Central Obesity, Incident Microalbuminuria, and Change in Creatinine Clearance in the Epidemiology of Diabetes Interventions and Complications Study
Bibliographic record
Abstract
Weight gain and central obesity are associated with insulin resistance, hypertension, and dyslipidemia in type 1 diabetes. These metabolic abnormalities are risk factors for kidney disease in the general population, but data addressing the relationship of central obesity with kidney disease in type 1 diabetes are limited. Whether waist circumference is associated with incident microalbuminuria and change in creatinine clearance was examined among 1279 participants who had type 1 diabetes and were enrolled in the Epidemiology of Diabetes Interventions and Complications Study, the observational extension of the Diabetes Control and Complications Trial (DCCT). Ninety-three of 1105 participants with normal albumin excretion rate (AER) at DCCT closeout developed incident microalbuminuria over 5.8 yr of follow-up. The hazard ratio for incident microalbuminuria that was associated with each 10-cm greater waist circumference at DCCT closeout was 1.34 (95% confidence interval 1.07 to 1.68), after adjustment for DCCT closeout age, gender, duration of diabetes, treatment group, smoking status, glycosylated hemoglobin, and AER. This increased risk was modestly attenuated when additional adjustment was made for levels of BP and serum lipids. Creatinine clearance declined by an average of 0.34 ml/min per 1.73 m2 each yr over 8 yr of follow-up. Greater rate of decline in creatinine clearance was associated with greater age, conventional insulin therapy during the DCCT, smoking, and greater glycosylated hemoglobin and AER at DCCT closeout but not with waist circumference. In conclusion, waist circumference predicts the subsequent development of microalbuminuria in type 1 diabetes. In contrast, no association of waist circumference with decline in creatinine clearance was observed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".