The Association Between Six Surrogate Insulin Resistance Indexes and Hypertension: A Population-Based Study
Bibliographic record
Abstract
Background: The relationship between insulin resistance and hypertension is well established, but the association of different surrogate insulin resistance indexes with the presence of hypertension is still under debate. The aim of this study was to compare the strength of the association between the presence of hypertension and six indexes: triglyceride/HDL cholesterol ratio (TG/HDL-C), Triglyceride Glucose (TyG) Index, Visceral adiposity index (VAI), Lipid accumulation product (LAP), TyG-Body mass index (TyG-BMI), and TyG-Waist circumference (TyG-WC). Methods: Data from a cross-sectional epidemiological study enrolling a sample representative for the Romanian population aged 18–80 years, excluding those with diabetes or requiring treatment for hypertriglyceridemia, were used to calculate the six indexes. The association with the presence of hypertension was examined with binomial and multinomial logistic regression. Results: In multinomial logistic models, which included age, gender, smoking, drinking, sedentary lifestyle, estimated glomerular filtration rate, urinary sodium, urinary albumin creatinine ratio, and use of medications known to influence insulin resistance as covariates, all individual components and surrogate insulin resistance indexes were independently associated with the presence of hypertension. Values of pseudo R square ranged from 0.342 for the multivariate model including TG/HDL-C to 0.357 for the model including TyG-WC, but with no clear superiority of any of the tested indexes over all others. Models including BMI and WC had a similar ability to predict the presence of hypertension as most of the surrogate indexes and they were slightly superior to TG/HDL-C and TyG. Conclusions: Although TG/HDL-C, VAI, LAP, TyG, TyG-BMI, and TyG-WC were independently associated with the presence of hypertension, no superiority could be demonstrated over the use of BMI and WC as predictors of hypertension in this cross-sectional study.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".