Predicting insulin resistance using the triglyceride-to-high-density lipoprotein cholesterol ratio in Taiwanese adults
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Bibliographic record
Abstract
BACKGROUND: The triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) has been advocated as a simple clinical indicator of insulin resistance. Thresholds of TG/HDL-C appeared to depend on ethnicity. However, no studies have specifically compared the accuracy of TG/HDL-C with and without other clinical and demographic factors in predicting insulin resistance in Taiwanese adults. The aim of the present investigation was to use TG/HDL-C and other clinical available factors to predict insulin resistance in Taiwanese adults. METHODS: A total of 812 subjects were recruited from at the time of their general health examination at the Buddhist Dalin Tzu Chi General Hospital, Taiwan. Demographic information and clinical characteristics were obtained. Insulin resistance was defined by the homeostasis model assessment for insulin resistance (HOMA-IR). Simple and multiple logistic regression analyses were used to obtain probabilities of insulin resistance (HOMA-IR > 2) using TG/HDL-C with (Model 2) and without (Model 1) other clinical variables. A receiver operating characteristic (ROC) analysis was conducted to evaluate the ability of the two models to correctly discriminate between subjects of low and elevated HOMA-IR. RESULTS: Female sex, greater waist circumferences, and higher ALT levels were significantly associated with the risk of elevated HOMA-IR in addition to TG/HDL-C in the multiple logistic regression (Model 2). The area under the ROC curve (AUC) of Model 2 was 0.71 [95% CI = 0.67-0.75] and was significantly higher (P = 0.007) than the AUC 0.66 [95% CI = 0.62-0.71] of Model 1. CONCLUSIONS: The diagnostic accuracy of insulin resistance, defined by HOMA-IR, using TG/HDL-C can be significantly enhanced by including three additional clinically available factors - sex, waist circumferences, and ALT levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it