The triglyceride-glucose index as an indicator of insulin resistance and cardiometabolic risk in Brazilian adolescents
Bibliographic record
Abstract
Objective: To set cutoff points for the triglyceride and glucose index (TyG) as a marker of insulin resistance (IR) for the pediatric population. Subjects and methods: This was a cross-sectional study with schoolchildren population-based data using data of 377 schoolchildren age 10 to 17 years of both sexes. We studied metabolic variables associated with IR indicators, such as fasting insulin and blood glucose, to calculate the homeostatic model assessment (HOMA-IR), and we studied triglycerides (TG) to determine the TyG index. We obtained TyG cutoff values for IR using the receiver operation characteristic (ROC), with definitions of sensitivity (Sen), specificity (Spe), and area under the ROC curve (AUC), with the HOMA-IR as reference. Results: The cutoff points of the TyG index for IR in adolescents are 7.94 for both sexes, 7.91 for boys, and 7.94 for girls, indicating moderate discriminatory power. When we also considered anthropometric variables of excess weight [TyG-BMI (body mass index)] and visceral fat [TyG-WC (waist circumference)], these indexes reached AUC values higher than 0.72, enhancing their potential use for a good diagnosis. Conclusion: TyG has proven to be a useful instrument for identifying IR in adolescent health screening, with high discrimination capacity when added to anthropometric variables, making it a feasible and inexpensive option.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".