Primate cerebrospinal fluid CHI3L1 reflects brain TREM2 agonism
Bibliographic record
Abstract
INTRODUCTION: Triggering receptor expressed on myeloid cells 2 (TREM2) agonists are being clinically evaluated as disease-modifying therapeutics for Alzheimer's disease. Clinically translatable pharmacodynamic (PD) biomarkers are needed to confirm drug activity and select the appropriate therapeutic dose in clinical trials. METHODS: We conducted multi-omic analyses on paired non-human primate brain and cerebrospinal fluid (CSF), and stimulation of human induced pluripotent stem cell-derived microglia cultures after TREM2 agonist treatment, followed by validation of candidate fluid PD biomarkers using immunoassays. We immunostained microglia to characterize proliferation and clustering. RESULTS: We report CSF soluble TREM2 (sTREM2) and CSF chitinase-3-like protein 1 (CHI3L1/YKL-40) as PD biomarkers for the TREM2 agonist hPara.09. The respective reduction of sTREM2 and elevation of CHI3L1 in brain and CSF after TREM2 agonist treatment correlated with transient microglia proliferation and clustering. DISCUSSION: CSF CHI3L1 and sTREM2 reflect microglial TREM2 agonism and can be used as clinical PD biomarkers to monitor TREM2 activity in the brain. HIGHLIGHTS: CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) reflects brain target engagement for a novel TREM2 agonist, hPara.09. CSF chitinase-3-like protein 1 reflects microglial TREM2 agonism. Both can be used as clinical fluid biomarkers to monitor TREM2 activity in brain.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".