Plasma protein risk scores for mild cognitive impairment and Alzheimer's disease in the Framingham heart study
Bibliographic record
Abstract
INTRODUCTION: It is unclear whether aggregated plasma protein risk scores (PPRSs) could be useful in predicting the risks of mild cognitive impairment (MCI) and Alzheimer's disease (AD). METHODS: The Cox proportional hazard model with the Least Absolute Shrinkage and Selection Operator penalty was used to build the PPRSs for MCI and AD in 1515 Framingham Heart Study Generation 2 with 1128 proteins measured in plasma at exam 5 (cognitively normal [CN] = 1258, MCI = 129, AD = 128). RESULTS: MCI PPRS had a hazard ratio (HR) of 6.97 [5.34, 9.12], with a discriminating power (C-index = 82.52%). AD PPRS had a HR of 5.74 [4.67, 7.05] (C-index = 88.15%). Both PPRSs were also significantly associated with cognitive changes, brain atrophy, and plasma AD biomarkers. Proteins in the MCI and AD PPRSs were involved in several pathways related to leukocyte, chemotaxis, immunity, inflammation, and cellular migration. DISCUSSION: This study suggests that PPRSs serve well to predict the risk of developing MCI and AD as well as cognitive changes and AD-related pathogenesis in the brain. HIGHLIGHTS: PPRSs were developed for the risk of AD and AD preclinical stage, MCI. PPRSs were developed for MCI and AD associated with cognitive changes, loss of brain volume, and increasing level of plasma AD biomarkers. Leukocyte, chemotaxis, immunity, inflammation, and cellular migration enriched in proteins were identified as being involved in MCI and AD PPRSs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".