Early Trajectory of Estimated Glomerular Filtration Rate and Long-term Advanced Kidney and Cardiovascular Complications in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Rapid loss of estimated glomerular filtration rate (eGFR) within its normal range has been proposed as a strong predictor of future kidney disease. We investigated this association of eGFR slope early in the course of type 1 diabetes with long-term incidence of kidney and cardiovascular complications. RESEARCH DESIGN AND METHODS: The annual percentage change in eGFR (slope) was calculated during the Diabetes Control and Complications Trial (DCCT) for each of 1,441 participants over a mean of 6.5 years and dichotomized by the presence or absence of early rapid eGFR loss (slope ≤-3% per year) as the exposure of interest. Outcomes were incident reduced eGFR (eGFR <60 mL/min/1.73 m2), composite cardiovascular events, or major adverse cardiovascular events (MACE) during the subsequent 24 years post-DCCT closeout follow-up. RESULTS: At DCCT closeout (the baseline for this analysis), diabetes duration was 12 ± 4.8 years, most participants (85.9%) had normoalbuminuria, mean eGFR was 117.0 ± 13.4 mL/min/1.73 m2, and 149 (10.4%) had experienced early rapid eGFR loss over the preceding trial phase. Over the 24-year subsequent follow-up, there were 187 reduced eGFR (6.3 per 1,000 person-years) and 113 MACE (3.6 per 1,000 person-years) events. Early rapid eGFR loss was associated with risk of reduced eGFR (hazard ratio [HR] 1.81, 95% CI 1.18-2.79, P = 0.0064), but not after adjustment for baseline eGFR level (HR 0.94, 95% CI 0.53-1.66, P = 0.84). There was no association with composite cardiovascular events or MACE. CONCLUSIONS: In people with type 1 diabetes primarily with normal eGFR and normoalbuminuria, the preceding slope of eGFR confers no additional association with kidney or cardiovascular outcomes beyond knowledge of an individual's current level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".