Tracking the progression of Alzheimer’s disease with peripheral blood monocytes
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is the most common form of dementia with the symptoms gradually worsening over the years. However, the driving pathological processes occur well before the appearance of symptoms. AD patients display signs of systemic inflammation, suggesting that it could precede the well-established AD hallmarks. We recently showed that the innate immune response in the form of monocyte activation is detectable at the pre-clinical stage. Objectives Our goal here is to characterize changes of gene expression in peripheral blood monocytes from patients at different stages of AD progression and validate potential biomarkers for a better prognosis and diagnosis of AD clinical spectrum. Results We performed a whole transcriptome analysis on monocytes purified from healthy subjects, Mild Cognitive Impairment (MCI) and AD patients, and established the list of genes differentially expressed in monocytes during the disease evolution. We observed that, in the top 500 genes differentially expressed, a majority of these genes were upregulated (65%) during AD progression. These genes are mainly involved in chemokine/cytokine-mediated signaling pathways. We further confirmed several biomarkers by quantitative PCR and immunoblotting and showed that they are often deregulated at pre-clinical stages of the disease (MCI stage), supporting the hyperactivation of monocytes in MCI patients. Perspectives Our findings provide evidence that the pre-clinical stage of AD can be detected in monocytes using a specific set of biomarkers, highlighting the importance to study the early innate immune response in AD. Our results open the possibility to use these biomarkers with different diagnostic methodologies to better predict and efficiently treat 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".