Microalbuminuria and the Risk for Early Progressive Renal Function Decline in Type 1 Diabetes
Bibliographic record
Abstract
This study aimed to establish the time of initiation and the determinants of renal function decline in type 1 diabetes. Until now, such decline has been assumed to be a late-occurring event associated with proteinuria. A total of 267 patients with normoalbuminuria and 301 patients with microalbuminuria were followed for 8 to 12 yr. Linear trends (slopes) in GFR were estimated by serial measurement of serum cystatin C. Cases of early renal function decline were defined by loss in cystatin C GFR that exceeded -3.3%/yr, a threshold that corresponds to the 2.5th percentile of the distribution of GFR slopes in an independent nondiabetic normotensive population. Cases of early renal function decline occurred in 9% (mean slope -4.4; range -5.9 to -3.3%/yr) of the normoalbuminuria group and 31% (mean slope -7.1; range -23.8 to -3.3%/yr) of the microalbuminuria group (P < 0.001). Risk for early renal function decline depended on whether microalbuminuria regressed, remained stable, or progressed, rising from 16 to 32 and 68%, respectively (P < 0.001). In multivariate analysis, risk for decline was higher after age 35 yr or when glycosylated hemoglobin exceeded 9% but did not vary with diabetes duration, smoking, BP, or angiotensin-converting enzyme inhibitor treatment. Contrary to the existing paradigm of diabetic nephropathy, progressive renal function decline in type 1 diabetes is an early event that occurs in a large proportion of patients with microalbuminuria. Together with testing for microalbuminuria, clinical protocols using cystatin C to diagnose early renal function decline and track response to therapeutic interventions should be developed.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".