Metabolomic Profiling of Aqueous Humor in Glaucoma Points to Taurine and Spermine Deficiency: Findings from the Eye-D Study
Bibliographic record
Abstract
We compared the metabolomic profile of aqueous humor from patients with primary open-angle glaucoma (POAG; n = 26) with that of a group of age- and sex-matched non-POAG controls (n = 26), all participants undergoing cataract surgery. Supervised paired partial least-squares discriminant analysis showed good predictive performance for test sets with a median area under the receiver operating characteristic of 0.89 and a p-value of 0.0087. Twenty-three metabolites allowed discrimination between the two groups. Univariate analysis after the Benjamini-Hochberg correction showed significant differences for 13 of these metabolites. The POAG metabolomic signature indicated reduced concentrations of taurine and spermine and increased concentrations of creatinine, carnitine, three short-chain acylcarnitines, 7 amino acids (glutamine, glycine, alanine, leucine, isoleucine, hydroxyl-proline, and acetyl-ornithine), 7 phosphatidylcholines, one lysophosphatidylcholine, and one sphingomyelin. This suggests an alteration of metabolites involved in osmoprotection (taurine and creatinine), neuroprotection (spermine, taurine, and carnitine), amino acid metabolism (7 amino acids and three acylcarnitines), and the remodeling of cell membranes drained by the aqueous humor (hydroxyproline and phospholipids). Five of these metabolic alterations, already reported in POAG plasma, concern spermine, C3 and C4 acylcarnitines, PC aa 34:2, and PC aa 36:4, thus highlighting their importance in the pathogenesis of glaucoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".