Multi-modal machine learning and gut microbiome pathway analysis for Alzheimer's risk prediction
Abstract
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 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.
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 9,832 participants or records.
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