420-P: Longitudinal Trajectories of Tubular Biomarkers in Type 1 Diabetes
Bibliographic record
Abstract
Background: Tubular biomarkers may provide insight into progression of kidney tubulointerstitial pathology that is complementary to traditional measures of glomerular function and damage. Methods: We examined longitudinal tubular biomarker trajectories in the DCCT/EDIC Study of T1D. For each of 220 randomly-selected participants, biomarkers were measured at up to 7 time points over 26 years. Measurements comprised KIM-1 and sTNFR1 in plasma, EGF and MCP1 in timed urine, and a composite tubular secretion score calculated from the urinary clearances of 8 small molecules secreted by the proximal tubule. Biomarker trends were described and associations with intensive diabetes therapy and glycemia changes over time were tested. Results: Mean age was 28 years at baseline, 45% were women, and 50% were assigned to intensive versus conventional therapy during DCCT. At baseline, participants had a mean estimated glomerular filtration rate (eGFR) of 125 ml/min/1.73m2, and 90% had a urinary albumin excretion rate (AER) < 30 mg/24h. Mean changes in biomarkers over time (in percent per decade) were: KIM-1 27.3% (95% CI 21.4, 33.5), sTNFR1 16.9% (95% CI 14.5, 19.3), MCP1 18.4% (95% CI 8.9, 28.8), EGF -13.5% (95% CI -16.7, -10.1), EGF/MCP1 -26.9% (95% CI -32.2, -21.3), and tubular secretion score -0.9% (95% CI -1.8, 0.0), compared with -12.0% (95% CI -12.9, -11.1) for eGFR and 10.9% (95% CI 2.5, 20.1) for AER. Intensive versus conventional therapy was associated with slower rise in sTNFR1 (relative difference in change 0.94; 95% CI 0.90, 0.98). Higher time-updated HbA1c was associated with faster rises in sTNFR1 (relative difference in change 1.06 per 1% higher HbA1c; 95% CI 1.05, 1.08) and KIM-1 (1.09; 95% CI 1.05, 1.14). Conclusion: Among people with T1D and normal eGFR at baseline, kidney tubular biomarkers changed significantly over long-term follow-up. Hyperglycemia was associated with larger increases in plasma sTNFR1 and KIM1 over time. Disclosure C.Limonte: None. I.Bebu: None. J.Seegmiller: None. M.Molitch: Consultant; Amryt Pharma Plc, Corcept Therapeutics, Janssen Pharmaceuticals, Inc., Sention, Takeda Pharmaceutical Co., Ltd. B.A.Perkins: Advisory Panel; Dexcom, Inc., Insulet Corporation, Novo Nordisk, Sanofi, Vertex Pharmaceuticals Incorporated, Other Relationship; Abbott, Medtronic, Sanofi, Research Support; Novo Nordisk, Bank of Montreal (BMO). A.B.Karger: Consultant; Roche Diagnostics, Research Support; Kyowa Kirin Co., Ltd., Siemens, Speaker's Bureau; Siemens, American Society of Nephrology, American Kidney Fund, National Kidney Foundation. I.De boer: Advisory Panel; AstraZeneca, Boehringer Ingelheim and Eli Lilly Alliance, Boehringer Ingelheim International GmbH, Otsuka America Pharmaceutical, Inc., Bayer Inc., Consultant; George Clinical, Gilead Sciences, Inc., Medscape, Research Support; Dexcom, Inc. Dcct/edic writing group: n/a. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK094176, U01DK094157)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".