From Neural Connectopathy to a Therapeutic Path: An Integrated Multi-omics Framework Identifies a Causal Gene and Drug Candidates for Parkinson's Disease with mild cognitive impairment

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Abstract Background Parkinson’s disease with mild cognitive impairment (PD-MCI) represents a devastating outcome for patients, yet its pathogenic drivers remain poorly understood, and predictive biomarkers are critically lacking. This study aims to establish an early diagnostic model for PD-MCI (from PD), delineate causal mechanisms and identify translational opportunities for PD-MCI. Methods We established an integrated analytical pipeline using the longitudinal Parkinson’s Progression Markers Initiative (PPMI) cohort. 1 Our framework combined plasma transcriptomics and cerebrospinal fluid proteomics with systems biology, machine learning, longitudinal proteomic trajectories analysis, clinical correlation analysis, single-nucleus RNA sequencing, Mendelian randomization, and computational drug repurposing. Results We identified a molecular signature of PD-MCI progression, revealing profound dysregulation of pathways governing neural connectivity—axon guidance, synaptogenesis, and neuron projection development—suggesting a "neural connectopathy" mechanism. Longitudinal proteomic trajectories analysis revealed that the pattern of dysregulated proteins changes years before PD-MCI diagnosis, underscoring an early pathological window. Gene-interaction network and protein-interaction network highlighted key hubs. We then implemented LASSO analysis to select key features and developed an interpretable machine learning model (Logistic Regression, transcripts + proteomics + demographics, AUC = 0.74) that accurately predicted cognitive decline, with SHAP analysis to interpret the order. Crucially, Mendelian randomization established putative causal drivers of PDD risk, and single-nucleus sequencing anchored these findings to the brain, showing altered expression of the causal candidate. specifically in excitatory neurons. Finally, computational drug repurposing nominated targeted candidates, including MEDRONIC ACID, ZILEUTON and ATENOLOL, for immediate therapeutic consideration. Conclusion Our study delivers an integrated translational advance by: (1) defining "neural connectopathy" as a core disease mechanism in PD-MCI; (2) establish an efficient machine learning model to early diagnose PD-MCI from PD; (3) providing a clinically actionable predictive tool, fingding causal genes and proposing a pathway to treatment via drug repurposing. This end-to-end framework establishes a foundation for early prediction, biological insight, and targeted intervention in PD-MCI.

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