Comparison of Formulas for Low-Density Lipoprotein (LDL) Calculation for Predicting the Risk of Metabolic Syndrome Babak Pezeshki, Mojtaba Golrazeghi, Sayed Reza Hojati, Fatemeh Rostamian, Hadi Raeisi Shahraki, Mojtaba Farjam, Reza Homayounfar

Abstract

Background: The correlation between serum cholesterol level and the risk of developing atherosclerosis and metabolic syndrome has been well established in previous studies. Serum low-density lipoprotein (LDL-C) measurement is conducted using different methods which are generally divided into two groups, namely direct and indirect. Using indirect methods or calculations such as the Friedewald or Iranian formula for measuring LDL, particularly in developing countries, is quite common. The present study has stepped in to compare the robustness of the extant formulas in prognosticating and determining the incidence of metabolic syndrome. Materials and Methods: In this cross-sectional study, the target population was the community of Fasa cohort study. According to the views of the statistical advisor, 9530 people were included in the study and clinical laboratory examinations were done for each person. Their serum LDL level was measured using the existing formulas. Then, the results of the serum LDL level that was computed with different formulas, were compared with both the status of metabolic syndrome and laboratory tests of individuals. Results: The Iranian formula has the highest area under curve, the sensitivity of 0.73, and specificity of 0.77, higher positive and negative predictive values among other formulas. In Friedewald formula, for example, sensitivity and specificity equal 0.28 and 0.80, respectively. After further analysis, two new models proposed for predicting metabolic syndrome. The results revealed that these two models even outperform the Iranian formula. Conclusion: The Iranian formula for plasma LDL calculation has higher precision and application for predicting and measuring the metabolic syndrome in the Iranian population due to its considerable features. It is required to develop a new formula for each population and even for each sex, if possible. [GMJ.2020;9:e1607]