Determinants of new onset cardiometabolic risk among normal weight children
Bibliographic record
Abstract
OBJECTIVE: To identify determinants for the development of "normal weight metabolically unhealthy" (NWMU) profiles among previously metabolically healthy normal weight children. METHODS: The QUALITY cohort comprises youth 8-10 years of age with a parental history of obesity (n = 630). Of these, normal weight children with no metabolic risk factors were identified and followed up 2 years later (n = 193). Children were classified as NWMU if they remained normal weight but developed at least one cardiometabolic risk factor. They were classified as normal weight metabolically healthy otherwise. Multivariable logistic regression models were used to identify whether adiposity (anthropometrics and DXA), lifestyle habits (physical activity, screen time, vegetables, and fruit- and sugar-sweetened beverages intake), fitness, and family history of cardiometabolic disease were associated with new onset NWMU. RESULTS: Of the 193 normal weight and metabolically healthy children at baseline, 45 (23%) became NWMU 2 years later (i.e., 48% had elevated HDL cholesterol, 13% had elevated triglycerides, and 4% had impaired fasting glucose). Changes in adiposity between baseline and follow-up were associated with an increased risk of NWMU for all adiposity measures examined (e.g., for ∆zBMI OR = 3.95; 95% CI: 1.76, 8.83). Similarly, a 2-year change in screen time was associated with incident NWMU status (OR = 1.24; 95% CI 1.04, 1.49). CONCLUSIONS: Children who increase their adiposity levels as they enter puberty, despite remaining normal weight, are at risk of developing cardiometabolic risk factors. Studies examining long-term consequences of NWMU profiles in pediatrics are needed to determine whether changes in screening practice are warranted.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".