Clinical usefulness of lipid ratios to identify men and women with metabolic syndrome: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Waist circumference, a metabolic syndrome (MetSy) criterion, is not routinely measured in clinical practice making early identification of individuals with MetSy challenging. It has been argued that ratios of commonly measured parameters such as lipids and lipoproteins may be an acceptable alternative for identifying individuals with MetSy. The objective of our study was to explore clinical utility of lipid ratios to identify men and women with MetSy; and to explore the association between lipid ratios and the number of MetSy components. METHODS: Men and women (N = 797) of Aboriginal, Chinese, European, and South Asian origin (35-60 years), recruited across ranges of body mass index (BMI), with no diagnosed cardiovascular disease (CVD) or on medications to treat CVD risk factors were assessed for anthropometrics, family history of CVD, MetSy components (waist circumference, blood pressure, glucose, triglycerides (TG), high-density-lipoprotein-cholesterol (HDL-C)), low-density-lipoprotein-cholesterol (LDL-C), nonHDL-C, and health-related behaviours. RESULTS: Mean levels of lipid ratios significantly increased with increasing number of MetSy components in men and women (p < 0.05). After adjustment for age, ethnicity, smoking, alcohol consumption, physical activity, family history of CVD and BMI, (and menopausal status in women), all lipid ratios were associated with the number of MetSy components in men and women (Poisson regression, p < 0.001). Compared to the rest of the lipid ratios (ROC curve analysis), TG/HDL-C was best able to discriminate between individuals with and without MetSy (AUC = 0.869 (95% CI: 0.830, 0.908) men; AUC = 0.872 (95% CI: 0.832, 0.912) women). The discriminatory power of TC/HDL-C and nonHDL-C/HDL-C to identify individuals with MetSY was the same (for both ratios, AUC = 0.793 (95% CI: 0.744, 0.842) men; 0.818 (95% CI: 0.772, 0.864) women). Additionally, LDL-C/HDL-C was a good marker for women (AUC = 0.759 (95% CI: 0.706, 0.812)), but not for men (AUC = 0.689 (95% CI: 0.631, 0.748)). Based on a multiethnic sample, we identified TG/HDL-C cut-off values of 1.62 in men and 1.18 in women that were best able to discriminate between men and women with and without MetSY. CONCLUSIONS: Our results indicate that TG/HDL-C is a superior marker to identify men and women with MetSy compared to TC/HDL-C, LDL-C/HDL-C, and nonHDL-C/HDL-C.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".