Baseline mood and non-motor symptom burden are associated with cognitive progression in Parkinson’s disease: an interpretable follow-up cohort analysis with separate neuroimaging, molecular, and digital analyses in independent samples

Clinically useful artificial intelligence and machine-learning studies in healthcare require interpretable features, internal validation, and explicit boundaries between primary inference and external context. Among 1,612 baseline Parkinson’s Progression Markers Initiative (PPMI) participants, 1,439 had evaluable post-baseline cognition and contributed 5,909 records through Year 5. Composite cognitive progression occurred in 720 participants (50.0%). Each standard deviation increase in baseline mood/non-motor burden was associated with higher odds of progression (adjusted odds ratio 1.37, 95% confidence interval 1.20–1.55). The 1,000-resample participant bootstrap interval was 1.20–1.57, and estimates were stable across 1- to 5-year windows (odds ratios 1.35–1.41). In 10 repetitions of stratified 5-fold cross-validation, adding composite burden to clinical covariates produced a modest increase in mean area under the receiver operating characteristic curve from 0.665 to 0.682. We interpreted the PPMI result alongside separate neuroimaging, transcriptomic, and digital analyses in other samples and specified a future same-participant study; the separate analyses were not used for participant-level integration or validation. In the small resting-state functional magnetic resonance imaging cohort, most static and dynamic comparisons did not survive false-discovery-rate correction; two threshold-specific network-based-statistic components were retained as exploratory hypotheses. Molecular rankings were consistent with previously reported Parkinson’s disease biology, while wearable and voice datasets demonstrated feasibility for the source-task only. Baseline mood/non-motor assessment may support future risk-enrichment research, but the limited cross-validated increment, absence of external clinical validation, and lack of participant-matched multimodal data preclude clinical implementation.