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What Are The 4 Body Types?: roomroom.info Explains Different Body Types \u0026 Belly FatAndroid vs gynoid fat metabolism -
However, these indicators were questioned as not being the best measures Dual-energy X-ray absorptiometry DEXA is one of the most precise direct measurements of adipose tissue distribution and quantity and may provide more basic evidence for the association between obesity and NAFLD.
The latest research showed that women had a significantly lower prevalence of NAFLD than men 3. Moreover, the pathogenesis of the sex-related epidemic of NAFLD remains unknown.
Previous studies have revealed notable sex differences in fat distribution. These two fat depots might interact with NAFLD, but no large cross-sectional study has investigated this interaction before.
Whether the two sex-related fat depots are correlated with NAFLD needs further exploration. This study aimed to examine whether there is an independent association between android and gynoid fat and the presence of NAFLD.
We also appraised the sex-specific association of android and gynoid fat with NAFLD prevalence. population groupings and health issues. We studied a subgroup of 13, people aged 20 and older with fasting laboratory measures. Finally, 10, individuals were included in this study Supplementary Figure S1.
The Fatty Liver Index FLI is a simple and accurate predictor of hepatic steatosis in the general population 19 , which had already been validated by magnetic resonance spectroscopy 20 , As the participants in this study were from the United States, NAFLD was determined using a modified version of the FLI—the United States Fatty Liver Index US FLI —developed by Ruhl et al.
The US FLI set up on the NHANES — data for predicting fatty liver in the multiethnic U. It was estimated using the following variables: ethnicity, age, gamma-glutamyl transferase, waist circumference, fasting insulin, and fasting glucose.
Fibrotic nonalcoholic steatohepatitis NASH was identified using the Fibrotic NASH Index FNI , developed by Tavaglione et al. The FNI incorporates the following variables: aspartate aminotransferase AST , high-density lipoprotein cholesterol HDL , and hemoglobin A1c HbA1c.
Dual-energy X-ray absorptiometry DXA was applied to estimate body adipose amounts. Android is defined as having fat distribution around the midsection or waist belly button. Gynoid refers to the area of the hips that is located at the tops of the thighs.
Hologic software automatically added the lines indicated above 24 — Anthropometric measures, including height, weight, body mass index BMI , waist circumference, and blood pressure, were extracted from examination data.
Laboratory data such as triglycerides, total cholesterol, high-density lipoprotein HDL cholesterol, low-density lipoprotein LDL cholesterol, alanine aminotransferase ALT , aspartate aminotransferase AST , free fatty acids, fasting blood glucose, insulin, glycohemoglobin, and uric acid were collected.
Masked variance pseudostrata and variance pseudo-PSU were also included to define the survey design. The prevalence and prevalence ratio were calculated as reported before 31 , For continuous variables on demographic characteristics, anthropometric measurements, and laboratory information, data are shown as the means and standard errors SEs , and for categorical variables, data are displayed as numbers percentages.
Logistic regression was applied to assess the association between risk factors and NAFLD. Adjustments were made to the models. Model 2 included model 1 covariates plus BMI, hypertension, ALT, AST, gamma-glutamyl-transpeptidase, total cholesterol, triglycerides, HDL, LDL, uric acid, and glycated hemoglobin.
We also conducted a logistic regression according to sex. A total of 10, participants The weighted baseline characteristics of the population are shown in Table 1. In contrast to individuals without NAFLD, those with NAFLD exhibited advanced age, higher values of body weight, BMI, waist circumference, glycohemoglobin, HOMA-IR, and uric acid, as well as worse lipid profiles.
Additionally, they demonstrated an increased incidence of hypertension and diabetes, and a lower proportion of female participants. The results showed that the prevalence of NAFLD was 5.
A correlation matrix of adipose allocation and other NAFLD risk factors is summarized in Figures 1A — C for all individuals and for male and female groups, respectively.
Figure 1. Correlation matrix of fat distribution and NAFLD-related risk factors by sex. A All people, B male subgroup, and C female subgroup. A complex sample logistic regression was used to investigate the relationship between fat depots and the prevalence of NAFLD Table 3.
In the crude model, android percent fat was positively related to NAFLD OR: 1. We further conducted multivariable logistic regression analyses, additionally adjusting for BMI, hypertension, diabetes, ALT, AST, gamma-glutamyl-transpeptidase, total cholesterol, triglycerides, HDL, LDL, and uric acid, in which there were similar OR values resembling the two previous models.
Fat distribution and NAFLD categorized by gender are displayed in Table 5. More body fat in both the android area and gynoid areas was found in women than in men.
Overall, the NAFLD group showed a similar pattern, except for the first and second quartiles, in which the proportion of women did not decline correspondingly as in the other two groups Figure 2. 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.
The DXA, or "Dual X-ray Absorptiometry", is a quick and pain free scan that can tell you a lot about your body. It provides you with an in-depth analysis of your fat tissue, lean mass and bone density. Due to its open design patients can comfortably enjoy the test without feeling claustrophobic.
It works by sending dual low power x-ray beams that can accurately and precisely differentiate between bone mineral, lean mass and fat mass. Example analysis from a DXA scan PDF. Dual X-ray Absorptiometry DXA is a quick and pain free scan that can tell you a lot about your body.
The main goal of the DXA is to provide you with an in-depth analysis of the main components of your body; fat, muscle and bone. After the scan, you will be given a multi-paged print out where you will see percentages, mass, and images accounting for the various data obtained.
The great thing about the DXA scan is that it requires very minimal preparation. For more accurate results you should make sure you are well hydrated and not have any food in your stomach at least 3 hours since your last meal. It is also important to not take calcium supplements 24 hours prior to your test to ensure accurate bone density readings.
Upon arriving at our medical office you will be greeted and taken back to meet with the licensed technologist who will perform your scan for you. After measuring your height and weight, you will be asked to lie down and get comfortable and the scan will begin.
The scan takes 6 minutes. Once the scan is over you will be able to sit down with the exercise specialist to go over your results.
Your results will be explained to you and suggestions will be given according to goals that you have i. You will be able to keep your packet of results as a reference in the case that a follow up is desired in the future.
Note: it is beneficial to do this scan every months for body composition and every year if you are looking to modify something specific such as bone density. Because this test gives so much detailed information regarding various components in your body, it is a scan that can be used for anyone.
Athletes can get this scan done if they are curious to track their muscle mass as well as overall fat percentage. Mean SD quantitative insulin-sensitivity check index QUICKI values in tertiles of android to gynoid fat ratio.
Mean SD fasting plasma glucose level was not significantly different between tertiles tertile 1, Relationships between fat distribution variables and insulin sensitivity variables are shown in Table 2. Neither body fat percentage nor lower limbs fat percentage were significantly correlated with insulin sensitivity variables or glucose and insulin concentrations.
None of the fat distribution variables had significant correlation with fasting glucose concentration. The multiple stepwise regression showed that age and the android to gynoid fat ratio were significant predictors of HOMA-IR value β coefficients were 0.
Adjusted R 2 was 0. Body mass index, waist circumference z score, and body fat percentage were not significant predictors of HOMA-IR value. Our hypothesis was that a preferential fat storage at the abdominal level rather than in the lower limbs would be associated with increased insulin resistance.
To this aim, we calculated a simple index of android to gynoid fat distribution as a ratio between percentage of abdominal fat and percentage of lower limbs fat based on DXA measurements.
Insulin resistance was estimated by using simple indexes based on fasting plasma glucose and insulin concentrations. Indexes such as HOMA-IR and the quantitative insulin-sensitivity check index calculated from fasting samples have been shown to be valid to assess insulin resistance during puberty when compared with direct measurement with a glucose clamp.
Furthermore, insulin resistance was associated with abdominal adiposity without distinction between subcutaneous and visceral fat depots. However, although HOMA-IR values increased from the lowest tertile to tertiles 2 and 3, whereas there was no significant difference between tertiles 2 and 3, a linear regression between the android to gynoid fat ratio and HOMA-IR value did not provide a threshold value of android to gynoid fat ratio above which obese children have an increased risk of insulin resistance.
Indeed, in the present study, there was no significant association between percentage of body fat and insulin resistance. Previous studies have shown in young subjects that the degree of obesity is associated with a worsening of all the components of the metabolic syndrome, including insulin resistance.
Despite a similar degree of obesity, a lower prevalence of impaired glucose tolerance and type 2 diabetes have been reported in European than in American children.
Hence, together with a reduced number of subjects with severe obesity in comparison with other studies, only mild alterations of insulin sensitivity may explain the lack of association between percentage of body fat and insulin resistance.
The development of abdominal obesity during puberty may be favored by pubertal insulin resistance and its consequent hyperinsulinemia. Logically, age was a significant predictor of insulin resistance.
Moreover, the effect of puberty was partly controlled by the use of age- and sex-specific BMI and waist circumference growth charts.
Several studies have already used DXA to provide measurements of abdominal fat mass. Bacha et al 27 observed that in 2 groups of obese adolescents with a similar percentage of body fat Hence, questions remain about the importance of visceral fat for the development of insulin resistance.
Finally, significant correlations between waist circumference or waist circumference z score and HOMA-IR confirm that simple anthropometric measurements are also reliable to assess an association between upper body adiposity and insulin resistance. We did not observe any association between lower body fat percentage and insulin resistance.
This result is similar to previous findings in adults. Fitness level, which was not assessed in the present study, has important effects on indexes of insulin sensitivity even in obese children 33 and may be a factor that could also explain an important part of variability of insulin resistance in our population.
To conclude, the present study showed that an android rather than gynoid fat distribution was associated with an increased insulin resistance in obese children and adolescents. Hence, an android to gynoid fat ratio based on DXA measurement may be a useful and simple technique to assess a pattern of body fat distribution associated with an increased insulin resistance.
This study also confirmed that the severity of insulin resistance is associated with abdominal obesity, which can be assessed by waist circumference measurement, whether fat is located essentially in visceral or subcutaneous adipose tissue in children and adolescents.
Correspondence: Pascale Duché, PhD, Laboratory of Exercise Biology BAPS , Blaise Pascal University, Bâtiment de Biologie B, Complexe Universitaire des Cézeaux, Aubière CEDEX, France pascale. duche univ-bpclermont. Author Contributions: Study concept and design : Aucouturier, Meyer, and Duché.
Acquisition of data : Aucouturier, Thivel, and Taillardat. Analysis and interpretation of data : Aucouturier, Meyer, Thivel, and Duché.
Drafting of the manuscript : Aucouturier. Critical revision of the manuscript for important intellectual content : Aucouturier, Meyer, Thivel, Taillardat, and Duché. Statistical analysis : Aucouturier, Thivel, Taillardat, and Duché. Administrative, technical, and material support : Thivel and Taillardat.
Study supervision : Aucouturier, Meyer, and Duché. Aucouturier J , Meyer M , Thivel D , Taillardat M , Duché P. Effect of Android to Gynoid Fat Ratio on Insulin Resistance in Obese Youth. Arch Pediatr Adolesc Med. Artificial Intelligence Resource Center. Select Your Interests Customize your JAMA Network experience by selecting one or more topics from the list below.
Save Preferences. Privacy Policy Terms of Use. X Facebook LinkedIn. This Issue. Citations View Metrics. Share X Facebook Email LinkedIn. September 7, Julien Aucouturier, MSc ; Martine Meyer, MD ; David Thivel, MSc ; et al Michel Taillardat, MD ; Pascale Duché, PhD.
Author Affiliations Article Information Author Affiliations: Laboratory of Exercise Biology BAPS , Blaise Pascal University, Aubière Drs Aucouturier, Thivel, and Duché , Department of Pediatrics, Hotel Dieu, University Hospital, Clermont-Ferrand Dr Meyer , and Children's Medical Center, Romagnat Dr Taillardat , France.
visual abstract icon Visual Abstract. Body composition. Blood samples. Statistical analysis. Descriptive statistics of the sample. View Large Download. Indexes of insulin resistance: fasting glucose and insulin concentrations.
Correlation coefficient. Correlation Coefficients for Association Between Fat Distribution Variables and Markers of Insulin Resistance. Multiple stepwise regression. Presse Med ; PubMed Google Scholar. Després JP Cardiovascular disease under the influence of excess visceral fat.
Crit Pathw Cardiol ;6 2 59 PubMed Google Scholar Crossref. Fujioka SMatsuzawa YTokunaga KTarui S Contribution of intra-abdominal fat accumulation to the impairment of glucose and lipid metabolism in human obesity.
The DEXA machine and distribution Android vs gynoid fat metabolism hynoid fat can vss widely among individuals and may mteabolism always fit neatly into these categories. Additionally, mrtabolism fat distribution Android vs gynoid fat metabolism not always correspond to overall health status or risk for obesity-related health problems. Sex and gender exist on spectrums. Click here to learn more. Many factors can contribute to the development of gynoid obesity. Here are some of the causes and risk factors of gynoid obesity:. Gynoid obesity, like any other form of obesity, can increase the risk of various health problems, which include :. Metabolis, Affiliations: Android vs gynoid fat metabolism of Exercise Mmetabolism BAPS cs, Blaise Pascal University, Aubière Drs Aucouturier, Thivel, and Duché ffat, Department of Pediatrics, Hotel Dieu, University Hospital, Clermont-Ferrand Dr Mehabolismand Children's Medical Center, Romagnat Dr TaillardatMetaboilsm. Background Biocidal materials body fat distribution is associated fay 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 Participants Data from 66 obese children and adolescents coming to the hospital for medical consultation were used in this study.
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