Urinary markers of renal inflammation in adolescents with Type 1 diabetes mellitus and normoalbuminuria
Bibliographic record
Abstract
AIMS: Patients with the highest albumin:creatinine ratio within the normal range are at an increased risk for developing microalbuminuria. The mechanistic basis for this is unknown, but may be related to renal inflammation. Our goal was to characterize the urinary excretion of cytokines/chemokines in normoalbuminuric adolescents with Type 1 diabetes to determine whether higher range normoalbuminuria is associated with evidence of renal inflammation. METHODS: Forty-two urinary cytokines/chemokines were measured in subjects who were screened for the Adolescent Type 1 Diabetes Cardio-Renal Intervention Trial. Urinary cytokines/chemokines were compared across low (n = 50), middle (n = 50) or high (n = 50) albumin:creatinine ratio tertile groups. RESULTS: At baseline, participants in the upper tertile were younger and had shorter diabetes duration compared with the other groups. Other clinical characteristics were similar. Urinary levels of interleukin 6, interleukin 8, platelet-derived growth factor-AA and RANTES differed across albumin:creatinine ratio tertiles, with higher values in patients in the middle and high tertiles compared with the lower tertile (ANCOVA P ≤ 0.01). CONCLUSIONS: Within the normal albumin:creatinine ratio range, higher urinary albumin excretion is associated with elevated urinary levels of inflammatory markers. Ultimately, this may provide mechanistic insights into disease pathophysiology and stratify the risk of nephropathy in 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 | 0.000 | 0.002 |
| 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 | 0.000 | 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 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".