Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework
Bibliographic record
Abstract
Abstract Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration. Grounded in the Antimicrobial Protection Hypothesis , this study introduces a sheaf-theoretic machine learning framework, Sheaf-ML , for integrating multimodal health data and assessing infection-related cognitive risk. Sheaf-ML constructs a unified patient-level representation that coherently combines diverse data streamsincluding serological infection markers, cognitive assessments, cardiovascular and metabolic measures, nutritional and behavioral evaluationswhile preserving the intrinsic structure and relationships of each modality. Applying this framework to the Harmonized LASI-DAD dataset ( N = 6168), we modeled six clinically motivated domains (Infection, Cognition, Mental Health, Cardiovascular, Nutrition, and Demographics) and integrated them into a topologically consistent representation using learnable cross-domain mappings and consistency constraints. The sheaf-integrated embeddings revealed clinically meaningful interactions: infection burden was linked with cardio-vascular, nutritional, and cognitive outcomes, highlighting system-level coordination across modalities. Using these embeddings, Sheaf-ML produced interpretable patient-level predictions and identified the most influential features both globally and individually. We further derived an Infection Burden Index (IBI) , which quantified patient-level infection-related risk. Patients exceeding the 80 th percentile were flagged as early-warning cases, corresponding to approximately 20% of the cohort, demonstrating actionable stratification for clinical monitoring. This study provides the first empirical evidence that sheaf-based architectures can integrate multimodal health data in a clinically interpretable manner, uncover biologically meaningful interactions, and support patient-specific risk prediction. By linking population-level patterns with individualized insights, Sheaf-ML establishes a foundation for scalable, interpretable, and equitable precision models of infection-related cognitive decline in Alzheimers disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".