Risk Factors for Kidney Disease in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE In type 1 diabetes (T1D), the course of microalbuminuria is unpredictable and timing of glomerular filtration rate (GFR) loss is uncertain. Thus, there is a need to identify the risk factors associated with the development of more advanced stages of kidney disease through large, long-term systematic analysis. RESEARCH DESIGN AND METHODS Multivariable Cox proportional hazards models assessed the association of baseline and time-dependent glycemic and nonglycemic risk factors for incident macroalbuminuria and reduced estimated GFR (eGFR; defined as <60 mL/min/1.73 m2) over a mean of 27 years in the Diabetes Control and Complications Trial (DCCT) cohort. RESULTS Higher mean HbA1c (hazard ratio [HR] 1.969 per 1% higher level [95% CI 1.671–2.319]) and male sex (HR 2.767 [95% CI 1.951–3.923]) were the most significant factors independently associated with incident macroalbuminuria, whereas higher mean triglycerides, higher pulse, higher systolic blood pressure (BP), longer diabetes duration, higher current HbA1c, and lower mean weight had lower magnitude associations. For incident reduced eGFR, higher mean HbA1c (HR 1.952 per 1% higher level [95% CI 1.714–2.223]) followed by higher mean triglycerides, older age, and higher systolic BP were the most significant factors. CONCLUSIONS Although several risk factors associated with macroalbuminuria and reduced eGFR were identified, higher mean glycemic exposure was the strongest determinant of kidney disease among the modifiable risk factors. These findings may inform targeted clinical strategies for the frequency of screening, prevention, and treatment of kidney disease in T1D.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".