{"paper_id":"1b2a7cc2-f75c-4d02-aeeb-16502e049933","body_text":"1 \n \nThe role of obesity in female reproductive conditions: 1 \nA  Mendelian Randomisation study 2 \nSamvida S. Venkatesh 1,2*, Teresa Ferreira 1,3, Stefania Benonisdottir 1, Nilufer Rahmioglu 2,3, 3 \nChristian M. Becker 3, Ingrid Granne 3, Krina T. Zondervan 2,3, Michael V. Holmes 4,5, Cecilia M. 4 \nLindgren1,2,3,4,6, Laura B. L. Wittemans1,3* 5 \n 6 \n1Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of 7 \nOxford, Oxford, United Kingdom 8 \n2Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, 9 \nOxford, United Kingdom 10 \n3Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, 11 \nUniversity of Oxford, Oxford, United Kingdom 12 \n4Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom 13 \n5Medical Research Council Population Health Research Unit, University of Oxford, Oxford, 14 \nUnited Kingdom 15 \n6Broad Institute of Harvard and MIT, Cambridge, MA, United States of America 16 \n 17 \n* Corresponding Author 18 \nE-mail: samvida@well.ox.ac.uk (S.S.V.); laura.wittemans@wrh.ox.ac.uk (L.B.L.W.)19 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n2 \n \nAbstract 20 \nBackground - Obesity is observationally associated with altered risk of many female reproductive 21 \nconditions. These include polycystic ovary syndrome (PCOS), abnormal uterine bleeding, 22 \nendometriosis, infertility, and pregnancy-related disorders. However, the roles and mechanisms of 23 \nobesity in the aetiology of reproductive disorders remain unclear.  24 \nMethods and Findings - We estimated observational and genetically predicted causal associations 25 \nbetween obesity, metabolic hormones, and female reproductive conditions using logistic 26 \nregression, generalised additive models, and Mendelian randomisation (two-sample, non-linear, 27 \nand multivariable) applied to data from UK Biobank and publicly available genome-wide 28 \nassociation studies (GWAS).  29 \nBody mass index (BMI), waist-hip ratio (WHR), and WHR adjusted for BMI (WHRadjBMI) were 30 \nobservationally (odds ratios (ORs) = 1.02 – 1.87 per 1 S.D. obesity trait) and  31 \ncausally (ORs = 1.06 – 2.09) associated with uterine fibroids (UF), PCOS, heavy menstrual 32 \nbleeding (HMB), and pre-eclampsia. Causal effect estimates of WHR and WHRadjBMI, but not 33 \nBMI, were attenuated compared to their observational counterparts. Genetically predicted visceral 34 \nadipose tissue mass was causal for the development of HMB, PCOS, and pre-eclampsia (ORs = 35 \n1.01 - 3.38). Increased waist circumference also posed a higher causal risk (ORs = 1.16 – 1.93) for 36 \nthe development of these disorders and UF than did increased hip circumference (ORs = 1.06 – 37 \n1.10). Leptin, fasting insulin, and insulin resistance each mediated between 20% -50% of the total 38 \ncausal effect of obesity on pre-eclampsia. Reproductive conditions clustered based on shared 39 \ngenetic components of their aetiological relationships with obesity. 40 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n3 \n \nConclusions - In this first systematic, large-scale, genetics-based analysis of the aetiological 41 \nrelationships between obesity and female reproductive conditions, we found that common indices 42 \nof overall and central obesity increased risk of reproductive disorders to heterogenous extents, 43 \nmediated by metabolic hormones. Our results suggest exploring the mechanisms mediating the 44 \ncausal effects of overweight and obesity on gynaecological health to identify targets for disease 45 \nprevention and treatment. 46 \nIntroduction 47 \nObesity is commonly understood as the excess accumulation of body fat which leads to increased 48 \nhealth risks. In women, body mass index (BMI) is associated with increased prevalence of 49 \ngynecological conditions, including excessive and abnormal menstrual bleeding (1, 2), 50 \nendometriosis and uterine fibroids (UF) (3, 4), polycystic ovary syndrome (PCOS) (5, 6), 51 \ncomplications of pregnancy such as pre-eclampsia and eclampsia (7), miscarriage (8, 9), and 52 \ninfertility (10, 11). These are often non-linear and heterogeneous relationships. While the risk of 53 \nanovulatory infertility and recurrent miscarriages are highest in obese women, underweight 54 \nwomen also have increased risk of infertility (9, 12). The association of BMI with endometriosis 55 \nvaries by disease severity, as women with advanced-stage endometriosis have lower BMI than 56 \nthose with minimal disease, and the inverse BMI-endometriosis association is stronger in women 57 \nwith infertility (13, 14). Finally, although the severity of PCOS and menstrual disorders increases 58 \nwith overall obesity, women presenting with these conditions are more likely to store fat in the 59 \nabdominal region, regardless of their BMI (2, 5).  60 \nObservational epidemiological studies are limited in assessing causality, due to confounding and 61 \nreverse causation. The Mendelian randomisation (MR) framework is a genetics-based instrumental 62 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n4 \n \nvariable approach that relies on the random and fixed assignment of genetic variants at conception 63 \nto estimate the causal effect size of genetically predicted exposures on an outcome. MR has 64 \npreviously indicated causal effects of genetically predicted BMI on the development of some 65 \nsubtypes of ovarian cancer (odds ratio (OR) = 1.29 per 5 units of BMI) (15), endometrial cancer 66 \n(OR = 2.06 per 5 units of BMI) (16), and PCOS (OR = 4.89 per 1 S.D. higher BMI) (17). However, 67 \nthe aetiological role of obesity and body fat distribution on many other female reproductive 68 \ndiseases has not been reported. It is especially relevant to investigate the effects of fat distribution, 69 \nas there are intricate metabolic and endocrine links between adipose tissue and female reproductive 70 \norgans. Yet, causal investigations of such relationships are lacking.  71 \nLeptin, which is a hormone secreted by adipocytes, and elevated in individuals with obesity, is 72 \nincreased in women with endometriosis, UF, and adverse pregnancy outcomes, even when 73 \nadjusted for BMI (7, 18-21). Obesity-induced insulin resistance additionally increases the risk and 74 \nseverity of PCOS and pre-eclampsia by dysregulating steroid hormone and metabolic pathways 75 \n(5, 22, 23). The dysregulation of sex hormones, including oestrogen and testosterone, is likely to 76 \nplay a role in the obesity-driven development of female reproductive disorders due to its close 77 \nassociations with body fat (22, 24). Yet, the causal impact of these factors in mediating the 78 \nrelationships between obesity and gynaecological diseases has not been detailed.  79 \nHere, we apply logistic regression, generalised additive models, two-sample, non-linear, and 80 \nmultivariable MR to dissect the relationships of overall obesity and body fat distribution with a 81 \nrange of female reproductive disorders, and investigate the mediating role of metabolic factors 82 \nincluding leptin and insulin.  83 \n 84 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n5 \n \nMethods 85 \nObservational associations in UK Biobank 86 \nUK Biobank (UKBB) is a prospective UK-based cohort study with approximately 500,000 87 \nparticipants aged 40-69 at recruitment on whom a range of medical, environmental, and genetic 88 \ninformation is collected (25). We included 257,193 individuals self-identifying as females of white 89 \nancestry in UKBB in our analyses. Baseline measurements of BMI (total body weight (kg) / 90 \nstanding height 2 (m 2)) and waist-to-hip ratio (WHR) (waist circumference (cm) / hip 91 \ncircumference (cm)), and WHR adjusted for BMI (WHRadjBMI) were used to estimate general 92 \nand central obesity, respectively. Cases of reproductive conditions were identified based on ICD9 93 \nand ICD10 primary and secondary diagnoses from hospital inpatient data, self-reported illness 94 \ncodes, and primary care records ( Table A in S1 Table). We fitted logistic regression models to 95 \nestimate the associations of BMI, WHR, and WHRadjBMI with prevalence of endometriosis 96 \n(7,703 cases, 249,490 controls), heavy menstrual bleeding (17,229 cases, 239,964 controls), 97 \ninfertility (2,194 cases, 254,999 controls), self-reported stillbirth, spontaneous miscarriage or 98 \ntermination (81,102 cases, 176,091 controls), PCOS (746 cases, 256,447 controls), pre-eclampsia 99 \n(2,242 cases, 254,951 controls), and uterine fibroids (19,192 cases, 238,001 controls). Case 100 \ndefinitions for pre-eclampsia included eclampsia cases to capture cases in which the former may 101 \nhave developed into the latter. For each disease, individuals not included in the case group were 102 \nused as controls. BMI, WHR and WHRadjBMI were adjusted for age, age-squared, assessment 103 \ncentre, and smoking status. The residuals were rank-based inverse normally transformed. Multiple 104 \ntesting correction was applied using the false discovery rate (FDR) to evaluate statistical 105 \nsignificance while minimising false negatives (26). 106 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n6 \n \nWe also tested associations without adjustment for smoking status, as it has previously been 107 \nsuggested that higher BMI increases risk of smoking (27) and adjustment for both could therefore 108 \ninduce collider bias. Adjustment for menopause status was not performed as up to 42% of women 109 \nwith reproductive disorders in UKBB report being unsure of their menopause status as compared 110 \nto 16% of women who do not have a recorded history or presence of a reproductive condition 111 \n(Table A in S1 Table).  112 \nTo evaluate the presence of non-linear observational associations between obesity and each 113 \nreproductive trait, fractional polynomial regression following the closed test procedure was 114 \nperformed using the mfp v1.5.2 R package (28). This algorithm tests for the presence of an overall 115 \nassociation, the likelihood of non-linearity, and selects the best-fitting fractional polynomial 116 \nfunction. We also fitted generalised additive models (GAM) to the same data, allowing for 117 \nsmoothing of the obesity trait with splines, using the mgcv 1.8-31 R package (29). All models were 118 \nadjusted for age, age-squared, assessment centre, and smoking status. Model fits were compared 119 \nwith Akaike’s Information Criterion (AIC) (30). 120 \nTwo-sample Mendelian Randomisation 121 \nGenetic instruments for BMI, WHR, and WHRadjBMI were selected based on the sentinel variants 122 \nat genome-wide significant loci ( P < 5E-9) reported in the largest publicly available European 123 \nancestry GWAS of Genetic Investigation of ANthropometric Traits (GIANT) and UKBB (max N 124 \nindividuals = 806,801) (31). Similarly, genetic instruments for predicted visceral adipose tissue 125 \n(VAT) mass (N individuals = 325,153) (32), waist circumference (N individuals = 462,166) and 126 \nhip circumference (N individuals = 462,117) (33), and waist-specific and hip-specific WHR (N 127 \nindividuals = 18,330) (34) were selected based on the largest publicly available GWAS.  128 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n7 \n \nThree instrument weighting strategies were considered where sex-stratified GWAS results were 129 \navailable: (i) SNPs from combined-sexes GWAS with combined-sexes weights (effect sizes), (ii) 130 \ncombined-sexes SNPs with female-specific weights, or (iii) female-specific SNPs with female-131 \nspecific weights. The method of female-specific SNPs with female-specific weights produced the 132 \nstrongest instruments as evaluated by F-statistics and was thus chosen for analysis (Table B in S1 133 \nTable). Additionally, due to concerns of ascertainment bias in UKBB (35, 36), sensitivity analyses 134 \nwith combined-sexes instruments (combined-sexes SNPs with combined-sexes weights) were also 135 \nperformed. 136 \nAssociations of the genetic instruments for obesity traits with female reproductive diseases were 137 \nobtained by performing a fixed-effect inverse-variance weighted meta-analysis of publicly 138 \navailable GWAS summary statistics from two large biobank projects - FinnGen and UKBB (37). 139 \nThe meta-analysis was performed using METAL (38) by matching the relevant ICD codes (Table 140 \nC in S1 Table) for the following traits: infertility (4,996 cases, 421,223 controls), pre-eclampsia 141 \n(2,711 cases, 480,373 controls), and uterine fibroids (21,835 cases, 456,551 controls). For 142 \nendometriosis, summary statistics were obtained by request from a recent European ancestry 143 \nGWAS (39) and meta-analysed as above with publicly available FinnGen and UKBB summary 144 \nstatistics (12,210 cases, 450,183  controls). For heavy menstrual bleeding (HMB) (9,813 cases, 145 \n210,946 controls), sporadic miscarriage, i.e. 1-2 miscarriages (50,060 cases, 174,109 controls), 146 \nand multiple consecutive miscarriage, i.e. >= 3 consecutive miscarriages (750 cases, 150,215 147 \ncontrols), publicly available summary statistics were obtained from recent European ancestry 148 \nGWAS that include UKBB individuals (3, 40). For PCOS, estimates were based on a fixed-effect 149 \ninverse variance-weighted meta-analysis of published GWAS summary statistics (38), publicly 150 \navailable GWAS results by FinnGen, and a European-ancestry GWAS run in UKBB using SAIGE 151 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n8 \n \n(11,186 cases, 273,812 controls). As a sensitivity analysis, all MR tests were performed using 152 \ndisease association estimates based on FinnGen only, where available, to alleviate bias due to 153 \nsample overlap between the exposure and outcome GWAS sources. 154 \nPower to detect MR associations was calculated using two methods, one designed for general two-155 \nsample MR (41) and the other for MR performed on binary outcomes (42). Briefly, these methods 156 \ncalculate power by accounting for GWAS sample size, proportion of cases in case-control GWAS, 157 \nand variance explained by genetic instruments for the exposure. The power to detect a true odds 158 \nratio (OR) association of 1.1 or more extreme at an unadjusted significance level of 0.05 was 159 \nestimated. 160 \nInstrument SNPs were extracted from the outcome GWAS results, harmonised for consistency in 161 \nthe alleles, and MR was performed using the TwoSample MR v0.5.4 R package (43). Three 162 \nmethods for MR - inverse-variance weighted (IVW), MR-Egger, and weighted median - were 163 \nevaluated, and the best method was selected via Rucker's framework (44). Briefly, this framework 164 \nadvises to choose the MR method with least heterogeneity as assessed by Cochran's Q-statistic, 165 \nwhile accounting for the trade-off between power and pleiotropy (45). Inverse-variance weighted 166 \nresults, which were the best method chosen by Rucker’s framework for all tested associations, are 167 \nreported in the main text, but results from all methods are calculated for robustness and displayed 168 \nin the supplementary information. Multiple-hypothesis testing correction was applied with the 169 \nFDR method and significance established at FDR < 0.05. MR-Egger intercept tests were performed 170 \nto detect horizontal pleiotropy, and single-SNP and leave-one-out analyses were used to identify 171 \noutlier SNPs driving relationships (43). 172 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n9 \n \nReverse MR for obesity traits regressed on female reproductive conditions was performed as 173 \ndetailed above. Genetic instruments for endometriosis (14,926 cases, 189,715 controls) (46), 174 \nPCOS (10,174 cases, 103,164 controls) (47), and uterine fibroids (20,406 cases, 223,918 controls) 175 \n(3) were constructed from index variants identified by the largest European ancestry GWAS for 176 \neach trait. Instrument strength was assessed by F-statistics (endometriosis, 16 SNPs, F = 5.13; 177 \nPCOS, 14 SNPs, F = 41.6; UF, 29 SNPs, F = 11.1). Associations of genetic instruments for these 178 \nreproductive conditions with BMI, WHR, and WHRadjBMI were obtained from female-specific 179 \nsummary statistics from the above-mentioned GIANT-UKBB meta-analysis (31). 180 \nNon-linear Mendelian Randomisation 181 \nFor non-linear MR analyses, we selected female UK Biobank participants of white British ancestry 182 \nwith no second-degree or closer relatives in the study, as identified by the UKBB team (48), to 183 \navoid violation of the MR assumption of random assignment of genetic variants; 207,705 women 184 \nwere retained following this selection. Genetic instruments for BMI were constructed for each 185 \nindividual using female-specific index variants from Pulit et al.'s GIANT-UKBB meta-analysis 186 \n(31). The instruments for BMI explained 4.15% of trait variance after adjustment for age, age-187 \nsquared, smoking status, assessment centre, genotyping array, and the first ten genetic principal 188 \ncomponents to account for population stratification. Binomial non-linear MR, a method designed 189 \nto assess causal relationships in different exposure strata while avoiding collider bias, was 190 \nperformed using the fractional polynomial method with 100 quantiles and the piecewise linear 191 \nmethod with 10 quantiles (49). Outcomes were restricted to female reproductive disorders with 192 \nprevalence > 5% in UKBB (i.e. HMB, miscarriage, and UF) to maintain sufficient sample sizes in 193 \neach quantile to estimate localised average causal effects. We assessed non-linearity with the 194 \nfractional polynomial non-linearity and Cochran's Q tests, and tested heterogeneity of the 195 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n10 \n \ninstrumental variable (IV) with the Cochran's Q and trend tests. All analyses were performed with 196 \nthe nlmr v2.0 R package (49).  197 \nMR with Mediation Analysis 198 \nTo investigate the extent to which obesity affects female reproductive disorders via hormone-199 \nrelated mediators, two-step MR by the product of coefficients method was performed using GWAS 200 \nsummary statistics. This method was chosen as female reproductive disease phenotypes are binary 201 \noutcomes with disease prevalence < 10% in UK Biobank, for which two-step MR provides the 202 \nleast biased estimates of mediation (50). Summary statistics for leptin (N = 33,987) (51), fasting 203 \ninsulin (N = 51,750) (52), and insulin sensitivity (N = 16,753) (53) were obtained from publicly 204 \navailable European-ancestry GWAS sources that do not include samples from UKBB to minimise 205 \nbias from sample overlap (Table B in S1 Table). 206 \nIn the first step of two-step MR, the mediators were regressed on obesity-related exposures using 207 \nsummary statistics MR methods described above. The direction of causality for all relationships 208 \nwas confirmed with the MR-Steiger directionality test (54) and reciprocal MR with mediator 209 \ninstruments and obesity-related exposures as outcomes were performed to ensure correct direction 210 \nof causality. In the second step, multivariable MR (MVMR) was performed using combined 211 \ngenetic instruments for each obesity trait and hormone to estimate the independent effect of the 212 \nmediator on each outcome after adjusting for the value of the exposure; and to estimate the 213 \nindependent effect of the exposure on outcome when adjusted for the value of each mediator. This 214 \nwas only done for traits where the total unadjusted effect of the exposure on the outcome was 215 \nsignificant (FDR < 0.05). Odds ratios (ORs) for binary outcomes were converted to log ORs to 216 \ncalculate mediated effect by the product of coefficients method. The proportion of effect mediated 217 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n11 \n \nwas calculated by dividing indirect effect over total effect. Standard errors were estimated with the 218 \ndelta method (55). 219 \nDisease and SNP Clustering 220 \nTo assess similarities in the aetiological relationships of different reproductive conditions with 221 \nobesity traits, we projected single SNP causal estimates for BMI, WHR and WHRadjBMI on the 222 \nreproductive traits, estimated using the Wald ratio, in a two-dimensional space using UMAP. SNPs 223 \nwere annotated to their nearest gene with SNPsnap (56). 224 \nTo identify the genetic instruments driving the causal obesity-reproductive trait association, and 225 \nidentify clusters of SNPs with distinct causal effect sizes, we clustered SNPs by the magnitude of 226 \ntheir causal effect using mixture model clustering in the MR-Clust v0.1.0 R package (56). For each 227 \nobesity trait-reproductive disease pair, the algorithm distinguishes the genetic instruments for the 228 \nobesity traits that do not have an effect on the disease (“null cluster”), from those which have a 229 \nsimilar scaled effect on the disease (the “substantial clusters”), and those that have a scaled effect 230 \nthat cannot be grouped with other variants (“junk cluster”).  231 \nCode availability 232 \nAll scripts used in analyses are deposited at 233 \nhttps://github.com/lindgrengroup/obesity_femrepr_MR 234 \n 235 \n 236 \n 237 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n12 \n \nResults 238 \nObesity traits are observationally associated with female reproductive diseases in UK 239 \nBiobank 240 \nBMI at baseline assessment (age 40-69) was positively associated with the prevalence of most 241 \nfemale reproductive disorders in UKBB, with the strongest association observed between BMI and 242 \npre-eclampsia (odds ratio (OR) per 1 S.D. higher BMI = 1.87, P = 1.90E-64). Associations with 243 \nWHRadjBMI were null or lower than those for WHR (ORs for WHRadjBMI v. WHR for PCOS 244 \n= 1.06 v. 1.48, pre-eclampsia = 1.02 v. 1.13, endometriosis = 1.02 v. 1.08, HMB = 1.06 v. 1.14, 245 \nand UF = 1.02 v. 1.08), indicating that BMI may be driving many of the associations between 246 \nWHR and female reproductive diseases (Figures 1 & 2, Table 1). Infertility was the only disorder 247 \nfor which BMI (OR = 0.894, P = 2.16E-07) and WHR (OR = 0.927, P = 4.08E-04) were inversely 248 \nassociated with disease. 249 \nNon-linear models explained the associations of BMI with many reproductive disorders better than 250 \nlinear models. We observed inverted-U and plateau relationships with endometriosis (linear AIC 251 \n= 67091, generalised additive model (GAM) AIC = 67051), uterine fibroids (linear AIC = 134160, 252 \nGAM AIC = 134094), HMB (linear AIC = 116687, GAM AIC = 116636), miscarriage (linear AIC 253 \n= 314828, GAM AIC = 314819), and pre-eclampsia (linear AIC = 24826, GAM AIC = 24814) 254 \n(Figure 1, Table D in S1 Table ). All three obesity traits displayed U-shaped relationships with 255 \nPCOS.  256 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n13 \n \nObservational estimates between all obesity traits and female reproductive disorders did not differ 257 \nwith or without adjustment for smoking status (Table E in S1 Table). Statistical significance after 258 \nmultiple-testing correction was established at FDR < 0.05, unadjusted P < 0.04.  259 \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n14 \n \n \nTable 1: Observational and genetic associations between obesity traits and female reproductive \ndisorders. \nDiagnosis Obesity \n  trait \nLogistic regression Mendelian randomisation \nOR (95% CI) Unadj. p-\nvalue # SNPs \nOR (95% CI)  \nper 1 S.D. higher \nobesity trait \nUnadj. p-\nvalue  \nEndometriosis \nBMI 1.14 (1.12 - 1.16) 4.45E-29 264 1.04 (0.902 - 1.19) 0.606 \nWHR 1.07 (1.05 - 1.10) 1.00E-09 190 1.24 (1.05 - 1.47) 1.00E-02 \nWHRadjBMI 1.02 (0.99 - 1.04) 0.188 250 1.24 (1.08 - 1.41) 1.67E-03 \nHeavy \nmenstrual \nbleeding \nBMI 1.20 (1.18 - 1.22) 7.78E-117 268 1.01 (1.004 - 1.013) 3.62E-04 \nWHR 1.15 (1.13 - 1.16) 8.35E-69 191 1.01 (1.005 - 1.02) 1.42E-04 \nWHRadjBMI 1.06 (1.04 - 1.07) 5.30E-13 251 1.01 (1.002 - 1.011) 5.20E-03 \nInfertility \nBMI 0.894 (0.852 - 0.936)  2.16E-07 267 0.982 (0.810 - 1.19)  0.856 \nWHR 0.927 (0.884 - 0.969)  4.08E-04 191 1.10 (0.901 - 1.34) 0.355 \nWHRadjBMI 0.971 (0.929 - 1.01) 0.176 251 1.21 (1.03 - 1.43) 0.0214 \nMiscarriage BMI 1.03 (1.02 - 1.04) 4.28E-14 265 1.06 (1.01 - 1.12) 0.0238 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n15 \n \n(sporadic) WHR 1.04 (1.04 - 1.05) 3.82E-24 190 0.998 (0.947 - 1.05) 0.933 \nWHRadjBMI 1.03 (1.02 - 1.04) 1.87E-13 250 0.996 (0.953 - 1.04) 0.878 \nMiscarriage \n  (multiple \nconsecutive) \nBMI     254 0.917 (0.570 - 1.48)  0.720 \nWHR     184 1.20 (0.743 - 1.92) 0.462 \nWHRadjBMI     240 0.978 (0.662 - 1.44)  0.911 \nPCOS \nBMI 1.87 (1.80 - 1.94) 1.90E-64 268 1.13 (1.08 - 1.19) 7.60E-08 \nWHR 1.48 (1.41 - 1.55) 3.32E-26 191 1.07 (1.02 - 1.11) 4.30E-03 \nWHRadjBMI 1.06 (0.986 - 1.13) 0.124 251 1.02 (0.990 - 1.06) 0.222 \nPre-eclampsia \nBMI 1.25 (1.21 - 1.29) 3.85E-25 266 2.09 (1.60 - 2.73) 5.16E-08 \nWHR 1.13 (1.09 - 1.17) 4.97E-09 191 1.57 (1.16 - 2.10) 2.92E-03 \nWHRadjBMI 1.02 (0.982 - 1.07) 0.272 250 1.43 (1.13 - 1.80) 2.46E-03 \nUterine \nfibroids \nBMI 1.14 (1.12 - 1.15) 2.43E-63 268 1.21 (1.08 - 1.35) 9.93E-04 \nWHR 1.08 (1.06 - 1.09) 2.75E-23 191 1.24 (1.10 - 1.41) 6.20E-04 \nWHRadjBMI 1.02 (1.01 - 1.04) 2.94E-03 251 1.17 (1.06 - 1.29) 1.95E-03 \n \nBody fat distribution is causally related to risk of female reproductive diseases 260 \nTwo-sample MR indicated that higher genetically predicted WHR and/or WHRadjBMI are causal 261 \nfor higher risk of pre-eclampsia (OR per 1 S.D. higher WHR = 1.57, P = 2.92E-03; WHRadjBMI 262 \n= 1.43, P = 2.46E-03), endometriosis (WHR = 1.24, P = 1.00E-02; WHRadjBMI = 1.24, P = 263 \n1.67E-03), uterine fibroids (WHR = 1.24, P = 6.20E-04; WHRadjBMI = 1.17, P = 1.95E-03), 264 \ninfertility (WHRadjBMI = 1.21, P = 2.14E-02), and PCOS (WHR = 1.07, P = 4.30E-03) (Table 265 \n1, Figure 2). The causal estimates of WHR and WHRadjBMI on most reproductive disorders were 266 \nhigher than their observational counterparts. While genetically predicted BMI also increased risk 267 \nof most female reproductive disorders (ORs per 1 S.D. higher BMI = 1.01 for HMB to 2.09 for 268 \npre-eclampsia), MR estimates of associations between BMI and HMB (OR = 1.01, P = 3.62E-04), 269 \nendometriosis (OR = 1.04, P = 0.606), and PCOS (OR = 1.13, P = 7.60E-08) were much attenuated 270 \ncompared to observational results. 271 \nGenetically predicted visceral adipose tissue (VAT) mass was causal for the development of pre-272 \neclampsia (OR per 1 kg increase in predicted VAT mass = 3.08, P = 6.65E-07), PCOS (OR = 1.15, 273 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n16 \n \nP = 3.24E-05), and HMB (OR = 1.01, P = 0.0125) ( Figure 3 and Table F in S1 Table ). The 274 \ndifferential effect of body fat distribution on female reproductive traits was further reflected in the 275 \nheterogeneous causal effects of waist circumference (WC) and hip circumference (HC) on disease 276 \ndevelopment. Increased WC posed a higher risk than did increased HC for pre-eclampsia (ORs per 277 \n1 S.D. higher WC = 1.93 v. HC = 1.40, heterogeneity P-het = 0.0373), uterine fibroids (WC = 1.32 278 \nv. HC = 1.12, P-het = 7.70E-03), and PCOS (WC = 1.16 v. HC = 1.10, P-het = 0.0325). We did 279 \nnot see this heterogeneity in observational associations (all P-het > 0.164) (Figure 3 and Table F 280 \nin S1 Table).  281 \nNo significant causal effects were found when restricting MR analyses to genetic instruments with 282 \na specific effect of waist but not hip circumference, or on hip but not waist circumference ( Table 283 \nH in S1 Table  and S1 Figure), but the power based on these instruments to detect odds ratios 284 \nmore extreme than 1.1 was limited to 5% - 20% (Table I in S1 Table). No non-linear MR models 285 \nexplained the causal effects of BMI on any reproductive disorder better than linear MR models 286 \n(S3 Figure). However, the power to detect non-linear effects was severely limited by the lower 287 \nnumber of cases in each quantile of the BMI distribution in which analyses were run. Statistical 288 \nsignificance after multiple-testing correction was established at FDR < 0.05, unadjusted P < 0.03.  289 \nSNPs identified in female-only GWAS and with female-specific weights for BMI, WHR, and 290 \nWHRadjBMI (29) were found to be the strongest instruments, with F-statistics > 60; instrument 291 \nstrength for waist- and hip-circumference was > 45 ( Table B in S1 Table ). We found MR 292 \nestimates to be consistent between the different MR methods (heterogeneity P > 0.321), when 293 \nbased only on FinnGen summary statistics (heterogeneity P > 0.163), or with combined-sex 294 \ninstruments (heterogeneity P > 0.999), suggesting that the findings were not dependent on the 295 \nadopted MR method, or substantially biased due to sample overlap between exposure and outcome 296 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n17 \n \nGWAS sources or ascertainment bias in UKBB (35, 36) (Tables F, J, K in S1 Table & S1 & S2 297 \nFigure).  298 \nWe did not find evidence for reverse causal effects of endometriosis, PCOS, or uterine fibroids on 299 \nBMI, WHR, and WHRadjBMI ( Table L in S1 Table ). However, these estimates may be biased 300 \nby weak genetic instruments for endometriosis (F-statistic = 5.13) and UF (F-statistic = 11.1), and 301 \nhigh heterogeneity for all associations (Cochran’s Q P < 4.71E-06). We were limited in assessing 302 \nreverse causality of other female reproductive conditions on obesity traits by the lack of large-303 \nscale publicly available GWAS summary statistics. 304 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n18 \n \n \nLeptin and insulin mediate the causal effects of obesity on female reproductive disorders 305 \nWe applied a series of MR-based mediation analyses (58, 59) to study the role of hormonal factors 306 \n- leptin and insulin resistance - in mediating the causal relationships between obesity and female 307 \nreproductive health (Figure 4A). The effects of BMI, WHR, and WHRadjBMI on endometriosis, 308 \nPCOS, pre-eclampsia, and UF were attenuated (95% CIs of ORs all contain 1) when adjusted for 309 \nleptin, fasting insulin, or insulin sensitivity as measured by the modified Stumvoll Insulin 310 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n19 \n \nSensitivity Index (ISI) (Figure 4B, Table M in S1 Table). Furthermore, these hormones influence 311 \nrisk of pre-eclampsia independently of obesity. After adjustment for BMI, leptin  (ꞵ = 0.887, P = 312 \n1.28E-04), fasting insulin (ꞵ = 1.42, P = 1.29E-03), and ISI (ꞵ = -0.503, P = 2.99E-03) were all 313 \nassociated with risk of pre-eclampsia (Figure 4C, Table 2). Similarly, fasting insulin (ꞵ = 1.27, P 314 \n= 7.15E-03) and ISI (ꞵ = -0.793, P = 1.46E-05) had causal effects on pre-eclampsia upon 315 \nadjustment for WHR. Leptin, fasting insulin, and ISI did not have significant causal effects on 316 \nendometriosis, PCOS, or UF after adjustment for obesity traits. Statistical significance after 317 \nmultiple-testing correction was established at FDR < 0.05, unadjusted P < 0.01. 318 \nWe calculated the proportion of total obesity effect mediated by the above hormones for disorders 319 \nwhere the effects of obesity traits and mediators were significant at unadjusted P < 0.05.  We found 320 \nthat leptin (50.2% of effect of BMI on pre-eclampsia), fasting insulin (between 27.7% - 36.6% of 321 \ndifferent effects), and ISI (between 19.1% - 50.1% of different effects) each mediated the total 322 \ncausal effect of obesity traits on female reproductive disorders (Table 3).   323 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n20 \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n21 \n \nTable 2: Multivariable MR estimates of female reproductive disorders regressed on metabolic \nhormones, adjusted for obesity traits. \nOutcome Exposure Adjusted for ꞵ +/- S.E. per  \n1 S.D. higher exposure \nUnadjusted P-\nvalue \nEndometriosis Leptin \nUnadjusted -0.0745 +/- 0.188 0.692 \nBMI -0.0457 +/- 0.102 0.654 \nWHR -0.131 +/- 0.126 0.297 \nWHRadjBMI -0.0964 +/- 0.128 0.453 \nUterine fibroids Leptin \nUnadjusted -0.132 +/- 0.323 0.683 \nBMI 0.0881 +/- 0.102 0.389 \nWHR -4.12E-03 +/- 0.138 0.976 \nWHRadjBMI -0.0834 +/- 0.14 0.550 \nPCOS Leptin \nUnadjusted -0.134 +/- 0.0701 0.0567 \nBMI 0.0491 +/- 0.0409 0.231 \nWHR 8.44E-03 +/- 0.0476 0.859 \nWHRadjBMI -0.0310 +/- 0.0473 0.511 \nPre-eclampsia Leptin \nUnadjusted 0.181 +/- 0.501 0.718 \nBMI 0.887 +/- 0.232 1.28E-04 \nWHR 0.468 +/- 0.309 0.129 \nWHRadjBMI -0.102 +/- 0.333 0.760 \nEndometriosis Fasting insulin \nUnadjusted 0.150 +/- 0.247 0.544 \nBMI 0.0996 +/- 0.209 0.634 \nWHR 0.186 +/- 0.254 0.465 \nWHRadjBMI 0.423 +/- 0.239 0.0770 \nUterine fibroids Fasting insulin \nUnadjusted 0.0263 +/- 0.342 0.939 \nBMI 0.311 +/- 0.183 0.090 \nWHR 0.313 +/- 0.209 0.135 \nWHRadjBMI 0.262 +/- 0.202 0.195 \nPCOS Fasting insulin \nUnadjusted -0.0334 +/- 0.158 0.833 \nBMI 0.0283 +/- 0.0772 0.714 \nWHR 0.0932 +/- 0.0788 0.237 \nWHRadjBMI 0.0289 +/- 0.0726 0.690 \nPre-eclampsia Fasting insulin \nUnadjusted -0.0379 +/- 0.873 0.965 \nBMI 1.42 +/- 0.441 1.29E-03 \nWHR 1.27 +/- 0.473 7.15E-03 \nWHRadjBMI 0.476 +/- 0.467 0.308 \nEndometriosis Insulin sensitivity \nUnadjusted -0.0940 +/- 0.133 0.478 \nBMI -0.101 +/- 0.0864 0.244 \nWHR -0.131 +/- 0.100 0.192 \nWHRadjBMI -0.102 +/- 0.106 0.336 \n \nUterine fibroids \n \nInsulin sensitivity \nUnadjusted -0.223 +/- 0.137 0.103 \nBMI -0.0951 +/- 0.0716 0.184 \nWHR -0.202 +/- 0.0830 0.0151 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n22 \n \nWHRadjBMI -0.120 +/- 0.0855 0.165 \nPCOS Insulin sensitivity \nUnadjusted -0.0239 +/- 0.0616 0.698 \nBMI -0.0257 +/- 0.0290 0.376 \nWHR -0.0355 +/- 0.0293 0.226 \nWHRadjBMI -0.0443 +/- 0.0294 0.132 \nPre-eclampsia Insulin sensitivity \nUnadjusted -0.354 +/- 0.295 0.229 \nBMI -0.503 +/- 0.169 2.99E-03 \nWHR -0.793 +/- 0.183 1.461E-05 \nWHRadjBMI -0.519 +/- 0.205 0.0110 \n \nTable 3: Proportion of effect mediated for exposure - mediator - outcome relationships. \nExposure Mediator Outcome \nLog OR  \n(S.E.) per 1 S.D. higher exposure \nUnadj. pval  \nProportion of \nEffect Mediated \n(95% CI) Exposure - \nOutcome \nMediator - \nOutcome \nExposure - \nMediator \nBMI \nLeptin \nPre-eclampsia \n0.737  \n(0.135)  \nP = 5.16E-08 \n0.887  \n(0.232) \nP = 1.28E-04   \n0.417  \n(0.0262) \nP = 7.94E-57 \n50.2%  \n(18.2% - 82.2%) \nFasting \ninsulin \n1.42  \n(0.441) \nP = 1.29E-03 \n0.144  \n(0.0141) \nP = 1.61E-24 \n27.7%  \n(7.40% - 48.0%) \nInsulin \nsensitivity \n-0.503  \n(0.169) \nP = 2.99E-03  \n-0.281  \n(0.0490) \nP = 9.83E-09 \n19.1%  \n(3.33% - 35.0%) \n \nWHR \nFasting \ninsulin \nPre-eclampsia \n0.449  \n(0.151) \nP = 2.92E-03  \n1.27  \n(0.473) \nP = 7.15E-03  \n0.129  \n(0.0159) \nP = 5.04E-16 \n36.6%  \n(0% - 73.7%) \nInsulin \nsensitivity \n-0.793  \n(0.183) \nP = 1.46E-05  \n-0.283  \n(0.0601) \nP = 2.44E-06   \n50.1%  \n(4.98% - 95.3% \nInsulin \nsensitivity Uterine fibroids \n0.218  \n(0.0637) \nP = 6.20E-04  \n-0.202  \n(0.083) \nP = 1.51E-02  \n-0.283  \n(0.0601) \nP = 2.44E-06  \n26.2%  \n(0% - 54.4%) \nWHRadjBMI Insulin \nsensitivity Pre-eclampsia \n0.358  \n(0.118) \nP = 2.46E-03  \n-0.519  \n(0.205) \nP = 1.14E-02  \n-0.168  \n(0.0427) \nP = 8.21E-05 \n24.4%  \n(0% - 51.8%) \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n23 \n \nOther metabolic and hormone pathways may drive the aetiological relationships of obesity 324 \nwith female reproductive diseases 325 \nWe assessed the similarities in the aetiological relationships of different reproductive conditions 326 \nwith obesity, by projecting the single SNP causal estimates for BMI, WHR and WHRadjBMI on 327 \nthe reproductive traits in a two-dimensional space using UMAP ( Figure 5A ). The UMAP 328 \nprojections based on all obesity traits clustered endometriosis and UF together with infertility and 329 \nHMB, which were further separated from miscarriage (sporadic and multiple consecutive). While 330 \nPCOS and pre-eclampsia were grouped closely in UMAP plots of the effect of WHR and 331 \nWHRadjBMI variants, they were separated by BMI-associated variants. This reflects a shared 332 \ngenetic component of the aetiological role of general and central obesity in the three groups of 333 \nreproductive conditions.     334 \nWe further examined if different aspects of obesity play an aetiological role in different 335 \nreproductive conditions. For each obesity trait-reproductive disease pair, we grouped the genetic 336 \ninstruments for the obesity traits by those that do not have an effect on the disease (“null cluster”), 337 \nthose which have a similar scaled effect on the disease (the “substantial clusters”), and those that 338 \nhave a scaled effect that cannot be grouped with other variants (“junk cluster”) using MRClust 339 \n(57). One substantial cluster was identified for each pair of obesity traits and reproductive 340 \nconditions. The only exception to this was with WHRadjBMI and UF, for which two substantial 341 \nclusters were identified, one with positive causal effect and the other with negative effect ( S4 342 \nFigure and Table N in S1 Table ). Of the 4 SNPs in the negative effect cluster, rs2277339 343 \n(missense variant in PRIM1 and upstream of HSD17B6, involved in steroid biosynthesis) is 344 \nassociated with primary ovarian insufficiency, early menopause, and PCOS (60, 61), and 345 \nrs11694173 is intronic to THADA, which is also associated with PCOS (47).  On the other hand, 4 346 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n24 \n \nof 10 SNPs in the positive effect cluster are associated with metabolic traits - rs12328675 and 347 \nrs2459732 with circulating leptin (62), rs6905288 with type 2 diabetes and thyroid stimulating 348 \nhormone (63, 64), and rs4686696 is intronic to insulin-like growth factor IGF2BP2. 349 \nSNPs with high probability of belonging to the substantial cluster (≥ 80% probability) were 350 \ngenerally unique to each obesity-disease relationship, with no more than 2 variants shared between 351 \nany two clusters ( Figure 5B). However, 6 BMI index SNPs had positive causal effect estimates 352 \nfor both PCOS and pre-eclampsia, including rs1121980 in the adipose-associated gene FTO and 353 \nrs7498665 in SH2B1, linked to insulin resistance in obesity. The BMI-associated variant 354 \nrs7084454 (intronic to MLLT10) was shared by substantial clusters for PCOS, endometriosis, and 355 \nUF, while rs114760566 (mapped to HMGA1, associated with type 2 diabetes and multiple 356 \nlipomatosis) was shared by endometriosis and UF. We evaluated the biological effect of the top 357 \nSNPs in each substantial cluster with the DEPICT algorithms for pathway enrichment and gene 358 \nprioritisation. We recapitulated the known associations of GEMIN5 subnetwork enrichment in 359 \nSNPs causal for BMI-PCOS, which has previously been implicated in the aetiology of PCOS (65). 360 \nGene prioritisation for WHR-endometriosis causal SNPs highlighted TBX15, an important 361 \nmesodermal transcription factor with roles in endometrial and ovarian cancer (66, 67). 362 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n25 \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n26 \n \nDiscussion 363 \nIn this first systematic genetics-based causal investigation of the aetiological role of obesity in 364 \nfemale reproductive health, we report evidence that common indices of obesity increase risk of a 365 \nbroad range of reproductive conditions whose effects may be non-uniform across the obesity 366 \nspectrum. The strongest effect of generalised obesity was found for pre-eclampsia, while more 367 \nmodest effects were observed for nearly all other studied conditions. We identified endocrine 368 \nmechanisms, including those related to leptin and insulin resistance, as potential drivers of 369 \naetiological relationships of both generalised and central obesity with female reproductive health. 370 \nFinally, we found genetic evidence that certain groups of reproductive conditions, such as UF and 371 \nendometriosis, may share a mechanistically similar relationship with obesity.  372 \nOur findings highlight that the relationships between obesity and female reproductive disorders 373 \nare (i) non-uniform in their nature and strength, and (ii) observationally non-linear across the 374 \nobesity spectrum. We report substantial differences in the causal effect estimates of BMI on 375 \nreproductive diseases, with each S.D. increase in BMI doubling the risk of pre-eclampsia, but more 376 \nmoderately (ORs = 1.01 - 1.25 for PCOS, miscarriage, UF, HMB) or not at all (infertility, 377 \nendometriosis) affecting other conditions. Conversely, central fat distribution independent of BMI 378 \nshowed substantial genetically predicted effects on both infertility and endometriosis (ORs per 1 379 \nS.D. increase in WHRadjBMI = 1.21 - 1.46) as well as on pre-eclampsia and UF (ORs = 1.17 - 380 \n1.43), but not on PCOS, HMB, and miscarriage. These findings highlight that the aetiological role 381 \nof obesity in female reproductive diseases is heterogeneous in its effect strength, and may be driven 382 \nby overall adiposity (PCOS, HMB, and miscarriage), isolated central obesity (infertility and 383 \nendometriosis), or by both generalised and central obesity (pre-eclampsia and UF).  384 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n27 \n \nFor several reproductive conditions, we found substantial differences between the observational 385 \nand genetically predicted causal effect estimates, which may indicate a bi-directional relationship 386 \nbetween obesity and reproductive health. For instance, while the observational analyses suggested 387 \nan 87% increase in PCOS risk per S.D. higher BMI, the MR analyses indicated that each S.D. 388 \nhigher BMI causally increases PCOS risk by only 13%. Similarly, 1 S.D. higher WHR and 389 \nWHRadjBMI causally increase endometriosis risk by 24%, while the observational analyses 390 \nsuggest a more modest increase in risk of 7% and 2% respectively. This discrepancy may in part 391 \nbe due to reverse causality, which we were not powered to detect in this study, as the number and 392 \nstrength of the available genetic instruments for reproductive conditions is substantially lower than 393 \nthose for BMI and WHR. The obesity traits upon which the observational analyses were based 394 \nwere measured at ages 44-67, which was for most conditions likely to be several years or decades 395 \nafter women developed the condition, and often post-menopause. While our observational analyses 396 \nadjusted for the effect of age on obesity traits, adjusting for menopause status proved to be 397 \nunreliable as up to 42% of women with reproductive diseases in UKB were unsure of their 398 \nmenopause status, as opposed to 16% of female participants without a diagnosis for any of the 399 \nstudied conditions. The observational estimates may therefore capture both the effect of obesity 400 \non disease risk as well as any downstream effects of the disease or commonly used treatments on 401 \nbody weight and fat distribution. For instance, the large observational effect of BMI on PCOS 402 \nprevalence may reflect both a causal effect of obesity on disease risk (68), as captured by the 403 \ngenetically predicted effect, as well as weight gain as a consequence of PCOS (69). Other potential 404 \ncontributing factors to the differences between genetic and observational estimates are 405 \nconfounding by unmeasured variables which lead to inflated observational associations (70, 71), 406 \nreferral bias wherein obesity status affects the likelihood of receiving a diagnosis (72, 73), or 407 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n28 \n \ndifferences in pre- and post-menopausal weight and body fat distribution not captured by age (74). 408 \nFinally, while the observational relationships between obesity and some female reproductive 409 \ndisorders were non-linear, we did not find non-linearity in the causal effects of BMI on these 410 \ndiseases. The non-linear MR analyses were likely under-powered to detect associations with few 411 \ncases in each quantile of the BMI spectrum.  412 \nWe noted that genetic estimates for the effect of fat distribution were not similarly attenuated when 413 \ncompared to BMI effects. This disparity may be due to the differing impacts of overall and 414 \nabdominal (central) adiposity, as the latter is thought to be biologically more directly linked to 415 \nfemale reproductive health than generalised obesity, via pathways including insulin resistance and 416 \nhyper-androgenaemia (5, 75-77). Supporting the stronger effect of central body fat, we also 417 \nreported higher causal effects of waist than hip circumference with HMB, PCOS, pre-eclampsia, 418 \nand UF. Genetically predicted visceral adipose tissue mass increased risk of PCOS and pre-419 \neclampsia, in line with observational studies (78). VAT mass is also observationally associated 420 \nwith uterine fibroids (76), yet we did not find a significant causal effect of genetically predicted 421 \nVAT mass on development of UF, which may suggest a bi-directional or reverse causal 422 \nrelationship.  423 \nEndometriosis and infertility were the only reproductive conditions which did not show a 424 \nconsistently positive link with obesity. The modest observational associations of both BMI and 425 \nWHR with higher endometriosis prevalence in UKBB contradict previous studies, including 426 \nprospective cohort studies, which reported that lower BMI was associated with increased disease 427 \nprevalence (14, 79, 80). The positive association with endometriosis may in part be due to weight 428 \ngain as a consequence of the disease, for instance due to hormonal treatments (81-83), chronic pain 429 \n(84), inflammation (85), or earlier onset of menopause (86). We however did not find evidence 430 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n29 \n \nthat generalised obesity plays a causal role in the aetiology of endometriosis, which suggests that 431 \nthe observational finding reflects a reverse causal relationship. Conversely, the positive genetically 432 \npredicted effect of WHRadjBMI on endometriosis risk indicates a causal role for abdominal fat 433 \ndistribution. For infertility, we observe a similar divergence between the observational and 434 \ngenetically predicted effects of obesity traits, with BMI showing a negative observational 435 \nassociation, but WHRadjBMI a genetically predicted positive association. The causes of female 436 \ninfertility are multiple, ranging from PCOS (87) and anovulation (88), to tubal disease (89), 437 \nendometriosis (90), low oocyte quality (91), hormonal and immunological dysfunction (92-95), 438 \nand yet unknown mechanisms. Each of these may have distinct and complex relationships with 439 \nobesity, which cannot be captured by studying the links with infertility of any cause. Nonlinear 440 \neffects, such as the increased association of under- and overweight with incidence of infertility 441 \n(12, 96), may also obscure these estimates, although our observational analyses did not provide 442 \nevidence for a non-linear relationship. 443 \nWe conducted the first genetics-based investigation of the mediating hormonal pathways 444 \nunderlying the causal relationships between obesity and female reproductive health. We identify 445 \nmechanisms related to insulin resistance and leptin as mediators of the effects of obesity traits on 446 \nUF and pre-eclampsia. The latter is consistent with hypotheses that obese women with metabolic 447 \ndysregulation are at highest risk of developing hypertensive disorders of pregnancy via angiogenic 448 \nand pro-inflammatory mechanisms. Increased circulating leptin may have a vasoconstructive, 449 \nhypertensive effect, which may be worsened by attenuation of insulin-induced vasorelaxation and 450 \nincreased levels of TNF-alpha and IL6 (7, 21).  451 \nFinally, genetic clustering of female reproductive conditions revealed common genetic causes of 452 \nobesity on endometriosis, UF, and HMB, which are known to share mechanisms of development 453 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n30 \n \n(3, 97). The projection of infertility with these diseases merits following up on the genetic basis of 454 \nendometriosis-related infertility with an eye to prevention and treatment.  455 \nThe main strength of our work is the systematic approach to characterising the relationship 456 \nbetween a broad range of obesity traits and common female reproductive conditions using both 457 \nobservational and genetic approaches. All observational associations were estimated in the same 458 \nlarge-scale cohort study, which tends to lead to less biased estimates than case-control studies upon 459 \nwhich most previous results were based. Moreover, we conducted the first genetics-based 460 \nmediation analyses to pinpoint the mechanisms driving the causal effect of obesity on risk of 461 \nreproductive diseases.  462 \nReproductive conditions remain underdiagnosed and underreported in the UK, which was reflected 463 \nin their low prevalence among female UKBB participants ( Table A in S1 Table ). This posed a 464 \nlimitation to our analyses in UK Biobank by reducing power to identify significant associations. 465 \nFor this reason, we opted to use broad case categories, such as infertility of any cause, as we had 466 \ninsufficient power and information to examine conditions by sub-types. Secondly, we restricted 467 \nour analyses to women of genetically European ancestry, due to a lack of genetic data on women 468 \nof other ancestries. Many of the reproductive diseases included here, with uterine fibroids being 469 \nthe most notable example (98, 99), are more prevalent in non-European populations and our results 470 \nmay not be transferable to women of other ancestries (100-102), which emphasises the urgent need 471 \nto set up large-scale studies similar to UK Biobank on participants of non-European ancestry. We 472 \nwere further limited in investigations of metabolic, hormonal, and inflammatory mediating 473 \nmechanisms by a lack of publicly available GWAS summary statistics for these traits. Finally, the 474 \nlack of data on BMI and WHR prior to disease onset, and limited information on the age at which 475 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n31 \n \nreproductive conditions were first diagnosed, complicated the interpretation of our findings from 476 \nobservational analyses in UK Biobank.  477 \nKey priorities for the future are the further exploration and validation of the pathways through 478 \nwhich obesity increases risk of female reproductive disease. Notably, our finding that insulin 479 \nresistance may be an important mediating mechanism warrants further attention, as cheap and safe 480 \ntreatments are available to increase insulin sensitivity. This is demonstrated by the successful use 481 \nof metformin as a first-line treatment in women with PCOS (103), but such a treatment strategy 482 \nhas not yet been explored for other reproductive conditions linked to obesity. More generally, 483 \nbetter and more detailed diagnostic information on reproductive health in large-scale cohort studies 484 \nis urgently required for future research on the causes, consequences and aetiological mechanisms  485 \nof female reproductive illnesses.  486 \nIn conclusion, we provide genetic evidence that both generalised and central obesity play an 487 \naetiological role in a broad range of female reproductive conditions, but the extent of this link 488 \ndiffers substantially between conditions. Our findings also highlight the importance of hormonal 489 \npathways, notably leptin and insulin resistance, as mediating mechanisms and potential targets for 490 \nintervention in the treatment and prevention of common female reproductive conditions.  491 \nAcknowledgements 492 \nWe acknowledge the participants and investigators of FinnGen for their contribution to this study. 493 \nThis research has been conducted using the UK Biobank Resource under Application Number 494 \n10844. The views expressed are those of the author(s) and not necessarily those of the NHS, the 495 \nNIHR or the Department of Health.  496 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n32 \n \nFunding 497 \nThe research was supported by the Wellcome Trust Core Award Grant Number 203141/Z/16/Z 498 \nwith additional support from the NIHR Oxford BRC. 499 \nS.S.V. is supported by the Rhodes Trust (https://www.rhodeshouse.ox.ac.uk/), Clarendon Fund 500 \n(http://www.ox.ac.uk/clarendon/about), and the Medical Sciences Doctoral Training Centre 501 \n(https://www.medsci.ox.ac.uk/) at the University of Oxford. S.B. is supported by the Li Ka Shing 502 \nFoundation. M.V.H. works in a unit that receives funding from the UK Medical Research 503 \nCouncil  and is supported by a British Heart Foundation Intermediate Clinical Research 504 \nFellowship (FS/18/23/33512) and the National Institute for Health Research Oxford Biomedical 505 \nResearch Centre. C.M.L. is supported by the Li Ka Shing Foundation, NIHR Oxford Biomedical 506 \nResearch Centre, Oxford, NIH (1P50HD104224-01), Gates Foundation (INV-024200), and a 507 \nWellcome Trust Investigator Award (221782/Z/20/Z). L.B.L.W. is supported by the Wellcome 508 \nTrust (221651/Z/20/Z).  509 \nCompeting Interests 510 \nC.M.B. reports grants from Bayer AG, AbbVie Inc, Volition Rx, MDNA Life Sciences, Roche 511 \nDiagnostics Inc., and consultancy for Myovant. He is a member of the independent data monitoring 512 \nboard at ObsEva; I.G. reports grants from Bayer AG; K.T.Z. reports grants from Bayer AG, 513 \nAbbVie Inc, Volition Rx, MDNA Life Sciences, Roche Diagnostics Inc, and non-financial 514 \nscientific collaboration with Population Diagnostics Ltd,  outside the submitted work; M.V.H. has 515 \nconsulted for Boehringer Ingelheim, and in adherence to the University of Oxford’s Clinical Trial 516 \nService Unit & Epidemiological Studies Unit (CSTU) staff policy, did not accept personal 517 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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(which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint \n\n10 \n \nSupporting Information Captions 794 \nS1 Figure. Comparison of different Mendelian randomisation methods for regression of 795 \nfemale reproductive diseases on various obesity traits. Summary statistics MR was performed 796 \nwith the R package TwoSampleMR using three different methods whose results are compared. 797 \nEffect sizes are displayed as odds ratios (ORs) with 95% confidence intervals (CIs). P-values are 798 \nadjusted for multiple testing with the false discovery rate (FDR) correction; solid lines indicate 799 \nassociations that are significant at an adjusted p-value threshold of 0.05. BMI = body mass index, 800 \nIVW = Inverse-variance weighted, PCOS = polycystic ovary syndrome, WHR = waist-hip ratio, 801 \nWHRadjBMI = WHR adjusted for BMI, VAT = genetically predicted visceral adipose tissue 802 \nmass. 803 \nS2 Figure. Mendelian randomisation sensitivity analyses for female reproductive disorders 804 \nregressed on obesity traits. (A) Female-specific genetic instruments vs combined-sexes genetic 805 \ninstruments. (B) Reproductive outcomes from meta-analysis of UKBB and FinnGen summary 806 \nstatistics vs those from FinnGen only. Summary statistics MR performed with TwoSampleMR R 807 \npackage and best method (displayed, IVW) chosen via Rucker’s framework. Odds ratios (ORs) 808 \nwith 95% confidence intervals (CIs) displayed.   809 \nS3 Figure. (A) Non-linear Mendelian randomisation (MR) estimates for relationships 810 \nbetween BMI and female reproductive disorders. Localised average causal estimates (LACE) 811 \nare calculated by dividing the IV-free exposure into 100 quantiles (fractional polynomial 812 \nmethod) or 10 quantiles (piecewise linear method) with the odds ratio (OR) and 95% confidence 813 \nintervals (CI) displayed. The reference point for OR = 1 is mean BMI, 27.0 kg/m2. (B) 814 \nHeterogeneity across BMI spectrum in instrument variables used for non-linear MR. 815 \nInstrument variable (IV)-free BMI was divided into quantiles as described in (A). Left: fractional 816 \npolynomial 100 quantiles, right: piecewise linear 10 quantiles. Proportion of variance in BMI 817 \nexplained by instrument SNPs in each quantile is plotted (β), with 95% confidence intervals 818 \nshown as standard error bars. Spont. misc. = spontaneous miscarriage. 819 \nS4 Figure. Single-SNP genetic effect estimates for obesity instruments on female 820 \nreproductive disorders. SNPs are annotated with their nearest gene by SNPsnap and clustered 821 \nby obesity-female reproductive disorder relationship. SNPs with >= 80% probability of 822 \nbelonging to a substantial cluster (MRClust) are displayed, with an * if the SNP belongs to 823 \nsubstantial clusters for multiple disorders. Effect size estimates are scaled to a variance of 1 824 \nwithin each disease. 825 \nS1 Table. Supplemental Tables A-P. 826 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.06.01.21257781doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}