Integrated Genetic, Molecular, and Wearable Sensor Biomarkers Enable Bayesian Machine Learning-Driven Precision Stratification in Parkinson's Disease: A Comprehensive Multi-Cohort Validation Study

We present a Bayesian machine learning framework integrating genetic, molecular, and wearable sensor biomarkers for precision medicine in Parkinsons disease. Using PPMI (4,775 patients, 14,473 longitudinal records) and LRRK2 Consortium (627 individuals, 2,958 biological specimens), we demonstrate: (1) LRRK2 G2019S confers 1.92-fold PD risk (individual-level{chi} 2 = 36.6, p = 1.4 x 10-9; sex-adjusted OR=2.73) with carriers exhibiting 4.35-point higher motor severity (95% CI [1.95, 6.47], rank-biserial r = -0.270); (2) Wearable IMU sensors quantified Arm Swing Asymmetry (27% prevalence, n = 178) and Dual-Task Cost (14.87% degradation, t = 14.98, p < 0.001), enabling continuous cognitive-motor network monitoring; (3) Molecular markers phospho-LRRK2 (n = 884) and CSF{varepsilon} -synuclein seed amplification (n = 145) provide therapeutic monitoring and differential diagnosis; (4) Prodromal screening identified olfactory dysfunction (50.2%, n = 5, 122) and RBD (37.5%, n = 1, 548). Bayesian clustering via Evidence Lower Bound selection achieved Silhouette=0.535 with bootstrap stability (Jaccard=0.769), outperforming alternatives (0.170-0.452). Risk prediction model: AUC=0.717, calibration slope=1.197. This reproducible framework (complete code-result traceability, TRIPOD+AI compliant) enables mechanism-targeted precision medicine aligned with SDGs 3, 9, 10.