Metabolic syndrome in healthy, multiethnic adolescents in Toronto: the use of fasting blood glucose as a simple indicator
Bibliographic record
Abstract
Objectives To assess the presence of metabolic syndrome (MetS) features in healthy, normal‐weight, multiethnic adolescents and to determine whether fasting blood glucose (FBG) may be a simple indicator of its presence. Methods A cross‐sectional study using a convenience sample of secondary school students. GLM ANCOVA adjusted for multiple pairwise comparisons by the post hoc Tukey‐Kramer test was used to assess differences among the tertiles of FBG. Results A total of 182 adolescents from 62 Greater Toronto Area (GTA) secondary schools were recruited (44% Caucasian, 34% South Asian, and 22% Chinese), with mean(SD age 17.4±0.9 years, BMI 22.1±3.4 kg/m2, and FBG 4.92±0.4 mmol/L. Analysis with GLM ANCOVA across the tertiles of FBG (3.83–4.78 mmol/L, 4.79–5.08 mmol/L, and 5.09–6.45 mmol/L) showed significant linear increases of BMI (p<0.005), waist circumference (p<0.001), systolic blood pressure (p<0.001), and diastolic blood pressure (p<0.05) with increasing FBG. Stepwise multiple regression analysis indicated systolic blood pressure and waist circumference as independent predictors of the increased FBG level. Conclusions MetS markers are present in a sample of healthy multiethnic adolescents in GTA. FBG could be used as a simple indicator of MetS to allow for early detection of MetS presence and introduction of preventive lifestyle measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".