Daytime sleepiness might increase the risk of ALS: a 2-sample Mendelian randomization study

Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation

While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.

Is there an association between daytime napping, cognitive function, and brain volume? A Mendelian randomization study in the UK Biobank

OBJECTIVES

Daytime napping has been associated with cognitive function and brain health in observational studies. However, it remains elusive whether these associations are causal. Using Mendelian randomization, we studied the relationship between habitual daytime napping and cognition and brain structure.

METHODS

Data were from UK Biobank (maximum n = 378,932 and mean age = 57 years). Our exposure (daytime napping) was instrumented using 92 previously identified genome-wide, independent genetic variants (single-nucleotide polymorphisms, SNPs). Our outcomes were total brain volume, hippocampal volume, reaction time, and visual memory. Inverse-variance weighted was implemented, with sensitivity analyses (Mendelian randomization-Egger and Weighted Median Estimator) for horizontal pleiotropy. We tested different daytime napping instruments to ensure the robustness of our results.

RESULTS

Using Mendelian randomization, we found an association between habitual daytime napping and larger total brain volume (unstandardized ß = 15.80 cm3 and 95% CI = 0.25; 31.34) but not hippocampal volume (ß = -0.03 cm3 and 95% CI = -0.13;0.06), reaction time (expß = 1.01 and 95% CI = 1.00;1.03), or visual memory (expß = 0.99 and 95% CI = 0.94;1.05). Additional analyses with 47 SNPs (adjusted for excessive daytime sleepiness), 86 SNPs (excluding sleep apnea), and 17 SNPs (no sample overlap with UK Biobank) were largely consistent with our main findings. No evidence of horizontal pleiotropy was found.

CONCLUSIONS

Our findings suggest a modest causal association between habitual daytime napping and larger total brain volume. Future studies could focus on the associations between napping and other cognitive or brain outcomes and replication of these findings using other datasets and methods.

Leveraging genetic discoveries for sleep to determine causal relationships with common complex traits

Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.

Association of insomnia and daytime sleepiness with low back pain: A bidirectional mendelian randomization analysis

Purpose: Observational research has indicated the presence of a causal relationship between sleep disturbances and low back pain (LBP). However, the link may have been biased by confounding factors. The purpose of this study was to examine the potential causal association of insomnia and daytime sleepiness with LBP by using mendelian randomization (MR). Methods: Genome-wide association study (GWAS) summary statistics of insomnia were obtained from a large-scale GWAS meta-analysis (n = 1,331,010; individuals from UK Biobank and 23andMe) or UK Biobank alone (n = 453,379). The summary statistics of daytime sleepiness were from UK Biobank (n = 452,071) and LBP were provided by the FinnGen Release 6 (210,645 individuals with 16,356 LBP cases and 194,289 controls) or UK Biobank (5,423 cases versus 355,771 controls). Linkage disequilibrium score (LDSC) regression and bidirectional MR analysis was employed to estimate genetic correlation and causal relationship. In the MR analysis, the inverse variance weighted method (IVW) was utilized as the main analysis procedure, while MR-Egger, Weighted median and Robust adjusted profile score (RAPS) were utilized for supplementary analyses. Results: LDSC analysis showed that LBP were significantly genetically correlated with insomnia (rg = 0.57, p = 2.26e-25) and daytime sleepiness (rg = 0.18, p = 0.001). The MR analysis revealed that genetically predicted insomnia was significantly associated with an increased risk of LBP (OR = 1.250, 95% CI: 1.186-1.318; p = 1.69e-16). However, the reverse causality was not confirmed. No evidence was identified supporting causality of daytime sleepiness and LBP. Conclusion: This study demonstrates a putative causal link of insomnia on LBP and a null causal effect of LBP on insomnia. Furthermore, a causal link between daytime sleepiness and LBP were not reported. This finding may stimulate new strategies for patient management in clinical practice, benefiting public health.

Sleep, Pain, and Neurodegeneration: A Mendelian Randomization Study

Our aim was to determine whether the genetic liability to sleep and pain-related traits have a causal effect on risk of neurodegeneration in individuals of predominantly European ancestry. We selected five neurodegenerative disorders, namely, age-related macular degeneration (AMD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and Parkinson's disease (PD). Sleep duration (SD), short sleep (SS), long sleep (LS), chronotype (CHR), morning person (MP), insomnia (INS), and multisite chronic pain (MCP) were considered as exposures. We conducted Mendelian randomization (MR) using an inverse-variance weighted (IVW) method to compute causal effect estimates using latest available GWAS data sets. The MP phenotype was observed as the strongest risk factor for genetic liability to AMD (OR_IVW = 1.192; 95% CI 1.078, 1.318, P = 0.0007). We observed suggestive evidence of risky effects of CHR on AMD (P = 0.0034), SS on AD (P = 0.0044), and INS on ALS (P = 0.0123). However, we failed to observe any role of pain. The results were robust on sensitivity analyses. Our study highlighted the role of MP as a risk factor for AMD.