Differences in body composition between metabolically healthy obese and metabolically abnormal obese adults

Relevance of body composition in phenotyping the obesities

Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic alterations such as type 2 diabetes, hypertension, and dyslipidemia. Body mass index (BMI) remains the most frequently used tool for classifying patients with obesity, but it does not accurately reflect body adiposity. In this document we review classical and new classification systems for phenotyping the obesities. Greater accuracy of and accessibility to body composition techniques at the same time as increased knowledge and use of cardiometabolic risk factors is leading to a more refined phenotyping of patients with obesity. It is time to incorporate these advances into routine clinical practice to better diagnose overweight and obesity, and to optimize the treatment of patients living with obesity.

Metabolically healthy obesity: Misleading phrase or healthy phenotype?

Obesity is a heterogenous condition with multiple different phenotypes. Among these a particular subtype exists named as metabolically healthy obesity (MHO). MHO has multiple definitions and its prevalence varies according to study. The potential mechanisms underlying the pathophysiology of MHO include the different types of adipose tissue and their distribution, the role of hormones, inflammation, diet, the intestinal microbiota and genetic factors. In contrast to the negative metabolic profile associated with metabolically unhealthy obesity (MUO), MHO has relatively favorable metabolic characteristics. Nevertheless, MHO is still associated with many important chronic diseases including cardiovascular disease, hypertension, type 2 diabetes, chronic kidney disease as well as certain types of cancer and has the risk of progression into the unhealthy phenotype. Therefore, it should not be considered as a benign condition. The major therapeutic alternatives include dietary modifications, exercise, bariatric surgery and certain medications including glucagon-like peptide-1 (GLP-1) analogs, sodium-glucose cotransporter-2 (SGLT-2) inhibitors and tirzepatide. In this review, we discuss the significance of MHO while comparing this phenotype with MUO.

Adipose Tissue Inflammation and Pulmonary Dysfunction in Obesity

Obesity is a chronic disease caused by an excess of adipose tissue that may impair health by altering the functionality of various organs, including the lungs. Excessive deposition of fat in the abdominal area can lead to abnormal positioning of the diaphragm and consequent reduction in lung volume, leading to a heightened demand for ventilation and increased exposure to respiratory diseases, such as chronic obstructive pulmonary disease, asthma, and obstructive sleep apnoea. In addition to mechanical ventilatory constraints, excess fat and ectopic deposition in visceral depots can lead to adipose tissue dysfunction, which promotes metabolic disorders. An altered adipokine-secretion profile from dysfunctional adipose tissue in morbid obesity fosters systemic, low-grade inflammation, impairing pulmonary immune response and promoting airway hyperresponsiveness. A potential target of these adipokines could be the NLRP3 inflammasome, a critical component of the innate immune system, the harmful pro-inflammatory effect of which affects both adipose and lung tissue in obesity. In this review, we will investigate the crosstalk between adipose tissue and the lung in obesity, highlighting the main inflammatory mediators and novel therapeutic targets in preventing pulmonary dysfunction.

The Association Between Hyperuricemia and Obesity Metabolic Phenotypes in Chinese General Population: A Retrospective Analysis

Purpose

Serum uric acid (UA) not only affects the development of obesity but also alters the metabolic status in obese subjects; thus we investigated the relationship between serum UA and the overweight/obese metabolic phenotypes.

Methods

The demographic, biochemical, and hematological data were collected for 12,876 patients undergoing routine physical examination, and 6,912 participants were enrolled in our study. Participants were classified into four obesity metabolic phenotypes according to their BMI and the presence of metabolic syndrome: metabolically healthy overweight/obese (MHOO), metabolically healthy and normal weighted (MHNW), metabolically abnormal and overweight/obese (MAOO), and metabolically abnormal but normal weighted (MANW). Univariate and multivariate logistic regression analysis, stratified analysis, and also interaction analysis were conducted to analyze the relationship between serum UA and obesity metabolic phenotypes.

Results

Multivariable logistic regression analysis showed that hyperuricemia was positively associated with MHOO, MANW, and MAOO phenotypes relative to MHNW. After adjusting for the confounding factors, the odds ratios (OR) for individuals with hyperuricemia to be MHOO, MANW, and MAOO phenotypes were 1.86 (1.42–2.45), 2.30 (1.44–3.66), and 3.15 (2.34–4.24), respectively. The ORs for having MHOO, MANW, and MAOO increased 6% [OR: 1.06 (1.05–1.07), P < 0.0001], 5% [OR: 1.05 (1.03–1.07), P < 0.0001], and 11% [OR: 1.11 (1.10–1.13), P < 0.0001] for each 10 unit (μmol/L) of increase in serum UA level. Stratification analysis as well as an interaction test showed that sex and age did not interfere with the association of hyperuricemia with each metabolic phenotype. In terms of the components of the metabolic syndrome, after adjusting for other confounding factors including all of the metabolic indicators except itself, hyperuricemia was positively associated with increased BMI [OR: 1.66 (1.32–2.09), P < 0.0001], hypertriglyceridemia [OR: 1.56 (1.21–2.02), P = 0.0006], and hypertension [OR: 1.22 (1.03–1.46), P = 0.0233], while it had no significant association with hyperglycemia and low HDL-C (all P > 0.05).

Conclusion

In our study, we discovered that hyperuricemia was positively associated with MHOO, MANW, and MAOO phenotypes, and this relationship was independent of sex and age.

Quality Matters as Much as Quantity of Skeletal Muscle: Clinical Implications of Myosteatosis in Cardiometabolic Health

Although age-related changes in skeletal muscles are closely associated with decreases in muscle strength and functional decline, their associations with cardiometabolic diseases in the literature are inconsistent. Such inconsistency could be explained by the fact that muscle quality-which is closely associated with fatty infiltration of the muscle (i.e., myosteatosis)-is as important as muscle quantity in cardiometabolic health. However, muscle quality has been less explored compared with muscle mass. Moreover, the standard definition of myosteatosis and its assessment methods have not been established yet. Recently, some techniques using single axial computed tomography (CT) images have been introduced and utilized in many studies, as the mass and quality of abdominal muscles could be measured opportunistically on abdominal CT scans obtained during routine clinical care. Yet, the mechanisms by which myosteatosis affect metabolic and cardiovascular health remain largely unknown. In this review, we explore the recent advances in the assessment of myosteatosis and its changes associated with aging. We also review the recent literature on the clinical implication of myosteatosis by focusing on metabolic and cardiovascular diseases. Finally, we discuss the challenges and unanswered questions that need addressing to set myosteatosis as a therapeutic target for the prevention or treatment of cardiometabolic diseases.

Metabolically Healthy Obesity (MHO) vs. Metabolically Unhealthy Obesity (MUO) Phenotypes in PCOS: Association with Endocrine-Metabolic Profile, Adherence to the Mediterranean Diet, and Body Composition

Obesity and obesity-related low-grade inflammation are common findings in polycystic ovary syndrome (PCOS), the most common endocrine-metabolic disorder-affecting women in reproductive age. The terms metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) have been introduced to define individuals with obesity in whom cardio-metabolic risk factors are absent or present, respectively. To date, evidence investigating differences in body composition and adherence to the Mediterranean diet (MD) between MHO and MUO-PCOS women are lacking. Aim of this study was to better characterize the determinants of the metabolic health status in PCOS patients with obesity according to MHO and MUO phenotypes by evaluating endocrine-metabolic profile, inflammatory status, adherence to the MD, and body composition. The study population consisted of 94 treatment-naïve women with PCOS and obesity (BMI = 38.23 ± 6.62 kg/m² and age = 24.12 ± 3.68 years). Compared PCOS MHO with PCOS MUO patients, the latter had higher levels of high-sensitivity C-reactive protein (hs-CRP) (p < 0.001), testosterone (p < 0.001), and insulin (p < 0.001), worse metabolic parameters, and higher Homeostatic Model Assessment of Insulin Resistance (HoMA-IR), Visceral Adiposity Index (VAI), and Fatty liver Index (FLI) (p < 0.001). Furthermore, PCOS MUO patients had lower adherence to the MD (p < 0.001) in spite of the same total energy intake (p = 0.102) as compared to PCOS MHO. The presence of MUO was associated with highest hs-CRP levels (OR = 1.49, p < 0.001), more severe hyperandrogenism and cardio-metabolic indices (p < 0.001). On the contrary, being PCOS MUO was associated with lower adherence to the MD (OR = 0.28, p < 0.001), and smaller PhAs (OR = 0.04, p < 0.001). Using a regression linear analysis model PREDIMED score entered at the first step (p < 0.001), followed by VAI (p < 0.001), and FLI (p = 0.032) in this analysis. At ROC analysis, a PREDIMED score of ≤4 (p < 0.001, AUC 0.926) could serve as a threshold for a significantly increased risk of presence the MUO-PCOS phenotype. To the best of our knowledge, this is the first study that characterized MHO and MUO-PCOS women on the basis of their adherence to the MD, body composition, and cardio-metabolic indices, providing evidence of the usefulness of adjunctive diagnostic parameters to better differentiate the MHO/MHO phenotypes in this cohort of PCOS patients with obesity.

Characterisation of body size phenotypes in a middle-aged Maltese population

Obesity is increasingly recognised as being a heterogeneous disease. Some obese individuals may present a metabolically healthy profile (metabolically healthy obese (MHO)), while some normal weight individuals exhibit an adverse cardiometabolic phenotype (metabolically unhealthy normal weight individuals (MUHNW)). The objectives of the present study were to examine the prevalence and associated characteristics of the different body composition phenotypes within a Maltese cohort. This was a cross-sectional analysis involving 521 individuals aged 41 ± 5 years. The metabolically unhealthy state was defined as the presence of ≥2 metabolic syndrome components (NCEP-ATPIII parameters), while individuals with ≤1 cardiometabolic abnormalities were classified as metabolically healthy. Overall, 70 % of the studied population was overweight or obese and 30⋅7 % had ≥2 cardiometabolic abnormalities. The prevalence of MHO and MUHNW was 10⋅7 and 2⋅1 %, respectively. Individuals with the healthy phenotype were more likely to consume alcohol, participate in regular physical activity and less likely to be smokers. While the MHO phenotype had similar values for waist, hip and neck circumferences, waist-hip ratio and insulin resistance when compared with MUHNW individuals, there was a lower proportion of MHO subjects having a high fasting plasma glucose, hypertriglyceridaemia or low HDL-C when compared with the unhealthy lean individuals. A high prevalence of the metabolically unhealthy phenotype was observed in this relatively young population which may result in significant future cardiovascular disease burden if timely assessment and management of modifiable risk factors are not implemented. Furthermore, the present study suggests that the MHO phenotype is not totally benign as previously thought.

Comparison of muscle mass and quality between metabolically healthy and unhealthy phenotypes

OBJECTIVE

The aim of this study was to examine whether higher skeletal muscle mass is associated with a metabolically healthy phenotype and whether muscle quality affects metabolic health.

METHODS

This cross-sectional analysis included 20,659 participants (7,966 women) who underwent abdominal computed tomography scans during health checkups. The total abdominal muscle area (TAMA) on the third lumbar vertebral level was demarcated. Intermuscular adipose tissue and skeletal muscle area were measured. The skeletal muscle area was divided into normal attenuation muscle area (NAMA) and low attenuation muscle area (LAMA). The NAMA/TAMA index was calculated. The metabolically unhealthy phenotype was defined as having two or more components of metabolic syndrome or the presence of hypertension or diabetes.

RESULTS

TAMA and skeletal muscle area were not significantly different or even lower in metabolically healthy phenotypes compared with metabolically unhealthy phenotypes. However, metabolically healthy phenotypes had significantly higher NAMA (except in women with obesity) and NAMA/TAMA index than in the metabolically unhealthy phenotypes. In people without obesity, lower NAMA/TAMA index was independently associated with higher risk of the metabolically unhealthy phenotype in the fully adjusted model.

CONCLUSIONS

The metabolically healthy phenotypes had more good-quality muscles than did the metabolically unhealthy phenotypes. These results suggest that not only muscle mass but also muscle quality (i.e., degree of myosteatosis) are associated with metabolic health.

Metabolically healthy obesity increases the prevalence of stroke in adults aged 40 years or older: Result from the China National Stroke Screening survey

Debate over the cardio-cerebrovascular risk associated with metabolically healthy obesity (MHO) continues. In this study we investigated the association of MHO with the risk of stroke among 221,114 individuals aged 40 years or older based on data from the China National Stroke Screening and Prevention Project (CNSSPP), a nationally representative cross-sectional study, during 2014 to 2015. Different metabolic health and obesity phenotypes were defined according to the Adult Treatment Panel III (ATP III) criteria, where obesity was defined as a body mass index (BMI) ≥28 kg/m². Logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for stroke risk associated with different metabolically healthy phenotypes. BMI was used to estimate the mediation effect for metabolic abnormalities to stroke. Compared with the metabolically healthy non-obesity (MHNO) group, individuals with MHO (adjusted OR: 1.21, 95% CI: 1.10,1.33), metabolically unhealthy non-obesity (MUNO) (adjusted OR:1.41, 95% CI: 1.36,1.46), or metabolically unhealthy obesity (MUO) (adjusted OR: 1.70, 95% CI: 1.61,1.80) were found to have an increased risk of stroke. The findings were confirmed robustly by various sensitivity analyses and subgroup analyses. Furthermore, obesity and metabolic abnormalities had an additive interaction for stroke risk with an attributable proportion (AP) of 14.0% in females. BMI played a partial mediating role with the proportion of the effect (PE) at 11.1% in the relationship between metabolic abnormalities and stroke. This study strengthens the evidence that management and interventions in the MHO population may contribute to the primary prevention of stroke.

Association of Visceral Adipose Tissue and Insulin Resistance with Incident Metabolic Syndrome Independent of Obesity Status: The IRAS Family Study

OBJECTIVE

Although increasing evidence suggests that visceral adipose tissue (VAT) is a major underlying cause of metabolic syndrome (MetS), few studies have measured VAT at multiple time points in diverse populations. VAT and insulin resistance were hypothesized to differ by MetS status within BMI category in the Insulin Resistance Atherosclerosis Study (IRAS) Family Study and, further, that baseline VAT and insulin resistance and increases over time are associated with incident MetS.

METHODS

Generalized estimating equations were used for differences in body fat distribution and insulin resistance by MetS status. Mixed effects logistic regression was used for the association of baseline and change in adiposity and insulin resistance with incident MetS across 5 years, adjusted for age, sex, race/ethnicity, and family correlation.

RESULTS

VAT and insulin sensitivity differed significantly by MetS status and BMI category at baseline. VAT and homeostatic model assessment of insulin resistance (HOMA-IR) at baseline (VAT odds ratio [OR] = 1.16 [95% CI: 1.12-2.31]; HOMA-IR OR = 1.85 [95% CI: 1.32-2.58]) and increases over time (VAT OR = 1.55 [95% CI: 1.22-1.98]; HOMA-IR OR = 3.23 [95% CI: 2.20-4.73]) were associated with incident MetS independent of BMI category.

CONCLUSIONS

Differing levels of VAT may be driving metabolic heterogeneity within BMI categories. Both overall and abdominal obesity (VAT) may play a role in the development of MetS. Increased VAT over time contributed additional risk.

Influence of the Obesity Phenotype on the Adequacy of Antibiotic Prophylaxis with Cefoxitin for Obese Patients Undergoing Bariatric Surgery: Lessons Learnt and Future Considerations

BACKGROUND AND OBJECTIVES

A high inter-individual variability in pharmacokinetic parameters in obese patients is observed. The objective of this study was to evaluate the effect of obesity parameters on the pharmacokinetics of cefoxitin administered for antibiotic prophylaxis during bariatric surgery.

METHODS

This a secondary analysis of a pharmacokinetic study involving 174 obese patients scheduled for bariatric surgery and receiving a 4-g dose of cefoxitin. Blood samples were collected at incision and wound closure. The total plasma concentrations were assessed utilising a validated high-performance liquid chromatography-tandem mass spectrometry method. The pharmacokinetic and pharmacodynamic target was defined as an estimated free concentration of cefoxitin at the time of wound closure >8 mg/L. Specific evaluated obesity parameters were fat body mass, fat body mass/height², lean body mass, lean body mass/height², visceral adipose tissue and presence of a metabolic syndrome.

RESULTS

A total of 174 patients (median age 47 years) with a majority of women (75.3%) and a median BMI of 44 kg/m² were analysed. The percentage of patients who met the pharmacokinetic and pharmacodynamic target was 85.1%. In the whole population, a tendency to fail to reach the target was observed with a higher lean mass over height² [OR = 0.79; 95% CI (0.62-1.01); P = 0.060]. In the female subgroup, higher lean mass over height² [OR = 0.63; 95% CI (0.41-0.97); P = 0.037] and the presence of a metabolic syndrome [OR = 0.17; 95% CI (0.03-0.83); P = 0.030] were associated with failure to reach the pharmacokinetic and pharmacodynamic target.

CONCLUSION

Obese patients with a higher lean mass and a metabolic syndrome could constitute a subgroup at risk for cefoxitin under-dosage.

Differences in body composition between metabolically healthy and unhealthy midlife women with respect to obesity status

Body composition (BC) characteristics across metabolic health-by-body mass index categories were examined. Metabolic health (MH) was defined by five biomarkers: waist circumference, blood pressure, levels of triglycerides, high density lipoprotein cholesterol, and fasting glucose. Potential differences in BC characteristics between metabolically healthy obese (MH-O) and metabolically unhealthy obese (MUH-O) women, and between MH normal weight (MH-NW) and MUH normal weight (MUH-NW) women were explored in 276 Slovak midlife women (39-65 years). Body composition parameters were measured with bioimpedance analyzer (BIA 101, Akern, S. r. l.). A simple comparison of the BC data between the subgroups showed significant differences in resistance (Rz, ohm) (p=0.035), muscle mass (MM, kg) (p=0.044), and total body water (TBW, kg) (p=0.047) between MH-O and MUH-O women. However, we did not observe any significant differences in BC characteristics between MH-NW and MUHNW. Specific logistic regression models were used to determine differences in BC characteristics between various obesity phenotypes, with controlling for age, menopausal status, smoking status and sport activity. Our results indicated that increasing age and decreasing Rz were statistically significantly associated with an increased likelihood of exhibiting MUH-O (p=0.031 for age; p=0.032 for Rz). Moreover, other logistic models which included age, menopausal status, biochemical variables and life style factors such as covariates, showed that increasing alanine aminotransferase (ALT) and uric acid (UA) were statistically significantly associated with an increased likelihood of exhibiting MUH-O (p=0.023 for ALT, p=0.010 for UA). In conclusion, MUH-O and MH-O cardiometabolic profiles are characterized by differences in the value of resistance and plasma levels of ALT and UA.

Circulating levels of IL-33 are elevated by obesity and positively correlated with metabolic disorders in Chinese adults

BACKGROUND

Interleukin-33 (IL-33) plays a pivotal role in regulating innate immune response and metabolic homeostasis. However, whether its circulating level is correlated with obesity and metabolic disorders in humans remains largely unknown. We aimed to address this gap by determining IL-33 serum level and its downstream type 2 inflammatory cytokines interleukin-5 (IL-5) and interleukin-13 (IL-13) in overweight/obese population, and analyzing the specific associations between IL-33 and obesity metabolic phenotypes.

METHODS

217 subjects were enrolled and divided into three groups: healthy control (HC) subjects, metabolically healthy overweight/obese (MHOO) subjects and metabolically unhealthy overweight/obese (MUOO) subjects. Circulating levels of IL-33, IL-5 and IL-13 were measured using ELISA analyses. Multivariate regression analyses were further performed to determine the independent association between IL-33 and obesity metabolic phenotypes.

RESULTS

Circulating levels of IL-33 were significantly elevated in subjects of MUOO group compared with HC group and MHOO group, while no significant difference was observed between the latter two groups in IL-33 levels. Consistent with this, serum levels of IL-5/13 were higher in the MUOO group compared with HC and MHOO groups. After adjusted for all confounders, MUOO phenotype was significantly associated with increased IL-33 serum levels (OR = 1.70; 95% CI 1.09-2.64; p = 0.019). With the MHOO group as the reference population, higher circulating level of IL-33 was also positively associated with MUOO phenotype after adjusting for confounders (OR = 1.50; 95% CI 1.20-1.88; p = 2.91E-4). However, there was no significant association between MHOO phenotype and IL-33 levels (p = 0.942). Trend analysis further confirmed the positive correlation between MUOO phenotype and IL-33 level (p for trend = 0.019). Additionally, IL-33 was significantly and positively correlated with diastolic blood pressure (DBP), total cholesterol (TC), alanine aminotransferase (ALT), aspartate aminotransferase (AST), white blood cell (WBC), neutrophil and IL-5 only in MUOO group, while inversely correlated with high density lipoprotein cholesterol (HDL-C) in MHOO subjects.

CONCLUSION

Circulating levels of IL-33 were significantly elevated in overweight/obese Chinese adults with metabolic disorders. Increased levels of IL-33 were positively associated with metabolically unhealthy overweight/obese phenotype and several metabolic syndrome risk factors.

Higher Serum Neuropeptide Y Levels Are Associated with Metabolically Unhealthy Obesity in Obese Chinese Adults: A Cross-Sectional Study

Objective

Neuropeptide Y (NPY), an orexigenic peptide known to cause hyperphagia, has been involved in the occurrence and development of obesity. However, differences in the distribution of serum NPY levels in obese phenotypes (including metabolically unhealthy obesity (MUO) phenotype and metabolically healthy obesity (MHO) phenotype) and the association of NPY with MUO phenotype have not been unequivocally established. We therefore determined associations of serum NPY levels with MUO phenotype in obese Chinese adults.

Methods

A cross-sectional study was conducted from 400 obese adults in Hunan province, who underwent a health examination in the Second Xiangya Hospital, and 164 participants were finally enrolled in the study and divided into MHO and MUO groups. Serum NPY levels were examined; univariate and multivariate analyses as well as smooth curve fitting analyses were conducted to measure the association of NPY serum levels with the MUO phenotype.

Results

Serum NPY levels were significantly elevated in the MUO group compared with the MHO group ((667.69 ± 292.90) pg/mL vs. (478.89 ± 145.53) pg/mL, p < 0.001). A threshold and nonlinear association between serum NPY levels and MUO was found (p = 0.001). When serum NPY levels exceeded the turning point (471.5 pg/mL), each 10 pg/mL increment in the NPY serum level was significantly associated with an 18% increased odds ratio of MUO phenotype (OR: 1.18, 95% CI: 1.07–1.29, p = 0.0007) after adjusted for confounders.

Conclusions

Higher NPY serum levels were positively correlated with MUO phenotype in obese Chinese adults.

Utility of Liver Function Tests and Fatty Liver Index to Categorize Metabolic Phenotypes in a Mediterranean Population

The aim of this study was to analyze the utility of liver function tests (LFT) and fatty liver index (FLI), a surrogate marker of non-alcoholic fatty liver disease, in the categorization of metabolic phenotypes in a Mediterranean population. A cross-sectional study was performed on a random representative sample of 2233 adults assigned to a health center in Málaga, Spain. The metabolic phenotypes were determined based on body mass index (BMI) categorization and the presence or absence of two or more cardiometabolic abnormalities (high blood pressure, low high-density lipoprotein (HDL) cholesterol, hypertriglyceridemia, pre-diabetes) or type 2 diabetes. No difference was observed between metabolically healthy and metabolically abnormal phenotypes on LFT. The mean FLI of the population was 41.1 ± 28.6. FLI was significantly higher (p < 0.001) in the metabolically abnormal phenotypes in all BMI categories. The proportion of individuals with pathological FLI (≥60) was significantly higher in the metabolically abnormal overweight and obese phenotypes (p < 0.001). On a multivariate model adjusted for sex, age, and waist circumference, a significant correlation was found between pathological FLI and metabolically abnormal phenotypes in the overweight and obese BMI categories. Area under the curve (AUC) of FLI as a biomarker was 0.76, 0.74, and 0.72 for the metabolically abnormal normal-weight, overweight, and obese groups, respectively. Liver biochemistry is poorly correlated with metabolic phenotypes. Conversely, a good correlation between FLI, as a marker of non-alcoholic fatty liver disease (NAFLD), and metabolically abnormal phenotypes in all BMI ranges was found. Our study suggests that FLI may be a useful marker for characterizing metabolically abnormal phenotypes in individuals who are overweight or obese.

Does the Metabolically Healthy Obese Phenotype Protect Adults with Class III Obesity from Biochemical Alterations Related to Bone Metabolism?

Obesity negatively affects the relationship between markers and micronutrients of bone metabolism. Testing the hypothesis that the metabolically healthy obese phenotype might be protected by those alterations was the aim of this study. A cross-sectional study was carried out in adults with class III obesity classified in Metabolically Healthy Obese (MHO) and Metabolically Unhealthy Obese (MUHO), according to the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP ATP III) criteria. Anthropometric, biochemical, and clinical variables were analyzed for sample characterization. To evaluate bone metabolism, markers (alkaline phosphatase and parathyroid hormone—PTH) and related nutrients (vitamin D, vitamin B12, calcium, phosphorus, magnesium, potassium and zinc) were analyzed. A total of 223 adults with class III obesity aged 41.20 ± 10.15 years were included. The MHO phenotype was identified in 32.73% of the sample. After logistic regression, it was observed that inadequacies of calcium (OR: 4.11; 95% CI: 2.33–6.66), phosphorus (OR: 3.03; 95% CI: 1.98–5.79), vitamin D (OR: 5.01; 95% CI: 2.92–6.71) and PTH (OR: 5.45; 95% CI: 4.49–6.74) were significantly higher in the MUHO group compared to the MHO Group. This study showed that the MHO phenotype does not protect adults from alterations in markers and micronutrients of bone metabolism. However, the MUHO phenotype presents a higher risk for alterations related to bone metabolism, which can favor the emergence of metabolic bone diseases.

Differences in dietary intakes, body compositions, and biochemical indices between metabolically healthy and metabolically abnormal obese Korean women

BACKGROUND/OBJECTIVES

There are various factors that affect metabolic abnormalities related to obesity. The purpose of this study is to analyze the differences in dietary intakes and body compositions of obese women according to metabolic risks and to classify them as metabolically healthy obese (MHO) or metabolically abnormal obese (MAO).

SUBJECTS/METHODS

This study was conducted on 59 obese Korean women aged 19 to 60 years. NCEP-ATPIII criteria were applied and the women classified as MHO (n = 45) or MAO (n = 14). Body composition of each subject was measured by using dual-energy x-ray absorptiometry (DEXA). Three-day food records were used to analyze dietary intake. Eating habits and health-related behaviors were determined through questionnaires. Indirect calorimetry was used to measure resting metabolic rate and respiratory rate.

RESULTS

The average age of the subjects was 43.7 years. The analysis of body composition according to phenotype revealed significantly higher body fat mass (P < 0.05), arm fat mass (P < 0.05), and android fat mass (P < 0.05), as measured by DEXA, in the MAO group than in the MHO group. There was no significant difference in the dietary intake of the two groups. However, eating behaviors differed. Compared to the MHO group, the MAO women had a shorter meal time (less than 10 minutes), a preference of oily foods, and a tendency to eat until full. Therefore, the eating habits of MHO women were more positive than those of MAO women.

CONCLUSIONS

The results suggest that fat distribution in each body region affects various metabolic abnormalities. A high level of arm fat mass in obese Korean women may increase metabolic risk. In addition, eating habits of obese Korean women are considered to be environmental factors affecting the metabolic phenotype of obese Korean women.

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.

Predicting Menstrual Recovery in Adolescents With Anorexia Nervosa Using Body Fat Percent Estimated by Bioimpedance Analysis

OBJECTIVE

To identify the threshold of total body fat percentage (TBF%) required for the resumption of menses (ROM) in hospitalized female adolescents with anorexia nervosa (AN) using bioimpedance analysis (BIA).

METHODS

All female adolescents hospitalized with AN in our medical center were evaluated in a longitudinal prospective study during the years of 2012-2017. Anthropometric data, body fat measured by BIA, and hormonal determinants were collected periodically, in addition to routine medical and gynecological assessments.

RESULTS

Sixty-two participants presented with secondary amenorrhea, of which 20 remained with amenorrhea and 42 had ROM during hospitalization. At discharge, participants with ROM regained significantly more weight, and had higher mean body mass index (BMI), BMI standard deviation scores, and TBF% than those who remained with amenorrhea. Receiver operating characteristic analysis identified that a TBF% of 21.2% had the highest discriminative ability for ROM (sensitivity = 88%, specificity = 85%, positive predictive value = 93%). Compared with the anthropometric parameters, TBF% had the highest area under curve (AUC = .895), which significantly differed from that of BMI standard deviation scores (AUC = .643, p = .007) and body weight (AUC = .678, p = .03).

CONCLUSIONS

BIA is a safe and relatively simple method to assess the TBF% required for the return of balanced menstrual cycles in female adolescents with AN. The TBF% with the highest discriminative ability for menstrual resumption as assessed by BIA is 21.2%.

Body Composition and Cardiorespiratory Fitness Between Metabolically Healthy Versus Metabolically Unhealthy Obese Black and White Adolescents

PURPOSE

We compared body composition and cardiorespiratory fitness (CRF) between metabolically healthy overweight/obese (MHO) versus metabolically unhealthy overweight/obese (MUO) adolescents in 189 black and white adolescents (BMI ≥ 85th percentile, 12-18 years of age).

METHODS

Participants were defined as MHO or MUO if their insulin-stimulated glucose disposal, measured by a 3-hour hyperinsulinemic-euglycemic clamp, was in the upper quartile or in the lower three quartiles. Total fat was measured by dual-energy X-ray absorptiometry, and visceral adiposity and liver fat were measured by magnetic resonance imaging and proton magnetic resonance spectroscopy, respectively. CRF was measured by a graded maximal treadmill test.

RESULTS

Black MHO adolescents had lower (p < .05) 2-hour oral glucose tolerance test glucose, triglycerides, very-low-density lipoprotein cholesterol, and higher high-density lipoprotein cholesterol, with a lower prevalence of impaired fasting glucose and impaired glucose tolerance compared with black MUO adolescents. White MHO adolescents had lower (p < .05) triglycerides and very-low-density lipoprotein cholesterol, with a lower prevalence of impaired fasting glucose compared with white MUO adolescents. Independent of race, CRF was higher in MHO versus MUO adolescents. After accounting for gender, Tanner stage, and BMI, there were no differences in total fat (kg, %) between MHO versus MUO in both races. MHO adolescents had significantly lower trunk fat, waist circumference, and visceral fat compared with MUO adolescents in both races. In whites, MHO adolescents had lower (p = .055) liver fat compared with MUO adolescents.

CONCLUSIONS

Independent of race, the MHO phenotype is characterized by high CRF, lower waist circumference and visceral fat, and lower rates of dysglycemia in youth.

Validity of DXA body volume equations in a four-compartment model for adults with varying body mass index and waist circumference classifications

The purpose of this investigation was to determine the validity of 4-compartment (4C) model body fat percent (BF%) estimates when using dual energy x-ray absorptiometry (DXA) derived body volume (BV) equations (4C-DXA1 and 4C-DXA2) in adults with varying body mass index (BMI) and waist circumference (WC) classifications. Each model was compared to a criterion 4C model with air-displacement plethysmography (ADP) generated BV (4C-ADP). Participants were categorized as normal weight (n = 40; NW = BMI<25.0kg/m²); overweight (n = 40; OW_BMI = BMI≥25.0 kg/m²); and overweight with at-risk WC (n = 35; OW_BMI+WC = BMI≥25.0 kg/m² and WC≥88.0cm for women and 102.0cm for men). 4C-DXA1 produced lower BF% than that derived using the 4C-ADP in NW (CE = -3.0%; p<0.001) while 4C-DXA2 was significantly higher (CE = 4.8%; p<0.001). The SEE and 95% limits of agreement (LOA) were lower for 4C-DXA2 (1.24% and ±2.5%, respectively) than 4C-DXA1 (2.59% and ±5.0%, respectively) and proportional bias was present for both (p<0.05). 4C-DXA1 BF% was not significant in OW_BMI (CE = -0.5%; p = 0.112) whereas 4C-DXA2 was higher (CE = 4.5%; p<0.001). The SEE and 95% LOA were lower for 4C-DXA2 (1.20% and ±2.9%, respectively) than 4C-DXA1 (1.92% and ±3.9%, respectively) in OW_BMI. Proportional bias was present for 4C-DXA1 (p = 0.007), but not 4C-DXA2 (p = 0.832). 4C-DXA1 and 4C-DXA2 produced significantly higher BF% in OW_BMI+WC (CE = 2.2 and 2.3%, respectively; both p<0.001). The SEE and 95% LOA remained lower for 4C-DXA2 (1.15% and ±2.5%, respectively) than 4C-DXA1 (1.84% and ±3.8%, respectively). There was proportional bias for 4C-DXA2 (p = 0.020), but not 4C-DXA1 (p = 0.183) in OW_BMI+WC. Only one prediction model (i.e., 4C-DXA1 in OW_BMI+WC) revealed valid estimates of BF%. Practitioners are encouraged to use criteria for both BMI and WC when utilizing DXA-derived BV in 4C-models for normal and overweight populations.

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.

Effect of Hand Positioning on DXA Total and Regional Bone and Body Composition Parameters, Precision Error, and Least Significant Change

Dual-energy X-ray absorptiometry (DXA) body composition measurements are performed in both clinical and research settings for estimations of total and regional fat mass, lean tissue mass, and bone mineral content. Subject positioning influences precision and positioning instructions vary between manufacturers. The aim of the study was to determine the effect of hand position and scan mode on regional and total body bone and body composition parameters and determine protocol-specific body composition precision errors. Thirty-eight healthy subjects (men; mean age: 27.1 ± 12.1 yr) received 4 consecutive total body GE-Lunar iDXA (enCORE v 15.0) scans with re-positioning, and scan mode was dependent on body size. Twenty-three subjects received scans in standard mode and 15 received scans in thick scan modes. Two scans per subject were conducted with subject hands prone and 2 with hands mid-prone. The precision error (root mean squared standard deviation; percentage coefficient of variation) and least significant change for each protocol were determined using the International Society for Clinical Densitometry calculator. Hands placed in the mid-prone position increased arm bone mineral density (BMD) (standard mode: 0.185 g*cm^-2, thick mode: 0.265 g*cm^-2; p < 0.05), total body BMD (standard mode: 0.051 g*cm^-2, thick mode: 0.069 g*cm^-2; p < 0.001), and total body BMD Z-score (standard mode: 0.5. thick mode: 0.7; p < 0.001). This was due to reductions in bone area and bone mineral content. In standard mode, hands mid-prone reduced fat mass (0.05 kg, p < 0.05) and increased lean mass (0.11 kg, p < 0.05). There were no differences in body composition for thick mode scans. Hands mid-prone reduced lean mass precision error at the arms, trunk, and total body (p < 0.01). DXA clinical and research centers are advised to maintain consistency in their hand positioning and scan mode protocols, and consideration should be given to the hand positioning used for reference data. As a best practice recommendation, published DXA-based studies and reports for clinic-based total body assessments should ensure that subject positioning is fully described.

DXA-measured visceral fat mass and lean body mass reflect abnormal metabolic phenotypes among some obese and nonobese Chinese children and adolescents

BACKGROUND AND AIM

The exact constellation of body composition characteristics among metabolically unhealthy obese (MUO) and nonobese (MUNO) children and adolescents remains unclear. The purpose of this study was to identify the major body composition determinants of metabolically unhealthy phenotypes among Chinese children and adolescents.

METHODS AND RESULTS

We used data from a cross-sectional survey in 2015 that included 1983 children and adolescents aged 6-18 years. Subjects were classified into two phenotypes based on a combination of body mass index (BMI) and metabolic syndrome components. Body composition was measured by dual-energy X-ray absorptiometry (DXA). Among all boys and among adolescent boys, those with MUNO phenotypes displayed significantly higher indices of body composition except for fat mass (FM) percentage and trunk-to-legs FM ratio compared with the metabolically healthy nonobese phenotype (all P < 0.05). MUO individuals had higher arm FM, lean body mass (LBM), and trunk lean mass compared to metabolically healthy obese individuals (all P < 0.05). Visceral fat mass (VFM) and BMI were the major independent determinants of MUNO (VFM, 6- to 9-year-old boys, OR = 1.02, 95% CI = 1.00-1.03, P = 0.021; BMI, 6- to 9-year-old girls, OR = 1.90, 95% CI = 1.31-2.84, P = 0.001; and adolescent boys, OR = 1.34, 95% CI = 1.23-1.44, P < 0.001). LBM was the major independent predictor of MUO among adolescent boys (OR = 1.90, 95% CI = 1.03-1.17, P = 0.003).

CONCLUSIONS

Among children and adolescents, the metabolically unhealthy phenotype was associated with excess of body composition, but with significant differences observed based on age and sex. VFM and LBM derived by DXA can predict the metabolically unhealthy phenotype effectively in specific sex and age groups.

Metabolically Healthy Obesity, Transition to Metabolic Syndrome, and Cardiovascular Risk

BACKGROUND

Debate over the cardiometabolic risk associated with metabolically healthy obesity (MHO) continues. Many studies have investigated this relationship by examining MHO at baseline with longitudinal follow-up, with inconsistent results.

OBJECTIVES

The authors hypothesized that MHO at baseline is transient and that transition to metabolic syndrome (MetS) and duration of MetS explains heterogeneity in incident cardiovascular disease (CVD) and all-cause mortality.

METHODS

Among 6,809 participants of the MESA (Multi-Ethnic Study of Atherosclerosis) the authors used Cox proportional hazards and logistic regression models to investigate the joint association of obesity (≥30 kg/m2) and MetS (International Diabetes Federation consensus definition) with CVD and mortality across a median of 12.2 years. We tested for interaction and conducted sensitivity analyses for a number of conditions.

RESULTS

Compared with metabolically healthy normal weight, baseline MHO was not significantly associated with incident CVD; however, almost one-half of those participants developed MetS during follow-up (unstable MHO). Those who had unstable MHO had increased odds of CVD (odds ratio [OR]: 1.60; 95% confidence interval [CI]: 1.14 to 2.25), compared with those with stable MHO or healthy normal weight. Dose response for duration of MetS was significantly and linearly associated with CVD (1 visit with MetS OR: 1.62; 95% CI: 1.27 to 2.07; 2 visits, OR: 1.92; 95% CI: 1.48 to 2.49; 3+ visits, OR: 2.33; 95% CI: 1.89 to 2.87; p value for trend <0.001) and MetS mediated approximately 62% (44% to 100%) of the relationship between obesity at any point during follow-up and CVD.

CONCLUSIONS

Metabolically healthy obesity is not a stable or reliable indicator of future risk for CVD. Weight loss and lifestyle management for CVD risk factors should be recommended to all individuals with obesity.

Waist Circumference Is an Anthropometric Parameter That Identifies Women with Metabolically Unhealthy Phenotypes

Metabolically healthy (MH) and metabolically unhealthy (MUH) phenotypes can be present in any subject independently of their body mass index (BMI). However, factors related to the presence of these phenotypes are poorly understood. Therefore, the aim of this cross-sectional study is to describe the prevalence and characteristics associated with the MH and MUH phenotypes in Mexican subjects with different BMI categories. Anthropometric and biochemical parameters were evaluated after 12 h of fasting. HMW (High Molecular Weight) adiponectin and insulin levels were measured by ELISA (enzyme-linked immunosorbent assay). A total of 345 subjects were included, of which, 73.9% were women. The prevalence of the MH phenotype was 69.9%, 46.7%, and 19% in normal weight, overweight, and obesity, respectively. ROC (receiver operating characteristic) curve analysis showed that the waist circumference demonstrated a statistical significance (p < 0.01) in detecting the MUH phenotype in each BMI group only in women. Furthermore, subjects with lower HMW adiponectin levels showed a 2.1 increased risk of presenting the MUH phenotype. In conclusion, in this Mexican population, waist circumference was an anthropometric parameter that identified women with the MUH phenotype in all BMI categories and hypoadiponectinemia was a risk factor for the presence of this phenotype.

The Science of Obesity Management: An Endocrine Society Scientific Statement

The prevalence of obesity, measured by body mass index, has risen to unacceptable levels in both men and women in the United States and worldwide with resultant hazardous health implications. Genetic, environmental, and behavioral factors influence the development of obesity, and both the general public and health professionals stigmatize those who suffer from the disease. Obesity is associated with and contributes to a shortened life span, type 2 diabetes mellitus, cardiovascular disease, some cancers, kidney disease, obstructive sleep apnea, gout, osteoarthritis, and hepatobiliary disease, among others. Weight loss reduces all of these diseases in a dose-related manner-the more weight lost, the better the outcome. The phenotype of "medically healthy obesity" appears to be a transient state that progresses over time to an unhealthy phenotype, especially in children and adolescents. Weight loss is best achieved by reducing energy intake and increasing energy expenditure. Programs that are effective for weight loss include peer-reviewed and approved lifestyle modification programs, diets, commercial weight-loss programs, exercise programs, medications, and surgery. Over-the-counter herbal preparations that some patients use to treat obesity have limited, if any, data documenting their efficacy or safety, and there are few regulatory requirements. Weight regain is expected in all patients, especially when treatment is discontinued. When making treatment decisions, clinicians should consider body fat distribution and individual health risks in addition to body mass index.

Metabolic health across the BMI spectrum in HIV-infected and HIV-uninfected men

OBJECTIVES

In the general population, metabolic health often declines as BMI increases. However, some obese individuals maintain metabolic health. HIV and antiretroviral therapy have been associated with metabolic disturbances. We hypothesized that HIV-infected (HIV) men on suppressive antiretroviral therapy experience less metabolic health than HIV-uninfected (HIV) men across all BMI categories.

DESIGN/METHODS

In a cross-sectional analysis of 1018 HIV and 1092 HIV men enrolled in the multicenter AIDS cohort study, Poisson regression with robust variance determined associations between HIV serostatus and metabolic health prevalence (defined as meeting ≤2 of 5 National Cholesterol Education Program Adult Treatment Panel III metabolic syndrome criteria), adjusting for age, race, BMI category, smoking, and hepatitis C virus infection status.

RESULTS

HIV men were younger (54 vs. 59 years) and had lower median BMI (25 vs. 27 kg/m). Nonobese HIV men had lower metabolic health prevalence than HIV men (BMI ≤25 kg/m: 80 vs. 94%, P < 0.001; BMI 25-29 kg/m: 64 vs. 71%, P = 0.05), but metabolic health prevalence among obese men did not differ by HIV serostatus (BMI 30-34 kg/m: 35 vs. 39%, P = 0.48; BMI ≥35 kg/m: 27 vs. 25%, P = 0.79). In the adjusted model, nonobese HIV men were less likely to demonstrate metabolic health than nonobese HIV men. Among HIV men, per year darunavir, zidovudine, and stavudine use were associated with lower metabolic health likelihood.

CONCLUSION

Metabolically healthy obesity prevalence does not differ by HIV serostatus. However, among nonobese men, HIV infection is associated with lower metabolic health prevalence, with associations between lack of metabolic health and darunavir and thymidine analog nucleoside reverse transcriptase inhibitor exposure observed.

Metabolically Healthy or Metabolically Unhealthy Obese HIV-Infected Patients: Mostly a Matter of Age?

Background: Life expectancy of HIV-infected patients has increased with antiretroviral treatment (ART). Chronic diseases associated with aging, including metabolic and cardiovascular diseases are becoming more prevalent in this population. We aimed to evaluate the association of obesity and aging with cardiometabolic comorbidities and metabolic health status among patients with HIV infection. Methods: We evaluated 580 HIV-1 infected patients (71.7% male, mean age of 47.7 ± 11.5 years). We analyzed the association of age and obesity (defined by and by central obesity) with gender, duration of HIV infection, and ART, anthropometric parameters, cardiometabolic comorbidities, Framingham risk score (FRS), blood pressure, lipid profile, uric acid, liver biochemical tests, and glycemic profile. Furthermore, we analyzed the above-mentioned associations according to the category and central obesity into the metabolically healthy (MH) and unhealthy (MUH) categories. To evaluate the association of anthropometric parameters with cardiometabolic comorbidities, we performed unadjusted and adjusted logistic regression models. Results: The prevalence of excessive weight and cardiometabolic comorbidities increased with age. Patients with normal weight were younger and there was a higher proportion of female patients in the obesity group. The prevalence of hypertension and metabolic syndrome were higher among patients who were overweight or with obesity. The FRS was higher among patients with obesity. The proportion of MUH patients was higher among patients with excessive weight and central obesity. MUH patients had more cardiometabolic comorbidities and a higher FRS. In the normal weight group, MUH patients were older, and in the obesity group they were more likely to be male. The anthropometric parameter most associated with metabolic syndrome was waist circumference and that most associated with hypertension was waist-to-height ratio. The anthropometric parameter most associated with diabetes and FRS was waist-to-hip ratio. Conclusion: Patients with HIV present a high prevalence of obesity and related comorbidities. Ageing significantly contributes to metabolic dysfunction in this population. The proportion of MUH patients is higher among groups with excessive weight and central obesity, with those patients presenting a higher cardiovascular risk. Our results highlight the importance of evaluating and addressing obesity in patients with HIV, as well as metabolic comorbidities and cardiovascular risk.

Fat Matters: Understanding the Role of Adipose Tissue in Health in HIV Infection

More than one-third of adults in the USA are obese and obesity-related disease accounts for some of the leading causes of preventable death. Mid-life obesity may be a strong predictor of physical function impairment later in life regardless of body mass index (BMI) in older age, highlighting the benefits of obesity prevention on health throughout the lifespan. Adipose tissue disturbances including lipodystrophy and obesity are prevalent in the setting of treated and untreated HIV infection. This article will review current knowledge on fat disturbances in HIV-infected persons, including therapeutic options and future directions.

Metabolic characterization of overweight and obese adults

OBJECTIVES

Traditional evaluations of metabolic health may overlook underlying dysfunction in individuals who show no signs of insulin resistance or dyslipidemia. The purpose of this study was to characterize metabolic health in overweight and obese adults using traditional and non-traditional cardiometabolic variables. A secondary purpose was to evaluate differences between overweight/obese and male/female cohorts, respectively.

METHODS

Forty-nine overweight and obese adults (Mean ± SD; Age = 35.0 ± 8.9 yrs; Body mass index = 33.6 ± 5.2 kg·m-2; Percent body fat [%fat] = 36.7 ± 7.9%) were characterized. Body composition (fat mass [FM], lean mass [LM], %fat) was calculated using a 4-compartment model; visceral adipose tissue (VAT) was quantified using B-mode ultrasound. Resting metabolic rate (RMR) and respiratory exchange ratio (RER) were evaluated using indirect calorimetry. Fasted blood and saliva samples were analyzed for total cholesterol (TC), high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglycerides (TRG), glucose (GLUC), insulin, leptin, estradiol, and cortisol.

RESULTS

The prevalence of individuals with two or more cardiometabolic risk factors increased from 13%, using traditional risk factors (GLUC, TRG, HDL), to 80% when non-traditional metabolic factors (VAT, LM, RMR, RER, TC, LDL, HOMA-IR) were considered. Between overweight/obese, there were no significant differences in %fat (p = 0.152), VAT (p = 0.959), RER (p = 0.493), lipids/GLUC (p > 0.05), insulin (p = 0.143), leptin (p = 0.053), or cortisol (p = 0.063); obese had higher FM, LM, RMR, and estradiol (p < 0.01). Males had greater LM, RMR, and TRG (p < 0.01); females had greater %fat, and leptin (p < 0.001). There were no significant sex differences in RER, estradiol, insulin, or cortisol (p > 0.05).

CONCLUSIONS

Evaluating metabolic health beyond BMI and traditional cardiometabolic risk factors can give significant insights into metabolic status. Due to high variability in metabolic health in overweight and obese adults and inherent sex differences, implementation of body composition and visceral fat measures in the clinical setting can improve early identification and approaches to disease prevention.

Dietary Interventions and Changes in Cardio-Metabolic Parameters in Metabolically Healthy Obese Subjects: A Systematic Review with Meta-Analysis

The aim of this systematic review was to assess the effect of diet on changes in parameters describing the body size phenotype of metabolically healthy obese subjects. The databases Medline, Scopus, Web of Knowledge and Embase were searched for clinical studies carried out between 1958 and June 2016 that reported the effect of dietary intervention on BMI, blood pressure, concentration of fasting triglyceride (TG), high density lipoprotein cholesterol (HDL-C), fasting glucose level, the homoeostatic model assessment of insulin resistance (HOMA-IR) and high sensitivity C-Reactive Protein (hsCRP) in metabolically healthy, obese subjects. Twelve clinical studies met inclusion criteria. The combined analyzed population consists of 1827 subjects aged 34.4 to 61.1 with a BMI > 30 kg/m². Time of intervention ranged from eight to 104 weeks. The baseline characteristics related to lipid profile were more favorable for metabolically healthy obese than for metabolically unhealthy obese. The meta-analyses revealed a significant associations between restricted energy diet and BMI (95% confidence interval (CI): -0.88, -0.19), blood pressure (systolic blood pressure (SBP): -4.73 mmHg; 95% CI: -7.12, -2.33; and diastolic blood pressure (DBP): -2.75 mmHg; 95% CI: -4.30, -1.21) and TG (-0.11 mmol/l; 95% CI: -0.16, -0.06). Changes in fasting glucose, HOMA-IR and hsCRP did not show significant changes. Sufficient evidence was not found to support the use of specific diets in metabolically healthy obese subjects. This analysis suggests that the effect of caloric restriction exerts its effects through a reduction in BMI, blood pressure and triglycerides in metabolically healthy obese (MHO) patients.

Does Metabolically Healthy Obesity Exist?

The relationship between obesity and other metabolic diseases have been deeply studied. However, there are clinical inconsistencies, exceptions to the paradigm of "more fat means more metabolic disease", and the subjects in this condition are referred to as metabolically healthy obese (MHO).They have long-standing obesity and morbid obesity but can be considered healthy despite their high degree of obesity. We describe the variable definitions of MHO, the underlying mechanisms that can explain the existence of this phenotype caused by greater adipose tissue inflammation or the different capacity for adipose tissue expansion and functionality apart from other unknown mechanisms. We analyze whether these subjects improve after an intervention (traditional lifestyle recommendations or bariatric surgery) or if they stay healthy as the years pass. MHO is common among the obese population and constitutes a unique subset of characteristics that reduce metabolic and cardiovascular risk factors despite the presence of excessive fat mass. The protective factors that grant a healthier profile to individuals with MHO are being elucidated.

Metabolic health is more closely associated with prevalence of cardiovascular diseases or stroke than obesity: A cross-sectional study in Korean populations

Mounting evidence suggests that not all obese subjects are at increased cardiovascular risk. However, the relationship between the metabolically healthy obese (MHO) phenotype and cardiovascular diseases (CVDs) or stroke remains unclear. Therefore, we retrospectively investigated the prevalence of CVDs or stroke according to metabolic health with obese.We studied 3695 subjects (40-85 years) from the Fifth Korean National Health and Nutrition Examination Survey. Participants were divided into 2 groups and 6 subgroups based on the body mass index (BMI) and metabolic syndrome (MetS) components: healthy (exhibiting none of the 5 MetS components) with the followings: healthy-normal weight (BMI < 23 kg/m), healthy-overweight (BMI = 23-24.9 kg/m), and healthy-obese (BMI ≥ 25 kg/m); and unhealthy (exhibiting 2 or more MetS components) with the followings: unhealthy-normal weight, unhealthy-overweight, and unhealthy-obese.In the healthy group (n = 1726), there were 76 CVDs or stroke patients (4.4%), whereas in the unhealthy group (n = 1969), there were 170 (8.6%). The prevalence was significantly different between the 2 groups (P < 0.001). However, the prevalence was not significantly different among healthy subgroups (P = 0.4072). The prevalence in unhealthy subgroups also demonstrated no statistically significant difference (P = 0.3798).We suggest that the prevalence of CVDs or stroke is different between metabolically healthy and unhealthy phenotype. Furthermore, MHO did not reveal higher CVDs or stroke prevalence rather than metabolically healthy other groups. Additional cohort studies are needed to explain causality between CVDs or stroke incidence and subjects exhibiting the MHO phenotype.

'Adipaging': ageing and obesity share biological hallmarks related to a dysfunctional adipose tissue

The increasing ageing of our societies is accompanied by a pandemic of obesity and related cardiometabolic disorders. Progressive dysfunction of the white adipose tissue is increasingly recognized as an important hallmark of the ageing process, which in turn contributes to metabolic alterations, multi-organ damage and a systemic pro-inflammatory state ('inflammageing'). On the other hand, obesity, the paradigm of adipose tissue dysfunction, shares numerous biological similarities with the normal ageing process such as chronic inflammation and multi-system alterations. Accordingly, understanding the interplay between accelerated ageing related to obesity and adipose tissue dysfunction is critical to gain insight into the ageing process in general as well as into the pathophysiology of obesity and other related conditions. Here we postulate the concept of 'adipaging' to illustrate the common links between ageing and obesity and the fact that, to a great extent, obese adults are prematurely aged individuals.

Correlations Between Abnormal Glucose Metabolism and Bone Mineral Density or Bone Metabolism

Background

The aim of this meta-analysis was to explore the correlations of abnormal glucose metabolism (AGM) with bone mineral density (BMD) and bone metabolism.

Material/Methods

Relevant studies were identified using computerized and manual search strategies. The included studies were in strict accordance with inclusion and exclusion criteria. Statistical analyses were conducted with the Comprehensive Meta-analysis 2.0 (Biostat Inc., Englewood, NJ, USA).

Results

Our present meta-analysis initially searched 844 studies, and 7 studies were eventually incorporated in the present meta-analysis. These 7 cohort studies included 1123 subjects altogether (560 patients with AGM and 563 healthy controls). The results showed that bone mass index (BMI), insulin, and insulin resistance (IR) of patients with AGM were significantly higher than that of the population with normal glucose metabolism (BMI: SMD=1.658, 95% CI=0.663~2.654, P=0.001; insulin: SMD=0.544, 95% CI=0.030~1.058, P=0.038; IR: SMD=8.767, 95% CI=4.178~13.356, P<0.001). However, the results also indicated there was no obvious difference in osteocalcin (OC) and BMD in patients with AGM and the population with normal glucose metabolism (OC: SMD=0.293, 95% CI=−0.023~0.609, P=0.069; BMD: SMD=0.805, 95% CI=−0. 212~1.821, P=0.121).

Conclusions

Our meta-analysis results suggest that AGM might lead to increased BMI, insulin, and IR, while it has no significant correlation with BMD or bone metabolism.

Estimation of risk for diabetes according to the metabolically healthy status stratified by degree of obesity in Korean men

Although obesity is clearly identified as a risk factor for diabetes, the relationship between diabetes and metabolically healthy status of obesity is less clear. This study was aimed to evaluate the incidental risk of diabetes according to metabolically healthy status of obesity. 31,834 Korean men without diabetes categorized into six groups according to their metabolically healthy status stratified by degree of obesity were followed up for 5 years: metabolically healthy normal weight (MH-NW), metabolically healthy overweight (MH-OW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MU-NW), metabolically unhealthy overweight (MU-OW), and metabolically unhealthy obese (MUO). Cox proportional hazards analysis was used to measure the risk for diabetes according to their categories. While overall incidence was 9.0 %, incidence of diabetes was in proportion to the degree of obesity and metabolically healthy status (MH-NW: 6.3 %, MH-OW: 7.5 %, MHO: 9.2 %, MU-NW: 11.8 %, MU-OW: 14.9 %, MUO: 20.1 %). When MH-NW was set as reference, the adjusted HRs (95 % CI) for diabetes of the MH-OW, MHO, MU-NW, MU-OW, MUO compared to MH-NW were 1.18 (1.06-1.32), 1.58 (1.03-2.41), 1.81 (1.61-2.04), 2.36 (2.11-2.63), and 3.47 (2.84-4.24), respectively. In conclusion, risk for diabetes was in proportion to the degree of obesity in both metabolically healthy and unhealthy group. Metabolically healthy status was more significant determinant for incident diabetes than obesity itself.

Imaging methods for analyzing body composition in human obesity and cardiometabolic disease

Advances in the technological qualities of imaging modalities for assessing human body composition have been stimulated by accumulating evidence that individual components of body composition have significant influences on chronic disease onset, disease progression, treatment response, and health outcomes. Importantly, imaging modalities have provided a systematic method for differentiating phenotypes of body composition that diverge from what is considered normal, that is, having low bone mass (osteopenia/osteoporosis), low muscle mass (sarcopenia), high fat mass (obesity), or high fat with low muscle mass (sarcopenic obesity). Moreover, advances over the past three decades in the sensitivity and quality of imaging not just to discern the amount and distribution of adipose and lean tissue but also to differentiate layers or depots within tissues and cells is enhancing our understanding of distinct mechanistic, metabolic, and functional roles of body composition within human phenotypes. In this review, we focus on advances in imaging technologies that show great promise for future investigation of human body composition and how they are being used to address the pandemic of obesity, metabolic syndrome, and diabetes.

The role of gut microbiota in the development of type 1, type 2 diabetes mellitus and obesity

Diabetes is a group of metabolic disorders characterized by persistent hyperglycemia and has become a major public health concern. Autoimmune type 1 diabetes (T1D) and insulin resistant type 2 diabetes (T2D) are the two main types. A combination of genetic and environmental factors contributes to the development of these diseases. Gut microbiota have emerged recently as an essential player in the development of T1D, T2D and obesity. Altered gut microbiota have been strongly linked to disease in both rodent models and humans. Both classic 16S rRNA sequencing and shot-gun metagenomic pyrosequencing analysis have been successfully applied to explore the gut microbiota composition and functionality. This review focuses on the association between gut microbiota and diabetes and discusses the potential mechanisms by which gut microbiota regulate disease development in T1D, T2D and obesity.

Visceral and subcutaneous adipose tissue FDG uptake by PET/CT in metabolically healthy obese subjects

OBJECTIVE

To measure FDG uptake in visceral (VAT) and subcutaneous (SAT) adipose tissue of metabolically healthy obese (MHO) and metabolically abnormal obese (MAO) compared to metabolically healthy lean (MHL) subjects. Given that MHO has increased metabolic risk, it was hypothesized that MHO and MAO display similar VAT FDG uptake.

METHODS

The 18F-FDG-PET/CT studies of 141 adults (n = 60 MHL, n = 20 MHO, n = 61 MAO) were examined to determine VAT and SAT volumes and FDG uptake. Data on CVD risk factors (BMI, abdominal circumference, blood pressure, serum lipids, and fasting plasma glucose) were collected.

RESULTS

MHO and MAO had similar VAT FDG uptake (P = 0.74), both significantly lower than MHL (P < 0.01) independent of age and gender. SAT FDG uptake was similar across all groups (P > 0.2) independent of age and gender. In all groups, VAT FDG uptake was higher than SAT (P < 0.0001). In separate sub-analyses of obese groups, VAT FDG uptake was more broadly negatively associated with whole-body adiposity than SAT FDG uptake, and FDG uptake in abdominal adipose depots was positively associated with liver density (P < 0.05).

CONCLUSIONS

FDG uptake in VAT of MHO is similar to MAO and lower than MHL, suggesting these subjects may present similar VAT dysfunction.

Consequences of obesity and weight loss: a devil's advocate position

Obesity is associated with multiple negative health consequences and current weight management guidelines recommend all obese persons to lose weight. However, recent evidence suggests that not all obese persons are negatively affected by their weight and that weight loss does not necessarily always improve health. The purpose of this review is not to trivialize the significant health risks associated with obesity, but to discuss subpopulations of obese people who are not adversely affected, or may even benefit from higher adiposity, and in who weight loss per se may not always be the most appropriate recommendation. More specifically, this review will take a devil's advocate position when discussing the consequences of obesity and weight loss for adults with established cardiovascular disease and type 2 diabetes, weight cyclers, metabolically healthy obese adults, youth, older adults and obese individuals who are highly fit.

Adiposity is associated with structural properties of the adolescent brain

Obesity, a major risk factor for cardiometabolic disease, is associated with variations in a number of structural properties in the adult brain, as assessed with magnetic resonance imaging (MRI). In this study, we investigated the cross-sectional relationship between visceral fat (VF), total body fat (TBF) and three MRI parameters in the brains of typically developing adolescents: (i) T1-weighted (T1W) signal intensity; (ii) T1W signal contrast between white matter (WM) and gray matter (GM); and (iii) magnetization transfer ratio (MTR). In a community-based sample of 970 adolescents (12-18 years old, 466 males), VF was quantified using MRI, and total body fat was measured using a multifrequency bioimpedance. T1W images of the brain were used to determine signal intensity in lobar GM and WM, as well as WM:GM signal contrast. A magnetization transfer (MT) sequence of MT(ON) and MT(OFF) was used to obtain MTR in GM and WM. We found that both larger volumes of VF and more TBF were independently associated with higher signal intensity in WM and higher WM:GM signal contrast, as well as higher MTR in both GM and WM. These relationships were independent of a number of potential confounders, including age, sex, puberty stage, household income and height. Our results suggest that both visceral fat and fat deposited elsewhere in the body are associated independently with structural properties of the adolescent brain. We speculate that these relationships suggest the presence of adiposity-related variations in phospholipid composition of brain lipids.