The causal relationship between sleep characteristics and multi-site pain perception: a two-sample Mendelian randomization study
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SUBMITTER: Yin Y
PROVIDER: S-EPMC11347297 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Observational studies indicate that the risk of prostatitis in sleep apnea patients is higher than those without sleep apnea. However, the causal relationships remain to be determined. This study aims to investigate the causal relationships of sleep apnea on prostatitis using Mendelian randomization (MR). Summary-level data for sleep apnea (16,761 cases and 201,194 controls) and prostatitis (1859 cases and 72,799 controls) were available from the GWAS summary data. Two-sample MR analyses were performed to investigate the causal relationship between sleep apnea and prostatitis. The inverse variance weighted (IVW) analysis was employed as the primary statistical method. In 2-sample MR analyses, we found that IVW estimates revealed that sleep apnea inferred an effect on risk of prostatitis at statistical significance (odds ratio [OR] = 1.370, 95% confidence interval [CI] = 0.094-0.535, P = .005). This MR study strengthens the evidence of a causal relationship between sleep apnea and prostatitis in Europeans.
| S-EPMC11651455 | biostudies-literature
Project description:IntroductionSleep is associated with psychiatric disorders. However, their causality remains unknown.MethodsThe study explored the causal relationship between seven sleep parameters (sleep duration, insomnia, sleep apnea, chronotype, daytime dozing, napping during the day, and snoring) and three psychiatric disorders including major depressive disorder (MDD), schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS) summary data for sleep parameters were obtained from the United Kingdom biobank, FinnGen biobank, and EBI databases. MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW(fixed effects) were used to perform the MR analysis. The heterogeneity was detected by Cochran's Q statistic. The horizontal pleiotropy was detected by MR Egger. The sensitivity was investigated by the leave-one-out analysis.ResultsInsomnia (OR = 2.02, 95%CI = 1.34-3.03, p = 0.001, False-discovery rate (FDR) corrected p-value = 0.011) and napping during the day (OR = 1.81, 95%CI = 1.34-2.44, FDR corrected p-value<0.001) were associated with an increased risk of MDD. Longer sleep duration (OR = 2.20, 95%CI = 1.24-3.90, FDR corrected p-value = 0.049) had an association with the increased risk of schizophrenia, while daytime dozing (OR = 4.44, 95%CI = 1.20-16.41, corrected p-value = 0.088)and napping during the day (OR = 2.11, 95%CI = 1.11-4.02, FDR corrected p-value = 0.088) had a suggestive association with an increased risk of schizophrenia. Longer sleep duration had a suggestive association with a decreased risk of ADHD (OR = 0.66, 95%CI = 0.42-0.93, FDR corrected p-value = 0.088).ConclusionThis study provides further evidence for a complex relationship between sleep and psychiatric disorders. Our findings highlight the potential benefits of addressing sleep problems in the prevention of psychiatric disorders.
| S-EPMC11215123 | biostudies-literature
Project description:Background Previous observational studies have shown that low back pain (LBP) often coexists with sleep disturbances, however, the causal relationship remains unclear. In the present study, the causal relationship between sleep disturbances and LBP was investigated and the importance of sleep improvement in the comprehensive management of LBP was emphasized. Methods Genetic variants were extracted as instrumental variables (IVs) from the genome-wide association study (GWAS) of insomnia, sleep duration, short sleep duration, long sleep duration, and daytime sleepiness. Information regarding genetic variants in LBP was selected from a GWAS dataset and included 13,178 cases and 164,682 controls. MR-Egger, weighted median, inverse-variance weighted (IVW), penalized weighted median, and maximum likelihood (ML) were applied to assess the causal effects. Cochran’s Q test and MR-Egger intercept were performed to estimate the heterogeneity and horizontal pleiotropy, respectively. Outliers were identified and eliminated based on MR-PRESSO analysis to reduce the effect of horizontal pleiotropy on the results. Removing each genetic variant using the leave-one-out analysis can help evaluate the stability of results. Finally, the reverse causal inference involving five sleep traits was implemented. Results A causal relationship was observed between insomnia-LBP (OR = 1.954, 95% CI: 1.119–3.411), LBP-daytime sleepiness (OR = 1.011, 95% CI: 1.004–1.017), and LBP-insomnia (OR = 1.015, 95% CI: 1.004–1.026), however, the results of bidirectional MR analysis between other sleep traits and LBP were negative. The results of most heterogeneity tests were stable and specific evidence was not found to support the disturbance of horizontal multiplicity. Only one outlier was identified based on MR-PRESSO analysis. Conclusion The main results of our research showed a potential bidirectional causal association of genetically predicted insomnia with LBP. Sleep improvement may be important in comprehensive management of LBP.
| S-EPMC9755499 | biostudies-literature
Project description:A correlation between sleep and systemic lupus erythematosus (SLE) has been observed in a number of prior investigations. However, little is known regarding the potential causative relationship between them. In this study, we selected genetic instruments for sleep traits from pooled data from published genome-wide association studies (GWAS). Independent genetic variants associated with six sleep-related traits (chronotype, sleep duration, short sleep duration, long sleep duration, insomnia, and daytime sleepiness) were selected as instrumental variables. A two-sample Mendelian randomization (TSMR) study was first conducted to assess the causal relationship between sleep traits and SLE (7219 cases versus 15,991 controls). The reverse MR analysis was then used to infer the causal relationship between SLE and sleep traits. Inverse variance weighted (IVW), MR Egger, Weighted median, and Weighted mode were applied to perform the primary MR analysis. MR Egger regression and the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test were used to detect horizontal pleiotropy, and Cochran's Q was used to detect heterogeneity. In studies of the effect of sleep traits on SLE risk, the IVW method demonstrated no causal relationship between chronotype, sleep duration, short sleep duration, long sleep duration, insomnia, daytime sleepiness and SLE risk. The remaining three methods agreed with the results of IVW. In studies of the effect of SLE on the risk of sleep traits, neither IVW, MR Egger, Weighted median, nor Weighted mode methods provided evidence of a causal relationship between SLE and the risk of sleep traits. Overall, our study found no evidence of a bidirectional causal relationship between genetically predicted sleep traits and SLE.
| S-EPMC9248809 | biostudies-literature
Project description:BackgroundSeveral observational studies have investigated the association between myeloperoxidase (MPO) and obstructive sleep apnea (OSA). However, the nature of this relationship remains uncertain due to potential selection and confounding biases. To resolve this, we conducted a bidirectional two-sample Mendelian randomization (MR) study to scrutinize the causal relationship between MPO and OSA.MethodsInstrumental variables (IVs) for OSA were sourced from the publicly available FinnGen dataset, encompassing 38,998 OSA cases and 336,659 controls. Data on MPO were sourced from a study of 21,758 individuals conducted by the European Bioinformatics Institute (EBI). The primary MR analysis utilized the inverse-variance weighted (IVW) method, with MR-Egger intercept and leave-one-out methods assessing pleiotropy and Cochran's Q test determining heterogeneity.ResultsThe IVW analysis indicated a causal relationship between heightened MPO levels and an increased incidence of OSA. Individuals with elevated MPO levels manifested a higher propensity to develop OSA, exhibiting an odds ratio (OR) of 1.075 and a 95% confidence interval (CI) of 1.011-1.143 (p = 0.021). Conversely, the reciprocal analysis unveiled no significant association between OSA and heightened MPO levels (p = 0.643). No directional pleiotropy was identified through the MR-Egger intercept test (p > 0.05).ConclusionOur study provides evidence of an association between elevated MPO levels and an increased incidence of OSA. However, OSA does not necessarily lead to elevated MPO levels. When patients present with high MPO levels, screening for OSA may be advisable, considering their clinical characteristics.
| S-EPMC10753018 | biostudies-literature
Project description:ObjectiveAdequate sleep is closely related to people's health. However, with increasing age, the quality of sleep worsens. At the same time, among elderly individuals, frailty is also a disturbing factor, which makes elderly individuals more vulnerable to negative factors. To explore the relationship between the two, we conducted this study.MethodsIn this paper, independent genetic variations related to insomnia, sleep duration and daytime sleepiness were selected as IVs, and related genetic tools were used to search published genome-wide association studies for a two-sample Mendelian randomization (TSMR) analysis. The inverse-variance weighted (IVW) method was used as the main Mendelian randomization analysis method. Cochran's Q test was used to test heterogeneity, MR‒Egger was used to test horizontal pleiotropy, and the MR-PRESSO test was used to remove outliers.ResultsAccording to our research, insomnia (OR = 1.10, 95% CI 1.03-1.17, P = 2.59e-97), long sleep duration (OR = 0.66, 95% CI 0.37-1.17, P = 0.02), short sleep duration (OR = 1.30, 95% CI 1.22-1.38, P = 2.23e-17) and daytime sleepiness (OR = 1.49, 95% CI 1.25-1.77, P = 0.96e-4) had a bidirectional causal relationship with frailty.ConclusionsOur research showed that there is a causal relationship between sleep disturbances and frailty. This result was obtained by a TSMR analysis, which involves the use of genetic variation as an IV to determine causal relationships between exposure and outcome. Future TSMR studies should include a larger sample for analysis.
| S-EPMC10951186 | biostudies-literature
Project description:To investigate the causal relationship between obesity and meniscal injuries using Mendelian randomization (MR). Genetic loci independently associated with obesity and meniscal injuries in people of European origin were selected as instrumental variables using pooled data from genome-wide association studies. Three MR analyses, MR-Egger, weighted median and inverse variance weighting, were used to investigate the causal relationship between obesity and meniscal injuries. The results were tested for robustness by heterogeneity and multiplicity tests, and sensitivity analyses were performed using the "leave-one-out" method. The inverse variance weighting results showed an OR (95% CI) of 1.13 (1.04-1.22), P = .003, indicating a causal relationship between obesity and the occurrence of meniscal injuries. And no heterogeneity and multiplicity were found by the test and sensitivity analysis also showed robust results. In this study, genetic data were analyzed and explored using 2-sample MR analysis, and the results showed that obesity is a risk factor for meniscal injuries.
| S-EPMC10695575 | biostudies-literature
Project description:To investigate the causal relationship between attention deficit and hyperactivity disorder (ADHD) and frozen shoulder using Mendelian randomization (MR). Data were pooled from large-scale genome wide association studies, and genetic loci that were independent of each other and associated with ADHD and frozen shoulder in people of European ancestry were selected as instrumental variables. Three MR analyses, inverse variance weighting, weighted median and MR-Egger, were used to investigate the causal relationship between ADHD and frozen shoulder. Heterogeneity and multiplicity tests were used, and sensitivity analyses were performed using the "leave-one-out" method to explore the robustness of the results. The inverse variance weighting results showed an OR (95 % CI) of 1.12 (1.00-1.25), P = .046, indicating a causal relationship between ADHD and frozen shoulder. And no heterogeneity and multiplicity were found by the test and sensitivity analysis also showed robust results. The present study used a two-sample MR analysis, and by analyzing and exploring the genetic data, the study showed that ADHD is a risk factor for developing frozen shoulder, and patients with ADHD are more likely to suffer from frozen shoulder.
| S-EPMC10627672 | biostudies-literature
Project description:BackgroundObservational studies suggest that sleep disturbances are commonly associated with schizophrenia. However, it is uncertain whether this relationship is causal. To investigate the bidirectional causal relation between sleep traits and schizophrenia, we performed a two-sample bidirectional Mendelian randomization (MR) study with the fixed effects inverse-variance weighted (IVW) method.MethodsAs genetic variants for sleep traits, we selected variants from each meta-analysis of genome-wide association studies (GWASs) conducted using data from the UK Biobank (UKB).ResultsWe found that morning diurnal preference was associated with a lower risk of schizophrenia, while long sleep duration and daytime napping were associated with a higher risk of schizophrenia. Multivariable MR analysis also showed that sleep duration was associated with a higher risk of schizophrenia after adjusting for other sleep traits. Furthermore, genetically predicted schizophrenia was negatively associated with morning diurnal preference and short sleep duration and was positively associated with daytime napping and long sleep duration.ConclusionsTherefore, sleep traits were identified as a potential treatment target for patients with schizophrenia.
| S-EPMC9202113 | biostudies-literature
Project description:BackgroundsMany studies have shown particulate matter has emerged as one of the major environmental risk factors for diabetes; however, studies on the causal relationship between particulate matter 2.5 (PM2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM2.5 using two sample mendelian randomization (TSMR).MethodsWe collected genetic data from European ancestry publicly available genome wide association studies (GWAS) summary data through the MR-BASE repository. The IEU GWAS information output PM2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship of PM2.5 (2010) were modeled for each address using a Land Use Regression model developed as part of the European Study of Cohorts for Air Pollution Effects. Diabetes GWAS information (Consortium = MRC-IEU, sample size: 461,578) were used, and the genetic variants were used as the instrumental variables (IVs). We performed three representative Mendelian Randomization (MR) methods: Inverse Variance Weighted regression (IVW), Egger, and weighted median for causal relationship using genetic variants. Furthermore, we used a novel method called MR Mixture to identify outlier SNPs.ResultsFrom the IVW method, we revealed the causal relationship between PM2.5 and diabetes (Odds ratio [OR]: 1.041, 95% CI: 1.008-1.076, P = 0.016), and the finding was substantiated by the absence of any directional horizontal pleiotropy through MR-Egger regression (β = 0.016, P = 0.687). From the IVW fixed-effect method (i.e., one of the MR machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC = 0.72) with a causal relationship between PM2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, P = 0.012).ConclusionWe identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods.
| S-EPMC10450337 | biostudies-literature