Utility of the hypertriglyceridemic waist phenotype in the cardiometabolic risk assessment of youth stratified by body mass index
Bibliographic record
Abstract
BACKGROUND: It is unclear whether the hypertriglyceridemic waist phenotype (HTWP) can be used to identify those at most risk of cardiometabolic disorders. OBJECTIVES: The utility of the HTWP as a useful predictor of cardiometabolic risk in youth stratified by body mass index was assessed. METHODS: Three hundred and eighty-seven children (12-17.5 years) were used within this cross-sectional study. Participants were classified as normal weight or overweight/obese according to the International Obesity Task Force criteria. The HTWP phenotype was defined as having a waist circumference ≥90th percentile for age and gender with concomitant triglyceride concentrations ≥1.24 mmol L(-1) . Cardiometabolic risk profiles were compared using MANCOVA. RESULTS: Normal weight participants with the HTWP had significantly higher levels of C-reactive protein 2.6 ± 0.4 vs. 1.6 ± 0.3 mg L(-1) (P < 0.05) and cardiometabolic risk scores (1.3 ± 0.3 vs. -0.7 ± 0.2 and 2.1 ± 0.4 vs. -0.5 ± 0.2; both P < 0.05) compared with those of a normal weight without the HTWP. Overweight/obese participants with the HTWP had significantly higher C-reactive protein levels (3.5 ± 0.6 vs. 2.6 ± 0.5; P < 0.05) as well as both cardiometabolic risk scores (1.6 ± 0.6 vs. 0.9 ± 0.2 and 2.2 ± 0.6 vs. 0.8 ± 0.2; both P < 0.001) when compared with overweight/obese participants without the HTWP. CONCLUSIONS: The HTWP may serve as a simple and clinically useful approach to identify youth at increased cardiometabolic risk.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".