A non-fasting marker of metabolic syndrome in a high-risk population
Bibliographic record
Abstract
OBJECTIVE: The rising prevalence of metabolic syndrome among young adults has prompted studies of fasting triglyceride-glucose (TyG) index as a marker of insulin resistance. We aimed to evaluate metabolic syndrome in young adults using non-fasting TyG index and a high-risk genetic model, 22q11.2 microdeletion. METHODS: We assessed metabolic syndrome and its components in 350 adults (50.6% female) aged 18-59 (median 27.7, IQR 22.5-38.1) years with typical 22q11.2 microdeletions. We used multivariable logistic regression and receiver operating characteristic (ROC) curves to evaluate the association of non-fasting TyG index with metabolic syndrome. RESULTS: Non-fasting TyG index was significantly associated with metabolic syndrome (OR 3.23, 95% CI 2.27-4.59, p < 0.0001), independent of age, sex, BMI, and hypothyroidism. Non-fasting TyG index was positively correlated with number of metabolic syndrome components per individual. In this high-risk population, prevalence of metabolic syndrome was 21.7% (60/277) among young adults (18-39 years), and 45.2% (33/73, p < 0.0001) among middle-aged adults (40-59 years). Non-fasting TyG index ≥4.81 was an effective indicator of prevalent metabolic syndrome, with an area under the ROC curve of 0.83 (95% CI 0.78-0.88). CONCLUSIONS: The results support non-fasting TyG index as a practical marker of metabolic syndrome, and by extension insulin resistance, encouraging future studies evaluating non-fasting TyG index in young adults as a predictor of cardiovascular disease later in life. The high prevalence of metabolic syndrome at a young age in 22q11.2 microdeletion demonstrates the potential value of this genetic high-risk population for future prospective studies, with animal and cellular models available.
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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 | 0.003 |
| 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 | 0.001 | 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".