Prediction of cognitive impairment in Parkinson’s disease using TabPFN: an integrative analysis of clinical data, longitudinal trajectories, and lipidomics

Abstract

Cognitive impairment is a common and disabling non-motor manifestation of Parkinson’s disease (PD). Early identification of patients at high risk remains challenging, and the metabolic mechanisms underlying cognitive decline are not fully understood. We conducted a retrospective cohort study including PD patients who were cognitively normal at baseline. A machine learning model based on a Tabular Prior-data Fitted Network (TabPFN) was developed using routinely collected clinical and laboratory data to predict future cognitive impairment. Model performance was evaluated using stratified group cross-validation and external validation in the Parkinson’s Progression Markers Initiative (PPMI) cohort. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Longitudinal analyses and untargeted lipidomics were performed to explore temporal and molecular alterations associated with cognitive decline. Among 91 patients in the internal cohort, 45 developed cognitive impairment during follow-up. The TabPFN model achieved an AUC of 0.822 (95% CI 0.709–0.889) in internal validation and 0.742 (95% CI 0.695–0.783) in external validation. In internal benchmark comparisons, TabPFN showed numerically higher discrimination than logistic regression (AUC = 0.728) and random forest (AUC = 0.787). MoCA and age were the most influential predictors, while triglycerides (TG) also contributed to model predictions. In the longitudinal analysis, TG trajectories differed between converters and stable participants after adjustment for relevant covariates (group-by-time interaction: β = −0.146, 95% CI − 0.248 to −0.044; p  = 0.005). In the independent lipidomics cohort, although total TG abundance did not differ significantly between cognitive groups, several individual lipid species, including specific TG species, showed differential abundance. A TabPFN-based model using routinely available clinical and biochemical variables showed promising discrimination for future cognitive impairment in PD. TG contributed to prediction, and longitudinal analyses identified different TG trajectories between converters and stable participants. The cross-sectional lipidomics findings provide complementary, hypothesis-generating information about molecular lipid heterogeneity but do not establish the mechanism underlying the longitudinal TG pattern. Prospective multicenter validation is required before clinical implementation.