Skip to main content

Thank you for visiting 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.

Open questions about metabolically normal obesity


Subsets of obese subjects without any cardiometabolic risk factors have been repeatedly described. This raises questions whether obesity ‘per se’ enhances the risk for cardiovascular or metabolic diseases and whether healthy obese subjects would benefit from a medical treatment. In order to answer these questions, as a first step, an expert consensus should be reached for the definition of metabolic normality. In fact, up to now, different parameters related to the metabolic syndrome and/or to insulin sensitivity have been utilized across studies. Once an agreement is reached, population-based studies should be undertaken to establish the incidence of metabolic normality among obese subjects. Furthermore, many other parameters such as age, sex, race, fat distribution and physical activity should be monitored to obtain results representative of a general population. Longitudinal studies aimed at investigating the evolution of the cardiometabolic profile of healthy obese subjects are also needed. In conclusion, data from the literature strongly suggest that a regular surveillance of the cardiometabolic parameters and a prevention of any further weight gain should be applied to healthy obese individuals, whereas possible benefits of a weight loss treatment are still a matter of debate.


Obesity is a well-established and independent risk factor for cardiovascular diseases and mortality in the general population.1, 2, 3 However, a subset of obese subjects seems to be protected from obesity-related cardiovascular and cardiometabolic abnormalities.4, 5, 6 This observation raises the possibility that obesity is not inevitably associated with co-morbidities and, therefore, does not necessarily need a treatment.

This review will address some questions that remain to be investigated, in order to establish sound guidelines for the management of obese subjects with a healthy cardiometabolic profile.

Which definition?

To date, there is no uniform definition for obesity phenotypes. Generally, metabolically normal obesity (MNO) describes the absence of any overt cardiometabolic disease: specifically, type 2 diabetes mellitus, dyslipidemia and hypertension in an individual with body mass index 30 kg m−2. In addition to the absence of overt pathologies, the associations and clustering of cardiometabolic risk factors, such as metabolic syndrome components or inflammatory markers, have also been used in categorizing subjects as metabolically normal or abnormal.

The metabolic syndrome, however, is defined in at least four different ways (WHO, IDF, ATP-III and EGIR). This discordance is a possible reason for inconsistencies in the prevalence of metabolic normality reported in the literature.

Data from the European Group for the Study of Insulin Resistance (EGIR)4 have shown that in obese subjects free from diabetes and hypertension, the prevalence of insulin resistance was relatively low (26%). Furthermore, other studies have demonstrated that both the prevalence7, 8, 9 and the incidence10 of cardiovascular risk factors and/or diseases are strongly related to insulin resistance. Thus, insulin sensitivity could be the key factor discriminating healthy from at-risk obese subjects.11 Some authors have, therefore, adopted insulin sensitivity as an additional12, 13 or even the sole6, 14, 15 criterion to define MNO.

The gold standard for insulin sensitivity assessment is the hyperinsulinemic euglycemic clamp technique. However, due to the complexity of this technique, different surrogate indexes have been used to estimate insulin sensitivity. Some of them16 are derived from oral glucose tolerance test; others, such as the homeostasis model assessment (HOMA) index17 or quantitative insulin sensitivity check index,18 use glucose and insulin fasting levels.

In conclusion, the comparison of different studies investigating MNO may be difficult because of the use of different parameters to categorize subjects as metabolically healthy. Thus, the absence of harmonized criteria is the main barrier to be overcome in examining the magnitude of metabolic normality among patients suffering from obesity.

Which prevalence?

Table 1 illustrates the variability of the prevalence of MNO across the studies. Clearly, the prevalence of MNO versus at-risk obesity mainly depends on selected criteria for the definition of cardiometabolic normality. Another critical point is also represented by the cut-off values of normality for a given parameter. This is particularly true when insulin sensitivity is taken into account. For instance, the threshold value for the HOMA index largely varies across the studies,9, 13, 19, 20 whereas the one of glucose disposal is often obtained from pre-established stratifications of the study groups.6, 15

Table 1 Prevalence of metabolically normal obesity across the studies

Furthermore, other factors such as sex, age or family history of diabetes can largely influence this prevalence, which, in fact, varies highly according to the studies.

Among 5440 subjects who participated in the National Health and Nutrition Examination Surveys (NHANES 1999–2004), 31.7% of obese US adults showed a healthy profile, defined as the presence of no more than one of six classical cardiometabolic risk factors (blood pressure, triglycerides, fasting glucose, C-reactive protein, low high-density lipoprotein cholesterol level and insulin sensitivity).19 In the same study, when using more stringent criteria (that is, none of the six cardiometabolic risk factors), only 16.6% of obese adults were categorized as metabolically healthy.19

It is interesting to note that, in this same population, only 6% of obese subjects were metabolically normal, when the cut-off point (HOMA index) for insulin sensitivity was lowered from 5.119 to <2.5.20 The results of these two studies clearly underline how the nature of selected parameters and the cut-off point for the same parameter heavily condition the prevalence of metabolic normality.

In an Italian population-based study of 681 obese subjects, 27.5% did not show adverse risk factors and obesity-related metabolic and cardiovascular co-morbidities, although the authors applied numerous and strict criteria for the definition of metabolic normality.12

Data from 314 adult Germans14 showed that a predefined upper quartile of insulin-sensitive obese subjects (as established by Matsuda or HOMA index) had cardiometabolic parameters closely similar to the ones measured in a normal-body-weight control group. It should be noted, however, that all the participants had a family history of diabetes and/or a previous diagnosis of impaired glucose tolerance or gestational diabetes. It is well known that all these situations are associated with reduced insulin sensitivity in both normal-body-weight and obese subjects.21 For this reason, it is difficult to apply the results of this study to a general population.

In a cohort of 113 obese postmenopausal women, the prevalence of metabolically healthy obese subjects was 25% using methods based on insulin sensitivity (clamp, Matsuda and HOMA index).22

In a study of 43 obese postmenopausal women,5 subjects were first selected on the basis of their percent body fat (35%). They were then submitted to a hyperinsulinemic euglycemic clamp, and a literature-based cut-off point of 8.0 mg min−1 kg−1 lean body mass of glucose uptake was used to categorize subjects with MNO. In this way, 39.5% of the subjects were identified as metabolically normal but obese. Because of the selection of sex and menopausal status and/or the relatively small number of subjects, the results of the two last studies can be applied only to a specific category of subjects.

In a recent study, performed in a European clinically healthy population, our group showed that the prevalence of MNO, as defined by the IDF criteria and the glucose disposal rate (clamp), was about 6%.23

Which determinants?


It is generally accepted that genetic background has an important role in the development of obesity and of its major related co-morbidity, diabetes. Consequently, MNO could also be the expression of a genetic trait. It has been shown, for instance, that fat deposition in the visceral area is influenced by genes.24, 25, 26 Furthermore, human27, 28 and animal29 studies have demonstrated that spontaneous physical activity is associated with genetic characteristics. Both the amount of visceral fat and physical activity are potential determinants of MNO. It is therefore possible that genetic background influences the metabolically normal phenotype expression by these factors. Up to now, however, no evidence supports this hypothesis.

Besides genes, epigenetic changes, that is, inheritable modifications of gene expression, can heavily contribute to the development of metabolic disturbances such as diabetes.30 It has also been proposed31 that, during pregnancy or lactation, a maternal metabolic deregulation could favor, in the offspring, the later development of metabolic syndrome, that is, one of the mostly used parameter for the definition of metabolic normality in obesity.

Visceral adipose tissue

Visceral adipose tissue mass, as determined by computed tomography in post-menopausal sedentary women, is smaller in MNO as compared with at-risk obese subjects.5, 6

The role of visceral fat is confirmed by the results obtained by Jennings et al.,32 who measured a smaller waist circumference in MNO in a population of African-American women. On the other hand, it is well established that visceral adipose tissue has an important role in the genesis of both insulin resistance and inflammation33 often present in obesity. Thus, a relatively low amount of visceral adipose tissue could explain the more favorable metabolic and inflammatory profile described in MNO.6

Omental adipocytes have a smaller size in healthy individuals than in at-risk obese individuals.34 This correlates with the degree of insulin resistance (HOMA established). It is of note that other studies have demonstrated that adiponectin secretion from the visceral depot is negatively related to the adipocyte size.35 This is consistent with the finding that MNO is characterized by elevated levels of circulating adiponectin.36 This study was conducted in 726 subjects whose body mass index ranged from 19 to >40 kg m−2. It is interesting to note that the prevalence of metabolic normality, defined as the absence of an overt pathology such as diabetes, hypertension or dyslipidemia, declined from 85% in normal-body-weight subjects to 46–48% in obese and 23% in morbidly obese individuals, thus suggesting that increasing body mass index drastically reduces the chances of being metabolically normal. Similar results have been obtained in the Wildmann et al.19 study, in which it was demonstrated that the prevalence of metabolic normality decreases with increasing age and body mass index.

Ectopic fat deposition

Obesity is often associated with ectopic fat deposition. The physiopathological mechanisms underlying this phenomenon are still unclear. On the contrary, its impact on numerous functions, such as insulin sensitivity and inflammation, are well established.37 Uncomplicated obesity is characterized by a lower degree of ectopic fat deposition, in particular in muscle and liver.14 This could, at least in part, explain why healthy obese subjects seem to be protected from the major obesity-linked co-morbidities.


A higher degree of physical activity has been reported by some authors,19 but not by others,5, 32 in MNO. It is not clear whether this healthier lifestyle characteristic is linked to possible genetic traits, as previously discussed, or to a more favorable socioeconomic status, which is often associated to more active leisure-time occupations.38 This last hypothesis is not supported by data found in the study by Jennings et al.,32 in which educational or economic levels did not distinguish the metabolically healthy obese subpopulation. Whatever the cause, increased physical activity could positively influence the cardiometabolic profile of these obese subjects by a reduction of visceral fat mass,19 which is a recognized determinant of cardiometabolic risk factors.

Up to now, no systematic investigation has analyzed the role of diet composition in MNO. Although obesity is almost invariably associated to high-fat, low-fiber and vegetable diet, it is possible that a minority of obese subjects have healthier alimentary habits that improve their cardiometabolic profile.

Psychological traits

Only one study has investigated this potentially major contributor to the healthy or unhealthy profile of obese subjects. Karelis et al.39 have proposed that some psychological traits could distinguish metabolically normal obese from the ‘at-risk’ population. Unfortunately, these authors could not provide evidence of any specific characteristics, in terms of quality of life, self-esteem or perceived stress. These negative results may be due to the small cohort of subjects who were investigated (20 metabolically normal vs 20 at-risk obese women). In the light of the well-documented cardiometabolic consequences of stress,40 it would be worthwhile to investigate further the potential role of stress as a discriminator between at-risk or not at-risk obesity.

Natural history of obesity

The existence of a subset of obese subjects who do not show any risk factor for cardiometabolic diseases is an indubitable fact. It remains to be elucidated whether this favorable profile represents a permanent characteristic or is just a step in the natural history of obesity, which will evolve through the appearance of risk factors and, then, of overt pathologies. Besides these two possibilities, a third intermediate and more likely scenario could be that some obese subjects potentially remain protected from the risk of co-morbidities provided that some changes, such as increase in body weight or reduction of physical activity, do not negatively interfere with their favorable cardiometabolic profile.

To answer this question, large-scale longitudinal studies are needed. Up to now, the only available data derive from two investigations. In the first,41 an 11-year follow-up study, the authors demonstrated a significantly increased risk for diabetes in MNO, whereas they did not observe a higher incidence of cardiovascular diseases. Another 9-year follow-up study, conducted on about 6000 subjects,20 revealed that obesity, even in the absence of metabolic alterations, is associated with an increased risk for all causes of mortality. In addition to their rather discouraging results, both these studies demonstrated that metabolically normal obese subjects were younger than their at-risk counterparts, further suggesting an age-related transition from normal to abnormal cardiometabolic profile.

Some reports seem to confirm this hypothesis: for instance, by examining a population of almost 5000 subjects, Janssen et al.42 have demonstrated that, in women, 10-year duration of overweight has a significant impact on the appearance of diabetes, dyslipidemia and hypertension.

In contrast with these findings, it has been reported that an early onset of obesity is associated with higher insulin sensitivity.5, 43 No well-defined adaptative mechanisms have been evoked to explain this surprising result, which, differently from the study by Janssens et al.,42 has been obtained in a very small sample (17 subjects in the study by Brochu et al.5 and 30 in the study by Muscelli et al.43). This finding, therefore, needs a confirmation based on a larger number of observations.

Which surveillance and/or treatment?

Before discussing whether metabolically normal obese individuals would benefit from treatment, the usefulness of a close surveillance should be examined.

The relative lack of longitudinal studies makes difficult the elaboration of sound guidelines for both surveillance and treatment of metabolically normal obese subjects. However, it is generally agreed that there is no evidence that these subjects are permanently protected from the risk of obesity-related co-morbidities. Furthermore, the fact that metabolically normal obese subjects show a healthier cardiometabolic profile, as compared with at-risk obese subjects, does not necessarily mean that they are perfectly normal. As realistically suggested by Marini et al.,15 metabolically normal obese subjects occupy an intermediate position between healthy, normal-body-weight and at-risk obese subjects, in terms of cardiovascular characteristics such as carotid intima–media thickness, systolic and diastolic blood pressure. When compared with normal-body-weight controls, healthy obese subjects also show an impaired aortic elastic function44 and a deterioration of the endothelial function.45

On the basis of this evidence, a prudent attitude would be to regularly monitor the risk factors in metabolically normal obese subjects, in order to detect early a possible negative evolution of their cardiometabolic profile. In particular, a special surveillance should be applied to prevent any increase in body weight. In fact, there is no evidence that healthy obese subjects could tolerate a further increase of their fat mass, without any consequences on their cardiometabolic profile. More generally, it is well established that worsening of body weight is strongly associated with the deterioration of inflammatory and metabolic syndrome-related parameters.46 Prevention of obesity aggravation should therefore be applied to any subgroup of obese subjects.

With regard to the possible treatment of MNO, the discussion should be limited to the usefulness of a weight loss intervention. In fact, the normality of blood pressure, glycemia and lipidemia does not justify any pharmacological therapy in these subjects. Very few data are available about the effect of weight loss in metabolically normal obese subjects: one study reports that a 3% weight reduction significantly improved the inflammatory and lipid profile in at-risk, but not in MNO individuals.47 The lack of effect in the healthy obese group could be explained, at least in part, by the fact that their cardiometabolic and inflammatory values are already within the limit of normality. In addition, as pointed out by Reaven,11 the largest benefits of weight reduction are observed in those individuals who show insulin resistance, which is, by definition, absent in MNO.

More surprisingly, Karelis et al.48 found a worsening in insulin sensitivity in healthy obese women after a 6-month weight reduction program. This result led the authors to suggest that healthy obese subjects may respond differently to weight loss and, therefore, such a type of intervention could be counterproductive. The implications of such a conclusion are too important to be based on a single study performed in a small group of patients (20 subjects). Although the possible adverse effect of weight loss must be considered with caution, one should remember that achieving permanent weight reduction is a difficult challenge for any obese person and the risk of weight regain is elevated. Furthermore, repeated episodes of weight loss followed by regain may have adverse health effects.49, 50 For these reasons, any weight loss program should be preceded by a careful evaluation of expected costs and benefits. This is particularly true when treating MNO. In these subjects, however, the indication for a weight reduction could come from the evaluation of other obesity-related co-morbidities, such as sleeping apnea, back pain, knee osteoarthritis or cholelithiasis.


An expert consensus must be established about the definition of MNO. This consensus would thus clarify whether metabolic normality has a quite elevated incidence (over 30%) in the obese population or whether it is a relatively rare observation (6%). We should also enhance our knowledge about the determinants of this favorable metabolic profile and about the possible transition from a healthy to an at-risk obesity. This last aspect should be investigated in population-based longitudinal studies. Any guideline for the treatment (if any) of healthy obese subjects can be proposed before answering these questions. A regular surveillance of the cardiometabolic risk parameters and prevention of any further weight gain seem to represent the most prudent and sound attitude in the management of metabolically normal obese subjects.


  1. 1

    Hubert HB, Feinleib M, McNamara PM, Castelli WP . Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation 1983; 67: 968–977.

    CAS  Article  Google Scholar 

  2. 2

    Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath Jr CW . Body-mass index and mortality in a prospective cohort of US adults. N Engl J Med 1999; 341: 1097–1105.

    CAS  Article  Google Scholar 

  3. 3

    Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH . The disease burden associated with overweight and obesity. JAMA 1999; 282: 1523–1529.

    CAS  Article  Google Scholar 

  4. 4

    Ferrannini E, Natali A, Bell P, Cavallo-Perin P, Lalic N, Mingrone G . Insulin resistance and hypersecretion in obesity. European Group for the Study of Insulin Resistance (EGIR). J Clin Invest 1997; 100: 1166–1173.

    CAS  Article  Google Scholar 

  5. 5

    Brochu M, Tchernof A, Dionne IJ, Sites CK, Eltabbakh GH, Sims EA et al. What are the physical characteristics associated with a normal metabolic profile despite a high level of obesity in postmenopausal women? J Clin Endocrinol Metab 2001; 86: 1020–1025.

    CAS  PubMed  Google Scholar 

  6. 6

    Karelis AD, Faraj M, Bastard JP, St-Pierre DH, Brochu M, Prud′homme D et al. The metabolically healthy but obese individual presents a favorable inflammation profile. J Clin Endocrinol Metab 2005; 90: 4145–4150.

    CAS  Article  Google Scholar 

  7. 7

    McLaughlin T, Allison G, Abbasi F, Lamendola C, Reaven G . Prevalence of insulin resistance and associated cardiovascular disease risk factors among normal weight, overweight, and obese individuals. Metabolism 2004; 53: 495–499.

    CAS  Article  Google Scholar 

  8. 8

    McLaughlin T, Abbasi F, Lamendola C, Reaven G . Heterogeneity in the prevalence of risk factors for cardiovascular disease and type 2 diabetes mellitus in obese individuals: effect of differences in insulin sensitivity. Arch Intern Med 2007; 167: 642–648.

    CAS  Article  Google Scholar 

  9. 9

    Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Targher G et al. Prevalence of insulin resistance in metabolic disorders: the Bruneck Study. Diabetes 1998; 47: 1643–1649.

    CAS  Article  Google Scholar 

  10. 10

    Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Meigs JB et al. Insulin resistance as estimated by homeostasis model assessment predicts incident symptomatic cardiovascular disease in caucasian subjects from the general population: the Bruneck study. Diabetes Care 2007; 30: 318–324.

    Article  Google Scholar 

  11. 11

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

    Article  Google Scholar 

  12. 12

    Iacobellis G, Ribaudo MC, Zappaterreno A, Iannucci CV, Leonetti F . Prevalence of uncomplicated obesity in an Italian obese population. Obes Res 2005; 13: 1116–1122.

    Article  Google Scholar 

  13. 13

    Karelis AD, Brochu M, Rabasa-Lhoret R . Can we identify metabolically healthy but obese individuals (MHO)? Diabetes Metab 2004; 30: 569–572.

    CAS  Article  Google Scholar 

  14. 14

    Stefan N, Kantartzis K, Machann J, Schick F, Thamer C, Rittig K et al. Identification and characterization of metabolically benign obesity in humans. Arch Intern Med 2008; 168: 1609–1616.

    Article  Google Scholar 

  15. 15

    Marini MA, Succurro E, Frontoni S, Hribal ML, Andreozzi F, Lauro R et al. Metabolically healthy but obese women have an intermediate cardiovascular risk profile between healthy nonobese women and obese insulin-resistant women. Diabetes Care 2007; 30: 2145–2147.

    Article  Google Scholar 

  16. 16

    Matsuda M, DeFronzo RA . Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 1999; 22: 1462–1470.

    CAS  Article  Google Scholar 

  17. 17

    Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC . Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28: 412–419.

    CAS  Article  Google Scholar 

  18. 18

    Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000; 85: 2402–2410.

    CAS  Article  Google Scholar 

  19. 19

    Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J 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.

    Article  Google Scholar 

  20. 20

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

    Article  Google Scholar 

  21. 21

    Perseghin G, Ghosh S, Gerow K, Shulman GI . Metabolic defects in lean nondiabetic offspring of NIDDM parents: a cross-sectional study. Diabetes 1997; 46: 1001–1009.

    CAS  Article  Google Scholar 

  22. 22

    Messier V, Karelis AD, Prud′homme D, Primeau V, Brochu M, Rabasa-Lhoret R . Identifying metabolically healthy but obese individuals in sedentary postmenopausal women. Obesity (Silver Spring) 2009; 18: 911–917.

    Article  Google Scholar 

  23. 23

    Pataky Z, Bobbioni-Harsch E, Makoundou V, Carpentier A, Golay A . Does metabolically normal obesity exist? Obesity Facts 2009; 2 (Suppl 2): 48.

    Google Scholar 

  24. 24

    Fox CS, Heard-Costa N, Cupples LA, Dupuis J, Vasan RS, Atwood LD . Genome-wide association to body mass index and waist circumference: the Framingham Heart Study 100 K project. BMC Med Genet 2007; 8 (Suppl 1): S18.

    Article  Google Scholar 

  25. 25

    Kunnas T, Lahtio R, Kortelainen ML, Kalela A, Nikkari ST . Gln27Glu variant of Beta2-adrenoceptor gene affects male type fat accumulation in women. Lipids Health Dis 2009; 8: 43.

    Article  Google Scholar 

  26. 26

    Peeters AV, Beckers S, Verrijken A, Mertens I, Roevens P, Peeters PJ et al. Association of SIRT1 gene variation with visceral obesity. Hum Genet 2008; 124: 431–436.

    CAS  Article  Google Scholar 

  27. 27

    Levine JA, Eberhardt NL, Jensen MD . Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science 1999; 283: 212–214.

    CAS  Article  Google Scholar 

  28. 28

    Choh AC, Demerath EW, Lee M, Williams KD, Towne B, Siervogel RM et al. Genetic analysis of self-reported physical activity and adiposity: the Southwest Ohio Family Study. Public Health Nutr 2009; 12: 1052–1060.

    Article  Google Scholar 

  29. 29

    Dumke CL, Rhodes JS, Garland Jr T, Maslowski E, Swallow JG, Wetter AC et al. Genetic selection of mice for high voluntary wheel running: effect on skeletal muscle glucose uptake. J Appl Physiol 2001; 91: 1289–1297.

    CAS  Article  Google Scholar 

  30. 30

    Dabelea D, Hanson RL, Lindsay RS, Pettitt DJ, Imperatore G, Gabir MM et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 2208–2211.

    CAS  Article  Google Scholar 

  31. 31

    Gallou-Kabani C, Junien C . Nutritional epigenomics of metabolic syndrome: new perspective against the epidemic. Diabetes 2005; 54: 1899–1906.

    CAS  Article  Google Scholar 

  32. 32

    Jennings CL, Lambert EV, Collins M, Joffe Y, Levitt NS, Goedecke JH . Determinants of insulin-resistant phenotypes in normal-weight and obese Black African women. Obesity (Silver Spring) 2008; 16: 1602–1609.

    Article  Google Scholar 

  33. 33

    Diamant M, Lamb HJ, van de Ree MA, Endert EL, Groeneveld Y, Bots ML et al. The association between abdominal visceral fat and carotid stiffness is mediated by circulating inflammatory markers in uncomplicated type 2 diabetes. J Clin Endocrinol Metab 2005; 90: 1495–1501.

    CAS  Article  Google Scholar 

  34. 34

    O'Connell J, Lynch L, Cawood TJ, Kwasnik A, Nolan N, Geoghegan J et al. The relationship of omental and subcutaneous adipocyte size to metabolic disease in severe obesity. PLoS One 2010; 5: e9997.

    Article  Google Scholar 

  35. 35

    Drolet R, Belanger C, Fortier M, Huot C, Mailloux J, Legare D et al. Fat depot-specific impact of visceral obesity on adipocyte adiponectin release in women. Obesity (Silver Spring) 2009; 17: 424–430.

    CAS  Article  Google Scholar 

  36. 36

    Aguilar-Salinas CA, Garcia EG, Robles L, Riano D, Ruiz-Gomez DG, Garcia-Ulloa AC et al. High adiponectin concentrations are associated with the metabolically healthy obese phenotype. J Clin Endocrinol Metab 2008; 93: 4075–4079.

    CAS  Article  Google Scholar 

  37. 37

    Szendroedi J, Roden M . Ectopic lipids and organ function. Curr Opin Lipidol 2009; 20: 50–56.

    CAS  Article  Google Scholar 

  38. 38

    McLaren L, Godley J, MacNairn IA . Social class, gender, and time use: implications for the social determinants of body weight? Health Rep 2009; 20: 65–73.

    PubMed  Google Scholar 

  39. 39

    Karelis AD, Fontaine J, Rabasa-Lhoret R, Prud′homme D, Doucet E, Blanchard C et al. Psychosocial profile of the metabolically healthy but obese postmenopausal woman. Diabetes Metab 2006; 32: 90–91.

    CAS  Article  Google Scholar 

  40. 40

    Kyrou I, Chrousos GP, Tsigos C . Stress, visceral obesity, and metabolic complications. Ann N Y Acad Sci 2006; 1083: 77–110.

    CAS  Article  Google Scholar 

  41. 41

    Meigs JB, Wilson PW, 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.

    CAS  Article  Google Scholar 

  42. 42

    Janssen I, Katzmarzyk PT, Ross R . Duration of overweight and metabolic health risk in American men and women. Ann Epidemiol 2004; 14: 585–591.

    Article  Google Scholar 

  43. 43

    Muscelli E, Camastra S, Gastaldelli A, Natali A, Masoni A, Pecori N et al. Influence of duration of obesity on the insulin resistance of obese non-diabetic patients. Int J Obes Relat Metab Disord 1998; 22: 262–267.

    CAS  Article  Google Scholar 

  44. 44

    Robinson MR, Scheuermann-Freestone M, Leeson P, Channon KM, Clarke K, Neubauer S et al. Uncomplicated obesity is associated with abnormal aortic function assessed by cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2008; 10: 10.

    Article  Google Scholar 

  45. 45

    Oflaz H, Ozbey N, Mantar F, Genchellac H, Mercanoglu F, Sencer E et al. Determination of endothelial function and early atherosclerotic changes in healthy obese women. Diabetes Nutr Metab 2003; 16: 176–181.

    CAS  PubMed  Google Scholar 

  46. 46

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

    CAS  Article  Google Scholar 

  47. 47

    Shin MJ, Hyun YJ, Kim OY, Kim JY, Jang Y, Lee JH . Weight loss effect on inflammation and LDL oxidation in metabolically healthy but obese (MHO) individuals: low inflammation and LDL oxidation in MHO women. Int J Obes (Lond) 2006; 30: 1529–1534.

    CAS  Article  Google Scholar 

  48. 48

    Karelis AD, Messier V, Brochu M, Rabasa-Lhoret R . Metabolically healthy but obese women: effect of an energy-restricted diet. Diabetologia 2008; 51: 1752–1754.

    CAS  Article  Google Scholar 

  49. 49

    Rzehak P, Meisinger C, Woelke G, Brasche S, Strube G, Heinrich J . Weight change, weight cycling and mortality in the ERFORT Male Cohort Study. Eur J Epidemiol 2007; 22: 665–673.

    Article  Google Scholar 

  50. 50

    Olson MB, Kelsey SF, Bittner V, Reis SE, Reichek N, Handberg EM et al. Weight cycling and high-density lipoprotein cholesterol in women: evidence of an adverse effect: a report from the NHLBI-sponsored WISE study. Women′s Ischemia Syndrome Evaluation Study Group. J Am Coll Cardiol 2000; 36: 1565–1571.

    CAS  Article  Google Scholar 

  51. 51

    Karelis AD, Brochu M, Rabasa-Lhoret R . Can we identify metabolically healthy but obese individuals (MHO)? Diabetes Metab 2004; 30: 569–572.

    CAS  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to E Bobbioni-Harsch.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pataky, Z., Bobbioni-Harsch, E. & Golay, A. Open questions about metabolically normal obesity. Int J Obes 34, S18–S23 (2010).

Download citation


  • metabolic normality
  • cardio-metabolic complications
  • metabolic syndrome

Further reading


Quick links