short sleep duration, insomnia, daytime napping) and the increased risk of MDD. Interventions targeting lifestyle factors may reduce the risk of MDD.

Causal relationship between sleep characteristics and thyroid function: A bidirectional Mendelian randomization study

BACKGROUND

Previous researches have revealed some links between thyroid function and sleep characteristics, however it remains unclear which one causes the other. The purpose of this study was to investigate the potential causal relationship between hyperthyroidism, hypothyroidism, and sleep characteristics.

METHODS

We utilized aggregated data from published genome-wide association studies (GWAS) to select genetic instruments for sleep variables. The 5 sleep-related traits (chronotype, short sleep duration, long sleep duration, daytime sleepiness, and insomnia) were associated with distinct genetic variants chosen as instrumental factors. Employing MR Egger's analysis of Mendelian randomization (MR), weighted median, weighted mode, and inverse variance weighted (IVW) methods to assess the 5 sleep traits in relation to hyperthyroidism and hypothyroidism, we subsequently conducted inverse MR analysis to examine the causal relationship between thyroid function and the 5 sleep characteristics.

RESULTS

The IVW technique did not reveal a causal association between chronotype, short sleep duration, long sleep duration, daytime sleepiness, or insomnia and the risk of abnormal thyroid function in the study investigating the influence of sleep characteristics on this risk. The outcomes of the IVW approach were consistent with the remaining 3 methods. The IVW, weighted median, MR Egger, and weighted mode methods in the reverse magnetic resonance imaging investigation did not yield evidence of a causative association between the risk of time type, long sleep duration, and insomnia and abnormal thyroid function. In contrast, the weighted median and weighted mode methods showed a possible causal relationship between hypothyroidism and short sleep duration and daytime sleepiness. Sensitivity analyses showed that the results were robust and no pleiotropy or heterogeneity was detected.

CONCLUSION

More precisely, our analysis did not uncover any indication of a reciprocal causal link between thyroid function and genetically predicted sleep characteristics.

Impact of sleep problems on the cardiometabolic risks: an integrated epidemiological and metabolomics study

BACKGROUND

We investigated the association between sleep problems and cardiometabolic risks and the potential linking effect of metabolites and metabolic pathways based on multi-layered research, including observational, mendelian randomization (MR), and metabolomics analysis.

METHODS

A cross-sectional analysis of the 2015-2018 National Health and Nutrition Examination Survey (NHANES) dataset was conducted to identify the association between sleep problems and cardiometabolic risks. A subsequent MR study based on genetic data was performed to explore the causal correlation of significant associations in the NHANES study. The underlying alteration of metabolism was explored by constructing zebrafish models and wide-targeted metabolomics analysis.

RESULTS

The cross-sectional analysis of the NHANES database revealed a significant association of snoring with obesity [OR = 2.65, 95% confidence intervals (CI): 1.87, 3.74]; sleep apnea with hypertension (OR = 1.68, 95% CI: 1.14, 2.48) and obesity (OR = 1.44, 95% CI: 1.05, 1.96); trouble sleeping with hypertension (OR = 1.84, 95% CI: 1.18, 2.86), obesity (OR = 1.56, 95% CI: 1.07, 2.26), and type 2 diabetes (T2DM) (OR = 1.52, 95% CI: 1.02, 2.25). MR analysis verified the causal relationship between genetically proxied sleep apnea or snoring and obesity. The decreased activity levels and altered expression levels of six circadian genes (bmal1b, cry1aa, cry1ab, clock1a, per1b, per2) were identified in the zebrafish of the sleep disorder group. Multiple metabolites related to disturbed glucose metabolism (e.g., 20-HETE), lipid metabolism (e.g., inosine), and vascular-related metabolites (e.g., riboflavin) were finally identified, indicating the latent effect of metabolism.

CONCLUSIONS

This study identified the chain of sleep-circadian rhythm-metabolism-cardiometabolic risks. These findings can promote improved prevention implementation and therapeutic strategies.

Genetic evidence for the causal impact of insomnia on gastrointestinal diseases and the mediating effects of adiposity traits

BACKGROUND AND AIM

Insomnia has been implicated in gastrointestinal diseases (GIs), but the causal effect between insomnia and GIs and underlying mechanisms remain unknown.

METHODS

By using the released summary-level data, we conducted a two-step Mendelian randomization (MR) analysis to examine the relationship between insomnia and four GIs and estimate the mediating role of candidate mediators. The first step was to investigate the causal association between insomnia and GIs using univariable MR analysis. The second step was to estimate the mediation proportion of selected mediators in these associations using multivariable MR analysis. Subsequently, results from different datasets were combined using the fixed-effect meta-analysis.

RESULTS

Univariable MR analysis provided strong evidence for the causal effects of insomnia on four GIs after Bonferroni correction for multiple comparisons, including peptic ulcer disease (PUD) (odds ratio [OR] = 1.15, 95% interval confidence [CI] = 1.10-1.20, P = 1.83 × 10-9), gastroesophageal reflux (GORD) (OR = 1.19, 95% CI = 1.16-1.22, P = 5.95 × 10-42), irritable bowel syndrome (IBS) (OR = 1.18, 95% CI = 1.15-1.22, P = 8.69 × 10-25), and inflammatory bowel disease (IBD) (OR = 1.09, 95% CI = 1.03-1.05, P = 3.46 × 10-3). In the mediation analysis, body mass index (BMI) and waist-to-hip ratio (WHR) were selected as mediators in the association between insomnia and PUD (BMI: mediation proportion [95% CI]: 13.61% [7.64%-20.70%]; WHR: 8.74% [5.50%-12.44%]) and GORD (BMI: 11.82% [5.94%-18.74%]; WHR: 7.68% [4.73%-11.12%]).

CONCLUSIONS

Our findings suggest that genetically instrumented insomnia has causal effects on PUD, GORD, IBS, and IBD, respectively. Adiposity traits partially mediated the associations between insomnia and GIs. Further clinical studies are warranted to evaluate the protective effect of insomnia treatment on GIs.

Genetically Predicted Sleep Traits and Sensorineural Hearing Loss: A Mendelian Randomization Study

OBJECTIVE

Observational studies suggest a potential association between sleep characteristics, sensorineural hearing loss (SNHL), and sudden SNHL (SSNHL), but causal evidence is scarce. We sought to clarify this issue using two-sample Mendelian randomization analysis.

METHODS

The inverse-variance weighted (IVW) method was performed as primary analysis to assess bidirectional causal associations between sleep traits (chronotype, sleep duration, insomnia, daytime sleepiness, and snoring) and SNHL/SSNHL using publicly available Genome-Wide Association Studies summary data from two large consortia (UK Biobank and FinnGen). Sensitivity analyses, including Mendelian randomization (MR)-Egger, Mendelian randomization pleiotropy residual sum and outlier, weight median, Cochran's Q test, leave-one-out analysis, and potential pleiotropy analysis, were conducted to ensure robustness.

RESULTS

IVW analysis found suggestive associations of morning chronotype (odds ratio [OR] = 1.08, 95% confidence interval [CI] = 1.01-1.16, p = 0.031) and daytime sleepiness (OR = 1.88, 95% CI = 1.24-2.87, p = 0.003) with SNHL onset. Additionally, morning chronotype was nominally associated with SSNHL onset using IVW method (OR = 1.37, 95% CI = 1.10-1.71, p = 0.006). However, there was no evidence for the causal effect of SNHL and SSNHL on different sleep traits (all p > 0.05). Sensitivity analysis showed that the results were stable.

CONCLUSION

Within the MR limitations, morning chronotype and daytime sleepiness were underlying causal contributors to the burden of SNHL, indicating that optimal sleep might facilitate the prevention and development of SNHL.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:4723-4729, 2024.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

Psychiatric disorders are complex and influenced by both genetic and environmental factors. However, studying the full spectrum of these disorders is hindered by practical limitations on measuring human behavior. This highlights the need for novel technologies that can measure behavioral changes at an intermediate level between diagnosis and genotype. Wearable devices are a promising tool in precision medicine, since they can record physiological measurements over time in response to environmental stimuli and do so at low cost and minimal invasiveness. Here we analyzed wearable and genetic data from a cohort of the Adolescent Brain Cognitive Development study. We generated >250 wearable-derived features and used them as intermediate phenotypes in an interpretable AI modeling framework to assign risk scores and classify adolescents with psychiatric disorders. Our model identifies key physiological processes and leverages their temporal patterns to achieve a higher performance than has been previously possible. To investigate how these physiological processes relate to the underlying genetic architecture of psychiatric disorders, we also utilized these intermediate phenotypes in univariate and multivariate GWAS. We identified a total of 29 significant genetic loci and 52 psychiatric-associated genes, including ELFN1 and ADORA3. These results show that wearable-derived continuous features enable a more precise representation of psychiatric disorders and exhibit greater detection power compared to categorical diagnostic labels. In summary, we demonstrate how consumer wearable technology can facilitate dimensional approaches in precision psychiatry and uncover etiological linkages between behavior and genetics.

Changes in sleep patterns in people with a history of depression during the COVID-19 pandemic: a natural experiment

BACKGROUND

The COVID-19 pandemic, while a major stressor, increased flexibility in sleep-wake schedules.

OBJECTIVES

To investigate the impact of the pandemic on sleep patterns in people with a history of depression and identify sociodemographic, clinical or genetic predictors of those impacts.

METHODS

6453 adults from the Australian Genetics of Depression Study (45±15 years; 75% women) completed surveys before (2016-2018) and during the pandemic (2020-2021). Participants were assigned to 'short sleep' (<6 hours), 'optimal sleep' (6-8 hours) or 'long sleep' (>8 hours). We focused on those having prepandemic 'optimal sleep'.

FINDINGS

Pre pandemic, the majority (70%, n=4514) reported optimal sleep, decreasing to 49% (n=3189) during the pandemic. Of these, 57% maintained optimal sleep, while 16% (n=725) shifted to 'short sleep' and 27% (n=1225) to 'long sleep'. In group comparisons 'optimal-to-short sleep' group had worse prepandemic mental health and increased insomnia (p's<0.001), along with an elevated depression genetic score (p=0.002). The 'optimal-to-long sleep' group were slightly younger and had higher distress (p's<0.05), a greater propensity to being evening types (p<0.001) and an elevated depression genetic score (p=0.04). Multivariate predictors for 'optimal-to-short sleep' included reported stressful life events, psychological or somatic distress and insomnia severity (false discovery rate-corrected p values<0.004), while no significant predictors were identified for 'optimal-to-long sleep'.

CONCLUSION AND IMPLICATIONS

The COVID-19 pandemic, a natural experiment, elicited significant shifts in sleep patterns among people with a history of depression, revealing associations with diverse prepandemic demographic and clinical characteristics. Understanding these dynamics may inform the selection of interventions for people with depression facing major challenges.

Search for signals of positive selection of circadian rhythm genes PER1, PER2, PER3 in different human populations

The diversity of geographically distributed human populations shows considerable variation in external and internal traits of individuals. Such differences are largely attributed to genetic adaptation to various environmental influences, which include changes in climatic conditions, variations in sleep and wakefulness, dietary variations, and others. Whole-genome data from individuals of different populations make it possible to determine the specific genetic sites responsible for adaptations and to further understand the genetic structure underlying human adaptive characteristics. In this article, we searched for signals of single nucleotide polymorphisms (SNPs) under selection pressure in people of different populations. To identify selection signals in different population groups, the PER1, PER2 and PER3 genes that are involved in the coordination of thermogenic functions and regulation of circadian rhythms, which is directly reflected in the adaptive abilities of the organism, were investigated. Data were analyzed using publicly available data from the 1000 Genomes Project for 23 populations. The Extended Haplotype Homozygosity Score statistical method was chosen to search for traces of selection. The comparative analysis performed identified points subject to selection pressure. The SNPs were annotated through the GWAS catalog and manually by analyzing Internet resources. This study suggests that living conditions, climate, and other external factors directly influence the genetic structure of populations and vary across races and geographic locations. In addition, many of the selection variants in the PER1, PER2, PER3 genes appear to regulate biological processes that are associated with major modern diseases, including obesity, cancer, metabolic syndrome, bipolar personality disorder, depression, rheumatoid arthritis, diabetes mellitus, lupus erythematosus, stroke and Alzheimer's disease, making them extremely interesting targets for further research aimed at identifying the genetic causes of human disease.

Mendelian randomization analysis using GWAS and eQTL data to investigate the relationship between chronotype and neuropsychiatric disorders and their molecular basis

Chronotype is a proxy sleep measure that has been associated with neuropsychiatric disorders. By investigating how chronotype influences risk for neuropsychiatric disorders and vice versa, we may identify modifiable risk factors for each phenotype. Here we used Mendelian randomization (MR), to explore causal effects by (1) studying the causal relationships between neuropsychiatric disorders and chronotype and (2) characterizing the genetic components of these phenotypes. Firstly, we investigated if a causal role exists between five neuropsychiatric disorders and chronotype using the largest genome-wide association studies (GWAS) available. Secondly, we integrated data from expression quantitative trait loci (eQTLs) to investigate the role of gene expression alterations on these phenotypes. Evening chronotype was causal for increased risk of schizophrenia and autism spectrum disorder and schizophrenia was causal for a tendency toward evening chronotype. We identified 12 eQTLs where gene expression changes in brain or blood were causal for one of the phenotypes, including two eQTLs for SNX19 in hippocampus and hypothalamus that were causal for schizophrenia. These findings provide important evidence for the complex, bidirectional relationship that exists between a sleep-based phenotype and neuropsychiatric disorders, and use gene expression data to identify causal roles for genes at associated loci.

CYP1B1-RMDN2 Alzheimer's disease endophenotype locus identified for cerebral tau PET

Determining the genetic architecture of Alzheimer's disease pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we perform a genome-wide association study of cortical tau quantified by positron emission tomography in 3046 participants from 12 independent studies. The CYP1B1-RMDN2 locus is associated with tau deposition. The most significant signal is at rs2113389, explaining 4.3% of the variation in cortical tau, while APOE4 rs429358 accounts for 3.6%. rs2113389 is associated with higher tau and faster cognitive decline. Additive effects, but no interactions, are observed between rs2113389 and diagnosis, APOE4, and amyloid beta positivity. CYP1B1 expression is upregulated in AD. rs2113389 is associated with higher CYP1B1 expression and methylation levels. Mouse model studies provide additional functional evidence for a relationship between CYP1B1 and tau deposition but not amyloid beta. These results provide insight into the genetic basis of cerebral tau deposition and support novel pathways for therapeutic development in AD.

Analysis of the Improvement Sequence in Insomnia Symptoms and Factors Influencing the Treatment Outcomes of Smartphone-Delivered CBT in Patients with Insomnia Disorder

Background

The effectiveness of medication combined with smartphone-delivered cognitive behavioral therapy for insomnia (CBT-I) has been well verified, but there are few studies on the sequence of remission of insomnia symptoms. This study aims to understand the sequence of symptom improvement and the factors influencing the treatment effectiveness in patients with insomnia.

Methods

Smartphone-delivered CBT, as a form of Online CBT, allows for training through mobile devices at any time and place. We utilized the Good Sleep 365 app to conduct a survey, involving 2820 patients who met the baseline inclusion criteria. These patients were assessed using a general demographic questionnaire and the Pittsburgh Sleep Quality Index (PSQI) to evaluate general demographic information and insomnia symptoms, and subsequently underwent CBT training using the Good Sleep 365 app. A total of 1179 patients completed follow-ups at 4 weeks, 8 weeks, 16 weeks, and 24 weeks.

Results

At 4 weeks and 8 weeks, the descending order of the reduction rates of PSQI components (excluding component 6: use of sleeping medication) was: sleep latency, subjective sleep quality, sleep efficiency, sleep disturbance, sleep maintenance, and daytime dysfunction. At 16 weeks and 24 weeks, the descending order was subjective sleep quality, sleep latency, sleep efficiency, daytime dysfunction, sleep maintenance, and sleep disturbance. There were significant differences in the reduction rates of PSQI components (excluding component 6: use of sleeping medication) both at the same follow-up times and at different follow-up times (all P<0.05). Multivariable logistic regression analysis showed that patients older than 30 years and those with a college degree or above had better treatment outcomes, whereas those with a disease duration of more than three years had worse outcomes.

Conclusion

The sequence of symptom improvement in patients with insomnia changes over time, and age, educational level, and duration of disease are factors influencing treatment outcomes.

A Large-Scale Genome-Wide Gene-Sleep Interaction Study in 732,564 Participants Identifies Lipid Loci Explaining Sleep-Associated Lipid Disturbances

We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify novel genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. We collected data from 55 cohorts with a combined sample size of 732,564 participants (87% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for the one-degree of freedom tests of interaction and two-degree of freedom joint tests of the main and interaction effect. In the cross-population meta-analyses, the one-degree of freedom variant-sleep interaction test identified 10 loci (P int <5.0e-9) not previously observed for lipids. Of interest, the ASPH locus (TG, LTST) is a target for aspartic and succinic acid metabolism previously shown to improve sleep and cardiovascular risk. The two-degree of freedom analyses identified an additional 7 loci that showed evidence for variant-sleep interaction (P joint <5.0e-9 in combination with P int <6.6e-6). Of these, the SLC8A1 locus (TG, STST) has been considered a potential treatment target for reduction of ischemic damage after acute myocardial infarction. Collectively, the 17 (9 with STST; 8 with LTST) loci identified in this large-scale initiative provides evidence into the biomolecular mechanisms underpinning sleep-duration-associated changes in lipid levels. The identified druggable targets may contribute to the development of novel therapies for dyslipidemia in people with sleep disturbances.

Metabolic Characteristics of Gut Microbiota and Insomnia: Evidence from a Mendelian Randomization Analysis

Insomnia is a common sleep disorder that significantly impacts individuals' sleep quality and daily life. Recent studies have suggested that gut microbiota may influence sleep through various metabolic pathways. This study aims to explore the causal relationships between the abundance of gut microbiota metabolic pathways and insomnia using Mendelian randomization (MR) analysis. This two-sample MR study used genetic data from the OpenGWAS database (205 gut bacterial pathway abundance) and the FinnGen database (insomnia-related data). We identified single nucleotide polymorphisms (SNPs) associated with gut bacterial pathway abundance as instrumental variables (IVs) and ensured their validity through stringent selection criteria and quality control measures. The primary analysis employed the inverse variance-weighted (IVW) method, supplemented by other MR methods, to estimate causal effects. The MR analysis revealed significant positive causal effects of specific carbohydrate, amino acid, and nucleotide metabolism pathways on insomnia. Key pathways, such as gluconeogenesis pathway (GLUCONEO.PWY) and TCA cycle VII acetate producers (PWY.7254), showed positive associations with insomnia (B > 0, p < 0.05). Conversely, pathways like hexitol fermentation to lactate, formate, ethanol and acetate pathway (P461.PWY) exhibited negative causal effects (B < 0, p < 0.05). Multivariable MR analysis confirmed the independent causal effects of these pathways (p < 0.05). Sensitivity analyses indicated no significant pleiotropy or heterogeneity, ensuring the robustness of the results. This study identifies specific gut microbiota metabolic pathways that play critical roles in the development of insomnia. These findings provide new insights into the biological mechanisms underlying insomnia and suggest potential targets for therapeutic interventions. Future research should further validate these causal relationships and explore how modulating gut microbiota or its metabolic products can effectively improve insomnia symptoms, leading to more personalized and precise treatment strategies.

Insomnia-related brain functional correlates in first-episode drug-naïve major depressive disorder revealed by resting-state fMRI

Introduction

Insomnia is a common comorbidity symptom in major depressive disorder (MDD) patients. Abnormal brain activities have been observed in both MDD and insomnia patients, however, the central pathological mechanisms underlying the co-occurrence of insomnia in MDD patients are still unclear. This study aimed to explore the differences of spontaneous brain activity between MDD patients with and without insomnia, as well as patients with different level of insomnia.

Methods

A total of 88 first-episode drug-naïve MDD patients including 44 with insomnia (22 with high insomnia and 22 with low insomnia) and 44 without insomnia, as well as 44 healthy controls (HC), were enrolled in this study. The level of depression and insomnia were evaluated by HAMD-17, adjusted HAMD-17 and its sleep disturbance subscale in all subjects. Resting-state functional and structural magnetic resonance imaging data were acquired from all participants and then were preprocessed by the software of DPASF. Regional homogeneity (ReHo) values of brain regions were calculated by the software of REST and were compared. Finally, receiver operating characteristic (ROC) curves were conducted to determine the values of abnormal brain regions for identifying MDD patients with insomnia and evaluating the severity of insomnia.

Results

Analysis of variance showed that there were significant differences in ReHo values in the left middle frontal gyrus, left pallidum, right superior frontal gyrus, right medial superior frontal gyrus and right rectus gyrus among three groups. Compared with HC, MDD patients with insomnia showed increased ReHo values in the medial superior frontal gyrus, middle frontal gyrus, triangular inferior frontal gyrus, calcarine fissure and right medial superior frontal gyrus, medial orbital superior frontal gyrus, as well as decreased ReHo values in the left middle occipital gyrus, pallidum and right superior temporal gyrus, inferior temporal gyrus, middle cingulate gyrus, hippocampus, putamen. MDD patients without insomnia demonstrated increased ReHo values in the left middle frontal gyrus, orbital middle frontal gyrus, anterior cingulate gyrus and right triangular inferior frontal gyrus, as well as decreased ReHo values in the left rectus gyrus, postcentral gyrus and right rectus gyrus, fusiform gyrus, pallidum. In addition, MDD patients with insomnia had decreased ReHo values in the left insula when compared to those without insomnia. Moreover, MDD patients with high insomnia exhibited increased ReHo values in the right middle temporal gyrus, and decreased ReHo values in the left orbital superior frontal gyrus, lingual gyrus, right inferior parietal gyrus and postcentral gyrus compared to those with low insomnia. ROC analysis demonstrated that impaired brain region might be helpful for identifying MDD patients with insomnia and evaluating the severity of insomnia.

Conclusion

These findings suggested that MDD patients with insomnia had wider abnormalities of brain activities in the prefrontal-limbic circuits including increased activities in the prefrontal cortex, which might be the compensatory mechanism underlying insomnia in MDD. In addition, decreased activity of left insula might be associated with the occurrence of insomnia in MDD patients and decreased activities of the frontal-parietal network might cause more serious insomnia related to MDD.

Enhanced cerebral blood flow similarity of the somatomotor network in chronic insomnia: Transcriptomic decoding, gut microbial signatures and phenotypic roles

Chronic insomnia (CI) is a complex disease involving multiple factors including genetics, gut microbiota, and brain structure and function. However, there lacks a unified framework to elucidate how these factors interact in CI. By combining data of clinical assessment, sleep behavior recording, cognitive test, multimodal MRI (structural, functional, and perfusion), gene, and gut microbiota, this study demonstrated that enhanced cerebral blood flow (CBF) similarities of the somatomotor network (SMN) acted as a key mediator to link multiple factors in CI. Specifically, we first demonstrated that only CBF but not morphological or functional networks exhibited alterations in patients with CI, characterized by increases within the SMN and between the SMN and higher-order associative networks. Moreover, these findings were highly reproducible and the CBF similarity method was test-retest reliable. Further, we showed that transcriptional profiles explained 60.4 % variance of the pattern of the increased CBF similarities with the most correlated genes enriched in regulation of cellular and protein localization and material transport, and gut microbiota explained 69.7 % inter-individual variance in the increased CBF similarities with the most contributions from Negativicutes and Lactobacillales. Finally, we found that the increased CBF similarities were correlated with clinical variables, accounted for sleep behaviors and cognitive deficits, and contributed the most to the patient-control classification (accuracy = 84.4 %). Altogether, our findings have important implications for understanding the neuropathology of CI and may inform ways of developing new therapeutic strategies for the disease.

Disadvantaged social status contributed to sleep disorders: An observational and genome-wide gene-environment interaction analysis

BACKGROUND

Sleep is a natural and essential physiological need for individuals. Our study aimed to research the associations between accumulated social risks and sleep disorders.

METHODS

In this study, we came up with a polysocial risk score (PsRS), which is a cumulative social risk index composed of 13 social determinants of health. This research includes 239,165 individuals with sleep disorders and social determinants of health data from the UK Biobank cohort. First, logistic regression models were performed to examine the associations of social determinants of health and sleep disorders, including chronotype, narcolepsy, insomnia, snoring, short and long sleep duration. Then, PsRS was calculated based on statistically significant social determinants of health for each sleep disorder. Third, a genome-wide gene-environment interaction study was conducted to explore the interactions between single-nucleotide polymorphisms and PsRS in relation to sleep disorders.

RESULTS

Higher PsRS scores were associated with worse sleep status, with the adjusted odds ratio (OR) ranging from 1.10 (95% Confidence interval [CI]: 1.09-1.11) to 1.29 (95% CI: 1.27-1.30) for sleep disorders. Emotional stress (OR = 1.36, 95% CI: 1.28-1.43) and not in paid employment (OR = 2.62, 95% CI: 2.51-2.74) were found to have significant contributions for sleep disorders. Moreover, multiple single-nucleotide polymorphisms were discovered to have interactions with PsRS, such as FRAS1 (P = 2.57 × 10-14) and CACNA1A (P = 8.62 × 10-14) for narcolepsy, and ACKR3 (P = 1.24 × 10-8) for long sleep.

CONCLUSIONS

Our findings suggested that cumulative social risks was associated with sleep disorders, while the interactions between genetic susceptibility and disadvantaged social status are risk factors for the development of sleep disorders.

Periostracum Cicadae Extract and N-Acetyldopamine Regulate the Sleep-Related Neurotransmitters in PCPA-Induced Insomnia Rats

Insomnia is the second most prevalent mental illness worldwide. Periostracum cicadae (PC), as an animal traditional Chinese medicine with rich pharmacological effects, has been documented as a treatment for children's night cries, and later extended to treat insomnia. This study aimed to investigate the effects of PC extract and N-acetyldopamine compounds in ameliorating insomnia. The UPLC-ESI-QTOF-MS analysis determined that PC extract mainly contained N-acetyldopamine components. Previously, we also isolated some acetyldopamine polymers from PC extract, among which acetyldopamine dimer A (NADA) was present in high content. Molecular docking and molecular dynamic simulations demonstrated that NADA could form stable complexes with 5-HT1A, BDNF, and D2R proteins, respectively. The effects of PC extract and NADA on insomnia were evaluated in the PCPA-induced insomnia model. The results indicated that PC extract and NADA could effectively ameliorate hypothalamic pathology of insomnia rats, increase the levels of 5-HT, GABA, and BDNF, and decrease the levels of DA, DOPAC, and HVA. Meanwhile, the PC extract and NADA also could significantly affect the expression of 5-HT1A, BDNF, and DARPP-32 proteins. This study proved that PC extract and acetyldopamine dimer A could effectively improve PCPA-induced insomnia in rats. It is speculated that the main pharmacological substances of PC were acetyldopamine components.

Novel insights into causal effects of serum lipids, lipid metabolites, and lipid-modifying targets on the risk of intracerebral aneurysm

INTRODUCTION

Different serum lipid and lipid-lowering agents are reported to be related to the occurrence of intracerebral aneurysm (IA). However, the causal relationship between them requires further investigation.

PATIENTS AND METHODS

Mendelian randomization (MR) analysis was performed on IA and its subtypes by using instrumental variants associated with six serum lipids, 249 lipid metabolic traits, and 10 lipid-lowering agents that were extracted from the largest genome-wide association study. Phenome-wide MR analyses were conducted to identify potential phenotypes associated with significant lipid-lowering agents.

RESULTS

After multiple comparison adjustments (p < 0.0083), genetically proxied triglyceride (TG) (odds ratio [OR] 1.25, 95% confidence interval [CI] 1.07-1.47, p = 0.005) and high-density lipoprotein cholesterol (HDL-C) levels (OR 0.93, 95% CI 0.89-0.98, p = 0.008) showed causal relationships with the risk of IA. Four lipid metabolic traits showed a causal relationship with the risk of IA (p < 0.0002). As confirmed by drug target MR, the causal relationship between the HMGCR target and IA, HMGCR target and subarachnoid hemorrhage (SAH), ANGPTL3 target and SAH, CETP target, and SAH remained statistically significant after multiple adjustments (p < 0.005). Additionally, phenome-wide MR did not identify other diseases linked to the significant lipid-lowering agent (p < 6.39 × 10-5).

DISCUSSION AND CONCLUSION

This study not only supports that serum lipids (TG and HDL-C) are associated with IA but also confirms the positive effect and absence of safety concerns of intervening HMGCR, ANGPTL3, and CETP targets in IA and its subtypes, opening new avenues for IA treatment.

Genetic variants affect diurnal glucose levels throughout the day

Circadian rhythms not only coordinate the timing of wake and sleep but also regulate homeostasis within the body, including glucose metabolism. However, the genetic variants that contribute to temporal control of glucose levels have not been previously examined. Using data from 420,000 individuals from the UK Biobank and replicating our findings in 100,000 individuals from the Estonian Biobank, we show that diurnal serum glucose is under genetic control. We discover a robust temporal association of glucose levels at the Melatonin receptor 1B ( MTNR1B) (rs10830963, P = 1e-22) and a canonical circadian pacemaker gene Cryptochrome 2 ( CRY2) loci (rs12419690, P = 1e-16). Furthermore, we show that sleep modulates serum glucose levels and the genetic variants have a separate mechanism of diurnal control. Finally, we show that these variants independently modulate risk of type 2 diabetes. Our findings, together with earlier genetic and epidemiological evidence, show a clear connection between sleep and metabolism and highlight variation at MTNR1B and CRY2 as temporal regulators for glucose levels.

Polygenic scores for psychiatric traits mediate the impact of multigenerational history for depression on offspring psychopathology

A family history of depression is a well-documented risk factor for offspring psychopathology. However, the genetic mechanisms underlying the intergenerational transmission of depression remain unclear. We used genetic, family history, and diagnostic data from 11,875 9-10 year-old children from the Adolescent Brain Cognitive Development study. We estimated and investigated the children's polygenic scores (PGSs) for 30 distinct traits and their association with a family history of depression (including grandparents and parents) and the children's overall psychopathology through logistic regression analyses. We assessed the role of polygenic risk for psychiatric disorders in mediating the transmission of depression from one generation to the next. Among 11,875 multi-ancestry children, 8,111 participants had matching phenotypic and genotypic data (3,832 female [47.2%]; mean (SD) age, 9.5 (0.5) years), including 6,151 [71.4%] of European ancestry). Greater PGSs for depression (estimate = 0.129, 95% CI = 0.070-0.187) and bipolar disorder (estimate = 0.109, 95% CI = 0.051-0.168) were significantly associated with higher family history of depression (Bonferroni-corrected P < .05). Depression PGS was the only PGS that significantly associated with both family risk and offspring's psychopathology, and robustly mediated the impact of family history of depression on several youth psychopathologies including anxiety disorders, suicidal ideation, and any psychiatric disorder (proportions mediated 1.39%-5.87% of the total effect on psychopathology; FDR-corrected P < .05). These findings suggest that increased polygenic risk for depression partially mediates the associations between family risk for depression and offspring psychopathology, showing a genetic basis for intergenerational transmission of depression. Future approaches that combine assessments of family risk with polygenic profiles may offer a more accurate method for identifying children at elevated risk.

Establishing causal relationships between insomnia and gestational diabetes mellitus using Mendelian randomization

Background

Gestational diabetes mellitus (GDM) is a common condition observed globally, and previous studies have suggested a link between GDM and insomnia. The objective of this study was to elucidate the causative relationship between insomnia and GDM, and to investigate the influence of factors related to insomnia on GDM.

Methods

We performed bidirectional Mendelian randomization (MR) analyses using single nucleotide polymorphisms (SNPs) as genetic instruments for exposure and mediators, thereby minimizing bias due to confounding and reverse causation. The Cochran Q test was utilized for heterogeneity analysis, MR-Egger regression for pleiotropy assessment, and the leave-one-out method for evaluating the robustness of the results. Additionally, we determined the causal relationships between GDM and other factors such as coffee consumption, alcohol intake, and household income.

Results

Insomnia was positively associated with GDM, as indicated by 39 SNPs (OR = 1.27, 95 % CI 1.12-1.439, P-value = 0.008). Conversely, the MR analysis did not reveal any causal relationship between GDM and insomnia (OR = 1.032, 95 % CI 0.994-1.071, P-value = 0.99). Additionally, no causal relationship was observed between coffee consumption, alcohol intake, household income, and GDM (all P-values >0.05).

Conclusion

Our study indicates that insomnia elevates the risk of GDM, thereby establishing a causal link with GDM, independent of coffee consumption, alcohol intake, and household income.

Perturbation of the insomnia WDR90 genome-wide association studies locus pinpoints rs3752495 as a causal variant influencing distal expression of neighboring gene, PIG-Q

Although genome-wide association studies (GWAS) have identified loci for sleep-related traits, they do not directly uncover the underlying causal variants and corresponding effector genes. The majority of such variants reside in non-coding regions and are therefore presumed to impact cis-regulatory elements. Our previously reported 'variant-to-gene mapping' effort in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs), combined with validation in both Drosophila and zebrafish, implicated phosphatidyl inositol glycan (PIG)-Q as a functionally relevant gene at the insomnia "WDR90" GWAS locus. However, importantly that effort did not characterize the corresponding underlying causal variant. Specifically, our previous 3D genomic datasets nominated a shortlist of three neighboring single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium within an intronic enhancer region of WDR90 that contacted the open PIG-Q promoter. We sought to investigate the influence of these SNPs collectively and then individually on PIG-Q modulation to pinpoint the causal "regulatory" variant. Starting with gross level perturbation, deletion of the entire region in NPCs via CRISPR-Cas9 editing and subsequent RNA sequencing revealed expression changes in specific PIG-Q transcripts. Results from individual luciferase reporter assays for each SNP in iPSCs revealed that the region with the rs3752495 risk allele (RA) induced a ~2.5-fold increase in luciferase expression. Importantly, rs3752495 also exhibited an allele-specific effect, with the RA increasing the luciferase expression by ~2-fold versus the non-RA. In conclusion, our variant-to-function approach and in vitro validation implicate rs3752495 as a causal insomnia variant embedded within WDR90 while modulating the expression of the distally located PIG-Q.

Dissecting the genetic and causal relationship between sleep-related traits and common brain disorders

BACKGROUND

There is a profound connection between abnormal sleep patterns and brain disorders, suggesting a shared influential association. However, the shared genetic basis and potential causal relationships between sleep-related traits and brain disorders are yet to be fully elucidated.

METHODS

Utilizing linkage disequilibrium score regression (LDSC) and bidirectional two-sample univariable Mendelian Randomization (UVMR) analyses with large-scale GWAS datasets, we investigated the genetic correlations and causal associations across six sleep traits and 24 prevalent brain disorders. Additionally, a multivariable Mendelian Randomization (MVMR) analysis evaluated the cumulative effects of various sleep traits on each brain disorder, complemented by genetic loci characterization to pinpoint pertinent genes and pathways.

RESULTS

LDSC analysis identified significant genetic correlations in 66 out of 144 (45.8 %) pairs between sleep-related traits and brain disorders, with the most pronounced correlations observed in psychiatric disorders (66 %, 48/72). UVMR analysis identified 29 causal relationships (FDR<0.05) between sleep traits and brain disorders, with 19 associations newly discovered according to our knowledge. Notably, major depression, attention-deficit/hyperactivity disorder, bipolar disorder, cannabis use disorder, and anorexia nervosa showed bidirectional causal relations with sleep traits, especially insomnia's marked influence on major depression (IVW beta 0.468, FDR = 5.24E-09). MVMR analysis revealed a nuanced interplay among various sleep traits and their impact on brain disorders. Genetic loci characterization underscored potential genes, such as HOXB2, while further enrichment analyses illuminated the importance of synaptic processes in these relationships.

CONCLUSIONS

This study provides compelling evidence for the causal relationships and shared genetic backgrounds between common sleep-related traits and brain disorders.

Shared genetic mechanisms underlying association between sleep disturbances and depressive symptoms

OBJECTIVES

Polygenic scores (PGS) for sleep disturbances and depressive symptoms in an epidemiological cohort were contrasted. The overlap between genes assigned to variants that compose the PGS predictions was tested to explore the shared genetic bases of sleep problems and depressive symptoms.

METHODS

PGS analysis was performed on the São Paulo Epidemiologic Sleep Study (EPISONO, N = 1042), an adult epidemiological sample. A genome wide association study (GWAS) for depression grounded the PGS calculations for Beck Depression Index (BDI), while insomnia GWAS based the PGS for Insomnia Severity Index (ISI) and Pittsburg Sleep Quality Index (PSQI). Pearson's correlation was applied to contrast PGS and clinical scores. Fisher's Exact and Benjamin-Hochberg tests were used to verify the overlaps between PGS-associated genes and the pathways enriched among their intersections.

RESULTS

All PGS models were significant when individuals were divided as cases or controls according to BDI (R2 = 1.2%, p = 0.00026), PSQI (R2 = 3.3%, p = 0.007) and ISI (R2 = 3.4%, p = 0.021) scales. When clinical scales were used as continuous variables, the PGS models for BDI (R2 = 1.5%, p = 0.0004) and PSQI scores (R2 = 3.3%, p = 0.0057) reached statistical significance. PSQI and BDI scores were correlated, and the same observation was applied to their PGS. Genes assigned to variants that compose the best-fit PGS predictions for sleep quality and depressive symptoms were significantly overlapped. Pathways enriched among the intersect genes are related to synapse function.

CONCLUSIONS

The genetic bases of sleep quality and depressive symptoms are correlated; their implicated genes are significantly overlapped and converge on neural pathways. This data suggests that sleep complaints accompanying depressive symptoms are not secondary issues, but part of the core mental illness.

Genetic correlation and causal associations between psychiatric disorders and lung cancer risk

BACKGROUND

Patients with certain psychiatric disorders have increased lung cancer incidence. However, establishing a causal relationship through traditional epidemiological methods poses challenges.

METHODS

Available summary statistics of genome-wide association studies of cigarette smoking, lung cancer, and eight psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism, depression, major depressive disorder, bipolar disorder, insomnia, neuroticism, and schizophrenia (range N: 46,350-1,331,010) were leveraged to estimate genetic correlations using Linkage Disequilibrium Score Regression and assess causal effect of each psychiatric disorder on lung cancer using two-sample Mendelian randomization (MR) models, comprising inverse-variance weighted (IVW), weighted median, MR-Egger, pleiotropy residual sum and outlier testing (MR-PRESSO), and a constrained maximum likelihood approach (cML-MR).

RESULTS

Significant positive correlations were observed between each psychiatric disorder and both smoking and lung cancer (all FDR < 0.05), except for the correlation between autism and lung cancer. Both univariable and the cML-MA MR analyses demonstrated that liability to schizophrenia, depression, ADHD, or insomnia was associated with an increased risk of overall lung cancer. Genetic liability to insomnia was linked specifically to squamous cell carcinoma (SCC), while genetic liability to ADHD was associated with an elevated risk of both SCC and small cell lung cancer (all P < 0.05). The later was further supported by multivariable MR analyses, which accounted for smoking.

LIMITATIONS

Participants were constrained to European ancestry populations. Causal estimates from binary psychiatric disorders may be biased.

CONCLUSION

Our findings suggest appropriate management of several psychiatric disorders, particularly ADHD, may potentially reduce the risk of developing lung cancer.

The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be 'objective' measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

Exploring the causal effects of sleep characteristics on TMD-related pain: a two-sample Mendelian randomization study

OBJECTIVES

The study was to explore the causal effects of sleep characteristics on temporomandibular disorder (TMD)-related pain using Mendelian randomization (MR) analysis.

MATERIALS AND METHODS

Five sleep characteristics (short sleep, insomnia, chronotype, snoring, sleep apnea) were designated as exposure factors. Data were obtained from previous publicized genome-wide association studies and single nucleotide polymorphisms (SNPs) strongly associated with them were utilized as instrumental variables (IVs). TMD-related pain was designed as outcome variable and sourced from the FinnGens database. MR analysis was employed to explore the causal effects of the five sleep characteristics on TMD-related pain. The causal effect was analyzed using the inverse variance-weighted (IVW), weighted median, and MR-Egger methods. Subsequently, sensitivity analyses were conducted using Cochran's Q tests, funnel plots, leave-one-out analyses, and MR-Egger intercept tests.

RESULTS

A causal effect of short sleep on TMD-related pain was revealed by IVW (OR: 1.60, 95% CI: 1.06-2.41, P = 0.026). No causal relationship was identified between other sleep characteristics (insomnia, chronotype, snoring, sleep apnea) and TMD-related pain.

CONCLUSIONS

Our study suggests that short sleep may increase the risk of TMD-related pain, while there was no causal relationship between other sleep characteristics and TMD-related pain. Further studies are warranted to deepen and definitively clarify their relationship.

CLINICAL RELEVANCE

These findings reveal that the short sleep may be a risk factor of TMD-related pain and highlight the potential therapeutical effect of extending sleep time on alleviating TMD-related pain.

Causality Investigation between Gut Microbiome and Sleep-Related Traits: A Bidirectional Two-Sample Mendelian Randomization Study

Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a bidirectional two-sample Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386,577). We employed multiple MR methods to assess causality, with Inverse Variance Weighted (IVW) as the primary method, alongside a Bonferroni correction ((p < 2.4 × 10-4) to determine significant causal associations. We further applied Cochran's Q statistical analysis, MR-Egger intercept, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) for heterogeneity and pleiotropy assessment. IVW estimates revealed 79 potential causal effects of microbial taxa and pathways on sleep-related traits and 45 inverse causal relationships, with over half related to pathways, emphasizing their significance. The results revealed two significant causal associations: genetically determined relative abundance of pentose phosphate decreased sleep duration (p = 9.00 × 10-5), and genetically determined increase in fatty acid level increased the ease of getting up in the morning (p = 8.06 × 10-5). Sensitivity analyses, including heterogeneity and pleiotropy tests, as well as a leave-one-out analysis of single nucleotide polymorphisms, confirmed the robustness of these relationships. This study explores the potential causal relationships between sleep and microbial taxa and pathways, offering novel insights into their complex interplay.

Large-scale neurophysiology and single-cell profiling in human neuroscience

Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain.

Insomnia Polygenic Component on Attention Deficit-Hyperactivity Disorder: Exploring this Association Using Genomic Data from Brazilian Families

Introduction  Insomnia is highly prevalent among individuals with Attention-Deficit/Hyperactivity Disorder (ADHD). However, the biological mechanisms shared between both conditions is still elusive. We aimed to investigate whether insomnia's genomic component is able to predict ADHD in childhood and adolescence. Methods  A Brazilian sample of 259 ADHD probands and their biological parents were included in the study. Their genomic DNA genotypes were used to construct the polygenic risk score for insomnia (Insomnia PRS), using the largest GWAS summary statistics as a discovery sample. The association was tested using logistic regression, under a case-pseudocontrol design. Results  Insomnia PRS was nominally associated with ADHD (OR = 1.228, p  = 0.022), showing that the alleles that increase the risk for insomnia also increase the risk for ADHD. Discussion  Our results suggest that genetic factors associated with insomnia may play a role in the ADHD genetic etiology, with both phenotypes likely to have a shared genetic mechanism.

Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

Associations of evening-type and insomnia symptoms with depressive symptoms among youths

BACKGROUND

Evening-type and insomnia symptoms are significantly related to each other and independently associated with depressive symptoms, yet few studies have examined the potential interaction between these two conditions. Therefore, we aimed to examine the associations of evening-type and insomnia symptoms with depressive symptoms among Chinese youths, with a specific focus on the joint effects of the two conditions on depressive symptoms.

METHODS

Participants aged between 12 and 25 were invited to participate in an online survey from December 15, 2022, to May 26, 2023. Multivariate logistic regression models and additive interaction models were used to examine the independent and joint effects of chronotypes and insomnia symptoms on depressive symptoms, respectively.

RESULTS

Of the 6145 eligible youths, the prevalence of evening-type and insomnia symptoms were 24.9 % and 29.6 %, respectively. Both evening-type (adjusted OR, [AdjOR]: 3.21, 95 % CI: 2.80-3.67) and insomnia symptoms (AdjOR: 10.53, 95 % CI: 9.14-12.12) were associated with an increased risk of depressive symptoms. In addition, the additive interaction models showed that there is an enhanced risk of depression related to interaction between evening-type and insomnia symptoms (relative excess risk due to interaction, [RERI]: 11.66, 95 % CI: 7.21-16.11).

CONCLUSIONS

The present study provided additional evidence demonstrating the presence of interaction between evening-type and insomnia symptoms, which can lead to a higher risk of depressive symptoms. Our findings argue the need for addressing both sleep and circadian factors in the management of depressive symptoms in young people.