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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".