Serum Uromodulin Predicts Less Coronary Artery Calcification and Diabetic Kidney Disease Over 12 Years in Adults With Type 1 Diabetes: The CACTI Study
Bibliographic record
Abstract
OBJECTIVE Novel biomarkers are needed to better predict coronary artery calcification (CAC), a marker of subclinical atherosclerosis, and diabetic kidney disease (DKD) in type 1 diabetes. We evaluated the associations between serum uromodulin (SUMOD [a biomarker associated with anti-inflammatory and renal protective properties]), CAC progression, and DKD development over 12 years. RESEARCH DESIGN AND METHODS Participants (n = 527, 53% females) in the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study were examined during 2002–2004, at a mean age of 39.6 ± 9.0 years and a median duration of diabetes of 24.8 years. Urine albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) determined by the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) creatinine equation were measured at baseline and after a mean follow-up period of 12.1 ± 1.5 years. Elevated albumin excretion was defined as ACR ≥30 mg/g, rapid GFR decline (>3 mL/min/1.73 m2/year), and impaired GFR as eGFR <60 mL/min/1.73 m2. SUMOD was measured on stored baseline plasma samples (Meso Scale Discovery). CAC was measured using electron beam computed tomography. CAC progression was defined as a change in the square root–transformed CAC volume of ≥2.5. RESULTS Higher baseline SUMOD level conferred lower odds of CAC progression (odds ratio 0.68; 95% CI 0.48–0.97), incident elevated albumin excretion (0.37; 0.16–0.86), rapid GFR decline (0.56; 0.35–0.91), and impaired GFR (0.44; 0.24–0.83) per 1 SD increase in SUMOD (68.44 ng/mL) after adjustment for baseline age, sex, systolic blood pressure, LDL cholesterol, and albuminuria/GFR. The addition of SUMOD to models with traditional risk factors also significantly improved the prediction performance for CAC progression and incident DKD. CONCLUSIONS Higher baseline SUMOD level predicted lower odds of both CAC progression and incident DKD over 12 years in adults with type 1 diabetes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".