Background: Metabolic syndrome (Met-S) is considered one of the most important health problems of the 21st century. It includes a group of metabolic disorders that increase the risk of cardiovascular diseases such as overweight and obesity, elevated lipid profile and blood pressure and insulin resistance (IR). Based on the information mentioned above in which there seems to be a relationship between IR and Met-S, the objective of this work was twofold: on the one hand, to assess the relationship between the values of different insulin resistance risk scales and Met-S determined with three different scales, and on the other, to determine whether any of the components of Met-S predispose more to the appearance of IR. Methods: A descriptive cross-sectional study of 418,343 workers. Waist circumference was measured and evaluated together with six formulas to assess the insulin resistance index. Categorical variables were evaluated by calculating the frequency and distribution of each one. For quantitative variables, mean and standard deviation were determined, and Student’s t-test was applied, while for qualitative variables, the chi-square test was performed. The usefulness of the different risk scales for insulin resistance for predicting metabolic syndrome was evaluated using ROC curves, the area under the curve (AUC), as well as their cut-off points for sensitivity, specificity, and the Youden index. Results: People with metabolic syndrome applying any criteria had higher values in the IR risk scales. The different IR scales made it possible to adequately classify people with metabolic syndrome. Of the three definitions of Met-S, the one that showed the greatest relationship with IR was IDF. Conclusions: Most risk scales for insulin resistance enable the presence of metabolic syndrome to be adequately classified, finding the best ones if the International Diabetes Federation (IDF) criteria are applied. Of the elements included in the Met-S, the one that seems to increase the risk of presenting IR the most is waist circumference; hence, the Met-S definition that is most related to IR is that of the IDF, which is the only one of the three in which a high value of waist circumference is necessary to be able to diagnose Met-S. Waist circumference can be considered the central essential component for detecting insulin resistance and, therefore, the early detection of metabolic syndrome.
Background: Obesity has become a public health problem in our society and is associated with many diseases, including type 2 diabetes mellitus, cardiovascular diseases, dyslipidemia, respiratory diseases, and cancer. Several studies relate weight loss in obese patients to improved anthropometric measurements and cardiometabolic risk. The objective of our study was to evaluate anthropometric changes, analytical parameters, insulin resistance, fatty liver, and metabolic scales, after a personalized weight loss program, through dietary advice to increase adherence to the Mediterranean diet and a motivational booster via mobile SMS messaging. Methods: Intervention study on a sample of 1964 workers, in which different anthropometric parameters were evaluated before and after dietary intervention: the metabolic score of insulin resistance; non-alcoholic fatty liver disease using different scales; metabolic syndrome; atherogenic dyslipidemia; and the cardiometabolic index. A descriptive analysis of the categorical variables was performed, by calculating the frequency and distribution of the responses for each one. For quantitative variables, the mean and standard deviation were calculated, since they followed a normal distribution. Bivariate association analysis was performed by applying the chi-squared test (corrected by Fisher’s exact statistic when conditions required it) and Student’s t-test for independent samples (for comparison of means). Results: The population subjected to the Mediterranean diet improved in all the variables evaluated at 12 months of follow-up and compliance with the diet. Conclusions: Dietary advice on a Mediterranean diet and its reinforcement with reminder messages through the use of mobile phones may be useful to improve the parameters evaluated in this study and reduce the cardiometabolic risk of patients.
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.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.