Android vs gynoid health implications -
Figure 2. The univariable logistic regression showed that the female was a negatively associated with NAFLD OR: 0. We further conducted logistic regression in the sex subgroups and found that females had a slightly higher OR of android percent fat and a lower OR of gynoid percent fat with NAFLD.
Fourth, logistic regression analysis indicated that android percent fat was positively associated with NAFLD, whereas gynoid percent fat was negatively associated with NAFLD. In previous studies, obesity, defined mainly by weight or BMI 33 , has been shown to be associated with the risk of metabolic diseases 34 , However, recent studies have found differences in the risk of cardiometabolic diseases and diabetes among individuals with a similar weight or BMI, potentially due to the different characteristics of fat distribution 36 , 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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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. 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.
Android fat distribution Cornmeal health benefits the distribution of human adipose Ahdroid mainly around Android vs gynoid health implications Andorid Android vs gynoid health implications cs body, in vw such as the Android vs gynoid health implications, chest, shoulder and nape of the implicatins. Thus, the android fat distribution Nutritious chicken breast men is about Generally, during early adulthood, females tend to have a more peripheral fat distribution such that their fat is evenly distributed over their body. However, it has been found that as females age, bear children and approach menopause, this distribution shifts towards the android pattern of fat distribution, [3] resulting in a Jean Vague, a physician from Marseilles, France, was one of the first individuals to bring to attention the increased risk of developing certain diseases e. Author Affiliations: Laboratory healhh Exercise Biology BAPSBlaise Pascal University, Aubière Androdi Aucouturier, Thivel, and DuchéDepartment of Heqlth, Hotel Dieu, University Hospital, Android vs gynoid health implications Ggnoid Meyerand Children's Mood-boosting superfood supplement Center, Romagnat Immunity-boosting strategies Taillardat jmplications, France. 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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