About the Calculator

Why a Severity Score?

Others (1) have recommended such a severity score to improve the ability to accurately predict disease. In forming a continuous score, an estimate of the individual components that commonly make up the classic definitions of MetS (obesity, lipids, blood pressure, and glucose) is summed up somehow to provide an estimate of overall MetS severity. This approach has two main benefits:

  1. Fewer “false negatives” – we would assign a severity score to an individual that just misses the above criteria but who is approaching MetS; this score would indicate this.
  2. Ability to track individuals over time – we would thus be able to monitor progression, and evaluate the effectiveness of any interventions designed to treat MetS.

This is not the first continuous score proposed; however, the methods used to develop the score allow for correlations among the MetS components, and allow for differences in how these components correlate with this underlying MetS factor by sex and race/ethnicity. This is important, as it has been shown repeatedly that the association between MetS and biomarkers of future disease differs by both sex and race/ethnicity (2, 3, 4). The weight given to the individual components of MetS (triglycerides, HDL cholesterol, systolic blood pressure) thus varies in this score by racial/ethnic group according to how closely the components cluster together in that group.

How is the Score Calculated and Interpreted?


We aimed at creating a score that was simply a continuous version of traditionally defined MetS while allowing for sex and racial/ethnic differences. Moreover, our primary motivation was a clinically accessible tool that could be easily calculated and interpreted. The resulting score is actually a z-score from the normal distribution, with a value of 0 indicating an “average” severity score, and higher scores reflecting greater severity of MetS. Percentiles associated with each score can be interpreted as one would interpret percentiles for growth in children.

Our severity score equations for adolescents (using BMI) as well as adults (using waist circumference) have been published. (5, 6) For ease of clinical use and due to substantial growth that occurs during adolescence, the score is based on BMI z-scores rather than waist circumference. The adult score uses waist circumference rather than BMI. Please click the “Calculator” link at the top of this page to access the calculator for both adolescents (12-19 years of age) and adults (20 years and older).

We have recently published a study examining the progression of MetS severity in a large cohort of adults (7); further validation studies are forthcoming.

Need Assistance?

If you have questions, or if you need assistance in calculating the score for participants in your data set(s), please contact Matthew Gurka, Ph.D., professor in the Department of Health Outcomes and Policy and associate director of the Institute for Child Health Policy.


This work was supported by NIH grants U54GM10492, R01HL120960, R21DK085363, and K08HD060739-03.

Equations for New Sex and Race/Ethnic-Specific Metabolic Syndrome Severity Score

Click here for a downloadable document  with the equations.


1. Kahn R, Buse J, Ferrannini E, Stern M. The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes.Diabetes Care. Sep 2005;28(9):2289-23043.

2. Walker SE, Gurka MJ, Oliver MN, Johns DW, DeBoer MD. Racial/ethnic discrepancies in the metabolic syndrome begin in childhood and persist after adjustment for environmental factors.Nutrition, Metabolism and Cardiovascular Disease 22: 141-148, 2012.

3. DeBoer MD, Gurka MJ, Sumner AE. A diagnosis of the metabolic syndrome is associated with a disproportionately high degree of inflammation demonstrated by high levels of hsCRP in non-Hispanic black adolescents: An analysis of NHANES 1999-2008.Diabetes Care 34: 734-740, 2011.

4. DeBoer MD, Dong L, Gurka MJ. Racial/ethnic and sex differences in the relationship between uric acid and metabolic syndrome in adolescents: an analysis of NHANES 1999-2006.Metabolism 61: 554-561, 2012.

5. Gurka MJ, Ice CL, Sun SS, DeBoer MD. A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences.Cardiovascular Diabetology 11: 128, 2012 (full text) (pdf)

6. Gurka MJ, Lilly CL, Oliver MN, DeBoer MD.  An examination of sex and racial/ethnic differences in the metabolic syndrome among adults: A confirmatory factor analysis and a resulting continuous severity score. Metabolism 63: 218-225 (2014) (full text).

7. Vishnu A, Gurka MJ, DeBoer MD. The severity of the metabolic syndrome increases over time within individuals, independent of baseline metabolic syndrome status and medication use: the Atherosclerosis Risk in Communities Study. Atherosclerosis 243: 278-285, 2015. (full text)