Metabolically healthy obesity: different prevalences using different criteria

[Prevalence of metabolic health in Mallorca obese patients]

Introduction

Aims: to assess the prevalence of metabolic health in Mallorca obese patients. Methods: participants were classified in metabolically healthy obese (MHO) and metabolically non-healthy obese (MNHO). Food, toxic and lifestyle habits, time of obesity evolution, breastfeeding, obesity in childhood and family history of obesity and diabetes mellitus, as well as glycemia, total cholesterol, HDL-cholesterol and triglyceridemia were evaluated in 457 obese patients. Results: prevalence of MHO was 49.2% and that of MNHO was 50.8%. MHO phenotype decreased with age. All patients showed inadequate habits. Consumption of fruits, salads and vegetables, tobacco and physical activity were similar between both groups; 37.4% of patients consumed sugary sweet drinks, and 52.9% consumed alcohol, higher in MNHO (4.3%) than in MHO (0.4%). MNHO showed higher values of BMI, abdominal circumference, fat percentage and visceral fatty index, as well as all metabolically studied outcomes. Conclusions: more than half of assessed obese population showed metabolic complications, but all obese population showed similar inadequate food and lifestyle habits. Increase of age, low educational level, years of obesity evolution, and visceral localization of fat are associated with a metabolically non-healthy status. Criteria to define and typify the metabolic state of obese subjects should be unified.

No Difference in Lactoferrin Levels between Metabolically Healthy and Unhealthy Obese Women

BACKGROUND

The aim of the study was to compare serum lactoferrin concentrations in metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO) women.

METHODS

Three hundred (101 MHO and 199 MUHO) women were recruited to the study. Basic anthropometric parameters and blood pressure were measured. Body mass index (BMI) was calculated. Fat mass and visceral adipose tissue mass were assessed using dual X-ray absorptiometry scan. Fasting glucose, insulin, lipid profile, high sensitivity C-reactive protein (hs-CRP) and lactoferrin levels were determined.

RESULTS

Lactoferrin levels did not differ between MHO and MUHO subjects (median (interquartile range): 1639 (1055-2396) vs. 1622 (1009-23345) ng/mL). However, in the total population insulin (r = 0.131, p = 0.0234) and hs-CRP (r = 0.165, p = 0.0045) levels were correlated with lactoferrin concentrations. In addition, a weak positive association between serum lactoferrin concentrations and anthropometric parameters was also detected, and predominantly referred to MHO group (body weight: r = 0.231, p = 0.0201; BMI: r = 0.286, p = 0.0037; waist circumference: r = 0.258, p = 0.0092). In addition, serum lactoferrin concentrations were negatively correlated with fasting glucose (r = -0.250, p = 0.0115) and HDL-C levels (r = -0.203, p = 0.0411) in MHO subjects.

CONCLUSIONS

Lactoferrin levels did not differ between MHO and MUHO women. However, some mild correlations between lactoferrin concentrations and anthropometric and metabolic parameters were observed mostly in MHO subjects.

Fasting triglycerides and glucose index is more suitable for the identification of metabolically unhealthy individuals in the Chinese adult population: A nationwide study

AIMS/INTRODUCTION

Metabolic unhealth can be defined by the components of metabolic syndrome, which is closely connected to insulin resistance. We aimed to determine a simple index to identify metabolic unhealth in the Chinese adult population.

MATERIALS AND METHODS

A total of 30,291 individuals were screened from the China National Diabetes and Metabolic Disorders Study carried out from June 2007 to May 2008. Metabolic unhealth was defined using components of metabolic syndrome, except waist circumference. We compared the three surrogate indices of insulin resistance: the product of fasting triglycerides and glucose (TyG), triglycerides divided by high-density lipoprotein cholesterol and the metabolic score for insulin resistance for the evaluation of metabolic status.

RESULTS

All indices had high sensitivity and specificity for the identification of metabolic unhealth, especially the TyG index with an area under the curve of 0.863 for men and 0.867 for women. Participants were divided into subgroups for further analysis. The TyG index also showed high diagnostic values, especially for younger individuals and men with normal waist circumference. Sex-specific cut-offs for three indices were also used to define metabolic unhealth. The TyG index showed the highest agreement with κ values of 0.603 and 0.605 for men and women between the components of metabolic syndrome and three indices.

CONCLUSIONS

We propose that the TyG index, just read in one blood laboratory test report, is simpler and more suitable for the identification of metabolically unhealthy individuals as well as who have high risk of cardiometabolic diseases of the Chinese adult population.

Associations of overweight and metabolic health with successful aging: 32-year follow-up of the Helsinki Businessmen Study

BACKGROUND & AIMS

Prognostic significance of metabolically healthy overweight and obesity (MHO) is under debate. However the relationship between MHO and health-related quality of life (HRQoL) is less studied. We compared successful aging (longevity plus HRQoL) in men with MHO, metabolically healthy normal weight (MHN) and metabolically unhealthy overweight and obesity (MUO).

METHODS

In the Helsinki Businessmen Study longitudinal cohort, consisting of men born 1919 to 1934. In 1985/86, overweight (BMI≥25 kg/m²) and metabolic health were determined in 1309 men (median age 60 years). HRQoL was assessed using RAND-36/SF-36 in 2000 and 2007, and all-cause mortality retrieved from registers up to 2018. The proportion of men reaching 90 years was also calculated.

RESULTS

Of the men, 469 (35.8%), 538 (41.1%), 276 (21.1%), and 26 (2.0%) were MHN, MHO, MUO and MUN, respectively. During the 32-year follow-up, 72.3% men died. With MHN as reference, adjusted hazard ratio with all-cause mortality was 1.08 (95% confidence interval [CI] 0.93 to 1.27) for MHO, and 1.18 (95% CI 0.95 to 1.47) for MUO. During follow-up, 273 men reached 90 years. With MHN as reference, adjusted odds ratio for MHO was 0.82 (95% CI 0.59 to 1.14) and 0.62 (95% CI 0.41 to 0.95) for MUO. Men in MHN group scored generally highest in RAND-36 HRQoL subscales in 2000 and 2007, of those significantly better in Physical functioning, Role physical, Role emotional, Bodily Pain, and General health sub-scales compared to MHO group in 2000.

CONCLUSIONS

As compared to MHN, MHO in late midlife does not increase mortality, but impairs odds for successful aging.

Abnormal Metabolic Phenotypes Among Urban Chinese Children: Epidemiology and the Impact of DXA-Measured Body Composition

OBJECTIVE

This study aimed to describe the prevalence rates of abnormal metabolic phenotypes among urban Chinese children and to explore the impact of body composition as measured by dual-energy x-ray absorptiometry (DXA) on metabolic disorders.

METHODS

A total of 7,926 children aged 6 to 17 years from seven cities across China were involved. Metabolically unhealthy was defined as having ≥ 2 risk factors (elevated blood pressure, high triglyceride levels, low levels of high-density lipoprotein cholesterol, and impaired fasting glucose [IFG]). Abnormal metabolic phenotypes were defined as metabolically unhealthy with normal weight (MUNW) and metabolically healthy with overweight or obesity (MHO).

RESULTS

Overall prevalence rates of MUNW and MHO were 10.6% and 15.3%, respectively. The prevalence of MUNW increased with age in boys, whereas the prevalence in girls was statistically higher in 11- to 15-year-olds than in other age groups. The prevalence of MHO decreased with age in both genders. Among children with normal weight, the levels of body composition indices were positively correlated with metabolically unhealthy phenotype. Fat-free mass had a protective effect on IFG in children with normal weight. Among children with overweight or obesity, body composition was related to elevated blood pressure and dyslipidemia but not to IFG.

CONCLUSIONS

The prevalence rates of abnormal metabolic phenotypes differed between genders and ages. Body composition could partly explain the different metabolic phenotypes at the same BMI status among children.

Is metabolically healthy obesity a useful concept?

Obesity poses significant public health concerns, including Type 2 diabetes mellitus, cardiovascular disease and stroke. Although deleterious metabolic profiles typically accompany individuals with obesity, various epidemiological studies have identified a subset of patients with a favourable metabolic phenotype referred to as the metabolically healthy obese. Metabolically healthy obesity is a novel concept that stratifies obese individuals according to their respective metabolic status. It has important implications for healthcare policies, particularly the efficient allocation of resources in the targeted treatment of obesity and prevention of metabolic ill-health. However, conflicting evidence in the literature regarding its risk profile questions its clinical relevance. In addition, the lack of a unified definition of metabolically healthy obesity and agreement on its progression further impede its utility as a stratification strategy. This review aims to describe current concepts of metabolically healthy obesity within the literature, evidence of this metabolic phenotype, the clinical implications of patient stratification and limitations of the concept.

Is normal body mass index a good indicator of metabolic health in Azar cohort population?

Introduction: Metabolic syndrome (Mets) has become most important public health problem in the world. We examined the association between Mets and different cardiometabolic phenotype in Azar cohort population. Methods: In the present study, the data of 13099 subjects who participated in Azar cohort study were cross-sectionally analyzed. Mets was defined according to the National Cholesterol Education Program's Adult Treatment Panel III report (ATPIII) criteria. Participants were categorized into four cardiometabolic phenotypes including metabolically healthy Lean (MHL), metabolically unhealthy lean (MUHL), metabolically healthy Obese (MHO), metabolically unhealthy obese (MUHO) according to BMI cut-off point (25 kg/m2 ), and the presence of Mets. Results: Totally, the prevalence of Mets was 33.20% with the higher prevalence in women (40.1%). About 46.7% of participants were MHO and 1.6% of them were MHL. In both genders, MUHL had the highest prevalence of hyperglycemia, hypertrigliceridemia, hypo-HDL-cholestrolemia and Frahmingham 10-year CVD risk. In both MUHL and MUHO phenotypes, hypertriglyceridemia (OR: 31.97 [95% CI: 22.31, 45.81] and OR: 20.28 [95% CI: 17.32, 23.75]) and hypo-HDL cholestrolemia (OR:27.97 [95% CI: 17.35, 45.09] and OR:11.0 [95% CI: 9.62, 12.58]) are the strongest predictor of incidence of Mets. Also, the results of multinominal regression analyses indicated that in all cardiometabolic phenotypes, Framingham 10- year CVD risks had the lowest power for predicting of Mets incidence. Conclusion: Based on the results, in addition to obese individuals, multiple metabolic abnormalities were seen in normal weight individuals and these subjects are even at higher risk of developing Mets compared with metabolically obese individuals. So, it seems that decision on initiation of lifestyle interventions should not be only based on the BMI; rather metabolic status seems to be even more important.

Metabolically healthy versus metabolically unhealthy obesity

Obesity-related disease complications reduce life quality and expectancy and increase health-care costs. Some studies have suggested that obesity not always entails metabolic abnormalities and increased risk of cardiometabolic complications. Because of the lack of universally accepted criteria to identify metabolically healthy obesity (MHO), its prevalence varies widely among studies. Moreover, the prognostic value of MHO is hotly debated, mainly because it likely shifts gradually towards metabolically unhealthy obesity (MUO). In this review, we outline the differential factors contributing to the metabolic heterogeneity of obesity by discussing the behavioral, genetic, phenotypical, and biological aspects associated with each of the two metabolic phenotypes (MHO and MUO) of obesity and their clinical implications. Particular emphasis will be laid on the role of adipose tissue biology and function, including genetic determinants of body fat distribution, depot-specific fat metabolism, adipose tissue plasticity and, particularly, adipogenesis. Finally, the emerging role of gut microbiota in obesity and adipose tissue dysfunction as well as the search for novel biomarkers for the obesity-related metabolic traits and associated diseases will be briefly presented. A better understanding of the main determinants of a healthy metabolic status in obesity would allow promotion of this favorable condition by targeting the relevant pathways.

Heterogeneity of obesity phenotypes in relation to cardiovascular risk

Obesity is associated with numerous metabolic disorders, the development and progression of a wide range of conditions and diseases, primarily cardiovascular disease and type 2 diabetes. One of the widely discussed phenomena of the association of obesity with cardiovascular diseases, their complications and mortality is the phenomenon of “heterogeneity of obesity phenotypes in relation to cardiovascular risk”. Currently, two main phenotypes have been identified — metabolically healthy and metabolically unhealthy obesity. At the same time, not only the expediency of separation, but also the existence of this phenotype is widely discussed today. The article provides an overview of existing approaches to the verification of metabolic phenotypes of obesity and data relating to the epidemiology and the association of metabolically healthy obesity with the risk of cardiovascular diseases and death, and also discusses the factors that determine the features of the obesity phenotype.

Heterogeneity of obesity phenotypes in relation to cardiovascular risk

Obesity is associated with numerous metabolic disorders, the development and progression of a wide range of conditions and diseases, primarily cardiovascular disease and type 2 diabetes. One of the widely discussed phenomena of the association of obesity with cardiovascular diseases, their complications and mortality is the phenomenon of “heterogeneity of obesity phenotypes in relation to cardiovascular risk”. Currently, two main phenotypes have been identified — metabolically healthy and metabolically unhealthy obesity. At the same time, not only the expediency of separation, but also the existence of this phenotype is widely discussed today. The article provides an overview of existing approaches to the verification of metabolic phenotypes of obesity and data relating to the epidemiology and the association of metabolically healthy obesity with the risk of cardiovascular diseases and death, and also discusses the factors that determine the features of the obesity phenotype.

Obesity and cardiovascular disease: revisiting an old relationship

A wealth of clinical and epidemiological evidence has linked obesity to a broad spectrum of cardiovascular diseases (CVD) including coronary heart disease, heart failure, hypertension, stroke, atrial fibrillation and sudden cardiac death. Obesity can increase CVD morbidity and mortality directly and indirectly. Direct effects are mediated by obesity-induced structural and functional adaptations of the cardiovascular system to accommodate excess body weight, as well as by adipokine effects on inflammation and vascular homeostasis. Indirect effects are mediated by co-existing CVD risk factors such as insulin resistance, hyperglycemia, hypertension and dyslipidemia. Adipose tissue (AT) quality and functionality are more relevant aspects for cardiometabolic risk than its total amount. The consequences of maladaptive AT expansion in obesity are local and systemic: the local include inflammation, hypoxia, dysregulated adipokine secretion and impaired mitochondrial function; the systemic comprise insulin resistance, abnormal glucose/lipid metabolism, hypertension, a pro-inflammatory and pro-thrombotic state and endothelial dysfunction, all of which provide linking mechanisms for the association between obesity and CVD. The present narrative review summarizes the major pathophysiological links between obesity and CVD (traditional and novel concepts), analyses the heterogeneity of obesity-related cardiometabolic consequences, and provides an overview of the cardiovascular impact of weight loss interventions.

Effect of a weight-loss stabilization following a weight reduction with or without meal replacement on cardiometabolic risk in overweight women. A randomized controlled trial

The objective of this study was to examine the effect of a 3-month weight-loss-stabilization phase (phase 2) following a successful 3-month weight-loss phase (phase 1), including a conventional energy-restricted diet with (MR) or without (C) meal replacement, on the cardiometabolic risk profile in 80 overweight women. In phase 2, both groups continued to significantly reduce weight and sustained the significant decreases in waist circumference and LDL-C. During the study, folic acid concentration significantly increased in the MR-group, while homocysteine concentration significantly worsened in the C-group. After 6 months, the number of women with hypertriglyceridemic waist was significantly reduced in both the groups, however with metabolic syndrome and metabolically unhealthy abdominal obesity (MUHAO) only in the MR-group. In conclusion, both strategies were equally effective for weight loss and weight-loss stabilization. The micronutrient supplementation with MR seemed to have an additional beneficial impact on the cardiometabolic risk in the MR-group versus the C-group.

The Prevalence of Metabolically Healthy and Unhealthy Obesity according to Different Criteria

OBJECTIVE

Obesity-related disease risks may vary depending on whether the subject has metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO). At least 5 definitions/criteria of obesity and metabolic disorders have been documented in the literature, yielding uncertainties in a reliable international comparison of obesity phenotype prevalence. This report aims to compare differences in MHO and MUO prevalence according to the 5 most frequently used definitions.

METHODS

A random sample of 4,757 adults aged 35 years and older (male 51.1%) was enrolled. Obesity was defined either according to body mass index or waist circumference, and the definitions of metabolic abnormalities were derived from 5 different criteria.

RESULTS

In MHO, the highest prevalence was obtained when using the homeostasis model assessment (HOMA) criteria (13.6%), followed by the Chinese Diabetes Society (11.4%), Adult Treatment Panel III (10.3%), Wildman (5.2%), and Karelis (4.2%) criteria; however, the MUO prevalence had an opposite trend to MHO prevalence. The magnitude of differences in the age-specific prevalence of MHO and MUO varied greatly and ranked in different orders. The proportion of insulin resistance for MHO and MUO individuals differed significantly regardless of which metabolic criterion was used.

CONCLUSION

The prevalence of MHO and MUO in the Chinese population varies according to different definitions of obesity and metabolic disorders.

Metabolically Healthy Obesity and High Carotid Intima-Media Thickness in Children and Adolescents: International Childhood Vascular Structure Evaluation Consortium

OBJECTIVE

It has been argued that metabolically healthy obesity (MHO) does not increase cardiovascular disease (CVD) risk. This study examines the association of MHO with carotid intima-media thickness (cIMT), a proxy of CVD risk, in children and adolescents.

RESEARCH DESIGN AND METHODS

Data were available for 3,497 children and adolescents aged 6-17 years from five population-based cross-sectional studies in Brazil, China, Greece, Italy, and Spain. Weight status categories (normal, overweight, and obese) were defined using BMI cutoffs from the International Obesity Task Force. Metabolic status (defined as "healthy" [no risk factors] or "unhealthy" [one or more risk factors]) was based on four CVD risk factors: elevated blood pressure, elevated triglyceride levels, reduced HDL cholesterol, and elevated fasting glucose. High cIMT was defined as cIMT ≥90th percentile for sex, age, and study population. Logistic regression model was used to examine the association of weight and metabolic status with high cIMT, with adjustment for sex, age, race/ethnicity, and study center.

RESULTS

In comparison with metabolically healthy normal weight, odds ratios (ORs) for high cIMT were 2.29 (95% CI 1.58-3.32) for metabolically healthy overweight and 3.91 (2.46-6.21) for MHO. ORs for high cIMT were 1.44 (1.03-2.02) for unhealthy normal weight, 3.49 (2.51-4.85) for unhealthy overweight, and 6.96 (5.05-9.61) for unhealthy obesity.

CONCLUSIONS

Among children and adolescents, cIMT was higher for both MHO and metabolically healthy overweight compared with metabolically healthy normal weight. Our findings reinforce the need for weight control in children and adolescents irrespective of their metabolic status.

Relations of major dietary patterns and metabolically unhealthy overweight/obesity phenotypes among Iranian women

OBJECTIVE

No studies have examined the contribution of major dietary patterns to MUH phenotypes in obese and overweight people based on Karelis criteria. This study was conducted to evaluate the association of major dietary patterns with MUHOW/O and MHOW/O phenotypes.

METHODS

This cross-sectional study was conducted on 290 overweight and obese women aged 18-50 (BMI≥25 kg/m2). Anthropometric measurements were assessed in all participants. The MH phenotype was defined according to the Karelis criteria. Major dietary patterns were determined using factor analysis of 21 foods groups using a valid and reliable FFQ containing 147 items. Participants' body composition was assessed by BIA. Serum HDL, LDL, TG, insulin, and hs-CRP levels were quantified by ELISA.

RESULTS

By the use of factor analysis, 3 major dietary patterns were extracted: healthy dietary pattern (HDP), western dietary pattern (WDP) and unhealthy dietary pattern (UNHDP). Binary logistic analysis showed that participants in the in the upper category of WDP had greater odds of MUH phenotype (OR = 2.33, 95%CI = 1.11-4.91, P = 0.02), after confounder factor control. Individuals with high adherence to the UNHDP score had high odds of MUH phenotype (OR = 1.75, 95%CI = 0.98-3.10, P = 0.05), after adjustment for BMI, age, and total EI, compared to those with low adherence. A positive relation was observed between WDP and levels of hs-CRP, HOMA-IR (OR = 1.94, 95%CI = 0.91-4.10, P = 0.05 and OR = 2.53, 95%CI = 1.26-5.11, P = 0.009) as well as a positive association between UHDP and plasma level of LDL (OR = 1.90, 95%CI = 1.04-3.47, P = 0.03), but an inverse association between HDP and hs-CRP level (OR = 0.56, 95%CI = 0.29-0.92, P = 0.03).

CONCLUSIONS

The present evidence indicates various significant associations among major dietary patterns and MUHOW/O phenotypes.

Early Onset of Obesity and Adult Onset of Obesity as Factors Affecting Patient Characteristics Prior to Bariatric Surgery

BACKGROUND

Patients who are slated for bariatric surgery vary in terms of their age at onset of obesity, duration of obesity, and their health complications. Therefore, we aimed to explore a relationship between the age at onset of obesity, metabolic parameters, and health problems in bariatric surgery candidates.

METHODS

A total of 469 unrelated adults with obesity prior to bariatric surgery were included in this study. The study group consisted of 246 individuals who became obese < 20 years of age, and 223 individuals who became obese ≥ 20 years. Clinical, biochemical, anthropometric assessments, and DXA-derived measures were taken.

RESULTS

Patients with early onset of obesity had a higher total body fat mass, and higher body fat percentage, and a 1.84 times higher risk of BMI above 40 kg/m2 than patients with adult onset of obesity (≥ 20 years). Multivariable logistic regression demonstrated that, among bariatric surgery candidates with early onset of obesity, the frequency of hypertension and type 2 diabetes was significantly lower than that in cases with an adult onset of obesity, despite a longer duration of obesity and higher BMI.

CONCLUSIONS

The age at which an individual reaches obesity has a significant impact on patient characteristics on the day he or she is evaluated for bariatric surgery. A younger age at obesity onset is a predicting factor for a higher BMI in patients, but they are less likely to clinically manifest well-established consequences of obesity, such as diabetes or hypertension, compared to patients with adult onset of obesity.

Association of dietary inflammatory index with metabolic profile in metabolically healthy and unhealthy obese people

AIM

Obesity is an emerging public health problem, with its incidence on the rise. An abnormal metabolic profile is a risk factor for developing obesity. Dietary factors play a central role in the regulation of inflammation and obesity. The aim of the present study was to determine the prevalence of metabolically healthy obese and metabolically unhealthy obese (MUO) phenotypes, and their association with dietary inflammatory index (DII) among obese Iranian people.

METHODS

A cross-sectional study was conducted from July to October 2017 among 300 obese participants in southern Tehran. DII scores were computed based on the overall inflammatory properties of 32 dietary components using dietary intake assessed by food frequency questionnaire. MUO phenotype was defined as having three or more of these metabolic abnormalities: high blood glucose, high triglycerides, low high-density lipoprotein cholesterol, elevated blood pressure or abdominal obesity. The association was determined using logistic regression analysis.

RESULTS

The MUO phenotype (n = 176) was found in 63.5% of obese participants. Compared with participants in the first quartile, those in the fourth quartile of DII score (more pro-inflammatory diet) had higher odds of MUO phenotype (odds ratio, OR: 2.58 (95% CI: 1.19-5.59), P = 0.04), and there was a significant association between the continuous form of DII score and the odds of MUO phenotype (OR: 1.18 (95% CI: 1.01-1.37)) after adjusting for potential confounders.

CONCLUSIONS

Higher DII scores were positively associated with the MUO phenotype. A more pro-inflammatory diet is a potential risk factor for MUO phenotype.

Genes that make you fat, but keep you healthy

Obesity prevalence continues to rise worldwide, posing a substantial burden on people's health. However, up to 45% of obese individuals do not suffer from cardiometabolic complications, also called the metabolically healthy obese (MHO). Concurrently, up to 30% of normal-weight individuals demonstrate cardiometabolic risk factors that are generally observed in obese individuals, the metabolically obese normal weight (MONW). Besides lifestyle, environmental factors and demographic factors, innate biological mechanisms are known to contribute to the aetiology of the MHO and MONW phenotypes, as well. Experimental studies in animal models have shown that adipose tissue expandability, fat distribution, adipogenesis, adipose tissue vascularization, inflammation and fibrosis, and mitochondrial function are the main mechanisms that uncouple adiposity from its cardiometabolic comorbidities. We reviewed current genetic association studies to expand insights into the biology of MHO/MONW phenotypes. At least four genetic loci were identified through genome-wide association studies for body fat percentage (BF%) of which the BF%-increasing allele was associated with a protective effect on glycemic and lipid outcomes. For some, this association was mediated through favourable effects on body fat distribution. Other studies that characterized the genetic susceptibility of insulin resistance found that a higher susceptibility was associated with lower overall adiposity due to less fat accumulation at hips and legs, suggesting that an impaired capacity to store fat subcutaneously or a preferential storage in the intra-abdominal cavity may be metabolically harmful. Clearly, more work remains to be done in this field, first through gene discovery and subsequently through functional follow-up of identified genes.

A comparison of the sensitivity and specificity of anthropometric measurements to predict unhealthy metabolic phenotype in overweight and obese women

AIM

Metabolically Healthy Obese (MHO) is called to obese people that their insulin sensitivity, lipid profiles and inflammatory profiles are favorable, and there are no signs of hypertension and cardio-metabolic diseases. The metabolically unhealthy obese (MUHO) is the opposite. A Body Shape Index (ABSI) is a marker to identifying abdominal obesity that is derived from weight, height and waist circumference (WC). Several studies have reported ABSI is associated with diabetes, metabolic syndrome, hypertension and higher mortality rate.

MATERIALS AND METHODS

A total of 305 overweight and obese women were included in the current comparative cross-sectional study. Body composition was measured using body composition analyzer. Blood samples were obtained. The usual food intake of evaluated through the use of a semi-quantitative food frequency questionnaire.

RESULTS

The results of this study revealed that there is a significant relationship between ABSI and MHO and MUHO (p = 0.04) and area under the ROC curve was 0.60. Also there is a significant relationship between BMI, fat mass index (FMI), free fat mass index (FFMI), neck circumference (NC), WC, fat mass (FM) and metabolic healthy status (MHS). The largest area under the ROC curve belonged to NC, WC, FM and BMI (0.66).

CONCLUSION

The findings of this study suggest that there is a significant relationship between ABSI, BMI, FMI, FFMI, NC, WC, FM and MHS. The largest area under the ROC curve was related to the NC, WC, FM and BMI not ABSI, that means NC, WC, FM and BMI have maximum sensitivity and specificity.

Incidence of type 2 diabetes, hypertension, and dyslipidemia in metabolically healthy obese and non-obese

BACKGROUND AND AIMS

Metabolically healthy obese (MHO) individuals are devoid of many metabolic abnormalities, but how this condition is maintained over time remains debated. We assessed the prevalence of MHO over time and the incidence of hypertension (HTN), dyslipidemia, and type 2 diabetes mellitus (T2DM) in MHO as compared with metabolically healthy non obese (MHNO).

METHODS AND RESULTS

Prospective, population-based study including 3038 participants (49.9 ± 9.9 years; 1753 women) free from metabolic syndrome and cardiovascular disease at baseline and examined after a follow-up of 5.6 years and 10.9 years on average. At each follow-up, prevalence of MHO, MHNO, metabolically unhealthy not obese (MUNO), and metabolically unhealthy obese (MUO), as well as of HTN, dyslipidemia, and T2DM, was calculated and stratified by sex, age group, and education. At baseline, 179 (5.7%) MHO participants were identified, of which 62 (34.6%) and 79 (44.1%) remained MHO at 5.6 and 10.9 years follow-up, respectively. At 5.6 years follow-up, MHO participants were more likely to develop low HDL or be on hypolipidemic medication [multivariable-adjusted OR (95% CI): 1.56 (1.02-2.38)], to have dyslipidemia [1.94 (1.33-2.82)], and high triglycerides [2.07 (1.36-3.14)] than MHNO. At 10.9 years follow-up, MHO participants were significantly more likely to develop T2DM [3.44 (1.84-6.43)], dyslipidemia [1.64 (1.14-2.38)], and low HDL or be prescribed hypolipidemic medication [1.57 (1.08-2.27)] than MHNO. Conversely, no differences were found regarding hypertension.

CONCLUSION

A considerable fraction of MHO individuals lose their status over time, and in metabolically healthy adults, obesity confers a higher risk of developing cardiovascular risk factors.

Early determinants of metabolically healthy obesity in young adults: study of the Northern Finland Birth Cohort 1966

BACKGROUND

A body of literature suggests a metabolically healthy phenotype in individuals with obesity. Despite important clinical implications, the early origins of metabolically healthy obesity (MHO) have received little attention.

OBJECTIVE

To assess the prevalence of MHO among the Northern Finland Birth Cohort 1966 (NFBC1966) at 31 years of age, examine its determinants in early life taking into account the sex specificity.

METHODS

We studied 3205 term-born cohort participants with data available for cardio-metabolic health outcomes at 31 years, and longitudinal height and weight data. After stratifying the population by sex, adult BMI and a strict definition of metabolic health (i.e., no risk factors meaning metabolic health), we obtained six groups. Repeated childhood height and weight measures were used to model early growth and early adiposity phenotypes. We employed marginal means adjusted for mother and child covariates including socio-economic status, birth weight and gestational-age, to compare differences between the groups.

RESULTS

The prevalence of adult MHO was 6% in men and 13.5% in women. Differences in adult metabolic status were linked to alterations in BMI and age at adiposity peak in infancy (p < 0.0003 in men and p = 0.027 in women), and BMI and age at adiposity rebound (AR) (p < 0.0001 irrespective of sex). Compared to MHO, metabolically unhealthy obese (MUO) women were five and a half months younger at AR (p = 0.007) with a higher BMI while MUO men were four months older (p = 0.036) with no difference in BMI at AR.

CONCLUSION

At the time of AR, MHO women appeared to be older than their MUO counterparts while MHO men were younger. These original results support potential risk factors at the time of adiposity rebound linked to metabolic health in adulthood. These variations by sex warrant independent replication.

High Body Adiposity Drives Glucose Intolerance and Increases Cardiovascular Risk in Normoglycemic Subjects

OBJECTIVE

The objective of this study was to assess the utility of the 2-hour oral glucose tolerance test (OGTT) value to discriminate between different cardiometabolic profiles and examine the role of body composition in predicting the associated increased risk for glucose impairment, beta-cell dysfunction, and cardiovascular disease (CVD).

METHODS

Subjects with normal fasting glucose completed a 2-hour OGTT and were categorized to the carbohydrate metabolism alterations (CMAs) or the control group based on a 2-hour glucose threshold of 7.8 mmol/L. Body composition, visceral adipose tissue, OGTT-based parameters, and cardiovascular risk factors (CVRFs) such as hypertension, dyslipidemia, obstructive sleep apnea, nonalcoholic fatty liver disease, and smoking status were measured.

RESULTS

Subjects with CMAs exhibited a significantly higher 1-hour postload glucose level and a greater decline in beta-cell function and CVRF profiles. After multivariate adjustment, an excess of total body and visceral fat was associated with an increased risk of CMAs, beta-cell dysfunction, CVRFs, and lower whole-body insulin sensitivity.

CONCLUSIONS

These data support the etiopathogenic role of body and visceral fat in the development of glucose derangements and CVRFs early on in the metabolic dysregulation process. Thus, body composition analysis and OGTT assessment performed in individuals with normal fasting glucose enable a better identification of patients at risk of developing type 2 diabetes and CVD.

Blood Concentrations of Persistent Organic Pollutants and Unhealthy Metabolic Phenotypes in Normal-Weight, Overweight, and Obese Individuals

Factors underlying metabolic phenotypes, such as the metabolically healthy but obese phenotype, remain unclear. Differences in metabolic phenotypes-particularly, among individuals with a similar body mass index-could be related to concentrations of persistent organic pollutants (POPs). To our knowledge, no studies have analyzed POPs and metabolic phenotypes in normal-weight persons. We investigated the relationships between serum concentrations of POPs and metabolic phenotypes in 860 normal-weight, overweight, and obese participants in the 2002 Catalan Health Interview Survey (Spain). POP concentrations were significantly higher in metabolically unhealthy than in metabolically healthy individuals. In models adjusting for body mass index and other confounders, hexachlorobenzene, β-hexachlorocyclohexane, and polychlorinated biphenyls were associated with the unhealthy metabolic phenotype and metabolic syndrome. Among normal-weight individuals, the adjusted prevalence ratio of having an unhealthy phenotype for the upper category of the sum of orders of the 6 mentioned POPs (all individually associated with metabolic phenotypes) was 4.1 (95% confidence interval: 1.7, 10.0). Among overweight and obese individuals, the corresponding prevalence ratio for the sum of polychlorinated biphenyls was 1.4 (95% confidence interval: 1.0, 1.8). Our results supported the hypothesis that POP concentrations are associated with unhealthy metabolic phenotypes, not only in obese and overweight individuals but also (and probably more strongly) in normal-weight individuals.

Distribution and related factors of cardiometabolic disease stage based on body mass index level in Chinese adults-The National Diabetes and Metabolic Disorders Survey

BACKGROUND

It is important to characterize distribution of cardiometabolic disease (CMD) based on different body mass index (BMI) levels in a population. This information remains scarce in China, so we investigated the proportions and related factors of cardiometabolic disease stages based on different BMI levels in Chinese adults.

METHODS

We included 45 093 participants aged ≥20 years from the National Diabetes and Metabolic Disorders Survey. Cardiometabolic disease (central obesity, elevated triglycerides, elevated blood pressure, elevated plasma glucose, reduced high-density lipoprotein cholesterol, and cardiovascular disease) was classified as stage 0 (no CMD), stage 1 (mild-to-moderate CMD), or stage 2 (severe CMD). Overweight/obesity was defined as BMI ≥25 kg/m² .

RESULTS

The standardized proportions of stage 0, stage 1, and stage 2 were 32.6%, 36.4%, and 30.9% in normal-weight men, 29.9%, 42.5%, and 27.7% in normal-weight women, 4.9%, 31.7%, and 63.4% in overweight/obese men, and 6.9%, 31.4%, and 61.7% in overweight/obese women, respectively. Multinomial regression showed that regardless of gender or region, the probability of severe cardiometabolic disease rapidly increased with increasing BMI. Severe cardiometabolic disease risk was positively associated with ageing, family history of diabetes, hypertension, or cardiovascular disease, but was inversely associated with higher levels of education and increased physical activity.

CONCLUSIONS

Of Chinese men and women with normal weight, more than one third had mild-to-moderate cardiometabolic disease, and less than one third had severe cardiometabolic disease, while of these with overweight or obesity, nearly one third had mild-to-moderate cardiometabolic disease, and nearly two thirds had severe cardiometabolic disease.

Obesity as a Disease

Obesity is a complex disease with many causal factors, associated with multiple comorbidities that contribute to significant morbidity and mortality. It is a highly prevalent disease that poses an enormous health and economic burden to society. This article reviews the mechanisms of obesity and its related comorbidities.

Inverse association between excessive alcohol drinking and cardiometabolically healthy status in middle-aged men with and without overweight and obesity

AIMS

The aim of this study was to clarify the relationship between drinking and metabolically healthy status in men with normal weight, overweight and obesity.

METHODS

The subjects were Japanese men aged from 35 to 60 years (n=31781) and they were divided by daily amount of drinking (g ethanol) into light (< 22), moderate (≥22 and <44), heavy (≥44 and <66) and very heavy (≥66) drinkers. Metabolically healthy subjects were defined as those without hypertension, dyslipidemia and diabetes.

RESULTS

The percentage of metabolically healthy subjects was much lower in the overweight (BMI≥25 and <30) and obese (BMI≥30) groups than in the normal weight group (BMI≥18.5 and <25) and was much lower in the obese group than in the overweight group. In each of the normal weight and overweight groups, percentages of metabolically healthy subjects were significantly lower in heavy and very heavy drinkers than in nondrinkers and were marginally significantly higher in light drinkers than in nondrinkers. The above associations between drinking and metabolically healthy status were confirmed by logistic regression analysis. In the obese group, the percentage of metabolically healthy subjects was significantly lower in regular drinkers (including all drinker categories) than in nondrinkers, and metabolically healthy subjects were rare (0.56%) among regular drinkers.

CONCLUSIONS

Regardless of absence and presence of overweight or obesity, excessive alcohol drinking is inversely associated with metabolically healthy status and should be avoided for prevention of cardiovascular disease.

Effects of low-fat compared with high-fat diet on cardiometabolic indicators in people with overweight and obesity without overt metabolic disturbance: a systematic review and meta-analysis of randomised controlled trials

Randomised controlled trials comparing low- v. high-fat diets on cardiometabolic risk factors in people with overweight or obesity have shown inconsistent results, which may be due to the mixed metabolic status of people with excess adiposity. The role of dietary fat manipulation in modifying cardiometabolic indicators in people with overweight or obese without metabolic disturbance is unclear. Thus, meta-analysis was conducted to compare low- v. high-fat diets on cardiometabolic indicators in people who are overweight or obese without metabolic disturbance in the present study. Databases were searched until October 2016. The pooled effects of outcomes with heterogeneity were calculated with a random-effects model, heterogeneities were analysed by subgroup and meta-regression. As a result, twenty studies with 2106 participants were included in the meta-analysis. Total cholesterol and LDL-cholesterol levels were lower following low-fat diets compared with high-fat diets: weighted mean difference (WMD) was −7·05 mg/dl (−0·18 mmol/l; 95 % CI −11·30, −2·80; P=0·001) and −4·41 mg/dl (−0·11 mmol/l; 95 % CI −7·81, −1·00; P=0·011), respectively. Conversely, significant higher level of TAG (WMD: 11·68 mg/dl (0·13 mmol/l), 95 % CI 5·90, 17·45; P<0·001) and lower level of HDL-cholesterol (WMD: −2·57 mg/dl (−0·07 mmol/l); 95 % CI −3·85, −1·28; P<0·001) were found following low-fat diets compared with high-fat diets. In conclusion, dietary fat manipulation has a significant influence on blood lipid levels in people with overweight or obesity without metabolic disturbances.

Obesity and Triple-Negative Breast Cancer: Disparities, Controversies, and Biology

Once considered a problem of Western nations, obesity (body mass index ≥30 kg/m²) has rapidly increased since the 1970s to become a major threat to world health. Since 1970, the face of obesity has changed from a disease of affluence and abundance to a disease of poverty. During the last 10 years, studies have mechanistically linked obesity and an obese tumor microenvironment with signaling pathways that predict aggressive breast cancer biology. For example, in the United States, African American women are more likely than non-Hispanic European American women to be obese and to be diagnosed with triple-negative breast cancer (TNBC). In 2008, the Carolina Breast Study found that obesity (increased waist/hip ratio) was linked to an increased incidence of TNBC in premenopausal and postmenopausal African American women. Subsequently, several groups have investigated the potential link between obesity and TNBC in African American women. To date, the data are complex and sometimes contradictory. We review epidemiologic studies that investigated the potential association among obesity, metabolic syndrome, and TNBC in African American women and mechanistic studies that link insulin signaling to the obese breast microenvironment, tissue inflammation, and aggressive TNBC biology.

Obesity and Triple-Negative Breast Cancer: Disparities, Controversies, and Biology

Once considered a problem of Western nations, obesity (body mass index ≥30 kg/m²) has rapidly increased since the 1970s to become a major threat to world health. Since 1970, the face of obesity has changed from a disease of affluence and abundance to a disease of poverty. During the last 10 years, studies have mechanistically linked obesity and an obese tumor microenvironment with signaling pathways that predict aggressive breast cancer biology. For example, in the United States, African American women are more likely than non-Hispanic European American women to be obese and to be diagnosed with triple-negative breast cancer (TNBC). In 2008, the Carolina Breast Study found that obesity (increased waist/hip ratio) was linked to an increased incidence of TNBC in premenopausal and postmenopausal African American women. Subsequently, several groups have investigated the potential link between obesity and TNBC in African American women. To date, the data are complex and sometimes contradictory. We review epidemiologic studies that investigated the potential association among obesity, metabolic syndrome, and TNBC in African American women and mechanistic studies that link insulin signaling to the obese breast microenvironment, tissue inflammation, and aggressive TNBC biology.

Prevalence and Clinical Characteristics of Metabolically Healthy Obesity in Korean Children and Adolescents: Data from the Korea National Health and Nutrition Examination Survey

Metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) are differentiated by the presence of cardiometabolic risk factors (CMRFs) and insulin resistance (IR). This study aimed to evaluate the prevalence and clinical characteristics of MHO in Korean children and adolescents and to investigate the anthropometric, laboratory, and lifestyle predictors of MHO. This study included data from 530 obese subjects, aged 10-19 years, obtained from the Fourth Korea National Health and Nutrition Examination Survey. Subjects were classified into MHO and MUO groups according to the presence of CMRF (MHO(CMRF)/MUO(CMRF)) and degree of IR (MHO(IR)/MUO(IR)). Demographic, anthropometric, cardiometabolic, and lifestyle factors were compared between the groups. Logistic regression analysis and receiver operating characteristic curve analysis were performed to identify factors that predicted MHO. The prevalence of MHO(CMRF) and MHO(IR) in obese Korean youth was 36.8% (n = 197) and 68.8% (n = 356), respectively. CMRF profiles were significantly less favorable in MUO children. Longer and more vigorous physical activity and less protein intake were associated with MHO(CMRF) phenotype. The best predictors of MHO(CMRF) and MHO(IR) were waist circumference (odds ratio [OR], 0.82; 95% confidence interval [CI], 0.77-0.88; P < 0.001) and body mass index (BMI) standard deviation score (OR, 0.24; 95% CI, 0.15-0.39; P < 0.001), respectively. The prevalence of MHO differed depending on how it was defined. To adequately manage obesity in youth, the approach to individuals with MHO and MUO should be personalized due to variation in clinical characteristics. Longitudinal studies are needed to evaluate long-term consequences of MHO.

The influence of physical activity on risk of cardiovascular disease in people who are obese but metabolically healthy

The metabolic outcomes of metabolically healthy obesity (MHO) remain controversial. The aim of the present study was to determine the effect of physical activity on the cardiovascular disease (CVD) outcomes of MHO. The study included participants who were followed for 10 years and recruited from the Korean Genome and Epidemiology Study (KoGES), a population-based cohort study. Participants with previously recorded CVDs or cancer, or who had received steroids or anticoagulants at baseline were excluded. A total of 8144 participants (3,942 men and 4,202 women) fulfilled inclusion criteria. In a multivariate Cox regression model adjusted for age and sex, MHO participants were not at elevated risk of CVD compared with their metabolically healthy non-obese (MHNO) counterparts (HR, 1.28; 95% CI, 0.96–1.71), although both the non-obese (HR, 1.50; 95% CI, 1.19–1.90) and obese (HR, 1.85; 95% CI, 1.48–2.30) participants with metabolic abnormalities were at elevated risk. However, in the subgroup analysis by physical activity, physically inactive MHO participants had a significantly higher HR for CVD events compared to active MHNO participants (HR, 1.54; 95% CI, 1.03–2.30), while active MHO participants were not at elevated risk (HR, 1.15; 95% CI, 0.70–1.89). Physically inactive MHO participants had significantly increased risk of CVD compared to physically active MHNO participants whereas physically active MHO participants did not.

Long-term metabolic risk for the metabolically healthy overweight/obese phenotype

BACKGROUND/OBJECTIVES

The clinical relevance of the metabolically healthy overweight/obese (MHO) phenotype is controversial and the relationships between weight change and the development of cardiometabolic risk factors is unknown. Therefore, we aim to: (1) Assess the long-term risk of developing one or more components of the metabolic syndrome in MHO adults compared with metabolically healthy normal weight (MHNW); (2) Evaluate risk of a composite of death, cardiovascular disease (CVD), and risk of developing type 2 diabetes between adults defined according to baseline body mass index and metabolic health.

SUBJECTS/METHODS

Retrospective cohort study of adults 18-65 years of age seen at our institution between 1998 and 2000 who lived in Olmsted County. Metabolically healthy was defined as the absence of all components of the metabolic syndrome (except for waist circumference). Main outcome was the development of metabolic risk factors. The secondary outcome was a composite of mortality, CVD and heart failure.

RESULTS

Of the 18 070 individuals with complete data at baseline, 1805 (10%) were MHO (mean age 38±11 years) and 3047 were MHNW (mean age 35±11 years). After a median follow-up of 15 years, interquartile range 10-17, 80% of MHO vs 68% of MHNW developed at least one cardiometabolic risk factor (P<0.001). In multivariate analysis, MHO individuals who gained ⩾10% of their body weight were more likely to have developed metabolic complications compared to MHO individuals that did not gain weight (P=0.001 for 10-15%, P<0.001 for >15% weight gain). The risk for the secondary composite end point was similar between MHO and MHNW, number of events 218/1805 vs 217/3048 for MHO and MHNW, respectively, (hazard ratio: 1.16, 95% confidence interval: 0.96-1.40).

CONCLUSIONS

MHO are more likely to develop metabolic complications than MHNW, especially if they gain weight.

Cardiometabolic healthy obesity paradigm and all-cause mortality risk

OBJECTIVE

To examine the cardiometabolic healthy obesity paradigm as it relates to all-cause mortality risk, with effect moderation evaluated for physical activity and demographic characteristics.

METHODS

Data from the 1999-2006 NHANES were used. The analytic sample included 7579 dietary fasting adults (20+ yrs). All-cause mortality was linked with participant data from the National Death Index. Metabolic health was based on fasting levels of triglycerides, high-density lipoprotein cholesterol, glucose and blood pressure. Weight status was determined from measured height and weight. Physical activity was assessed via self-report. Six mutually exclusive groups were evaluated, including 1) Metabolically Healthy and Normal Weight (Referent), 2) Metabolically Healthy and Overweight, 3) Metabolically Healthy and Obese, 4) Metabolically Abnormal and Normal Weight, 5) Metabolically Abnormal and Overweight, and 6) Metabolically Abnormal and Obese. A Cox proportional hazards model was used to evaluate the association between these 6 groups and all-cause mortality.

RESULTS

The unweighted median follow-up was 103months; 770,568 person-months occurred with an incidence rate of 1.18 deaths per 1000 person-months. When compared to those who were metabolically healthy and of normal BMI, all other metabolic and weight configurations had an increased mortality risk. There was no evidence of effect modification by physical activity or demographic characteristics.

CONCLUSIONS

These findings emphasize the importance of optimizing body habitus and increasing public awareness of the detrimental effects of metabolic abnormalities.

Metabolically healthy obesity: a friend or foe?

Obesity is associated with a reduced life expectancy, largely because obese individuals are at an increased risk of type 2 diabetes, cardiovascular disease, and several types of cancer. Much interest has recently focused on the concept of "all obesity is not created equally." Obese individuals without the metabolic abnormalities that commonly accompany excess adiposity, a condition known as metabolically healthy obesity (MHO), account for a substantial proportion of the obese adult population. Numerous possible mechanisms underlying MHO have been suggested, including adipose tissue distribution and inflammation. However, the prognostic value of MHO is controversial and considerably challenging. The lack of a standard definition for metabolic health and obesity as well as the dynamic properties of MHO may have contributed to these inconsistent results. This review aimed to present several current issues regarding MHO including its definition, epidemiology, natural course, suggested mechanisms, and clinical implications in the context of patient prognosis.

Apolipoproteins A-I, B, and C-III and Obesity in Young Adult Cherokee

Since young adult Cherokee are at increased risk for both diabetes and cardiovascular disease, we assessed association of apolipoproteins (A-I, B, and C-III in non-HDL and HDL) with obesity and related risk factors. Obese participants (BMI ≥ 30) aged 20–40 years (n = 476) were studied. Metabolically healthy obese (MHO) individuals were defined as not having any of four components of the ATP-III metabolic syndrome after exclusion of waist circumference, and obese participants not being MHO were defined as metabolically abnormal obese (MAO). Associations were evaluated by correlation and regression modeling. Obesity measures, blood pressure, insulin resistance, lipids, and apolipoproteins were significantly different between groups except for total cholesterol, LDL-C, and HDL-apoC-III. Apolipoproteins were not correlated with obesity measures with the exception of apoA-I with waist and the waist : height ratio. In a logistic regression model apoA-I and the apoB : apoA-I ratio were significantly selected for identifying those being MHO, and the result (C-statistic = 0.902) indicated that apoA-I and the apoB : apoA-I ratio can be used to identify a subgroup of obese individuals with a significantly less atherogenic lipid and apolipoprotein profile, particularly in obese Cherokee men in whom MHO is more likely.

Expression of MYD88 in Adipose Tissue of Obese People: Is There Some Role in the Development of Metabolic Syndrome?

BACKGROUND

The mechanism leading to the development of metabolic complications in obese individuals is not fully understood. Thus, the objective of this study was to examine differences in insulin resistance, inflammation, cytokine and adipokine levels, and expression of selected genes across obese individuals with different number of metabolic syndrome (MetS) components.

METHODS

Forty obese individuals who underwent bariatric surgery, divided in three groups based on the number of components of MetS, in addition to abdominal obesity (0, 1, and 2-3 additional components), were studied. Levels of inflammatory proteins, insulin resistance, cytokines, adipokines, and gene expression in subcutaneous (SAT) and visceral adipose tissue (VAT) were compared.

RESULTS

There was a significantly higher expression of MYD88 in SAT among those with more components of MetS (P = 0.008). In SAT, but not in VAT, MYD88 expression was significantly correlated with toll-like receptor 4 expression (r = 0.7, P < 0.05). Expression of adipsin in SAT was also associated with the presence of more components of MetS, but with borderline statistical significance (P = 0.05). There were no significant differences in insulin resistance, inflammation, and cytokine and adipokine levels by the number of components of MetS.

CONCLUSIONS

Our study suggests that MYD88 expression in SAT of obese subjects could be associated with the development of components of MetS.

Risk factors that affect metabolic health status in obese children

BACKGROUND

While some obese children are metabolically healthy (MHO), some have additional health problems, such as hypertension, dyslipidemia, insulin resistance, and hepatosteatosis, which increase mortality and morbidity related to cardiovascular diseases (CVD) during adulthood. These children are metabolically unhealthy obese (MUO) children. In this study we assessed the factors that affect metabolic health in obesity and the clinical and laboratory findings that distinguish between MHO and MUO children.

METHODS

In total, 1085 patients aged 6-18 years, with age- and sex-matched BMI exceeding the 95th percentile were included in the study (mean 11.1±2.9 years, 57.6% female, 59.7% pubertal). Patients without dyslipidemia, insulin resistance, hepatosteatosis, or hypertension were considered as MHO. Dyslipidemia was defined as total cholesterol level over 200 mg/dL, triglyceride over 150 mg/dL, LDL over 130 mg/dL, or HDL under 40 mg/dL. Insulin resistance was calculated using the homeostasis model of assesment for insulin resistance (HOMA-IR) index. Hepatosteatosis was evaluated with abdominal ultrasound. Duration of obesity, physical activity and nutritional habits, screen time, and parental obesity were questioned. Thyroid and liver function tests were performed.

RESULTS

Six hundred and forty-two cases (59.2%) were MUO. Older age, male sex, increased BMI-SDS, and sedentary lifestyle were associated with MUO. Excessive junk food consumption was associated with MUO particularly among the prepubertal obese patients.

CONCLUSIONS

Our results revealed that the most important factors that affect metabolic health in obesity are age and BMI. Positive effects of an active lifestyle and healthy eating habits are prominent in the prepubertal period and these habits should be formed earlier in life.

Characterization of Metabolically Healthy Obese People and Metabolically Unhealthy Normal-Weight People in a General Population Cohort of the ABCD Study

There is actually no consensus about the possibility that in some instances, obesity may be a benign metabolically healthy (MH) condition as opposed to a normal-weight but metabolically unhealthy (MUH) state. The aim of this study was to characterize MH condition and to investigate possible associations with metabolic and cardiovascular complications. One thousand nineteen people (range of age 18-90 years) of the cohort of the ABCD_2 study were investigated. Participants were classified as normal weight (BMI < 24.9 kg/m²) or overweight-obese (BMI ≥25 kg/m²); they were also classified as MH in the presence of 0-1 among the following conditions: (a) prediabetes/type 2 diabetes, (b) hypertension, (c) hypertriglyceridemia or low HDL cholesterolemia, and (d) hypercholesterolemia. MUH condition was diagnosed if ≥2 of the conditions listed were found. The prevalence of overweight/obese people was 71.1%, of whom 27.4% were found to be MH. In addition, 36.7% of the normal-weight participants were MUH. HOMA-IR, high sensitivity C-reactive protein, and the carotid intima-media thickness were significantly different in the 4 subgroups (P < 0.001), with higher values observed in the MUH normal-weight and obese groups. In conclusion, this study highlights the importance of identifying a MH condition in normal-weight and in obese people in order to offer better treatment.

Are all metabolically healthy individuals with obesity at the same risk of diabetes onset?

OBJECTIVE

To examine the risk of diabetes and the development of an unhealthy status according to metabolic health. To assess the effect of changes in metabolic health among participants with metabolically healthy obesity (MHO) on the risk of diabetes.

METHODS

A total of 4,340 subjects were included. Unhealthy metabolic status was defined as having three or more risk factors of the Adult Treatment Panel-III criteria. A Cox proportional-hazard analysis was conducted to estimate the hazard ratio (HR) of developing diabetes across the change in the metabolic status of subjects with MHO.

RESULTS

After 40,622 person-years of follow-up, the risk of becoming unhealthy was 1.53 times higher for participants with MHO, compared with lean or overweight healthy subjects. A greater risk of diabetes was found in MHO, but it was attributable to those who progressed to an unhealthier status over time: HR of 4.78 (95% CI: 3.38-6.78). The combination of being metabolically unhealthy and obesity heightened the risk of diabetes: HR of 10.09 (95% CI: 4.82-21.55).

CONCLUSIONS

The increased risk of diabetes in MHO is attributed to the progression to an unhealthier state. "Healthy obesity" is not a permanent situation but a transitory state.

Disability, Physical Inactivity, and Impaired Health-Related Quality of Life Are Not Different in Metabolically Healthy vs. Unhealthy Obese Subjects

BACKGROUND

Obesity represents a major health hazard, affecting morbidity, psychological status, physical functionality, quality of life, and mortality. The aim of the present study was to explore the differences between metabolically healthy (MHO) and metabolically unhealthy (MUO) obese subjects with regard to physical activity, disability, and health-related quality of life (HR-QoL).

METHODS

All subjects underwent a multidimensional evaluation, encompassing the assessment of body composition, metabolic biomarkers and inflammation, physical activity level (IPAQ questionnaire), disability (TSD-OC test), and HR-QoL (SF-36 questionnaire). MHO and MUO were defined based on the absence or the presence of the metabolic syndrome, respectively.

RESULTS

253 subjects were included (54 men and 199 women; age: 51.7 ± 12.8 vs. 50.3 ± 11.7 years, p = 0.46; BMI: 38.1 ± 5.7 vs. 38.9 ± 6.7 kg/m², p = 0.37). No significant difference was observed in body composition. There was no difference between MHO and MUO considering inflammation (hs-CRP: 6517.1 ± 11,409.9 vs. 5294.1 ± 5612.2 g/L; p = 0.37), physical inactivity (IPAQ score below 3000 METs-min/week in 77.6% of MHO vs. 80% of MUO subjects; p = 0.36), obesity-related disability (TSD-OC score > 33%, indicating a high level of obesity-related disability, in 20.2% of MHO vs. 26.5% of MUO subjects; p = 0.28), and the HR-QoL (SF-36 total score: 60 ± 20.8 vs. 62.8 ± 18.2, p = 0.27).

DISCUSSION AND CONCLUSION

The metabolic comorbidity and the impairment of functional ability and psycho-social functioning may have a different timing in the natural history of obesity. Alterations in the physical activity level and mobility disabilities may precede the onset of metabolic abnormalities. (Trial registration 2369 prot 166/12-registered 23 February 2012; Amendment 223/14-registered 13 February 2014).