Unraveling the Causal Nexus Between Reproductive Characteristics and Non-Alcoholic Fatty Liver Disease

Unraveling the Causal Nexus Between Reproductive Characteristics and Non-Alcoholic Fatty Liver Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unraveling the Causal Nexus Between Reproductive Characteristics and Non-Alcoholic Fatty Liver Disease Heng Yang, Qiaoxia Chen, Xue Liu, Xuemei Jiang, Yishun Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3845511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aim Non-alcoholic fatty liver disease (NAFLD), a prevalent global health concern, stems from intricate interactions between genetic and environmental factors. The primary aim of this study is to employs Mendelian randomization (MR) to investigate the causal relationship between key female reproductive characteristics—age at first birth (AFB), age at first sexual intercourse (AFS), and age at menarche (AAM)—and the risk of NAFLD. Methods: Genome-wide association data on AFB, AFS, AAM, and NAFLD were pooled for two-sample MR analysis. Instrumental variables were meticulously selected to meet MR assumptions. The primary analysis used the inverse variance weighting (IVW) approach, supplemented by MR-Egger regression and weighted median methods. Multivariate MR (MVMR) analysis considered confounding variables: educational attainment, BMI, and household income. Results: The MR analysis revealed significant causal associations between later AFB (OR 0.89; 95% CI: 0.83–0.96; P = 0.003), AFS (OR 0.64; 95% CI: 0.53–0.76; P = 1.47×10 − 5 ), and AAM (OR 0.83; 95% CI: 0.75–0.91; P = 0.0002) with a reduced risk of NAFLD. MVMR, after accounting for confounders, sustained the significance of AFS (P = 0.003) and AAM (P = 0.02), with a weaker association for AFB (P = 0.3). Conclusion: This study provides compelling evidence that later reproductive events—later AFB, AFS, and AAM—are causally associated with a reduced risk of NAFLD. The observed associations persist even after adjusting for confounding variables. Further research is warranted to delve into the underlying mechanisms of this causality, emphasizing the importance of women's reproductive health awareness in mitigating NAFLD risk. AFB AFS AAM NAFLD Mendelian randomization causality Figures Figure 1 Figure 2 1. Introduction Non-alcoholic fatty liver disease (NAFLD) stands as the prevailing cause of liver-related morbidity globally, contributing significantly to mortality rates (1) . Its prevalence is staggering, with estimates reaching up to 25% in the general global population (2) . The histological spectrum of NAFLD spans from non-alcoholic fatty liver to more severe stages such as non-alcoholic steatohepatitis (NASH), potentially progressing to cirrhosis, hepatocellular carcinoma (HCC), and necessitating liver transplantation. The intricate pathogenesis of NAFLD is unequivocally multifactorial, encompassing metabolic factors such as obesity, insulin resistance, type 2 diabetes mellitus (T2DM), hypertension, and hyperlipidemia. Genetic susceptibility further accentuates the complexity, underlining these factors as pivotal drivers of the disease (3) . The interplay between genetics and the environment emerges as a critical influencer in both the susceptibility and progression of NAFLD, with studies indicating an increased risk among first-degree relatives independent of obesity (4) . Given the lack of universally accepted pharmacological treatments, NAFLD imposes a substantial economic and clinical burden, emphasizing the paramount significance of early recognition and prevention (5) . In the realm of women's health, reproductive characteristics such as age at first birth (AFB), age at first sexual intercourse (AFS), age at menarche (AAM), and age at natural menopause (ANM) wield substantial influence on later-life health. Recent investigations into the association between AFB and women's health yield inconsistent results; some studies posit an elevated risk of metabolic disease and cancer-related death with early AFB (6, 7, 8) , while others suggest a decreased cancer risk (9) . Additionally, earlier AAM has been linked to an increased risk of NAFLD (10, 11) . Despite these correlations, the causal relationship between female reproductive characteristics and NAFLD remains uncertain. To disentangle the intricacies of this relationship, we leverage Mendelian Randomization (MR), employing single nucleotide polymorphisms (SNPs) as instrumental variables (IVs). SNPs, intimately tied to phenotypes, offer a powerful means to address confounding and reverse causation inherent in traditional observational analyses. These genetic variants, transmitted randomly from parents to offspring during pregnancy, are less susceptible to external influences, providing a robust foundation for causal inference (12) . This article aims to elucidate the potential causal association between AFB, AFS, AAM, and the risk of NAFLD through a comprehensive two-sample MR study. 2. Materials and Methods 2.1 Data Sources: Genome-wide association analysis data pertaining to exposure and outcome were sourced from the Integrated Epidemiology Unit (IEU) Open Genome-Wide Association Studies (GWAS) project, accessible at https://gwas.mrcieu.ac.uk/ . Pooled statistics for Age at First Birth (AFB) comprised 9,702,772 SNPs from 542,901 European samples (13) ; Age at First Sexual Intercourse (AFS) data encompassed 16,359,424 SNPs from 397,338 European individuals (13) ; Age at Menarche (AAM) data involved 244,816 SNPs from 182,416 individuals of European ancestry (14) . Genome-wide meta-analyses data related to NAFLD were obtained from four European cohorts, constituting 8,434 cases and 770,180 controls. (Table 1 )NAFLD diagnoses were ascertained from electronic health records in these cohorts (15) . Table 1 Details of Studies and Datasets Used in the Study. Phenotype Sample size SNP Size Year PMID GWAS ID AFB 542,901 9,702,772 2021 34211149 ebi-a-GCST90000050 AFS 397,338 16,359,424 2021 34211149 ebi-a-GCST90000047 AAM 182,416 2,441,816 2014 25231870 ieu-a-1095 NAFLD 778,614 6,784,388 2021 34841290 ebi-a-GCST90091033 2.2 Study Design and Statistical Analysis Methods: Mendelian Randomization (MR) analyses were conducted under three fundamental assumptions: 1) a robust correlation between genetic variations and exposure factors; 2) no association of genetic variations with potential confounding variables influencing the progression from exposure to outcome; 3) genetic variations exerting an impact on the outcome solely through their correlation with exposure (16) . Initially, we assessed the independent associations between Single Nucleotide Polymorphisms (SNPs) and the three reproductive characteristics, eliminating linkage disequilibrium (LD) (P < 5 × 10 − 8 , R 2 = 0.001, kb = 10,000). Subsequently, we examined the relationship between each SNP and NAFLD risk. Synthesizing these results, we conducted Mendelian randomization analyses to evaluate the causal link between reproductive characteristics and NAFLD, employing the Inverse Variance Weighting (IVW) method as the primary statistical analysis. The IVW method utilizes meta-analysis techniques, offering reliable estimates of the causal effect of exposure on outcomes, assuming each genetic variant fulfills IV conditions (17) . To ensure result consistency and identify potential horizontal pleiotropy, three sensitivity analyses were performed: the Weighted Median method (18) , MR-Egger regression (19) , and Mendelian Randomization Polytomous Residuals and Outliers (MR-PRESSO) analysis (20) . Educational attainment, BMI, and household income were identified as significant confounders, and after correction, multivariate MR (MVMR) was conducted to explore the potential causal relationship between reproductive characteristics and NAFLD, using MVMR-IVW as the analytical method. All analyses were performed using the R statistical software version 4.3.1 with the TwoSampleMR and MRPRESSO packages. 2.3 Heterogeneity and Sensitivity Analysis: Cochran's Q statistic and I 2 statistic were employed to assess heterogeneity among SNPs (21) . A "leave-one-out sensitivity" analysis was conducted to evaluate the impact of individual SNPs on causality, ensuring the reliability of MR findings (22, 23) . 3. Results 3.1 Selection of Instrumental Variables: For the Mendelian randomization analysis, instrumental variables were meticulously chosen to adhere to Mendelian randomization assumptions, ensuring high relevance and independence from exposure factors while accounting for linkage disequilibrium (LD) (P < 5 × 10 − 8 , R 2 = 0.001, kb = 10,000). Each instrumental variable's efficacy was assessed using the F statistic (F = beta 2 / se 2 ), with values below 10 considered weak instruments (24) . A total of 53, 146, and 60 SNPs were identified as instrumental variables for AFB, AFS, and AAM, respectively (Table S1 ,S2,S3). 3.2.1 Univariable MR Results: The Inverse Variance Weighting (IVW) analysis revealed compelling evidence supporting older age at AFB, AFS, and AAM as associated with a reduced risk of NAFLD (Fig. 1 , Figure S8 ). The odds ratios (OR) and corresponding confidence intervals (95% CI) were as follows: AFB (OR 0.89; 95% CI: 0.83–0.96; P = 0.003), AFS (OR 0.64; 95% CI: 0.53–0.76; P = 1.47×10^(-5)), AAM (OR 0.83; 95% CI: 0.75–0.91; P = 0.0002). MR-Egger regression intercepts indicated no effect of horizontal pleiotropy on the results (P = 0.93, P = 0.63, P = 0.52). Sensitivity analyses, including leave-one-out analysis and funnel plots, supported result reliability(Figure S1 ,S2,S3,S7.). 3.2.2 Multivariate MR Results: In multivariate MR, instrumental variables for confounders (educational attainment, BMI, and household income) were obtained from IEU OpenGWAS (Table S4 , S5, S6.). After controlling for these confounders, the effect of AFB on NAFLD diminished (OR 1.07; 95% CI: 0.94–1.22; P = 0.3). However, in MVMR, controlling for education, BMI, and household income showed that earlier AFS (OR 0.62; 95% CI: 0.45–0.85; P = 0.003) and AAM (OR 0.87; 95% CI: 0.76–0.98; P = 0.02) were associated with a lower sub-risk of NAFLD(Fig. 2 ). 3.3 Heterogeneity: Heterogeneity, indicative of variability in causal estimates among SNPs, was assessed using Cochran's Q statistic, demonstrating low heterogeneity for all three outcomes (AFB, AFS, and AAM). The I 2 statistic supported this finding, indicating enhanced reliability of MR estimates (Table 2 ). Table 2 Heterogeneity results estimated by IVW method and MR-Egger method. Exposure Methord Cochran'sQ I 2 P-value AFB IVW 51.123 0.017 0.508 MR-Egger 51.115 0.002 0.469 AFS IVW 153.845 0.05 0.292 MR-Egger 153.596 0.06 0.277 AAM IVW 53.538 0.10 0.676 MR-Egger 53.118 0.09 0.657 I 2 =(Q − df)/Q (35) 4. Discussion This groundbreaking study represents the initial endeavor to unravel the causal relationship between female reproductive factors (AAM, AFS, AFB) and NAFLD using a 2-sample MR analysis. Employing three distinct MR analysis estimation methods—inverse variance weighting, weighted median method, and MR-Egger regression—our findings suggest a causal link between AFB, AFS, and AAM, and NAFLD. Despite some inconsistencies in estimates obtained through MR-Egger and IVW analyses, as well as the weighted median analysis, the concurrent support from IVW and weighted median methods underpins the existence of this causal relationship. The robustness of the weighted median method, even in the presence of invalid instruments, coupled with its higher estimation precision compared to MR-Egger analysis, suggests a compelling genetic predisposition to early AAM, AFB, and AFS causally associating with an increased risk of NAFLD. Post MVMR correction for confounding variables such as education, household income, and BMI, AFS and AAM at earlier ages remained associated with a heightened risk of NAFLD. Potential mechanisms linking AFB and NAFLD may be multifaceted. Pregnancy and childbirth induce increased adiposity, lipolysis, insulin resistance, and an inflammatory response, factors that may persist post-childbirth, placing young mothers at a higher risk of developing NAFLD. (25) Furthermore, intricate interactions between metabolism and reproduction suggest that adolescent mothers, experiencing prolonged growth post-delivery, might undergo more profound changes in the cardiovascular system, potentially irreversibly heightening the risk of NAFLD in subsequent years. (26,27) Despite the initial significance of the association between AFB and NAFLD diminishing post MVMR correction for confounders, the role of BMI and education on AFB may explain this weakening association. The association of AFS appears linked to socioeconomic factors. Women experiencing AFS at an earlier age may exhibit lower education levels and household incomes, potentially leading to more hazardous health behaviors such as smoking, overeating, and increased alcohol consumption. Although the association between AFS and NAFLD remains statistically significant after correcting for confounders, including educational attainment, household income, and BMI, it appears somewhat influenced by these factors. Studies suggest that a year-earlier age at menarche is associated with a 10% increased risk of NAFLD in mid-adulthood (44–45 years). (28) Premature maturation in women may be a risk factor for increased body fat accumulation, with hormonal pathways mediating the association between AAM and NAFLD. Early menarche may expose women to elevated estrogen and progesterone levels, affecting body fat distribution and adipocyte differentiation, thus increasing the likelihood of NAFLD later in life. (29,30,31,32,33,34) The complex physiological changes during pregnancy make the underlying mechanisms linking AFB and NAFLD less significant after MVMR correction for confounders, including education, family income, and BMI. Our study boasts several strengths, being the first to utilize MR to assess the connection between reproductive factors and NAFLD. Examining three distinct reproductive characteristics (AFB, AFS, AAM) and employing significant sensitivity analyses enhances the reliability of our MR model. However, limitations include the challenge of horizontal pleiotropy, the predominantly European study population, and the intricate nature of reproductive factors influenced by various genetic, environmental, and socioeconomic factors. While the study does not completely rule out environmental influences, it signifies a significant step in understanding the nuanced relationship between reproductive factors and NAFLD. 5. Summary This study underscores the causal association between later ages of AFB, AFS, and AAM with a reduced risk of NAFLD. Further exploration into the underlying mechanisms of this causality is imperative. Empowering women with knowledge of reproductive health care may emerge as a pivotal strategy in reducing the risk of NAFLD. Abbreviations NAFLD non-alcoholic fatty liver disease MR mendelian randomization AFB age at first birth AFS age at first sexual intercourse AAM age at menarche ANM age at natural menopause IV instrumental variables SNP single nucleotide polymorphisms GWAS Genome-Wide Association Studies IEU Integrated Epidemiology Unit LD linkage disequilibrium MR-PRESSO Mendelian Randomization Polytomous Residuals and Outliers IVW inverse variance weighting UVMR univariable mendelian randomization MVMR Multivariate mendelian randomization EA educational attainment BMI body mass index HI household income HCC hepatocellular carcinoma NASH non-alcoholic steatohepatitis T2DM type 2 diabetes mellitus OR odds ratio 95%cl 95% confidence interval Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding Not applicable. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions J-XM, YH and C-QX contributed to the study conception, design, and manuscript drafting. J-XM, LX, and C-YS contributed to the acquisition and analysis of data. All authors approved the final manuscript. Acknowledgments We thank all participants and investigators involved in the Mills,Perry,Ghodsion et al. GWAS. Data availability statement The original contributions presented in this study areincluded in the article/Supplementary material, further inquiries can be directed to the corresponding author. References Younossi ZM, Koenig AB, Abdelatif D, et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016 Jul;64(1):73-84. doi: 10.1002/hep.28431. Younossi ZM. 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Supplementary Files SupplementaryFigureS1SensitivityanalysisplotoftheleaveoneoutmethodforthecausalrelationshipbetweenAFBandNAFLD.pdf SupplementaryFigureS2SensitivityanalysisplotoftheleaveoneoutmethodforthecausalrelationshipbetweenAFSandANFLD.pdf SupplementaryFigureS3SensitivityanalysisplotoftheleaveoneoutmethodforthecausalrelationshipbetweenAAMandNAFLD.pdf SupplementaryFigureS4ForestplotofthecausaleffectsofsinglenucleotidepolymorphismsassociatedwithAFBonNAFLD..pdf SupplementaryFigureS5ForestplotofthecausaleffectsofsinglenucleotidepolymorphismsassociatedwithAFSonNAFLD..pdf SupplementaryFigureS6ForestplotofthecausaleffectsofsinglenucleotidepolymorphismsassociatedwithAAMonNAFLD..pdf SupplementaryFigureS7FunnelplotofcausalitybetweenAFBAFSAAMandNAFLD.pdf SupplementaryFigureS8ScatterplotofcausalitybetweenAFBAFSAAMandNAFLD.pdf SupplementaryTableS1.GeneticinformationofSNPsassociatedwithAFBinUVMRandMVMR.xlsx SupplementaryTableS2.GeneticinformationofSNPsassociatedwithAFSinUVMRandMVMR..xlsx SupplementaryTableS3.GeneticinformationofSNPsassociatedwithAAMinUVMRandMVMR..xlsx SupplementaryTableS4.GeneticinformationofSNPsassociatedwithEAinMVMR..xlsx SupplementaryTableS5.GeneticinformationofSNPsassociatedwithBMIinMVMR..xlsx SupplementaryTableS6.GeneticinformationofSNPsassociatedwithhouseholdincomeinMVMR..xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":816405,"visible":true,"origin":"","legend":"\u003cp\u003eGenetically predicted causal associations of age at first birth, age at first sexual intercourse , and age at menarche with non-alcoholic fatty liver disease.CI confidence interval,OR odds ratio，AFB age at first birth , AFS age at first sexual intercourse , AAM age at menarche,NAFLD non-alcoholic fatty liver disease.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/b29c6545ee2f3d61b6d70751.png"},{"id":49675146,"identity":"34aee436-c631-4327-b7a9-ab7222acd9e1","added_by":"auto","created_at":"2024-01-16 09:54:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":822084,"visible":true,"origin":"","legend":"\u003cp\u003eGenetically predicted causal associations of age at first birth, age at first sexual intercourse , and age at menarche with nonalcoholic fatty liver disease after adjusting for education, body mass index, and household income confounders in multivariate Mendelian randomization.CI confidence interval,OR odds ratio，AFB age at first birth , AFS age at first sexual intercourse , AAM age at menarche,NAFLD non-alcoholic fatty liver disease.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/3f67e76c0ef8a7a891021d17.png"},{"id":56913644,"identity":"6e0eb1a8-84d7-49e1-bea9-e7b31ef87ac8","added_by":"auto","created_at":"2024-05-22 05:50:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2394333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/ae3b7658-7ae0-4262-93f7-04cd77824085.pdf"},{"id":49675148,"identity":"a91acf16-3e04-4d50-b8a1-959b5e18af0a","added_by":"auto","created_at":"2024-01-16 09:54:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":148142,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1SensitivityanalysisplotoftheleaveoneoutmethodforthecausalrelationshipbetweenAFBandNAFLD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/a33b28ed2e5d2d061f72313d.pdf"},{"id":49675159,"identity":"a22eb158-0832-4d84-88ac-b6cea473ba40","added_by":"auto","created_at":"2024-01-16 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10:02:18","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":117390,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS3SensitivityanalysisplotoftheleaveoneoutmethodforthecausalrelationshipbetweenAAMandNAFLD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/7baf400b2d7edb8597f0bd1d.pdf"},{"id":49676150,"identity":"594cc1fa-73ff-4863-9668-b3b9c16eab1c","added_by":"auto","created_at":"2024-01-16 10:10:18","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":121304,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS4ForestplotofthecausaleffectsofsinglenucleotidepolymorphismsassociatedwithAFBonNAFLD..pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/cc52c2113226f074d6743d06.pdf"},{"id":49677036,"identity":"652febaa-32d8-40ef-8493-37174b4a12bf","added_by":"auto","created_at":"2024-01-16 10:18:18","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":148109,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS5ForestplotofthecausaleffectsofsinglenucleotidepolymorphismsassociatedwithAFSonNAFLD..pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/504d30f9ff1e860f13b36192.pdf"},{"id":49675640,"identity":"6e789722-cd4b-476e-b4ba-c0b8a2040960","added_by":"auto","created_at":"2024-01-16 10:02:18","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":119199,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS6ForestplotofthecausaleffectsofsinglenucleotidepolymorphismsassociatedwithAAMonNAFLD..pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/27c283bcc747adc231fd50eb.pdf"},{"id":49675161,"identity":"364efa53-2584-4d4f-b7c6-e603263e631a","added_by":"auto","created_at":"2024-01-16 09:54:18","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":204295,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS7FunnelplotofcausalitybetweenAFBAFSAAMandNAFLD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/0324e5e5bf4067fd204ace21.pdf"},{"id":49675158,"identity":"57bf8e32-4955-42fa-a641-c96a0b45e46b","added_by":"auto","created_at":"2024-01-16 09:54:18","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":363199,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS8ScatterplotofcausalitybetweenAFBAFSAAMandNAFLD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/1d0abaa6ff05f927daed01f6.pdf"},{"id":49675157,"identity":"89be6fa0-af45-4677-971e-c5d2ee1e37fd","added_by":"auto","created_at":"2024-01-16 09:54:18","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":14909,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.GeneticinformationofSNPsassociatedwithAFBinUVMRandMVMR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/3d0e5c9b7b4190ddacb221d2.xlsx"},{"id":49992655,"identity":"23af4602-ddc7-4df9-a94a-041a418a596f","added_by":"auto","created_at":"2024-01-22 18:58:03","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":24832,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.GeneticinformationofSNPsassociatedwithAFSinUVMRandMVMR..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/7256f833b22f94cb4459aa34.xlsx"},{"id":49675155,"identity":"2e2fc55b-efc4-4cfd-bd03-49380f2e809e","added_by":"auto","created_at":"2024-01-16 09:54:18","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":15083,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.GeneticinformationofSNPsassociatedwithAAMinUVMRandMVMR..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/f3781804168ca7f263eda21d.xlsx"},{"id":49677038,"identity":"5dc3883d-fd29-429b-9054-372b04612dcf","added_by":"auto","created_at":"2024-01-16 10:18:18","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":28872,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.GeneticinformationofSNPsassociatedwithEAinMVMR..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/4661579582e6b6d3d60cbe5b.xlsx"},{"id":49677287,"identity":"994f0a8d-41ad-4141-b4a4-78813f13f5bc","added_by":"auto","created_at":"2024-01-16 10:26:18","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":15176,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS5.GeneticinformationofSNPsassociatedwithBMIinMVMR..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/33d6fb070b6ae7fc686d1ca9.xlsx"},{"id":49675160,"identity":"110dcb00-efbb-4f45-a810-cd9ca8be409a","added_by":"auto","created_at":"2024-01-16 09:54:18","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":13583,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS6.GeneticinformationofSNPsassociatedwithhouseholdincomeinMVMR..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3845511/v1/5b3c8b5024923c68e0b8c5db.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unraveling the Causal Nexus Between Reproductive Characteristics and Non-Alcoholic Fatty Liver Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) stands as the prevailing cause of liver-related morbidity globally, contributing significantly to mortality rates \u003csup\u003e(1)\u003c/sup\u003e. Its prevalence is staggering, with estimates reaching up to 25% in the general global population \u003csup\u003e(2)\u003c/sup\u003e. The histological spectrum of NAFLD spans from non-alcoholic fatty liver to more severe stages such as non-alcoholic steatohepatitis (NASH), potentially progressing to cirrhosis, hepatocellular carcinoma (HCC), and necessitating liver transplantation. The intricate pathogenesis of NAFLD is unequivocally multifactorial, encompassing metabolic factors such as obesity, insulin resistance, type 2 diabetes mellitus (T2DM), hypertension, and hyperlipidemia. Genetic susceptibility further accentuates the complexity, underlining these factors as pivotal drivers of the disease \u003csup\u003e(3)\u003c/sup\u003e. The interplay between genetics and the environment emerges as a critical influencer in both the susceptibility and progression of NAFLD, with studies indicating an increased risk among first-degree relatives independent of obesity \u003csup\u003e(4)\u003c/sup\u003e. Given the lack of universally accepted pharmacological treatments, NAFLD imposes a substantial economic and clinical burden, emphasizing the paramount significance of early recognition and prevention \u003csup\u003e(5)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the realm of women's health, reproductive characteristics such as age at first birth (AFB), age at first sexual intercourse (AFS), age at menarche (AAM), and age at natural menopause (ANM) wield substantial influence on later-life health. Recent investigations into the association between AFB and women's health yield inconsistent results; some studies posit an elevated risk of metabolic disease and cancer-related death with early AFB \u003csup\u003e(6, 7, 8)\u003c/sup\u003e, while others suggest a decreased cancer risk \u003csup\u003e(9)\u003c/sup\u003e. Additionally, earlier AAM has been linked to an increased risk of NAFLD \u003csup\u003e(10, 11)\u003c/sup\u003e. Despite these correlations, the causal relationship between female reproductive characteristics and NAFLD remains uncertain.\u003c/p\u003e \u003cp\u003eTo disentangle the intricacies of this relationship, we leverage Mendelian Randomization (MR), employing single nucleotide polymorphisms (SNPs) as instrumental variables (IVs). SNPs, intimately tied to phenotypes, offer a powerful means to address confounding and reverse causation inherent in traditional observational analyses. These genetic variants, transmitted randomly from parents to offspring during pregnancy, are less susceptible to external influences, providing a robust foundation for causal inference \u003csup\u003e(12)\u003c/sup\u003e. This article aims to elucidate the potential causal association between AFB, AFS, AAM, and the risk of NAFLD through a comprehensive two-sample MR study.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources:\u003c/h2\u003e \u003cp\u003eGenome-wide association analysis data pertaining to exposure and outcome were sourced from the Integrated Epidemiology Unit (IEU) Open Genome-Wide Association Studies (GWAS) project, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Pooled statistics for Age at First Birth (AFB) comprised 9,702,772 SNPs from 542,901 European samples \u003csup\u003e(13)\u003c/sup\u003e; Age at First Sexual Intercourse (AFS) data encompassed 16,359,424 SNPs from 397,338 European individuals \u003csup\u003e(13)\u003c/sup\u003e; Age at Menarche (AAM) data involved 244,816 SNPs from 182,416 individuals of European ancestry \u003csup\u003e(14)\u003c/sup\u003e. Genome-wide meta-analyses data related to NAFLD were obtained from four European cohorts, constituting 8,434 cases and 770,180 controls. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)NAFLD diagnoses were ascertained from electronic health records in these cohorts \u003csup\u003e(15)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of Studies and Datasets Used in the Study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e542,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,702,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34211149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eebi-a-GCST90000050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e397,338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,359,424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34211149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eebi-a-GCST90000047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,441,816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25231870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eieu-a-1095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e778,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,784,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34841290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eebi-a-GCST90091033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Design and Statistical Analysis Methods:\u003c/h2\u003e \u003cp\u003eMendelian Randomization (MR) analyses were conducted under three fundamental assumptions: 1) a robust correlation between genetic variations and exposure factors; 2) no association of genetic variations with potential confounding variables influencing the progression from exposure to outcome; 3) genetic variations exerting an impact on the outcome solely through their correlation with exposure \u003csup\u003e(16)\u003c/sup\u003e. Initially, we assessed the independent associations between Single Nucleotide Polymorphisms (SNPs) and the three reproductive characteristics, eliminating linkage disequilibrium (LD) (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001, kb\u0026thinsp;=\u0026thinsp;10,000). Subsequently, we examined the relationship between each SNP and NAFLD risk. Synthesizing these results, we conducted Mendelian randomization analyses to evaluate the causal link between reproductive characteristics and NAFLD, employing the Inverse Variance Weighting (IVW) method as the primary statistical analysis. The IVW method utilizes meta-analysis techniques, offering reliable estimates of the causal effect of exposure on outcomes, assuming each genetic variant fulfills IV conditions \u003csup\u003e(17)\u003c/sup\u003e. To ensure result consistency and identify potential horizontal pleiotropy, three sensitivity analyses were performed: the Weighted Median method \u003csup\u003e(18)\u003c/sup\u003e, MR-Egger regression \u003csup\u003e(19)\u003c/sup\u003e, and Mendelian Randomization Polytomous Residuals and Outliers (MR-PRESSO) analysis \u003csup\u003e(20)\u003c/sup\u003e. Educational attainment, BMI, and household income were identified as significant confounders, and after correction, multivariate MR (MVMR) was conducted to explore the potential causal relationship between reproductive characteristics and NAFLD, using MVMR-IVW as the analytical method.\u003c/p\u003e \u003cp\u003eAll analyses were performed using the R statistical software version 4.3.1 with the TwoSampleMR and MRPRESSO packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Heterogeneity and Sensitivity Analysis:\u003c/h2\u003e \u003cp\u003eCochran's Q statistic and I\u003csup\u003e2\u003c/sup\u003e statistic were employed to assess heterogeneity among SNPs \u003csup\u003e(21)\u003c/sup\u003e. A \"leave-one-out sensitivity\" analysis was conducted to evaluate the impact of individual SNPs on causality, ensuring the reliability of MR findings \u003csup\u003e(22, 23)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Selection of Instrumental Variables:\u003c/h2\u003e \u003cp\u003eFor the Mendelian randomization analysis, instrumental variables were meticulously chosen to adhere to Mendelian randomization assumptions, ensuring high relevance and independence from exposure factors while accounting for linkage disequilibrium (LD) (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001, kb\u0026thinsp;=\u0026thinsp;10,000). Each instrumental variable's efficacy was assessed using the F statistic (F\u0026thinsp;=\u0026thinsp;beta\u003csup\u003e2\u003c/sup\u003e / se\u003csup\u003e2\u003c/sup\u003e), with values below 10 considered weak instruments \u003csup\u003e(24)\u003c/sup\u003e. A total of 53, 146, and 60 SNPs were identified as instrumental variables for AFB, AFS, and AAM, respectively (Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e,S2,S3).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Univariable MR Results:\u003c/h2\u003e \u003cp\u003eThe Inverse Variance Weighting (IVW) analysis revealed compelling evidence supporting older age at AFB, AFS, and AAM as associated with a reduced risk of NAFLD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). The odds ratios (OR) and corresponding confidence intervals (95% CI) were as follows: AFB (OR 0.89; 95% CI: 0.83\u0026ndash;0.96; P\u0026thinsp;=\u0026thinsp;0.003), AFS (OR 0.64; 95% CI: 0.53\u0026ndash;0.76; P\u0026thinsp;=\u0026thinsp;1.47\u0026times;10^(-5)), AAM (OR 0.83; 95% CI: 0.75\u0026ndash;0.91; P\u0026thinsp;=\u0026thinsp;0.0002). MR-Egger regression intercepts indicated no effect of horizontal pleiotropy on the results (P\u0026thinsp;=\u0026thinsp;0.93, P\u0026thinsp;=\u0026thinsp;0.63, P\u0026thinsp;=\u0026thinsp;0.52). Sensitivity analyses, including leave-one-out analysis and funnel plots, supported result reliability(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e,S2,S3,S7.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Multivariate MR Results:\u003c/h2\u003e \u003cp\u003eIn multivariate MR, instrumental variables for confounders (educational attainment, BMI, and household income) were obtained from IEU OpenGWAS (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, S5, S6.). After controlling for these confounders, the effect of AFB on NAFLD diminished (OR 1.07; 95% CI: 0.94\u0026ndash;1.22; P\u0026thinsp;=\u0026thinsp;0.3). However, in MVMR, controlling for education, BMI, and household income showed that earlier AFS (OR 0.62; 95% CI: 0.45\u0026ndash;0.85; P\u0026thinsp;=\u0026thinsp;0.003) and AAM (OR 0.87; 95% CI: 0.76\u0026ndash;0.98; P\u0026thinsp;=\u0026thinsp;0.02) were associated with a lower sub-risk of NAFLD(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Heterogeneity:\u003c/h2\u003e \u003cp\u003eHeterogeneity, indicative of variability in causal estimates among SNPs, was assessed using Cochran's Q statistic, demonstrating low heterogeneity for all three outcomes (AFB, AFS, and AAM). The I\u003csup\u003e2\u003c/sup\u003e statistic supported this finding, indicating enhanced reliability of MR estimates (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity results estimated by IVW method and MR-Egger method.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethord\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCochran'sQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAFB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAFS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eI\u003csup\u003e2\u003c/sup\u003e=(Q\u0026thinsp;\u0026minus;\u0026thinsp;df)/Q\u003csup\u003e(35)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis groundbreaking study represents the initial endeavor to unravel the causal relationship between female reproductive factors (AAM, AFS, AFB) and NAFLD using a 2-sample MR analysis. Employing three distinct MR analysis estimation methods\u0026mdash;inverse variance weighting, weighted median method, and MR-Egger regression\u0026mdash;our findings suggest a causal link between AFB, AFS, and AAM, and NAFLD. Despite some inconsistencies in estimates obtained through MR-Egger and IVW analyses, as well as the weighted median analysis, the concurrent support from IVW and weighted median methods underpins the existence of this causal relationship. The robustness of the weighted median method, even in the presence of invalid instruments, coupled with its higher estimation precision compared to MR-Egger analysis, suggests a compelling genetic predisposition to early AAM, AFB, and AFS causally associating with an increased risk of NAFLD. Post MVMR correction for confounding variables such as education, household income, and BMI, AFS and AAM at earlier ages remained associated with a heightened risk of NAFLD.\u003c/p\u003e \u003cp\u003ePotential mechanisms linking AFB and NAFLD may be multifaceted. Pregnancy and childbirth induce increased adiposity, lipolysis, insulin resistance, and an inflammatory response, factors that may persist post-childbirth, placing young mothers at a higher risk of developing NAFLD. \u003csup\u003e(25)\u003c/sup\u003eFurthermore, intricate interactions between metabolism and reproduction suggest that adolescent mothers, experiencing prolonged growth post-delivery, might undergo more profound changes in the cardiovascular system, potentially irreversibly heightening the risk of NAFLD in subsequent years. \u003csup\u003e(26,27)\u003c/sup\u003eDespite the initial significance of the association between AFB and NAFLD diminishing post MVMR correction for confounders, the role of BMI and education on AFB may explain this weakening association.\u003c/p\u003e \u003cp\u003eThe association of AFS appears linked to socioeconomic factors. Women experiencing AFS at an earlier age may exhibit lower education levels and household incomes, potentially leading to more hazardous health behaviors such as smoking, overeating, and increased alcohol consumption. Although the association between AFS and NAFLD remains statistically significant after correcting for confounders, including educational attainment, household income, and BMI, it appears somewhat influenced by these factors.\u003c/p\u003e \u003cp\u003eStudies suggest that a year-earlier age at menarche is associated with a 10% increased risk of NAFLD in mid-adulthood (44\u0026ndash;45 years).\u003csup\u003e(28)\u003c/sup\u003e Premature maturation in women may be a risk factor for increased body fat accumulation, with hormonal pathways mediating the association between AAM and NAFLD. Early menarche may expose women to elevated estrogen and progesterone levels, affecting body fat distribution and adipocyte differentiation, thus increasing the likelihood of NAFLD later in life. \u003csup\u003e(29,30,31,32,33,34)\u003c/sup\u003eThe complex physiological changes during pregnancy make the underlying mechanisms linking AFB and NAFLD less significant after MVMR correction for confounders, including education, family income, and BMI.\u003c/p\u003e \u003cp\u003eOur study boasts several strengths, being the first to utilize MR to assess the connection between reproductive factors and NAFLD. Examining three distinct reproductive characteristics (AFB, AFS, AAM) and employing significant sensitivity analyses enhances the reliability of our MR model. However, limitations include the challenge of horizontal pleiotropy, the predominantly European study population, and the intricate nature of reproductive factors influenced by various genetic, environmental, and socioeconomic factors. While the study does not completely rule out environmental influences, it signifies a significant step in understanding the nuanced relationship between reproductive factors and NAFLD.\u003c/p\u003e"},{"header":"5. Summary","content":"\u003cp\u003eThis study underscores the causal association between later ages of AFB, AFS, and AAM with a reduced risk of NAFLD. Further exploration into the underlying mechanisms of this causality is imperative. Empowering women with knowledge of reproductive health care may emerge as a pivotal strategy in reducing the risk of NAFLD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-alcoholic fatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage at first birth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage at first sexual intercourse\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage at menarche\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage at natural menopause\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstrumental variables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-Wide Association Studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIEU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Epidemiology Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR-PRESSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian Randomization Polytomous Residuals and Outliers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einverse variance weighting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUVMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eunivariable mendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMVMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultivariate mendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeducational attainment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehousehold income\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNASH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-alcoholic steatohepatitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etype 2 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e95%cl\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eJ-XM, YH and C-QX contributed to the study conception, design, and manuscript drafting. J-XM, LX, and C-YS\u0026nbsp;contributed to the acquisition and analysis of data. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants and investigators involved in the Mills,Perry,Ghodsion et al. GWAS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study areincluded in the article/Supplementary material, further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYounossi ZM, Koenig AB, Abdelatif D, et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016 Jul;64(1):73-84. doi: 10.1002/hep.28431. \u003c/li\u003e\n\u003cli\u003eYounossi ZM. Non-alcoholic fatty liver disease - A global public health perspective. J Hepatol. 2019 Mar;70(3):531-544. doi: 10.1016/j.jhep.2018.10.033.\u003c/li\u003e\n\u003cli\u003eZhao ZH, Zou J, Huang X, et al. Assessing causal relationships between sarcopenia and nonalcoholic fatty liver disease: A bidirectional Mendelian randomization study. Front Nutr. 2022 Nov 9;9:971913. doi: 10.3389/fnut.2022.971913.\u003c/li\u003e\n\u003cli\u003eEslam M, Valenti L, Romeo S. Genetics and epigenetics of NAFLD and NASH: Clinical impact. J Hepatol. 2018 Feb;68(2):268-279. doi: 10.1016/j.jhep.2017.09.003. \u003c/li\u003e\n\u003cli\u003ePaternostro R, Trauner M. Current treatment of non-alcoholic fatty liver disease. J Intern Med. 2022 Aug;292(2):190-204. doi: 10.1111/joim.13531. \u003c/li\u003e\n\u003cli\u003eSim JH, Chung D, Lim JS, et al. Maternal age at first delivery is associated with the risk of metabolic syndrome in postmenopausal women: from 2008-2010 Korean National Health and Nutrition Examination Survey. PLoS One. 2015 May 26;10(5):e0127860. doi: 10.1371/journal.pone.0127860.\u003c/li\u003e\n\u003cli\u003eRosendaal NTA, Pirkle CM. Age at first birth and risk of later-life cardiovascular disease: a systematic review of the literature, its limitation, and recommendations for future research. BMC Public Health. 2017 Jul 5;17(1):627. doi: 10.1186/s12889-017-4519-x.\u003c/li\u003e\n\u003cli\u003eMerritt MA, Riboli E, Murphy N, et al.Reproductive factors and risk of mortality in the European Prospective Investigation into Cancer and Nutrition; a cohort study. BMC Med. 2015 Oct 30;13:252. doi: 10.1186/s12916-015-0484-3.\u003c/li\u003e\n\u003cli\u003eWu CH, Chan TF, Changchien CC, et al. Parity, age at first birth, and risk of death from liver cancer: Evidence from a cohort in Taiwan. J Gastroenterol Hepatol. 2011 Feb;26(2):334-9. doi: 10.1111/j.1440-1746.2010.06365.x.\u003c/li\u003e\n\u003cli\u003eRyu S, Chang Y, Choi Y, et al. Age at menarche and non-alcoholic fatty liver disease. J Hepatol. 2015 May;62(5):1164-70. doi: 10.1016/j.jhep.2014.11.041.\u003c/li\u003e\n\u003cli\u003eWang J, Wu AH, Stanczyk FZ, et al. Associations Between Reproductive and Hormone-Related Factors and Risk of Nonalcoholic Fatty Liver Disease in a Multiethnic Population. Clin Gastroenterol Hepatol. 2021 Jun;19(6):1258-1266.e1. doi: 10.1016/j.cgh.2020.08.012.\u003c/li\u003e\n\u003cli\u003eDavey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014 Sep 15;23(R1):R89-98. doi: 10.1093/hmg/ddu328. \u003c/li\u003e\n\u003cli\u003eMills MC, Tropf FC, Brazel DM, et al.Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat Hum Behav. 2021 Dec;5(12):1717-1730. doi: 10.1038/s41562-021-01135-3.\u003c/li\u003e\n\u003cli\u003ePerry JR, Day F, Elks CE, et al. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature. 2014 Oct 2;514(7520):92-97. doi: 10.1038/nature13545. \u003c/li\u003e\n\u003cli\u003eGhodsian N, Abner E, Emdin CA, et al. Electronic health record-based genome-wide meta-analysis provides insights on the genetic architecture of non-alcoholic fatty liver disease. Cell Rep Med. 2021 Nov 3;2(11):100437. doi: 10.1016/j.xcrm.2021.100437. \u003c/li\u003e\n\u003cli\u003eChen L, Fan Z, Sun X, et al. Mendelian Randomization Rules Out Causation Between Inflammatory Bowel Disease and Non-Alcoholic Fatty Liver Disease. Front Pharmacol. 2022 May 19;13:891410. doi: 10.3389/fphar.2022.891410.\u003c/li\u003e\n\u003cli\u003ePierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013 Oct 1;178(7):1177-84. doi: 10.1093/aje/kwt084. \u003c/li\u003e\n\u003cli\u003eYavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017 Dec 1;46(6):1734-1739. doi: 10.1093/ije/dyx034.\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017 May;32(5):377-389. doi: 10.1007/s10654-017-0255-x. \u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018 May;50(5):693-698. doi: 10.1038/s41588-018-0099-7.\u003c/li\u003e\n\u003cli\u003eEgger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ. 1997 Dec 6;315(7121):1533-7. doi: 10.1136/bmj.315.7121.1533.\u003c/li\u003e\n\u003cli\u003eBowden J, Del Greco M F, Minelli C, et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016 Dec 1;45(6):1961-1974. doi: 10.1093/ije/dyw220.\u003c/li\u003e\n\u003cli\u003eHiggins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002 Jun 15;21(11):1539-58. doi: 10.1002/sim.1186.\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011 Jun;40(3):755-64. doi: 10.1093/ije/dyr036. \u003c/li\u003e\n\u003cli\u003eHerrera E. Lipid metabolism in pregnancy and its consequences in the fetus and newborn. Endocrine. 2002 Oct;19(1):43-55. doi: 10.1385/ENDO:19:1:43.\u003c/li\u003e\n\u003cli\u003eSanghavi M, Rutherford JD. Cardiovascular physiology of pregnancy. Circulation. 2014 Sep 16;130(12):1003-8. doi: 10.1161/CIRCULATIONAHA.114.009029.\u003c/li\u003e\n\u003cli\u003eMay L. Cardiac Physiology of Pregnancy. Compr Physiol. 2015 Jul 1;5(3):1325-44. doi: 10.1002/cphy.c140043.\u003c/li\u003e\n\u003cli\u003ePeng H, Wu X, Wen Y, et al .Age at first birth and lung cancer: a two-sample Mendelian randomization study. Transl Lung Cancer Res. 2021 Apr;10(4):1720-1733. doi: 10.21037/tlcr-20-1216. \u003c/li\u003e\n\u003cli\u003eMueller NT, Pereira MA, Demerath EW, et al. Earlier menarche is associated with fatty liver and abdominal ectopic fat in midlife, independent of young adult BMI: The CARDIA study. Obesity (Silver Spring). 2015 Feb;23(2):468-74. doi: 10.1002/oby.20950.\u003c/li\u003e\n\u003cli\u003eLu J, Zhang J, Du R, et al. Age at menarche is associated with the prevalence of non-alcoholic fatty liver disease later in life. J Diabetes. 2017 Jan;9(1):53-60. doi: 10.1111/1753-0407.12379.\u003c/li\u003e\n\u003cli\u003eKaplowitz PB. Link between body fat and the timing of puberty. Pediatrics. 2008 Feb;121 Suppl 3:S208-17. doi: 10.1542/peds.2007-1813F.\u003c/li\u003e\n\u003cli\u003eDunger DB, Ahmed ML, Ong KK. Early and late weight gain and the timing of puberty. Mol Cell Endocrinol. 2006 Jul 25;254-255:140-5. doi: 10.1016/j.mce.2006.04.003. \u003c/li\u003e\n\u003cli\u003eWilson ME, Fisher J, Chikazawa K, et al. Leptin administration increases nocturnal concentrations of luteinizing hormone and growth hormone in juvenile female rhesus monkeys. J Clin Endocrinol Metab. 2003 Oct;88(10):4874-83. doi: 10.1210/jc.2003-030782. \u003c/li\u003e\n\u003cli\u003eKitawaki J, Kusuki I, Koshiba H, et al. Leptin directly stimulates aromatase activity in human luteinized granulosa cells. Mol Hum Reprod. 1999 Aug;5(8):708-13. doi: 10.1093/molehr/5.8.708. \u003c/li\u003e\n\u003cli\u003eGill D, Brewer CF, Del Greco M F, et al. Age at menarche and adult body mass index: a Mendelian randomization study. Int J Obes (Lond). 2018 Sep;42(9):1574-1581. doi: 10.1038/s41366-018-0048-7. \u003c/li\u003e\n\u003cli\u003eDunger DB, Ahmed ML, Ong KK. Early and late weight gain and the timing of puberty. Mol Cell Endocrinol. 2006 Jul 25;254-255:140-5. doi: 10.1016/j.mce.2006.04.003. \u003c/li\u003e\n\u003cli\u003eApter D, Reinil\u0026auml; M, Vihko R. Some endocrine characteristics of early menarche, a risk factor for breast cancer, are preserved into adulthood. Int J Cancer. 1989 Nov 15;44(5):783-7. doi: 10.1002/ijc.2910440506. \u003c/li\u003e\n\u003cli\u003eLeeners B, Geary N, Tobler PN, et al. Ovarian hormones and obesity. Hum Reprod Update. 2017 May 1;23(3):300-321. doi: 10.1093/humupd/dmw045. \u003c/li\u003e\n\u003cli\u003eApter D, Reinil\u0026auml; M, Vihko R. Some endocrine characteristics of early menarche, a risk factor for breast cancer, are preserved into adulthood. Int J Cancer. 1989 Nov 15;44(5):783-7. doi: 10.1002/ijc.2910440506.\u003c/li\u003e\n\u003cli\u003eHiggins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002 Jun 15;21(11):1539-58. doi: 10.1002/sim.1186. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AFB, AFS, AAM, NAFLD, Mendelian randomization, causality","lastPublishedDoi":"10.21203/rs.3.rs-3845511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3845511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aim\u003c/h2\u003e \u003cp\u003eNon-alcoholic fatty liver disease (NAFLD), a prevalent global health concern, stems from intricate interactions between genetic and environmental factors. The primary aim of this study is to employs Mendelian randomization (MR) to investigate the causal relationship between key female reproductive characteristics\u0026mdash;age at first birth (AFB), age at first sexual intercourse (AFS), and age at menarche (AAM)\u0026mdash;and the risk of NAFLD.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eGenome-wide association data on AFB, AFS, AAM, and NAFLD were pooled for two-sample MR analysis. Instrumental variables were meticulously selected to meet MR assumptions. The primary analysis used the inverse variance weighting (IVW) approach, supplemented by MR-Egger regression and weighted median methods. Multivariate MR (MVMR) analysis considered confounding variables: educational attainment, BMI, and household income.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe MR analysis revealed significant causal associations between later AFB (OR 0.89; 95% CI: 0.83\u0026ndash;0.96; P\u0026thinsp;=\u0026thinsp;0.003), AFS (OR 0.64; 95% CI: 0.53\u0026ndash;0.76; P\u0026thinsp;=\u0026thinsp;1.47\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and AAM (OR 0.83; 95% CI: 0.75\u0026ndash;0.91; P\u0026thinsp;=\u0026thinsp;0.0002) with a reduced risk of NAFLD. MVMR, after accounting for confounders, sustained the significance of AFS (P\u0026thinsp;=\u0026thinsp;0.003) and AAM (P\u0026thinsp;=\u0026thinsp;0.02), with a weaker association for AFB (P\u0026thinsp;=\u0026thinsp;0.3).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis study provides compelling evidence that later reproductive events\u0026mdash;later AFB, AFS, and AAM\u0026mdash;are causally associated with a reduced risk of NAFLD. The observed associations persist even after adjusting for confounding variables. Further research is warranted to delve into the underlying mechanisms of this causality, emphasizing the importance of women's reproductive health awareness in mitigating NAFLD risk.\u003c/p\u003e","manuscriptTitle":"Unraveling the Causal Nexus Between Reproductive Characteristics and Non-Alcoholic Fatty Liver Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 09:54:13","doi":"10.21203/rs.3.rs-3845511/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9aee187-5665-4f01-aae4-94fed35c0720","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-22T05:42:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 09:54:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3845511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3845511","identity":"rs-3845511","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}