APOE4 Copy Number-Dependent Proteomic Changes in the Cerebrospinal Fluid1
Bibliographic record
Abstract
BACKGROUND: APOE4 has been hypothesized to increase Alzheimer's disease risk by increasing neuroinflammation, though the specific neuroinflammatory pathways involved are unclear. OBJECTIVE: Characterize cerebrospinal fluid (CSF) proteomic changes related to APOE4 copy number. METHODS: We analyzed targeted proteomic data from ADNI CSF samples using a linear regression model adjusting for age, sex, and APOE4 copy number, and additional linear models also adjusting for AD clinical status or for CSF Aβ, tau, or p-tau levels. False discovery rate was used to correct for multiple comparisons correction. RESULTS: Increasing APOE4 copy number was associated with a significant decrease in a CRP peptide level across all five models (q < 0.05 for each), and with significant increases in ALDOA, CH3L1 (YKL-40), and FABPH peptide levels (q < 0.05 for each) except when controlling for AD clinical status or neurodegeneration biomarkers (i.e., CSF tau or p-tau). In all models except the one controlling for CSF Aβ levels, though not statistically significant, there was a consistent inverse direction of association between APOE4 copy number and the levels of all 24 peptides from all 8 different complement proteins measured. The odds of this happening by chance for 24 unrelated peptides would be less than 1 in 16 million. CONCLUSION: Increasing APOE4 copy number was associated with decreased CSF CRP levels across all models, and increased CSF ALDOA, CH3L1, and FABH levels when controlling for CSF Aβ levels. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".