Diabetologia

, Volume 60, Issue 7, pp 1261–1270

Independent associations between a metabolic syndrome severity score and future diabetes by sex and race: the Atherosclerosis Risk In Communities Study and Jackson Heart Study

  • Matthew J. Gurka
  • Sherita H. Golden
  • Solomon K. Musani
  • Mario Sims
  • Abhishek Vishnu
  • Yi Guo
  • Michelle Cardel
  • Thomas A. Pearson
  • Mark D. DeBoer
Article

Abstract

Aims/hypothesis

The study aimed to assess for an association between the degree of severity of the metabolic syndrome and risk of type 2 diabetes beyond that conferred by the individual components of the metabolic syndrome.

Methods

We assessed HRs for an Adult Treatment Panel III (ATP-III) metabolic syndrome score (ATP-III MetS) and a sex- and race-specific continuous metabolic syndrome severity z score related to incident diabetes over a median of 7.8 years of follow-up among participants of two observational cohorts, the Atherosclerosis Risk in Communities study (n = 10,957) and the Jackson Heart Study (n = 2137).

Results

The ATP-III MetS had an HR for incident diabetes of 4.36 (95% CI 3.83, 4.97), which was attenuated in models that included the individual metabolic syndrome components. By contrast, participants in the fourth quartile of metabolic syndrome severity (compared with the first quartile) had an HR of 17.4 (95% CI 12.6, 24.1) for future diabetes; in models that also included the individual metabolic syndrome components, this remained significant, with an HR of 3.69 (95% CI 2.42, 5.64). There was a race × metabolic syndrome interaction in these models such that HR was greater for black participants (5.30) than white participants (2.24). When the change in metabolic syndrome severity score was included in the hazard models, this conferred a further association, with changes in metabolic syndrome severity score of ≥0.5 having a HR of 2.66 compared with changes in metabolic syndrome severity score of ≤0.

Conclusions/interpretation

Use of a continuous sex- and race-specific metabolic syndrome severity z score provided an additional prediction of risk of diabetes beyond that of the individual metabolic syndrome components, suggesting an added risk conferred by the processes underlying the metabolic syndrome. Increases in this score over time were associated with further risk, supporting the potential clinical utility of following metabolic syndrome severity over time.

Keywords

Insulin resistance Metabolic syndrome Risk Type 2 diabetes mellitus 

Abbreviations

AIC

Akaike’s information criterion

ARIC

Atherosclerosis Risk in Communities Study

ATP-III

Adult Treatment Panel III

ATP-III MetS

ATP-III metabolic syndrome criteria

CVD

Cardiovascular disease

JHS

Jackson Heart Study

ROC

Receiver operating characteristic

WC

Waist circumference

Supplementary material

125_2017_4267_MOESM1_ESM.pdf (448 kb)
ESM(PDF 447 kb)

References

  1. 1.
    Shulman GI (2014) Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med 371:2237–2238CrossRefPubMedGoogle Scholar
  2. 2.
    de Ferranti S, Mozaffarian D (2008) The perfect storm: obesity, adipocyte dysfunction, and metabolic consequences. Clin Chem 54:945–955CrossRefPubMedGoogle Scholar
  3. 3.
    DeBoer MD (2013) Obesity, systemic inflammation, and increased risk for cardiovascular disease and diabetes among adolescents: a need for screening tools to target interventions. Nutrition 29:379–386CrossRefPubMedGoogle Scholar
  4. 4.
    Grundy SM, Cleeman JI, Daniels SR et al (2005) Diagnosis and management of the metabolic syndrome—an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112:2735–2752CrossRefPubMedGoogle Scholar
  5. 5.
    Ford ES, Li C, Sattar N (2008) Metabolic syndrome and incident diabetes: current state of the evidence. Diabetes Care 31:1898–1904CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Kahn R, Buse J, Ferrannini E, Stern M (2005) 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 28:2289–2304CrossRefPubMedGoogle Scholar
  7. 7.
    Gurka MJ, Ice CL, Sun SS, DeBoer MD (2012) A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences. Cardiovasc Diabetol 11:128CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Gurka MJ, Lilly CL, Norman OM, DeBoer MD (2014) 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 63:218–225CrossRefPubMedGoogle Scholar
  9. 9.
    Lee AM, Gurka MJ, DeBoer MD (2016) A metabolic syndrome severity score to estimate risk in adolescents and adults: current evidence and future potential. Expert Rev Cardiovasc Ther 14:411–413CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    DeBoer MD, Gurka MJ, Woo JG, Morrison JA (2015) Severity of metabolic syndrome as a predictor of cardiovascular disease between childhood and adulthood: the Princeton Lipid Research Cohort Study. J Am Coll Cardiol 66:755–757CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    DeBoer MD, Gurka MJ, Woo JG, Morrison JA (2015) Severity of the metabolic syndrome as a predictor of type 2 diabetes between childhood and adulthood: the Princeton Lipid Research Cohort Study. Diabetologia 58:2745–2752CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    DeBoer MD, Gurka MJ, Hill Golden S et al (2016) Independent associations between metabolic syndrome severity and future coronary heart disease by sex and race. J Am Coll Cardiol 69:1204–1205CrossRefGoogle Scholar
  13. 13.
    The ARIC Investigators (1989) The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol 129: 687–702Google Scholar
  14. 14.
    Collaborative Studies Coordinating Center (2017) Atherosclerosis Risk in Communities Study Description. Available from: https://www2.cscc.unc.edu/aric/. Accessed 20 Mar 2017
  15. 15.
    Jackson Heart Study Investigators (2015) Welcome to the Jackson Heart Study Home Page. Available from https://www.jacksonheartstudy.org/Research/Ancillary-Studies. Accessed 20 Mar 2017
  16. 16.
    Taylor HA, Wilson JG, Jones DW et al (2005) Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study. Ethn Dis 15:S6-4-17PubMedGoogle Scholar
  17. 17.
    McNeill AM, Schmidt MI, Rosamond WD et al (2005) The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care 28:385–390CrossRefPubMedGoogle Scholar
  18. 18.
    DeBoer MD, Gurka MJ (2010) Ability among adolescents for the metabolic syndrome to predict elevations in factors associated with type 2 diabetes and cardiovascular disease: data from the national health and nutrition examination survey 1999–2006. Metab Syndr Relat Disord 8:343–353CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    DeBoer MD, Gurka MJ, Morrison JA, Woo JG (2016) Inter-relationships between the severity of metabolic syndrome, insulin and adiponectin and their relationship to future type 2 diabetes and cardiovascular disease. Int J Obes 40:1353–1359CrossRefGoogle Scholar
  20. 20.
    Schmidt MI, Duncan BB, Bang H et al (2005) Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities study. Diabetes Care 28:2013–2018CrossRefPubMedGoogle Scholar
  21. 21.
    Effoe VS, Correa A, Chen H, Lacy ME, Bertoni AG (2015) High-sensitivity C-reactive protein is associated with incident type 2 diabetes among African Americans: the Jackson Heart Study. Diabetes Care 38:1694–1700CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344CrossRefPubMedGoogle Scholar
  23. 23.
    Vishnu A, Gurka MJ, DeBoer MD (2015) 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 243:278–285CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Aguilar-Salinas CA, Garcia EG, Robles L et al (2008) High adiponectin concentrations are associated with the metabolically healthy obese phenotype. J Clin Endocrinol Metab 93:4075–4079CrossRefPubMedGoogle Scholar
  25. 25.
    Hanley AJ, Karter AJ, Williams K et al (2005) Prediction of type 2 diabetes mellitus with alternative definitions of the metabolic syndrome: the Insulin Resistance Atherosclerosis Study. Circulation 112:3713–3721CrossRefPubMedGoogle Scholar
  26. 26.
    Sumner AE (2009) Ethnic differences in triglyceride levels and high-density lipoprotein lead to underdiagnosis of the metabolic syndrome in black children and adults. J Pediatr 155:e7–e11CrossRefGoogle Scholar
  27. 27.
    DeBoer MD (2010) Underdiagnosis of metabolic syndrome in non-Hispanic black adolescents: a call for ethnic-specific criteria. Curr Cardiovasc Risk Rep 4:302–310CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Cowie CC, Rust KF, Byrd-Holt DD et al (2010) Prevalence of diabetes and high risk for diabetes using A1C criteria in the U.S. population in 1988–2006. Diabetes Care 33:562–568CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Mozaffarian D, Benjamin EJ, Go AS et al (2016) Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation 133:e38–e360CrossRefPubMedGoogle Scholar
  30. 30.
    Walker SE, Gurka MJ, Oliver MN, Johns DW, DeBoer MD (2012) Racial/ethnic discrepancies in the metabolic syndrome begin in childhood and persist after adjustment for environmental factors. Nutr Metab Cardiovasc Dis 22:141–148CrossRefPubMedGoogle Scholar
  31. 31.
    DeBoer MD, Gurka MJ, Sumner AE (2011) Diagnosis of the metabolic syndrome is associated with disproportionately high levels of high-sensitivity C-reactive protein in non-Hispanic black adolescents: an analysis of NHANES 1999–2008. Diabetes Care 34:734–740CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    DeBoer MD, Dong L, Gurka MJ (2011) Racial/ethnic and sex differences in the ability of metabolic syndrome criteria to predict elevations in fasting insulin levels in adolescents. J Pediatr 159:975–981CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    DeBoer MD, Dong L, Gurka MJ (2012) Racial/ethnic and sex differences in the relationship between uric acid and metabolic syndrome in adolescents: an analysis of National Health and Nutrition Survey 1999–2006. Metabolism 61:554–561CrossRefPubMedGoogle Scholar
  34. 34.
    DeBoer MD (2011) Ethnicity, obesity and the metabolic syndrome: implications on assessing risk and targeting intervention. Expert Rev Endocrinol Metab 6:279–289CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Mottillo S, Filion KB, Genest J et al (2010) The metabolic syndrome and cardiovascular risk: a systematic review and meta-analysis. J Am Coll Cardiol 56:1113–1132CrossRefPubMedGoogle Scholar
  36. 36.
    Goff DC, Lloyd-Jones DM, Bennett G et al (2014) 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129:S49–S73CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Matthew J. Gurka
    • 1
  • Sherita H. Golden
    • 2
    • 3
  • Solomon K. Musani
    • 4
  • Mario Sims
    • 4
  • Abhishek Vishnu
    • 1
    • 5
  • Yi Guo
    • 1
  • Michelle Cardel
    • 1
  • Thomas A. Pearson
    • 6
  • Mark D. DeBoer
    • 7
  1. 1.Department of Health Outcomes and Policy, College of MedicineUniversity of FloridaGainesvilleUSA
  2. 2.Department of MedicineJohns Hopkins UniversityBaltimoreUSA
  3. 3.Department of EpidemiologyJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Medicine, Jackson Heart StudyUniversity of Mississippi Medical CenterJacksonUSA
  5. 5.Icahn School of Medicine at Mount SinaiNew YorkUSA
  6. 6.Department of Epidemiology, College of MedicineUniversity of FloridaGainesvilleUSA
  7. 7.Department of Pediatrics, Division of Pediatric EndocrinologyUniversity of VirginiaCharlottesvilleUSA

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