Longitudinal Trajectories of Biomarkers of Kidney Tubular Function in Type 1 Diabetes
Bibliographic record
Abstract
Introduction: Tubular biomarkers may shed insight into progression of kidney tubulointerstitial pathology complementary to traditional measures of glomerular function and damage. Methods: We examined trajectories of tubular biomarkers in the Diabetes Control and Complications Trial and the Epidemiology of Diabetes Interventions and Complications Study (DCCT/EDIC Study) of type 1 diabetes (T1D). Biomarkers were measured in a subset of 220 participants across 7 time points over 26 years. Measurements included the following: kidney injury molecule 1 (KIM-1), soluble tumor necrosis factor 1 (sTNFR1) in serum or plasma, epidermal growth factor (EGF), monocyte chemoattractant protein-1 (MCP1) in timed urine, and a composite tubular secretion score. We described biomarker trajectories and examined how these were affected by intensive glucose-lowering therapy and glycemia. Results: and 90% of participants had a urinary albumin excretion rate (AER) <30 mg/24h. Mean changes in biomarkers over time (percent/decade) were: KIM-1: 27.3% (95% confidence interval [CI]: 21.4-33.5), sTNFR1: 16.9% (14.5-19.3), MCP1: 18.4% (8.9-28.8), EGF: -13.5% (-16.7 to -10.1), EGF-MCP1 ratio: -26.9% (-32.2 to -21.3), and tubular secretion score -0.9% (-1.8 to 0.0), versus -12.0% (CI: -12.9 to -11.1) for eGFR and 10.9% (2.5-20.1) for AER. Intensive versus conventional glucose-lowering therapy was associated with slower increase in sTNFR1 (relative difference in change: 0.94 [0.90-0.98]). Higher HbA1c was associated with faster increases in sTNFR1 (relative difference in change: 1.06 per 1% higher HbA1c [1.05-1.08]) and KIM-1 (1.09 [1.05-1.14]). Conclusion: Among participants with T1D and normal eGFR at baseline, kidney tubular biomarkers changed significantly over long-term follow-up. Hyperglycemia was associated with larger increases in serum or plasma sTNFR1 and KIM-1, when followed-up longitudinally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".