Android vs gynoid fat distribution impact on clothing size -
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Category Commons Wikiproject Portal Outline. Gasperino JA, Wang J, Pierson RN, Heymsfield SB. Age-related-changes in musculoskeletal mass between Black-and-White women. Metab Clin Exp ; 44 : 30— Deurenberg P, Deurenberg-Yap M, Guricci S. Obes Rev ; 3 : — Wagner DR, Heyward VH.
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Relationships between insulin sensitivity and measures of body fat in asymptomatic men and women. Download references. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,.
Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX, USA. Department of Epidemiology and Biostatistics, State University of New York, Albany, NY, USA. Bureau of Economic Geology, University of Texas at Austin, Austin, TX, USA. You can also search for this author in PubMed Google Scholar.
Correspondence to M A Stults-Kolehmainen. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3. Reprints and permissions. Stults-Kolehmainen, M. et al. DXA estimates of fat in abdominal, trunk and hip regions varies by ethnicity in men. Download citation.
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Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content Thank you for visiting nature. Download PDF. Subjects Epidemiology Fats Medical imaging. Abstract Objective: 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 AA , Asian AS , Hispanic HI and non-Hispanic White NHW men.
Results: HI men Conclusions: Fat deposition and body fat patterning varies by ethnicity. Materials and methods Subjects A cohort of men was assessed for body composition from June to May Protocol Height and weight Stature was measured to the nearest 0. Dual-emission X-ray absorptiometry Body composition was determined from DXA technology using a Lunar Prodigy G.
DXA regional fat analysis Prodigy enCORE software automatically demarcates the regional boundaries. Sample DXA scan showing demarcations between body regions generated by enCORE software. Full size image.
Results Reliability measures for regional body composition measures were obtained from a separate sample of men. Table 1 Subject characteristics by ethnicity Full size table. Figure 2. Discussion The central purpose of this study was to determine whether differences exist among ethnic and racial groups of young men in central that is, android and trunk and peripheral that is, arm, leg and gynoid regional fat mass.
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Article CAS Google Scholar Mazess RB, Barden HS, Bisek JP, Hanson J. Article CAS Google Scholar Kiebzak GM, Leamy LJ, Pierson LM, Nord RH, Zhang ZY. Summary: The weight scale does not tell an accurate story.
Weight loss may not necessarily come from fat. A weight loss goal should focus on reducing body fat percentage to improve body composition, which increases lean body mass. Cutting Further Into the Fat of Men vs.
Females Gynoid vs. Males Android - Women have a higher body fat percentage and different fat distribution compared to men. The gynoid fat distribution — commonly referred to as pear shape — deposits adipose tissue in the hips and thighs gluteofemoral fat of women. The android fat distribution apple shape , predominantly found in males, is classified as mostly visceral fat and is found in the upper thoracic region.
Insulin is crucial in metabolism and insulin resistance may be the missing link in obesity, cardiovascular disease and type 2 diabetes. Android fat distribution is mostly associated with cardiovascular risk in comparison to gynoid. The gynoid distribution actually provides protection against diseases like type 2 diabetes and atherosclerosis.
Men usually have a higher lean mass and greater visceral fat and liver fat tissue while women have higher general adiposity. The gender differences in fat tissue distribution might facilitate a more insulin-sensitive how well your body responds to insulin environment in women because both visceral and liver fat as seen more in men are associated with increased insulin resistance.
Caption: Body fat storage locations on men is traditionally at andriod locations, but fat stores in the lower body is possibility as well.
Men who are more physically active usually have less body fat than men who are less active, but this is not necessarily true for women. One study found that exercise is not associated with a significant fat loss in women.
An explanation for this relationship between physical activity and body fat in women could be because women tend to increase their food intake more so than men. Geer and Shen suggest that body composition has a stronger relationship to the type of food intake in women than it does in men.
However, the gender differences between energy expenditure and body composition has not been completely determined by food intake or differences in metabolism. Summary: Women have a gynoid fat distribution fat deposits in thighs and hips.
Men tend to have an android fat distribution visceral fat associated with the upper body. Android fat is associated with cardiovascular risk more than gynoid fat.
Track Your Body Composition, Not Just Your Weight. Measuring body fat helps identify health risks and evaluate body composition. A weight scale provides us with a number signifying the weight of ALL our body tissues. Body composition provides us with percentages of body fat and lean body mass.
It is important to note that body mass index BMI and waist circumference WC do not assess body fat percentage. BMI is calculated by dividing weight in kilograms by height in meters squared. Understanding body composition is important to understanding weight loss.
The person could have a low body fat percentage, which would classify them as lean. Athletes are a prime example of this because they have more muscle mass.
And muscle weighs more than fat. It can assess our risk for obesity-related complications, but it still does not tell us the composition of our weight. WC also suggests our increased risk for obesity-related health problems because of the location of excess fat — in the abdominal area where the dangerous visceral fat is located.
Summary: Weight loss does not always come from fat. Focus should be on reducing body fat percentage to build a leaner body composition. Lean body composition, not solely weight loss, should be your primary gauge of your physique.
Body fat assessments vary in their precision and accuracy. Common anthropometric measures include: weight, waist circumference and skinfold measurements using skin calipers. To measure WC, stand relaxed and place a cloth tape measure around the smallest part of the waist.
More complex methods include: bioelectrical impedance analysis BIA , the BOD POD and dual-energy X-ray absorptiometry DEXA or DXA. You should a methodology based on what is appropriate for you because each methodology has pros and cons.
Skin Calipers: Quick and Easy. A couple of bucks can get you skin calipers. This feasible skin fold assessment gathers measurements from a few body sites. Following a pinch of the skin, the thickness of the skin fold is measured using skin calipers. Measurements, with specific protocols, are taken from the chest, arm, abdominals and thighs.
The measurements are then plugged into an equation to estimate body composition. Body fat percentage can be determined within minutes, but the margin of error should be considered.
This type of method requires accurate readings. It is suggested to measure from the same spots each time. Fortunately with this measurement, a study found that using skin calipers to calculate total body fat percentage did not significantly differ from the value calculated using a portable ultra sound.
Yet, skin calipers provide regional body fat data because it does not measure deep belly fat. Therefore, it is a good relative measure of body fat. Bioelectrical Impedance Analysis BIA.
BIA scales range from simple — a scale with electrodes under the feet - to complex — a handheld scale with electrodes. BIA relies on electric current that flows at different rates through the body depending on body composition.
Android fat distribution describes clothihg distribution of fta adipose tissue impaact around the trunk and upper body, in areas such as the abdomen, chest, Kiwi fruit jam recipes and nape of the neck. Distributon, the android fat distribution of men is about Android vs gynoid fat distribution impact on clothing size 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.
Android vs gynoid fat distribution impact on clothing size -
Similarly, the slope and the BEI derived from the cardiac baroreflex function analysis were not different. None of the HRV parameters differed between the two groups.
Table 2. Blood pressure, heart rate, muscle sympathetic nerve activity MSNA , and cardiac baroreflex function. High sensitivity-CRP, NEFA and leptin plasma levels were not different. Among the 26 classes analyzed, 5 lipid classes were significantly elevated in subjects with higher android fat content.
Those were: Ceramide CER , Diacylglycerol DG , phosphatidylethanolamine PE , phosphatidylglycerol PG and triacylglycerol TAG Table 3. Among the liver enzymes, ALT was slightly not but significantly higher in subjects with higher android fat content.
Reactive hyperemia index and pulse amplitude tonometry ratio were significantly less in those with higher android fat content compared to those with lower android fat RHI: 1. Arterial stiffness as assessed by AI 75 was similar between the two groups Figure 1. Figure 1.
Endothelial function as assessed by the reactive hyperemia index RHI and Pulse Amplitude Tonometry PAT ratio and augmentation index AI 75 in subjects with low and high android fat content.
In this study, we show that for the same level of BMI and fat mass, young overweight males with preferential fat in the android region present with an impaired metabolic profile and endothelial function compared to those with lower android fat content.
These differences were observed in the absence of any difference in blood pressure and sympathetic tone. The group of subjects with higher android fat content presented with reduced insulin sensitivity and decreased glucose tolerance as measured by fasting insulin concentrations and OGTT respectively compared to individuals with lower android fat depot, after correction for BMI.
Our study is in line with previous findings demonstrating that excess body fat in abdominal rather than in peripheral fat depot is involved in the development of insulin resistance in adults Peterson et al.
This is of particular relevance because decreased insulin sensitivity is thought to be the underlying linkage between obesity, type 2 diabetes and CV disease Reaven, Decreased insulin sensitivity in the setting of high android fat depot may reflect structural and functional differences between android and peripheral fat tissue with android tissue possibly expressing higher pro-inflammatory, lipogenic and lipolytic genes and containing higher proportions of saturated fatty acids Marinou et al.
We found no difference however between the 2 groups in serum CRP and leptin concentrations and, although serum NEFA tended to be higher in the group with higher android fat, it did not reach significance.
Of note in this study was the finding that endothelial function was significantly lower in the group of young males with higher android fat content. Impaired endothelial function is considered an early marker of atherosclerotic disease, with important clinical implications including cardiac dysfunction, coronary artery disease, hypertension, diabetes, and neurologic disorders, leading to increased mortality and morbidity Kim et al.
Endothelial dysfunction is detectable in overweight children and young adults and develops even after a rapid and modest weight gain of 4 kg Romero-Corral et al. Decreased insulin sensitivity observed in the group with high android fat may have important consequences in the development of endothelial dysfunction and atherosclerosis Muniyappa and Sowers, The pathway involving decreased endothelial function in this setting of higher android fat remains to be established.
In addition, subjects with higher android fat content were characterized by an abnormal lipid profile in the form of elevated plasma concentration of TG and five other lipidomic classes.
Elevated fasting TG levels are a common dyslipidemic feature that accompanies the prediabetic state and is associated with CV risk in young men Tirosh et al. Serum TG have previously been reported to be positively associated with android fat in a large study in adults in the general population Min and Min, Such abnormal serum TG in those with higher android fat content may negatively impact endothelial function as a strong link between serum TG and endothelial function was demonstrated in a large community-based study Kajikawa et al.
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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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. In summary, we found that abdominal fat mass and the ratio of abdominal to gynoid fat mass, measured by DEXA, were strongly associated with hypertension, IGT, and elevated triglycerides.
Gynoid fat mass was positively associated with several cardiovascular risk factors, whereas the ratio of gynoid to total fat mass showed a negative association with the same risk factors.
Assessing the influence of fat distribution, and gynoid fat mass in particular, on CVD endpoints such as stroke and heart infarctions merits further investigation.
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Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Subjects and Methods. Journal Article. Abdominal and Gynoid Fat Mass Are Associated with Cardiovascular Risk Factors in Men and Women.
Peder Wiklund , Peder Wiklund. Oxford Academic. Fredrik Toss. Lars Weinehall. Göran Hallmans. Paul W. Anna Nordström. Peter Nordström.
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txt Medlars, RefWorks Download citation. Permissions Icon Permissions. Open in new tab Download slide. TABLE 1 Descriptive characteristics of the male and female part of the cohort. Mean ± sd. Age yr Open in new tab. TABLE 2 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.
When it comes to discussing obesity and sze impact on health, body fat distribution plays a gnyoid role. Raspberry ketones capsules distinct patterns of fat distribuution, known as gynoid and android obesity, Android vs gynoid fat distribution impact on clothing size garnered attention due to Citrus fruit supplement for digestive enzymes varying health implications. Understanding the differences between gynoid and android obesity is essential for recognizing the potential risks and taking proactive measures to maintain a healthy lifestyle. Body fat distribution refers to how fat is distributed throughout the body. The accumulation of fat can occur in different regions, with the two main patterns being android and gynoid obesity. Gynoid fat mass is characterized by the excessive accumulation of fat in the lower body, particularly in the hips, thighs, and buttocks.
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