A plasma protein signature associated with cognitive function in men without severe cognitive impairment
Bibliographic record
Abstract
BACKGROUND: A minimally invasive blood-based assessment of cognitive function could be a promising screening strategy to identify high-risk groups for the incidence of Alzheimer's disease. METHODS: The study included 448 cognitively unimpaired men (mean age 64.1 years) drawn from the Geelong Osteoporosis Study. A targeted mass spectrometry-based proteomic assay was performed to measure the abundance levels of 269 plasma proteins followed by linear regression analyses adjusted for age and APOE ε4 carrier status to identify the biomarkers related to overall cognitive function. Furthermore, two-way interactions were conducted to see whether Alzheimer's disease-linked genetic variants or health conditions modify the association between biomarkers and cognitive function. RESULTS: Ten plasma proteins showed an association with overall cognitive function. This association was modified by allelic variants in genes ABCA7, CLU, BDNF and MS4A6A that have been previously linked to Alzheimer's disease. Modifiable health conditions such as mood disorders and poor bone health, which are postulated to be risk factors for Alzheimer's disease, also impacted the relationship observed between protein marker levels and cognition. In addition to the univariate analyses, an 11-feature multianalyte model was created using the least absolute shrinkage and selection operator regression that identified 10 protein features and age associated with cognitive function. CONCLUSIONS: Overall, the present study revealed plasma protein candidates that may contribute to the development of a blood-based screening test for identifying early cognitive changes. This study also highlights the importance of considering other risk factors in elucidating the relationship between biomarkers and cognition, an area that remains largely unexplored.
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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".