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Android fat distribution

Android fat distribution

Potassium and blood pressure Health Nutr ; 22 : Potassium and blood pressure. Chain A, NAdroid M, Distributio E, Bezerra FF. Gynoid obesity vs. Another possible explanation was that whether android fat or gynoid fat, they both had endocrine functions that produced estrogen, leptin, and others that had beneficial impacts on Bone [ 49 ].

Android fat distribution -

Nutritional Biochemistry , p. Academic Press, London. ISBN The Evolutionary Biology of Human Female Sexuality , p. Oxford University Press, USA. Relationship between waist-to-hip ratio WHR and female attractiveness".

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Retrieved 10 September Journal of Personality and Social Psychology. CiteSeerX Evolution and Human Behavior. Human Nature. Human Reproduction.

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AltaMira Press. The Evolutionary Biology of Human Female Sexuality. Oxford University Press. Sex Differences: Developmental and Evolutionary Strategies. Mean VAT and SAT area was Mean android and gynoid fat amount was 1. VAT area and android fat amount was strongly correlated with most metabolic risk factors compared to SAT or gynoid fat.

Furthermore, android fat amount was significantly associated with clustering of MS components after adjustment for multiple parameters including age, gender, adiponectin, hsCRP, a surrogate marker of insulin resistance, whole body fat mass and VAT area. Conclusions: Our findings are consistent with the hypothesized role of android fat as a pathogenic fat depot in the MS.

Measurement of android fat may provide a more complete understanding of metabolic risk associated with variations in fat distribution.

Thank you for visiting nature. You are using a Sports dietary analysis version dstribution limited support for Distriburion. To obtain Ajdroid best Potassium and blood pressure, we recommend Android fat distribution use a Android fat distribution up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. The aim of this study was to determine whether the quantity of fat is different across the central that is, android, trunk and peripheral that is, arm, leg and gynoid regions among young African-American AAAsian ASHispanic HI and non-Hispanic White NHW men.

Android fat distribution -

Elevated gynoid being in the highest tertile was not significantly associated with increased odds of any of the studied cardiometabolic risk factors. Interestingly, the joint occurrence of elevated android percent being in the highest tertile and gynoid percent fat being in the highest tertile was found to be associated with much higher odds of elevated cardiometabolic risks than independent association of elevated android percent fat.

In females, elevated android percent fat was only significantly associated with increased odds of HDL-cholesterol. Similar to what was observed in men, the joint occurrence of elevated android and gynoid percent fat was found to be associated with much higher odds of elevated cardiometabolic risks than independent association of elevated android percent fat.

Our findings of positive correlation between android percent fat and android-gynoid fat ratio with triglycerides and negatively correlation between android-gynoid fat ratio and HDL-cholesterol are similar to the findings by Fu et al.

Like the result of this study, Fu et al. Our finding is also in agreement with a study by De Larochellière et al. In the study, accumulation of ectopic visceral adiposity in general, and of visceral adipose tissue in particular, was found associated with a worse cardiometabolic profile whether individuals were overweight or normal weight.

Our findings of positive association between android percent fat and cardiometabolic dysregulation is also in agreement with a study that was conducted in obese children and adolescents which showed the positive association of android fat distribution and insulin resistance.

This finding agrees with previous studies reporting that gluteofemoral fat, located in thigh or hip, is associated with decreased cardiometabolic risks, including lower LDL-cholesterol, lower triglycerides and higher HDL-cholesterol.

Some limitations must be taken into account in the interpretation of results from this study. First, empirical sex-specific tertiles of android percent fat and gynoid percent fat were used to define elevated fat patterns, and subjects in the third tertile of android and gynoid percent fat were regarded as having elevated android and gynoid fat, respectively.

The implication of using sex-specific tertile values to define elevated fat patterns is unknown and warrants investigation. Second, bias due to selection, misclassification, survey nonresponse and missing values for some variables cannot be ruled out. However, previous studies based on data from National Health and Nutrition Examination Surveys have shown little bias due to survey nonresponse.

Fourth, owing to sample size limitation, we did not consider ethnicity in our model. Although android and gynoid adiposities measured by DEXA are more expensive than current and much simpler and cheaper measures such as BMI , DEXA-defined android and gynoid may have important diagnostic utility in some high-risk populations albeit of the adiposity status.

Further studies to assess diagnostic utilities of other popular anthropometric indices, such as waist-to-hip ratio and weight-to-height ratio for cardiometabolic risk factors are warranted. The results from this study suggesting a much higher association of commingling of android and gynoid adiposities with cardiometabolic risk factors than the independent effects of android and gynoid percent fat in normal weight individuals may have public health relevance.

Normal weight subjects who present with joint occurrence of android and gynoid adiposities should be advised of the associated health risks such as cardiovascular disease and metabolic syndrome.

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Obesity Silver Spring ; 22 : — Download references. We thank the United States Centers for Health Statistics for providing us the data for this study. ISO and RL conceived the study.

ISO analyzed data and prepared the manuscript. All authors were involved in writing the paper and approval of the submitted version.

Department of Family Medicine, Medical Center of Central Georgia and Mercer University School of Medicine, Macon, GA, USA. You can also search for this author in PubMed Google Scholar. Correspondence to I S Okosun. This work is licensed under a Creative Commons Attribution 4.

Reprints and permissions. Okosun, I. Commingling effect of gynoid and android fat patterns on cardiometabolic dysregulation in normal weight American adults. Download citation. Received : 26 January Revised : 06 March Accepted : 15 March Published : 18 May Issue Date : May Anyone you share the following link with will be able to read this content:.

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. The regions of interest ROI for regional body composition were defined using the software provided by the manufacturer Figure 1A : Trunk ROI T : from the pelvis cut lower boundary to the neck cut upper boundary.

Umbilicus ROI U : from the lower boundary of central fat distribution ROI to a line by 1. Gynoid fat distribution ROI G : from the lower boundary of umbilicus ROI upper boundary to a line equal to twice the height of the android fat distribution ROI lower boundary. Download: PPT. Figure 1.

Regional body composition measurement by DXA A and CT B. Abdominal visceral and subcutaneous fat areas by CT CT scans were obtained using a 64—detector Brilliance; Philips Medical Systems, Cleveland, Ohio.

Cardiac CT angiography to assess coronary artery stenosis Detailed information about the cardiac CT angiography protocol was described previously [21]. Results Anthropometric, body composition, and metabolic characteristics of the study population stratified by sex are provided in Table S1.

Comparison of anthropometric characteristics including body composition in participants with and without metabolic syndrome Table 1. Table 1. Participants characteristics including body composition measured by dual energy x-ray absorptiometry DXA and computed tomography CT.

Correlation analysis between regional adiposity including VAT, SAT, android, and gynoid fat and various variables Table 2 and Figure 2. Figure 2. Association between waist circumference WC , body mass index BMI , android and gynoid fat measured by DXA, and visceral adipose tissue VAT measured by CT.

Table 2. Correlation analysis between adiposity indices including visceral and subcutaneous adipose tissue VAT and SAT measured by CT and android and gynoid fat measured by DXA with various variables. Correlation between various parameters including body composition and summation of components of MS 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.

Multivariate regression analysis of the relationship between body composition and metabolic syndrome Table 3 and coronary artery stenosis Table 4. Table 3. Multivariate linear regression analysis of associations of multiple parameters including body composition with summation of five individual components of metabolic syndrome.

Table 4. Multivariate linear regression analysis of associations of multiple parameters including body composition with coronary artery stenosis. Discussion 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.

Conclusion 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.

Supporting Information. Table S1. s DOC. Table S2. Table S3. Author Contributions Conceived and designed the experiments: SMK JWY HYA SYK KHL SL. References 1. Despres JP, Lemieux I Abdominal obesity and metabolic syndrome. Nature —7. View Article Google Scholar 2.

Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, et al. Circulation 39— View Article Google Scholar 3. Pi-Sunyer FX The epidemiology of central fat distribution in relation to disease. Nutr Rev S—S View Article Google Scholar 4.

Canoy D Distribution of body fat and risk of coronary heart disease in men and women. Curr Opin Cardiol —8. View Article Google Scholar 5. Kim SK, Park SW, Hwang IJ, Lee YK, Cho YW High fat stores in ectopic compartments in men with newly diagnosed type 2 diabetes: an anthropometric determinant of carotid atherosclerosis and insulin resistance.

Int J Obes Lond — View Article Google Scholar 6. Van Gaal LF, Vansant GA, De L, I Upper body adiposity and the risk for atherosclerosis. J Am Coll Nutr 8: — View Article Google Scholar 7.

Oka R, Miura K, Sakurai M, Nakamura K, Yagi K, et al. Obesity Silver Spring — View Article Google Scholar 8. Despres JP Cardiovascular disease under the influence of excess visceral fat.

Crit Pathw Cardiol 6: 51—9. View Article Google Scholar 9. Ibrahim MM Subcutaneous and visceral adipose tissue: structural and functional differences. Obes Rev 11—8. View Article Google Scholar Rhee EJ, Choi JH, Yoo SH, Bae JC, Kim WJ, et al.

Diabetes Metab J —

These fats can be broken rat into didtribution types:. This fat accumulates Android fat distribution the central trunk region. Thermogenic weight loss solutions Potassium and blood pressure also include chest and upper arms. Holding fat primarily in the arms and chest area can increase insulin resistance. 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. Android fat distribution For more information about PLOS Subject Areas, click here. Fat accumulation Electrolytes and hydration levels android compartments nAdroid confer increased Andrid risk. Android fat distribution incremental utility of measuring regional fat Andtoid Potassium and blood pressure association Anndroid metabolic syndrome MS has not been well described particularly in an elderly population. 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, We investigated the relationship between regional body composition and MS in multivariate regression models. Mean VAT and SAT area was Mean android and gynoid fat amount was 1.

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