Identification of blood-derived DNA methylation biomarkers of glaucoma and intraocular pressure measurements in three European ancestry cohorts including the Canadian longitudinal study on aging
Bibliographic record
Abstract
Glaucoma is a major cause of blindness globally and its prevalence rises with age. This study explored systemic blood-derived DNA methylation epigenetic biomarkers for association with glaucoma and intraocular pressure (IOP). Blood-derived DNA methylation (DNAm) was analyzed in 1,201 European participants from the Canadian Longitudinal Study on Aging (CLSA; Illumina EPIC v1 array) and 843 European participants from TwinsUK (450k array). An Epigenome-Wide Association Study (EWAS) for glaucoma and IOP was conducted, adjusting for age, sex, tobacco smoking, and leukocyte cell types. DNAm-based EpiScores estimates for 108 plasma protein levels were evaluated for associations with glaucoma and IOP. Additionally, ‘biological’ age acceleration, estimated using five established DNAm ‘clocks,’ was assessed for glaucoma and IOP and replicated in The Health and Retirement Study (HRS; n = 3,453). EWAS analyses of glaucoma and IOP in individual cohorts did not identify genome-wide significant associations. However, a combined EWAS for overlapping probes in both cohorts identified two epigenome-wide significant CpGs: cg03498697 in the FRMD3 promoter (p = 6.86x10−8) and cg06044751 intronically within PALLD (p = 1.76x10−7). EpiScore analysis revealed one IOP Bonferroni-significant association with TNFRSF1B levels in the meta-analysis of both cohorts (p = 1.31x10−4). DNAm ‘clock’ analysis in the HRS identified a GrimAge-positive age acceleration associated with glaucoma (p = 0.01). This study identified significant epigenetic blood-derived biomarkers that are associated with glaucoma and IOP. These findings warrant replication in larger and more diverse populations as well as via longitudinal analysis to assess their robustness and potential predictive power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".