Comparison of Formulas for Low-Density Lipoprotein (LDL) Calculation for Predicting the Risk of Metabolic Syndrome

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.


Introduction
T he correlation between serum cholesterol and the risk of developing atherosclerosis has been well established in previous research, such as Framingham study [1,2]. In human blood, much of the circulating cholesterol is carried by low-density lipoprotein (LDL); therefore, total cholesterol concentration is a good indicator of LDL cholesterol (LDL-C) level. According to the latest NCEP guidelines for the adult treatment panel, the diagnosis and treatment of hypercholesterolemia are entirely based on the measurement of total cholesterol and LDL-C levels [3]. Treatment aims to diminish LDL-C below the target values [2]. The LDL-C is a reliable marker for prognosticating the coronary heart disease. To date, various studies have asserted the strong correlation between increased LDL-C and coronary heart disease [4][5][6]. Serum LDL-C measurement is conducted using different methods which are generally divided into two groups, namely direct and indirect. There are various direct methods to this end; however, the reference and direct measurement of LDL is performed using a combination of ultracentrifugation-polianion precipitation, which is neither easily available nor feasible in routine labs. The new method for direct measurement of LDL is homogenous assay which is highly accurate, albeit considerably costly [7]. Considering the limitations mentioned above, using indirect methods or calculations such as the Friedewald formula for measuring LDL, particularly in developing countries, is quite common. However, evidence and some reports have revealed inconsistencies between the results of the homogeneous methods and those of the Friedewald formula, and this has led to a concerted effort to reach a more precise formula [8]. One of these attempts was made by Ahmadi et al. [9] who developed an Iranian formula for calculating LDL. Many other researchers, including Anandaraja [2], Vujovic [6], and Chen [10], also put forth new formulas for measuring LDL. Hitherto, no study, whether in Iran or abroad, has compared the robustness of all these formulas and their precision in predicting the status of metabolic syndrome in patients, hence the present study has stepped in to compare the robust-ness of the extant formulas in prognosticating and determining the incidence of metabolic syndrome.

Materials and Methods
In Fasa Cohort Study, a part of the Persian cohort study, more than 10,000 people with age range of 35 to 70 years were investigated. The demographics, socioeconomic status, nutritional status, medical history, body composition, electrocardiogram test, and clinical laboratory examinations, were collected from each person. Also, a biobank of urine, blood, hair, and nail samples was compiled for further research. All information is recorded online for ease of access [11].

Exclusion and inclusion criteria
All individuals with complete information were included in the study.

Research ethical
Research committee registration code: IR. FUMS.REC.1397.096 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 their LDL level was measured using the following formu- The metabolic syndrome is assumed to be present provided that three or more of the following parameters are met [14]: 1. The waistline is more than 40 inches (102 centimeters) for men and more than 35 inches (89 centimeters) for women 2. HDL is less than 40 mg/dL in men and less than 50 mg/dL in women 3 Triglyceride level is 150 mg/dL or higher 4. Blood pressure is 130/85 mm Hg or higher 5. Fasting blood sugar is 100 mg/dL or higher Then, the results of the LDL level computed with different formulas were compared with both the status of metabolic syndrome and laboratory tests of individuals. After reviewing the results, more analysis was carried out to find more robust formulas..

Data analysis
Descriptive statistics were reported as number (percentage) or mean± SD. Independent T-test was used to compare quantitative variables between two groups and logistic regression analysis performed for modeling associated factors with metabolic syndrome. Moreover, ROC curve analysis was used to obtain the area under the curve, sensitivity, and specificity of different LDL formulas. All the statistical analyses performed in SPSS (IBM Co., Armonk, NY, USA) 18.0 and MedCalc (Medcalc software, Ostend, Belgium) 14.0 software and P<0.05 considered as statistically significant.

Results
Table-1 depicts the comparison of the variables under scrutiny between people with and without metabolic syndrome. The results confirm that except for HCT, SGOT, and RBC, other variables have significant differences between the two groups.  Table-3. The columns represent the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and optimum cut-off point, respectively. As observed, the Iranian formula has the highest area under the curve, sensitivity, and specificity. Figure-2 plots the formulas' AUC. After further analysis, the results of two new models proposed for predicting metabolic syndrome are presented in Table-4. The results revealed that  at the end of the table, which portrays the superiority of the proposed models to all existing formulas. In the second model, the sex is also considered (in computations, one and zero stand for female and male, respectively).

Discussion
According to the results of the study, 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, i.e., sensitivity of 0.73, specificity of 0.77, higher positive and negative predictive values, and area under the curve. In Friedewald formula, for example, sensitivity and specificity equal 0.28 and 0.80, respectively.
In Table-4 that illustrates the first model, it can be observed that this model is more precise than the previous methods due to its sensitivity (0.76), specificity (0.77), and area under the curve (0.83). The second model, which also considers sex, is more robust than both the first model and other existing methods. According to the results presented in Table-1, metabolic syndrome has a significant correlation with most demographic indicators and laboratory data. The results exhibited in Figure-1 Schectman et al., it was shown that despite this widespread perception that the indirect formula, encompassing TC, TG, and HDL, may cause an error in the calculation of LDL or give an unrealistic estimation as a result of a change in a parameter and deviation from the normal range, the indirect method is not significantly different from the direct method [17].