Prevalence and Correlates of Multiple Cardiovascular Risk Factors in Children with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although prevalence of traditional cardiovascular risk factors (CVRF) has been described in children with CKD, the frequency with which these CVRF occur concomitantly and the clinical characteristics associated with multiple CVRF are unknown. This study determined the prevalence and characteristics of multiple CVRF in children in the Chronic Kidney Disease in Children study. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using cross-sectional data from first follow-up visits, we determined the prevalence of four CVRF: hypertension (casual BP >95(th) percentile or self-reported hypertension with concurrent use of anti-hypertensive medication), dyslipidemia (triglycerides >130 mg/dl, HDL <40 mg/dl, non-HDL >160 mg/dl, or use of lipid-lowering medication), obesity (BMI >95(th) percentile), and abnormal glucose metabolism (fasting glucose >110 mg/dl, insulin >20 μIU/ml, or HOMA-IR >2.20, >3.61, or >3.64 for those at Tanner stage 1, 2 to 3, or 4 to 5, respectively) in 250 children (median age 12.2 years, 74% Caucasian, median iohexol-based GFR 45.2 ml/min per 1.73 m(2)). RESULTS: Forty-six percent had hypertension, 44% had dyslipidemia, 15% were obese, and 21% had abnormal glucose metabolism. Thirty-nine percent, 22%, and 13% had one, two, and three or more CVRF, respectively. In multivariate ordinal logistic regression analysis, glomerular disease and nephrotic-range proteinuria were associated with 1.96 (95% confidence interval, 1.04 to 3.72) and 2.04 (95% confidence interval, 0.94 to 4.43) higher odds of having more CVRF, respectively. CONCLUSIONS: We found high prevalence of multiple CVRF in children with mild to moderate CKD. Children with glomerular disease may be at higher risk for future cardiovascular events.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".