BackgroundInsulin resistance is closely associated with metabolic profiles, including obesity and dyslipidemia. However, there are few studies to demonstrate a relationship between lipid profiles and insulin resistance, categorized by BMI, especially in Chinese. The aim of the present study was to examine how lipid profiles were associated with insulin resistance in non-obese middle-aged and elderly Chinese population.MethodsThis cross-sectional study included 1608 (596 men and 1012 women) subjects, without prior known diabetes mellitus and lipid regulating therapy history, older than 45 years. Insulin resistance was defined by homeostasis model assessment of insulin resistance (HOMA-IR) of at least 2.5. The areas under the curve of the receiver operating characteristic curves (AROC) were used to compare the power of these serum markers. SPSS 17.0 software was used for the statistical analysis.ResultsIn non-obese subjects (BMI < 25 kg/m2, n= 996), triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) ratio (OR = 1.43, 95% CI 1.13-1.81, P = 0.003), and SBP (OR = 1.01, 95% CI 1.00-1.02, P = 0.03) were independently risk factors for the insulin resistance. The best marker for insulin resistance in non-obese subjects was triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) ratio with the AROC of 0.73 (95% CI 0.68-0.77, P < 0.001), and the positive likelihood ratio was greatest for TG/HDL-C ratio in the metabolic profiles including BMI. The optimal cut-off point to identifying insulin resistance for TG/HDL-C ratio was ≥ 1.50 in the non-obese population. The BMI, TG, total cholesterol (TC)/HDL-C ratio and HDL-C also discriminated insulin resistance, as the values for AROC were 0.70 (95% CI 0.66-0.75, P < 0.001), 0.71 (95% CI 0.67-0.76, P < 0.001), 0.70 (95% CI 0.65-0.74, P < 0.001), 0.34 (95% CI 0.29-0.38, P < 0.001), respectively. In overweight subjects (BMI ≥ 25.0 kg/m2, n = 612), BMI was still the best marker for insulin resistance with the AROC of 0.65 (95% CI 0.60-0.69, P < 0.001). ConclusionsTG/HDL-C ratio may be the best reliable marker for insulin resistance in the non-obese population.
Background Primary aldosteronism (PA) is a common form of secondary hypertension, which usually manifests low blood potassium levels. The fractional excretion of urine potassium (FEK) has been proposed as a useful tool to measure urinary potassium excretion. However, the role of the FEK in PA remains unclear. In the current study, we assessed the diagnostic value of FEK in PA. Methods A total of 155 hypertension patients were included in this cross-sectional study, of which 62 were confirmed by a positive screening test for PA. We collected the serum, 24-hr urine samples, and spot urine samples to evaluate the diagnostic value of the spot and 24-hr FEK in the diagnosis of PA and renal potassium loss compared to other indices. The sensitivity and specificity of the related diagnostic indexes were analyzed using receiver operating characteristic (ROC) curves, and the optimal cut-point value of the diagnostic index was determined according to the Youden index (YI) (sensitivity + specificity − 1). Correlation analysis was performed between the spot FEK and 24-hr FEK using Pearson’s correlation coefficient. Results The spot FEK (7.3 vs. 5.9) and 24-hr FEK (9.3 vs. 8.0) levels were statistical differences between the PA and essential hypertension groups. PA patients had a significant tendency to lose potassium through the kidneys. We found that FEK from spot urine distinguished renal potassium loss with a sensitivity of 86.7% and a specificity of 87.1% at a cut-off of 9.8%. The sensitivity and specificity of the spot FEK in screening PA were 51.6% and 76.3%, respectively. Conclusions FEK is significantly related to renal potassium loss. Spot FEK and 24-hr FEK performed a certain diagnostic value for PA, which may be potential indicators for the differential diagnosis of PA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.