While it is uncertain why Androkd lipid desearch are elevated Preventing fatigue through diet those with higher android fat, it may add Gynod their nody CV risk. Retinol-binding protein-4 and nonalcoholic fatty liver disease. Methods Subjects, anthropometric and biochemical parameters This study was part of the Korean Longitudinal Study on Health and Aging KLoSHAwhich 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.

Android vs gynoid body fat distribution research -

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? Click through the PLOS taxonomy to find articles in your field. Article Authors Metrics Comments Media Coverage Reader Comments Figures. Abstract Background Fat accumulation in android compartments may confer increased metabolic risk.

Methods and Findings As part of the Korean Longitudinal Study on Health and Aging, which is a community-based cohort study of people aged more than 65 years, subjects male, Conclusions Our findings are consistent with the hypothesized role of android fat as a pathogenic fat depot in the MS.

Introduction Obesity is a heterogeneous disorder characterized by multi-factorial etiology. Methods Subjects, anthropometric and biochemical parameters 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.

Regional body composition by DXA DXA measures were recorded using a bone densitometer Lunar, GE Medical systems, Madison, WI. Among the many lipid classes, some have been implicated in metabolic and CV disease development in animal models and in humans. Within the system-wide lipid network, Stegemann et al.

While it is uncertain why these lipid species are elevated in those with higher android fat, it may add to their elevated CV risk. Individuals with higher android fat content were characterized by elevated serum UA compared to those with lower android fat.

UA has emerged as an important marker of end organ damage Lambert et al. Therefore, increased UA in those with elevated android fat content may be an additional CV risk factor. In line with our findings, a previous study conducted in a large cohort of Chinese subjects indicated that increasing risk of blood pressure outcomes across UA quartiles was most prominent in individuals with abdominal obesity Yang et al.

Hyperuricemia is strongly associated with an increased risk of atherosclerosis and UA has also been shown to induce vascular endothelial dysfunction via oxidative stress and inflammatory responses Puddu et al. However, whether elevated UA in the group of young males with high levels of android fat affects their endothelial function is uncertain because lowering UA fails to improve endothelial function Borgi et al.

While low endothelial function was noticed in individuals with higher fat content, we noticed that the arterial stiffness assessed from the augmentation index from the digits as well as the renal function were not different between subjects with higher or lower android fat content.

Both arterial stiffness Corrigan et al. The young age and absence of cardiometabolic abnormalities in our participants even in the presence of higher android fat may explain the lack of difference. Our results of a lower endothelial function in those with higher android fat depot are different to those of Weil et al.

who found that abdominal obesity assessed with waist circumference was not associated with greater impairment in endothelial function in overweight and obese adult men Weil et al. Discrepancies in the findings may be due to differences in subject age, assessment of endothelial function and assessment of abdominal fat content.

Our findings are however in agreement with the data from Romero-Corral et al. Overweight is a well-recognized risk factor for pre-hypertension and hypertension and studies have suggested that the risk of developing hypertension may be linked to body fatness and body fat distribution Wiklund et al.

Similarly, excess adiposity is characterized by elevated sympathetic nervous system activity, even in young healthy individuals, which is likely to impact on their CV risk including hypertension development Lambert et al.

Contrary to expectation, we found that MSNA, expressed as bursts incidence was not different between our subjects with high and low android fat content.

Of note burst frequency was significantly higher in participants with higher android fat but this increase was no longer noticed after adjusting for the heart rate. This is surprising considering that sympathetic activation to the skeletal muscle is usually observed in the presence of glucose intolerance Straznicky et al.

Blood pressure and cardiac vagal baroreflex function were also found to be similar between the 2 groups suggesting that in this cohort of young overweight males, excess android fat may not further alter hemodynamic control.

One exception was noticed for the heart rate which, as noticed above, was higher in those with high android fat content. As the HRV data indicated no differences in cardiac vagal control between the two group, perhaps higher heart rate may reflect preferential sympathetic activation to the heart Esler et al.

Limitations of the study include the small number of participants and the cross-sectional aspect of our study which does not permit the determination of causality. The EndoPat technique uses pulse volume changes at the fingertips after an occlusion of the brachial artery as an index of endothelial function.

Although the method has been validated Kuvin et al. Dietary habits and physical activity were not assessed in these participants hence we are not able to determine if these factors may have influenced our results. Strengths of the study includes the number of different outcomes assessed with regards to both metabolic and end organ damage as well as direct sympathetic nervous system activity measurements and the use of iDXA.

In conclusion, our study indicated that in young overweight but otherwise healthy males, preferential fat depot in the android region was associated with impaired glucose and lipid profile, increased serum UA concentrations and worsening of endothelial function.

On the other hand renal function and arterial stiffness were comparable. Contrary to expectation, sympathetic tone as assessed with MSNA and expressed as burst incidence was not elevated in participants with higher android fat content. These data suggest that elevated android fat may confer heightened CV risk and interventions to slow down the development of CV disease should specifically target android fat.

MS received research support and speaker fees from Abbott. GH received research support from Boehringer Ingelheim. The datasets generated for this study are available on request to the corresponding author. EL, CS, NE, GH, MS, and GL contributed to the conception and design of the study.

CS collected the clinical data, organized the database, and performed the statistical analysis. NE and PM performed all the lipidomic analysis. EL and CS wrote the first draft of the manuscript. All authors contributed to the manuscript revision, and read and approved the submitted version.

This study was supported by a project grant from the National Health and Medical Research Council of Australia.

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.

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Peder Wiklund, Researchh Toss, Researcb Weinehall, Göran Hallmans, Paul W. Context: Abdominal Androd is an established Brain health tips factor for cardiovascular disease Faat. However, the correlation of dual-energy x-ray absorptiometry DEXA measurements of regional Android vs gynoid body fat distribution research mass with CVD risk factors has not been completely investigated. Objective: The aim of this study was to investigate the association of estimated regional fat mass, measured with DEXA and CVD risk factors. Design, Setting, and Participants: This was a cross-sectional study of men and women. DEXA measurements of regional fat mass were performed on all subjects, who subsequently participated in a community intervention program. Thank Body fat percentage vs muscle mass for fay nature. Ggnoid are using a browser Android vs gynoid body fat distribution research with limited support for CSS. To obtain Bs best distributoin, we recommend you use a more up to date browser boyd turn Andrroid compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. To determine the independent and commingling effect of android and gynoid percent fat measured using Dual Energy X-Ray Absorptiometry on cardiometabolic dysregulation in normal weight American adults. Associations of android percent fat, gynoid percent fat and their joint occurrence with risks of cardiometabolic risk factors were estimated using prevalence odds ratios from logistic regression analyses.