Impact of sarcosine on diabetic retinopathy: Findings based on weighted gene co‐expression network analysis and machine learning techniques
Bibliographic record
Abstract
AIM: To quantify the association between serum sarcosine and diabetic retinopathy (DR) using weighted gene co-expression network analysis (WGCNA). METHODS: We measured serum metabolites in 69 pairs of type 2 diabetes (T2D) patients with and without DR matched by age, gender, body mass index（BMI and HbA1c, using a propensity score matching-based approach. To identify modules and metabolites linked to DR, pathway analysis was performed using WGCNA, the Kyoto Encyclopedia of Genes and Genomes and Small-Molecule Pathway Database. The association of sarcosine with DR was estimated by restricted cubic spline and conditional logistic regression models. Its joint effects with covariates on DR were also extensively examined. RESULTS: With per interquartile range elevation of sarcosine, the adjusted odds ratio (AOR) of DR significantly decreased by 67% (AOR: 0.33, 95% confidence interval [CI]: 0.19-0.58). Similar results were also found in the tertile analysis. Compared with those in the first tertile of sarcosine, the AOR significantly decreased by 54% (AOR: 0.46, 95% CI: 0.18-1.17) and 78% (AOR: 0.22, 95% CI: 0.08-0.59) for subjects in the second and third tertiles, respectively. Compared with subjects with lower sarcosine and lower HDL-C levels, those with higher sarcosine and lower HDL-C levels had the lowest odds of DR (OR: 0.13, 95% CI: 0.04, 0.43). CONCLUSIONS: Serum sarcosine was inversely related to DR, especially in T2D patients with insufficient HDL-C. This study provides insights on a possible novel target for DR precision prevention and control, as well as a better understanding of the DR mechanism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".