Development and Progression of Renal Insufficiency With and Without Albuminuria in Adults With Type 1 Diabetes in the Diabetes Control and Complications Trial and the Epidemiology of Diabetes Interventions and Complications Study
Bibliographic record
Abstract
OBJECTIVE: This multicenter study examined the impact of albumin excretion rate (AER) on the course of estimated glomerular filtration rate (eGFR) and the incidence of sustained eGFR <60 ml/min/1.73 m(2) in type 1 diabetes up to year 14 of the Epidemiology of Diabetes Interventions and Complications (EDIC) study (mean duration of 19 years in the Diabetes Control and Complications Trial [DCCT]/EDIC). RESEARCH DESIGN AND METHODS: Urinary albumin measurements from 4-h urine collections were obtained from participants annually during the DCCT and every other year during the EDIC study, and serum creatinine was measured annually in both the DCCT and EDIC study. GFR was estimated from serum creatinine using the abbreviated Modification of Diet in Renal Disease equation. RESULTS: A total of 89 of 1,439 subjects developed an eGFR <60 ml/min/1.73 m(2) (stage 3 chronic kidney disease on two or more successive occasions (sustained) during the DCCT/EDIC study (cumulative incidence 11.4%). Of these, 20 (24%) had AER <30 mg/24 h at all prior evaluations, 14 (16%) had developed microalbuminuria (AER 30-300 mg/24 h) before they reached stage 3 chronic kidney disease, and 54 (61%) had macroalbuminuria (AER >300 mg/24 h) before they reached stage 3 chronic kidney disease. Macroalbuminuria is associated with a markedly increased rate of fall in eGFR (5.7%/year vs. 1.2%/year with AER <30 mg/24 h, P < 0.0001) and risk of eGFR <60 ml/min/1.73 m(2) (adjusted hazard ratio 15.3, P < 0.0001), whereas microalbuminuria had weaker and less consistent effects on eGFR. CONCLUSIONS: Macroalbuminuria was a strong predictor of eGFR loss and risk of developing sustained eGFR <60 ml/min/1.73 m(2). However, screening with AER alone would have missed 24% of cases of sustained impaired eGFR.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".