BackgroundDementia is a common complication of type 2 diabetes mellitus (T2DM), influenced by both genetic susceptibility and social disadvantages. While polygenic risk scores (PRS) have been widely applied to assess genetic vulnerability, the contribution of social determinants and their interaction with genetic risk are less understood.ObjectiveThis study aimed to investigate the independent and joint effects of PRS and polysocial risk scores (PsRS) on post-T2DM dementia risk.MethodsA prospective cohort study was conducted using UK Biobank data. PsRS and PRS were derived from multidimensional social and genetic indicators, respectively. Cox proportional hazards models were used to examine their associations with dementia outcomes. In genetically susceptible individuals, seven machine learning models were applied to predict dementia risk. SHAP and ALE were used to interpret feature importance.ResultsAmong 5,624 participants with T2DM, those in the highest PsRS group had a markedly elevated risk of all-cause dementia (HR = 2.738; 95% CI: 1.556-4.818, p < 0.001). This association persisted among genetically susceptible individuals. Machine learning analyses in the medium-to-high PRS group showed that eXtreme Gradient Boosting (XGBoost) achieved the best predictive performance (F1 = 0.735, AUC = 0.726). SHAP interpretation highlighted employment status (mean |SHAP value| = 0.45) and educational level (mean |SHAP value| = 0.19) as the strongest social contributors to dementia risk.ConclusionsBoth social disadvantages and genetic susceptibility contribute to dementia risk in individuals with T2DM. These findings underscore the importance of addressing modifiable social factors in targeted dementia prevention strategies for high-genetic-risk populations.
Keywords: Alzheimer's disease; machine learning; polygenic risk score; type 2 diabetes.