Relationships of PGRN with sTREM2 in AD continuum and non-AD pathophysiology and their reciprocal roles in modulating amyloid pathology: two population-based study
Bibliographic record
Abstract
Progranulin (PGRN) and soluble triggering receptor expressed on myeloid cells-2 (sTREM2) are emerging biomarkers of Alzheimer’s disease (AD). This study explores the roles of their interplay in modulating amyloid pathology. We analyzed data from 905 participants (mean age = 62.0) in the CABLE cohort and 973 participants (mean age = 73.1) in the ADNI, classified using the A/T/N biomarker framework. One-way ANOVA was used to assess whether cerebrospinal fluid (CSF) PGRN and sTREM2 differed across biomarker profiles and clinical stages. Multiple linear regression models and linear mixed-effects models were used to test the relationships among PGRN, sTREM2, and CSF Aβ 1–42 levels. Mediation analysis was used to explore the reciprocal relationships between sTREM2 and PGRN in influencing amyloid pathology. CSF proteomic and bioinformatic analyses were finally used to investigate the underlying biological mechanisms. In both cohorts, PGRN and sTREM2 were higher in individuals within the TN+ profile irrespective of the A status, and followed similar trajectory across different clinical and biomarker stage. CSF PGRN was associated with higher sTREM2 across AD continuum and non-AD pathophysiology. Bidirectional mediation was observed between PGRN (14.6% in CABLE, 15.6% in ADNI) and sTREM2 (29.7% in CABLE, 33.5% in ADNI) in modulating Aβ pathology ( p < 0.0001). Proteomic analysis identified 1539 CSF proteins (Bonferroni-corrected p < 7.13 × 10 −6 ) simultaneously associated with PGRN, sTREM2, and Aβ 1–42 . These proteins are mainly enriched in immune processes and neural plasticity. These findings suggest that the interplay between lysosome function and microglia-related neuroinflammation plays key roles in amyloid metabolism.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".