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Background
Clinical data on the relationship between triglycerides (TG)/HDL ratio and insulin resistance (IR) suggest that TG/HDL ratio may be a risk factor for IR. However, there is evidence that different races have different risk of developing IR. The relationship on TG/HDL ratio and IR in various populations needs to be improved. Therefore, we investigated whether TG/HDL ratio was linked to IR in different groups in the United States after controlling for other covariates.
Methods
The current research was conducted in a cross-sectional manner. From 2009 to 2018, the National Health and Nutrition Examination Survey (NHANES) had a total of 49,696 participants, all of whom were Americans. The target-independent variable was TG/HDL ratio measured at baseline, and the dependent variable was IR. Additionally, the BMI, waist circumference, education, race, smoking, alcohol use, alanine transaminase, aspartate transaminase, and other covariates were also included in this analysis.
Results
The average age of the 10,132 participants was 48.6 ± 18.4 years, and approximately 4936 (48.7%) were males. After correcting for confounders, fully adjusted logistic regression revealed that TG/HDL ratio was correlated with IR (odds ratio = 1.51, 95% CI 1.42–1.59). A nonlinear interaction between TG/HDL ratio and IR was discovered, with a point of 1.06. The impact sizes and CIs on the left and right sides of the inflection point were 6.28 (4.66–8.45) and 1.69 (1.45–1.97), respectively. According to subgroup analysis, the correlation was strong in females, alcohol users, and diabetes patients. Meanwhile, the inverse pattern was observed in the aged, obese, high-income, and smoking populations.
Conclusion
In the American population, the TG/HDL ratio is positively associated with IR in a nonlinear interaction pattern.
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Background
In recent decades, with the development of the global economy and the improvement of living standards, insulin resistance (IR) has become a common phenomenon. Current studies have shown that IR varies between races. Therefore, it is necessary to develop individual prediction models for each country. The purpose of this study was to develop a predictive model of IR applicable to the US population.
Method
In total, 11 cycles of data from the NHANES database were selected for this study. Of these, participants from 1999 to 2010 (n = 14931) were used to establish the model, and participants from 2011 to 2020 (n = 13,646) were used to validate the model. Univariate and multivariable logistic regression was used to analyze the factors associated with IR. Optimal subset regression was used to filter the best modeling variables. ROC curves, calibration curves, and decision curve analysis were used to determine the strengths and weaknesses of the model.
Results
After screening the variables by optimal subset regression, variables with covariance were excluded, and a total of seven factors (including HDL, LDL, ALB, GLB, GLU, BMI, and waist) were finally included to establish the prediction model. The AUCs were 0.851 and 0.857 in the training and validation sets, respectively, and the Brier value of the calibration curve was 0.153.
Conclusion
The optimal subset predictive model proposed in this study has a great performance in predicting IR, and the decision curve analysis shows that it has a high net clinical benefit, which can help clinicians and epidemiologists easily detect IR and take appropriate interventions as early as possible.
