APOE4 Copy Number-Dependent Proteomic changes in the Cerebrospinal Fluid
Bibliographic record
Abstract
Abstract Background APOE4 has been hypothesized to increase Alzheimer’s disease risk by increasing neuroinflammation, though the specific neuroinflammatory pathways involved are unclear. Objectives To characterize CSF proteomic changes as a function of APOE4 copy number. Methods We analyzed targeted proteomic data obtained on ADNI CSF samples using a linear regression model adjusting for age, sex, and APOE4 copy number, and a second linear model also adjusting for AD clinical status. False Discovery Rate (FDR) was used to correct for multiple comparisons. Results In the first model, increasing APOE4 copy number was associated with significant expression decreases in a CRP peptide (q=0.006), and significant expression increases in peptides from ALDOA, CH3L1 (YKL-40), and FABPH (q<0.05 for each). In the second model (controlling for age, sex, and AD clinical status), increasing APOE4 copy number was associated with significant expression decreases in a CRP peptide (q=0.009). In both models, increased APOE4 copy number was associated with trends towards lower expression of all 24 peptides from all 8 different complement proteins measured here, although none of these differences were statistically significant. The odds of this happening by chance for 24 unrelated peptides would be less than 1 in 16 million. Conclusions Increasing APOE4 copy number was associated with decreased CSF CRP levels and increased CSF ALDOA, CH3L1 and FABH levels; the CRP decrease remained significant after controlling for AD clinical status. Increased APOE4 copy number may also be associated with decreased CSF complement pathway protein levels, a hypothesis for investigation in future studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".