Metabolic Abnormalities, Cardiovascular Disease Risk Factors, and GFR Decline in Children with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Metabolic abnormalities and cardiovascular disease (CVD) risk factors have rarely been systematically assessed in children with chronic kidney disease (CKD). We examined the prevalence of various CKD sequelae across the GFR spectrum. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data were used from 586 children participating in the Chronic Kidney Disease in Children (CKiD) study (United States and Canada) with GFR measured by iohexol plasma disappearance. Laboratory values and CVD risk factors were compared across GFR categories and with an age-, gender-, and race-matched community sample. RESULTS: CKiD participants were 62% male, 66% Caucasian, 23% African American, and 15% Hispanic with a median age of 11 years and a median GFR of 44 ml/min per 1.73 m(2). Compared with those with a GFR ≥ 50 ml/min per 1.73 m(2), having a GFR < 30 ml/min per 1.73 m(2) was associated with a three-fold higher risk of acidosis and growth failure and a four- to five-fold higher risk of anemia and elevated potassium and phosphate. Median GFR change was -4.3 ml/min per 1.73 m(2) and -1.5 ml/min per 1.73 m(2) per year in children with glomerular and nonglomerular diagnoses, respectively. Despite medication and access to nephrology care, uncontrolled systolic hypertension was present in 14%, and 16% had left ventricular hypertrophy. Children with CKD frequently were also shorter and had lower birth weight, on average, compared with norms. CONCLUSIONS: Growth failure, metabolic abnormalities, and CVD risk factors are present at GFR >50 ml/min per 1.73 m(2) in children with CKD and, despite therapy, increase in prevalence two- to four-fold with decreasing GFR.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".