An inflammatory and trophic disconnect biomarker profile revealed in Down syndrome plasma: Relation to cognitive decline and longitudinal evaluation
Bibliographic record
Abstract
INTRODUCTION: Given that Alzheimer's pathology develops silently over decades in Down syndrome (DS), prognostic biomarkers of dementia are a major need. METHODS: We investigated the plasma levels of Aβ, proNGF, tPA, neuroserpin, metallo-proteases and inflammatory molecules in 31 individuals with DS (with and without dementia) and in 31 healthy controls. We examined associations between biomarkers and cognitive decline. RESULTS: Aβ40 and Aβ42 were elevated in DS plasma compared to controls, even in DS individuals without dementia. Plasma Aβ correlated with the rate of cognitive decline across 2 years. ProNGF, MMP-1, MMP-3, MMP-9 activity, TNF-α, IL-6, and IL-10 were higher in DS plasma, even at AD-asymptomatic stages. Declining plasma Aβ42 and increasing proNGF levels correlated with cognitive decline. A combined measure of Aβ and inflammatory molecules was a strong predictor of prospective cognitive deterioration. CONCLUSIONS: Our findings support the combination of plasma and cognitive assessments for the identification of DS individuals at risk of dementia.
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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 | 0.001 |
| 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".