Association between different growth curve definitions of overweight and obesity and cardiometabolic risk in children
Bibliographic record
Abstract
BACKGROUND: Overweight and obesity in young people are assessed by comparing body mass index (BMI) with a reference population. However, two widely used reference standards, the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) growth curves, have different definitions of overweight and obesity, thus affecting estimates of prevalence. We compared the associations between overweight and obesity as defined by each of these curves and the presence of cardiometabolic risk factors. METHODS: We obtained data from a population-representative study involving 2466 boys and girls aged 9, 13 and 16 years in Quebec, Canada. We calculated BMI percentiles using the CDC and WHO growth curves and compared their abilities to detect unfavourable levels of fasting lipids, glucose and insulin, and systolic and diastolic blood pressure using receiver operating characteristic curves, sensitivity, specificity and kappa coefficients. RESULTS: The z scores for BMI using the WHO growth curves were higher than those using the CDC growth curves (0.35-0.43 v. 0.12-0.28, p < 0.001 for all comparisons). The WHO and CDC growth curves generated virtually identical receiver operating characteristic curves for individual or combined cardiometabolic risk factors. The definitions of overweight and obesity had low sensitivities but adequate specificities for cardiometabolic risk. Obesity as defined by the WHO or CDC growth curves discriminated cardiometabolic risk similarly, but overweight as defined by the WHO curves had marginally higher sensitivities (by 0.6%-8.6%) and lower specificities (by 2.6%-4.2%) than the CDC curves. INTERPRETATION: The WHO growth curves show no significant discriminatory advantage over the CDC growth curves in detecting cardiometabolic abnormalities in children aged 9-16 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".