Development of metabolic syndrome components in adults with a healthy obese phenotype: A 3‐year follow‐up

Objective:

There is a lack of data on the progression from a healthy obese phenotype toward an unhealthy obese phenotype and the development of metabolic syndrome (MetS). Our aim was to assess the development of MetS 3 years after screening in centrally obese individuals with a healthy obese phenotype and to evaluate the usefulness of repeated screening.

Design and Methods:

Eighty‐eight individuals (mean age 47 years, 88% female) with central obesity as their only MetS component (ATP III criteria) at baseline screening were re‐evaluated for MetS status after 3 years.

Results:

At follow‐up, the cardiometabolic risk profile in centrally obese individuals with a healthy phenotype showed a tendency toward deterioration. Thirty‐two percent developed at least one additional MetS component, 7% had developed MetS. Nobody had developed type 2 diabetes. An increased triglyceride level (n = 16) and an increased blood pressure (n = 18) were the components most often present at follow‐up. The people developing additional MetS components had a lower education level compared with the group that preserved the healthy centrally obese phenotype (80 vs. 71% lower educated, P = 0.35). They also had slightly worse baseline levels of the risk factors.

Conclusion:

The number of centrally obese individuals developing an unhealthy phenotype in this relatively short follow‐up period emphasizes the need for a regular surveillance of cardiometabolic parameters in centrally obese individuals. However, it is questionable whether a repeated screening for type 2 diabetes every 3 years, as recommended by the American Diabetes Association, in this category of patients is appropriate.     

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.     

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.     

PROP taster status interacts with the built environment to influence children's food acceptance and body weight status

Objectives:

Eating behaviors and obesity are complex phenotypes influenced by genes and the environment, but few studies have investigated the interaction of these two variables. The purpose of this study was to use a gene‐environment interaction model to test for differences in children's food acceptance and body weights.

Design and Methods:

Inherited ability to taste 6‐n‐propylthiouracil (PROP) was assessed as a marker of oral taste responsiveness. Food environment was classified as “healthy” or “unhealthy” based on proximity to outlets that sell fruits/vegetables and fast foods using Geographic Information Systems (GIS). The cohort consisted of 120 children, ages 4‐6 years, recruited from New York City over 2005‐2010. Home address and other demographic variables were reported by parents and PROP status, food acceptance, and anthropometrics were assessed in the laboratory. Based on a screening test, children were classified as PROP tasters or non‐tasters. Hierarchical linear models analysis of variance was performed to examine differences in food acceptance and body mass index (BMI) z‐scores as a function of PROP status, the food environment (“healthy” vs. “unhealthy”), and their interaction.

Results and Conclusion:

Results showed an interaction between taster status and the food environment on BMI z‐score and food acceptance. Non‐taster children living in healthy food environments had greater acceptance of vegetables than taster children living in healthy food environments (P ≤ 0.005). Moreover, non‐tasters from unhealthy food environments had higher BMI z‐scores than all other groups (P ≤ 0.005). Incorporating genetic markers of taste into studies that assess the built environment may improve the ability of these measures to predict risk for obesity and eating behaviors. Obesity (2012)     

Serum amyloid A is found on ApoB‐containing lipoproteins in obese humans with diabetes

Objective:

In murine models of obesity/diabetes, there is an increase in plasma serum amyloid A (SAA) levels along with redistribution of SAA from high‐density lipoprotein (HDL) to apolipoprotein B (apoB)‐containing lipoprotein particles, namely, low‐density lipoprotein and very low‐density lipoprotein. The goal of this study was to determine if obesity is associated with similar SAA lipoprotein redistribution in humans.

Design and Methods:

Three groups of obese individuals were recruited from a weight loss clinic: healthy obese (n = 14), metabolic syndrome (MetS) obese (n = 8), and obese with type 2 diabetes (n = 6). Plasma was separated into lipoprotein fractions by fast protein liquid chromatography, and SAA was measured in lipid fractions using enzyme‐linked immunosorbent assay and Western blotting.

Results:

Only the obese diabetic group had SAA detectable in apoB‐containing lipoproteins, and SAA reverted back to HDL with active weight loss.

Conclusions:

In human subjects, SAA is found in apoB‐containing lipoprotein particles only in obese subjects with type 2 diabetes, but not in healthy obese or obese subjects with MetS.     

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.     

Insulin Resistance is the Best Predictor of the Metabolic Syndrome in Subjects With a First‐Degree Relative With Type 2 Diabetes

Although obesity is associated with insulin resistance and the metabolic syndrome (MetS), some obese individuals are metabolically healthy. Conversely, some lean individuals are insulin resistant (IR) and at increased cardiometabolic risk. To determine the relative importance of insulin sensitivity, BMI and waist circumference (WC) in predicting MetS, we studied these two extreme groups in a high‐risk population. One thousand seven hundred and sixty six subjects with a first‐degree relative with type 2 diabetes were stratified by BMI and homeostasis model assessment of insulin resistance (HOMA_IR) into groups. IR groups had higher triglycerides, fasting glucose, and more diabetes than their BMI‐group insulin sensitive (IS) counterparts. Within both IS and IR groups, obesity was associated with higher HOMA_IR and diastolic blood pressure (BP), but no difference in other metabolic variables. MetS (Adult Treatment Panel III (ATPIII)) prevalence was higher in IR groups (P < 0.001) and more subjects met each MetS criterion (P < 0.001). Within each BMI category, HOMA_IR independently predicted MetS (P < 0.001) whereas WC did not. Within IS and IR groups, age and WC, but not BMI, were independent determinants of MetS (P < 0.001). WC was a less meaningful predictor of MetS at higher values of HOMA_IR. HOMA_IR was a better predictor of MetS than WC or BMI (receiver operating characteristic (ROC) area under the curve 0.76 vs. 0.65 vs. 0.59, P < 0.001). In conclusion, insulin sensitivity rather than obesity is the major predictor of MetS and is better than WC at identifying obese individuals with a healthier metabolic profile. Further, as many lean individuals with a first‐degree relative with type 2 diabetes are IR and metabolically unhealthy, they may all benefit from metabolic testing.     

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.     

Prevalence of metabolic syndrome in Iranian professional drivers: results from a population based study of 12,138 men

Background

It is evident that professional driving is associated with substantial changes in lifestyle habits. Professional drivers are prone to metabolic syndrome (MetS) and its complications because their working environment is characterized by numerous stress factors such as lack of physical activity due to working in a fixed position, disruption in diet, and irregular sleep habits. The aim of the present study was to estimate the prevalence of MetS among long distance drivers residing in West Azerbaijan province in Iran.

Materials

To assess the prevalence of metabolic syndrome among professional long distance drivers, 12138 participants were enrolled in this cross sectional study. The MetS was defined using International Diabetes Federation criteria.

Results

Among12138 participants, 3697 subjects found to be MetS. The crude and age-adjusted rates of MetS were 30.5% and 32.4% respectively. Based on Body mass index (BMI), 5027 subjects (41.4%) were overweight (BMI ≥25.01–30 kg/m2), and 2592 (21.3%) were obese (BMI ≥30.01 kg/m2). The presence of central obesity was more common than other components. The associations of MetS with BMI, pack-year smoking, age, weekly driving duration and driving experiences were significant in the logistic regression. By increasing BMI, pack-year smoking, age, weekly driving duration and driving experiences, odds ratio of MetS was increased.

Conclusion

The study suggests that MetS has become a noteworthy health problem among Iranian long distance drivers. This might be due to the following facts: sitting in a fixed position for long hours while working, cigarette smoking, job stress, unhealthy diet and lack of physical activity. Educational programs should be established for promoting healthy lifestyle and also for early detection and appropriate interventions     

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.     

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.     

Combined Effects of Smoking and Alcohol on Metabolic Syndrome: The LifeLines Cohort Study

Introduction

The development of metabolic syndrome (MetS) is influenced by environmental factors such as smoking and alcohol consumption. We determined the combined effects of smoking and alcohol on MetS and its individual components.

Methods

64,046 participants aged 18–80 years from the LifeLines Cohort study were categorized into three body mass index (BMI) classes (BMI<25, normal weight; BMI 25–30, overweight; BMI≥30 kg/m², obese). MetS was defined according to the revised criteria of the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP ATP III). Within each BMI class and smoking subgroup (non-smoker, former smoker, <20 and ≥20 g tobacco/day), the cross-sectional association between alcohol and individual MetS components was tested using regression analysis.

Results

Prevalence of MetS varied greatly between the different smoking-alcohol subgroups (1.7–71.1%). HDL cholesterol levels in all alcohol drinkers were higher than in non-drinkers (0.02 to 0.29 mmol/L, P values<0.001). HDL cholesterol levels were lower when they were also a former or current smoker (<20 and ≥20 g tobacco/day). Consumption of ≤1 drink/day indicated a trend towards lower triglyceride levels (non-significant). Concurrent use alcohol (>1 drink/day) and tobacco showed higher triglycerides levels. Up to 2 drinks/day was associated with a smaller waist circumference in overweight and obese individuals. Consumption of >2 drinks/day increased blood pressure, with the strongest associations found for heavy smokers. The overall metabolic profile of wine drinkers was better than that of non-drinkers or drinkers of beer or spirits/mixed drinks.

Conclusion

Light alcohol consumption may moderate the negative associations of smoking with MetS. Our results suggest that the lifestyle advice that emphasizes smoking cessation and the restriction of alcohol consumption to a maximum of 1 drink/day, is a good approach to reduce the prevalence of MetS.     

Accuracy of prediction scores and novel biomarkers for predicting nonalcoholic fatty liver disease in obese children

Background:

Accurate prediction scores for liver steatosis are demanded to enable clinicians to noninvasively screen for nonalcoholic fatty liver disease (NAFLD). Several prediction scores have been developed, however external validation is lacking.

Objective:

The aim was to determine the diagnostic accuracy of four existing prediction scores in severely obese children, to develop a new prediction score using novel biomarkers and to compare these results to the performance of ultrasonography.

Design and Results:

Liver steatosis was measured using proton magnetic resonance spectroscopy in 119 severely obese children (mean age 14.3 ± 2.1 years, BMI z‐score 3.35 ± 0.35). Prevalence of steatosis was 47%. The four existing predictions scores (“NAFLD liver fat score,” “fatty liver index,” “hepatic steatosis index,” and the pediatric prediction score) had only moderate diagnostic accuracy in this cohort (positive predictive value (PPV): 70, 61, 61, 69% and negative predictive value (NPV) 77, 69, 68, 75%, respectively). A new prediction score was built using anthropometry, routine biochemistry and novel biomarkers (leptin, adiponectin, TNF‐alpha, IL‐6, CK‐18, FGF‐21, and adiponutrin polymorphisms). The final model included ALT, HOMA, sex, and leptin. This equation (PPV 79% and NPV 80%) did not perform substantially better than the four other equations and did not outperform ultrasonography for excluding NAFLD (NPV 82%).

Conclusion:

The conclusion is in severely obese children and adolescents existing prediction scores and the tested novel biomarkers have insufficient diagnostic accuracy for diagnosing or excluding NAFLD.     

Health-Related Quality of Life in Relation to Obesity Grade,Type 2 Diabetes,Metabolic Syndrome and Inflammation

Background

Health-related quality of life (HR-QoL) may be compromised in obese individuals, depending on the presence of other complications. The aim of this study is to assess the effect of obesity-related conditions on HR-QoL. These conditions are i) grade of obesity with and without type 2 diabetes (T2D), ii) metabolic syndrome (MetS), and iii) level of inflammation.

Methods

From the Dutch LifeLines Cohort Study we included 13,686 obese individuals, aged 18–80 years. HR-QoL was measured with the RAND 36-Item Health Survey which encompasses eight health domains. We calculated the percentage of obese individuals with poor HR-QoL, i.e. those scoring below the domain and sex specific cut-off value derived from the normal weight population. Logistic regression analysis was used to calculate the probability of having poor domain scores according to the conditions under study.

Results

Higher grades of obesity and the additional presence of T2D were associated with lower HR-QoL, particularly in the domains physical functioning (men: odds ratios (ORs) 1.48–11.34, P<0.005, and women: ORs 1.66–5.05, P<0.001) and general health (men: ORs 1.44–3.07, P<0.005, and women: ORs 1.36–3.73, P<0.001). A higher percentage of obese individuals with MetS had a poor HR-QoL than those without MetS. Furthermore, we observed a linear trend between inflammation and the percentage of obese individuals with poor scores on the HR-QoL domains. Individuals with MetS were more likely to have poor scores in the domains general health, vitality, social functioning and role limitations due to emotional problems. Obese women with increased inflammation levels were more likely to have poor scores on all domains except role limitations due to emotional problems and mental health.

Conclusions

The impact of obesity on an individual’s quality of life is enhanced by grade of obesity, T2D, MetS and inflammation and are mainly related to reduced physical health. The mental well-being is less often impaired.     

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.     

Snoring, inflammatory markers, adipokines and metabolic syndrome in apparently healthy Chinese

Objective

Chronic low-grade inflammation and adipokines dysregulation are linked to mechanisms underscoring the pathogenesis of obesity-related metabolic disorders. Little is known about roles of these cytokines on the association between snoring and metabolic syndrome (MetS). We aimed to investigate whether a cluster of cytokines are related to snoring frequency and its association with MetS in apparently healthy Chinese.

Methods

Current analyses used a population-based sample including 1059 Shanghai residents aged 35–54 years. Self-reported snoring frequency was classified as never, occasionally and regularly. Fasting plasma glucose, lipid profile, insulin, C-reactive protein, interleukin-6, interleukin-18, lipopolysaccharide binding protein, high-molecular-weight adiponectin and leptin were measured. MetS was defined by the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian-Americans.

Results

Overweight/obese subjects had significantly higher prevalence of regular snorers than their normal-weight counterparts (34.8% vs. 11.5%, P<0.001). Regular snoring was associated with unfavorable profile of inflammatory markers and adipokines. However, those associations were abolished after adjustment for body mass index (BMI) or waist circumference. The MetS risk (multivariate-adjusted odds ratio 5.41, 95% confidence interval 3.72–7.88) was substantially higher in regular snorers compared with non-snorers. Controlling for BMI remarkably attenuated the association (2.03, 1.26–3.26), while adjusting for inflammatory markers and adipokines showed little effects.

Conclusion

Frequent snoring was associated with an elevated MetS risk independent of lifestyle factors, adiposity, inflammatory markers and adipokines in apparently healthy Chinese. Whether snoring pattern is an economic and no-invasive indicator for screening high-risk persons needs to be addressed prospectively.     

Associations of the PTEN − 9C>G polymorphism with insulin sensitivity and central obesity in Chinese

Background

Phosphatase and tensin homolog on chromosome 10 gene (PTEN) is known as a tumor-suppressor gene. Previous studies demonstrated that PTEN dysfunction affects the function of insulin. However, investigations of PTEN single nucleotide polymorphisms (SNPs) and IR-related disease associations are limited. The aim of the present study was to investigate whether its polymorphism could be involved in the risk of metabolic syndrome (MetS).

Methods

The genotype frequency of PTEN − 9C>G polymorphism was determined by using a Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) method in 530 subjects with MetS and 202 healthy control subjects of the Han Ethnic Chinese population in a case–control analysis.

Results

The PTEN − 9C>G polymorphism was not associated with MetS or its hyperglycemia, hypertension and hypertriglyceridemia components. In the control individuals aged < 60 years or ≥ 60 years, the CG genotype individuals had lower insulin sensitivity than CC individuals (P < 0.05). In the < 60-year-old MetS group and normal glucose tolerance (NGT) subgroup, the CG individuals had lower insulin sensitivity and higher waist circumference (WC) and waist-height-ratio (WHtR) than CC individuals (P < 0.05). Multiple linear regression analysis showed that the PTEN polymorphism (P = 0.001) contributed independently to 4.2% (adjusted R²) of insulin sensitivity variance (estimated by Matsuda ISI), while age (P = 0.004), gender (P = 0.000) and the PTEN polymorphism (P = 0.032) contributed independently to 5.6% (adjusted R²) of WHtR variance.

Conclusions

The CG genotype of PTEN − 9C>G polymorphism was not associated with MetS and some of its components as well. However, it may not only decrease insulin sensitivity in the healthy control and MetS in pre-elderly or NGT subjects, but may also increase the risk of central obesity among these MetS individuals.     

Influence of metabolic syndrome on biomarkers of oxidative stress and inflammation in obese adults

Objective: Both obesity and the metabolic syndrome (MetS) have been independently linked with increased oxidative and inflammatory stress. This study tested the hypothesis that obesity with MetS is associated with greater oxidative and inflammatory burden compared with obesity alone. Research Methods and Procedures: Forty‐eight normal‐weight and 40 obese (20 without MetS; 20 with MetS) adults were studied. MetS was defined according to National Cholesterol Education Program Adult Treatment Panel III criteria. Plasma concentrations of oxidized low‐density lipoprotein, C‐reactive protein, tumor necrosis factor‐α, interleukin (IL)‐6, and IL‐18 were determined by enzyme immunoassay. Results: Plasma biomarkers of oxidative stress and inflammation were lowest in normal‐weight controls. Of note, obese MetS adults demonstrated significantly higher plasma concentrations of oxidized low‐density lipoprotein (62.3 ± 3.2 vs. 54.0 ± 4.0 U/L; p < 0.05), C‐reactive protein (3.0 ± 0.6 vs. 1.5 ± 0.3 mg/L; p < 0.01), tumor necrosis factor‐α (2.1 ± 0.1 vs. 1.6 ± 0.1 pg/mL; p < 0.05), IL‐6 (2.8 ± 0.4 vs. 1.4 ± 0.2 pg/mL; p < 0.01), and IL‐18 (253 ± 16 vs. 199 ± 16 pg/mL; p < 0.01), compared with obese adults without MetS. Discussion: These results suggest that MetS heightens oxidative stress and inflammatory burden in obese adults. Increased oxidative and inflammatory stress may contribute to the greater risk of coronary heart disease and cerebrovascular disease in obese adults with MetS.     

Serum Ferritin Is Inversely Correlated with Testosterone in Boys and Young Male Adolescents: A Cross-Sectional Study in Taiwan

Objective

The transition from childhood to teenaged years is associated with increased testosterone and a decreased iron status. It is not clear whether higher testosterone levels cause the decreased iron status, and to what extent, obesity-related inflammation influences the iron-testosterone relationship. The aim of the present study was to examine relationships of testosterone, iron status, and anti-/proinflammatory cytokines in relation to nutritional status in boys and young adolescent Taiwanese males.

Methods

In total, 137 boys aged 7~13 yr were included. Parameters for obesity, the iron status, testosterone, and inflammatory markers were evaluated.

Results

Overweight and obese (ow/obese) boys had higher mean serum testosterone, interleukin (IL)-1β, and nitric oxide (NO) levels compared to their normal-weight counterparts (all p<0.05). Mean serum ferritin was slightly higher in ow/obese boys compared to normal-weight boys, but this did not reach statistical significance. A multiple linear regression showed that serum ferritin (β = -0.7470, p = 0.003) was inversely correlated with testosterone, while serum IL-10 (β = 0.3475, p = 0.009) was positively associated with testosterone after adjusting for covariates. When normal-weight boys were separately assessed from ow/obesity boys, the association between testosterone and serum ferritin became stronger (β = -0.9628, p<0.0001), but the association between testosterone and IL-10 became non-significant (β = 0.1140, p = 0.4065) after adjusting for covariates. In ow/obese boys, only IL-10 was weakly associated with serum testosterone (β = 0.6444, p = 0.051) after adjusting for age.

Conclusions

Testosterone and serum ferritin are intrinsically interrelated but this relationship is weaker in ow/obese boys after adjusting for age.