Blood and Brain Gene Expression Trajectories Underlying Neuropathology and Cognitive Impairment in Neurodegeneration
Bibliographic record
Abstract
SUMMARY Neurodegenerative disorders take decades to develop and their early detection is challenged by confounding non-pathological aging processes. For all neurodegenerative conditions, we lack longitudinal gene expression (GE) data covering their large temporal evolution, which hinders the fully understanding of the underlying dynamic molecular mechanisms. Here, we aimed to overcome this limitation by introducing a novel GE contrastive trajectory inference (GE-cTI) method that reveals enriched temporal patterns in a diseased population. Evaluated on 1969 subjects in the spectrum of late-onset Alzheimer’s and Huntington’s diseases (from ROSMAP, HBTRC and ADNI studies), this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak, Amyloid and Vonsattel stages). Furthermore, when applied to in-vivo blood samples (ADNI), it predicts 97% of the variance in memory deterioration and its future declining rate, supporting the identification of a powerful and minimally invasive (blood-based) tool for early clinical screening and disease prevention. This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. Eighty percent of the most predictive molecular pathways identified in the brain were also top predictors in the blood. The GE-cTI is a promising tool for revealing complex neuropathological mechanisms, with direct implications for implementing personalized dynamic treatments in neurology. HIGHLIGHTS - Unsupervised learning detects enriched gene expression (GE) trajectories in disease - These plasma and brain GE trajectories predict neuropathology and future cognitive impairment - Most predictive molecular functions/pathways in the brain are also top predictors in the plasma - By identifying plasma GE trajectories, patients can be easily screened and follow dynamic treatments
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".