Obesity and risk of female reproductive conditions: A Mendelian randomisation study

Abstract

Background: Obesity is observationally associated with altered risk of many female reproductive conditions. These include polycystic ovary syndrome (PCOS), abnormal uterine bleeding, endometriosis, infertility, and pregnancy-related disorders. However, the roles and mechanisms of obesity in the aetiology of reproductive disorders remain unclear. Thus, we aimed to estimate observational and genetically predicted causal associations between obesity, metabolic hormones, and female reproductive disorders.

Methods and findings: Logistic regression, generalised additive models, and Mendelian randomisation (MR) (2-sample, non-linear, and multivariable) were applied to obesity and reproductive disease data on up to 257,193 women of European ancestry in UK Biobank and publicly available genome-wide association studies (GWASs). Body mass index (BMI), waist-to-hip ratio (WHR), and WHR adjusted for BMI were observationally (odds ratios [ORs] = 1.02-1.87 per 1-SD increase in obesity trait) and genetically (ORs = 1.06-2.09) associated with uterine fibroids (UF), PCOS, heavy menstrual bleeding (HMB), and pre-eclampsia. Genetically predicted visceral adipose tissue (VAT) mass was associated with the development of HMB (OR [95% CI] per 1-kg increase in predicted VAT mass = 1.32 [1.06-1.64], P = 0.0130), PCOS (OR [95% CI] = 1.15 [1.08-1.23], P = 3.24 × 10-05), and pre-eclampsia (OR [95% CI] = 3.08 [1.98-4.79], P = 6.65 × 10-07). Increased waist circumference posed a higher genetic risk (ORs = 1.16-1.93) for the development of these disorders and UF than did increased hip circumference (ORs = 1.06-1.10). Leptin, fasting insulin, and insulin resistance each mediated between 20% and 50% of the total genetically predicted association of obesity with pre-eclampsia. Reproductive conditions clustered based on shared genetic components of their aetiological relationships with obesity. This study was limited in power by the low prevalence of female reproductive conditions among women in the UK Biobank, with little information on pre-diagnostic anthropometric traits, and by the susceptibility of MR estimates to genetic pleiotropy.

Conclusions: We found that common indices of overall and central obesity were associated with increased risks of reproductive disorders to heterogenous extents in a systematic, large-scale genetics-based analysis of the aetiological relationships between obesity and female reproductive conditions. Our results suggest the utility of exploring the mechanisms mediating the causal associations of overweight and obesity with gynaecological health to identify targets for disease prevention and treatment.

Fig 1. Predicted probability of developing female reproductive disorders as a function of obesity-related traits.

A series of logistic regression, fractional polynomial, and generalised additive models were fitted to estimate the probability of developing female reproductive disorders as a function of obesity-related traits in UK Biobank. Predicted fits for logistic and best-fitting non-linear models were better than those for logistic regression (as evaluated with Akaike information criterion), and 95% confidence intervals about the mean are displayed. Asterisks indicate that non-linear models fit the data better than linear models. All models were adjusted for age, age squared, assessment centre, and smoking status. BMI, body mass index; spont. miscarr., spontaneous miscarriage; WHRadjBMI, waist-to-hip ratio adjusted for BMI.

Fig 2. Comparison of observational and genetically causal relationships between obesity-related traits and female reproductive disorders.

Odds ratios and 95% confidence intervals per 1 SD higher obesity trait displayed. Significant relationships (false discovery rate [FDR]–adjusted P value < 0.05) are in solid lines while non-significant (n.s.) ones are shown with dotted lines. For observational results, BMI, WHR, and WHRadjBMI adjusted for age, age squared, region (assessment centre), and smoking status are used as predictors in a logistic regression model. Causal relationships between genetically predicted obesity-related traits and female reproductive disorders are assessed by 2-sample Mendelian randomisation (MR). The displayed method (inverse-variance-weighted) was determined via Rucker’s model selection framework to minimise heterogeneity of the estimate. “Miscarriage (sporadic)” is self-reported stillbirth, spontaneous miscarriage, or termination for observational results and sporadic miscarriage for MR results. BMI, body mass index; HMB, heavy menstrual bleeding; Misc. (mult.), multiple consecutive miscarriage; PCOS, polycystic ovary syndrome; WHR, waist-to-hip ratio; WHRadjBMI, waist-to-hip ratio adjusted for BMI.

Fig 3. Causal associations of genetically predicted visceral adipose tissue mass, waist circumference, and hip circumference with female reproductive disorders.

Odds ratios (ORs) per 1-kg increase in predicted visceral adipose tissue (VAT) mass or per 1-SD increase in waist circumference or hip circumference and 95% confidence intervals (CI) are estimated by 2-sample Mendelian randomisation. The displayed method (inverse-variance-weighted) was determined via Rucker’s model selection framework. The number of cases for each disease is indicated. Significant relationships (false discovery rate [FDR]–adjusted P value < 0.05) are in solid lines while non-significant (n.s.) ones are shown with dotted lines. c., circumference; HMB, heavy menstrual bleeding; PCOS, polycystic ovary syndrome.

Fig 4. Hormone mediation of genetically predicted causal effect of obesity on female reproductive disorders.

(A) Method outline for 2-step Mendelian randomisation (MR) to estimate hormonally mediated effects between obesity and female reproductive disorders. In step 1, the effect of exposures on mediators is estimated using instruments for the exposure alone, while in step 2 the independent effect of the mediators on outcomes is estimated using multivariable MR (MVMR) adjusted for exposures. All SNP–phenotype effect estimates come from different genome-wide association studies. (B) Estimated effects of obesity traits on female reproductive disorders adjusted for mediators. MVMR was performed with combined genetic instruments for each exposure–mediator combination, displayed here for relationships where unadjusted (unadj.) exposure–outcome effect was significant (false discovery rate [FDR] < 0.05). (C) Example of a mediated relationship, shown here for BMI effect on pre-eclampsia. Estimated effects for exposure–mediator (betas and standard errors) and for mediator–outcome (log odds ratios and standard errors) effects are shown. Repr., reproductive; BMI, body mass index; n.s., not significant; PCOS, polycystic ovary syndrome; sensitiv., sensitivity; WHR, waist-to-hip ratio; WHRadjBMI, waist-to-hip ratio adjusted for BMI.

Fig 5. Clustering of the genetic effect of obesity on female reproductive disorders.

(A) Female reproductive disorders separated by the genetic effects of different obesity traits on reproductive conditions. Uniform Manifold Approximation and Projection (UMAP) was used to plot the separation of diseases based on genetic effects of obesity instruments, evaluated by 2-sample Mendelian randomisation. (B) Number of SNPs in substantial clusters for each obesity × female reproductive condition relationship. SNPs with ≥80% probability of belonging to a substantial cluster (MRClust) are displayed with their nearest gene as annotated by SNPsnap and ‘+’ or ‘−’ representing causal effect direction. BMI, body mass index; Endo, endometriosis; HMB, heavy menstrual bleeding; Infert., infertility; M. misc., multiple consecutive miscarriage; PCOS, polycystic ovary syndrome; PE, pre-eclampsia; S. misc., sporadic miscarriage; UF, uterine fibroids; WHR, waist-to-hip ratio; WHRadjBMI, waist-to-hip ratio adjusted for BMI.