Multi-modal machine learning and gut microbiome pathway analysis for Alzheimer's risk prediction

Early Alzheimer's disease (AD) risk assessment requires accessible alternatives to invasive biomarkers. We developed a multi-modal machine learning framework using questionnaire metadata from participants with concurrent microbiome sequencing data.

We analyzed 9832 participants with 120 metadata features across five categories (demographic, dietary, lifestyle, nutritional, medical). Features were selected via Pearson correlation and chi-squared tests. Four algorithms were trained using 10-fold cross-validation with synthetic minority oversampling technique (SMOTE), validated on 1967 samples. The 16S rRNA sequencing data from the same cohort with 2000 samples enabled microbiome composition analysis.

Medical history (area under the curve [AUC] = 0.871) and dietary patterns (AUC = 0.874) achieved best performance, outperforming demographic (0.795), lifestyle (0.660), and nutritional (0.569) domains (p < 0.001). Microbiome analysis revealed dysbiosis markers (Prevotella/Bacteroides ratio: 1.921) linking dietary factors to potential neuroinflammatory pathways.

These findings support non-invasive, multi-modal screening combining medical and dietary evaluation for AD risk stratification, with preliminary microbiome evidence suggesting gut-brain axis dysbiosis as a mechanistic pathway warranting validation in larger cohorts.