Mathematical Translation of Metabolic Syndrome: Assessment of siMS Score for Metabolic Syndrome and Biochemical Risks ()
Affiliation(s)
1Department of Pathology, PNS Hafeez, Islamabad, Pakistan.
2Bedford Hospital, NHS, Bedford, UK.
3Department of Neurology, Pak Emirates Military Hospital (PEMH), Rawalpindi, Pakistan.
4Department of Biochemistry, Islamabad Medical & Dental College, Islamabad, Pakistan.
5Department of Radiology, PNS Hafeez, Islamabad, Pakistan.
6PNS Hafeez, Islamabad, Pakistan.
ABSTRACT
Background: Metabolic syndrome over decades has undergone multiple diagnostic criteria announced by National Cholesterol Education Program (NCEP), WHO, International Diabetic Federation (IDF) and certain regional criteria. Recently, Soldatovic et al. have provided a mathematical model for evaluating metabolic syndrome. We aimed to compare siMS score among subjects with and without metabolic syndrome and other biochemical risks including insulin resistance. Methods: The study was conducted at PNS HAFEEZ hospital from July-2017 to Jan-2019. A comparative cross-sectional analysis was carried out among 232 subjects to evaluate siMS score among metabolic syndrome and those without metabolic syndrome. Pearson’s correlation was performed for siMS score with other anthropometric and biochemical measures. Finally ROC curve analysis was performed to evaluate various biomarkers along with siMS score for diagnosis of metabolic syndrome. Results: Insulin resistance between subjects was higher among subjects with metabolic syndrome [Mean = 3.27 ± 4.45] than non-metabolic syndrome subjects [Mean = 2.10 ± 1.89] (p = 0.012). Differences in siMS score was higher in subjects with metabolic syndrome (Mean = 3.58 ± 0.725, N = 121) than subjects without metabolic syndrome (Mean = 2.83 ± 0.727, N = 108). AUC for various biochemical parameters was highest for sdLDL cholesterol and siMS score. Conclusion: siMS score has shown better performance than HOMAIR, sdLDL cholesterol, non-HDL cholesterol, HbA1c, and fasting plasma glucose in diagnosing metabolic syndrome.
Share and Cite:
Khan, S. , Khan, A. , Hashmat, A. , Anwar, R. , Shahid, R. and Chaudhry, T. (2020) Mathematical Translation of Metabolic Syndrome: Assessment of siMS Score for Metabolic Syndrome and Biochemical Risks.
Open Journal of Endocrine and Metabolic Diseases,
10, 95-106. doi:
10.4236/ojemd.2020.107010.