Triglyceride/High‐Density Lipoprotein Cholesterol Ratio: A Clue to Metabolic Syndrome, Insulin Resistance, and Severe Atherosclerosis
Bibliographic record
Abstract
High serum levels of triglycerides (Tg) and low levels of high-density lipoprotein cholesterol (HDL-C) are characteristic of the Metabolic Syndrome (MetS). We assessed the ratio of Tg to HDL-C as a way to identify MetS and insulin resistance. We also evaluated its association with severity of carotid atherosclerosis. Data were analyzed from three cohorts totaling 13,908 participants. MetS was defined according to the International Diabetes Federation criteria. Optimal cut-off for Tg/HDL-C ratio was obtained using Youden's index in receiver-operating characteristic (ROC) curve analyses. The risk of MetS and IR in those with a Tg/HDL-C ratio above the optimum cutoff was evaluated by logistic regression analysis. A Tg/HDL-C ratio above the optimal cutoff level significantly increased the odds ratio for MetS in the three cohorts (OR 6.00, 4.04, and 3.50, least in the healthy population), identified insulin resistance defined by the homeostatic model of insulin resistance (HOMA-IR) (p < 0.0001), and was strongly associated with atherosclerosis severity (p = 0.0001). Tg/HDL-C ratio identifies persons with MetS, insulin resistance, and severe atherosclerosis. It should be used more widely to identify patients at high risk. This is clinically important because insulin resistance is treatable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".