Multiple Variants in Vascular Endothelial Growth Factor (VEGFA) Are Risk Factors for Time to Severe Retinopathy in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: We sought to determine if any common variants in the gene for vascular endothelial growth factor (VEGFA) are associated with long-term renal and retinal complications in type 1 diabetes. RESEARCH DESIGN AND METHODS: A total of 1,369 Caucasian subjects with type 1 diabetes from the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study had an average of 17 retinal photographs and 10 renal measures over 15 years. In the DCCT/EDIC, we studied 18 single nucleotide polymorphisms (SNPs) in VEGFA that represent all linkage disequilibrium bins (pairwise r(2) > or = 0.64) and tested them for association with time to development of severe retinopathy, three or more step progression of retinopathy, clinically significant macular edema, persistent microalbuminuria, and severe nephropathy. RESULTS: In a global multi-SNP test, there was a highly significant association of VEGFA SNPs with severe retinopathy (P = 6.8 x 10(-5))-the four other outcomes were all nonsignificant. In survival analyses controlling for covariate risk factors, eight SNPs showed significant association with severe retinopathy (P < 0.05). The most significant single SNP association was rs3025021 (hazard ratio 1.37 [95% CI 1.13-1.66], P = 0.0017). Family-based analyses of severe retinopathy provide evidence of excess transmission of C at rs699947 (P = 0.029), T at rs3025021 (P = 0.013), and the C-T haplotype from both SNPs (P = 0.035). Multi-SNP regression analysis including 15 SNPs, and allowing for pairwise interactions, independently selected 6 significant SNPs (P < 0.05). CONCLUSIONS: These data demonstrate that multiple VEGFA variants are associated with the development of severe retinopathy in type 1 diabetes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".