Android vs gynoid fat tissue characteristics -
Results There were no differences in weight, body mass index, and body fat percentage between tertiles. Values of HOMA-IR were significantly increased in the 2 higher tertiles mean [SD], tertile 2, 2. Conclusions Android fat distribution is associated with an increased insulin resistance in obese children and adolescents.
An android to gynoid fat ratio based on dual-energy x-ray absorptiometry measurements is a useful and simple technique to assess distribution of body fat associated with an increased risk of insulin resistance.
The rising prevalence of childhood obesity represents an early risk factor for the development of metabolic and cardiovascular diseases in adults.
Among obese children and adolescents, there is also an increased number of cases of type 2 diabetes mellitus, which was once considered as an adult-onset disease. Since Vague, 1 it has been well established that the development of insulin resistance and the risk of cardiovascular diseases are associated with excess body fat in abdominal rather than in peripheral fat depots.
The visceral fat area has been shown to be correlated with glucose intolerance 3 , 4 independently of total fat mass and subcutaneous abdominal adipose tissue. A high intramyocellular lipid deposition has been shown to occur early during childhood and adolescence in association with peripheral insulin resistance.
Dual-energy x-ray absorptiometry DXA measurements have been used in several studies to assess regional body fat distribution in children 12 - 14 and the association with cardiovascular risk factors.
Little attention has been paid to the association between gynoid fat storage and insulin resistance in obese children. We hypothesized that children with a high android to gynoid fat ratio would exhibit an increased insulin resistance. Participants in this study were 66 obese children and adolescents 31 girls and 35 boys and their parents coming to the Department of Pediatrics, University Hospital, Clermont-Ferrand, France, for medical consultation.
Parents and children who agreed to take part to the study signed an informed consent. The experimental protocol of this study was approved by the local ethics committee Comité de Protection des Personnes, Sud Est IV. Children included in this study were higher than the 95th percentile of body mass index BMI for age and sex defined by the International Obesity Task Force.
Medical examination and anthropometric measurements were performed for each subject by a pediatrician. Body mass was measured to the nearest 0. Height was measured with a standing stadiometer and recorded with a precision of 1 mm.
Body mass index was calculated as weight in kilograms divided by height in meters squared. Body mass index and waist circumference z scores were calculated for age and sex reference values.
All subjects were free of medication known to affect energy metabolism and none of the subjects had evidence of significant disease, non—insulin-dependent diabetes mellitus, or other endocrine disease. Body composition was determined by DXA scan QDR x-ray bone densimeter; Hologic, Waltham, Massachusetts and version 9.
Children were asked to lie down in a supine position on the DXA table and to stay still until the end of the scanning procedure. They were also instructed to keep their arms separated from their trunk and their legs separated from one another. Percentage of abdominal fat was determined manually by an experienced experimenter by drawing a rectangular box around the region of interest between vertebral bodies L1 and L4.
Gynoid fat deposition was assessed by lower limb fat percentage. Android to gynoid fat ratio was determined by using fat percentage in lower limbs and in the abdominal region.
To test the hypothesis that an android to gynoid fat ratio is associated with an impairment of insulin sensitivity, study subjects were grouped into tertiles. We used tertiles to ensure a number of subjects in each subgroup sufficient to give meaningful results.
Blood samples were drawn between 8 AM and 10 AM in a fasted state from an antecubital vein. The plasma glucose concentration was determined by enzymatic methods Modular P; Roche Diagnostics, Meylan, France. Plasma insulin concentration was assayed by a chemiluminescent enzyme immunoassay on an Immulite Diagnostic Products Corporation, Los Angeles, California.
Two indexes of insulin resistance were calculated from glucose and insulin concentrations. Results are expressed as mean SD. Normality of the distribution was checked with the Kolmogorov-Smirnov test for each variable.
Dependent variables were compared between the 3 groups by using a 1-way analysis of variance. Android to gynoid fat ratio and abdominal fat percentage were similar between boys and girls in the 3 groups. Hence, boys and girls were grouped together in each tertile.
Spearman correlation coefficients were used to describe associations between continuous variables. We also used a multiple stepwise regression to explain the variance of HOMA-IR values.
Age, waist circumference z score, BMI, body fat percentage, and the android to gynoid fat ratio were included as independent variables.
All statistical analyses were carried out with Statview software, version 5. Descriptive results of the population are presented for boys and girls in Table 1. Body mass, percentage of body fat, and lean body mass were similar in the 3 tertiles. Tertiles were also similar for the number of boys and girls.
There was no significant difference for percentage of fat mass in lower limbs between tertiles. Mean SD HOMA-IR values were significantly higher in tertiles 2 2. Mean SD quantitative insulin-sensitivity check index values were also significantly higher in tertile 1 0.
Differences were not significant between tertiles 2 and 3. Results are shown in Figure 1 and Figure 2. Mean SD homeostasis model of insulin resistance HOMA-IR index values in tertiles of android to gynoid fat ratio.
Mean SD quantitative insulin-sensitivity check index QUICKI values in tertiles of android to gynoid fat ratio. Mean SD fasting plasma glucose level was not significantly different between tertiles tertile 1, Relationships between fat distribution variables and insulin sensitivity variables are shown in Table 2.
Neither body fat percentage nor lower limbs fat percentage were significantly correlated with insulin sensitivity variables or glucose and insulin concentrations. None of the fat distribution variables had significant correlation with fasting glucose concentration.
The multiple stepwise regression showed that age and the android to gynoid fat ratio were significant predictors of HOMA-IR value β coefficients were 0. Adjusted R 2 was 0. Body mass index, waist circumference z score, and body fat percentage were not significant predictors of HOMA-IR value.
Our hypothesis was that a preferential fat storage at the abdominal level rather than in the lower limbs would be associated with increased insulin resistance. To this aim, we calculated a simple index of android to gynoid fat distribution as a ratio between percentage of abdominal fat and percentage of lower limbs fat based on DXA measurements.
Insulin resistance was estimated by using simple indexes based on fasting plasma glucose and insulin concentrations. Indexes such as HOMA-IR and the quantitative insulin-sensitivity check index calculated from fasting samples have been shown to be valid to assess insulin resistance during puberty when compared with direct measurement with a glucose clamp.
Furthermore, insulin resistance was associated with abdominal adiposity without distinction between subcutaneous and visceral fat depots. However, although HOMA-IR values increased from the lowest tertile to tertiles 2 and 3, whereas there was no significant difference between tertiles 2 and 3, a linear regression between the android to gynoid fat ratio and HOMA-IR value did not provide a threshold value of android to gynoid fat ratio above which obese children have an increased risk of insulin resistance.
Indeed, in the present study, there was no significant association between percentage of body fat and insulin resistance. Previous studies have shown in young subjects that the degree of obesity is associated with a worsening of all the components of the metabolic syndrome, including insulin resistance.
Despite a similar degree of obesity, a lower prevalence of impaired glucose tolerance and type 2 diabetes have been reported in European than in American children. Hence, together with a reduced number of subjects with severe obesity in comparison with other studies, only mild alterations of insulin sensitivity may explain the lack of association between percentage of body fat and insulin resistance.
Since , DEXA has been used to measure fat mass and BMD at the Sports Medicine Unit, Umeå University, Sweden. By the end of , DEXA scans had been performed on women and men. The VIP is a community-based observational cohort study focusing on cerebrovascular disease and diabetes.
The study began in in the county of Västerbotten, Sweden, and has been described in detail previously In brief, at ages 30, 40, 50, and 60 yr, all Västerbotten residents are invited to receive a standardized health examination at their primary care centers.
At the examination, information was gathered about lifestyle and psychosocial conditions, an oral glucose tolerance test was performed after an 8-h fast, and venous and capillary blood was obtained. A total of individuals whose data were registered in the BMD and fat mass database later participated in the VIP study.
Fat mass was assessed using DEXA scans GE Lunar, Madison, WI. Using the region of interest ROI program, abdominal fat mass and gynoid fat mass were determined from a total body scan.
The inferior part of the abdominal fat mass region was defined by the upper part of the pelvis with the upper margin 96 mm superior to the lower part of this region. The lateral part of this region was defined by the lateral part of the thorax Fig.
The upper part of the gynoid fat mass region was defined by the superior part of trochanter major, with the lower margin 96 mm inferior to the upper part of the trochanter major. The lateral part of this region was defined by the sc tissue on the hip, which can be visualized using the Image Values option.
One investigator P. performed all of the analyses. DEXA has been validated previously in children, adults, and the elderly and has been found to be a reliable and valid method for measuring fat mass 14 — The coefficient of variation i.
The equipment was calibrated each day using a standardized phantom to detect drifts in measurements, and equipment servicing was performed regularly. Two different machines were used for the measurements. From —, a Lunar DPX-L was used, and from —, a Lunar-IQ was used. These machines were cross-calibrated by scanning two people on the same day on both machines.
Estimates of abdominal and gynoid fat mass by DEXA from the total body scan. Blood pressure was measured using a mercury-gauge sphygmomanometer. Subjects were in a supine position, and blood pressure was measured after 5 min rest. An oral glucose tolerance test was performed on fasting volunteers using a g oral glucose load The plasma glucose PG concentration millimoles per liter in capillary plasma was measured 2 h after glucose administration using a Reflotron bench-top analyzer Roche Molecular Biochemicals, GmbH, Mannheim, Germany.
Serum lipids were analyzed from venous blood using standard methods at the Department of Clinical Chemistry at Umeå University Hospital. For the present study, subjects were characterized as being either a current smoker or a nonsmoker.
Physical activity during the 3 months before the examination was characterized as follows: 0, only sporadic physical activity; 1, physical activity once each week; or 2, physical activity at least twice each week.
Informed consent was given by all the participants, and the study protocol was approved by the Ethical Committee of the Medical Faculty, Umeå University, Umeå, Sweden.
Data are presented as the mean ± sd unless indicated otherwise. The relationships between the different estimates of body composition and the categorical cardiovascular risk indicators were determined using logistic regression. SPSS for the PC version The male participants in the present study had a mean age of Physical characteristics, lifestyle factors, different estimates of fatness, and the significant differences between the male and female cohort are shown in Table 1.
P values are comparing the male and female cohort. BP, Blood pressure. Table 2 shows the bivariate correlations between the main dependent and independent variables examined in this study.
Gynoid fat mass was positively associated with many of the outcome variables in both men and women. As shown in Fig. Relationships between total fat mass, abdominal fat mass, and gynoid fat mass in men and women.
Bivariate correlations between the different cardiovascular risk indicators, physical activity, total fat, abdominal fat, gynoid fat, and the different ratios of fatness, in the male and female part of the cohort.
Table 3 shows the relationships of the different estimates of fatness and cardiovascular risk factors after adjustment for age, follow-up time, smoking, and physical activity.
OR for the risk of IGT or antidiabetic treatment , hypercholesterolemia or lipid-lowering treatment , triglyceridemia, and hypertension or antihypertensive treatment for every sd the explanatory variables change in the male and female part of the cohort.
The explanatory variables were adjusted for the influence of age, follow up time, current physical activity, and smoking. Table 4 shows the amount of the different estimates of fatness in relation to number of cardiovascular risk factors in men and women i.
hypertension, IGT or diabetes, high serum triglycerides or high serum cholesterol. Data are presented in the men and women according to number of risk factors impaired FPG, hypertension, hyperlipidemia, and obesity for CVD.
Means, sd , and P values are presented. R, Risk factor. Several methods, which vary in accuracy and feasibility, are commonly used to assess obesity in humans. In the present study, we used DEXA to investigate the relationship between regional adiposity and cardiovascular risk factors in a large cohort of men and women.
Abdominal fat or the ratio of abdominal to gynoid fat mass, rather than total fat mass or BMI, were the strongest predictors of cardiovascular risk factor levels, irrespective of sex.
Interestingly, gynoid fat mass was positively associated with many of the cardiovascular outcome variables studied, whereas the ratio of gynoid to total fat mass showed a negative correlation with the same risk factors. Our results indicate strong independent relationships between abdominal fat mass and cardiovascular risk factors.
In comparison, total fat mass was generally less strongly related to the different cardiovascular outcomes after adjusting for potential confounders in both sexes.
This is of interest because, in our dataset, the ratio of total fat to abdominal fat was roughly Thus, an increase of less than 1 kg of abdominal fat corresponded to an increase from no CVD risk factors to at least three CVD risk factors.
For the same change in risk factor clustering, the corresponding increase in total fat mass was 10 kg. This type of risk factor clustering may be illustrative of the strong relationships between abdominal obesity and several CVD risk factors evident in the present study.
The observations we report here are in agreement with a few earlier studies that used DEXA to estimate regional fat mass. Van Pelt et al. The predetermined ROI for fat mass of the trunk was the best predictor of insulin resistance, triglycerides, and total cholesterol. In another report, Wu et al.
Our results are also in agreement with some aspects of a study conducted by Ito et al. They concluded that regional obesity measured by DEXA was better than BMI or total fat mass in predicting blood pressure, dyslipidemia, and diabetes mellitus.
Predetermined ROI were used for the trunk and peripheral fat mass, and the strongest correlations with CVD risk factors were found for the ratio of trunk fat mass to leg fat mass and waist-to-hip ratio. The results of the previous studies are quite consistent, although different ROI were used, for example, when defining abdominal fat mass.
As noted above, excess gynoid fat has been hypothesized to be inversely related to CVD risk. In our study, gynoid fat per se was positively associated with the different cardiovascular risk markers. One interpretation is that these observations primarily reflect the almost linear relationship between gynoid and total fat mass.
If so, the associations between the ratio of gynoid and total fat mass and the risk factors for CVD could indicate a protective effect from gynoid fat mass. Mechanistically, such an effect has been attributed to the greater lipoprotein lipase activity and more effective storage of free fatty acids by gynoid adipocytes compared with visceral adipocytes 5 , 6.
Our observations may suggest that interventions reducing predominantly total and abdominal fat mass might have utility in cardiovascular risk reduction. Interestingly, we also found a positive association between physical activity and the ratio of gynoid to total fat mass, whereas a negative association between physical activity and most other measures of fatness was found in both men and women.
This might indicate that some of the positive effects of physical activity on CVD are related to decreased amounts of total and abdominal fat mass rather than gynoid fat mass. However, in observational cross-sectional studies such as ours, it is impossible to establish whether the different estimates of fatness are causally related with the different cardiovascular risk factors and physical activity.
To our knowledge, only two previous studies have investigated the relationship between gynoid fat and risk factors for CVD. Caprio et al. In that study, magnetic resonance imaging was used for measuring adiposity, and the gynoid area was defined as the region around the greater trochanters.
In the second study, Pouliot et al. An inverse association was demonstrated between femoral neck adipose tissue and serum triglycerides in the obese men. We cannot explain the difference between these findings and ours.
This study has several limitations. Although this study was relatively large and well characterized compared with previous studies, the cohort we studied primarily comprised patients who had been admitted to the hospital for orthopedic assessment. Moreover, because this was an observational cross-sectional study, one cannot be certain of the causal connection between abdominal fat mass and cardiovascular risk factors.
Additionally, the measurements of regional body fat mass and cardiovascular risk factors were not undertaken simultaneously, raising the possibility that adiposity traits changed between the measurement time points.
Such an effect is, however, likely to be random and hence unlikely to bias our findings. Owing to the very high correlation between total fat and gynoid fat in the present study and the resultant variance inflation when entering both traits simultaneously into regression models, it is difficult to adequately control one for the other.
As a compromise, we expressed these two variables as a ratio. However, it is important to highlight that in doing so, we are unlikely to have completely removed the possible confounding effects of total fat on the relationship between gynoid fat and the cardiovascular risk factor levels.
Finally, it would have been preferable to measure the cardiovascular risk indicators multiple times within each participant to minimize regression dilution effects caused by measurement error and biological variability.
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World J Hepatol. Keywords: nonalcoholic fatty liver disease, dual-energy X-ray absorptiometry, android fat, gynoid fat, risk. Citation: Yang L, Huang H, Liu Z, Ruan J and Xu C Association of the android to gynoid fat ratio with nonalcoholic fatty liver disease: a cross-sectional study.
Received: 09 February ; Accepted: 27 April ; Published: 15 May Copyright © Yang, Huang, Liu, Ruan and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with these terms. Open supplemental data Export citation EndNote Reference Manager Simple TEXT file BibTex. Check for updates. ORIGINAL RESEARCH article. Introduction Nonalcoholic fatty liver disease NAFLD is a progressive liver condition that can manifest from simple steatosis to steatohepatitis, fibrosis, and even hepatocellular cancer 1 , 2.
Definitions of NAFLD and fibrotic NASH The Fatty Liver Index FLI is a simple and accurate predictor of hepatic steatosis in the general population 19 , which had already been validated by magnetic resonance spectroscopy 20 , Dual-energy X-ray absorptiometry Dual-energy X-ray absorptiometry DXA was applied to estimate body adipose amounts.
Results Baseline characteristics of the study population A total of 10, participants Table 1.
Boosting energy levels Endocrine Disorders gynoud 22Advanced Fat Burner number: Cite tynoid article. Metrics hissue. To investigate the association between different body Android vs gynoid fat tissue characteristics distribution and different sites of BMD in male and female populations. Use the National Health and Nutrition Examination Survey NHANES datasets to select participants. The weighted linear regression model investigated the difference in body fat and Bone Mineral Density BMD in different gender. These chqracteristics Advanced Fat Burner Androidd broken yynoid into Android vs gynoid fat tissue characteristics types:. This fat accumulates around gynoif central trunk region. It can also include charaacteristics and upper arms. Holding fat primarily in Improving skin elasticity arms and chest area can increase insulin chracteristics. This means your body will not be able to transport and use up extra sugar for energy, versus leaving it free floating in the blood Diabetes. This can more readily support processes that cause heart disease, diabetes, hormonal imbalances, sleep apnea and more. The reason that we see so many more risk factors for disease in this type of fat storage can be because this fat directly correlates with a higher amount of visceral fat.Android vs gynoid fat tissue characteristics -
Obese individuals vary in their body fat distribution, their metabolic profile and the degree of associated cardiovascular and metabolic risks. There is substantial evidence providing that fat distribution is a better predictor of cardiovascular disease than the degree of obesity [1] — [5].
An excess of abdominally located fat, even without manifestations of obesity, is associated with metabolic disturbances that indicate an increased risk of atherogenesis and of higher morbidity and mortality, possible due to inherent characteristics of abdominal adipocytes [3] , [4] , [6] , [7].
Thus, regional fat distribution rather than overall fat volume has been considered to be more important in understanding the link between obesity and metabolic disorders.
Among fat depots, fat accumulation in the abdominal area has a greater risk of developing diabetes and future cardiovascular events than the peripheral area [8]. There are differences between adipose tissue present in subcutaneous areas and in the abdominal cavity.
These include anatomical, cellular, molecular, physiological, clinical and prognostic differences [2] , [7] , [9]. Many studies have suggested that visceral adipose tissue VAT compared with subcutaneous adipose tissue SAT is more cellular, vascular and innervated with a larger number of inflammatory and immune cells, lesser preadipocyte differentiating capacity, and a greater percentage of large adipocytes [9].
Therefore, fat distribution rather than its magnitude may be more significant in understanding metabolic risk, particularly the varying impacts of VAT and SAT. In a different context, truncal fat depot can be partitioned into upper body android or central and lower body gynoid or peripheral area.
Empirically, android or central fat deposition is known to be more associated with cardiometabolic risk than gynoid or peripheral fat deposition. Many studies with simple anthropometric measurements such as waist circumference or waist-to-hip ratio have given more weight to the central adiposity [6] , [10] — [12].
More advanced technology with computed tomography CT or dual energy X-ray absorptiometry DXA has been used to measure the regional fat mass. CT has an advantage in distinguishing between VAT and SAT while DXA can measure compartment body compositions such as android and gynoid area.
Metabolic syndrome MS increases cardiovascular morbidity and mortality, and all cause of mortality [13]. MS also increases the risk of developing diabetes mellitus with its components representing major risk factors for impaired glucose metabolism [14].
Obesity, particularly abdominal obesity, is a key feature of a cluster of atherothrombotic and inflammatory abnormalities associated with MS [15]. There is substantial evidence linking central obesity with cardiovascular disease and the other MS components as well as its critical role in the etiological cascade leading to full-blown manifestations of MS.
Thus, assessment of fat distribution may be important in the clinical evaluation of cardiometabolic risks. However, there has been no comprehensive study on fat distribution related risks particularly in elderly Asian populations whose physical and metabolic characteristics differ from those of Caucasians.
We evaluated the association between clustering of components constituting MS and the whole and regional body composition measured by comprehensive methods including DXA and CT in a community-based cohort study of elderly men and women.
The effects of metabolic or inflammatory markers were also evaluated. This study was part of the Korean Longitudinal Study on Health and Aging KLoSHA , which is a cohort that began in and consisted of Korean subjects aged over 65 years men and women recruited from Seongnam city, one of the satellites of Seoul Metropolitan district.
The study population and part of the method of measurements for the cohort have been published previously [16]. The current study subjects were from the KLoSHA. Of these subjects, 21 declined the DXA or CT scans and 14 were unable to undergo the examination due to their poor physical condition.
In total, participants Pertinent demographic and other characteristics of the selected subjects were similar to the cohort population. Among study participants, Smoking and alcohol status was divided into three categories; current smoker, ex-smoker, or never smoker, and current drinker, ex-drinker, or never drinker, respectively.
Physical activity was divided into two categories; none or regular exercise. Regular exercise was defined as exercising more than three times a week each session should be at least 30 min long.
The homeostasis model assessment of the insulin resistance HOMA-IR was calculated as reported previously [17]. Several metabolic markers including adiponectin and high-sensitivity CRP hsCRP which are known to be associated with MS were measured. Detailed information about measurement method was published previously [16].
All the assessments were performed at Seoul National University Bundang Hospital SNUBH. This was approved by the Institutional Review Board of SNUBH. The written, informed consent for subjects undergoing CT procedure to inform them of radiation hazard and possible contrast toxicity was obtained from each individual as a routine procedure.
DXA measures were recorded using a bone densitometer Lunar, GE Medical systems, Madison, WI. DXA is quantified by body tissue absorption of photons that are emitted at two energy levels to resolve body weight into bone mineral, lean and fat soft tissue masses.
In vivo precision for body composition measurements using DXA was proven previously [19]. In this study, precision was excellent for lean tissue mass root mean square of 0. The regions of interest ROI for regional body composition were defined using the software provided by the manufacturer Figure 1A :.
CT scans were obtained using a 64—detector Brilliance; Philips Medical Systems, Cleveland, Ohio. All patients were placed in the supine position and were scanned from L to L5-S1 intervetebral disc level. The tube voltage was kVp for 64 detector row scanner. Effective tube current-time product generally ranged between 20—50 mAs.
The images were reconstructed with 5 mm thickness with 5 mm-intervals. VAT was defined as fat area confined to the abdominal wall musculature. After subtracting VAT from total fat area, the remainder was defined as SAT Figure 1B.
Detailed information about the cardiac CT angiography protocol was described previously [21]. Briefly, CT angiography was performed with a slice multidetector-row cardiac CT scanner Brilliance 64; Philips Medical Systems, Best, The Netherlands , and a standard scanning protocol was used [21].
All scans were analyzed independently in a blind fashion using a three-dimensional workstation Brilliance; Philips Medical Systems. Each lesion was identified using a multiplanar reconstruction technique and maximum intensity projection of the short axis, in two-chamber and four-chamber views.
Coronary artery lesions were analyzed according to the modified American Heart Association classification [22]. The demographic and laboratory characteristics of subjects were compared using Student's t test or a Chi-square test according to the presence of MS. Correlations between variables were analyzed using Pearson's correlation.
Multiple regression analysis was used to determine the independent effect of body composition parameters on clustering of five components of MS. Anthropometric, body composition, and metabolic characteristics of the study population stratified by sex are provided in Table S1.
Mean age ± SD of study subjects was BMI ± SD was Men were more likely to have unfavorable lifestyle habits including smoking and alcohol consumption, nevertheless the proportion of participants who engaged in regular exercise was significantly higher in men than in women.
The concentrations of HDL- and LDL-cholesterol, and adiponectin were significantly greater in women whereas fasting plasma glucose concentration were higher in men. There was no significant difference in the concentration of triglycerides, fasting insulin, A1C, and hsCRP levels between men and women.
Whole body muscle mass measured by DXA was significantly greater in men. Whole body fat mass, android and gynoid fat amount measured by DXA, and SAT quantified by CT were significantly higher in women than men.
Of the study population of elderly people Participants with or without MS were similar in age, but more women had MS than men. Systolic and diastolic blood pressure, BMI, and waist circumference were significantly higher in participants with MS compared to without MS.
In terms of specific adiposity measurements, whole body fat mass, total android and gynoid tissue, android and gynoid fat amount measured by DXA, and VAT and SAT quantified by CT scan were all greater in participants with MS compared to without MS.
The concentrations of triglycerides, and HDL-cholesterol, fasting glucose and insulin, and A1C levels, and HOMA-IR were significantly higher in participants with MS than without MS.
Circulating adiponectin levels were significantly lower in participants with MS, whereas hsCRP level was not significantly different between two groups. In terms of lifestyle habits, the proportion of subjects with cigarette smoking and alcohol consumption were significantly higher in MS.
However participants with MS were more likely to engage in regular exercise. Past medical history of coronary heart disease i.
angina, myocardial infarction, percutaneous coronary intervention, and coronary artery bypass surgery or strokes were not different. VAT at the level of umbilicus was significantly correlated with adiposity measurements by DXA including whole body fat mass, android and gynoid fat amount.
The concentration of triglycerides was associated with all of the four adiposity indices including VAT and SAT, and android and gynoid fat amount whereas HDL-cholesterol showed negative association with adiposity indices. Android fat amount was associated with fasting glucose and insulin levels, HOMA-IR, and A1C, whereas gynoid fat was not associated with fasting glucose and A1C levels.
Both VAT and android fat amount were correlated negatively with circulating adiponectin level and positively with coronary artery stenosis. Figure 2 shows the greatest association between android fat with VAT compared to BMI, waist circumference, and gynoid fat. Indices of adiposity including BMI, whole body fat mass, android and gynoid fat amount, VAT and SAT area were associated with the five components of MS Table S2.
In particular, BMI, whole body fat mass and android fat amount, and visceral and subcutaneous fat quantified by CT were strongly correlated with summation of five components of MS.
Alanine aminotransferase and γ-glutamyl transferase levels were weakly correlated with MS, and fasting insulin level and HOMA-IR were more strongly correlated. Adiponectin levels were negatively associated with clustering of MS components. Multivariate linear regression models were used to assess whether android fat amount measured by DXA was associated with the summation of five components of MS i.
central obesity, hypertension, high triglyceride and low HDL-cholesterol, dysglycemia controlling for VAT quantified by CT. To investigate the differential effects of body composition measured by each method, four models were constructed according to each method. In Model 2, VAT area was added as an independent variable.
In Model 3, android fat was further added to Model 1 as an independent variable. Lastly, VAT area and android fat amount were added as independent variables in Model 4. In model 1, age, female gender, BMI, hsCRP and HOMA-IR were positively associated with clustering of MS components, whereas adiponectin was negatively associated.
Adjusting for VAT resulted in a positive association of MS with age, female gender, hsCRP, HOMA-IR, and VAT, and a negative association with adiponectin Model 2.
Association with BMI was attenuated after including VAT in the model. Adjusting for android fat with MS, age, gender, BMI, HOMA-IR, and android fat were positively associated with MS, and negatively associated with adiponectin Model 3.
Finally, adjusting for both VAT and android fat in Model 4 yielded a consistent and unchanged positive association of android fat with MS, whereas an association with VAT was attenuated. When the combined VAT area between L and L5-S1 was used instead of a single level of VAT In univariate analysis, android fat and VAT were significantly associated with the degree of coronary artery stenosis.
After adjusting for the risk factors previously used in Table 3 , android fat amount or VAT was an independent risk factor for significant coronary stenosis. When both android fat amount and VAT were included in the multivariate regression model, the associations with coronary artery stenosis were not retained Table 4.
In this study with community-based elderly population, of the various body compositions examined using advanced techniques, android fat and VAT were significantly associated with clustering of five components of MS in multivariate linear regression analysis adjusted for various factors.
When android fat and VAT were both included in the regression model, only android fat remained to be associated with clustering of MS components. The results suggest that android fat is strongly associated with MS in the elderly population even after adjusting for VAT.
Abdominal obesity is well recognized as a major risk factor of cardiovascular disease and type 2 diabetes [11]. Although anthropometric measurements such as BMI and waist circumference are widely used to estimate abdominal obesity, distinguishing between visceral and subcutaneous fat or between fat and lean mass cannot be ascertained.
Moreover, anthropometric measurements are subject to intra- and inter-examiner variations. Alternatively, more accurate methods used to measure regional fat depot are DXA and CT.
DXA and CT provide a comprehensive assessment of the component of body composition with each contributing its unique advantages.
CT can distinguish between visceral and subcutaneous fat, and has been useful in measuring fat or muscle distribution at specific regions [23] , [24]. However, there are several limitations in the VAT quantification using CT scan. Even though VAT from a single scan obtained at the level of umbilicus was well correlated with the total visceral volume [25] , there could be a potential concern for over- or underestimation if we measure fat area at one selected level instead of measuring total fat volume.
In addition, CT scan has a greater risk of radiation hazards than DXA and is not appropriate for repetitive measurements [20] , [26]. In contrast, DXA has the ability to accurately identify where fat or muscle is distributed throughout the body with high precision [12].
The measurement of body composition is an area, which has attracted great interest because of the relationships between fat and lean tissue mass with health and disease. In addition, DXA with advanced software is able to quantify android and gynoid fat accumulation [27] , and have been used for investigations of cardiovascular risk [28].
Adipose tissue in the android region quantified by DXA has been found to have effects on plasma lipid and lipoprotein concentrations [29] and correlate strongly with abdominal visceral fat [30] , [31].
Thus, DXA is emerging as a new standard for body composition assessment due to its high precision, reliability and repeatability [32] , [33]. In the current study, adiponectin levels were negatively and hsCRP levels were positively associated with MS with at least borderline significance except for hsCRP in model 4, where both VAT and android fat were included as covariates in the regression model.
Mechanistically and theoretically, fat deposition in android area is suggested to have deleterious effects on the heart function, energy metabolism and development of atherosclerosis. However, studies on android fat depot are limited [23].
A recent study suggested varying effects of fat deposition by observing inconsistent associations of waist and hip measurements with coronary artery disease, particularly with an underestimated risk using waist circumference alone without accounting for hip girth measurement [4]. A more recent study demonstrated that central fat based on simple anthropometry was associated with an increased risk of acute myocardial infarction in women and men while peripheral subcutaneous fat predicted differently according to gender: a lower risk of acute myocardial infarction in women and a higher risk in men [34].
Another study with obese youth confirmed harmful effects of android fat distribution on insulin resistance [35]. These results suggest that in addition to visceral fat, accumulation of fat in android area is also important in the pathogenesis of MS.
Of note, in this study, android fat was more closely associated with a clustering of metabolic abnormalities than visceral fat. There is no clear answer for this but several explanations can be postulated. First, android area defined in this study includes liver, pancreas and lower part of the heart.
For example, the adipokines released from pericardial fat may act locally on the adjacent metabolically active organs and coronary vasculature, thereby aggravating vessel wall inflammation and stimulating the progression of atherosclerosis via outside-to-inside signaling [40] , [41].
Second, the android fat represents whole fat amount in the upper abdomen area while VAT measurement was performed at a single umbilicus level.
This different methodology may possibly contribute to greater association between metabolic impairments and android fat than VAT.
This interpretation is supported by the borderline significance of VAT in the association with MS when combined VAT area was used instead of a single level of VAT. A recent study also showed that the whole fat amount between L1—L5 vertebra showed a stronger relationship with insulin resistance than that of the single L3 level [39].
In this study, both android fat amount and VAT were associated with coronary artery stenosis. Android fat is closely related with VAT because of their proximity and correlation with various cardiovascular risk factors.
The attenuated associations of both variables without statistical significance in the regression model where android fat and VAT were simultaneously included may be due to a shared systemic effect as a result of shared risk factors for the development of atherosclerosis. This study has several strengths.
First, DXA with its advanced technology was used to measure regional fat depot. Second, the subjects were recruited from a well-defined population, which represented a single ethnic group and were older than 65 years.
Third, the regression analysis was adjusted for important factors including whole body fat mass, insulin resistance, and biochemical markers including adiponectin and hsCRP that might affect MS. This study also has several limitations. First, since our study is limited by its cross-sectional nature, it is impossible to confirm clinically meaningful role of android fat depot.
Therefore, further studies are needed to determine a predictive role of android fat for a clustering of cardiometabolic risk factors and subsequent incidence of cardiovascular diseases.
Second, this is a single cohort study with a small number of subjects and the results are confined to this specific cohort. Of the various body compositions examined using advanced techniques, android fat measured by DXA was significantly associated with clustering of five components of MS even after accounting for various factors including visceral adiposity.
Participants characteristics including body composition measured by dual energy x-ray absorptiometry DXA and computed tomography CT subdivided by sex.
Correlation between summation of components of metabolic syndrome and multiple parameters including body composition. Multivariate linear regression analysis of associations of multiple parameters including body composition with summation of five individual components of metabolic syndrome VAT from L to L5-S1 was used.
Conceived and designed the experiments: SMK JWY HYA SYK KHL SL. Performed the experiments: SMK SL. Analyzed the data: HS SHC KSP HCJ. Wrote the paper: SMK SL. Browse Subject Areas? In this cross-sectional study, we provide new evidence that different regional fat depots have different threats independent of BMI: android percent fat in this study was proven to be positively related to NAFLD prevalence, whereas gynoid percent fat was negatively related to NAFLD.
This finding provides a novel and vital indicator of NAFLD for individuals in health screening in the future. A possible explanation for our findings is a disorder of lipid metabolism. Individuals with high android fat and low gynoid fat tend to have excessive triacylglycerols, which might accumulate in hepatocytes in the long run and finally trigger the development of NAFLD Another possibility is that different fat accumulation depots confer different susceptibilities to insulin resistance A recent study highlighted that apple-shaped individuals high android fat had a higher risk of insulin resistance than BMI-matched pear-shaped high gynoid fat individuals Aucouturier et al.
Uric acid has previously been shown to regulate hepatic steatosis and insulin resistance via the NOD-like receptor family pyrin domain containing 3 inflammasome and xanthine oxidase 43 , It is a widely established fact that female adults have a lower epidemic of NAFLD, but there is no definite reason 3 , In addition, morbid obesity was reported to be related to fibrosis of NAFLD by Ciardullo et al.
This result is possibly associated with different effects of sex hormones on adipose tissue. Sex steroid hormones were reported to have an direct effect on the metabolism, accumulation, and distribution of adiposity Additionally, several loci displayed considerable sexual dimorphism in modulating fat distribution independent of overall adiposity 12 , Several limitations should also be acknowledged.
First, the diagnosis of NAFLD was based on US FLI, which is not precise enough compared to the gold standard technique for diagnosing NAFLD. However, this score has been modified for the United States multiracial population and has a more accurate diagnostic capacity than the original FLI To address racial disparities in the prevalence and severity of NAFLD, the US FLI includes race-ethnicity as a standard to enhance diagnostic capacity.
When studying different populations, the race of the population should be fully considered in order to better diagnose NAFLD Second, US FLI is derived from a population aged 20 and older, so our study based on US FLI also used this standard, resulting in a lack of analysis of adolescents.
Third, Given the lack of data, selection bias might exist. Last, the cross-sectional methodology of the study makes it impossible to draw conclusions regarding the cause-and-effect relationship between body composition and NAFLD.
Additional studies investigating the reasons are needed. Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements.
Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. LY and CX conceived the study idea and designed the study.
LY, HH, ZL, and JR performed the statistical analyses. LY wrote the manuscript. HH and CX revised the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by the National Key Research and Development Program YFA , the National Natural Science Foundation of China , and the Key Research and Development Program of Zhejiang Province C The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Author Affiliations: Android vs gynoid fat tissue characteristics aft Exercise Biology Pre-workout nutrition guideBlaise Pascal University, Aubière Drs Aucouturier, Thivel, and FfatChzracteristics of Pediatrics, Hotel Dieu, Android vs gynoid fat tissue characteristics Chzracteristics, Clermont-Ferrand Dr Advanced Fat Burner characteristcis, and Eco-friendly living Medical Center, Charactefistics Dr TaillardatFrance. Background Upper body fat distribution is associated with the early development of insulin resistance in obese children and adolescents. Objective: To determine if an android to gynoid fat ratio is associated with the severity of insulin resistance in obese children and adolescents, whereas peripheral subcutaneous fat may have a protective effect against insulin resistance. Setting The pediatric department of University Hospital, Clermont-Ferrand, France. Design A retrospective analysis using data from medical consultations between January and January
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