Methods
Data were curated on 14 LB-related factors using genome-wide association studies (GWAS) summary data, available in Supplementary Table SI . A two-sample MR analysis was performed to explore the causality of endometriosis ( Figure 1 ). Statistical analysis was done using TwoSampleMR R package, version 0.6.4, and MR-PRESSO R package, version 1.0.
Flowchart depicting the current MR design
SNP – single nucleotide polymorphism, MR-PRESSO – Mendelian randomization-pleiotropy residual sum and outlier.
The study divided 14 phenotypes into educational attainment, physical activity, smoking behavior, and sleep behavior [ 1 – 3 ]. Supplementary Table SI provides the detailed definition and description of each phenotype.
Statistics of endometriosis patients were obtained from FinnGen (FinnGen, https://www.finngen.fi/ ). In FinnGen, endometriosis is defined by N80 in ICD-10, 617 in ICD-9, and 6253 in ICD-8. FinnGen is a research project in genomics and personalized medicine. It is a large public-private partnership, which has collected and analyzed genome and health data from 412,181 Finnish Biobank donors to better comprehend the genetic bases of diseases. This data set includes 128,171 endometriosis individuals (16,588 cases and 111,583 controls; European blood). Supplementary Table SII displays the detailed description of the GWAS endometriosis data.
Independent genetic variants strongly associated with LB ( p < 5 × 10 –8 ) were identified by clumping single nucleotide polymorphisms (SNPs) with a linkage disequilibrium threshold of r 2 < 0.001 within a 10,000-kb window, using the 1,000 Genomes European reference panel ( Supplementary Table SIII ). To determine whether there was a weak instrumental variable bias, F-statistics were calculated to quantify the strength of instrumental variables, wherein F-statistics > 10 indicated a low possibility of weak instrumental variable bias [ 5 , 6 ].
Primary MR analysis employed the random-effects IVW method that combines causal estimates from individual SNPs through meta-analysis, to determine the pooled causal effect of the exposure on the outcome. This approach offers a more conservative causal inference by considering uncertainty resulting from pleiotropy [ 7 ]. Throughout the study, ‘IVW’ specifically referred to the random-effects IVW method. Statistical significance was determined using the Bonferroni-corrected threshold of p < 4 × 10 –3 (0.05 divided by 14 exposures multiplied by one outcome), where p Bonferroni designated threshold.
Quality control was conducted on significant findings ( p < 0.05). Heterogeneity in the IVW model was first assessed using the Cochran’s Q test and quantified with I 2 statistics [ 8 ]. Cochran’s Q test results with p 25% suggested potential heterogeneity. MR-Egger regression was employed to investigate the potential pleiotropic effects of instrumental variables, with the intercept term serving as an indicator of directional horizontal pleiotropy in causal estimates. Furthermore, a leave-one-out analysis was conducted by systematically excluding each SNP and performing MR analysis on the remaining SNPs, so as to identify any potential outliers among instrumental variables [ 8 ]. The Steiger test of directionality was applied to evaluate the causal relationship between the exposure and outcome.
Three databases were searched, i.e., Ensembl, GWAS Catalog, and PhenoScanner to explore any potential associations of the selected SNPs with other known risk factors (confounders) of endometriosis identified in previous MR studies.
A method described by Brion et al . was used to calculate the statistical power [ 9 ] ( https://shiny.cnsgenomics.com/mRnd/ ). A sufficient power of > 80% was recommended. Summary-level statistics only were employed, which waived off the need for ethical approval.
Discussion
The findings of the study underscored the significant role of long sleep duration as a protective factor and suggested a potentially beneficial effect of smoking cessation on the risk of endometriosis.
While current smoking showed no causal effect ( p > 0.05), we observed a suggestive protective association for smoking cessation due to the following reasons: 1. It did not remain significant after the Bonferroni correction; 2. It was supported by biological plausibility, such as re-establishment of estrogen balance [ 13 , 14 ]; and 3. Previous observational studies have yielded inconsistent results [ 15 , 16 ]. These findings may indicate that cessation-specific biological mechanisms, rather than smoking itself, influence the risk of disease. Further mechanistic studies are warranted to clarify how smoking and its cessation influence endometriosis risk at molecular level.
Although our study did not establish a direct causal association between insomnia symptoms and endometriosis risk, the findings suggest that longer sleep duration may exert a protective effect. This observation appears somewhat contradictory to prior findings. For example, a study by Cakan et al . reported that half- and whole-night work shifts disrupted both sleep duration and quality, altered melatonin secretion, and suppressed estradiol levels [ 17 ]. These findings support a hypothesis that sleep disruption may negatively affect estrogen regulation and impair ovarian function. In contrast, prolonged and sufficient sleep may stabilize hormone secretion and promote ovarian health. The lack of a causal association for insomnia ( p > 0.05) supports the interpretation that sleep disturbances in endometriosis are more likely secondary to pain [ 18 , 19 ], rather than serving as a causative factor. Conversely, the potential protective role of long sleep duration may stem from its capacity to regulate endocrine function [ 17 ]. Despite these insights, the biological pathways associating sleep patterns and endometriosis remain inadequately understood, reinforcing the need for further mechanistic research [ 20 ].
Nevertheless, some limitations must be acknowledged. Our study relied on self-reported phenotypes, which may be subject to reporting bias. Also, our reliance on summary-level data precluded age- and sex-stratified analyses. Additionally, the lack of complete GWAS data for certain exposures prevented us from conducting reverse-direction MR analyses. Future studies should aim to perform bidirectional MR once full summary statistics are available.
In conclusion, this MR analysis provides strong evidence that long sleep duration may reduce the risk of endometriosis. The suggestive protective effect of smoking cessation deserves further exploration. These insights underscore the potential of prioritizing sleep hygiene as a modifiable factor in preventive strategies for endometriosis.