Background The comorbidity between polycystic ovary syndrome (PCOS) and obesity has long been observed in clinical settings, but their shared genetic basis remains unclear. Methods Leveraging summary statistics of large-scale GWAS(s) conducted in European-ancestry populations on body mass index (adult BMI, Nfemale=434,794; childhood BMI, N=39,620), waist-to-hip ratio (WHR, Nfemale=381,152), WHR adjusted for BMI (WHRadjBMI, Nfemale=379,501), and PCOS (Ncase=10,074, Ncontrol=103,164), we performed a large-scale genome-wide cross-trait analysis to quantify overall and local genetic correlation, to identify shared loci, and to infer causal relationship. Results We found positive genetic correlations between PCOS and adult BMI (rg=0.47, P=2.19×10−16), childhood BMI (rg=0.31, P=6.72×10−5), and WHR (rg=0.32, P=1.34×10−10), all withstanding Bonferroni correction. A suggestive significant genetic correlation was found between PCOS and WHRadjBMI (rg=0.09, P=0.04). Partitioning the whole genome into 1703 nearly independent regions, we observed a significant local genetic correlation for adult BMI and PCOS at chromosome 18: 57630483–59020751. We identified 16 shared loci underlying PCOS and obesity-related traits via cross-trait meta-analysis including 9 loci shared between BMI and PCOS (adult BMI and PCOS: 5 loci; childhood BMI and PCOS: 4 loci), 6 loci shared between WHR and PCOS, and 5 loci shared between WHRadjBMI and PCOS. Mendelian randomization (MR) supported the causal roles of both adult BMI (OR=2.92, 95% CI=2.33–3.67) and childhood BMI (OR=2.76, 95% CI=2.09–3.66) in PCOS, but not WHR (OR=1.19, 95% CI=0.93–1.52) or WHRadjBMI (OR=1.03, 95% CI=0.87–1.22). Genetic predisposition to PCOS did not seem to influence the risk of obesity-related traits. Conclusions Our cross-trait analysis suggests a shared genetic basis underlying obesity and PCOS and provides novel insights into the biological mechanisms underlying these complex traits. Our work informs public health intervention by confirming the important role of weight management in PCOS prevention.

Aims/hypothesis The link underlying abnormal glucose metabolism, type 2 diabetes and polycystic ovary syndrome (PCOS) that is independent of BMI remains unclear in observational studies. We aimed to clarify this association using a genome-wide cross-trait approach. Methods Summary statistics from the hitherto largest genome-wide association studies conducted for type 2 diabetes, type 2 diabetes mellitus adjusted for BMI (T2DMadjBMI), fasting glucose, fasting insulin, 2h glucose after an oral glucose challenge (all adjusted for BMI), HbA1c and PCOS, all in populations of European ancestry, were used. We quantified overall and local genetic correlations, identified pleiotropic loci and expression–trait associations, and made causal inferences across traits. Results A positive overall genetic correlation between type 2 diabetes and PCOS was observed, largely influenced by BMI (rg=0.31, p=1.63×10–8) but also independent of BMI (T2DMadjBMI–PCOS: rg=0.12, p=0.03). Sixteen pleiotropic loci affecting type 2 diabetes, glycaemic traits and PCOS were identified, suggesting mechanisms of association that are independent of BMI. Two shared expression–trait associations were found for type 2 diabetes/T2DMadjBMI and PCOS targeting tissues of the cardiovascular, exocrine/endocrine and digestive systems. A putative causal effect of fasting insulin adjusted for BMI and type 2 diabetes on PCOS was demonstrated. Conclusions/interpretation We found a genetic link underlying type 2 diabetes, glycaemic traits and PCOS, driven by both biological pleiotropy and causal mediation, some of which is independent of BMI. Our findings highlight the importance of controlling fasting insulin levels to mitigate the risk of PCOS, as well as screening for and long-term monitoring of type 2 diabetes in all women with PCOS, irrespective of BMI. Graphical abstract

Aims/hypothesis We conducted an updated Mendelian randomization (MR) study to explore the associations of moderate-to-vigorous physical activity (MVPA) and leisure screen time (LST) with type 2 diabetes. Methods Genetic variants strongly associated with MVPA or LST with low linkage disequilibrium were selected as instrumental variables from a genome-wide meta-analysis including >600,000 individuals. Summary-level data on type 2 diabetes were obtained from the DIAbetes Genetics Replication And Meta-analysis consortium including 898,130 individuals. Data on possible intermediates (adiposity indicators, lean mass, glycemic traits, and inflammatory biomarkers) were extracted from large-scale genome-wide association studies (n=21,758-681,275). Univariable and multivariable MR analyses were performed to estimate the total and direct effects of MVPA and LST on type 2 diabetes. Methylation MR analysis was performed for MVPA in relation to diabetes. Results The odds ratio of type 2 diabetes was 0.70 (95% confidence interval 0.55-0.88; P=0.002) per unit increase in the log-odds ratio of having MVPA and 1.45 (95% confidence interval 1.30-1.62; P=7.62×10-11) per standard deviation increase in genetically-predicted LST. These associations attenuated in multivariable MR analyses adjusted for genetically-predicted waist-to-hip ratio, body mass index, lean mass, and circulating C-reactive protein. The association between genetically-predicted MVPA and type 2 diabetes attenuated after adjusting for genetically-predicted fasting insulin levels. Two physical activity-related methylation biomarkers (cg17332422 in ADAMTS2 and cg09531019) were associated with the risk of type 2 diabetes (P<0.05). Conclusions/interpretation The study suggests causal associations of MVPA and LST with type 2 diabetes that appear to be mediated by obesity, lean mass, and chronic low-grade inflammation.