Abstract Background Although obesity is a well-established risk factor for endometrial cancer, its relationship with genetic susceptibility in determining cancer risk remains unexplored. Current endometrial cancer risk prediction relies primarily on epidemiological factors, with limited consideration of genetic risk. We hypothesized that integrating polygenic risk score (PRS) information with established epidemiological factors could improve risk stratification and reveal whether genetic and lifestyle factors operate independently or jointly. Methods We generated a polygenic risk score for endometrial cancer in 129,829 unrelated female participants of European genetic ancestry (including 956 incident cases with endometrial cancer) in the UK Biobank cohort. We evaluated the prediction model performance using area under the receiver operating characteristic curves (AUCs) and assessed individual and joint associations of body mass index (BMI) and PRS with endometrial cancer using Cox proportional hazards models. Results The integrated model incorporating PRS and epidemiological risk factors achieved statistically significant improvement in predicting endometrial cancer compared with epidemiologic factors alone (AUC = 0.739 versus 0.728; P = 3.98 × 10 −5 ). Participants in the top 1% PRS distribution had a 3.06-fold increased risk (95% CI 1.97–4.76), with a number needed to screen of 58 individuals. BMI and PRS demonstrated independent effects on endometrial cancer risk, with participants with a BMI ≥ 30 kg/m 2 in the top PRS tertile showing the highest endometrial cancer risk (HR = 4.94; 95% CI 3.65–6.68). Even participants with a BMI < 25 kg/m 2 in the top PRS tertile had a significantly increased risk (HR = 2.01; 95% CI 1.45–2.78). Conclusions Integrating PRS with epidemiological risk factors provides potential for enhanced endometrial cancer risk stratification. PRS effects persist independently of BMI, suggesting genetic risk assessment could complement current screening approaches focused on Lynch Syndrome and identify additional high-risk individuals for targeted prevention strategies.
Wang et al. (Tue,) studied this question.