Skip to main content
Download PDF

Prevalence rates of metabolic health and body size phenotypes by different criteria and association with insulin resistance in a Maltese Caucasian population

BMC Endocrine Disorders volume 22, Article number: 160 (2022) Cite this article

Abstract

Introduction

Hyperinsulinemia and insulin resistance are known to be associated with increased cardiovascular morbidity and mortality. A metabolically unhealthy phenotype is frequently used as a surrogate marker for insulin resistance. The aims of the current study were to compare the prevalence of the body size phenotypes using different definitions of metabolic health and to investigate which one of them is most strongly associated with insulin resistance in men and women.

Methods

We conducted a cross-sectional study in a middle-aged cohort of Maltese Caucasian non-institutionalized population. Metabolic health was defined using the various currently used definitions.

Results

There were significant differences in the prevalence of body size phenotypes according to the different definitions. We also found significant sex differences in the predictive value of the various definitions of the metabolically unhealthy phenotype to predict insulin resistance. The strongest association was for the definition of having >2 NCEP-ATPIII criteria to characterize the metabolic unhealthy phenotype in women (odds ratio of 19.7). On the other hand, the Aguilar-Salinas et al. definition had the strongest association in men (odds ratio of 18.7).

Conclusions

We found large differences in the prevalence of the various body size phenotypes when using different definitions, highlighting the need for having standard criteria. Our data also suggest the need for sex-specific definitions of metabolic health.

Peer Review reports

Introduction

Evidence from several epidemiological studies demonstrates that hyperinsulinemia and insulin resistance are associated with an increased risk of both cardiovascular disease and of all-cause cardiovascular and cancer mortality [1,2,3]. For example, in the Paris Prospective Study, fasting and 2-hour post-load plasma insulin levels were found to be independent predictors of coronary artery disease and death over an 11-year follow-up period [4]. In the Helsinki Policemen Study, patients in the highest quintile of the area under the insulin response curve during an oral glucose tolerance test had higher all-cause mortality at 10 and 22 years [5]. The Diabetes Epidemiology: Collaborative analysis of Diagnostic Criteria in Europe (DECODE) Insulin Study Group reported similar results and confirmed the association between hyperinsulinemia and higher risk of cardiovascular mortality in both males and females [6].

The direct quantification of insulin resistance can be challenging in clinical practice. Therefore, metabolic health phenotypes are frequently used as surrogate indices of insulin resistance. The concept of metabolic health comprises multiple anthropometric and biochemical parameters that are easily ascertained clinically. The metabolically unhealthy state describes individuals with increased insulin resistance [7, 8] and higher cardiovascular risk [9] irrespective of body mass index (BMI). To date, there is no universally accepted definition of what constitutes metabolic health, and several definitions are currently in use (Table 1). These definitions vary with regard to which parameters are employed, their respective cut-offs and in the number of abnormal parameters needed to characterize a subject as being metabolically unhealthy.

Table 1 Criteria currently in use to define metabolic health
Full size table

Obesity (as defined by the BMI) is strongly correlated with insulin resistance [19] and with a pro-atherogenic cardiometabolic profile [20,21,22,23]. Increased adiposity is also associated with increased risk of type 2 diabetes [24, 25], and weight loss by lifestyle intervention reduces this risk [26]. Nonetheless, there are some overweight and obese individuals who do not exhibit the typical cardiometabolic abnormalities associated with obesity and are thus referred to as being metabolically healthy overweight/obese (MHOw/O), thereby distinguishing them from the commoner metabolically unhealthy overweight/obese categories (MUHOw/O). Conversely, there are also some normal weight individuals who are metabolically unhealthy (metabolically unhealthy normal weight (MUHNW)), distinguishing them from the commoner metabolically healthy normal weight (MHNW) state [27]. Thus, these four metabolic health phenotypes enable superior stratification of individual cardiometabolic risk than obesity indices alone.

The aims of the current study were to 1) compare the prevalence of body composition phenotypes using different definitions of metabolic health 2) explore sex differences in the relationship of body composition phenotypes to metabolic health and insulin resistance and 3) evaluate which definition of metabolic health is the strongest predictor of insulin resistance. Since there are sex differences in the relationship of anthropometric parameters to metabolic health and insulin resistance [28], we studied males and females separately.

Methods

Study subjects

We conducted a cross-sectional study in a middle-aged cohort (41 ± 10 years) of Maltese Caucasian non-institutionalized population using convenience sampling similar to that used in other studies [29]. Individuals with type 1 diabetes, known underlying genetic or endocrine causes of overweight or underweight (apart from controlled thyroid disorders), active malignancy or terminal illness were excluded. Persons who were unable to give their own voluntary informed consent and pregnant females were also excluded. Anthropometric measurements were recorded with the participants dressed in light clothing and without shoes, using validated equipment which was calibrated in accordance with WHO recommendations [30]. Body weight was measured to the nearest 0.1 kg; height and waist circumference were measured to the nearest 0.1 cm. Normal weight was defined as BMI of 18.5–24.9 kg/m2; overweight as BMI of 25.0–29.9 kg/m2; and obesity as BMI ≥30 kg/m2. Obesity was further subcategorized according to WHO cutoffs as class 1 obesity (BMI 30.0–34.9 kg/m2), class 2 obesity (BMI of 35–39.9 kg/m2) or class 3 obesity (BMI >40.0 kg/m2). Waist index (WI) was calculated as the waist circumference (WC) (cm) / 94 for males and WC (cm) / 80 for females [31]. Fasting insulin and high sensitivity CRP (hsCRP) were quantified at baseline by sandwich ELISA (Diagnostic Automation, USA) according to the manufacturer’s instructions and using a Mithras® microplate reader for absorbance determination. Samples were assayed in duplicate using 50 μL of serum.

Body size definitions

In the study cohort, metabolic health was defined using the various criteria outlined in Table 1. These include the classifications proposed by Wildman et al., (National Health and Nutrition Examination Survey of the United States), Doumatey et al., Hamer et al., Lynch et al., Aguilar-Salinas et al., Karelis et al. and the harmonization criteria proposed by Lavie et al [11,12,13,14,15,16,17]. In addition, the NCEP-ATPIII cut-offs for the metabolic syndrome were also applied [10], with subjects being classified as metabolically healthy if they met none of these criteria (NCEP-0), if they had a maximum of one abnormal criterion (NCEP-1), and if they had a maximum of two abnormal criteria (NCEP-2). HOMA-IR was used to evaluate insulin resistance using the formula: fasting serum insulin (μU/ml) x fasting plasma glucose (mmol/L) /22.5 [32]. This is a validated measure of insulin resistance [33, 34]. For binary logistic regression, we defined insulin resistance as HOMA-IR of ≥2.5. We chose this cut-off since it has been linked to increased mortality in large population-based studies [34,35,36]. Ethical approval for the study was granted by the University of Malta Research Ethics Committee (approval code 06/2016; approval date 08/08/2016).

Statistics

Normality of distribution was assessed by the Kolmogorov-Smirnov test. Since HOMA-IR exhibited a skewed non-normal distribution, the Mann-Whitney test was used to compare HOMA-IR between metabolically healthy and unhealthy individuals. In order to investigate the predictive value of the various definitions of metabolic health in males and females, we performed logistic regression with HOMA-IR ≥ 2.5 as the dependent variable and a metabolic unhealthy phenotype as the independent variable separately for each of the definitions of metabolic health, except for those by Wildman et al. and Karelis et al. The latter two were not entered in logistic regression analysis since HOMA-IR is one of the criteria they use to define metabolic health. To investigate the association between BMI category and insulin resistance, logistic regression with HOMA-IR ≥ 2.5 as the dependent variable and BMI category (normal, overweight, obese classes I-III) as the independent variable was performed. Finally, we repeated logistic regression with metabolic unhealthy phenotype using each of the above-mentioned definitions as the independent variable adjusted for BMI category. All analyses were performed using IBM SPSS version 26. A p-value of <0·05 was considered significant.

Results

We studied 520 individuals of Maltese Caucasian ethnicity (331 females and 190 males). Complete data was obtained for all 520 subjects. The median age in the cohort was 41 years (IQR 6), with a range of 30–51 years. The median age in males was 42 years (IQR 6) and 40 years (IQR 7) in females. The prevalence of the various body size phenotypes according to the different definitions are shown in Fig. 1. The prevalence of metabolically healthy normal weight (MHNW) ranged from 16.3 to 27.5%; metabolically healthy overweight (MHOw) from 11.9 to 32.9%; metabolically healthy obese (MHO) from 2.1 to 19.0%; metabolically unhealthy normal weight (MUHNW) from 0.6 to 13.5%; metabolically unhealthy overweight (MUHOw) from 3.8 to 25.0% and metabolically unhealthy obese (MUHO) from 14.6 to 31.2%.

Fig. 1
figure 1

Prevalence (as %) of body size phenotypes using different definitions. NCEP = National Cholesterol Education Program/Adult Treatment Panel III. MHNW = metabolically healthy normal weight; MHoW = metabolically healthy overweight; MHO = metabolically healthy obese; MUHNW = metabolically unhealthy normal weight; MUHoW = metabolically unhealthy overweight; MUHO = metabolically unhealthy obese

Full size image

In males, a metabolically unhealthy phenotype was associated with a higher median HOMA-IR for all definitions used (p < 0.001 for all), as shown in Table 2. In females, a metabolically unhealthy phenotype was also associated with a higher median HOMA-IR for all definitions, except when using the Doumatey et al. criteria, as shown in Table 2. Logistic regression analysis revealed that a metabolically unhealthy phenotype was consistently associated with insulin resistance (defined as HOMA-IR ≥ 2.5) across all definitions in both sexes. However, there were notable sex differences in the performance of the various definitions of the metabolically unhealthy phenotype to predict insulin resistance, as evidenced by the odds ratio. This is shown in Table 3 and Fig. 2. In females, the strongest observed association was for the NCEP-2 definition (i.e. having ≤2 NCEP-ATPIII parameters to characterize the metabolically healthy phenotype), with an odds ratio (OR) of 19.7. Conversely, the Doumatey et al. criteria had lowest predictive ability in the female cohort (OR of 2.6). In males, the strongest association was for the Aguilar-Salinas et al. definition of the metabolically healthy phenotype (OR of 18.7), followed by the Lynch et al. (OR of 16.6) and the NCEP-2 (OR of 13.1) definitions (Table 3 and Fig. 2). The Doumatey et al. definition performed better in males than in females (OR of 12.2 in males compared to 2.6 in females). When considering BMI category as the sole independent predictor of HOMA-IR ≥2.5, a lower predictive performance relative to a metabolic unhealthy phenotype using any definitions was noted, with OR of 1.90 in females and 2.07 in males (p < 0.001).

Table 2 Comparison of Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) in the metabolically healthy and unhealthy subgroups as classified by the various definitions and stratified by sex
Full size table
Table 3 Performance of the various criteria currently in use to define metabolic health to predict insulin resistance, defined as Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) ≥2.5
Full size table
Fig. 2
figure 2

Odds ratio for having a Homeostatic Model Assessment for Insulin Resistance >2.5 in metabolically healthy and unhealthy according to different definitions. Error bars refer to the standard error

Full size image

Even after adjusting for BMI category, the metabolically unhealthy phenotype was associated with a higher prevalence of having HOMA-IR ≥ 2.5 for all definitions used in both sexes (Table 3 and Fig. 2). After adjusting for BMI category, the metabolically unhealthy phenotype as defined by the NCEP-2 criteria retained the strongest association with insulin resistance in females (adjusted OR of 16.1), whilst in males a metabolically unhealthy phenotype as defined by the Aguilar-Salinas et al. criteria retained the strongest association with insulin resistance (adjusted OR of 15.3).

Discussion

Our data show considerable differences in the prevalence of body size phenotypes in a contemporary middle-aged population when using different diagnostic criteria. This finding is consistent with evidence from previous studies [8, 37, 38]. Our study thus reinforces the need to adopt a population-specific approach in the definition of metabolic health, as criteria developed in North European/American Caucasians might not be generalizable. Specifically, the different diagnostic criteria currently in use require reproduction and validation in specific populations to account for regional differences in genetic admixture, demographics, background prevalence of obesity and variation in anthropometric characteristics.

The prevalence of the different body size phenotypes reported in this study is markedly different from that reported by other authors. For example, when using the Aguilar-Salinas et al. definition of metabolic health in an Irish population, Phillips & Perry et al. [37] found a much lower prevalence of MHOw/O (2.2% compared to 45.2% in our cohort when using the same definition) and of MHNW (8.8% compared to 25.0% in our cohort). Direct comparison between studies is limited by population-specific differences in life-style factors, variable patient ascertainment criteria, the impact of genetic factors on adiposity and fat distribution patterns as well as temporal changes in the prevalence of body size phenotypes.

All definitions of metabolic health had a higher predictive value with respect to insulin resistance than BMI alone in both males and females. This reinforces the importance of the concept of metabolic health over a simple BMI-based classifier, which does not fully capture the underlying adverse cardiometabolic risks. Furthermore, a metabolically unhealthy phenotype was still strongly predictive of insulin resistance for all definitions used even after adjusting for BMI category in both sexes. Although, there are many studies on metabolic health, few have compared the strength of the association of metabolic health with insulin resistance when using the different definitions currently in use. Our data is important because it shows which definition has the strongest association.

This study also identifies important sex-specific effects in the ability of the various definitions of metabolic health to predict insulin resistance. A cut-off of HOMA-IR ≥ 2.5 was selected since population-based research has shown that this threshold is associated with increased mortality [34,35,36]. Our data shows that in females, the NCEP-2 definition demonstrated the strongest association with insulin resistance compared to other definitions. Conversely, in males, the definition by Aguilar-Salinas et al. had the strongest association with insulin resistance (OR of 18.7), followed by that of Lynch et al. and NECP-2. The Doumatey et al. definition also performed much better in males than in females (OR of 12.2 compared to 2.4). The NCEP-2 definition remained the best predictor of insulin resistance even after adjusting for BMI category in females.

There are possible physiological mechanisms that underlie the observed sex-specific associations. Females exhibit greater blood pressure variability than males [39]. This may be mediated by greater baroreceptor sensitivity [40] and by greater sensitivity to changes in dietary sodium [41] in females compared to males. The increased blood pressure variability in females would be expected to create greater inaccuracy in body size characterization in women, especially when using definitions which only require one abnormal criterion in order to be classified as being metabolically unhealthy (such as those by Aguilar-Salinas et al., Lynch et al., Doumatey et al., NCEP-0, and Lavie et al.). On the other hand, the NCEP-ATPIII criteria use a higher cut-off for waist circumference in males than in females. There is evidence that this cut-off may be too high in males [28]. This may explain the stronger association of the NCEP-2 definition of metabolic health with insulin resistance in females compared to males. The Aguilar-Salinas et al. definition uses identical cut-offs for HDL-cholesterol in males and females. In females, the HDL-cholesterol cut-off is therefore significantly lower than the one used by NCEP-ATPIII (1.0 vs 1.3 mmol/L). The former may be too low, and this may explain why the NCEP-2 definition performs better than that by Aguilar-Salinas et al. in females. Women are known to have higher HDL-C [42, 43]. Data from the US National Health and Nutrition Examination Survey indicates that the optimal HDL-C cut-off to predict cardiovascular disease is 1.45 mmol/L in females compared to 1.06 mmol/L in males [44]. These cut-offs are likely to differ amongst populations. For example, in Koreans the optimal HDL-C cut-offs are 1.24 mmol/L in females and 1.11 mmol/L in males [44].

The various definitions of metabolic health carry important caveats that impact on broad clinical interpretation. The metabolic health definitions currently in use are based on the findings of investigations carried out in different ethnicities. There is extensive between-study heterogeneity, with the use of different sample sizes and different gender proportions. These factors might contribute to the observed variation between males to females reported in this study. Furthermore, most definitions were not derived from studying the association of metabolic health with insulin resistance or cardiovascular disease.

An additional definition of the metabolically healthy phenotype, proposed by Meigs et al. is based on HOMA-IR values below the 75th centile [18]. In the current study, we did not explore this classification since we wanted to investigate which body composition phenotype is most predictive of insulin resistance without the need of determination of HOMA-IR. Furthermore, this definition results in a fixed prevalence of the metabolically unhealthy phenotype (25%) in all populations and at all times. Thus, it does not account for the dynamic nature of insulin resistance based on population-specific differences in cardiometabolic risk. The definitions proposed by Wildman et al. and Karelis et al. also incorporate HOMA-IR as one of their criteria, but they also use additional biochemical and anthropometric criteria. Hence, we calculated the prevalence rates of the various body size phenotypes when using these definitions, but we did not enter them in the logistic regression analyses to predict a high HOMA-IR.

Strengths and limitations

The findings from this study need to be interpreted in the context of several limitations. The use of convenience sampling rather than stratified random sampling led to more females than males being recruited, which may thus explain the skewed gender ratios observed in this study. Nonetheless, the number of males recruited (190) gives a statistical power of 0.75 to detect an odds ratio of 1.75 at α = 0.05 [45]. We had 0.95 power to detect an odds ratio of 1.75 at α = 0.05 in females [45]. We used HOMA-IR as a measure of insulin resistance rather than a euglycaemic clamp, but previous studies have shown a very good correlation between the two [31, 32]. Furthermore, high HOMA-IR has been shown to be associated with increased cardiovascular, cancer and all-cause mortality [1,2,3]. The cross-sectional design of our study precludes drawing of definitive conclusions on long-term outcomes.

This investigation is strengthened by the use of a well-characterized and reasonably sized representative sample of middle-aged adult subjects. There are significant age-related changes in muscle mass, fat distribution and in the prevalence of body size phenotypes. Thus, the relationship between anthropometric parameters and metabolic health is likely to vary across different age groups, which should be studied separately. Anthropometric parameters were measured using standard procedures and biochemical variables were centrally analyzed under appropriate quality controls. The Maltese population consists largely of Caucasians, with other races being largely under-represented. Since there are racial differences in the relation of insulin resistance to anthropometric and biochemical parameters, we restricted our study to subjects of Caucasian ethnicity. It is therefore important that other authors replicate our findings in other ethnic groups.

Conclusions

This study demonstrated large differences in prevalence of the various body size phenotypes when using different criteria, thereby highlighting the need for standardization of definitions. Irrespective of the definition used, the metabolically unhealthy phenotype was more strongly associated with insulin resistance than BMI category. Furthermore, a metabolically unhealthy phenotype using any definition was associated with insulin resistance even after adjusting for BMI category. This study lends further support to incorporating metabolic health in patient stratification since this offers additional information on cardiometabolic risk compared to BMI alone.

The strongest association with insulin resistance as measured by HOMA-IR in females was obtained by using the presence of >2 NCEP-ATPIII criteria to define a metabolically unhealthy phenotype, whilst in males the strongest association with insulin resistance was specified by the Aguilar-Salinas et al. definition. Our data therefore suggest the need for sex-specific definitions of metabolic health. Future studies should seek to replicate these findings in other age groups and populations, and to evaluate the longitudinal relationship of the different definitions to long-term cardiometabolic outcomes.

Availability of data and materials

The datasets used and/or analysed during the current study are not publicly available due to legal constraints but are available from the corresponding author on reasonable request.

References

  1. Hellgren MI, Daka B, Jansson PA, Lindblad U, Larsson CA. Insulin resistance predicts early cardiovascular morbidity in men without diabetes mellitus, with effect modification by physical activity. Eur J Prev Cardiol. 2015;22:940–9. https://doi.org/10.1177/2047487314537917.

    Article  PubMed  Google Scholar 

  2. Pan K, Nelson RA, Wactawski-Wende J, Lee DJ, Manson JE, Aragaki AK, et al. Insulin resistance and cancer-specific and all-cause mortality in postmenopausal women: the women's health initiative. J Natl Cancer Inst. 2020;112(2):170–8. https://doi.org/10.1093/jnci/djz069 PMID: 31184362; PMCID: PMC7019097.

    Article  PubMed  Google Scholar 

  3. Perseghin G, Calori G, Lattuada G, Ragogna F, Dugnani E, Garancini MP, et al. Insulin resistance/hyperinsulinemia and cancer mortality: the cremona study at the 15th year of follow-up. Acta Diabetol. 2012;49:421–8. https://doi.org/10.1007/s00592-011-0361-2 Epub 2012 Jan 4. PMID: 22215126.

    Article  CAS  PubMed  Google Scholar 

  4. Fontbonne AM, Eschwège EM. Insulin and cardiovascular disease. Paris prospective study. Diabetes Care. 1991;14:461–9. https://doi.org/10.2337/diacare.14.6.461 PMID: 1864219.

    Article  CAS  PubMed  Google Scholar 

  5. Pyörälä M, Miettinen H, Laakso M, Pyörälä K. Plasma insulin and all-cause, cardiovascular, and noncardiovascular mortality: the 22-year follow-up results of the Helsinki policemen study. Diabetes Care. 2000;23:1097–102. https://doi.org/10.2337/diacare.23.8.1097 PMID: 10937504.

    Article  PubMed  Google Scholar 

  6. DECODE Insulin Study Group. Plasma insulin and cardiovascular mortality in non-diabetic European men and women: a meta-analysis of data from eleven prospective studies. Diabetologia. 2004;47(7):1245–56. https://doi.org/10.1007/s00125-004-1433-4 Epub 2004 Jul 7. PMID: 15241592.

    Article  Google Scholar 

  7. Genovesi S, Antolini L, Orlando A, Gilardini L, Bertoli S, Giussani M, et al. Cardiovascular risk factors associated with the Metabolically Healthy Obese (MHO) phenotype compared to the Metabolically Unhealthy Obese (MUO) phenotype in children. Front Endocrinol (Lausanne). 2020;11:27. https://doi.org/10.3389/fendo.2020.00027 PMID: 32117055; PMCID: PMC7025459.

    Article  Google Scholar 

  8. Liu C, Wang C, Guan S, Liu H, Wu X, Zhang Z, et al. The prevalence of metabolically healthy and unhealthy obesity according to different criteria. Obes Facts. 2019;12:78–90. https://doi.org/10.1159/000495852 Epub 2019 Feb 27. PMID: 30814477; PMCID: PMC6465689.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kim NH, Seo JA, Cho H, Seo JH, Yu JH, Yoo HJ, et al. Risk of the development of diabetes and cardiovascular disease in metabolically healthy obese people: the Korean genome and epidemiology study. Medicine (Baltimore). 2016;95:e3384. https://doi.org/10.1097/MD.0000000000003384 PMID: 27082607; PMCID: PMC4839851.

    Article  CAS  Google Scholar 

  10. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of The National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97. https://doi.org/10.1001/jama.285.19.2486 PMID: 11368702.

    Article  Google Scholar 

  11. 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(15):1617–24. https://doi.org/10.1001/archinte.168.15.1617 PMID: 18695075.

    Article  PubMed  Google Scholar 

  12. Doumatey AP, Bentley AR, Zhou J, Huang H, Adeyemo A, Rotimi CN. Paradoxical hyperadiponectinemia is associated with the Metabolically Healthy Obese (MHO) phenotype in African Americans. J Endocrinol Metab. 2012;2:51–65. https://doi.org/10.4021/jem95W PMID: 23293696; PMCID: PMC3534968.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hamer M, Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality. J Clin Endocrinol Metab. 2012;97:2482–8. https://doi.org/10.1210/jc.2011-3475 Epub 2012 Apr 16. PMID: 22508708; PMCID: PMC3387408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Aguilar-Salinas CA, García EG, Robles L, Riaño D, Ruiz-Gomez DG, García-Ulloa AC, et al. High adiponectin concentrations are associated with the metabolically healthy obese phenotype. J Clin Endocrinol Metab. 2008;93(10):4075–9. https://doi.org/10.1210/jc.2007-2724 Epub 2008 Aug 5. PMID: 18682512.

    Article  CAS  PubMed  Google Scholar 

  15. Lynch LA, O'Connell JM, Kwasnik AK, Cawood TJ, O'Farrelly C, O'Shea DB. Are natural killer cells protecting the metabolically healthy obese patient? Obesity (Silver Spring). 2009;17(3):601–5. https://doi.org/10.1038/oby.2008.565 Epub 2008 Dec 18. PMID: 19238145.

    Article  CAS  Google Scholar 

  16. Karelis AD, Brochu M, Rabasa-Lhoret R. Can we identify metabolically healthy but obese individuals (MHO)? Diabetes Metab. 2004;30(6):569–72.

    Article  CAS  Google Scholar 

  17. Lavie CJ, Laddu D, Arena R, Ortega FB, Alpert MA, Kushner RF. Healthy weight and obesity prevention: JACC health promotion series. J Am Coll Cardiol. 2018;72(13):1506–31.

    Article  Google Scholar 

  18. 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;91:2906–12.

    Article  CAS  Google Scholar 

  19. Kahn CR, Wang G, Lee KY. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J Clin Invest. 2019;129:3990–4000.

    Article  Google Scholar 

  20. Doğan K, Şeneş M, Karaca A, Kayalp D, Kan S, Gülçelik NE, et al. HDL subgroups and their paraoxonase-1 activity in the obese, overweight and normal weight subjects. Int J Clin Pract. 2021;9:e14969. https://doi.org/10.1111/ijcp.14969 Epub ahead of print. PMID: 34626508.

    Article  CAS  Google Scholar 

  21. Dyer AR, Stamler J, Garside DB, Greenland P. Long-term consequences of body mass index for cardiovascular mortality: the Chicago heart association detection project in industry study. Ann Epidemiol. 2004;14:101–8.

    Article  Google Scholar 

  22. Jousilahti P, Tuomilehto J, Vartiainen E, Pekkanen J, Puska P. Body weight, cardiovascular risk factors, and coronary mortality. 15-year follow-up of middle-aged men and women in eastern Finland. Circulation. 1996;93:1372–9.

    Article  CAS  Google Scholar 

  23. Flegal K, Kit B, Orpana H, Graubard B. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309:71–82.

    Article  CAS  Google Scholar 

  24. Diabetes Prevention Program Research Group. Relationship of body size and shape to the development of diabetes in the diabetes prevention program. Obesity (Silver Spring). 2006;14:2107–17. https://doi.org/10.1038/oby.2006.246.

    Article  Google Scholar 

  25. Janghorbani M, Salamat MR, Amini M, Aminorroaya A. Risk of diabetes according to the metabolic health status and degree of obesity. Diabetes Metab Syndr. 2017;11 Suppl 1:S439–44. https://doi.org/10.1016/j.dsx.2017.03.032.

    Article  PubMed  Google Scholar 

  26. Fujimoto WY, Jablonski KA, Bray GA, Kriska A, Barrett-Connor E, Haffner S, et al. Diabetes prevention program research group. Body size and shape changes and the risk of diabetes in the diabetes prevention program. Diabetes. 2007;56:1680–5. https://doi.org/10.2337/db07-0009.

    Article  CAS  PubMed  Google Scholar 

  27. Fava MC, Agius R, Fava S. Obesity and cardio-metabolic health. Br J Hosp Med (Lond). 2019;80(8):466–71. https://doi.org/10.12968/hmed.2019.80.8.466 Erratum in: Br J Hosp Med (Lond) 2019;80(10):619.

    Article  Google Scholar 

  28. Agius R, Pace NP, Fava S. Sex differences in cardiometabolic abnormalities in a middle-aged Maltese population. Can J Public Health. 2022. https://doi.org/10.17269/s41997-021-00592-7 Epub ahead of print. PMID: 35006592.

  29. Buscemi S, Chiarello P, Buscemi C, Corleo D, Massenti MF, Barile AM, et al. Characterization of metabolically healthy obese people and metabolically unhealthy normal-weight people in a general population cohort of the ABCD study. J Diabetes Res. 2017;2017:9294038.

    Article  Google Scholar 

  30. Agius R, Pace NP, Fava S. Characterisation of body size phenotypes in a middle-aged Maltese population. J Nutr Sci. 2021;10:e81.

    Article  CAS  Google Scholar 

  31. Magri CJ, Fava S, Galea J. Prediction of insulin resistance in type 2 diabetes mellitus using routinely available clinical parameters. Diabetes Metab Syndr. 2016;10(2 Suppl 1):S96–S101.

    Article  Google Scholar 

  32. 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;28:412–9.

    Article  CAS  Google Scholar 

  33. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care. 2000;23(1):57–63.

    Article  CAS  Google Scholar 

  34. Durward CM, Hartman TJ, Nickols-Richardson SM. All-cause mortality risk of metabolically healthy obese individuals in NHANES III. J Obes. 2012;2012:460321. https://doi.org/10.1155/2012/460321 Epub 2012 Dec 5. PMID: 23304462; PMCID: PMC3523154.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kuk JL, Ardern CI. Are metabolically normal but obese individuals at lower risk for all-cause mortality? Diabetes Care. 2009;32:2297–9. https://doi.org/10.2337/dc09-0574 Epub 2009 Sep 3. PMID: 19729521; PMCID: PMC2782994.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Calori G, Lattuada G, Piemonti L, Garancini MP, Ragogna F, Villa M, et al. Prevalence, metabolic features, and prognosis of metabolically healthy obese Italian individuals: the Cremona study. Diabetes Care. 2011;34:210–5.

    Article  Google Scholar 

  37. Phillips CM, Perry IJ. Does inflammation determine metabolic health status in obese and nonobese adults? J Clin Endocrinol Metab. 2013;98:E1610–9.

    Article  CAS  Google Scholar 

  38. Velho S, Paccaud F, Waeber G, Vollenweider P, Marques-Vidal P. Metabolically healthy obesity: different prevalences using different criteria. Eur J Clin Nutr. 2010;64:1043–51.

    Article  CAS  Google Scholar 

  39. Boubouchairopoulou N, Ntineri A, Kollias A, Destounis A, Stergiou GS. Blood pressure variability assessed by office, home, and ambulatory measurements: comparison, agreement, and determinants. Hypertens Res. 2021;44:1617–24.

    Article  CAS  Google Scholar 

  40. Veiz E, Kieslich SK, Staab J, Czesnik D, Herrmann-Lingen C, Meyer T. Men show reduced cardiac baroreceptor sensitivity during modestly painful electrical stimulation of the forearm: exploratory results from a sham-controlled crossover vagus nerve stimulation study. Int J Environ Res Public Health. 2021;18(21):11193.

    Article  CAS  Google Scholar 

  41. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-Sodium Collaborative Research Group. N Engl J Med. 2001;344:3–10.

    Article  CAS  Google Scholar 

  42. Palmer MK, Toth PP. Trends in lipids, obesity, metabolic syndrome, and diabetes mellitus in the United States: an NHANES analysis (2003–2004 to 2013–2014). Obesity (Silver Spring). 2019;27:309–14.

    Article  CAS  Google Scholar 

  43. Cho KH, Kim JR. Rapid Decrease in HDL-C in the Puberty Period of Boys Associated with an Elevation of Blood Pressure and Dyslipidemia in Korean Teenagers: An Explanation of Why and When Men Have Lower HDL-C Levels Than Women. Med Sci (Basel). 2021;9:35.

    CAS  Google Scholar 

  44. Moon JH, Koo BK, Moon MK. Optimal high-density lipoprotein cholesterol cutoff for predicting cardiovascular disease: Comparison of the Korean and US National Health and Nutrition Examination Surveys. J Clin Lipidol. 2015;9:334–42.

    Article  Google Scholar 

  45. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods. 2009;41:1149–60.

    Article  Google Scholar 

Download references

Funding

There was no external funding.

Author information

Authors and Affiliations

  1. Faculty of Medicine and Surgery, University of Malta Medical School, Tal-Qroqq, Msida, Malta

    Rachel Agius, Nikolai Paul Pace & Stephen Fava

  2. Mater Dei Hospital, Triq Dun Karm, Msida, MSD2090, Malta

    Rachel Agius, Marie Claire Fava & Stephen Fava

Authors
  1. Rachel Agius
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marie Claire Fava
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikolai Paul Pace
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephen Fava
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the conception of the study and to the study methodology. RA collected the data. RA and MCF inputted & coded the data. SF and MCF drafted the manuscript. All authors reviewed the manuscript and contributed to its writing. We confirm that all methods were carried out in accordance with relevant guidelines and regulations. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Stephen Fava.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the University Research Ethics Committee of the University of Malta. Informed consent was obtained from all subjects. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agius, R., Fava, M.C., Pace, N.P. et al. Prevalence rates of metabolic health and body size phenotypes by different criteria and association with insulin resistance in a Maltese Caucasian population. BMC Endocr Disord 22, 160 (2022). https://doi.org/10.1186/s12902-022-01071-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12902-022-01071-x

Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

Contact us