Discovery‐based proteomics identifies plasma proteins that predict longitudinal cognitive decline in older adults over a 7‐year follow‐up period
Bibliographic record
Abstract
Abstract Background Recent technological advances have enabled large‐scale proteomic profiling of plasma and especially in studies of Alzheimer’s disease. However, studies that have comprehensively examined longitudinal cognitive decline as an outcome from plasma protein levels prior to the onset of clinical dementia are limited. This project aimed to identify plasma proteins predictive of cognitive decline across a robust neuropsychological protocol over a 7‐year follow‐up period. Methods Vanderbilt Memory and Aging Project participants (n = 333, 73±7 years, 41% female) free of clinical dementia at study entry underwent fasting blood draw and comprehensive serial neuropsychological assessment over a 7‐year period (mean follow‐up = 5.8 years). Plasma samples were subjected to multiplex liquid chromatography tandem mass spectrometry analysis to quantify cross‐sectional protein abundances for each participant at study entry. Normalization of proteomic data adjusted for batch effects, high levels of missingness, and log2 transformation. Linear mixed‐effects regressions related protein levels to longitudinal neuropsychological outcomes, adjusting for age, sex, race/ethnicity, education, baseline cognitive status, apolipoprotein E ε4 status, and follow‐up time. False discovery rate correction was applied to the a priori significance threshold. Results Initial proteomics analyses yielded 3,784 proteins, of which 686 were used as analytical predictors following quality control, post‐acquisition filtering, and data normalization. Regression analyses resulted in 86 proteins which predicted longitudinal decline in global cognition, as assessed by the Montreal Cognitive Assessment. In analyses with individual neuropsychological domains as outcomes, 144 proteins predicted longitudinal decline in at least one neuropsychological domain [episodic memory (40 proteins), language (72 proteins), information processing speed (99 proteins), executive function (22 proteins), visuospatial skills (55 proteins)]. There was a smaller number of proteins (i.e., five) that were predictive of longitudinal cognitive decline across all domains. Conclusion We conducted large‐scale proteomics analyses and identified 144 plasma proteins at study entry among a cohort of older adults free of clinical dementia that predict subsequent decline in cognition over a 7‐year follow‐up period. Five of these proteins were predictive of decline across all domains suggesting these proteins may represent biological pathways that adversely affect brain health with increasing age. Replication is needed to validate identified proteins as potential biomarkers of cognitive decline.
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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 | 0.001 | 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 | 0.001 | 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".