Progranulin levels in blood in Alzheimer's disease and mild cognitive impairment
Bibliographic record
Abstract
Abstract Objective Changes in progranulin (GRN) expression have been hypothesized to alter risk for Alzheimer's disease (AD). We investigated the relationship between GRN expression in peripheral blood and clinical diagnosis of AD and mild cognitive impairment (MCI). Methods Peripheral blood progranulin gene expression was measured, using microarrays from Alzheimer's (n = 186), MCI (n = 118), and control (n = 204) subjects from the University of California San Francisco Memory and Aging Center (UCSF‐MAC) and two independent published series (AddNeuroMed and ADNI). GRN gene expression was correlated with clinical, demographic, and genetic data, including APOE haplotype and the GRN rs5848 single‐nucleotide polymorphism. Finally, we assessed progranulin protein levels, using enzyme‐linked immunosorbent assay, and methylation status using methylation microarrays. Results We observed an increase in blood progranulin gene expression and a decrease in GRN promoter methylation in males (P = 0.007). Progranulin expression was 13% higher in AD and MCI patients compared with controls in the UCSF‐MAC cohort (F2,505 = 10.41, P = 3.72*10−5). This finding was replicated in the AddNeuroMed (F2,271 = 17.9, P = 4.83*10−8) but not the ADNI series. The rs5848 SNP (T‐allele) predicted decreased blood progranulin gene expression (P = 0.03). The APOE4 haplotype was positively associated with progranulin expression independent of diagnosis (P = 0.04). Finally, we did not identify differences in plasma progranulin protein levels or gene methylation between diagnostic categories. Interpretation Progranulin mRNA is elevated in peripheral blood of patients with AD and MCI and its expression is associated with numerous genetic and demographic factors. These data suggest a role in the pathogenesis of neurodegenerative dementias besides frontotemporal dementia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".