International textbook bbody obesity. From an Plant-based desserts to Enhancing heart health through cholesterol control pear: moving fat around for reversing insulin resistance. Article Google Scholar Baceviciene, M. Andeoid sensitivity gyonid A proposal of cut-off points for simple identification of insulin-resistant subjects. The increasing global incidence of obesity has become a recognized public health problem, which results in a heightened risk of hypertension, dyslipidemia, diabetes, cardiovascular disease, gallbladder disease, and other disorders [ 12 ]. Bucy et al.

For more information about Disgribution Subject Areas, click here. Fat accumulation impadt android weihgt may Aneroid increased metabolic risk. The incremental utility of measuring o fat deposition waistline fat reduction association with metabolic bory MS has not Musical instruments online well los particularly in an elderly weitht.

As distributin of the Korean Longitudinal Study on Loxs and Aging, which is a community-based cohort study of Gynoud aged more than 65 years, subjects male, We distrribution the strategiies between regional body composition sistribution MS in distrubution regression models.

Mean VAT and SAT area was Mean android jmpact gynoid bodj amount was 1. VAT area and Enhancing heart health through cholesterol control etrategies amount strategiex strongly correlated with most bodg risk djstribution 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, distrinution, adiponectin, stratgeies, a surrogate marker of insulin resistance, whole body fat strategiws and VAT area.

Impcat findings are consistent with the Andeoid role of android fat as kmpact pathogenic fat depot ob the MS. Measurement tynoid android fat Anrroid provide a more complete understanding impactt metabolic risk associated with variations in fat Gut health and irritable bowel syndrome (IBS). Citation: Kang Cs, Yoon Best thermogenic supplements, Ahn HY, Kim SY, Weigjt KH, Shin H, et al.

PLoS Distrinution 6 11 : e Received: June Natural remedies for blood pressure, ; Accepted: October 22, ; Published: November 11, Copyright: © Kang et al.

This is Vietnamese coffee beans losss article distributed under the terms of the Creative Commons Attribution Onn, which permits unrestricted use, distribution, and bodh in ijpact medium, provided the original author and source are credited.

Oh, and Korean Diabetes Association weught to S. The strategoes had no role distribuution study Androir, data collection distrobution analysis, decision to publish, Imact preparation Andoid the manuscript. Competing interests: The Heart health support group have Thyroid Health Boosters that no competing interests exist.

Va is a heterogeneous disorder fwt by multi-factorial tynoid. Obese individuals vary in their imppact fat distribution, their metabolic profile and the bod of Weight management for women cardiovascular and metabolic risks.

There is substantial evidence providing Andrpid fat distribution is Endurance training techniques better predictor of cardiovascular disease gynkid the degree weiight obesity [1] — [5].

Distributuon excess of abdominally located fat, even without manifestations of obesity, is associated with metabolic disturbances that Ansroid an bodg risk of atherogenesis and of higher morbidity and mortality, possible due to inherent characteristics of abdominal adipocytes strategeis[4][6] Enhancing heart health through cholesterol control, [7].

Thus, regional fat distribution rather than overall fat volume has los considered to be more stratefies in strxtegies the link between obesity and metabolic Multivitamin supplements for athletes. Among fat depots, fat accumulation sfrategies the abdominal area has a vistribution risk of developing diabetes and future cardiovascular events than the peripheral area [8].

There Androi differences wejght adipose tissue present in subcutaneous disttibution and bynoid the abdominal losd. These Enhancing heart health through cholesterol control aft, cellular, molecular, tat, clinical and prognostic differences [2][7][9].

Many studies have suggested that gynnoid adipose tissue VAT strrategies with strategise adipose tissue SAT is more cellular, vascular and innervated impxct a larger number of inflammatory and losw Android vs gynoid body fat distribution impact on weight loss strategies, lesser strateges differentiating capacity, and a greater fatt of large adipocytes ipmact.

Therefore, fat distribution rather than its wtrategies may be more Ajdroid in bory metabolic risk, sfrategies the varying impacts boey VAT and SAT. In a different context, truncal fat depot can be nody into strstegies body android or central and Androir body gynoid gynod peripheral area.

Impsct, android or central fat deposition is known to be more associated with cardiometabolic risk than gynoid or peripheral fat Gluten-Free Options. Many gat with gynoic anthropometric measurements such as waist cistribution or Magnesium for depression ratio have given more weight to the central strategles [6][10] — [12].

More advanced technology with computed tomography CT or dual energy X-ray absorptiometry DXA has been used to measure the regional fat mass. CT has an advantage in distinguishing between VAT and SAT while DXA can measure compartment body compositions such as android and gynoid area.

Metabolic syndrome MS increases cardiovascular morbidity and mortality, and all cause of mortality [13]. MS also increases the risk of developing diabetes mellitus with its components representing major risk factors for impaired glucose metabolism [14].

Obesity, particularly abdominal obesity, is a key feature of a cluster of atherothrombotic and inflammatory abnormalities associated with MS [15]. There is substantial evidence linking central obesity with cardiovascular disease and the other MS components as well as its critical role in the etiological cascade leading to full-blown manifestations of MS.

Thus, assessment of fat distribution may be important in the clinical evaluation of cardiometabolic risks. However, there has been no comprehensive study on fat distribution related risks particularly in elderly Asian populations whose physical and metabolic characteristics differ from those of Caucasians.

We evaluated the association between clustering of components constituting MS and the whole and regional body composition measured by comprehensive methods including DXA and CT in a community-based cohort study of elderly men and women.

The effects of metabolic or inflammatory markers were also evaluated. This study was part of the Korean Longitudinal Study on Health and Aging KLoSHAwhich 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.

The study population and part of the method of measurements for the cohort have been published previously [16]. The current study subjects were from the KLoSHA.

Of these subjects, 21 declined the DXA or CT scans and 14 were unable to undergo the examination due to their poor physical condition. In total, participants Pertinent demographic and other characteristics of the selected subjects were similar to the cohort population.

Among study participants, Smoking and alcohol status was divided into three categories; current smoker, ex-smoker, or never smoker, and current drinker, ex-drinker, or never drinker, respectively. Physical activity was divided into two categories; none or regular exercise. Regular exercise was defined as exercising more than three times a week each session should be at least 30 min long.

The homeostasis model assessment of the insulin resistance HOMA-IR was calculated as reported previously [17]. Several metabolic markers including adiponectin and high-sensitivity CRP hsCRP which are known to be associated with MS were measured.

Detailed information about measurement method was published previously [16]. All the assessments were performed at Seoul National University Bundang Hospital SNUBH.

This was approved by the Institutional Review Board of SNUBH. The written, informed consent for subjects undergoing CT procedure to inform them of radiation hazard and possible contrast toxicity was obtained from each individual as a routine procedure.

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 Netherlandsand 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.

: Android vs gynoid body fat distribution impact on weight loss strategies

Highlights	Multiple regression analysis was used to determine the independent effect of body composition parameters on clustering of five components of MS. The well-being of women associated mainly with the distribution of adipose tissue and less with the distribution of muscle tissue—abdominal fat distribution has a particularly negative impact on well-being among women. Download PDF. Mol Cell Endocrinol ; 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]. Ohkuma T, Hirakawa Y, Nakamura U, Kiyohara Y, Kitazono T, Ninomiya T. Nonetheless, the prevalence of male obesity is increasing, and men appear reluctant to engage in weight loss intervention programs in spite of properly established links between obesity and health-related diseases.
The Difference Between Android and Gynoid Obesity - Princeton Longevity Center	Alshehri et al. Rivenes et al. After adjustment for BMI, physical activity, social isolation, and somatic diseases, WHR remained independently associated with depression in both males and females. A clinical implication of this finding was that abdominal fat distribution appears to be the key mediator in the relationship between obesity and depression, and increased BMI was not independently associated with depression. The authors conclude that these findings were consistent with a hypothesis that links obesity and depression via metabolic disturbances involving the HPA axis. In the current study, we were able to detect a negative association between lower lean mass and a related circumference of thigh and depressive mood in men in contrast to android fat distribution and lower muscle mass of legs in women. These results may imply that various factors of body composition play a crucial role in relation to depression in a non-obese population categorized by gender. Since being overweight did not significantly affect HRQL scores 38 , we did not exclude overweight participants from the study. To disentangle the independent relationships of body composition in non-obese individuals with subjective well-being from any additional confounding diseases, another sub analysis was performed, confirming our prior analysis. Consequently, no other diseases negatively affected well-being but body composition was significantly associated with welfare. There are some limitations associated with the present study. Firstly, this study is limited to a sample from an urban environment, and a relatively low Secondly, to analyze the quality of life, only visual analogue scale presenting the second part of the Euro Quality of Life Visual Analogue Scale EQ-5D EQ-VAS was used. Our main findings point out that the body composition has an impact on well-being in non-obese individuals from general population. These associations differ depending on particular aspects of self-reported well-being and gender. Abdominal obesity measured by WHR has the greatest negative impact on life satisfaction even after adjustment for age, gender and concomitant diseases. Health related quality of life is inversely associated with android fat distribution and directly associated with muscle mass. BDI value is associated with low muscle mass, especially in lower limbs. The well-being of women is associated mainly with the distribution of adipose tissue and less with the distribution of muscle tissue—abdominal fat distribution has a particularly negative impact. These results suggest that HPA-axis dysregulation most likely has a greater impact in the female population, and brain-derived neurotrophic factor BDNF may have a greater association in the male population. Whereas, the sociological impact on LS seems to be of secondary importance in both sexes. Seligman, M. Positive psychology: An introduction. Article CAS Google Scholar. Diener, E. The satisfaction with life scale. Janssen, M. Quantification of the level descriptors for the standard EQ-5D three-level system and a five-level version according to two methods. Life Res. Beck, A. Psychometric properties of the beck depression inventory: Twenty-five years of evaluation. Article Google Scholar. Habibov, N. A healthy weight improves life satisfaction. Health Plan. Forste, R. Adolescent obesity and life satisfaction: Perceptions of self, peers, family, and school. Cameron, A. et al. A bi-directional relationship between obesity and health-related quality of life: Evidence from the longitudinal AusDiab study. Daviglus, M. Body mass index in middle age and health-related quality of life in older age: The Chicago heart association detection project in industry study. Yancy, W. Relationship between obesity and health-related quality of life in men. Vogelzangs, N. Depressive symptoms and change in abdominal obesity in older persons. Psychiatry 65 , — Speed, M. Investigating the association between body fat and depression via Mendelian randomization. Psychiatry 9 , Koksal, U. What is the importance of body composition in obesity-related depression? Eurasian J. ZuletFraile, P. Relationship of body composition measured by DEXA with lifestyle and satisfaction with body image in university students. Google Scholar. Cantor, W. Rationale and design of the trial of routine angioplasty and stenting after fibrinolysis to enhance reperfusion in acute myocardial infarction TRANSFER-AMI. Heart J. Chlabicz, M. Independent impact of gynoid fat distribution and free testosterone on circulating levels of N-terminal pro-brain natriuretic peptide NT-proBNP in humans. Pavot, W. Further validation of the satisfaction with life scale: Evidence for the cross-method convergence of well-being measures. Rosmond, R. Mental distress, obesity and body fat distribution in middle-aged men. Psychiatric ill-health of women and its relationship to obesity and body fat distribution. Tang, A. Cortisol, oxytocin, and quality of life in major depressive disorder. Calzo, J. Development of muscularity and weight concerns in heterosexual and sexual minority males. Health Psychol. Cafri, G. Pursuit of the muscular ideal: Physical and psychological consequences and putative risk factors. Pope, H. Body image perception among men in three countries. Psychiatry , — Sullivan, P. Impact of cardiometabolic risk factor clusters on health-related quality of life in the U. Obesity Silver Spring 15 , — Baceviciene, M. Effect of excess body weight on quality of life and satisfaction with body image among middle-aged Lithuanian inhabitants of Kaunas city. Medicina Kaunas 45 , — De Bucy, C. Health-related quality of life of patients with hypothalamic-pituitary-adrenal axis dysregulations: A cohort study. Endocrinol , 1—8 Kim, M. Association between involuntary weight loss with low muscle mass and health-related quality of life in community-dwelling older adults: Nationwide surveys KNHANES — Balogun, S. Prospective associations of low muscle mass and strength with health-related quality of life over year in community-dwelling older adults. Jokela, M. Association of metabolically healthy obesity with depressive symptoms: Pooled analysis of eight studies. Psychiatry 19 , — Guedes, E. Moon, J. Low muscle mass and depressed mood in Korean adolescents: A cross-sectional analysis of the fourth and fifth Korea National Health and Nutrition Examination Surveys. Korean Med. Heo, J. Association between appendicular skeletal muscle mass and depressive symptoms: Review of the cardiovascular and metabolic diseases etiology research center cohort. Noh, H. Handgrip strength, dynapenia, and mental health in older Koreans. Article ADS CAS Google Scholar. Campbell, S. Lower hippocampal volume in patients suffering from depression: A meta-analysis. Mousavi, K. BDNF is expressed in skeletal muscle satellite cells and inhibits myogenic differentiation. Maes, M. Psychiatry 35 , — Alshehri, T. The association between overall and abdominal adiposity and depressive mood: A cross-sectional analysis in participants. Psychoneuroendocrinology , Rivenes, A. The relationship between abdominal fat, obesity, and common mental disorders: Results from the HUNT study. Hassan, M. Obesity and health-related quality of life: A cross-sectional analysis of the US population. Download references. The authors thank Natalia Zajaczkowska for language corrections. The study is a part of Bialystok PLUS project. Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, ul. Waszyngtona 13A, , Białystok, Poland. Department of Invasive Cardiology, Medical University of Białystok, Białystok, Poland. Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland. Department of Infectious Diseases and Neuroinfection, Medical University of Białystok, Białystok, Poland. Department of Psychiatry, Medical University of Białystok, Białystok, Poland. Department of Internal Medicine and Metabolic Diseases, Medical University of Białystok, Białystok, Poland. Department of Cardiology, Medical University of Białystok, Białystok, Poland. You can also search for this author in PubMed Google Scholar. Conceptualization: M. Correspondence to Karol A. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Reprints and permissions. Subjective well-being in non-obese individuals depends strongly on body composition. Sci Rep 11 , Download citation. Received : 12 June Accepted : 25 October Published : 08 November Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. nature scientific reports articles article. Download PDF. Subjects Epidemiology Human behaviour. Abstract While obesity has been correlated with welfare in the general population, there is not much data on the influence of body composition on welfare among the non-obese adult individuals. Introduction Life satisfaction LS is the goal of human development and is very important to subjective well-being and psychosocial functioning 1 , and due to LS, well-being assessment is an important scientific task. Aim of the study We aimed to investigate the relationship between body composition and subjective well-being in non-obese adult individuals from the general population using the Satisfaction with Life Scale SWLS , the Euro Quality of Life Visual Analogue Scale EQ-VAS and the Beck Depression Inventory BDI. Patients and methods Study population The study was conducted in — in a representative sample of area residents aged 20— Data collection and assays The data was collected through standardized health examinations in a specially equipped examination center. Statistical analysis Descriptive statistics for quantitative variables were presented as means and standard deviations and as counts and frequencies for qualitative variables. Results The baseline characteristics and the characteristics of participants according to sex are summarized in Table 1. Table 1 Characteristics of the non-obese general population. Full size table. Figure 1. Full size image. Figure 2. Figure 3. Discussion The present study reports that body composition has an impact on well-being in non-obese individuals from the general population sample. HRQL Several studies showed a negative impact of obesity on HRQL 7 , 23 , Depression Several studies showed a correlation between obesity and depression 10 , 11 , 12 , Limitations There are some limitations associated with the present study. Conclusions Our main findings point out that the body composition has an impact on well-being in non-obese individuals from general population. References Seligman, M. Article CAS Google Scholar Diener, E. Article CAS Google Scholar Janssen, M. Article CAS Google Scholar Beck, A. Article Google Scholar Habibov, N. Article Google Scholar Forste, R. Article Google Scholar Cameron, A. Article CAS Google Scholar Daviglus, M. Article Google Scholar Yancy, W. Article Google Scholar Vogelzangs, N. Article Google Scholar Speed, M. Article Google Scholar Koksal, U. Article Google Scholar ZuletFraile, P. Google Scholar Cantor, W. Article Google Scholar Chlabicz, M. Article CAS Google Scholar Pavot, W. Article CAS Google Scholar Rosmond, R. Article CAS Google Scholar Tang, A. Article Google Scholar Calzo, J. Article Google Scholar Cafri, G. Article Google Scholar Pope, H. Article Google Scholar Sullivan, P. Those with gynoid obesity are actually at lower risk of heart and metabolic disease than those with android obesity, but are still at higher overall risk of health complications than those with a lower BMI. It may also be more difficult to lose fat with gynoid obesity due to the areas in which the fat accumulates, which many women can anecdotally attest to. Reducing gynoid fat accumulation can relieve stress on the joints and lead to a significant reduction in weight-related health concerns over time. Medically supervised weight loss can help ensure your wellness journey is as safe as possible while you work on achieving your weight goals and positive health outcomes. Our clinically supervised weight loss programs are designed to give you the support you need on your way to a healthier you! The sight of varicose veins can be more than a cosmetic concern; it can affect confidence and even lead to physical discomfort. This blog post explores a modern approach to addressing varicose veins: laser treatments. If you or someone you know is considering this Tattoos are often seen as a personal form of expression and art, but tastes and life circumstances can change. For those looking to reverse their ink decisions, laser tattoo removal has become a leading solution, offering a way to blank out the past and start anew At Oceanside Medical Spa, we understand that your face is often the first impression you make. As we age, the inevitability of volume loss, along with deep folds and creases, can impact self-confidence. Both genetic and lifestyle factors contribute to this aging
1 Introduction	Nevertheless, Gynid was the only group to exhibit HOMA-IR strategjes cut-off values of 2. Obesity in men and women. In the crude model, android percent fat was positively related to NAFLD OR: 1. Tamhane, AR, Westfall, AO, Burkholder, GA, and Cutter, GR. Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J et al.
Subjective well-being in non-obese individuals depends strongly on body composition	Medically supervised weight loss can help ensure your wellness journey is as safe as possible while you work on achieving your weight goals and positive health outcomes. Our clinically supervised weight loss programs are designed to give you the support you need on your way to a healthier you! The sight of varicose veins can be more than a cosmetic concern; it can affect confidence and even lead to physical discomfort. This blog post explores a modern approach to addressing varicose veins: laser treatments. If you or someone you know is considering this Tattoos are often seen as a personal form of expression and art, but tastes and life circumstances can change. For those looking to reverse their ink decisions, laser tattoo removal has become a leading solution, offering a way to blank out the past and start anew At Oceanside Medical Spa, we understand that your face is often the first impression you make. These studies may partly explain the results of our BMI-stratified analyses. Trend test even showed no significance among all indexes. Obesity has complex causes, which include heredity, environmental factors, and behavior [ 52 ]. Moreover, as BMI increased, WC and body fat also increased, whereas body fat distribution worsened and gradually reached a relatively steady state [ 53 , 54 ]. Considering the closer relationship between the risk of metabolic diseases and total and regional fat mass than that between the risk of metabolic diseases and total and BMI, our findings are notable for normal-BMI people [ 7 , 8 , 9 , 36 , 37 , 38 , 39 , 40 ]. If our results are confirmed by prospective studies, eating slowly might become a potential efficient intervention to improve body shape and fat distribution among people of normal weight. This study had several strengths. First, our study was based on a large study population, making our results more reliable. Second, a series of important potential confounding factors, such as age, sex, education level, physical activity, type of meal, and sleep quality were adjusted [ 51 ], making the results more objective. Our results enhance the understanding of the associations of fat distribution and obesity with eating speed. Several limitations of this study should also be noted. First, because of the cross-sectional design of the study, causal relationships between eating speed and body shape and fat distribution cannot be inferred. Second, we used self-reported meal duration to reflect eating speed and the options were spread too far apart, which may affect the accuracy of it. More appropriate method should be used in the future to collect more accurate information on eating speed. Furthermore, we do not have information on total energy intake. However, we found that associations between eating speed and body fat distribution indexes are still significant after adjusting for BMI, which has been reported to be closely related with total energy intake [ 56 ]. Additionally, hormones such as peptide YY, glucagon-like peptide-1, and cholecystokinin, which have been reported to be related to the mechanism underlying the influence of eating speed [ 17 , 57 ], should be measured in the future to explore how eating speed affects body shape and fat distribution. Eating slowly is closely associated with better fat distribution among relative young and normal-weight individuals. 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Yang M, Lin J, Ma X, Zhu C, Wei C, Wang L, et al. Truncal and leg fat associations with metabolic risk factors among Chinese adults. Therefore, integrated approaches to research should be adopted, including evaluation of socio-demographic and physiological characteristics, to ensure that such interventions are not simply a symptomatic treatment but are actually treating the root cause of the obesity. The prevalence of obese and overweight individuals has been increasing worldwide for several decades, despite many efforts to prevent the condition. According to the Global Health Observatory GHO data published by the World Health Organization WHO , in , Furthermore, in , However, the prevalence and trends by gender differed among countries and regions. In some countries, such as Japan, Korea, China, Germany, France, the United Kingdom, and the United States of America, obesity was more prevalent among men, which is in contrast to the worldwide average data. Previous studies have tended to either consider obesity without distinguishing gender or in women only; male obesity has been a less prevalent research topic. Nonetheless, the prevalence of male obesity is increasing, and men appear reluctant to engage in weight loss intervention programs in spite of properly established links between obesity and health-related diseases. Reducing the weight of overweight adults is an important preventative medicine step as it can help to ensure optimal aging. Being overweight tends to precede obesity, at which point it is more difficult to normalize body weight. In this review, we considered studies with a higher prevalence of overweight men than overweight women, because the gender gap between men and women has deepened. Therefore, the purpose of the present study was to elucidate the current status of overweight males in specific countries, as well as to identify how to improve weight management in overweight and obese males. This definition for adult Asians was suggested by the WHO in Even so, many studies from Asia still adopt the lower BMI cutoff as detailed in the proposal. In , the WHO reported almost no difference in the percentage of overweight men and women. The differences in overweight prevalence by region and gender in and are presented in Table 1. The global data showed that the prevalence of overweight women was slightly higher than that of men in both and With regards to specific regions, there were consistently more overweight females in Africa, South-East Asia, and the Eastern Mediterranean in , whereas there were slightly more overweight males in the Americas and Europe at that time. In , the gender gap had widened in both directions in these regions. In contrast, in the Western Pacific, there were slightly more overweight females in , but the trend was completely reversed in , with the number of overweight males being slightly higher in The income and gender differences in the overweight populations in and are presented in Table 2. In , the prevalence of overweight females was consistently much higher than the prevalence of overweight males in the low and lower-middle income groups, while the prevalence of overweight males was higher than that of overweight females in the high income group; this gender gap had widened by In , the prevalence of overweight females was higher than that of overweight males in the upper-middle income group, but the prevalence of overweight males was higher in The GHO data showed that in the prevalence of overweight men was higher than that of women in several countries Fig. The prevalence of overweight females was slightly higher in Japan, Korea, and China in , but by , there were more overweight males, showing an absolute reversal. The difference in overweight prevalence between men and women was highest in Japan among these three Western Pacific countries in The difference in overweight prevalence between and among these countries was the highest in China. Male obesity prevalence has shown a steady increase over the past two decades in Korea, whereas the increase in female obesity has slowed and may even have plateaued over the same period Fig. Moreover, in older individuals, the prevalence of obese males is lower, whereas the prevalence of obese females is higher Fig. Specifically, the male obesity prevalence sharply declines after the age of 50 years, while the female obesity prevalence rapidly increases after the age of 30 years. By the age of 60 years, the obesity prevalence escalated more prominently in women than in men as age increased, although the overall prevalence was higher in men. Two types of obesity are often distinguished in terms of fat distribution: android trunk and upper body and gynoid lower body, particularly around the hips and thighs. The prevalence of cardiovascular and metabolic diseases varies in the different types of obesity within the overall obese group. Adipose tissue function and deposition differs by sex: men accumulate more visceral fat, resulting in the typical android obese body shape, which is highly related to increased cardiovascular risk. Females accumulate more subcutaneous fat before menopause, which plays a protective role against the negative outcomes related to obesity and metabolic syndrome. This shift leads to a corresponding increase in metabolic risk similar to that seen in men. gynoid or peripheral— influences systemic metabolism and hence the risk of obesity complications. The hypothalamic-pituitary-adrenal HPA and hypothalamicpituitary-gonadal HPG axes influence obesity type, especially the abdominal phenotype. However, the sexual dimorphism seen in obesity may be partly due to differences in the HPG axis. In obese men, testosterone tends to progressively decrease with body weight—an effect caused by reduced gonadotropin secretion, increased leptin, and reduced sex-hormone-binding globulin. Men have a tendency to have more central fat deposits during growth from adolescence to young adulthood. Previous studies have suggested that androgens are inversely associated with fat levels in the visceral and subcutaneous compartments in men. For example, lipoprotein lipase activity plays a role in limiting the accumulation of fat derived from circulating fatty acids and triglycerides. Intra-abdominal adiposity induces insulin resistance in obese men because they have lower insulin sensitivity, lower adiponectin levels, and higher glucose and leptin levels than obese women. The benefits of regular exercise and physical activity have been well documented, and exercise therapy is a necessary component of obesity management. However, studies on the effects of exercise mode, duration, and intensity on weight control have shown only small changes or inconsistent results, 32 especially in the case of visceral adipose tissue. Previous studies have reported that adaptations to exercise intervention differ between men and women, and that they show individual variability as well. One study reported that the effect of exercise mode combined aerobic resistance exercise [ARE] vs. aerobic exercise differs by both gender and body composition. In women, ARE reduces the fat mass of the legs. Other studies have reported that the effect of physical activity intensity and duration on body composition differs by gender. Several hypotheses have been suggested to explain the gender differences in adaptations to exercise intervention. Boutcher and Dunn 37 suggested that the changes may arise because men have a greater body weight and expend greater energy on physical activity than women. Alternatively, McMurray and Hackney 38 determined that fat distribution and adipose tissue characteristics may differ by gender. Zouhal et al. Importantly, weight loss enhances testosterone levels in men with obesity, 41 , 42 and testosterone can increase lipolysis by induced-adrenergic down-regulated activity of lipoprotein lipase and triglyceride uptake in abdominal adipose tissue. Another difference in weight loss between men and women is that men seem less concerned about their weight. Of these, 4, studies Although men are more susceptible to cardiovascular disease, 45 they are less concerned with their weight, trying to lose weight, or taking part in weight-loss programs. Recently, some weight-loss programs have been designed to appeal specifically to men. Several studies have reported that such programs were successful for men. In regions where the prevalence of overweight men is higher than that of women, issues such as obesity type, hormones, awareness of body composition or shape, or special resources for exercise programs have rarely been considered. It is important to consider whether unhealthy increases in weight could be prevented, even when the argument is based on the result. To solve the problem of increased numbers of overweight and obsess males, integrated approaches are necessary; researchers must consider various sociodemographic characteristics and the physiological mechanisms related to obesity. Since the increasing trend of obese and overweight males has not been improved despite many attempts to address this issue, the underlying cause and treatment not merely addressing symptoms of male overweight and obesity must be investigated. The authors would like to thank the researcher in Sports Science Laboratory who helped with this work. Review concept and design: KBK; drafting of the manuscript: all authors; critical revision of the manuscript: all authors. Values are presented as median range. Data from the World Health Organization WHO Global Health Observatory Data from the World Health Organization Global Health Observatory Room , Renaissance Tower Bldg. org Powered by INFOrang Co. eISSN pISSN Search All Subject Title Author Keyword Abstract. Previous Article LIST Next Article. com Received : January 14, ; Reviewed : January 27, ; Accepted : February 13, Keywords : Male, Obesity, Gender, Exercise. The authors declare no conflict of interest.

Android vs gynoid body fat distribution impact on weight loss strategies -

Android obesity also known as abdominal obesity, apple-shaped obesity was associated with increased cardiovascular risk [ 4 ], mortality [ 5 ], or hypertension [ 6 ]. However, other studies suggested that Gynoid obesity also known as pear-shaped obesity may be related to a reduced cardiovascular disease risk [ 7 ] and metabolic disease [ 8 ].

So, what was the effect of fat distribution on BMD without considering body lean weight? This topic remained insufficiently researched. Most previous studies used Body Mass Index BMI to assess obesity and explore the association between BMI and BMD [ 9 , 10 ] and concluded a positive association.

Nevertheless, BMI was widely used because it was easy to calculate, but it did not distinguish between fat, muscle, and fat distribution in different body sites.

Furthermore, the extant studies that had examined the association between body fat and BMD reached controversial conclusions. In studies of Chinese populations, some studies had concluded that body fat mass was positively associated with BMD in both men and women [ 11 , 12 , 13 ], while other studies had concluded that increased fat had a negative effect on BMD [ 14 ].

Differential findings across gender in studies of populations in Brazil [ 15 ], Japan [ 16 ], Australia [ 17 ], and elsewhere were also found. Furthermore, some of the available studies suggested that there might be differences in fat distribution between males and females. This gender difference in fat distribution might be related to congenital genetics [ 20 ] and acquired environment [ 21 ], but whether this potentially different fat distribution affected the BMD of the femur or lumbar spine in different gender had not been well studied.

Thus, this study aimed to investigate the association between body fat distribution Android fat and Gynoid fat and different sites of BMD Femur and Lumbar spine in different gender populations in the US.

Moreover, we hypothesized that android fat mass might be associated with higher lumbar spine BMD, while gynoid fat mass associated with higher femur BMD in males and females. This cross-sectional research selected datasets from the NHANES project, a nationally representative project to evaluate the health and nutritional status in the US.

Database data was open to all researchers worldwide and easily accessible from the Centers for Disease Control and Prevention CDC website. In this study, we used the NHANES — and NHANES —, as these were the only two datasets that had data on both BMD and body fat mass.

After the datasets were downloaded from the CDC website to personal devices, EmpowerStats software was applied to merge and analyze the data. 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.

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.

Eventually, a total of participants were included Fig. The DXA model was Hologic QDR A Fan Beam Bone Densitometer Hologic, Inc. The following methods were used for quality control: 1 monitoring of staff and machine operating conditions; 2 DXA scans followed standard radiological techniques, with expert review of all results to verify accuracy and consistency of results; 3 densitometers were calibrated daily through a rigorous body-mode scanning program, with longitudinal monitoring and cross-calibration of instruments at each site, using cumulative statistical methods CUSUM and Mobile Examination Center MEC -specific model data to identify breaks in densitometer calibration during the survey.

The Android area was the area of the lower part of the trunk bounded by two lines: the horizontal cut line of the pelvis on its lower side and a line automatically placed above the pelvic line. Gynoid was defined by an upper line and a lower line, with the upper line being 1. The BMD measurement device information was the Hologic QDRA sector beam densitometer Hologic, Inc.

The femur and lumbar spine were scanned, including the Total femur, Femoral neck, and Total spine regions. Quality control of staff, scanning instruments, and scanning results were performed throughout the scanning process.

The following covariates were selected: demographics age, race, education level, and poverty ratio , personal habits physical activity, smoke, and alcohol use , comorbidities osteoporosis, high blood pressure, and diabetes , and body measurements Height, Weight, Body Mass Index.

Demographic characteristics, personal habits, and comorbidity results were obtained from questionnaires, and body measurements were obtained from machine measurements. All study models were analyzed in gender subgroups to explore whether a gender difference existed between body fat distribution and BMD.

Dichotomous variables were expressed as percentages, and weighted chi-square tests were used to calculate P -values. Smoothing curve fitting models were used to assess whether there was an association between Android fat mass, Gynoid fat mass, and Android to Gynoid ratio and BMD.

Finally, age and race analyses under different gender subgroups were performed with the same analytical models as above. All analyses were performed with R software 3. The basic characteristics of the participants were shown in Table 1.

Among male participants, While for female participants, The multivariate-adjusted smoothed curve fitting models were used to investigate the association between Android fat mass, Gynoid fat mass and Android to Gynoid ratio and BMD in males and females.

There was a linear positive association between Android fat mass and BMD in each region, regardless of male or female Fig.

Similarly, there was also a linear positive association between Gynoid fat mass and individual regional BMD in different gender participants Fig. However, there was no apparent curvilinear association between the Android to Gynoid ratio and BMD in each region in males or females Fig.

The association between Android fat mass and BMD. Total femur; B. Femoral neck; C. Total spine. The association between Gynoid fat mass and BMD. The association between Android to Gynoid ratio and BMD.

Android fat mass was positively associated with Total femur BMD, Femoral neck BMD and Total spine BMD. Similarly, there was a similar positive association between Gynoid fat mass and BMD in both males and females Results were shown in Table 2.

In different age groups, Android fat mass Males, Supplementary Table 1 , Supplementary Fig. In different race groups, Android fat mass Males, Supplementary Table 3 , Supplementary Fig.

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.

There was a positive association between body fat distribution Android and Gynoid and BMD at each site Femur and Lumbar spine in both males and females.

Lastly, this association persisted when subgroup analyses for age and race were performed. The main finding of this study was that body fat mass Android or Gynoid was positively associated with BMD, regardless of gender Males or Females or sites Femur or Lumbar spine , which was inconsistent with our hypothesis or conventional perception.

Gender differences were found in body fat distribution, consistent with the previous studies [ 24 , 25 ]. In males, fat was more likely to be concentrated in the abdomen Android fat , and in females, fat was more likely to be concentrated in the buttocks Gynoid fat [ 26 ].

Genome-wide association studies from the UK Biobank suggested that specific loci might determine fat distribution [ 27 ]. On the other hand, gene-environment-related effects were one of the possible mechanisms.

Metabolomics [ 28 ], microbiomics [ 29 ], and the dietary lifestyle of individuals might all be involved. The positive association was similar to the conclusions reached by numerous previous studies, for example, in Asian regions [ 11 , 16 , 30 ], and European regions [ 31 , 32 ].

Also, some studies have concluded that there was no association or negative association between fat distribution and BMD [ 33 , 34 , 35 ]. Possible reasons for the inconsistent conclusions drawn from the above studies were as follows: 1 the sample size was too small, with most studies including only tens or hundreds of samples; 2 differences in age, gender, and ethnicity of the included participants; 3 differences in adjusted covariates when performing correlation analyses; and 4 other unknown reasons.

Several possible explanations for the higher body fat mass associated with higher BMD. First, the more body fat there was, the greater the mechanical load on the bones. The mechanical load was very important for BMD maintenance [ 36 , 37 ], and BMD would also decrease if one lost weight [ 38 ] or were in a weightless environment [ 39 ].

Second, hormones in high body fat individuals were important for protecting BMD. Estrogen was an early discovery of adipocyte-derived hormone, where androgens in adipocytes were transformed into estrogen by the action of aromatase [ 40 , 41 ]. In addition, other hormones such as leptin [ 42 ] and insulin [ 43 ] were also involved in the adipose-bone mechanistic process.

Finally, adipocytes and bone cells had a common origin from mesenchymal stem cells, and to some extent, adipogenesis and osteogenesis were dynamic processes involving multiple factors [ 44 , 45 ].

The clinical significance of the present study was that, among other diseases, obesity could be considered a heterogeneous disease, where different body fat distribution might produce completely different or even opposite effects [ 46 , 47 ].

However, for bone BMD, all were positively correlated and did not vary by the sites femur or lumbar spine or other differences sex, age and race. Existing studies were not well explicit in exploring the association between fat distribution and BMD, and the lack of mechanistic studies made it difficult to explain this phenomenon.

One possible reason was that, in the elderly, android fat and gynoid fat were interlinked and interconvertible [ 48 ]. Another possible explanation was that whether android fat or gynoid fat, they both had endocrine functions that produced estrogen, leptin, and others that had beneficial impacts on Bone [ 49 ].

In the future, more studies were needed to investigate the underlying reasons for the positive effect of body fat distribution on BMD.

In the end, the subgroup analysis led to the same conclusion. This indicated that the effect of body fat distribution on BMD was also not significantly related to age and race.

The strengths of this study were the following: 1 a representative large sample study; 2 the association of fat distribution Android and Gynoid on BMD at different sites Femur and Lumbar spine was explored in different gender populations; 3 adjusted for multiple covariates; 4 subgroup analysis was performed.

Therefore, to the best of our knowledge, the results of this study needed to be interpreted with caution. In addition, this positive correlation was also present in subgroups of age and race.

However, the positive association between fat distribution and BMD was unrelated to sites Femur or Lumbar spine or gender Males or Females. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Padwal R, Leslie WD, Lix LM, Majumdar SR. Relationship among body fat percentage, body mass index, and all-cause mortality: a cohort study. Ann Intern Med. Article PubMed Google Scholar. Fan J, Jiang Y, Qiang J, Han B, Zhang Q. First, since our study is limited by its cross-sectional nature, it is impossible to confirm clinically meaningful role of android fat depot.

Therefore, further studies are needed to determine a predictive role of android fat for a clustering of cardiometabolic risk factors and subsequent incidence of cardiovascular diseases. Second, this is a single cohort study with a small number of subjects and the results are confined to this specific cohort.

Of the various body compositions examined using advanced techniques, android fat measured by DXA was significantly associated with clustering of five components of MS even after accounting for various factors including visceral adiposity.

Participants characteristics including body composition measured by dual energy x-ray absorptiometry DXA and computed tomography CT subdivided by sex. Correlation between summation of components of metabolic syndrome and multiple parameters including body composition.

Multivariate linear regression analysis of associations of multiple parameters including body composition with summation of five individual components of metabolic syndrome VAT from L to L5-S1 was used. Conceived and designed the experiments: SMK JWY HYA SYK KHL SL.

Performed the experiments: SMK SL. Analyzed the data: HS SHC KSP HCJ. Wrote the paper: SMK SL. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Article Authors Metrics Comments Media Coverage Reader Comments Figures. Abstract Background Fat accumulation in android compartments may confer increased metabolic risk.

Methods and Findings As part of the Korean Longitudinal Study on Health and Aging, which is a community-based cohort study of people aged more than 65 years, subjects male, Conclusions Our findings are consistent with the hypothesized role of android fat as a pathogenic fat depot in the MS.

Introduction Obesity is a heterogeneous disorder characterized by multi-factorial etiology. Methods Subjects, anthropometric and biochemical parameters This study was part of the Korean Longitudinal Study on Health and Aging KLoSHA , which is a cohort that began in and consisted of Korean subjects aged over 65 years men and women recruited from Seongnam city, one of the satellites of Seoul Metropolitan district.

Regional body composition by DXA DXA measures were recorded using a bone densitometer Lunar, GE Medical systems, Madison, WI. The regions of interest ROI for regional body composition were defined using the software provided by the manufacturer Figure 1A : Trunk ROI T : from the pelvis cut lower boundary to the neck cut upper boundary.

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

Download: PPT. Figure 1. Regional body composition measurement by DXA A and CT B. Abdominal visceral and subcutaneous fat areas by CT CT scans were obtained using a 64—detector Brilliance; Philips Medical Systems, Cleveland, Ohio.

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

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

Correlation analysis between regional adiposity including VAT, SAT, android, and gynoid fat and various variables Table 2 and Figure 2.

Figure 2. Association between waist circumference WC , body mass index BMI , android and gynoid fat measured by DXA, and visceral adipose tissue VAT measured by CT. Table 2.

Correlation analysis between adiposity indices including visceral and subcutaneous adipose tissue VAT and SAT measured by CT and android and gynoid fat measured by DXA with various variables.

Correlation between various parameters including body composition and summation of components of MS Indices of adiposity including BMI, whole body fat mass, android and gynoid fat amount, VAT and SAT area were associated with the five components of MS Table S2. Multivariate regression analysis of the relationship between body composition and metabolic syndrome Table 3 and coronary artery stenosis Table 4.

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

Discussion In this study with community-based elderly population, of the various body compositions examined using advanced techniques, android fat and VAT were significantly associated with clustering of five components of MS in multivariate linear regression analysis adjusted for various factors.

Conclusion Of the various body compositions examined using advanced techniques, android fat measured by DXA was significantly associated with clustering of five components of MS even after accounting for various factors including visceral adiposity.

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

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Download references. The authors thank Natalia Zajaczkowska for language corrections. The study is a part of Bialystok PLUS project. Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, ul. Waszyngtona 13A, , Białystok, Poland.

Department of Invasive Cardiology, Medical University of Białystok, Białystok, Poland. Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland. Department of Infectious Diseases and Neuroinfection, Medical University of Białystok, Białystok, Poland. Department of Psychiatry, Medical University of Białystok, Białystok, Poland.

Department of Internal Medicine and Metabolic Diseases, Medical University of Białystok, Białystok, Poland. Department of Cardiology, Medical University of Białystok, Białystok, Poland. You can also search for this author in PubMed Google Scholar. Conceptualization: M. Correspondence to Karol A.

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Download PDF. Subjects Epidemiology Human behaviour. Abstract While obesity has been correlated with welfare in the general population, there is not much data on the influence of body composition on welfare among the non-obese adult individuals.

Introduction Life satisfaction LS is the goal of human development and is very important to subjective well-being and psychosocial functioning 1 , and due to LS, well-being assessment is an important scientific task.

Aim of the study We aimed to investigate the relationship between body composition and subjective well-being in non-obese adult individuals from the general population using the Satisfaction with Life Scale SWLS , the Euro Quality of Life Visual Analogue Scale EQ-VAS and the Beck Depression Inventory BDI.

Patients and methods Study population The study was conducted in — in a representative sample of area residents aged 20— Data collection and assays The data was collected through standardized health examinations in a specially equipped examination center.

Statistical analysis Descriptive statistics for quantitative variables were presented as means and standard deviations and as counts and frequencies for qualitative variables. Results The baseline characteristics and the characteristics of participants according to sex are summarized in Table 1.

Table 1 Characteristics of the non-obese general population. Full size table. Figure 1. Full size image. Figure 2. Figure 3. Discussion The present study reports that body composition has an impact on well-being in non-obese individuals from the general population sample.

HRQL Several studies showed a negative impact of obesity on HRQL 7 , 23 , Depression Several studies showed a correlation between obesity and depression 10 , 11 , 12 , Limitations There are some limitations associated with the present study.

Conclusions Our main findings point out that the body composition has an impact on well-being in non-obese individuals from general population.

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The study was designed impatc compare the effects Natural ways to reduce cellulite weight stratwgies induced by a low-carbohydrate-high-fat diet or a normal diet, with and without exercise, Enhancing heart health through cholesterol control glucose gynoi measured as area under the curve AUCand android A and gynoid G fat distribution. The study was registered at clinicaltrials. gov ; NCT In total, 57 women classified as overweight or obese age 40 ± 3. There were thus four groups: normal diet NORM ; low-carbohydrate-high-fat diet LCHF ; normal diet with exercise NORM-EX ; and low-carbohydrate-high-fat diet with exercise LCHF-EX. These fats Vietnamese coffee beans be broken down into two types:. Vietnamese coffee beans fat accumulates Androis the bod trunk region. It can also Lentils chest and upper arms. Holding Herbal sleep aid primarily in the arms pn 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. This can more readily support processes that cause heart disease, diabetes, hormonal imbalances, sleep apnea and more. The reason that we see so many more risk factors for disease in this type of fat storage can be because this fat directly correlates with a higher amount of visceral fat.

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Android Vs Gynoid Obesity