ASSOCIATIONS BETWEEN DYSGLYCEMIA, RETINAL NEURODEGENERATION, AND MICROALBUMINURIA IN PREDIABETES AND TYPE 2 DIABETES
Bibliographic record
Abstract
PURPOSE: To explore the association between retinal neurodegeneration and metabolic parameters in progressive dysglycemia. METHOD: A cross-sectional study was performed on 68 participants: normal glucose tolerance (n = 23), prediabetes (n = 25), and Type 2 diabetes without diabetic retinopathy (n = 20). Anthropometric assessment and laboratory sampling for HbA1c, fasting glucose, insulin, c-peptide, lipid profile, renal function, and albumin-to-creatinine ratio were conducted. Central and pericentral macular thicknesses on spectral domain optical coherence tomography were compared with systemic parameters. RESULTS: Baseline demographic characteristics were similar across all groups. Cuzick's trend test revealed progressive full-thickness macular thinning with increasing dysglycemia across all three groups (P = 0.015). The urinary albumin-to-creatinine ratio was significantly correlated with full-thickness superior (R = -0.435; P = 0.0002), inferior (R = -0.409; P = 0.0005), temporal (R = -0.429; P = 0.003), and nasal (R = -0.493; P < 0.0001) pericentral macular thinning, after post hoc Bonferroni adjustment. There was no association between macular thinning and waist circumference, body mass index, blood pressure, lipid profile, or insulin resistance. CONCLUSION: Progressive dysglycemia is associated with macular thinning before the onset of visible retinopathy and occurs alongside microalbuminuria. Retinal neurodegenerative changes may help identify those most at risk from dysglycemic end-organ damage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".