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.     

Correlation between body composition and risk factors for cardiovascular disease and metabolic syndrome

Body mass index (BMI) is an important diagnostic tool for determining obesity; however, while BMI reflects the influence of body height over body weight, it does not reveal body fat percentage (BF%). We explored whether BF% correlated with risk factors for cardiovascular disease and metabolic syndrome and whether metabolically obese, normal weight people were at risk for these diseases. A total of 2,867 healthy volunteers participated in this study. Blood pressure, height, weight, waist circumference, BMI, BF%, lipid profile, fasting glucose, uric acid, and lifestyle factors were collected from healthy subjects during their annual health examinations. In both males and females, BF% correlated positively with BMI and waist circumference. Participants were divided into three groups according to BF% and data were compared between groups. The results suggest that BF% correlates with risk factors for cardiovascular disease and metabolic syndrome for both men and women, and that BF% may be a useful predictor of risk, particularly in metabolically obese, normal weight individuals. ? 2012 International Union of Biochemistry and Molecular Biology, Inc.     

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.     

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.     

Body composition determinants of metabolic phenotypes of obesity in nonobese and obese postmenopausal women

Objective : Although obesity is typically associated with increased cardiovascular risk, a subset of obese individuals display a normal metabolic profile (“metabolically healthy obese,” MHO) and conversely, a subset of nonobese subjects present with obesity‐associated cardiometabolic abnormalities (“metabolically obese nonobese,” MONO). The aim of this cross‐sectional study was to identify the most important body composition determinants of metabolic phenotypes of obesity in nonobese and obese healthy postmenopausal women. Design and Methods : We studied a total of 150 postmenopausal women (age 54 ± 7 years, mean ± 1 SD). Based on a cardiometabolic risk score, nonobese (body mass index [BMI] ≤ 27) and obese women (BMI > 27) were classified into “metabolically healthy” and “unhealthy” phenotypes. Total and regional body composition was assessed with dual‐energy X‐ray absorptiometry (DXA). Results : In both obese and nonobese groups, the “unhealthy” phenotypes were characterized by frequent bodyweight fluctuations, higher biochemical markers of insulin resistance, hepatic steatosis and inflammation, and higher anthropometric and DXA‐derived indices of central adiposity, compared with “healthy” phenotypes. Indices of total adiposity, peripheral fat distribution and lean body mass were not significantly different between “healthy” and “unhealthy” phenotypes. Despite having increased fat mass, MHO women exhibited comparable cardiometabolic parameters with healthy nonobese, and better glucose and lipid levels than MONO. Two DXA‐derived indices, trunk‐to‐legs and abdominal‐to‐gluteofemoral fat ratio were the major independent determinants of the “unhealthy” phenotypes in our cohort. Conclusions : The “metabolically obese phenotype” is associated with bodyweight variability, multiple cardiometabolic abnormalities and an excess of central relative to peripheral fat in postmenopausal women. DXA‐derived centrality ratios can discriminate effectively between metabolic subtypes of obesity in menopause.     

Effect of obesity on repeat revascularization in patients undergoing percutaneous coronary intervention with drug-eluting stents

Obesity is a major risk factor for developing coronary artery disease. The impact of obesity on prognosis among those with established coronary disease is less clear. The objective of this study was to evaluate the effect of obesity on repeat revascularization in patients undergoing percutaneous coronary intervention (PCI) with drug-eluting stents (DES). We examined 6,083 patients who were divided into three groups according to BMI: normal (BMI 18.5-24.9 kg/m(2), n = 1,592); overweight (BMI 25-29.9 kg/m(2), n = 3,026) and obese (BMI >30 kg/m(2), n = 1,465). The follow-up focused on clinical-driven repeat revascularization, including target lesion revascularization (TLR) and nonTLR. Median follow-up was 26 months (interquartile range 20-32). There was no significant difference in the incidence of TLR among normal, overweight, and obese patients (6.3% vs. 6.1% vs. 7.1%; P = 0.423). In contrast, the incidence of nonTLR was significantly higher in obese patients compared with normal and overweight (8.4% vs. 6.0% vs. 5.8%, P = 0.003). Multivariate analysis showed that obesity was an independent predictor of nonTLR during follow-up (hazard ratio = 1.39; 95% confidence interval = 1.06-1.83; P = 0.019), along with diabetes and hypercholesterolemia. Concomitant use of statins was independently associated with decreased risk of nonTLR (hazard ratio = 0.75; 95% confidence interval = 0.62-0.92; P = 0.005). In conclusion, among patients undergoing PCI with DES, obesity was not associated with TLR, but was associated with a higher risk of nonTLR.     

Obesity and body fat classification in the metabolic syndrome: Impact on cardiometabolic risk metabotype

Objective:

Obesity is a key factor in the development of the metabolic syndrome (MetS), which is associated with increased cardiometabolic risk. We investigated whether obesity classification by BMI and body fat percentage (BF%) influences cardiometabolic profile and dietary responsiveness in 486 MetS subjects (LIPGENE dietary intervention study).

Design and Methods:

Anthropometric measures, markers of inflammation and glucose metabolism, lipid profiles, adhesion molecules, and hemostatic factors were determined at baseline and after 12 weeks of four dietary interventions (high saturated fat (SFA), high monounsaturated fat (MUFA), and two low fat high complex carbohydrate (LFHCC) diets, one supplemented with long chain n‐3 polyunsaturated fatty acids (LC n‐3 PUFAs)).

Results:

About 39 and 87% of subjects classified as normal and overweight by BMI were obese according to their BF%. Individuals classified as obese by BMI (≥30 kg/m²) and BF% (≥25% (men) and ≥35% (women)) (OO, n = 284) had larger waist and hip measurements, higher BMI and were heavier (P < 0.001) than those classified as nonobese by BMI but obese by BF% (NOO, n = 92). OO individuals displayed a more proinflammatory (higher C reactive protein (CRP) and leptin), prothrombotic (higher plasminogen activator inhibitor‐1 (PAI‐1)), proatherogenic (higher leptin/adiponectin ratio) and more insulin resistant (higher HOMA‐IR) metabolic profile relative to the NOO group (P < 0.001). Interestingly, tumor necrosis factor‐α (TNF‐α) concentrations were lower post‐intervention in NOO individuals compared with OO subjects (P < 0.001).

Conclusions:

In conclusion, assessing BF% and BMI as part of a metabotype may help to identify individuals at greater cardiometabolic risk than BMI alone.     

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.     

Correlation among soluble receptors for advanced glycation end-products,soluble vascular adhesion protein-1/semicarbazide-sensitive amine oxidase (sVAP-1) and cardiometabolic risk markers in apparently healthy adolescents: a cross-sectional study

In non-diabetics, low levels of soluble receptor for advanced glycations end products (sRAGE) associate with an increased risk of development of diabetes, cardiovascular afflictions, or death. The majority of studies in non-diabetics report an inverse relationship between measures of obesity, cardiometabolic risk factors and sRAGE and/or endogenous secretory RAGE (esRAGE) levels. To elucidate whether this inconsistency is related to the metabolically healthy obese phenotype, or a different impact of the risk factors in presence and absence of obesity, we analyzed data from 2206 apparently healthy adolescents (51 % girls) aged 15-to-19 years. The association of sRAGE levels with soluble vascular adhesion protein-1/semicarbazide sensitive amine oxidase (sVAP-1/SSAO) was also investigated. Centrally obese, including metabolically healthy, adolescents present significantly lower sRAGE and esRAGE, but not sVAP-1, levels in comparison with their lean counterparts. An increasing number of cardiometabolic risk factors did not associate with significant changes in sRAGE, esRAGE or sVAP-1 levels either in lean or in obese subjects. In multivariate analyses, WHtR, hsCRP, markers of glucose homeostasis, renal function, adiponectin, and sVAP-1 associated significantly with sRAGE and esRAGE. SVAP-1 correlated significantly with glycemia, adiponectin, hsCRP, and sRAGE. Thus, in adolescents, a decline in sRAGE and esRAGE precedes the development of metabolic syndrome. When combined, standard and non-standard cardiometabolic risk factors explain only minor proportion in a variability of sRAGE and esRAGE (8 %–11 %); or sVAP-1 (12 %–20 %). Elucidation of pathogenetic mechanisms underlying early decline in sRAGE and esRAGE levels in obese adolescents and their clinical impact with regard to future cardiometabolic health requires further studies.     

Longitudinal associations between BMI,waist circumference,and cardiometabolic risk in US youth: Monitoring implications

Objective:

This study examined whether change in body mass index (BMI) or waist circumference (WC) is associated with change in cardiometabolic risk factors and differences between cardiovascular disease specific and diabetes specific risk factors among adolescents. We also sought to examine any differences by gender or baseline body mass status.

Design:

The article is a longitudinal analysis of pre‐ and post‐data collected in the HEALTHY trial. Participants were 4,603 ethnically diverse adolescents who provided complete data at 6th and 8th grade assessments.

Methods:

The main outcome measures were percent change in the following cardiometabolic risk factors: fasting triglycerides, systolic and diastolic blood pressure, high density lipoprotein cholesterol, and glucose as well as a clustered metabolic risk score. Main exposures were change in BMI or WC z‐score. Models were run stratified by gender; secondary models were additionally stratified by baseline BMI group (normal, overweight, or obese).

Results:

Analysis showed that when cardiometabolic risk factors were treated as continuous variables, there was strong evidence (P < 0.001) that change in BMI z‐score was associated with change in the majority of the cardiovascular risk factors, except fasting glucose and the combined risk factor score for both boys and girls. There was some evidence that change in WC z‐score was associated with some cardiovascular risk factors, but change in WC z‐score was consistently associated with changes in fasting glucose.

Conclusions:

In conclusion, routine monitoring of BMI should be continued by health professionals, but additional information on disease risk may be provided by assessing WC.     

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.     

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.     

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.     

Beta and alpha cell function in metabolically healthy but obese subjects: Relationship with entero‐insular axis

Objective:

Obesity is widely acknowledged as a critical risk factor for metabolic complications. Among obese subjects, there is a phenotype of metabolically healthy but obese (MHO) individuals that shows a favorable cardiometabolic risk profile. We aimed to evaluate the potential mechanisms underlying the metabolic profile of this subset, including alpha and beta cell function and entero‐insular axis.

Design and Methods:

One hundred twenty‐nine obese and 24 nonobese subjects were studied. Obese participants were defined as MHO or at‐risk obese, according to the homeostasis model of assessment‐insulin resistance (HOMA‐IR) index (MHO: lower tertile of HOMA‐IR, n = 43; at‐risk: upper tertile of HOMA‐IR index, n = 41). Insulin, glucagon, and incretin responses after a 120′ oral glucose tolerance test (75‐g OGTT) were investigated.

Results:

During OGTT, MHO individuals showed in comparison with at‐risk subjects: lower fasting and afterloads plasma levels of glucose, insulin, and C‐peptide; higher disposition index; lower fasting (P = 0.004) and at 30′ (P = 0.01) plasma glucose‐dependent insulinotropic polypeptide (GIP) levels; lower area under the curve (AUC) (0‐30) for GIP (P = 0.008); higher glucagon‐like peptide‐1 (GLP‐1) plasma levels at 90′ (P = 0.02) and 120′ (P = 0.02); lower glucagon plasma levels at baseline (P = 0.04) and at 30′ (P = 0.03); and appropriate glucagon suppression after the oral glucose load.

Conclusions:

MHO subjects show, as well as normal‐weight individuals, a lower diabetogenic profile by virtue of higher disposition index and unaffected entero‐insular axis. At‐risk obese individuals present increased GIP levels that might play a role in determining increased glucagon secretion and inappropriate glucagon responses after glucose load, thus contributing to impaired glucose homeostasis.     

Increased plasma apoA-IV level is a marker of abnormal postprandial lipemia: a study in normoponderal and obese subjects.

Plasma apolipoprotein A-IV (apoA-IV) levels are found elevated in hypertriglyceridemic patients. However, the relationship between plasma apoA-IV level and postprandial lipemia is not well known and remains to be elucidated. Thus, our objective was to study the relationship between plasma apoA-IV and postprandial TG after an oral fat load test (OFLT). Plasma apoA-IV was measured at fast and during an OFLT in 16 normotriglyceridemic, normoglucose-tolerant android obese subjects (BMI = 34.6 +/- 2.9 kg/m(2)) and 30 normal weight controls (BMI = 22.2 +/- 2.3 kg/m(2)). In spite of not statistically different fasting plasma TG levels in controls and obese patients, the former group showed an altered TG response after OFLT, featuring increased nonchylomicron TG area under the curve (AUC) compared with controls (516 +/- 138 vs. 426 +/- 119 mmol/l x min, P < 0.05). As compared to controls, obese patients showed increased apoA-IV levels both at fast (138.5 +/- 22.4 vs. 124.0 +/- 22.8 mg/l, P < 0.05) and during the OFLT (apoA-IV AUC: 79,833 +/- 14,281 vs. 68,176 +/- 17,463 mg/l x min, P < 0.05). Among the whole population studied, as among the control and obese subgroups, fasting plasma apoA-IV correlated significantly with AUC of plasma TG (r = 0.60, P < 0.001), AUC of chymomicron TG (r = 0.45, P < 0.01), and AUC of nonchylomicron TG (r = 0.62, P < 0.001). In the multivariate analysis, fasting apoA-IV level constituted an independent and highly significant determinant of AUC of plasma TG, AUC of chymomicron TG, AUC of nonchylomicron TG, and incremental AUC of plasma TG. In conclusion, we show a strong link between fasting apoA-IV and postprandial TG metabolism. Plasma fasting apoA-IV is shown to be a good marker of TG response after an OFLT, providing additional information on post-load TG response in conjunction with other known factors such as fasting TGs.     

Acute changes in dietary omega-3 fatty acid intake lowers soluble interleukin-6 receptor in healthy adult normal weight and overweight males

OBJECTIVE: To determine the effect of a short-term isocaloric exchange of alpha-linolenic acid (ALA, 18:3n3) for linoleic acid (LA, 18:2n6) on fasting levels of soluble interleukin-6 receptor (sIL6R), and soluble tumor necrosis factor-alpha receptors 1 and 2 (sTNFR1 and sTNFR2) in healthy normal weight and overweight/obese adult males. DESIGN: Four-day clinical intervention study with 0.5% or 5% of total energy from ALA. Fasting (10 h) blood samples were obtained on the morning of day 5 in both diet treatments to measure sTNFR1, sTNFR2, and sIL6R. SUBJECTS: Nine normal weight (BMI < 25 kg/m2) and seven overweight (BMI > or = 25 kg/m2) healthy males. RESULTS: Fasting sIL6R decreased significantly from the control (C) diet following four days on the high ALA isocaloric (ISO) diet in normal weight and overweight/obese subjects (normal weight: C = 34.89 +/- 3.17 ng/ml, ISO = 30.91 +/- 2.24 ng/ml, p < 0.05; overweight/obese: C = 38.19 +/- 3.92 ng/ml, ISO = 33.57 +/- 2.47 ng/ml, p , 0.05). The dietary intervention did not have a significant effect on fasting sTNFR1 or sTNFR2. CONCLUSIONS: The results suggest that an isocaloric exchange of ALA for LA can reduce fasting sIL6R concentration by approximately 11% after a four-day dietary intervention in both overweight/obese and normal weight subjects. The data also suggest that longer exposure to a similar diet may have the potential to reduce inflammatory burden and thus lower the risk of both cardiovascular disease as well as diabetes.     

Metabolic Abnormalities Are Common among South American Hispanics Subjects with Normal Weight or Excess Body Weight: The CRONICAS Cohort Study

Objective

We aimed to characterize metabolic status by body mass index (BMI) status.

Methods

The CRONICAS longitudinal study was performed in an age-and-sex stratified random sample of participants aged 35 years or older in four Peruvian settings: Lima (Peru’s capital, costal urban, highly urbanized), urban and rural Puno (both high-altitude), and Tumbes (costal semirural). Data from the baseline study, conducted in 2010, was used. Individuals were classified by BMI as normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥30 kg/m²), and as metabolically healthy (0–1 metabolic abnormality) or metabolically unhealthy (≥2 abnormalities). Abnormalities included individual components of the metabolic syndrome, high-sensitivity C-reactive protein, and insulin resistance.

Results

A total of 3088 (age 55.6±12.6 years, 51.3% females) had all measurements. Of these, 890 (28.8%), 1361 (44.1%) and 837 (27.1%) were normal weight, overweight and obese, respectively. Overall, 19.0% of normal weight in contrast to 54.9% of overweight and 77.7% of obese individuals had ≥3 risk factors (p<0.001). Among normal weight individuals, 43.1% were metabolically unhealthy, and age ≥65 years, female, and highest socioeconomic groups were more likely to have this pattern. In contrast, only 16.4% of overweight and 3.9% of obese individuals were metabolically healthy and, compared to Lima, the rural and urban sites in Puno were more likely to have a metabolically healthier profile.

Conclusions

Most Peruvians with overweight and obesity have additional risk factors for cardiovascular disease, as well as a majority of those with a healthy weight. Prevention programs aimed at individuals with a normal BMI, and those who are overweight and obese, are urgently needed, such as screening for elevated fasting cholesterol and glucose.     

Age-Dependent Influence of Gender on the Association Between Obesity and a Cluster of Cardiometabolic Risk Factors

BackgroundObesity is a main risk factor in metabolic syndrome. Gender is known to influence the risk of obesity and other cardiovascular risk factors. However, it remains to be determined whether there is a gender-specific difference in the relationship between obesity and accumulation of other cardiometabolic risk factors such as hypertension, dyslipidemia, and diabetes.ObjectiveThe aim of this study was to determine whether the association between obesity and a cluster of other cardiometabolic risk factors is modified by gender.MethodsThe subjects were 17,791 Japanese men and women who were divided into younger (35–40 years) and older (60–70 years) age groups. The relationships between obesity (body mass index [BMI] ≥25 kg/m² or waist-to-height ratio [WHtR] ≥0.5) and multiple cardiometabolic risk factors (≥2 of the risk factors of high blood pressure, dyslipidema, and hyperglycemia) were compared between men and women in each age group.ResultsIn the younger group, the crude odds ratios (ORs) for multiple cardiometabolic risk factors in obese versus nonobese subjects were significantly higher in women than in men (BMI: 6.23 [range, 5.53–7.02] in men vs 16.63 [range, 12.37–22.37] in women, P < 0.01; WHtR: 6.04 [range, 5.36–6.81] in men vs 9.77 [range, 7.14–13.37] in women, P < 0.01), whereas this difference was not found in the older group (BMI: 3.03 [range, 2.69–3.42] in men vs 2.92 [range, 2.33–3.67] in women P = 0.076; WHtR: 3.11 [range, 2.78–3.47] in men vs 2.50 [range, 2.02–3.09] in women, P < 0.05). On multivariate logistic regression analysis, the ORs for multiple cardiometabolic risk factors after adjusting for age, smoking, alcohol consumption, and regular exercise in subjects with versus subjects without a large waist circumference tended to be higher in women than in men in the younger group but not in the older group. The ORs of the interaction term consisting of gender and each adiposity index for multiple cardiometabolic risk factors were significantly higher than a reference level of 1.00 in the younger group (BMI: 2.68 [range, 1.95–3.69], P < 0.01; WHtR: 1.62 [range, 1.16–2.27], P < 0.01) but not in the older group (BMI: 0.95 [range, 0.74–1.23], P = 0.712; WHtR: 0.80 [range, 0.63–1.02], P = 0.066).ConclusionThe results suggest that the association between obesity and a cluster of cardiometabolic risk factors is stronger in women than in men, and this gender-specific difference exists in younger (35–40 years) but not in older (60–70 years) individuals.     

Exenatide as a weight-loss therapy in extreme pediatric obesity: a randomized, controlled pilot study

The objective of this pilot study was to evaluate the effects of exenatide on BMI (primary endpoint) and cardiometabolic risk factors in nondiabetic youth with extreme obesity. Twelve children and adolescents (age 9-16 years old) with extreme obesity (BMI ≥1.2 times the 95th percentile or BMI ≥35 kg/m(2)) were enrolled in a 6-month, randomized, open-label, crossover, clinical trial consisting of two, 3-month phases: (i) a control phase of lifestyle modification and (ii) a drug phase of lifestyle modification plus exenatide. Participants were equally randomized to phase-order (i.e., starting with control or drug therapy) then crossed-over to the other treatment. BMI, body fat percentage, blood pressure, lipids, oral glucose tolerance tests (OGTT), adipokines, plasma biomarkers of endothelial activation, and endothelial function were assessed at baseline, 3-, and 6-months. The mean change over each 3-month phase was compared between treatments. Compared to control, exenatide significantly reduced BMI (-1.7 kg/m(2), 95% confidence interval (CI) (-3.0, -0.4), P = 0.01), body weight (-3.9 kg, 95% CI (-7.11, -0.69), P = 0.02), and fasting insulin (-7.5 mU/l, 95% CI (-13.71, -1.37), P = 0.02). Significant improvements were observed for OGTT-derived insulin sensitivity (P = 0.02) and β-cell function (P = 0.03). Compliance with the injection regimen was excellent (≥94%) and exenatide was generally well-tolerated (the most common adverse event was mild nausea in 36%). These preliminary data suggest that exenatide should be evaluated in larger, well-controlled trials for its ability to reduce BMI and improve cardiometabolic risk factors in youth with extreme obesity.     

Severe Obesity and Cardiometabolic Risk in Children: Comparison from Two International Classification Systems

Objectives

There is no agreed-upon definition for severe obesity (Sev-OB) in children. We compared estimates of Sev-OB as defined by different cut-points of body mass index (BMI) from the Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) curves and the ability of each set of cut-points to screen for the presence of cardiometabolic risk factors.

Research Design and Methods

Cross-sectional, multicenter study involving 3,340 overweight/obese young subjects. Sev-OB was defined as BMI ≥99^th percentile or ≥1.2 times the 95^th percentile of the CDC or the WHO curves. High blood pressure, hypertriglyceridemia, low High Density Lipoprotein -cholesterol and impaired fasting glucose were considered as cardiometabolic risk factors.

Results

The estimated prevalence of Sev-OB varied widely between the two reference systems. Either using the cut-point ≥99^th percentile or ≥1.2 times the 95^th percentile, less children were defined as Sev-OB by CDC than WHO (46.8 vs. 89.5%, and 63.3 vs. 80.4%, respectively p<0.001). The CDC 99^th percentile had lower sensitivity (58.5 vs 94.2), higher specificity (57.6 vs 12.3) and higher positive predictive value (34.4 vs 28.9) than WHO in identifying obese children with ≥2 cardiometabolic risk factors. These differences were mitigated using the 1.2 times the 95^th percentile (sensitivity 73.9 vs. 88.1; specificity 40.7 vs. 22.5; positive predictive value 32.1 vs. 30.1). Substantial agreement between growth curves was found using the 1.2 times the 95^th percentile, in particular in children ≤10 years.

Conclusions

Estimates of Sev-OB and cardiometabolic risk as defined by different cut-points of BMI are influenced from the reference systems used. The 1.2 times the 95^th percentile of BMI of either CDC or WHO standard has a discriminatory advantage over the 99^th percentile for identifying severely obese children at increased cardiometabolic risk, particularly under 10 years of age.