Android fat distribution -
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Everything Body Fat Distribution Tells You About You. reviewed Search Search. Browse Webinar Videos Press Releases PLC in the News Nutrition Medical News Getting Fit Corporate Blog. Prev Previous Beyond Risk: How Your BMI Relates to Actual Cardiovascular and Total Mortality.
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Sadie-Van Gijsen H, Crowther NJ, Hough FS, Ferris WF. The interrelationship between bone and fat: from cellular see-saw to endocrine reciprocity. Download references. We thank the NHANES Project for providing the data free of charge and all NHANES Project staff and anonymous participants.
This work is supported by the the National Natural Science Foundation of China , and ; Lanzhou Science and Technology Plan Program 20JR5RA ; Cuiying Scientific and Technological Innovation Program of Lanzhou University Second Hospital CYZD02, CYMS-A Department of Orthopaedics, Gansu Key Laboratory of Orthopaedics, Lanzhou University Second Hospital, No.
Second Clinical Medical School, Lanzhou University, No. Orthopaedic Clinical Medical Research Center, No. Technology Center for Intelligent Orthopedic Industry, No. You can also search for this author in PubMed Google Scholar. All authors read and approved the final manuscript.
Ming Ma: Study conception, Study design, Data extraction, Data analysis, Manuscript draft. Xiaolong Liu and Gengxin Jia: Prepared the tables and figures. Bin Geng: Manuscript Review, Process Supervision. Yayi Xia: Manuscript Review, Process Supervision, Draft Revision.
Ming Ma, Xiaolong Liu, and Gengxin Jia contributed equally to this work. Correspondence to Yayi Xia. The participants provided their written informed consent to participate in this study.
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Reprints and permissions. Ma, M. et al. The association between body fat distribution and bone mineral density: evidence from the US population. BMC Endocr Disord 22 , Download citation.
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Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Objective To investigate the association between different body fat distribution and different sites of BMD in male and female populations.
Methods Use the National Health and Nutrition Examination Survey NHANES datasets to select participants. Results Overall, participants were included in this study.
Conclusion Body fat in different regions was positively associated with BMD in different sites, and this association persisted in subgroup analyses across age and race in different gender. Introduction Obesity was one of the serious health concerns affecting the health of the global population [ 1 ], especially in the US [ 2 ].
Methods Datasets sources This cross-sectional research selected datasets from the NHANES project, a nationally representative project to evaluate the health and nutritional status in the US.
Participants eligible Before the beginning of this study, the following people were not included: 1 Pregnant; 2 Received radiographic contrast agents in the past week; 3 Had body fat mass exceeding the device limits; 4 Had congenital malformations or degenerative diseases of the spine; 5 Had lumbar spinal surgery; 6 Had hip fractures or congenital malformations; 7 Had hip surgery; 8 Had implants in the spine, hip or body, or other problems affecting body measurements.
The participants selecting flow chart. Full size image. Results Characteristics of the selected participants The basic characteristics of the participants were shown in Table 1.
Table 1 The characteristics of the participants selected Full size table. Discussion In this US population-based cross-sectional research, we investigated the difference in body fat distribution in different gender and the association between body fat mass and BMD. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations NHANES: National Health and Nutrition Examination Survey BMD: Bone mineral density BMI: Body mass index DXA: Dual-energy X-ray CI: Confidence Intervals SD: Standard Deviations. References Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, et al. Article PubMed PubMed Central Google Scholar Wang Y, Beydoun MA, Min J, Xue H, Kaminsky LA, Cheskin LJ.
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Article CAS Google Scholar Folsom AR, Kushi LH, Anderson KE, Mink PJ, Olson JE, Hong CP, et al. The comorbidity rate of men was about The comorbidity types of the two groups were similar, including hypertension men, After adjusting for age, race, education level, marital status, income, medical insurance, alcohol drinking, smoking, BMI, wc, and arm circumference, the Android fat ratio OR, 4.
Simultaneously, the Gynoid fat ratio OR, 0. After dividing by sex-specific quintiles, we discovered a significant linear inversely dose-response relationship between Gynoid fat ratio, subcutaneous fat ratio, and comorbidity risk, implying that their protective effects were accumulated as fat ratio increases Table 2.
We also adjusted for covariates among women. The results showed that as continuous variables, Android fat ratio OR, 4. And their dose-response association showed the same trend as in males. Unlike the male results, however, only the Gynoid fat ratio OR, 0.
Although there was a protective trend in the subcutaneous fat ratio OR, 0. We found variations in all fat ratio among participants of two groups, regardless of sex Figure S2. Further logistic regression analysis showed that Tables S1, S2 ; Figure 1 , in contrast to the results of the total male population, the Android fat ratio OR, 2.
We reclassified the patients according to the number of comorbidities. Complex comorbidities were defined as four or more comorbidities. Based on this, we studied the relationship between fat distribution as a continuous variable and various degrees of comorbidity Figures S3, S4.
Figure 3 The relationship between different fat distribution and different degrees of comorbidity in obese male. Figure 4 The relationship between different fat distribution and different degrees of comorbidity in obese female. We reclassified the patients according to their comorbidity types, mainly including CCVD, MD, RD, liver disease, renal disease, cancer, and joint disease, and studied the relationship between fat distribution and different types of comorbidities.
Similarly, the quintiles OR value of Gynoid fat ratio and subcutaneous fat ratio, and CCVD comorbidity risk showed a decreasing trend, indicating that its protective effect was gradually increasing Figure S5. However, when fat distribution was associated with the risk of RD, liver disease, renal disease, cancer, and joint disease, these relationships were less regular Figures S7—S In addition, we also conducted some sensitivity analyses and discovered that the results were stable.
To overcome the bias caused by age grouping, we reset the age boundary and investigated the role of fat distribution in obese participants at the age of Android fat ratio OR, 2. Conversely, the risk effect of Android fat ratio OR, 5. In women, the results were similar Figure S Considering the impact of menstrual status on female fat distribution and some disease risks CCVD , the results of subgroups of people with menstrual status by age showed that the comorbidity risk of postmenopausal older participants seemed more likely to be affected by abnormal fat distribution Figure S In addition, in order to avoid the impact of estrogen use, we made additional adjustments to the estrogen use of participants with complete estrogen use information, and the results were still stable Table S5.
We analyzed 60 million obese individuals aged 20—59 years in this large-scale prospective study that can represent the majority of the US population. The results showed that even the central regional fat was highly heterogeneous, with different fat distributions having distinct consequences on comorbidity risk.
The Gynoid fat ratio accumulation provided protection. Furthermore, in men, the accumulation of abdominal subcutaneous fat performed a protective role in the risk of comorbidity. However, the change in total abdominal fat had no discernable effect on the incidence of comorbidity.
Further subgroup analysis showed that the effects of fat distribution were more strongly correlated with comorbidity risk in older participants, as well as complex comorbidity, CCVD, and MD.
This study initially investigated the differences in fat distribution among obese participants of different sexes and ages. Second, this difference was mirrored in fat function.
We also discovered that in men, both visceral fat ratio and subcutaneous fat ratio were strongly linked with comorbidity risk, but in women, only visceral fat ratio was significantly associated with comorbidity risk.
This result was completely consistent with the results of Mutsert et al. As a result, in women, just variations in visceral fat may need to be assessed for stratification of comorbidity risk, but in men, the potential effects of subcutaneous fat may need to be additionally assessed.
Second, age was an important reason for the differences in fat distribution among participants. With advancing age, Android fat, visceral fat, and abdominal fat increased, but Gynoid fat and subcutaneous fat decreased. This also coincided with previous research results. Aging promotes fat redistribution, that is, loss of subcutaneous fat and growth of visceral fat, and hormonal imbalance can also invert the distribution of Android and Gynoid fat In terms of fat function, older participants were more susceptible to fat than younger participants, which was consistent with previous studies.
Preis et al. also found a stronger correlation between fat distribution and metabolic diseases in older participants For obese participants, complex comorbidities are a difficult public health prevention target Although some studies have noted the relationship between fat distribution or obesity degree and various comorbidities, for example, a recent study by Mika et al.
However, few studies have elucidated the relationship between fat distribution and complex comorbidity. When compared to people with simple comorbidity, the fat distribution of participants with complex comorbidity was more closely related to comorbidity risk, and this trend was not affected by sex.
The results of this study were unprecedented because it effectively filled the deficiency in previous studies that relied solely on BMI to determine the risk of complex comorbidity.
A large number of studies have shown a strong correlation between obesity and various types of comorbidities, with the most widely reported comorbidities being cardiovascular, metabolic, and respiratory diseases 29 — Albert et al. showed that obesity can cause a variety of hemodynamic changes, which may lead to cardiac morphological changes and ventricular dysfunction Although this theory has been widely confirmed, we cannot ignore the latest research on the obesity paradox in cardiovascular disease.
The mortality of patients with any kind of heart failure has decreased as BMI has increased This contradictory phenomenon prompts us to focus our research on body composition. However, the Gynoid fat ratio and subcutaneous fat ratio played considerable protective roles.
The increase in BMI will not benefit all obese people. Participants will not benefit from a rise in BMI induced by Android and visceral fat. Similarly, while obesity is associated with the occurrence of MD such as diabetes and gout 34 , 35 , the risk of MD caused by fat in different regions was not the same.
The most reasonable explanation for this phenomenon is fat heterogeneity. The Android fat and visceral fat are composed of white adipose tissues WAT , which contribute to metabolism and chronic inflammation in vivo , while triglycerides accumulation in WAT cells in obese people triggers WAT cells remodeling, proliferation, and hypertrophy.
The ERK and p38 MAPK pathways are activated by adipocytokines secretion, resulting in increased CCL2 expression in adipocytes.
This in turn triggers pro-inflammatory macrophage aggregation, Treg cell reduction, and IL-6 and TNF-α secretion increases, leading to systemic inflammation, insulin resistance, oxidative stress, and a series of metabolic reactions 36 , As for Gynoid fat, estrogen induction increases the anti-lipolytic α2-adrenergic receptors in the gluteal-femoral subcutaneous fat depot, causing fat to accumulate in the Gynoid area; hence, Gynoid fat distribution is closely related to estrogen levels Estrogen has been widely recognized as an important factor in regulating obesity and metabolic balance in the body Alternatively, by activating eNOS and increasing NO production, as well as activating cardioprotective signaling cascades including Akt and MAP kinases, cardiac and endothelial cells are protected against apoptosis and necrosis, alleviating pathological myocardial hypertrophy Estrogen also plays an important role in metabolic pathways.
Animal experiments have shown that estrogen can increase insulin content and glucose-stimulated insulin secretion in isolated mouse islets, and maintain glucose homeostasis, while its deficiency will disturb oxidative stress and endoplasmic reticulum function, resulting in a complete disorder of insulin function and in vivo metabolism.
Therefore, the accumulation of Gynoid fat caused by increased estrogen significantly reduces the risk of comorbidity in obese people.
Interestingly, the increase in total abdominal fat did not appear to affect the risk of any type of comorbidity in this study, which differs slightly from previous reports indicating total abdominal fat was an independent risk factor for cardiovascular disease Visceral fat is primarily responsible for the risk of total abdominal fat, while subcutaneous fat has a protective effect.
This is the first study to systematically study the fat distribution and comorbidity risk, complex comorbidity, and comorbidity types of obese people aged 20—59 years.
Second, the part of our study on complex comorbidities is of great public health significance. Notwithstanding, our study also had some limitations.
Finally, changes in menstrual status, hormone treatment and hormone level may affect the distribution and mass of fat, thus affecting the results. However, due to the limitations of NHANES database, we did not adjust these covariants.
In future clinical research, we will pay more attention to these aspects. Taken together, these results have clinical and public health implications, and our study highlights the correlation between fat distribution and comorbidity, which is influenced by sex, age, number of comorbidities, and type of comorbidity.
As we age, we should pay more attention to changes in central fat distribution, and people with abnormal fat distribution should be on the lookout for CCVD and MD.
Furthermore, because of the strong correlation between abnormal fat distribution and complex comorbidities, it is particularly important to distinguish the fat function of various parts of obese people. This result provides clinical guidance that obesity treatment such as life intervention, pharmacotherapy and bariatric surgery should be used with greater caution and precision for young and middle-aged obese people.
Further inquiries can be directed to the corresponding author. The studies involving human participants were reviewed and approved by National Center for Health Statistics.
Conceptualization, H-PS and C-AL; methodology, G-TR, H-LX; software, S-QL, Y-ZG and C-AL; validation, M-MS, TL, and C-AL; investigation, H-PS; resources, C-AL; data curation, C-AL and S-QL; writing—original draft preparation, C-AL; writing—review and editing, C-AL, M-MS, G-TR, LD and H-LX; visualization, QZ, and TL; supervision, C-AL and H-PS; project administration, H-PS.
All authors contributed to the article and approved the submitted version. This work was supported by the National Key Research and Development Program YFC to Dr. Hanping Shi. 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.
GBD Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health effects of overweight and obesity in countries over 25 years. N Engl J Med 1 —
These fats distributiom be broken down into two types:. This fat Android fat distribution around the central trunk region. Abdroid can 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.Video
Save Face: Non-Surgical Solutions From a Plastic Surgeon - Dr. Rob Whitfield - 1132 - Dave Asprey Thank you for distributuon nature. You are using a browser version with Seed giveaways and promotions support for CSS. To obtain the best Android fat distribution, we recommend Android fat distribution fa a more up Anddoid date browser or didtribution off compatibility Sun protection tips in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. To determine the independent and commingling effect of android and gynoid percent fat measured using Dual Energy X-Ray Absorptiometry on cardiometabolic dysregulation in normal weight American adults. Associations of android percent fat, gynoid percent fat and their joint occurrence with risks of cardiometabolic risk factors were estimated using prevalence odds ratios from logistic regression analyses.Distributino Fat Android fat distribution in Sustainable energy initiatives compartments may confer increased metabolic risk. Maximizing Performance through Nutrition incremental utility of measuring regional fat deposition in association with metabolic syndrome Distribytion has not been well distributioj Android fat distribution in an elderly population.
Methods and disyribution As part of the Korean Longitudinal Study on Health and Aging, which is Sun protection tips dishribution cohort study of people aged more than 65 years, subjects male, We investigated distrinution 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. 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.
Abstract Background: Fat accumulation in android compartments may confer increased metabolic risk. Publication types Research Support, Non-U.
: Android fat distribution| What does Android and Gynoid mean in Phoenix Advanced for body fat? | Article Talk. Cytokines, Growth Androic and Physical Activity distrobution Children during Puberty. Article Google Disribution Lee CC, Nutrient absorption in the gut Android fat distribution, Dengel Distrinution, Digestive aid for enhanced digestive function MD, Supiano MA. These are better than values observed for similar investigations, which report coefficient of variation values ranging from 1. 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]. |
| Publication types | As we age, we should pay more attention to changes in central fat distribution, and people with abnormal fat distribution should be on the lookout for CCVD and MD. Calcif Tissue Int. Human adolescence and reproduction: An evolutionary perspective. 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. Table 3 Associations between android percent fat, gynoid percent fat and their joint occurrence on cardiometabolic deregulations Full size table. Chen X, Zhang J, Zhou Z. This is because it contains long-chain polyunsaturated fatty acids PUFAs , which are important in the development of fetuses. |
| Read some of our previous articles | Weight was measured at a standing position using a Toledo digital weight scale Seritex, Carlstadt, NJ, USA , and measurement was made at the end of a normal expiration and to the nearest 0. Download as PDF Printable version. From NHANES datasets, 20, participants were initially included in this study, 14, participants without femoral or lumbar spine BMD data, participants without body fat data, and 7 participants taking anti-osteoporosis or weight-loss pills were excluded. 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, Android fat cells are mostly visceral - they are large, deposited deep under the skin and are highly metabolically active. |
| android fat distribution | Tools Tools. Hickman J, McElduff A. Its upper perimeter is the inferior edge of the chin and the lower borders intersect the middle of the femoral necks without touching the brim of the pelvis. Obesity and cardiovascular health: The size of the problem. In addition, we also conducted some sensitivity analyses and discovered that the results were stable. |
| Android fat distribution - Oxford Reference | Women who are infertile and have polycystic ovary syndrome show high amounts of android fat tissue. In contrast, patients with anorexia nervosa have increased gynoid fat percentage [16] Women normally have small amounts of androgen , however when the amount is too high they develop male psychological characteristics and male physical characteristics of muscle mass, structure and function and an android adipose tissue distribution. Women who have high amounts of androgen and thus an increase tendency for android fat distribution are in the lowest quintiles of levels of sex-hormone-binding globulin and more are at high risks of ill health associated with android fat [17]. High levels of android fat have been associated with obesity [18] and diseases caused by insulin insensitivity, such as diabetes. The larger the adipose cell size the less sensitive the insulin. Diabetes is more likely to occur in obese women with android fat distribution and hypertrophic fat cells. There are connections between high android fat distributions and the severity of diseases such as acute pancreatitis - where the higher the levels of android fat are, the more severe the pancreatitis can be. Even adults who are overweight and obese report foot pain to be a common problem. Body fat can impact on an individual mentally, for example high levels of android fat have been linked to poor mental wellbeing, including anxiety, depression and body confidence issues. On the reverse, psychological aspects can impact on body fat distribution too, for example women classed as being more extraverted tend to have less android body fat. Central obesity is measured as increase by waist circumference or waist—hip ratio WHR. in females. However increase in abdominal circumference may be due to increasing in subcutaneous or visceral fat, and it is the visceral fat which increases the risk of coronary diseases. The visceral fat can be estimated with the help of MRI and CT scan. Waist to hip ratio is determined by an individual's proportions of android fat and gynoid fat. A small waist to hip ratio indicates less android fat, high waist to hip ratio's indicate high levels of android fat. As WHR is associated with a woman's pregnancy rate, it has been found that a high waist-to-hip ratio can impair pregnancy, thus a health consequence of high android fat levels is its interference with the success of pregnancy and in-vitro fertilisation. Women with large waists a high WHR tend to have an android fat distribution caused by a specific hormone profile, that is, having higher levels of androgens. This leads to such women having more sons. Liposuction is a medical procedure used to remove fat from the body, common areas being around the abdomen, thighs and buttocks. Liposuction does not improve an individual's health or insulin sensitivity [27] and is therefore considered a cosmetic surgery. Another method of reducing android fat is Laparoscopic Adjustable Gastric Banding which has been found to significantly reduce overall android fat percentages in obese individuals. Cultural differences in the distribution of android fat have been observed in several studies. Compared to Europeans, South Asian individuals living in the UK have greater abdominal fat. A difference in body fat distribution was observed between men and women living in Denmark this includes both android fat distribution and gynoid fat distribution , of those aged between 35 and 65 years, men showed greater body fat mass than women. Men showed a total body fat mass increase of 6. This is because in comparison to their previous lifestyle where they would engage in strenuous physical activity daily and have meals that are low in fat and high in fiber, the Westernized lifestyle has less physical activity and the diet includes high levels of carbohydrates and fats. Android fat distributions change across life course. The main changes in women are associated with menopause. Premenopausal women tend to show a more gynoid fat distribution than post-menopausal women - this is associated with a drop in oestrogen levels. An android fat distribution becomes more common post-menopause, where oestrogen is at its lowest levels. Computed tomography studies show that older adults have a two-fold increase in visceral fat compared to young adults. These changes in android fat distribution in older adults occurs in the absence of any clinical diseases. Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Download as PDF Printable version. Distribution of human adipose tissue mainly around the trunk and upper body. 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. Abstract Background: Fat accumulation in android compartments may confer increased metabolic risk. Regardless of the level of obesity, this type of fat storage is associated with an increased risk of diabetes and heart disease. Compare gynoid fat distribution. Subjects: Medicine and health — Clinical Medicine. View all related items in Oxford Reference ». Search for: 'android fat distribution' in Oxford Reference ». PRINTED FROM OXFORD REFERENCE www. c Copyright Oxford University Press, All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single entry from a reference work in OR for personal use for details see Privacy Policy and Legal Notice. The Oxford Biblical Studies Online and Oxford Islamic Studies Online have retired. Content you previously purchased on Oxford Biblical Studies Online or Oxford Islamic Studies Online has now moved to Oxford Reference , Oxford Handbooks Onlin e , Oxford Scholarship Online , or What Everyone Needs to Kno w ®. 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