Identification of Metabolic Pathway Dysregulation in Diabetic Retinopathy: A Propensity Score-Matched Metabolomic Study
Bibliographic record
Abstract
Abstract Background Diabetic retinopathy (DR) is a major diabetes-related disease linked to metabolism. However, scientifically assessment of serum metabolic alterations in DR is scarce. We aimed to investigate the changes in metabolic coregulation from type 2 diabetic patients (T2DM) to DR and identify corresponding metabolite predictors via a widely targeted metabolomics approach.Methods In this case-control study, we tested 613 serum metabolites in 69 pairs of T2DM with DR (case) and propensity score-matched T2DM without DR (control) utilizing the ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system. The discrimination capability of differentially expressed metabolites (DEMs) in DR identification was also evaluated using a least absolute shrinkage and selection operator (LASSO) regression-based linear support vector machine (SVM) classifier. Metabolic pathway dysregulation in DR were comprehensively investigated by metabolic pathway analysis, chemical similarity enrichment analysis and MetaMapp approaches.Results A total of 89 DEMs were identified after paired univariate analysis and partial least squares discriminant analysis. The linear-SVM model based on LASSO regression selected DEMs had an excellent discrimination with an area under the ROC curve (AUC) as 0.99 (95% confidence interval: 0.95, 1.00). The biosynthesis of polyunsaturated fatty acids (PUFAs), thiamine metabolism, amino acids (mainly glycine, serine and threonine metabolism), hydroxyeicosatetraenoic acid (HETE), disaccharides, indoles and nucleotides were significantly enriched in DR. Conclusions This study systematically demonstrates that distinct metabolic alterations are linked to DR initiation. n-3 PUFAs, trehalose and vitamin B1 play an important role in inhibiting DR progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".