Hypothesis: cerebrospinal fluid protein markers suggest a pathway toward symptomatic resilience to AD pathology
Bibliographic record
Abstract
Abstract Introduction We sought biological pathways that explained discordance between Alzheimer's disease (AD) pathology and symptoms. Methods In 306 Alzheimer's Disease Neuroimaging Initiative (ADNI)‐1 participants across the AD clinical spectrum, we investigated association between cognitive outcomes and 23 cerebrospinal fluid (CSF) analytes associated with abnormalities in the AD biomarkers amyloid β1‐42 and total‐tau. In a 200‐person “training” set, Least Absolute Shrinkage and Selection Operator regression estimated model weights for the 23 proteins, and for the AD biomarkers themselves, as predictors of ADAS‐Cog11 scores. In the remaining 106 participants (“validation” set), fully adjusted regression models then tested the Least Absolute Shrinkage and Selection Operator–derived models and a related protein marker summary score as predictors of ADAS‐Cog11, ADNI diagnostic category, and longitudinal cognitive trajectory. Results AD biomarkers alone explained 26% of the variance in validation set cognitive scores. Surprisingly, the 23 AD‐related proteins explained 31% of this variance. The biomarkers and protein markers appeared independent in this respect, jointly explaining 42% of test score variance. The composite protein marker score also predicted ADNI diagnosis and subsequent cognitive trajectory. Cognitive outcome prediction redounded principally to ten markers related to lipid or vascular functions or to microglial activation or chemotaxis. In each analysis, apoE protein and four markers in the latter immune‐activation group portended better outcomes. Discussion CSF markers of vascular, lipid‐metabolic and immune‐related functions may explain much of the disjunction between AD biomarker abnormality and symptom severity. In particular, our results suggest the hypothesis that innate immune activation improves cognitive outcomes in persons with AD pathology. This hypothesis should be tested by further study of cognitive outcomes related to CSF markers of innate immune activation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".