Role of Matrix Metalloproteinase-9 During Diabetic Ketoacidosis: Results From the Diabetic Kidney Alarm (DKA) Study
Bibliographic record
Abstract
Background: Matrix metalloproteinases (MMPs) are involved in the pathophysiology of acute and chronic kidney disease. However, their role in acute kidney injury (AKI) and proximal tubular dysfunction, a common complication of diabetic ketoacidosis (DKA), is unknown. We examined changes in MMP-9 during and 3 months after episodes of DKA in youth with known or new onset type 1 diabetes (T1D). Methods: Serum samples were collected from youth with DKA at 2 time points: 0-8 hours after starting an insulin infusion and 3 months after hospital discharge. Mixedeffects models evaluated the changes in serum MMP9 and associations with serum copeptin and uric acid and adjustments were made for estimated glomerular filtration rate (eGFR) calculated by serum creatinine and cystatin C. Data are reported as mean and standard deviation (SD) or standard error (SE), or β-estimates and SE for mixed-effects models. Results: We enrolled 40 youth (52% boys, age [mean±SD] 11±4 years, venous pH 7.2±0.1, blood glucose 451±163 mg/dL). 17% of participants (n= 7) met criteria for AKI. Concentrations of MMP-9 were significantly higher during episodes of DKA compared to 3 months follow-up (mean±SE: 1504.6±137 vs. 668.7±159 ng/mL, p=0.0003). At 0-8 hours, participants with AKI had significantly higher MMP-9 (2256.9±310.1 vs. 1344.7±143.5 ng/mL, p=0.01). Higher serum MMP9 was associated with higher serum copeptin, a surrogate marker of vasopressin, (β±SE: 12.4±3.6 per 1 pmol/L increment in copeptin) and higher uric acid (β±SE: 123.9±42.2 per 1 mg/dL increment in uric acid). Conclusions: In our study, DKA and accompanying AKI associated with elevated concentrations of serum MMP-9, a marker of oxidative stress and remodeling, potentially highlighting the underlying mechanisms of kidney injury during DKA. Funding: Other NIH Support - NIH CTSA Grant UL1 TR002535, Private Foundation Support
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".