Usefulness of the metabolic syndrome criteria as predictors of insulin resistance among obese Korean women

Regression models for log-normal data: comparing different methods for quantifying the association between abdominal adiposity and biomarkers of inflammation and insulin resistance

We compared six methods for regression on log-normal heteroscedastic data with respect to the estimated associations with explanatory factors (bias and standard error) and the estimated expected outcome (bias and confidence interval). Method comparisons were based on results from a simulation study, and also the estimation of the association between abdominal adiposity and two biomarkers; C-Reactive Protein (CRP) (inflammation marker,) and Insulin Resistance (HOMA-IR) (marker of insulin resistance). Five of the methods provide unbiased estimates of the associations and the expected outcome; two of them provide confidence intervals with correct coverage.

Comparing abilities of different lipid measures in diagnosis of insulin resistance: a Survey of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007) study

BACKGROUND

Dyslipidemia is a distinguishing characteristic of subjects with insulin resistance. Our aim was to determine the predictive abilities and appropriate cutoffs of different lipid variables for insulin resistance.

METHODS

We used the data of the individuals without history of known diabetes mellitus, aged 25-64 years, from the third national Survey of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007). Fasting blood was collected for lipids, glucose and insulin. Receiver operating characteristics (ROC) curves were used to evaluate the predictive abilities of lipid measures for insulin resistance reflected by a homeostasis model assessment value >1.775. The optimal cutoffs of the selected measures were determined using the maximum Youden index and the point closest to (0,1) on the ROC curve, and were compared with metabolic syndrome definitions to diagnose hyperinsulinemia (fasting insulin ≥10 in men and ≥11 U/L in women).

RESULTS

Among lipid measures, low-density lipoprotein cholesterol (LDL-C) was the most tenuously related parameter to insulin resistance. On the contrary, triglycerides and triglycerides to high-density lipoprotein cholesterol ratio (triglycerides/HDL-C) had the highest abilities to determine insulin resistance, regardless of sex and body mass index (BMI). The optimal triglycerides and triglycerides/HDL-C cutoffs were 145 mg/dL and 3.75 for men and 109 mg/dL and 3.00 for women, respectively, which had similar abilities to metabolic syndrome definitions in identifying hyperinsulinemia.

CONCLUSION

Among lipid measures, triglycerides and the triglycerides/HDL-C ratio have the highest predictive abilities for insulin resistance, and the diagnostic values of their appropriate cutoffs are similar to those of metabolic syndrome definitions.

Biomarkers for predicting postmenopausal coronary heart disease

Coronary heart disease (CHD) is the main cause of death in post-menopausal women (PMW). Beyond the ‘traditional’ cardiovascular risk factors of CHD, newer biomarkers, reflecting inflammation, endothelial function and oxidative stress, have received growing consideration. We systematically reviewed the literature on the biomarkers for predicting CHD in PMW. C-reactive protein, IL-6 and Lipoprotein (a) have been consistently found to be associated with CHD risk in PMW. However, no evidence supports the existence of a causal and independent link between such biomarkers and CHD in PMW. Also, the new biomarkers only marginally improve cardiovascular risk prediction. Upcoming studies are needed to provide further evidence on the validity of the new biomarkers in PMW and to understand their relationships with hormone therapy, opening new avenues for prevention.