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

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. DISCUSSION: 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.