Body Mass Index Trajectories among Adolescents and Young Adults with CKD
Bibliographic record
Abstract
Background: Nearly 1 in 5 US children and adolescents have obesity, a risk factor for cardiovascular disease. We examine longitudinal body mass index (BMI) patterns among adolescents and young adults (AYA) with chronic kidney disease (CKD) during the age of transition to adulthood. Methods: We conducted an historical cohort study of AYA with CKD stages 1-5 aged 13-25 at a southeastern health system in the USA. Electronic medical record (EMR) appointment and laboratory data from Jan 2005-May 2015 were extracted, and CKD patients with ≥4 height and weight measurements were included (N=7,978 observations from 255 patients). Age- and sex-specific BMI Z-scores and percentiles were calculated using the 2000 CDC growth chart (normal weight: BMI >5th and <85th percentile; overweight: BMI ≥85th and <95th percentile; obese: BMI >95th percentile). We modeled BMI Z-score as a linear function of age allowing for different intercepts and slopes for adolescents (aged 13-19) and young adults (aged 20-25), adjusting for sex, race/ethnicity, birth year, and CKD etiology (glomerular, non-glomerular, other primary diagnosis). Logistic regression estimated the proportion of patients with overweight or obesity by age. Results: At baseline, patients’ mean age was 14.9 years, 53% were male; 35% had glomerular conditions, 32% non-glomerular conditions, and 33% had other primary diagnoses. BMI Z-scores decreased with age among adolescents (-0.066 standard deviations [SD] per year of age; P = 0.022) and increased with age for young adults (0.170 SD per year of age; P < 0.001). The proportion of adolescents with overweight or obesity did not change with age (P = 0.643), but the proportion of young adults with overweight or obesity increased by 0.037 with each year of age (P < 0.001), reaching 0.62 by age 25. Conclusion: Trends in BMI increase as adolescents with CKD move into young adulthood.Adjusted for age, race/ethnicity, birth year, and CKD etiology. 95% confidence intervals are shaded.
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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 | 0.001 |
| 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".