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siMS Score: Simple Method for Quantifying Metabolic Syndrome

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  • Ivan Soldatovic
  • Rade Vukovic
  • Djordje Culafic
  • Milan Gajic
  • Vesna Dimitrijevic-Sreckovic

Abstract

Objective: To evaluate siMS score and siMS risk score, novel continuous metabolic syndrome scores as methods for quantification of metabolic status and risk. Materials and Methods: Developed siMS score was calculated using formula: siMS score = 2*Waist/Height + Gly/5.6 + Tg/1.7 + TAsystolic/130—HDL/1.02 or 1.28 (for male or female subjects, respectively). siMS risk score was calculated using formula: siMS risk score = siMS score * age/45 or 50 (for male or female subjects, respectively) * family history of cardio/cerebro-vascular events (event = 1.2, no event = 1). A sample of 528 obese and non-obese participants was used to validate siMS score and siMS risk score. Scores calculated as sum of z-scores (each component of metabolic syndrome regressed with age and gender) and sum of scores derived from principal component analysis (PCA) were used for evaluation of siMS score. Variants were made by replacing glucose with HOMA in calculations. Framingham score was used for evaluation of siMS risk score. Results: Correlation between siMS score with sum of z-scores and weighted sum of factors of PCA was high (r = 0.866 and r = 0.822, respectively). Correlation between siMS risk score and log transformed Framingham score was medium to high for age groups 18+,30+ and 35+ (0.835, 0.707 and 0.667, respectively). Conclusions: siMS score and siMS risk score showed high correlation with more complex scores. Demonstrated accuracy together with superior simplicity and the ability to evaluate and follow-up individual patients makes siMS and siMS risk scores very convenient for use in clinical practice and research as well.

Suggested Citation

  • Ivan Soldatovic & Rade Vukovic & Djordje Culafic & Milan Gajic & Vesna Dimitrijevic-Sreckovic, 2016. "siMS Score: Simple Method for Quantifying Metabolic Syndrome," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-10, January.
  • Handle: RePEc:plo:pone00:0146143
    DOI: 10.1371/journal.pone.0146143
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    References listed on IDEAS

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    1. Andrew Woolston & Yu-Kang Tu & Paul D Baxter & Mark S Gilthorpe, 2012. "A Comparison of Different Approaches to Unravel the Latent Structure within Metabolic Syndrome," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
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    1. Katarína Šebeková & Radana Gurecká & Melinda Csongová & Ivana Koborová & Jozef Šebek, 2020. "Sex Differences in Association of Elevated Blood Pressure with Variables Characterizing Cardiometabolic Risk in Young Subjects with or Without Metabolic Abnormalities," IJERPH, MDPI, vol. 17(10), pages 1-21, May.
    2. Rade Vukovic & Tatjana Milenkovic & George Stojan & Ana Vukovic & Katarina Mitrovic & Sladjana Todorovic & Ivan Soldatovic, 2017. "Pediatric siMS score: A new, simple and accurate continuous metabolic syndrome score for everyday use in pediatrics," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-10, December.
    3. Tharrey, Marion & Malisoux, Laurent & Klein, Olivier & Bohn, Torsten & Perchoux, Camille, 2023. "Urban densification over 9 years and change in the metabolic syndrome: A nationwide investigation from the ORISCAV-LUX cohort study," Social Science & Medicine, Elsevier, vol. 331(C).
    4. Katarína Šebeková & Radana Gurecká & Gabriela Repiská & Ivana Koborová & Ľudmila Podracká, 2022. "The Presence of Hyperhomocysteinemia Does Not Aggravate the Cardiometabolic Risk Imposed by Hyperuricemia in Young Individuals: A Retrospective Analysis of a Cross-Sectional Study," IJERPH, MDPI, vol. 19(20), pages 1-12, October.

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