Chronic Kidney Disease Prevalence and Glomerular Filtration Rate Trends in Children With Type 1 Diabetes
Bibliographic record
Abstract
Abstract Background and Objectives: Children with type 1 diabetes (T1D) are at risk for acute kidney injury (AKI) secondary to diabetic ketoacidosis, as well as chronic kidney disease (CKD) from diabetic nephropathy. The primary objective of this study was to assess the prevalence of abnormalities in estimated glomerular filtration rate (eGFR) in children with T1D. As a secondary objective, we sought to explore the relationship between clinical characteristics and trends in eGFR. Design, Setting, Participants, and Measurements: This ambispective cohort study involved children aged 18 years or younger with T1D (n = 420), followed in the diabetes clinic at British Columbia Children’s Hospital (BCCH), the tertiary pediatric hospital in Vancouver, British Columbia, Canada. Data was collected from the BCCH paper and electronic health records. CKD was defined as eGFR less than 60 mL/min/1.73 m2. Being at risk of CKD was defined as having a mildly decreased eGFR (60-<90 mL/min/1.73 m2) and/or hyperfiltration (eGFR ≥140 mL/min/1.73 m2). eGFR was calculated using the modified Schwartz formula (36.5 x height in cm / serum creatinine in μmol/L). Linear regression analysis was used to describe the relationship between eGFR and duration of T1D. Covariates included in the analysis included sex, history of DKA, A1c, and BMI. Results: Of the 420 participants, 225 (54%) were male, with a median age at T1D diagnosis of 6.1 years and T1D duration of 4.8 years (range <1.0–15.0 years). One-hundred and eighty-six (44%) children were hospitalized for DKA, of which 89 (48%) developed AKI. No participants had an eGFR < 60 ml/min/1.73m2, and 317 (76%) had normal renal function. Fifty-one participants (12%) had an eGFR < 90 ml/min/1.73 m2, and 52 (12%) demonstrated hyperfiltration. When analyzed as a cohort cross-sectionally based on duration of T1D, there was a significant linear decline in eGFR of 1.4 ml/min/1.73 m2 per year (95% CI -1.95, -0.87 ml/min/1.73 m2). Conclusion: In a large group of pediatric patients with type 1 diabetes, 24% were at risk for chronic kidney disease based on a mildly decreased GFR and/or hyperfiltration. The pattern of eGFR decline over time is concerning and relevant, as this cohort is at risk for CKD secondary to diabetic kidney disease. Strategies are needed to improve the follow-up and management of early CKD in children with type 1 diabetes to maintain their renal function into adulthood, and more studies are needed to quantify further change in eGFR in the young adult population.
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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 | 0.000 |
| 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) | 0.000 | 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".