Short Communication | Published:

Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005–2012

International Journal of Obesity volume 40, pages 883886 (2016) | Download Citation

Abstract

The United States Equal Employment Opportunity Commission has proposed rules allowing employers to penalize employees up to 30% of health insurance costs if they fail to meet ‘health’ criteria, such as reaching a specified body mass index (BMI). Our objective was to examine cardiometabolic health misclassifications given standard BMI categories. Participants (N=40 420) were individuals aged 18+ in the nationally representative 2005–2012 National Health and Nutrition Examination Survey. Using the blood pressure, triglyceride, cholesterol, glucose, insulin resistance and C-reactive protein data, population frequencies/percentages of metabolically healthy versus unhealthy individuals were stratified by BMI. Nearly half of overweight individuals, 29% of obese individuals and even 16% of obesity type 2/3 individuals were metabolically healthy. Moreover, over 30% of normal weight individuals were cardiometabolically unhealthy. There was no significant race-by-BMI interaction, but there was a significant gender-by-BMI interaction, F(4,64)=3.812, P=0.008. Using BMI categories as the main indicator of health, an estimated 74 936 678 US adults are misclassified as cardiometabolically unhealthy or cardiometabolically healthy. Policymakers should consider the unintended consequences of relying solely on BMI, and researchers should seek to improve diagnostic tools related to weight and cardiometabolic health.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from $8.99

All prices are NET prices.

References

  1. 1.

    , , . The obesity epidemic. Clin Chest Med 2009; 30: 415–444.

  2. 2.

    , , , , , . The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Public Health 2009; 9: 88.

  3. 3.

    , , , , , et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: Prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med 2008; 168: 1617–1624.

  4. 4.

    . The impact of obesity on wages. J Hum Resour 2004; 39: 451–474.

  5. 5.

    , , , . Incentivizing wellness in the workplace: Sticks (not carrots) send stigmatizing signals. Psychol Sci 2013; 24: 1512–1522.

  6. 6.

    , , , . Weighed down by stigma: How weight-based social identity threat influences weight gain and health. Soc Psychol Personal Compass 2015; 9: 255–268.

  7. 7.

    , , . A review and meta-analysis of the effect of weight loss on all-cause mortality risk. Nutr Res Rev 2009; 22: 93–108.

  8. 8.

    National Health and Nutrition Examination Survey: Questionnaires, datasets, and related documentation. Available at: .

  9. 9.

    Task 2: When and how to construct weights when combining survey cycles. Available at: .

  10. 10.

    National Health and Nutrition Examination Survey: Analytic guidelines, 1999–2010. Available at: .

  11. 11.

    , , , , , et al. Weight bias in 2001 versus 2013: Contradictory attitudes among obesity researchers and health professionals. Obesity (Silver Spring) 2015; 23: 46–53.

  12. 12.

    , , , , , . Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obes Rev 2015; 16: 319–326.

  13. 13.

    , , . Moving focus from weight to health. What Are the components used in interventions to improve cardiovascular health in children? PLoS One 2015; 10: e0135115.

  14. 14.

    , . A call to shift the public health focus away from weight. Am J Public Health 2015; 105: e3–e3.

  15. 15.

    , , , , . Changing the endpoints for determining effective obesity management. Prog Cardiovasc Dis 2015; 57: 330–336.

  16. 16.

    , , , , , et al. The intriguing metabolically healthy but obese phenotype: Cardiovascular prognosis and role of fitness. Eur Heart J 2013; 34: 389–397.

  17. 17.

    , , , , , . Fitness vs. fatness on all-cause mortality: A meta-analysis. Prog Cardiovasc Dis 2014; 56: 382–390.

  18. 18.

    , , . Healthy obese versus unhealthy lean: The obesity paradox. Nat Rev Endocrinol 2015; 11: 55–62.

  19. 19.

    , . Current issues in the identification and treatment of metabolically healthy but obese individuals. Nutr Metab Cardiovasc Dis 2014; 24: 455–459.

  20. 20.

    , , . Occurrence and timing of childhood overweight and mortality: Findings from the Third Harvard Growth Study. J Pediatr 2012; 160: 743–750.

Download references

Acknowledgements

AJT was supported by the Hellman Fellows fund. The Hellman Fellows Fund had no further role in the design of the study, in the collection, analysis and interpretation of the data, in writing the report and in deciding to submit the paper for publication. AJT and JMH conceived the study. AJT obtained funding. CW analyzed the data. AJT, JMH, JNC and CW interpreted the analyses and drafted the manuscript.

Author information

Affiliations

  1. Department of Psychology, University of California, Los Angeles, CA, USA

    • A J Tomiyama
    •  & J Nguyen-Cuu
  2. Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA

    • J M Hunger
  3. Office of Information Technology, University of California, Los Angeles, CA, USA

    • C Wells

Authors

  1. Search for A J Tomiyama in:

  2. Search for J M Hunger in:

  3. Search for J Nguyen-Cuu in:

  4. Search for C Wells in:

Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to A J Tomiyama.

Supplementary information

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/ijo.2016.17

Supplementary Information accompanies this paper on International Journal of Obesity website (http://www.nature.com/ijo)