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.
References
[1]
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (2001) Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA, 285, 2486-2497. https://doi.org/10.1001/jama.285.19.2486
[2]
Alberti, K.G. and Zimmet, P.Z. (1998) Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications. Part 1: Diagnosis and Classification of Diabetes Mellitus Provisional Report of a WHO Consultation. Diabetic Medicine, 15, 539-553. https://doi.org/10.1002/(SICI)1096-9136(199807)15:7%3C539::AID-DIA668%3E3.0.CO;2-S
[3]
Herath, H.M.M., Weerasinghe, N.P., Weerarathna, T.P. and Amarathunga, A.A. (2018) Comparison of the Prevalence of the Metabolic Syndrome among Sri Lankan Patients with Type 2 Diabetes Mellitus Using WHO, NCEP-ATP III, and IDF Definitions. International Journal of Chronic Diseases, 2018, Article ID: 7813537. https://doi.org/10.1155/2018/7813537
[4]
Yamagishi, K. and Iso, H. (2017) The Criteria for Metabolic Syndrome and the National Health Screening and Education System in Japan. Epidemiology and Health, 39, e2017003. https://doi.org/10.4178/epih.e2017003
[5]
Soldatovic, I., Vukovic, R., Culafic, D., Gajic, M. and Dimitrijevic-Sreckovic, V. (2016) siMS Score: Simple Method for Quantifying Metabolic Syndrome. PLoS ONE, 11, e0146143. https://doi.org/10.1371/journal.pone.0146143
[6]
Reuter, C.P., Burgos, M.S., Barbian, C.D., Renner, J.D.P., Franke, S.I.R. and De Mello, E.D. (2018) Comparison between Different Criteria for Metabolic Syndrome in Schoolchildren from Southern Brazil. European Journal of Pediatrics, 177, 1471-1477. https://doi.org/10.1007/s00431-018-3202-2
[7]
Kang, E.S., Yun, Y.S., Park, S.W., Kim, H.J., Ahn, C.W., Song, Y.D., et al. (2005) Limitation of the Validity of the Homeostasis Model Assessment as an Index of Insulin Resistance in Korea. Metabolism, 54, 206-211. https://doi.org/10.1016/j.metabol.2004.08.014
[8]
Gurka, M.J., Ice, C.L., Sun, S.S. and Deboer, M.D. (2012) A Confirmatory Factor Analysis of the Metabolic Syndrome in Adolescents: An Examination of Sex and Racial/Ethnic Differences. Cardiovascular Diabetology, 11, Article No. 128. https://doi.org/10.1186/1475-2840-11-128
[9]
Huh, J.H., Lee, J.H., Moon, J.S., Sung, K.C., Kim, J.Y. and Kang, D.R. (2019) Metabolic Syndrome Severity Score in Korean Adults: Analysis of the 2010-2015 Korea National Health and Nutrition Examination Survey. Journal of Korean Medical Science, 34, e48. https://doi.org/10.3346/jkms.2019.34.e48
[10]
National Health and Nutrition Examination Survey (NHANES). Anthropometry Procedures Manual. https://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf
[11]
Guerrero-Romero, F. and Rodríguez-Morán, M. (2003) Abdominal Volume Index. An Anthropometry-Based Index for Estimation of Obesity Is Strongly Related to Impaired Glucose Tolerance and Type 2 Diabetes Mellitus. Archives of Medical Research, 34, 428-432. https://doi.org/10.1016/S0188-4409(03)00073-0
[12]
Alberti, K.G., Zimmet, P., Shaw, J. and IDF Epidemiology Task Force Consensus Group (2005) The Metabolic Syndrome—A New Worldwide Definition. The Lancet, 366, 1059-1062. https://doi.org/10.1016/S0140-6736(05)67402-8
[13]
American Diabetes Association (2018) 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care, 41, S13-S27. https://doi.org/10.2337/dc18-S002
[14]
Matthews, D.R., Hosker, J.P., Rudenski, A.S., Naylor, B.A., Treacher, D.F. and Turner, R.C. (1985) Homeostasis Model Assessment: Insulin Resistance and Beta-Cell Function from Fasting Plasma Glucose and Insulin Concentrations in Man. Diabetologia, 28, 412-419. https://doi.org/10.1007/BF00280883
[15]
Vukovic, R., Milenkovic, T., Stojan, G., Vukovic A, Mitrovic K, Todorovic, S. and Soldatovic, I. (2017) Pediatric siMS score: A New, Simple and Accurate Continuous Metabolic Syndrome Score for Everyday Use in Pediatrics. PLoS ONE, 12, e0189232. https://doi.org/10.1371/journal.pone.0189232
[16]
Khoshhali, M., Heshmat, R., Esmaeil Motlagh, M., Ziaodini, H., Hadian, M., Aminaei, T., et al. (2019) Comparing the Validity of Continuous Metabolic Syndrome Risk Scores for Predicting Pediatric Metabolic Syndrome: The CASPIAN-V Study. Journal of Pediatric Endocrinology and Metabolism, 32, 383-389. https://doi.org/10.1515/jpem-2018-0384
[17]
Srećković, B., Soldatovic, I., Colak, E., Mrdovic, I., Sumarac-Dumanovic, M., Janeski, H., et al. (2018) Homocysteine Is the Confounding Factor of Metabolic Syndrome-Confirmed by siMS Score. Drug Metabolism and Personalized Therapy, 33, 99-103. https://doi.org/10.1515/dmpt-2017-0013
[18]
Sebekova, K. and Sebek, J. (2018) Continuous Metabolic Syndrome Score (siMS) Enables Quantification of Severity of Cardiometabolic Affliction in Individuals Not Presenting with Metabolic Syndrome. Bratislavské Lekárske Listy, 119, 675-678. https://doi.org/10.4149/BLL_2018_121
[19]
Murillo-González, F.E., Ponce-Ruiz, N., Rojas-García, A.E., Rothenberg, S.J., Bernal-Hernández, Y.Y., Cerda-Flores, R.M., et al. (2019) PON1 Lactonase Activity and Its Association with Cardiovascular Disease. Clinica Chimica Acta, 500, 47-53.
[20]
Zhang, W.H., Xin, L.L. and Lu, Y. (2017) Integrative Analysis to Identify Common Genetic Markers of Metabolic Syndrome, Dementia, and Diabetes. Medical Science Monitor, 23, 5885-5891. https://doi.org/10.12659/MSM.905521
[21]
Stols-Gonçalves, D., Tristão, L.S., Henneman, P. and Nieuwdorp, M. (2019) Epigenetic Markers and Microbiota/Metabolite-Induced Epigenetic Modifications in the Pathogenesis of Obesity, Metabolic Syndrome, Type 2 Diabetes, and Non-Alcoholic Fatty Liver Disease. Current Diabetes Reports, 19, Article No. 31 https://doi.org/10.1007/s11892-019-1151-4
