Long-Term Prognosis of Acute Myocardial Infarction Associated With Metabolic Health and Obesity Status

ObjectiveEmerging evidence supports the favorable cardiovascular health in nonobese subjects with healthy metabolism. However, little is known regarding the prognosis across the range of metabolic phenotypes once cardiovascular disease is established. We examined the prognosis of patients with acute myocardial infarction (AMI) stratified according to metabolic health and obesity status.MethodsThis is a retrospective study on consecutive patients with AMI admitted to a tertiary hospital between 2014 and 2021. Patients were allocated into the following 4 groups based on metabolic and obesity profile: (1) metabolically healthy obese (MHO), (2) metabolically healthy nonobese (MHNO), (3) metabolically unhealthy obese (MUO), and (4) metabolically unhealthy nonobese (MUNO). Metabolic health was defined in accordance to the Biobank Standardisation and Harmonisation for Research Excellence in the European Union Healthy Obese Project. The primary outcome was all-cause mortality. The Cox regression analysis examined the independent association between mortality and metabolic phenotypes, adjusting for age, sex, AMI type, chronic kidney disease, smoking status, and left ventricular ejection fraction.ResultsOf 9958 patients, the majority (68.5%) were MUNO, followed by MUO (25.1%), MHNO (5.6%), and MHO (0.8%). MHO had the lowest mortality (7.4%), followed by MHNO (9.7%), MUO (19.2%), and MUNO (22.6%) (P < .001). Compared with MHNO, MUO (hazard ratio [HR], 1.737; 95% confidence interval [CI], 1.282-2.355; P < .001) and MUNO (HR, 1.482; 95% CI, 1.108-1.981; P = .008) had a significantly higher mortality risk but not MHO (HR, 1.390; 95% CI, 0.594-3.251; P = .447), after adjusting for confounders. The Kaplan-Meier curves showed favorable survival in the metabolically healthy and obesity groups, with the highest overall survival in the MHO, followed by MHNO, MUO, and MUNO (P < .001).ConclusionMetabolically healthy and obese patients with AMI have favorable prognosis compared with metabolically unhealthy and nonobese patients. It is equally important to prioritize intensive metabolic risk factor management to weight reduction in the early phase after AMI.     

Identifying Metabolically Healthy but Obese Individuals in Sedentary Postmenopausal Women

The purpose of this study was to compare different methods to identify metabolically healthy but obese (MHO) individuals in a cohort of obese postmenopausal women. We examined the anthropometric and metabolic characteristics of 113 obese (age: 57.3 ± 4.8 years; BMI: 34.2 ± 2.7 kg/m²), sedentary postmenopausal women. The following methods were used to identify MHO subjects: the hyperinsulinemic–euglycemic clamp (MHO: upper quartile of glucose disposal rates); the Matsuda index (MHO: upper quartile of the Matsuda index); the homeostasis model assessment (HOMA) index (MHO: lower quartile of the HOMA index); having 0–1 cardiometabolic abnormalities (systolic/diastolic blood pressure ≥130/85 mm Hg, triglycerides (TG) ≥1.7 mmol/l, glucose ≥5.6 mmol/l, HOMA >5.13, high‐sensitive C‐reactive protein (hsCRP) >0.1 mg/l, high‐density lipoprotein‐cholesterol (HDL‐C) <1.3 mmol/l); and meeting four out of five metabolic factors (HOMA ≤2.7, TG ≤1.7 mmol/l, HDL‐C ≥1.3 mmol/l, low‐density lipoprotein‐cholesterol ≤2.6 mmol/l, hsCRP ≤3.0 mg/l). Thereafter, we measured insulin sensitivity, body composition (dual‐energy X‐ray absorptiometry), body fat distribution (computed tomography scan), energy expenditure, plasma lipids, inflammation markers, resting blood pressure, and cardiorespiratory fitness. We found significant differences in body composition (i.e., peripheral fat mass, central lean body mass (LBM)) and metabolic risk factors (i.e., HDL‐C, hsCRP) between MHO and at risk individuals using the different methods to identify both groups. In addition, significant differences between MHO subjects using the different methods to identify MHO individuals were observed such as age, TG/HDL, hsCRP, and fasting insulin. However, independently of the methods used, we noted some recurrent characteristics that identify MHO subjects such as TG, apolipoprotein B, and ferritin. In conclusion, the present study shows variations in body composition and metabolic profile based on the methods studied to define the MHO phenotype. Therefore, an expert consensus may be needed to standardize the identification of MHO individuals.     

Diet composition and activity level of at risk and metabolically healthy obese american adults

Objective:

Obesity often clusters with other major cardiovascular disease risk factors, yet a subset of the obese appears to be protected from these risks. Two obesity phenotypes are described, (i) “metabolically healthy” obese, broadly defined as body mass index (BMI) ≥ 30 kg/m² and favorable levels of blood pressure, lipids, and glucose; and (ii) “at risk” obese, BMI ≥ 30 with unfavorable levels of these risk factors. More than 30% of obese American adults are metabolically healthy. Diet and activity determinants of obesity phenotypes are unclear. We hypothesized that metabolically healthy obese have more favorable behavioral factors, including less adverse diet composition and higher activity levels than at risk obese in the multi‐ethnic group of 775 obese American adults ages 40‐59 years from the International Population Study on Macro/Micronutrients and Blood Pressure (INTERMAP) cohort.

Design and Methods:

In gender‐stratified analyses, mean values for diet composition and activity behavior variables, adjusted for age, race, and education, were compared between metabolically healthy and at risk obese.

Results:

Nearly one in five (149/775 or 19%) of obese American INTERMAP participants were classified as metabolically healthy obese. Diet composition and most activity behaviors were similar between obesity phenotypes, although metabolically healthy obese women reported higher sleep duration than at risk obese women.

Conclusions:

These results do not support hypotheses that diet composition and/or physical activity account for the absence of cardiometabolic abnormalities in metabolically healthy obese.     

Prevalence of Metabolically Discordant Phenotypes in a Mediterranean Population—the Imap Study

ObjectiveTo study the prevalence and correlates of body size phenotypes in an adult Spanish population.MethodsWe undertook a cross-sectional analysis in a random sample of 2,270 individuals. We defined six body size phenotypes based on body mass index category (normal-weight, 18.5 to 24.9 kg/m²; overweight, 25 to 29.9 kg/m²; obese, ≥30.0 kg/m²) and the presence of ≤1 (meta- bolically healthy) or ≥2 (metabolically abnormal) cardio- metabolic abnormalities: metabolically healthy normal- weight (MHNW), metabolically abnormal normal-weight (MANW), metabolically healthy overweight (MHOW), metabolically abnormal overweight (MAOW), metaboli- cally healthy obese (MHO), and metabolically abnormal obese (MAO). We considered four cardiometabolic abnormalities: systolic and/or diastolic blood pressure ≥130/85 mm Hg, triglycerides ≥150 mg/dL, high-density-lipopro- tein cholesterol levels <40/<50 mg/dL in men/women, and elevated glucose (fasting plasma glucose ≥100 mg/dL or previous diabetes).ResultsSubmitted for publication October 30, 2012 Accepted for publication April 1, 2013From the ¹The prevalence of the MHO, MHOW, and MANW phenotypes was 2.2, 13.9, and 7.9%, respectively. Whereas 9.6% of obese and 32.6% of overweight individuals were metabolically healthy, 21.3% of the normal- weight subjects were metabolically abnormal. A multivariate regression model (adjusted for age, sex, and waist circumference) showed that age >40 years, male sex, and higher waist circumference were independently associated with the metabolically abnormal phenotype MANW, whereas younger age, female sex, and lower waist circumference were independently associated with the metabolically healthy phenotypes.ConclusionThe prevalence of MHO in our population is low and is more common in women and younger people. In contrast, a high proportion of normal-weight individuals (mainly over 40 years of age) in our population show cardiometabolic abnormalities. (Endocr Pract. 2013;19:758-768)     

Defining Metabolically Healthy Obesity: Role of Dietary and Lifestyle Factors

Background

There is a current lack of consensus on defining metabolically healthy obesity (MHO). Limited data on dietary and lifestyle factors and MHO exist. The aim of this study is to compare the prevalence, dietary factors and lifestyle behaviours of metabolically healthy and unhealthy obese and non-obese subjects according to different metabolic health criteria.

Method

Cross-sectional sample of 1,008 men and 1,039 women aged 45-74 years participated in the study. Participants were classified as obese (BMI ≥30kg/m²) and non-obese (BMI <30kg/m²). Metabolic health status was defined using five existing MH definitions based on a range of cardiometabolic abnormalities. Dietary composition and quality, food pyramid servings, physical activity, alcohol and smoking behaviours were examined.

Results

The prevalence of MHO varied considerably between definitions (2.2% to 11.9%), was higher among females and generally increased with age. Agreement between MHO classifications was poor. Among the obese, prevalence of MH was 6.8% to 36.6%. Among the non-obese, prevalence of metabolically unhealthy subjects was 21.8% to 87%. Calorie intake, dietary macronutrient composition, physical activity, alcohol and smoking behaviours were similar between the metabolically healthy and unhealthy regardless of BMI. Greater compliance with food pyramid recommendations and higher dietary quality were positively associated with metabolic health in obese (OR 1.45-1.53 unadjusted model) and non-obese subjects (OR 1.37-1.39 unadjusted model), respectively. Physical activity was associated with MHO defined by insulin resistance (OR 1.87, 95% CI 1.19-2.92, p = 0.006).

Conclusion

A standard MHO definition is required. Moderate and high levels of physical activity and compliance with food pyramid recommendations increase the likelihood of MHO. Stratification of obese individuals based on their metabolic health phenotype may be important in ascertaining the appropriate therapeutic or intervention strategy.     

Altered expression of genes involved in lipid metabolism in obese subjects with unfavourable phenotype

Obesity (BMI ≥30 kg/m²) increases the risk of developing lifestyle-related diseases. A subgroup of obese individuals has been described as “metabolically healthy, but obese” (MHO). In contrast to at-risk obese (ARO), the MHO phenotype is defined by a favourable lipid profile and a normal or only slightly affected insulin sensitivity, despite the same amount of body fat. The objective was to characterize the metabolic phenotype of MHO subjects. We screened a variety of genes involved in lipid metabolism and inflammation in peripheral blood mononuclear cells (PBMC). Obese subjects (men and women; 18–70 years) with BMI ≥30 kg/m² were characterized as MHO (n = 9) or as ARO (n = 10). In addition, eleven healthy, normal weight subjects characterized as healthy by the same criteria as described for the MHO subjects were included. We found that with similar weight, total fat mass and fat mass distribution, the ARO subjects have increased plasma levels of gamma-glutamyl transpeptidase and free fatty acids. This group also has altered expression levels of a number of genes linked to lipid metabolism in PBMC with reduced gene expression levels of uncoupling protein 2, hormone-sensitive lipase and peroxisome proliferator-activated receptor δ compared with MHO subjects. The present metabolic differences between subgroups of obese subjects may contribute to explain some of the underlying mechanisms causing the increased risk of disease among ARO subjects compared with MHO subjects.     

Comparison of DXA and CT in the Assessment of Body Composition in Premenopausal Women With Obesity and Anorexia Nervosa

Accurate methods for assessing body composition in subjects with obesity and anorexia nervosa (AN) are important for determination of metabolic and cardiovascular risk factors and to monitor therapeutic interventions. The purpose of our study was to assess the accuracy of dual‐energy X‐ray absorptiometry (DXA) for measuring abdominal and thigh fat, and thigh muscle mass in premenopausal women with obesity, AN, and normal weight compared to computed tomography (CT). In addition, we wanted to assess the impact of hydration on DXA‐derived measures of body composition by using bioelectrical impedance analysis (BIA). We studied a total of 91 premenopausal women (34 obese, 39 with AN, and 18 lean controls). Our results demonstrate strong correlations between DXA‐ and CT‐derived body composition measurements in AN, obese, and lean controls (r = 0.77–0.95, P < 0.0001). After controlling for total body water (TBW), the correlation coefficients were comparable. DXA trunk fat correlated with CT visceral fat (r = 0.51–0.70, P < 0.0001). DXA underestimated trunk and thigh fat and overestimated thigh muscle mass and this error increased with increasing weight. Our study showed that DXA is a useful method for assessing body composition in premenopausal women within the phenotypic spectrum ranging from obesity to AN. However, it is important to recognize that DXA may not accurately assess body composition in markedly obese women. The level of hydration does not significantly affect most DXA body composition measurements, with the exceptions of thigh fat.     

Metabolic Health Is a More Important Determinant for Diabetes Development than Simple Obesity: A 4-Year Retrospective Longitudinal Study

Background

Recent studies report the importance of metabolic health beyond obesity. The aim of this study is to compare the risk for diabetes development according to different status of metabolic health and obesity over a median follow-up of 48.7 months.

Methods

6,748 non-diabetic subjects (mean age 43 years) were divided into four groups according to the baseline metabolic health and obesity status: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUHNO) and metabolically unhealthy obese (MUHO). Being metabolically healthy was defined by having less than 2 components among the 5 components, that is, high blood pressure, high fasting blood glucose, high triglyceride, low high-density lipoprotein cholesterol and being in the highest decile of homeostasis model assessment-insulin resistance (HOMA-IR) index. Obesity status was assessed by body mass index (BMI) higher than 25 kg/m². The development of diabetes was assessed annually from self-questionnaire, fasting glucose and HbA1c.

Results

At baseline, 45.3% of the subjects were MHNO, 11.3% were MHO, 21.7% were MUHNO, and 21.7% were MUHO. During a median follow-up of 48.7 months, 277 subject (4.1%) developed diabetes. The hazard ratio for diabetes development was 1.338 in MHO group (95% CI 0.67–2.672), 4.321 in MUHNO group (95% CI 2.702–6.910) and 5.994 in MUHO group (95% CI 3.561–10.085) when MHNO group was considered as the reference group. These results were similar after adjustment for the changes of the risk factors during the follow-up period.

Conclusion

The risk for future diabetes development was higher in metabolically unhealthy subgroups compared with those of metabolically healthy subjects regardless of obesity status.     

Determinants of insulin-resistant phenotypes in normal-weight and obese Black African women

Objective: Subsets of metabolically “healthy obese” and “at‐risk” normal‐weight individuals have been previously identified. The aim of this study was to explore the determinants of these phenotypes in black South African (SA) women. Methods and Procedures: From a total of 103 normal‐weight (BMI ≤ 25 kg/m²) and 122 obese (BMI ≥ 30 kg/m²) black SA women, body composition, fat distribution, blood pressure, fasting glucose levels, insulin resistance, and lipid profiles were measured. Questionnaires relating to family history, physical activity energy expenditure (PAEE), and socio‐demographic variables were administered. The subjects were classified as insulin sensitive or insulin resistant according to the homeostasis model assessment of insulin resistance (HOMA‐IR) (≥1.95 insulin resistant). Results: Our study showed that 22% of the normal‐weight women were insulin resistant and 38% of the obese women were insulin sensitive. Increased visceral adipose tissue (VAT) (P = 0.001) and decreased VAT/leg fat mass (P ≤ 0.001), independent of total body fatness, distinguished between the phenotypes. Moreover, the insulin‐sensitive women were of higher socioeconomic status, did more leisure and vigorous PAEE and were less likely to use injectable contraceptives. Using a regression model, body fat distribution, percent body fat, age, log leisure PAEE, and use of injected contraception accounted for 35% of the variance in HOMA‐IR in the normal‐weight women. In the obese women, 34% of the variance in HOMA‐IR was explained by the same variables, excluding PAEE. No differences in smoking status or family history of metabolic disease were found between the phenotypes. Discussion: Central fat distribution, total adiposity, socioeconomic status, leisure PAEE, and use of injectable contraceptives distinguished between insulin‐sensitive and insulin‐resistant black SA women.     

Uric acid best predicts metabolically unhealthy obesity with increased cardiovascular risk in youth and adults

Objective:

The obesity prevalence is growing worldwide and largely responsible for cardiovascular disease, the most common cause of death in the western world. The rationale of this study was to distinguish metabolically healthy from unhealthy overweight/obese young and adult patients as compared to healthy normal weight age matched controls by an extensive anthropometric, laboratory, and sonographic vascular assessment.

Design and Methods:

Three hundred fifty five young [8 to < 18 years, 299 overweight/obese(ow/ob), 56 normal weight (nw)] and 354 adult [>18‐60 years, 175 (ow/ob), 179 nw)] participants of the STYJOBS/EDECTA (STYrian Juvenile Obesity Study/Early DEteCTion of Atherosclerosis) cohort were analyzed. STYJOBS/EDECTA (NCT00482924) is a crossectional study to investigate metabolic/cardiovascular risk profiles in normal and ow/ob people free of disease except metabolic syndrome (MetS).

Results:

From 299 young ow/ob subjects (8‐< 18 years), 108 (36%), and from 175 adult ow/ob subjects (>18‐60 years), 79 (45%) had positive criteria for MetS. In both age groups, prevalence of MetS was greater among males. Overweight/obese subjects were divided into “healthy” (no MetS criterion except anthropometry fulfilled) and “unhealthy” (MetS positive). Although percentage body fat did not differ between “healthy” and “unhealthy” ow/ob, nuchal and visceral fat were significantly greater in the “unhealthy” group which had also significantly higher values of carotid intima media thickness (IMT). With MetS as the dependent variable, two logistic regressions including juveniles < 18 years or adults >18 years were performed. The potential predictor variables selected with the exception of age and gender by t test comparisons included IMT, ultrasensitive c‐reactive protein (US‐CRP), IL‐6, malondialdehyde (MDA), oxidized LDL, leptin, adiponectin, uric acid (UA), aldosterone, cortisol, transaminases, fibrinogen. In both groups, uric acid and in adults only, leptin and adiponectin, turned out as the best predictor.

Conclusion:

Serum levels of UA are a significant predictor of unhealthy obesity in juveniles and adults.     

Body Fat Distribution and Inflammation Among Obese Older Adults With and Without Metabolic Syndrome

The protective mechanisms by which some obese individuals escape the detrimental metabolic consequences of obesity are not understood. This study examined differences in body fat distribution and adipocytokines in obese older persons with and without metabolic syndrome. Additionally, we examined whether adipocytokines mediate the association between body fat distribution and metabolic syndrome. Data were from 729 obese men and women (BMI ≥30 kg/m²), aged 70–79 participating in the Health, Aging and Body Composition (Health ABC) study. Thirty‐one percent of these obese men and women did not have metabolic syndrome. Obese persons with metabolic syndrome had significantly more abdominal visceral fat (men: P = 0.04; women: P < 0.01) and less thigh subcutaneous fat (men: P = 0.09; women: P < 0.01) than those without metabolic syndrome. Additionally, those with metabolic syndrome had significantly higher levels of interleukin‐6 (IL‐6), tumor necrosis factor‐α (TNF‐α), and plasminogen activator inhibitor‐1 (PAI‐1) than individuals without metabolic syndrome. Per standard deviation higher in visceral fat, the likelihood of metabolic syndrome significantly increased in women (odds ratio (OR): 2.16, 95% confidence interval (CI): 1.59–2.94). In contrast, the likelihood of metabolic syndrome decreased in both men (OR: 0.56, 95% CI: 0.39–0.80) and women (OR: 0.49, 95% CI: 0.34–0.69) with each standard deviation higher in thigh subcutaneous fat. These associations were partly mediated by adipocytokines; the association between thigh subcutaneous fat and metabolic syndrome was no longer significant in men. In summary, metabolically healthy obese older persons had a more favorable fat distribution, characterized by lower visceral fat and greater thigh subcutaneous fat and a more favorable inflammatory profile compared to their metabolically unhealthy obese counterparts.     

Body size phenotypes and inflammation in the Women's Health Initiative Observational Study

Individuals with "metabolically benign" obesity (obesity unaccompanied by hypertension, dyslipidemia, and diabetes) are not at elevated 10-year risk of cardiovascular disease (CVD) compared to normal weight individuals. It remains unclear whether these obese individuals or normal weight individuals with clustering of cardiometabolic factors display heightened immune activity. Therefore, we characterized levels of acute-phase reactants (C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), white blood cell (WBC) count), adhesion molecules (E-selectin, vascular cell adhesion molecule-1), and coagulation products (fibrinogen, plasminogen activator inhibitor-1 (PAI-1)) among four body size phenotypes (normal weight with 0/1 vs. ≥2 metabolic syndrome components/diabetes and overweight/obesity with 0/1 vs. ≥2 metabolic syndrome components/diabetes) in cross-sectional analyses of 1,889 postmenopausal women from the Women's Health Initiative Observational Study (WHI-OS) nested case-control stroke study. Higher levels of all three inflammatory marker categories were found among women with overweight/obesity or ≥2 metabolic syndrome components or diabetes. Compared to normal weight women with 0 or 1 metabolic syndrome components, normal weight women with ≥2 metabolic syndrome components or diabetes were more likely to have ≥3 inflammatory markers in the top quartile (multivariate odds ratio (OR) 2.0, 95% confidence interval (CI): 1.3-3.0), as were overweight/obese women with 0 or 1 metabolic syndrome components (OR 2.3; 95% CI: 1.5-3.5). Overweight/obese women with ≥2 metabolic syndrome components or diabetes had the highest OR (OR 4.2; 95% CI: 2.9-5.9). Despite findings that metabolically benign obese individuals are not at increased 10-year risk of CVD compared to normal weight individuals, the current results suggest that overweight/obese women without clustering of cardiometabolic risk factors still possess abnormal levels of inflammatory markers.     

Metabolically Healthy Obesity and the Risk of Cardiovascular Disease in the Elderly Population

BackgroundWhether being metabolically healthy obese (MHO)—defined by the presence of obesity in the absence of metabolic syndrome—is associated with subsequent cardiovascular disease (CVD) remains unclear and may depend on the participants’ age. We examined the association of being MHO with CVD risk in the elderly.ConclusionsIn our elderly population, we found that the presence of obesity without metabolic syndrome did not confer a higher CVD risk. However, metabolic syndrome was strongly associated with CVD risk, and was associated with an increased risk in all BMI categories. Therefore, preventive interventions targeting cardiometabolic risk factors could be considered in elderly, regardless of weight status.     

The Prevalence of Metabolically Healthy Obese Subjects Defined by BMI and Dual‐Energy X‐Ray Absorptiometry

Nearly one‐third of obese (OB) people are reported to be metabolically healthy based on BMI criteria. It is unknown whether this holds true when more accurate adiposity measurements are applied such as dual‐energy X‐ray absorptiometry (DXA). We compared differences in the prevalence of cardiometabolic abnormalities among adiposity groups classified using BMI vs. DXA criteria. A total of 1,907 adult volunteers from Newfoundland and Labrador participated. BMI and body fat percentage (%BF; measured using DXA) were measured following a 12‐h fasting period. Subjects were categorized as normal weight (NW), overweight (OW), or OB based on BMI and %BF criteria. Cardiometabolic abnormalities considered included elevated triglyceride, glucose, and high‐sensitivity C‐reactive protein (hsCRP) levels, decreased high‐density lipoprotein (HDL) cholesterol levels, insulin resistance, and hypertension. Subjects were classified as metabolically healthy (0 or 1 cardiometabolic abnormality) or abnormal (≥2 cardiometabolic abnormalities). We found low agreement in the prevalence of cardiometabolic abnormalities between BMI and %BF classifications (κ = 0.373, P < 0.001). Among NW and OW subjects, the prevalence of metabolically healthy individuals was similar between BMI and %BF (77.6 vs. 75.7% and 58.8 vs. 62.5%, respectively) however, there was a pronounced difference among OB subjects (34.0 vs. 47.7%, P < 0.05). Similar trends were evident using three additional definitions to characterize metabolically healthy individuals. Our findings indicate that approximately one‐half of OB people are metabolically healthy when classified using %BF criteria which is significantly higher than previously reported using BMI. Caution should therefore be taken when making inferences about the metabolic health of an OB population depending on the method used to measure adiposity.     

Lipokines and oxysterols: Novel adipose-derived lipid hormones linking adipose dysfunction and insulin resistance

The expansion of adipose tissue (AT) is, by definition, a hallmark of obesity. However, not all increases in fat mass are associated with pathophysiological cues. Indeed, whereas a “healthy” fat mass accrual, mainly in the subcutaneous depots, preserves metabolic homeostasis, explaining the occurrence of the metabolically healthy obese phenotype, “unhealthy” AT expansion is importantly associated with insulin resistance/type 2 diabetes and the metabolic syndrome. The development of a dysfunctional adipose organ may find mechanistic explanation in a reduced ability to recruit new and functional (pre)adipocytes from undifferentiated precursor cells. Such a failure of the adipogenic process underlies the “AT expandability” paradigm. The inability of AT to expand further to store excess nutrients, rather than obesity per se, induces a diabetogenic milieu by promoting the overflow and the ectopic deposition of fatty acids in insulin-dependent organs (i.e., lipotoxicity), the secretion of various metabolically detrimental adipose-derived hormones (i.e., adipokines and lipokines), and the occurrence of local and systemic inflammation and oxidative stress. Hitherto, fatty acids (i.e., lipokines) and the oxidation by-products of cholesterol and polyunsaturated fatty acids, such as nonenzymatic oxysterols and reactive aldehyde species, respectively, emerge as key modulators of (pre)adipocyte signaling through Wnt/β-catenin and MAPK pathways and potential regulators of glucose homeostasis. These and other mechanistic insights linking adipose dysfunction, oxidative stress, and impairment of glucose homeostasis are discussed in this review article, which focuses on adipose peroxidation as a potential instigator of, and a putative therapeutic target for, obesity-associated metabolic dysfunctions.     

Are Natural Killer Cells Protecting the Metabolically Healthy Obese Patient?

With the emerging obesity pandemic, identifying those who appear to be protected from adverse consequences such as type 2 diabetes and certain malignancies will become important. We propose that the circulating immune system plays a role in the development of these comorbidities. Clinical data and blood samples were collected from 52 patients with severe obesity attending a hospital weight‐management clinic and 11 lean healthy controls. Patients were classified into metabolically “healthy obese” (n = 26; mean age 42.6 years, mean BMI 46.8 kg/m²) or “unhealthy obese” (n = 26; mean age 45 years, mean BMI 47.5 kg/m²) groups, based upon standard cutoff points for blood pressure, lipid profile, and fasting glucose. Circulating lymphoid populations and phenotypes were assessed by flow cytometry. Obese patients had significantly less circulating natural killer (NK) and cytotoxic T lymphocytes (CTL) compared to lean controls. There were significantly higher levels of NK cells and CTLs in the healthy obese group compared to the unhealthy obese group (NK: 11.7% vs. 6.5%, P < 0.0001, CD8 13.4% vs. 9.3%, P = 0.04), independent of age and BMI and these NK cells were also less activated in the healthy compared to the unhealthy group (CD69, 4.1% vs. 11.8%, P = 0.03). This is the first time that quantitative differences in the circulating immune system of obese patients with similar BMI but different metabolic profiles have been described. The significantly higher levels of CTLs and NK cells, which express fewer inhibitory molecules, could protect against malignancy, infection, and metabolic disease seen in obesity.     

DXA: Can It Be Used as a Criterion Reference for Body Fat Measurements in Children?

Objective: Dual‐energy X‐ray absorptiometry (DXA) is often cited as a criterion method for body composition measurements. We have previously shown that a new DXA software version (Hologic Discovery V12.1) will affect whole‐body bone mineral results for subjects weighing <40 kg. We wished to reanalyze pediatric whole‐body scans in order to assess the impact of the new software on pediatric soft‐tissue body composition estimates. Methods and Procedures: We reanalyzed 1,384 pediatric scans (for ages 1.7–17.2 years) using Hologic software V12.1, previously analyzed using V11.2. Regression analysis and ANCOVA were used to compare body fat (total body fat (TBF), percentage fat (%BF)), and non‐bone lean body mass (LBM) for the two versions, adjusting for gender, age and weight. Results: Software V12.1 yielded values that were higher for TBF, lower for LBM, and unchanged for DXA‐derived weight in subjects weighing <40 kg. Body composition values for younger, smaller subjects were most affected, and girls were more affected than boys. Using the new software, 14% of the girls and 10% of the boys were reclassified from the “normal” %BF range to “at risk of obesity,” while 7 and 5%, respectively, were reclassified as obese. Discussion: Hologic's newest DXA software has a significant effect on soft‐tissue results for children weighing <40 kg. The effect is greater for girls than boys. Comparison of TBF estimates with previous studies that use older DXA instruments and software should be done with caution. DXA has not yet achieved sufficient reliability to be considered a “gold standard” for body composition assessment in pediatric studies.     

Effect of sarcopenia on cardiovascular disease risk factors in obese postmenopausal women

Objective: To compare sarcopenic‐obese and obese postmenopausal women for risk factors predisposing to cardiovascular disease (CVD) and determine whether there may be a relationship between muscle mass and metabolic risk in obese postmenopausal women. Research Methods and Procedures: In this cross‐sectional study, 22 healthy obese postmenopausal women (mean age, 66 ± 5 years; mean BMI, 27 ± 3 kg/m²) were divided into two groups matched for age (±2 years) and fat mass (FM) (±2%). Sarcopenia was defined as a muscle mass index of <14.30 kg fat‐free mass (FFM)/m² (which corresponds to 1 standard deviation below the values of a young reference population), and obesity was defined as an FM of >35% (which corresponds to the World Health Organization guidelines). FM, FFM (measured by DXA), daily energy expenditure (accelerometry), dietary intake (3‐day dietary record), and blood biochemical analyses (lipid profile, insulin, glucose, and C‐reactive protein) were obtained. Visceral fat mass (VFM) was calculated by the equation of Bertin, which estimates VFM from DXA measurements. Results: Obese women had more FFM (p = 0.006), abdominal FM (p = 0.047), and VFM (p = 0.041) and a worse lipid profile [p = 0.040 for triglycerides; p = 0.004 for high‐density lipoprotein (HDL); p = 0.026 for total cholesterol/HDL] than sarcopenic‐obese postmenopausal women. Obese women also ingested significantly more animal (p = 0.001) and less vegetal proteins (p = 0.013), although both groups had a similar total protein intake (p = 0.967). Discussion: Sarcopenia seems to be associated with lower risk factors predisposing to CVD in obese postmenopausal women. With the increase in the number of aging people, the health implications of being sarcopenic‐obese merit more attention.     

Discordant risk: Overweight and cardiometabolic risk in Chinese adults

Objective:

Recent US work identified “metabolically healthy overweight” and “metabolically at risk normal weight” individuals. Less is known for modernizing countries with recent increased obesity.

Design and Methods:

Fasting blood samples, anthropometry and blood pressure from 8,233 adults aged 18‐98 in the 2009 nationwide China Health and Nutrition Survey, were used to determine prevalence of overweight (Asian cut point, BMI ≥23 kg/m²) and five risk factors (prediabetes/diabetes (hemoglobin A1c ≥5.7%) inflammation (high‐sensitivity C‐reactive protein (hsCRP) ≥3 mg/l), prehypertension/hypertension (Systolic blood pressure/diastolic blood pressure≥130/85 mm Hg), high triglycerides (≥150 mg/dl), low high‐density lipoprotein cholesterol (<40 (men)/ <50 mg/dl (women)). Sex‐stratified, logistic, and multinomial logistic regression models estimated concurrent obesity and cardiometabolic risk, with and without abdominal obesity, adjusting for age, smoking, alcohol consumption, physical activity, urbanicity, and income.

Results:

Irrespective of urbanicity, 78.3% of the sample had ≥1 elevated cardiometabolic risk factor (normal weight: 33.2% had ≥1 elevated risk factor; overweight: 5.7% had none). At the age of 18‐30 years, 47.4% had no elevated risk factors, which dropped to 6% by the age 70, largely due to age‐related increase in hypertension risk (18‐30 years: 11%; >70 years: 73%). Abdominal obesity was highly predictive of metabolic risk, irrespective of overweight (e.g., “metabolically at risk overweight” relative to “metabolically healthy normal weight” (men: relative risk ratio (RRR) = 39.06; 95% confidence interval (CI): 23.47, 65.00; women: RRR = 22.26; 95% CI: 17.49, 28.33)).

Conclusion:

A large proportion of Chinese adults have metabolic abnormalities. High hypertension risk with age, underlies the low prevalence of metabolically healthy overweight. Screening for cardiometabolic‐related outcomes dependent upon overweight will likely miss a large portion of the Chinese at risk population.     

DXA‐derived Abdominal Fat Mass,Waist Circumference,and Blood Lipids in Postmenopausal Women

The purpose of this study was to determine the utility of dual‐energy X‐ray absorptiometry (DXA)‐derived fat mass indices for predicting blood lipid profile in postmenopausal women. A secondary purpose was to determine whether waist circumference is comparable with DXA‐derived measurements in predicting blood lipid profile. Subjects were 423 postmenopausal women (age 58.1 ± 6.3 years). Fat mass was assessed at abdomen, trunk, and total body using DXA. Anthropometric measurements included BMI and waist circumference. Blood samples were analyzed for total cholesterol (TC), triglyceride (TAG), high‐density lipoprotein (HDL), low‐density lipoprotein (LDL), and cholesterol/HDL ratio. Of the DXA‐derived measures, abdominal‐fat mass was the best predictor of blood lipid profiles. DXA‐derived abdominal fat mass and waist girth explained 20 and 16.5% of variation in TC/HDL ratio, respectively, in univariate analysis, with no difference between the slopes of the regression coefficients. Eighty‐four percent of subjects were common to the top quartiles of waist circumference and abdominal fat mass, and blood lipid profiles generally worsened across increasing quartiles. DXA‐derived abdominal fat mass and waist circumference are of equivalent utility for predicting alterations in blood lipids. Waist circumference is, therefore, ideal as an inexpensive means in primary health‐care services for predicting risk of cardiovascular diseases in postmenopausal women.