Body Adiposity Index versus Body Mass Index and Other Anthropometric Traits as Correlates of Cardiometabolic Risk Factors

Objective

The worldwide prevalence of obesity mandates a widely accessible tool to categorize adiposity that can best predict associated health risks. The body adiposity index (BAI) was designed as a single equation to predict body adiposity in pooled analysis of both genders. We compared body adiposity index (BAI), body mass index (BMI), and other anthropometric measures, including percent body fat (PBF), in their correlations with cardiometabolic risk factors. We also compared BAI with BMI to determine which index is a better predictor of PBF.

Methods

The cohort consisted of 698 Mexican Americans. We calculated correlations of BAI, BMI, and other anthropometric measurements (PBF measured by dual energy X-ray absorptiometry, waist and hip circumference, height, weight) with glucose homeostasis indices (including insulin sensitivity and insulin clearance from euglycemic clamp), lipid parameters, cardiovascular traits (including carotid intima-media thickness), and biomarkers (C-reactive protein, plasminogen activator inhibitor-1 and adiponectin). Correlations between each anthropometric measure and cardiometabolic trait were compared in both sex-pooled and sex-stratified groups.

Results

BMI was associated with all but two measured traits (carotid intima-media thickness and fasting glucose in men), while BAI lacked association with several variables. BAI did not outperform BMI in its associations with any cardiometabolic trait. BAI was correlated more strongly than BMI with PBF in sex-pooled analyses (r = 0.78 versus r = 0.51), but not in sex-stratified analyses (men, r = 0.63 versus r = 0.79; women, r = 0.69 versus r = 0.77). Additionally, PBF showed fewer correlations with cardiometabolic risk factors than BMI. Weight was more strongly correlated than hip with many of the cardiometabolic risk factors examined.

Conclusions

BAI is inferior to the widely used BMI as a correlate of the cardiometabolic risk factors studied. Additionally, BMI’s relationship with total adiposity may not be the sole determinate of its association with cardiometabolic risk.     

Sex-specific associations of adiposity with cardiometabolic traits in the UK: A multi–life stage cohort study with repeat metabolomics

BackgroundSex differences in cardiometabolic disease risk are commonly observed across the life course but are poorly understood and may be due to different associations of adiposity with cardiometabolic risk in females and males. We examined whether adiposity is differently associated with cardiometabolic trait levels in females and males at 3 different life stages.Methods and findingsData were from 2 generations (offspring, Generation 1 [G1] born in 1991/1992 and their parents, Generation 0 [G0]) of a United Kingdom population-based birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). Follow-up continues on the cohort; data up to 25 y after recruitment to the study are included in this analysis. Body mass index (BMI) and total fat mass from dual-energy X-ray absorptiometry (DXA) were measured at mean age 9 y, 15 y, and 18 y in G1. Waist circumference was measured at 9 y and 15 y in G1. Concentrations of 148 cardiometabolic traits quantified using nuclear magnetic resonance spectroscopy were measured at 15 y, 18 y, and 25 y in G1. In G0, all 3 adiposity measures and the same 148 traits were available at 50 y. Using linear regression models, sex-specific associations of adiposity measures at each time point (9 y, 15 y, and 18 y) with cardiometabolic traits 3 to 6 y later were examined in G1. In G0, sex-specific associations of adiposity measures and cardiometabolic traits were examined cross-sectionally at 50 y. A total of 3,081 G1 and 4,887 G0 participants contributed to analyses. BMI was more strongly associated with key atherogenic traits in males compared with females at younger ages (15 y to 25 y), and associations were more similar between the sexes or stronger in females at 50 y, particularly for apolipoprotein B–containing lipoprotein particles and lipid concentrations. For example, a 1 standard deviation (SD) (3.8 kg/m²) higher BMI at 18 y was associated with 0.36 SD (95% confidence interval [CI] = 0.20, 0.52) higher concentrations of extremely large very-low-density lipoprotein (VLDL) particles at 25 y in males compared with 0.15 SD (95% CI = 0.09, 0.21) in females, P value for sex difference = 0.02. By contrast, at 50 y, a 1 SD (4.8 kg/m²) higher BMI was associated with 0.33 SD (95% CI = 0.25, 0.42) and 0.30 SD (95% CI = 0.26, 0.33) higher concentrations of extremely large VLDL particles in males and females, respectively, P value for sex difference = 0.42. Sex-specific associations of DXA-measured fat mass and waist circumference with cardiometabolic traits were similar to findings for BMI and cardiometabolic traits at each age. The main limitation of this work is its observational nature, and replication in independent cohorts using methods that can infer causality is required.ConclusionsThe results of this study suggest that associations of adiposity with adverse cardiometabolic risk begin earlier in the life course among males compared with females and are stronger until midlife, particularly for key atherogenic lipids. Adolescent and young adult males may therefore be high priority targets for obesity prevention efforts.

Linda O’Keeffe and colleagues investigate sex-specific associations between cardiometabolomic traits and BMI, fat mass, and waist circumference across childhood, adolescence, early adulthood, and mid life in two generations of a UK population-based birth cohort.     

The Severity of Nocturnal Hypoxia but Not Abdominal Adiposity Is Associated with Insulin Resistance in Non-Obese Men with Sleep Apnea

Background

Beyond obesity, sleep apnea syndrome is frequently associated with excess abdominal adiposity that could contribute to the deteriorated cardiometabolic risk profile of apneic patients.

Methods

The present study addressed the respective contribution of the severity of sleep apnea syndrome and excess abdominal adiposity to the cardiometabolic risk profile of 38 non obese men with polysomnography-diagnosed sleep apnea syndrome (apnea-hypopnea index >15 events/hour). These otherwise healthy men performed a 75g-oral glucose tolerance test (OGTT) with plasma lipid/inflammatory and redox profiles. Twenty-one apneic men with high-waist circumference (>94 cm) were compared to 17 apneic men with low-waist circumference.

Results

Apneic men with high-waist circumference had higher AUC glucose and AUC insulin than apneic men with low-waist circumference. Accordingly, apneic men with high-waist circumference had higher hepatic insulin resistance as reflected by higher HOMA-resistance index, and lower global insulin sensitivity as reflected by lower insulin sensitivity index of Matsuda (derived from OGTT). The sleep structure and the apnea-hypopnea index were not different between the two groups. However, apneic men with high-waist circumference presented with lower mean nocturnal oxyhemoglobin (SpO₂). In the 38 men, waist circumference and mean nocturnal SpO₂ were inversely correlated (r = −0.43, p = 0.011) and were both associated with plasma glucose/insulin homeostasis indices: the higher the waist circumference, the lower the mean nocturnal SpO₂, the lower the insulin-sensitivity. Finally, in multivariable regression model, mean nocturnal SpO₂ and not waist circumference was associated with insulin-resistance.

Conclusion

Thus, excess abdominal adiposity in non obese apneic men was associated with a deteriorated insulin-sensitivity that could be driven by a more severe nocturnal hypoxemia.     

Body adiposity index and other indexes of body composition in the SAPHIR study: Association with cardiovascular risk factors

Objective:

The accuracy of anthropometric surrogate markers such as the body adiposity index (BAI) and other common indexes like the body mass index (BMI), waist‐to‐hip ratio (WHR) and waist‐to‐height ratio (WHtR) to predict metabolic sequelae is essential for its use in clinical practice.

Design and Methods:

Thus, we evaluated the strength of BAI and other indexes to relate with anthropometric parameters, adipocytokines, blood lipids, parameters of glucose‐homeostasis and blood pressure in 1,770 patients from the Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk (SAPHIR) study in a crosssectional design. Measurements were BAI, BMI, WHR, WHtR, abdominal subcutaneous and visceral adipose tissue (aSAT and VAT), total body adipose tissue mass, body weight, waist‐ and hip circumference (WC and HC), leptin, adiponectin, high‐density lipoprotein‐cholesterol (HDL‐C), low‐density lipoprotein‐cholesterol (LDL‐C), triglycerides (TG), fasting plasma glucose, fasting plasma insulin, the homeostasis model assessment of insulin resistance (HOMAIR), systolic and diastolic blood pressure.

Results and Conclusions:

BAI was significantly associated with leptin and HC. We conclude that BAI was the best calculator for leptin. BAI was inferior to BMI to predict anthropometric parameters other than HC, adiponectin, blood lipids, parameters of glucose homeostasis, and blood pressure in this cross‐sectional study.     

Skinfolds and coronary heart disease risk factors are more strongly associated with BMI than with the body adiposity index

Objective:

A recent, cross‐sectional analysis of adults found that the hip circumference divided by height^1.5 minus 18 (the body adiposity index, BAI) was strongly correlated (r = 0.79) with percent body fat determined by dual energy X‐ray absorptiometry. The BAI was proposed as a more accurate index of body fatness than BMI. We examined whether BAI was more strongly related, than was BMI and waist circumference, to skinfold thicknesses and levels of various risk factors for coronary heart disease.

Design and Methods:

Cross‐sectional analyses of adults (n = 14,263 for skinfold thickness; n=6291 for fasting lipid levels) in the National Health and Nutrition Examination Survey (NHANES) III, 1988‐1994.

Results:

As compared with BMI and waist circumference, we found that BAI was less strongly associated with the skinfold sum and with risk factor levels. For example, correlations with the skinfold sum were r = 0.79 (BMI) vs. r = 0.70 (BAI) among men, and r = 0.86 (BMI) vs. r = 0.79 (BAI) among women; p < 0.001 for the difference between each pair of correlations. An overall index of the 7 risk factors was also more strongly associated with BMI and waist circumference than BAI in analyses stratified by sex, race‐ethnicity and age. Multivariable analyses indicated that if BMI was known, BAI provided little additional information on risk factor levels.

Conclusions:

Based on the observed associations with risk factor levels and skinfold thicknesses, we conclude that BAI is unlikely to be a better index of adiposity than BMI.     

Lipid Accumulation Product,Visceral Adiposity Index,And Chinese Visceral Adiposity Index As Markers Of Cardiometabolic Risk In Adult Growth Hormone Deficiency Patients: A Cross-Sectional Study

Objective: Adult growth hormone deficiency (AGHD) is associated with cardiometabolic risk factors. Given that cardiovascular disease (CVD) is an important cause of morbidity and mortality in the AGHD population, there is a need for alternative, noninvasive methods of assessing cardiometabolic risk in this population. The Chinese visceral adiposity index (CVAI) is a new marker of visceral fat dysfunction based on age, body mass index (BMI), waist circumference (WC), and metabolic parameters. CVAI is well correlated with insulin resistance (IR) and is better at predicting metabolic syndrome (MS) than BMI and WC. This study aims to examine the reliability of the lipid accumulation product (LAP), visceral adiposity index (VAI), and CVAI as cardiometabolic risk markers in AGHD patients.Methods: A total of 91 patients diagnosed with AGHD were divided into 4 groups according to CVAI quartile. We investigated the relationship between the patients' clinical and biochemical features, cardiometabolic risk assessed by cardiometabolic risk indices, the Framingham and atherosclerotic cardiovascular disease (ASCVD) risk scores, LAP, VAI, and CVAI.Results: The CVAI scores of patients were significantly higher than those of control patients. Increased CVAI significantly correlated with higher BMI, WC, waist-hip ratio (WHR), and triglycerides (TG), Framingham risk score and atherosclerotic cardiovascular disease lifetime risk score (P≤.001), with lower growth hormone (GH) and high-density lipoprotein cholesterol (HDL-C) levels (P≤.001).Conclusion: Our results suggest that CVAI may be a good marker of cardiometabolic risk in AGHD patients and could be used to diagnose CVD development and vascular accidents.Abbreviations: AGHD = adult growth hormone deficiency; ASCVD = atherosclerotic cardiovascular disease; AUROC = area under the receiver operating characteristic curve; BMI = body mass index; CVAI = Chinese visceral adiposity index; CVD = cardiovascular disease; DBP = diastolic blood pressure; GH = growth hormone; GHRT = GH replacement therapy; HDL-C = high-density lipoprotein cholesterol; IGF-1 = insulin like factor-1; IGFBP-3 = insulin like factor binding protein-3; IR = insulin resistance; LAP = lipid accumulation product; MS = metabolic syndrome; SBP = systolic blood pressure; TC = total cholesterol; TG = triglycerides; VAI = visceral adiposity index; WC = waist circumference; WHR = waist-to-hip ratio     

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.     

Is endocan a novel potential biomarker of liver steatosis and fibrosis?

BackgroundStudies that evaluated endocan levels in nonalcoholic fatty liver disease (NAFLD) and liver fibrosis are scarce. We aimed to explore endocan levels in relation to different stages of liver diseases, such as NAFLD, as determined with fatty liver index (FLI) and liver fibrosis, as assessed with BARD score.MethodsA total of 147 participants with FLI≥60 were compared with 64 participants with FLI <30. An FLI score was calculated using waist circumference, body mass index, gamma-glutamyl transferase and triglycerides. Patients with FLI≥60 were further divided into those with no/mild fibrosis (BARD score 0-1 point; n=23) and advanced fibrosis (BARD score 2-4 points; n=124). BARD score was calculated as follows: diabetes mellitus (1 point) + body mass index≥28 kg/m2 (1 point) + aspartate amino transferase/alanine aminotransferase ratio≥0.8 (2 points).ResultsEndocan was independent predictor for FLI and BARD score, both in univariate [OR=1.255 (95% CI= 1.104-1.426), P=0.001; OR=1.208 (95% CI=1.029-1.419), P=0.021, respectively] and multivariate binary logistic regression analysis [OR=1.287 (95% CI=1.055-1.570), P=0.013; OR=1.226 (95% CI=1.022-1.470), P=0.028, respectively]. Endocan as a single predictor showed poor discriminatory capability for steatosis/fibrosis [AUC=0.648; (95% CI=0.568-0.727), P=0.002; AUC= 0.667 (95% CI=0.555-0.778), P=0.013, respectively], whereas in a Model, endocan showed an excellent clinical accuracy [AUC=0.930; (95% CI=0.886-0.975), P<0.001, AUC=0.840 (95% CI=0.763-0.918), P<0.001, respectively].ConclusionsEndocan independently correlated with both FLI and BARD score. However, when tested in models (with other biomarkers), endocan showed better discriminatory ability for liver steatosis/fibrosis, instead of its usage as a single biomarker.     

Blood Pressure in Adolescence and Early Adulthood Related to Obesity and Birth Size

Objective: Obesity is an established risk factor for higher systolic (SBP) and diastolic (DBP) blood pressure in adolescence and early adulthood, but birth size may also have a role. We analyzed the effects of adolescent and adult obesity and birth size on BP in the young adult. Research Methods and Procedures: In a prospective longitudinal study, anthropometric measurements were obtained at birth on 67 boys and 67 girls bom in Boston. Their body mass indices (BMI) and BP were recorded 17 years and 30 years later. Results: For women, adolescent and early adult obesity appeared to be the stronger determinants of higher BP, although smaller head and chest circumferences at birth may also be related. We found some evidence of birth (ponderal index [PI] and head circumference) anthropometric influences on age 17 BP levels in boys. By age 30, body mass variables were the dominant predictors of male BP levels. Female BMI at age 17 was positively correlated with birth adiposity (PI), but BMI at 30 was related only to age 17 BMI. Similarly, male BMI at 17 years was higher for those who weighed more at birth, but BMI at 30 years was again related only to age 17 BMI. Discussion: We conclude that adult weight and weight gain are the major determinants of adult BP.     

Circulating Endocannabinoids and the Polymorphism 385C>A in Fatty Acid Amide Hydrolase (FAAH) Gene May Identify the Obesity Phenotype Related to Cardiometabolic Risk: A Study Conducted in a Brazilian Population of Complex Interethnic Admixture

The dysregulation of the endocannabinoid system is associated with cardiometabolic complications of obesity. Allelic variants in coding genes for this system components may contribute to differences in the susceptibility to obesity and related health hazards. These data have mostly been shown in Caucasian populations and in severely obese individuals. We investigated a multiethnic Brazilian population to study the relationships among the polymorphism 385C>A in an endocannabinoid degrading enzyme gene (FAAH), endocannabinoid levels and markers of cardiometabolic risk. Fasting plasma levels of endocannabinoids and congeners (anandamide, 2-arachidonoylglycerol, N-oleoylethanolamide and N-palmitoylethanolamide) were measured by liquid chromatography-mass spectrometry in 200 apparently healthy individuals of both genders with body mass indices from 22.5 ± 1.8 to 35.9 ± 5.5 kg/m² (mean ± 1 SD) and ages between 18 and 60 years. All were evaluated for anthropometric parameters, blood pressure, metabolic variables, homeostatic model assessment of insulin resistance (HOMA-IR), adiponectin, leptin, C-reactive protein, and genotyping. The endocannabinoid levels increased as a function of obesity and insulin resistance. The homozygous genotype AA was associated with higher levels of anandamide and lower levels of adiponectin versus wild homozygous CC and heterozygotes combined. The levels of anandamide were independent and positively associated with the genotype AA position 385 of FAAH, C-reactive protein levels and body mass index. Our findings provide evidence for an endocannabinoid-related phenotype that may be identified by the combination of circulating anandamide levels with genotyping of the FAAH 385C>A; this phenotype is not exclusive to mono-ethnoracial populations nor to individuals with severe obesity.     

Comparative evaluation of obesity-related parameters in junior sumo wrestlers and children with obesity

[Purpose]Exercise is a key factor in preventing obesity and metabolic syndrome. Sumo wrestlers increase their body size from childhood for athletic advantage; however, the risk of metabolic syndrome in junior sumo wrestlers is undetermined. Preventive measures against pediatric obesity should be initiated during childhood to prevent obesity in adulthood, considering its high global incidence. We comparatively evaluated the risk factors for metabolic syndrome in junior sumo wrestlers and children with obesity.[Methods]We enrolled 70 male children (age 9–17 years [sumo group, n = 14] and 9–14 years [other sports and non-exercise groups, n = 28 each]) and evaluated their anthropometric parameters (height, weight, body mass index z-score, obesity rate, waist circumference, waist to height ratio) and hematological parameters (total, low-density, high-density, and non-high-density lipoprotein-cholesterol; triglycerides; plasma glucose, and glycated hemoglobin levels).[Results]The BMI z-score, obesity rate, waist circumference (p < 0.05, along with the non-exercise group), and systolic blood pressure were significantly higher and the high-density cholesterol level was lower in the sumo group than in the other sports group (p < 0.05). The waist to height ratio was significantly higher in the non-exercise group than in the other sports group (p < 0.05). No significant difference was found in other blood lipid, plasma glucose (significantly lower level than the reference range in the sumo group, p < 0.05), and glycated hemoglobin (within the reference range in all groups) levels among the three groups.[Conclusion]Junior sumo wrestlers had a larger body size and higher blood pressure than children with obesity who exercised regularly. This provides direction for future research into targeted preventive interventions against metabolic syndrome for junior sumo wrestlers with large body size.     

The Body Adiposity Index (Hip Circumference ÷ Height1.5) Is Not a More Accurate Measure of Adiposity Than Is BMI,Waist Circumference,or Hip Circumference

Based on cross‐sectional analyses, it was suggested that hip circumference divided by height^1.5 ?18 (the body adiposity index (BAI)), could directly estimate percent body fat without the need for further correction for sex or age. We compared the prediction of percent body fat, as assessed by dual‐energy X‐ray absorptiometry (PBF_DXA), by BAI, BMI, and circumference (waist and hip) measurements among 1,151 adults who had a total body scan by DXA and circumference measurements from 1993 through 2005. After accounting for sex, we found that PBF_DXA was related similarly to BAI, BMI, waist circumference, and hip circumference. In general, BAI underestimated PBF_DXA among men (2.5%) and overestimated PBF_DXA among women (4%), but the magnitudes of these biases varied with the level of body fatness. The addition of covariates and quadratic terms for the body size measures in regression models substantially improved the prediction of PBF_DXA, but none of the models based on BAI could more accurately predict PBF_DXA than could those based on BMI or circumferences. We conclude that the use of BAI as an indicator of adiposity is likely to produce biased estimates of percent body fat, with the errors varying by sex and level of body fatness. Although regression models that account for the nonlinear association, as well as the influence of sex, age, and race, can yield more accurate estimates of PBF_DXA, estimates based on BAI are not more accurate than those based on BMI, waist circumference, or hip circumference.     

A better index of body adiposity

Obesity is a growing problem in the United States and throughout the world. It is a risk factor for many chronic diseases. The BMI has been used to assess body fat for almost 200 years. BMI is known to be of limited accuracy, and is different for males and females with similar %body adiposity. Here, we define an alternative parameter, the body adiposity index (BAI = ((hip circumference)/((height)(1.5))-18)). The BAI can be used to reflect %body fat for adult men and women of differing ethnicities without numerical correction. We used a population study, the "BetaGene" study, to develop the new index of body adiposity. %Body fat, as measured by the dual-energy X-ray absorptiometry (DXA), was used as a "gold standard" for validation. Hip circumference (R = 0.602) and height (R = -0.524) are strongly correlated with %body fat and therefore chosen as principal anthropometric measures on which we base BAI. The BAI measure was validated in the "Triglyceride and Cardiovascular Risk in African-Americans (TARA)" study of African Americans. Correlation between DXA-derived %adiposity and the BAI was R = 0.85 for TARA with a concordance of C_b = 0.95. BAI can be measured without weighing, which may render it useful in settings where measuring accurate body weight is problematic. In summary, we have defined a new parameter, the BAI, which can be calculated from hip circumference and height only. It can be used in the clinical setting even in remote locations with very limited access to reliable scales. The BAI estimates %adiposity directly.     

Infrequent Breakfast Consumption Is Associated with Higher Body Adiposity and Abdominal Obesity in Malaysian School-Aged Adolescents

Unhealthy dietary pattern increases the risk of obesity and metabolic disorders in growing children and adolescents. However, the way the habitual pattern of breakfast consumption influences body composition and risk of obesity in adolescents is not well defined. Thus, the aim of the present study was to assess any associations between breakfast consumption practices and body composition profiles in 236 apparently healthy adolescents aged 12 to 19 years. A self-administered questionnaire on dietary behaviour and lifestyle practices and a dietary food frequency questionnaire were used. Body composition and adiposity indices were determined using standard anthropometric measurement protocols and dual energy χ-ray absorptiometry (DXA). Mean age of the participants was 15.3±1.9 years. The majority of participants (71.2%) fell in the normal body mass index (BMI) ranges. Breakfast consumption patterns showed that only half of the participants (50%) were consuming breakfast daily. Gender-specific multivariate analyses (ANCOVA) showed that in both boys and girls, those eating breakfast at least 5 times a week had significantly lower body weight, body mass index (BMI), BMI z-scores, waist circumference, body fat mass and percent body fat (%BF) compared to infrequent breakfast eaters, after adjustment for age, household income, pubertal status, eating-out and snacking practices, daily energy intakes, and daily physical activity levels. The present findings indicate that infrequent breakfast consumption is associated with higher body adiposity and abdominal obesity. Therefore, daily breakfast consumption with healthy food choices should be encouraged in growing children and adolescents to prevent adiposity during these critical years of growth.     

Gly40Ser Polymorphism of the Glucagon Receptor Gene Is Associated with Central Adiposity in Men

Objective: To study the association between the Gly40Ser polymorphism of the glucagon receptor gene (GCG‐R) and central adiposity. Research Methods and Procedures: Data from 985 working men (The Olivetti Heart Study) examined in 1994 were used in a cross‐sectional design. A complete anthropometry was performed; body mass index and waist circumference were taken as measures of total and central adiposity, respectively. The GCG‐R Gly40Ser polymorphism was characterized. Biochemical variables linked to energy metabolism were measured. Results: The GCG‐R Gly40Ser variant was present in 37 individuals only in heterozygous form and was significantly associated with anthropometric indices of central adiposity, accounting for age and body mass (odds ratio for waist circumference > 94 cm; 95% confidence interval: 3.14, 1.26 to 7.81), whereas no difference between the two groups was found with regard to biochemical indices of insulin resistance or plasma leptin levels. Discussion: The Gly40Ser polymorphism of the GCG‐R gene is associated with central adiposity independently from total body mass in men.     

The Associations between Anthropometric Indices and Obstructive Sleep Apnea in a Korean Population

Background

Obesity is a major risk factor for the development of obstructive sleep apnea (OSA). Although clinical and epidemiological studies have shown that OSA and obesity are strongly associated, few Asian studies have examined the associations between anthropometric obesity indices and OSA, especially in the Korean population. The purpose of this study was to evaluate the influence of anthropometric obesity indices on OSA in a Korean population.

Methods

Anthropometric indices, including neck circumference, waist circumference, and body mass index, were assessed in 383 consecutive subjects with suspected OSA.

Results

Of the 383 subjects assessed, 316 (82.5%) were diagnosed with OSA. Neck circumference (r = 0.518), waist circumference (r = 0.570), and body mass index (r = 0.512) were correlated with the apnea-hypopnea index (p<0.001, for all). After adjusting for age, sex, alcohol consumption, and smoking, a logistic regression model showed that neck circumference [odds ratio (OR), 1.414; p<0.001)], waist circumference (OR, 1.114; p<0.001), and body mass index (OR, 1.364; p<0.001) were associated with OSA. The linear regression model showed that neck circumference (β = 3.748, p<0.001), waist circumference (β = 1.272, p<0.001), and body mass index (β = 3.082, p<0.001) were associated with apnea-hypopnea index. The cut-off values for predicting OSA were determined as 34.5 cm for neck circumference, 76.5 cm for waist circumference, and 23.05 kg/m² for body mass index for females, and 38.75 cm for neck circumference, 88.5 cm for waist circumference, and 24.95 kg/m² for body mass index for males.

Conclusion

Increased anthropometric indices were significantly associated with the presence and severity of OSA in a Korean population. In addition, this study demonstrated the cut-off values for body mass index, waist circumference, and neck circumference for increased OSA risk.     

Brief communication: Body mass index,body adiposity index,and percent body fat in asians

Human obesity is a growing epidemic throughout the world. Body mass index (BMI) is commonly used as a good indicator of obesity. Body adiposity index (BAI = hip circumference (cm)/stature (m)^1.5 ? 18), as a new surrogate measure, has been proposed recently as an alternative to BMI. This study, for the first time, compares BMI and BAI for predicting percent body fat (PBF; estimated from skinfolds) in a sample of 302 Buryat adults (148 men and 154 women) living in China. The BMI and BAI were strongly correlated with PBF in both men and women. The correlation coefficient between BMI and PBF was higher than that between BAI and PBF for both sexes. For the linear regression analysis, BMI better predicted PBF in both men and women; the variation around the regression lines for each sex was greater for BAI comparisons. For the receiver operating characteristic (ROC) analysis, the area under the ROC curve for BMI was higher than that for BAI for each sex, which suggests that the discriminatory capacity of the BMI is higher than the one of BAI. Taken together, we conclude that BMI is a more reliable indicator of PBF derived from skinfold thickness in adult Buryats. Am J Phys Anthropol 152:294–299, 2013. © 2013 Wiley Periodicals, Inc.     

Sumac fruit supplementation improve glycemic parameters in patients with metabolic syndrome and related disorders: A systematic review and meta-analysis

BackgroundMetabolic syndrome (MetS) is the one of the main causes of mortality worldwide. Several randomized controlled trials (RCTs) have revealed the beneficial effects of sumac (Rhus coriaria) on cardiometabolic risk factors. However, the entirety of the evidence has yet to be summarized in a systematic review.ObjectiveThe aim of this systematic review and meta-analysis was to evaluate the effects of sumac on several cardiometabolic risk factors in patients with MetS and related disorders.MethodsWe reviewed Medline, Scopus, Web of Science and Cochrane CENTRAL for RCTs published from inception to December 2020 evaluating the impact of sumac in adults with MetS or related disorders. Outcome measures included anthropometric measures, glycemic indices, blood lipids, blood pressure and liver enzymes. Pooled effect sizes were reported as standard mean differences (SMDs) and 95% confidence intervals (CIs). Trials were pooled using a random effects model.ResultsNine studies enrolling 526 participants met the inclusion criteria for this meta-analysis. Our results indicate that sumac intake significantly decrease fasting blood sugar (FBS) (SMD: −0.28; 95% CI: −0.54, -0.02; I² = 00.0%), insulin (SMD: −0.67; 95% CI: −0.99, -0.36; I² = 03.7%), and insulin resistance (measured through the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)) (SMD: −0.79; 95% CI: −1.24, -0.34; I² = 50.1%). Sumac intake did not have a significant impact on weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist to hip ratio (WHR), HbA1c, total cholesterol (TC), triglycerides (TG), high density lipoproteins (HDL), low density lipoprotein (LDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), aspartate transaminase (AST) and alanine transaminase (ALT).ConclusionSumac, as an adjuvant therapy, may decrease serum levels of FBS, insulin and HOMA-IR. However, due to high heterogeneity in the included studies, these findings must be interpreted with great caution. Larger, well-designed placebo-controlled clinical trials are still needed to further evaluate the capacity of sumac as a complementary treatment to control MetS risk factors.     

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.