Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Clinical Studies and Practice

Metabolic phenotypes of obesity: frequency, correlates and change over time in a cohort of postmenopausal women

International Journal of Obesity volume 41, pages 170–177 (2017)Cite this article

Subjects

Abstract

Objective:

The possibility that a subset of persons who are obese may be metabolically healthy—referred to as the ‘metabolically healthy obese’ (MHO) phenotype—has attracted attention recently. However, few studies have followed individuals with MHO or other obesity phenotypes over time to assess change in their metabolic profiles. The aim of the present study was to examine transitions over a 6-year period among different states defined simultaneously by body mass index (BMI) and the presence/absence of the metabolic syndrome (MetS).

Methods:

We used repeated measurements available for a subcohort of participants enrolled in the Women’s Health Initiative (N=3512) and followed for an average of 6 years to examine the frequency of different metabolic obesity phenotypes at baseline, the 6-year transition probabilities to other states and predictors of the risk of different transitions. Six phenotypes were defined by cross-tabulating BMI (18.5–<25.0, 25.0–<30.0, ⩾30.0 kg m−2) by MetS (yes, no). A continuous-time Markov model was used to estimate 6-year transition probabilities from one state to another.

Results:

Over the 6 years of follow-up, one-third of women with the healthy obese phenotype transitioned to the metabolically unhealthy obese (MUO) phenotype. Overall, there was a marked tendency toward increased metabolic deterioration with increasing BMI and toward metabolic improvement with lower BMI. Among MHO women, the 6-year probability of becoming MUO was 34%, whereas among unhealthy normal-weight women, the probability of ‘regressing’ to the metabolically healthy normal-weight phenotype was 52%.

Conclusions:

The present study demonstrated substantial change in metabolic obesity phenotypes over a 6-year period. There was a marked tendency toward metabolic deterioration with greater BMI and toward metabolic improvement with lower BMI.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Flegal KM, Carroll MD, Kit BK, Ogden CL . Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. JAMA 2012; 307: 491–497.

    Article  Google Scholar 

  2. Ford ES, Giles WH, Dietz WH . Prevalence of the metabolic syndrome among US adults: findings from the Third National Health and Nutrition Examination Survey. JAMA 2002; 287: 356–359.

    Article  Google Scholar 

  3. Meigs JB . Epidemiology of the insulin resistance syndrome. Curr Diab Rep 2003; 3: 73–79.

    Article  Google Scholar 

  4. Sathyaprakash R, Henry RR . Preventing diabetes by treating aspects of the metabolic syndrome. Curr Diab Rep 2002; 2: 416–422.

    Article  Google Scholar 

  5. Alexander CM, Landsman PB, Teutsch SM, Haffner SM . NCEP defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes 2003; 52: 1210–1214.

    Article  CAS  Google Scholar 

  6. Bianchini F, Kaaks R, Vainio H . Overweight, obesity, and cancer risk. Lancet Oncol 2002; 3: 565–574.

    Article  Google Scholar 

  7. Wildman RP . Healthy obesity. Curr Opin Nutr Metab Care 2009; 12: 438–443.

    Article  Google Scholar 

  8. Reaven G . All obese individuals are not created equal: insulin resistance is the major determinant of cardiovascular disease in overweight/obese individuals. Diabetes Vasc Dis Res 2005; 2: 105–112.

    Article  Google Scholar 

  9. Field AE, Camargo CA, Ogino S . The merits of subtyping obesity: one size does not fit all. JAMA 2013; 310: 2147–2148.

    Article  CAS  Google Scholar 

  10. Karelis AD, St-Pierre DH, Conus F, Rabassa-Lhoret R, Poehlman ET . Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab 2004; 89: 2569–2575.

    CAS  Google Scholar 

  11. Pataky Z, Bobbioni-Harsch E, Golay A . Open questions about metabolically normal obesity. Int J Obesity 2010; 34: S18–S23.

    Article  Google Scholar 

  12. Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, Messier V et al. Characterizing the profile of obese patients who are metabolically healthy. Int J Obesity 2011; 35: 971–981.

    Article  CAS  Google Scholar 

  13. Den Engelsen C, Gorter KJ, Salomé PL, Rutten GE . Development of metabolic syndrome components in adults with a healthy obese phenotype: a 3-year follow-up. Obesity 2013; 21: 1025–1030.

    Article  CAS  Google Scholar 

  14. Hwang YC, Hayashi T, Fujimoto WY, Kahn SE, Leonetti DL, McNeely MJ et al. Visceral fat accumulation predicts the conversion of metabolically healthy obese subjects to an unhealthy phenotype. Int J Obesity 2015; 39: 1365–1370.

    Article  CAS  Google Scholar 

  15. Appleton SL, Seaborn CJ, Visvanathan R, Hill CL, Gill TK, Taylor AW et al. Diabetes and cardiovascular disease outcomes in the metabolically healthy obese phenotype. Diabetes Care 2013; 36: 2388–2394.

    Article  Google Scholar 

  16. Bell JA, Hamer M, Sabia S, Singh-Manoux A, Batty GD, Kivimaki M . The natural course of healthy obesity over 20 years (letter). J Am Coll Cardiol 2015; 65: 101–109.

    Article  Google Scholar 

  17. Eshtiaghi R, Keihani S, Hosseinpnah F, Barzin M, Azizi F . Natural course of metabolically healthy abdominal obese adults after 10 years of follow-up: the Tehran Lipid and Glucose Study. Int J Obesity 2015; 39: 514–519.

    Article  CAS  Google Scholar 

  18. Hamer M, Bell JA, Sabia S, Batty GD, Kivimäki M . Stability of metabolically healthy obesity over 8 years: the English Longitudinal Study of Aging. Eur J Endocrinol 2015; 173: 703–708.

    Article  CAS  Google Scholar 

  19. Bell JA, Kivimaki M, Hamer M . Metabolically healthy obesity and risk of incident type 2 diabetes: a meta-analysis of prospective cohort studies. Obesity Rev 2014; 15: 504–515.

    Article  CAS  Google Scholar 

  20. Lotta LA, Abbasi A, Sharp SJ, Sahlqvist A-S, Waterworth D, Brosnan JM et al. Definitions of metabolic health and risk of future type 2 diabetes in BMI categories: a systemic review and network meta-analysis. Diabetes Care 2015; 38: 2177–2187.

    Article  Google Scholar 

  21. Fan J, Song Y, Chen Y, Hui R, Zhang W . Combined effect of obesity and cardio-metabolic abnormality on the risk of cardiovascular disease: a meta-analysis of prospective cohort studies. Int J Cardiol 2013; 168: 4761–4768.

    Article  Google Scholar 

  22. Kramer CK, Zinman B, Retnakaran R . Are metabolically healthy overweight and obesity benign conditions: a systematic review and meta-analysis. Ann Int Med 2013; 159: 758–769.

    Article  Google Scholar 

  23. Rey-Lopez JP, de Rezende LF, de Sá TH, Stamatakis E . Is the metabolically healthy obesity phenotype an irrelevant artifact for public health? Am J Epidemiol 2015; 182: 737–741.

    Article  Google Scholar 

  24. Bradshaw PT, Stevens J . Invited commentary: limitations and usefulness of the metabolically healthy obesity phenotype. Am J Epidemiol 2015; 182: 742–744.

    Article  Google Scholar 

  25. Ray-Lopez, de Rezende LF, de Sá TH, Stamatakis E, Ray-Lopez et al. Respond to ‘the metabolically healthy obesity phenotype.’. Am J Epidemiol 2015; 182: 745–746.

    Article  Google Scholar 

  26. Ruderman N, Chisholm D, Pi-Sunyer X, Schneider S . The metabolically obese, normal-weight individual revisited. Diabetes 1998; 47: 699–713.

    Article  CAS  Google Scholar 

  27. Meigs JB, Wilson WF, Fox CS, Vasan RS, Nathan DM, Sullivan LM et al. Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J Clin Endocrinol Metab 2006; 91: 2906–2912.

    Article  CAS  Google Scholar 

  28. St Pierre AC, Cantin B, Mauriège P, Bergeron J, Dagenais GR, Després JP et al. Insulin resistance syndrome, body mass index and the risk of ischemic heart disease. CMAJ 2005; 172: 1301–1305.

    Article  Google Scholar 

  29. Song Y, Manson JE, Meigs JB, Ridker PM, Buring JE, Liu S . Comparison of usefulness of body mass index versus metabolic risk factors in predicting 10-year risk of cardiovascular events in women. Am J Cardiol 2007; 100: 1654–1658.

    Article  Google Scholar 

  30. Kuk JL, Arfern CI . Are metabolically normal but obese individuals at lower risk for all-cause mortality? Diabetes Care 2009; 32: 2297–2299.

    Article  Google Scholar 

  31. Anderson GL, Manson J, Wallace R, Lund B, Hall D, Davis S et al. Implementation of the Women’s Health Initiative study design. Ann Epidemiol 2003; 13: S5–S17.

    Article  Google Scholar 

  32. Langer RD, White E, Lewis CE, Kotchen JM, Hendrix SL, Trevisan M . The Women’s Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003; 13 (9 Suppl): S107–S121.

    Article  Google Scholar 

  33. Howard BV, Adams-Campbell L, Allen C, Black H, Passaro M, Rodabough RJ et al. Int J Obes Relat Metab Disord 2004; 28: 1039–1047.

  34. McTiernan A, Kooperberg C, White E, Wilcox S, Coates R, Adams-Campbell LL et al. Recreational physical activity and the risk of breast cancer in postmenopausal women: the Women’s Health Initiative Cohort Study. JAMA 2003; 290: 1331–1336.

    Article  CAS  Google Scholar 

  35. Bergmeyer HU, Bernt E, Schmidt F, Stork H . Determination of hexiokinase and glucose-6-phosphate dehydrogenase. In: Bergmeyer HU (eds). Methods of Enzymatic Analysis. 2nd edn. Academic Press: New York, NY, USA, 1974, pp 1196.

    Google Scholar 

  36. Peterson JI, Young DS . Evaluation of the hexokinase-glucose-6-phosphate dehydrogenase method of determination of glucose in urine. Anal Biochem 1968; 23: 301–316.

    Article  CAS  Google Scholar 

  37. Tietz NW . Fundamentals of Clinical Chemistry, 3rd edn. WB Saunders Co: Philadelphia, PA, USA, 1987; p 544.

    Google Scholar 

  38. Steiner P, Freidel J, Bremmer W, Stein E . Standardization of micromethods for plasma cholesterol, triglyceride and HDL-cholesterol with the lipid clinics’ methodology. J Clin Chem Clin Biochem 1981; 19: 850–851.

    Google Scholar 

  39. National Heart, Lung, and Blood Institute. Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III), final report. NIH Publication No. 02–5215, September 2002.

  40. Grundy SM, Cleeman JI, Daniels ST, Donato KA, Eckel RH, Franklin BA et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005; 112: 2735–2752.

    Article  Google Scholar 

  41. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157–163.

    Article  Google Scholar 

  42. Kuo HS, Chang HJ, Chou P, Teng L, Chen THH A . Markov chain model to assess the efficacy of screening for non-insulin dependent diabetes mellitus (NIDDM). Int J Epidemiol 1999; 28: 233–240.

    Article  CAS  Google Scholar 

  43. Hinnouho G-M, Czernichow S, Dugravot, Nabi H, Brunner EJ, Kivimaki M et al. Metabolically healthy obesity and risk of mortality. Diabetes Care 2013; 36: 2294–2300.

    Article  CAS  Google Scholar 

  44. Thomsen M, Nordesgaard BC . Myocardial infarction and ischemic heart disease in overweight and obesity with and without the metabolic syndrome. JAMA 2013; 174: 15–22.

    Google Scholar 

  45. Kip KE, Marroquin OC, Kelley DE, Johnson D, Kelsey SF, Shaw LJ et al. Clinical importance of obesity versus the metabolic syndrome in cardiovascular risk in women: a report from the Women’s Ischemia Syndrome Evaluation (WISE) study. Circulation 2004; 109: 706–713.

    Article  Google Scholar 

  46. Berrahmoune H, Herbeth B, Samara A, Marteau J-B, Visvikis-Siest S . Five-year alterations in BMI are associated with clustering of changes in cardiovascular risk factors in a gender-dependent way: the Stanislas study. Int J Obesity 2008; 32: 1279–1288.

    Article  CAS  Google Scholar 

  47. Rutter MK, Sullivan LM, Fox CS, Wilson PWF, Nathan DM, Vasan RS et al. Baseline levels, and changes over time in body mass index and fasting insulin, and their relationship to change in metabolic trait clustering. Metab Syndr Relat Disord 2014; 12: 372–380.

    Article  CAS  Google Scholar 

  48. Bradshaw PT, Monda KL, Stevens J . Metabolic syndrome in healthy obese, overweight, and normal weight individuals: the Atherosclerosis Risk in Communities Study. Obesity 2013; 21: 203–209.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by institutional funds from the Albert Einstein College of Medicine.

Author information

Author notes

  1. G C Kabat and W Y-Y Wu: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    G C Kabat, S Wassertheil-Smoller & T E Rohan

  2. Department of Radiation Sciences Oncology, Umeå University, Umeå, Sweden

    W Y-Y Wu

  3. Graduate School of Epidemiology and Department of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

    W Y-Y Wu

  4. Department of Medicine, Arizona Cancer Center, Tucson, AZ, USA

    J W Bea

  5. Department of Epidemiology, School of Public Health, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    C Chen

  6. Department of Public Health Sciences, School of Medicine, University of California at Davis, Davis, CA, USA

    L Qi

  7. Stefanick Department of Medicine, Stanford University, Stanford, CA, USA

    M L Stefanick

  8. Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA

    R T Chlebowski

  9. Department of Preventive Medicine, School of Medicine, Stony Brook University, Stony Brook, NY, USA

    D S Lane

  10. Department of Social and Preventive Medicine, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA

    J Wactawski-Wende

Authors
  1. G C Kabat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W Y-Y Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J W Bea
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M L Stefanick
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. R T Chlebowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. D S Lane
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. J Wactawski-Wende
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. S Wassertheil-Smoller
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. T E Rohan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G C Kabat.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kabat, G., Wu, WY., Bea, J. et al. Metabolic phenotypes of obesity: frequency, correlates and change over time in a cohort of postmenopausal women. Int J Obes 41, 170–177 (2017). https://doi.org/10.1038/ijo.2016.179

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ijo.2016.179

This article is cited by

Search

Advanced search

Quick links