Leveraging multiomic approaches to elucidate mechanisms of heterogeneity in Alzheimer's disease: Neuropsychiatric symptoms, co‐pathologies, and sex differences
Bibliographic record
Abstract
The heterogeneity of Alzheimer's disease (AD) is multi-dimensional, encompassing clinical features such as neuropsychiatric symptoms (NPS), rate of progression, age of onset, comorbidities, and neuropathological features such as co-pathologies, and represents the diverse outcomes of manifold genetic and environmental risk determinants. These diverse features of AD also vary significantly between sexes and across ancestral backgrounds, but the specific variations and causal mechanisms are not well understood. Recent technological advances, particularly single-cell and spatial omics, have provided new tools to dissect the molecular underpinnings of AD heterogeneity and its multifactorial nature. This perspective review highlights molecular differences, general and sex-specific, that contribute to the heterogeneity of AD in aspects such as NPS, co-pathology prevalence, and general disease trajectories. We further examined the potential for multiomic approaches to direct future translational studies aimed at the development of precision medicine strategies for the treatment of AD in all its diverse forms. HIGHLIGHTS: Alzheimer's disease (AD) represents diverse subtypes characterized by comorbid clinical symptoms and co-pathologies. Integration of bulk, single-cell, spatial multiomics reveals factors underlying AD variation. Multiomics studies indicate shared and distinct mechanisms between major psychiatric disorders and AD. Multiomics data have transformative implications for sex- and population-specific AD therapies. New tailored precision medicine strategies are needed to address the full range of complexity in AD.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".