Haptoglobin Genotype and the Rate of Renal Function Decline in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study
Bibliographic record
Abstract
Many patients with type 1 diabetes develop renal disease despite moderately good metabolic control, suggesting other risk factors may play a role. Recent evidence suggests that the haptoglobin (HP) 2-2 genotype, which codes for a protein with reduced antioxidant activity, may predict renal function decline in type 1 diabetes. We examined this hypothesis in 1,303 Caucasian participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study. HP genotype was determined by polyacrylamide gel electrophoresis. Glomerular filtration rate was estimated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and albumin excretion based on timed urine samples. Participants were followed up for a mean of 22 years. HP genotype was significantly associated with the development of sustained estimated glomerular filtration rate (GFR) <60 mL/min/1.73 m(2) and with end-stage renal disease (ESRD), with HP 2-2 having greater risk than HP 2-1 and 1-1. No association was seen with albuminuria. Although there was no treatment group interaction, the associations were only significant in the conventional treatment group, where events rates were much higher. We conclude that the HP genotype is significantly associated with the development of reduced GFR and ESRD in the DCCT/EDIC study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".