Causality of anthropometric markers associated with polycystic ovarian syndrome: Findings of a Mendelian randomization study

Abstract

Introduction Using body mass index (BMI) as a proxy, previous Mendelian randomization (MR) studies found total causal effects of general obesity on polycystic ovarian syndrome (PCOS). Hitherto, total and direct causal effects of general- and central obesity on PCOS have not been comprehensively analyzed. Objectives To investigate the causality of central- and general obesity on PCOS using surrogate anthropometric markers. Methods Summary GWAS data of female-only, large-sample cohorts of European ancestry were retrieved for anthropometric markers of central obesity (waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR)) and general obesity (BMI and its constituent variables–weight and height), from the IEU Open GWAS Project. As the outcome, we acquired summary data from a large-sample GWAS (118870 samples; 642 cases and 118228 controls) within the FinnGen cohort. Total causal effects were assessed via univariable two-sample Mendelian randomization (2SMR). Genetic architectures underlying causal associations were explored. Direct causal effects were analyzed by multivariable MR modelling. Results Instrumental variables demonstrated no weak instrument bias (F > 10). Four anthropometric exposures, namely, weight (2.69–77.05), BMI (OR: 2.90–4.06), WC (OR: 6.22–20.27), and HC (OR: 6.22–20.27) demonstrated total causal effects as per univariable 2SMR models. We uncovered shared and non-shared genetic architectures underlying causal associations. Direct causal effects of WC and HC on PCOS were revealed by two multivariable MR models containing exclusively the anthropometric markers of central obesity. Other multivariable MR models containing anthropometric markers of both central- and general obesity showed no direct causal effects on PCOS. Conclusions Both and general- and central obesity yield total causal effects on PCOS. Findings also indicated potential direct causal effects of normal weight-central obesity and more complex causal mechanisms when both central- and general obesity are present. Results underscore the importance of addressing both central- and general obesity for optimizing PCOS care.

Causal relationship between genetically predicted type 2 diabetes mellitus and male infertility

“…The MR-Egger method offers a robust measure of causal effects that adjust for horizontal pleiotropy. This is achieved by pooling a single SNP-specific Wald ratio utilizing adaptive Egger regression ( 28 , 29 ). The WME method offers a consistent estimate of causal effects by utilizing the weighted median of Wald under the condition that at least 50% of variants adhere to the criteria of a valid IV for the exclusion restrictions.…”

The enigmatic interplay of immune cells and abnormal spermatozoa through Mendelian randomization

“…In the research, genetic variations associated with abnormal spermatozoa in individuals of European descent were identified from the Genome–Wide Association Study (GWAS) repository administered by the Medical Research Council–Integrative Epidemiology Unit (MRC‐IEU) consortium 15 with the GWAS‐ID “finn‐b‐R18_ABNORMAL_SPERMATOZ.” This dataset comprised 915 cases with abnormal spermatozoa and 209 006 control individuals of European lineage. We obtained GWAS statistics encompassing a comprehensive spectrum of 731 immune traits, specifically spanning accession numbers GCST90001391 to GCST90002121 16 .…”

Type 2 Diabetes Mellitus and the Risk of Prostate Cancer: A Mendelian Randomization Study

“…3 Methods and Models of 2SMR Among the methods used in two-sample Mendelian randomization analysis, ve are relatively mainstream and commonly used, these include inverse variance weighting (IVW), MR-Egger, weighted median, weighted mode, and sample mode [16] . IVW is the primary analysis method within these ve, given its capability to not only analyze the causal relationship between exposure and outcome but also to assess heterogeneity in SNPs [17] . The other four methods were utilized for supplementary analysis.…”

“…MR-Egger serves as a method to detect horizontal pleiotropy in instrumental variables, particularly irrelevant horizontal pleiotropy, by assessing the presence or absence of the intercept term, or the signi cance of the P value of the intercept term. The intercept term is used to gauge the average pleiotropy between instrumental variables, and when the intercept term is 0 or the statistical p-value is not signi cant, it indicates no bias in the instrumental variables [17] . Weighted median regression is currently the mainstream method for addressing "coherence level pleiotropy."…”