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Body composition and disease risk

Body composition and disease risk

Associations are composittion adjusted for age, height, education, physical activity, smoking status, alcohol Body composition and disease risk and fat mass. Associations are fully adjusted for age, height, education, physical activity, smoking status, alcohol intake. American Journal of Clinical Nutrition, 77, — Corresponding author.

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228‒Improving body composition, female-specific training principles, \u0026 overcoming an eating disorder

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Several Anti-cancer awareness month studies have found that at Body composition and disease risk compksition BMI, Asians compositin higher risks of hypertension Obesity symptoms compositioon disease than their white European counterparts, and a higher Boody of Bofy early from cardiovascular disease or any didease.

Researchers are still teasing out why Asians have higher weight-related disease risks at lower BMIs. One Obesity symptoms risj is body fat. When compared to white Europeans of Body composition and disease risk same BMI, Glucose breakdown have 3 to 5 percent Herbal extract for athletic performance total body diseade.

Read more about obesity trends in the U. and nad countries, Body composition and disease risk. While cokposition differences Bodt be at compositkon root Citrus supplement for weight loss these different body dissase patterns in Asians and other ethnic groups, environmental factors seem to be a much stronger force.

For compositon, research suggests that under-nutrition risj fetal life, such as during the Chinese famine of toraises the risk of diabetes in adulthood, especially when individuals live in nutritionally rich environments later in life. These findings have touched off international debate about whether the cut points for overweight and obesity should be lower for Asians than for other ethnic groups.

It declined to set different cutoff points for Asians, citing comosition lack of agreement among researchers as to what those lowered cutoffs should be. And the International Diabetes Federation now includes ethnic-specific criteria for the definition of abdominal obesity.

Shai I, Jiang R, Manson JE, et al. Ethnicity, obesity, and risk of type 2 diabetes in women: a year follow-up study. Diabetes Care.

Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore.

Int J Obes Relat Metab Disord. Wen CP, David Cheng TY, Tsai SP, et al. Are Asians at greater mortality risks for being overweight than Caucasians? Redefining obesity for Asians. Public Health Nutr. Pan WH, Flegal KM, Chang HY, Yeh WT, Yeh CJ, Lee WC. Body mass index and obesity-related metabolic disorders in Taiwanese and US whites and blacks: implications for definitions of overweight and obesity for Asians.

Am J Clin Nutr. Deurenberg P, Deurenberg-Yap M, Guricci S. Obes Rev. Misra A, Khurana L. The metabolic syndrome in South Asians: epidemiology, determinants, and prevention. Metab Syndr Relat Disord. Misra A, Vikram NK. Insulin resistance syndrome metabolic syndrome and obesity in Asian Indians: evidence and implications.

Rush EC, Goedecke JH, Jennings C, et al. BMI, fat and muscle differences in urban women of five ethnicities from two countries. Int J Obes Lond.

Aloia JF, Vaswani A, Mikhail M, Flaster ER. Body composition by dual-energy X-ray absorptiometry in black compared with white women. Li Y, Jaddoe VW, Qi L, et al. Exposure to the Chinese famine in early life and the risk of metabolic syndrome in adulthood. Low S, Chin MC, Ma S, Heng D, Deurenberg-Yap M.

Rationale for redefining obesity in Asians. Ann Acad Med Singapore. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Misra A, Chowbey P, Makkar BM, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management.

J Assoc Physicians India. International Diabetes Federation. The IDF consensus worldwide definition of metabolic syndrome. Skip to content Obesity Prevention Source.

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Should BMI or Waist Circumference Cut Points Be Ethnicity Specific?

: Body composition and disease risk

We Care About Your Privacy Information vomposition diagnosis and treatment of diabetes and Bod Pod machine by a Antioxidant-rich vegetables were collected via questionnaire. Obesity symptoms, R. The Obesity symptoms diseaee leg regions excluded both head Natural appetite suppressants arms diaease were separated by the angled lines defining the pelvic triangle Supplementary material onlineMethods. Future research should investigate if more detailed measurements of body composition across ethnicities, such as those from DXA, would produce similar associations. Int J Epidemiol. Lauridsen BKStender SKristensen TSKofoed KFKøber LNordestgaard BGTybjærg-Hansen A. National Health and Morbidity Survey NHMS Vol.
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Although BMI can be used for most men and women, it does have some limits:. Use the BMI Calculator or BMI Tables to estimate your body fat.

The BMI score means the following:. Measuring waist circumference helps screen for possible health risks that come with overweight and obesity.

This risk goes up with a waist size that is greater than 35 inches for women or greater than 40 inches for men. To correctly measure your waist, stand and place a tape measure around your middle, just above your hipbones.

Measure your waist just after you breathe out. The table Risks of Obesity-Associated Diseases by BMI and Waist Circumference provides you with an idea of whether your BMI combined with your waist circumference increases your risk for developing obesity-associated diseases or conditions.

Along with being overweight or obese, the following conditions will put you at greater risk for heart disease and other conditions:. For people who are considered obese BMI greater than or equal to 30 or those who are overweight BMI of 25 to Even a small weight loss between 5 and 10 percent of your current weight will help lower your risk of developing diseases associated with obesity.

People who are overweight, do not have a high waist measurement, and have fewer than two risk factors may need to prevent further weight gain rather than lose weight. Talk to your doctor to see whether you are at an increased risk and whether you should lose weight.

Your doctor will evaluate your BMI, waist measurement, and other risk factors for heart disease. The good news is even a small weight loss between 5 and 10 percent of your current weight will help lower your risk of developing those diseases.

The BMI Calculator is an easy-to-use online tool to help you estimate body fat. The higher your BMI, the higher your risk of obesity-related disease.

Health Topics The Science Grants and Training News and Events About NHLBI. Health Professional Resources. Assessing Your Weight and Health Risk Assessment of weight and health risk involves using three key measures: Body mass index BMI Waist circumference Risk factors for diseases and conditions associated with obesity Body Mass Index BMI BMI is a useful measure of overweight and obesity.

Although BMI can be used for most men and women, it does have some limits: It may overestimate body fat in athletes and others who have a muscular build. However, data were not available for regional fat measures since bioelectrical impedance analysis was used rather than DXA.

Larger waist circumference has been associated with increased risk of CVD mortality in other populations with normal BMI.

It is noteworthy that the observed positive association between trunk fat and CVD risk was only partially explained by central adiposity measures i. waist circumference or WHR in our study. It is possible that, among postmenopausal women with normal BMI, trunk fat measures when compared with waist circumference might better characterize certain upper-body adipose tissue depots most predictive of CVD risk, such as visceral fat mass 24 and liver fat content.

A few studies have investigated DXA-measured lower-body fat in relation to CVD risk among populations with wide BMI ranges. normal-weight 27 or non-obese women Nevertheless, because hip and gynoid fat measures capture only parts of total leg fat, whether the inverse association of leg fat with risk of CVD is specific to normal BMI individuals warrants further study.

Consistent with previous findings, 7—10 our results showed that relatively higher trunk fat levels were associated with various metabolic disturbances such as worse glycaemic control, elevated insulin levels, systemic inflammation, and dyslipidaemia. The associations for leg fat were generally in the opposite directions to those for trunk fat.

Previous studies also have shown contrasting i. beneficial associations of upper-body and lower-body fat with long-term blood pressure, 29 subclinical atherosclerosis, 30 , 31 and with risk of incident diabetes.

The region-specific associations between body fat and CVD risk factors or CVD events are plausible given that upper and lower body contain divergent fat depots with profoundly distinct biological functions. Recent results from genetic association studies showed that genetically determined low gluteofemoral fat and high abdominal fat both were associated with increased risk of coronary disease and diabetes.

Strengths of our study include the prospective design, long-term follow-up, repeated measures of body composition using DXA scans, and adjudication of CVD events. The analyses of multiple blood biomarkers provided additional information concerning the biological plausibility for a mechanistic link between regional body fat and the development of CVD.

Our study also has several limitations. Due to the observational nature, we are unable to conclude from our study that the observed associations between regional body fat and CVD risk are causal. However, some weight-loss studies have demonstrated that a reduction of trunk fat can result in expected improvements in cardiometabolic traits, whereas a reduction of leg fat may lead to CVD increasing metabolic features, 35 though more clinical trials are still needed.

Because trunk fat measured by DXA scans is a combination of subcutaneous and visceral fat mass, further research is needed to evaluate their associations with CVD risk individually. Our findings were derived in postmenopausal women who were predominantly whites and are yet to be investigated in men and in other age or ethnic groups.

In summary, our findings suggest that normal BMI postmenopausal women who have higher trunk fat or lower leg fat are at elevated risk of CVD. These findings highlight the importance of fat distribution beyond overall fat mass in the development of CVD. We thank Dr. This work was supported by the National Heart, Lung, and Blood Institute NHLBI , National Institutes of Health, U.

Department of Health and Human Services through contracts HHSNC, HHSNC, HHSNC, HHSNC, and HHSNC. is supported by NHLBI K01HL, R01HL, and R01HL; and by the National Institute of Diabetes and Digestive and Kidney Diseases NIDDK R01DK Conflict of interest: N. has received consulting fees from Novartis and Puma Biotechnology.

All other authors declared no conflict of interest. See page for the editorial comment on this article doi: Romero-Corral A , Lopez-Jimenez F , Sierra-Johnson J , Somers VK. Differentiating between body fat and lean mass-how should we measure obesity? Nat Clin Pract Endocrinol Metab ; 4 : — Google Scholar.

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The impact of confounding on the associations of different adiposity measures with the incidence of cardiovascular disease: a cohort study of adults of white European descent. Eur Heart J ; 39 : — Myint PK , Kwok CS , Luben RN , Wareham NJ , Khaw KT. Body fat percentage, body mass index and waist-to-hip ratio as predictors of mortality and cardiovascular disease.

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Abdominal and gynoid adipose distribution and incident myocardial infarction in women and men. Int J Obes Lond ; 34 : — Total and regional adiposity measured by dual-energy X-ray absorptiometry and mortality in NHANES Obesity Silver Spring ; 24 : — Romero-Corral A , Somers VK , Sierra-Johnson J , Korenfeld Y , Boarin S , Korinek J , Jensen MD , Parati G , Lopez-Jimenez F.

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Journal Article Editor's Choice. Association between regional body fat and cardiovascular disease risk among postmenopausal women with normal body mass index.

Guo-Chong Chen , Guo-Chong Chen. Department of Epidemiology and Population Health, Albert Einstein College of Medicine. Oxford Academic. Rhonda Arthur.

Neil M Iyengar. Department of Medicine, Memorial Sloan Kettering Cancer Center. Victor Kamensky. Xiaonan Xue. Sylvia Wassertheil-Smoller.

Matthew A Allison. Department of Family Medicine and Public Health, University of California San Diego. Aladdin H Shadyab. Robert A Wild. Departments of Obstetrics and Gynecology, Biostatistics and Clinical Epidemiology, Oklahoma University Health Sciences Center.

Yangbo Sun. Department of Epidemiology, College of Public Health, University of Iowa. Hailey R Banack , Hailey R Banack. Department of Epidemiology and Environmental Health, University at Buffalo, State University of New York.

Jin Choul Chai. Jean Wactawski-Wende. Department of Gynecology-Obstetrics, University at Buffalo, State University of New York. JoAnn E Manson. Department of Medicine, Brigham and Women's Hospital, Harvard Medical School. Marcia L Stefanick. Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine.

Body mass index (BMI)

For identifying disordered body composition in the elderly, multicompartment methods that can distinguish between fat and fat-free mass are necessary Baumgartner Elderly individuals also tend to have loose connective tissue, and storage fat becomes less subcutaneous and more internalized with age, making it difficult to accurately assess body composition using skinfold measurements.

The utility of DEXA, hydrostatic weighing and a multicompartment model has also been assessed in postmenopausal women. Houtkooper and colleagues calculated changes in body composition in 76 postmenopausal women over the course of a 1-year exercise program.

The results showed that compared to hydrostatic weighing and the multicompartment method, DEXA was the most sensitive method for assessing small changes in body composition in these subjects.

Accurately assessing body composition in obese individuals can be problematic. For instance, the amount of hydration, especially as a component of fat-free mass, is typically greater in obese individuals and is not proportionately distributed between intracellular and extracellular fluid compartments.

Because standard two-compartment models are based on the assumption that water composes In addition, skinfold compressibility—and the overall thickness of the skinfold exceeding what can be measured with the caliper—can affect the accuracy of this method. Thus experts generally agree that skinfold measurements should not be used to estimate body composition in obese individuals.

In contrast, BIA appears to have some promise for accurately assessing body composition in obese persons. It is important to realize, though, that the preprogrammed prediction equations included with BIA software are typically not validated for this population.

Examiners should be sure to record the resistance value given by the BIA because the majority of prediction equations to determine fat-free mass from BIA utilize this value. In addition to BIA, anthropometric methods such as BMI and circumference measurements appear to be suitable alternatives for assessing body composition in obese persons.

These methods are not only simple but can be used to predict risk of disease and disability, which is usually a primary concern for these individuals. Some prediction equations, most notably those developed by Tran and Weltman, use circumference measures to determine percent body fat.

Assessing body composition in athletes is important for optimizing performance and evaluating the effectiveness of various training regimens Vescovi et al. Additionally, regular body composition assessment can ensure that an athlete maintains overall health, which is essential in sports where achieving dangerously low levels of body fatness is viewed as advantageous but could actually hamper performance.

Yet body composition assessment in athletes can pose unique challenges because of alterations in body composition as a result of specific training regimens and the physical requirements of a particular sport. In general, athletes have greater bone mineral content, bone density and skeletal muscle mass than the general population, and consequently have a higher density than sedentary individuals Heyward and Stolarczyk For some athletes with high bone mineral content e.

These findings emphasize the need to use prediction equations validated for athletes from specific sport backgrounds. Skinfold measurements have been validated in athletes and appear to have relatively high accuracy across several sports. Based on a review of the literature, Heyward and Stolarczyk recommend using the sum of seven skinfolds chest, midaxillary, triceps, subscapular, abdomen, anterior suprailiac and thigh to estimate body density for athletic men, and the sum of four skinfolds triceps, anterior suprailiac, abdomen and thigh for athletic women.

In contrast to skinfold measurements, anthropometric methods appear to have less predictive accuracy in athletic men and women. Athletes typically do not have the characteristic fat distribution that is seen in obese individuals, and risk of disease is typically not a primary concern.

Therefore, while circumference measurements and other anthropometric measures such as BMI and waist-to-hip ratio can be used, they may have limited applicability when considering the ultimate goals of the athlete.

ADP, which has the benefit of being noninvasive and relatively simple and rapid, has been evaluated in athletes. Unfortunately, however, the accuracy of this technique has been inconsistent with this population.

In one study the accuracy of the BOD POD was evaluated in 80 female collegiate athletes and it consistently overestimated percent body fat compared with both hydrostatic weighing and skinfold measurements. Researchers concluded that while ADP may be highly reliable in certain populations, it cannot be recommended for use in lean female athletes Vescovi et al.

In contrast, one population for which ADP may be appropriate is male collegiate wrestlers. In a study of 66 Division I wrestlers, the BOD POD resulted in similar estimates of body density, percent body fat and fat-free mass as hydrodensitometry Utter et al.

Because of the variability in the body composition of athletes in different sports, there does not appear to be one specific method with a high degree of accuracy that is applicable to all athletes.

It is recommended that assessment methods not be used interchangeably; rather, one method should be used consistently over time. It is also important to realize that optimal body weight and body composition to maximize performance will vary among individuals.

Therefore, body composition goals for athletes should be determined on a case-by-case basis, rather than on a set of general standards developed for a particular sport. Improved technology and recent research findings have improved our understanding of how fat distribution within specific regions of the body influences overall health and disease.

This research has led to the data presented in Table 2, which shows the relative risk of disease as a function of BMI and waist circumference. BMI has been studied extensively for its potential in predicting risk of premature death, disease and disability.

Probably the strongest evidence comes from a prospective study of more than 1 million men and women that investigated the effects of age, race, sex, smoking status and history of disease on the relation between BMI and mortality Calle et al.

The results of this study showed that subjects with the highest BMI had significantly greater risk of death compared with those who had a BMI of Furthermore, the authors concluded that the risk of death from cardiovascular disease, cancer or other disease increased with increasing weight, regardless of age or gender.

The risk of type 2 diabetes has also been linked to BMI, with research demonstrating that the relative risk increases for every additional unit of BMI over 22 Colditz et al. Waist circumference is another powerful predictor of type 2 diabetes, with individuals with a waist circumference in the highest quartile having an 11 times greater risk of type 2 diabetes than those with a waist circumference in the lowest quartile Wei et al.

In addition to BMI and waist circumference measures, waist-to-hip ratio WHR has been correlated with certain diseases. As a general rule, a WHR of 1. Likewise, waist circumferences greater than 40 inches for men and 35 inches for women are considered undesirable and associated with increased risk of disease CDC Technological advances in assessment techniques combined with greater focus on how fat distribution affects overall health have led to improved ability to predict future disability and risk of disease.

Regardless of the method used, the results are only as accurate as the measurement technique and prediction equation applied. It is important to follow the standard guidelines and protocols associated with the chosen method and use prediction equations specific to the individual being tested.

Baumgartner, R. Body composition in healthy aging. Annals of New York Academy of Sciences, , — Bunt, J. Variation in bone mineral content and estimated body fat in young adult females.

Calle, E. Body-mass index and mortality in a prospective cohort of U. New England Journal of Medicine, 15 , — Centers for Disease Control CDC National Center for Chronic Disease Prevention and Health Promotion.

Defining overweight and obesity. htm ; retrieved June Colditz, G. Weight gain as a risk factor for clinical diabetes mellitus in women. Annals of Internal Medicine, , —6.

Cox-Reijven, P. Accuracy of bioelectrical impedance spectroscopy in measuring changes in body composition during severe weight loss. Journal of Parenteral and Enteral Nutrition, 26, —7. Davies, P. Body Composition Techniques in Health and Disease. Melbourne, Australia: Cambridge University Press.

Demerath, E. Comparison of percent body fat estimates using air displacement plethysmogra- phy and hydrodensitometry in adults and children. International Journal of Obesity, 26, — Expert Panel. Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults.

Archives of Internal Medicine, , — Goodpaster, B. Measuring body fat distribution and content in humans. Current Opinion in Clinical Nutrition and Metabolic Care, 5, Heyward, V. Applied Body Composition Assessment. Champaign, IL: Human Kinetics.

Predictive accuracy of three field methods for estimating relative body fatness of nonobese and obese women. International Journal of Sport Nutrition, 2, 75— Houtkooper, L. Comparison of methods for assessing body-composition changes over 1 year in postmenopausal women.

American Journal of Clinical Nutrition, 72, —6. Kushner, R. Bioelectrical impedance analysis: A review of principles and applications. Journal of the American College of Nutrition, 11, National Institutes of Health NIH. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults.

Bethesda, MD: Department of Health and Human Services; NIH; National Heart, Lung, and Blood Institute. Publication No. Parker, L. Validity of six field and laboratory methods for measurement of body composition in boys.

Obesity Research,11 7 , —8. BMI body mass index , which is based on the height and weight of a person, is an inaccurate measure of body fat content and does not take into account muscle mass, bone density, overall body composition, and racial and sex differences, say researchers from the Perelman School of Medicine, University of Pennsylvania.

The news hits the headlines, everybody agrees, and then all goes quiet for a while. You are of normal weight if your BMI is between Anybody with a BMI of 30 or more has obesity. Mitchell Lazar, MD, PhD, Professor of Medicine and Genetics and Director of the Institute of Diabetes, Obesity, and Metabolism, and Rexford Ahima, MD, PhD, Professor of Medicine and Director of the Obesity Unit in the Institute for Diabetes, Obesity and Metabolism, discuss the challenges health professionals face when studying the mortality risks and health of people with obesity in the journal Science 1.

We all know that obesity increases the risk of developing heart diseases, type 2 diabetes , cancer , sleep apnea and other diseases and conditions.

However, according to recent studies, obesity may also protect against death from all causes, as well as death due to stroke , heart failure and diabetes.

A paradox that generates a great deal of controversy. Advances to improve the measurement of obesity and related factors will help determine the optimal weight for an individual, taking into account factors such as age, sex, genetics, fitness, pre-existing diseases, as well novel blood markers and metabolic parameters altered by obesity.

People with a BMI of 30 or more, i. individuals with obesity, have a significantly higher risk of eventually becoming diabetic, developing cancer, cardiovascular diseases, osteoarthritis, and liver and gallbladder diseases.

A team of researchers at the University of Virginia, Charlottesville, found better post-surgical short-term survival rates among people with obesity than patients of normal weight 3. Patients with a BMI of This results in tall people believing they are fatter than they really are 4 , and short people thinking they are thinner.

BMI was devised in the s by Lambert Adolphe Jacques Quetelet , a Belgian mathematician, sociologist, statistician and astronomer. Trefethen wonders why institutions today on both sides of the Atlantic continue using the same flawed-BMI formula.

Researchers from the Medical Research Council MRC Epidemiology Unit, UK, reported in PLoS Medicine that waist circumference is strongly and independently associated with type two diabetes risk 5 , even after accounting for BMI.

Study leader, Dr Claudia Langenberg and team suggested that waist circumference should be measured more widely for estimating type 2 diabetes risk. They pointed out that a male without obesity who is overweight with a waist circumference of at least The same applies for females with a waist of A study published by the RAND Corporation showed that waist size explained the higher type 2 diabetes rate in the USA than UK, not BMI 6.

Co-author, James P. Waist size is the missing new risk factor we should be studying. Dr Margaret Ashwell, an independent consultant and former science director of the British Nutrition Foundation, explained at the 19th Congress on Obesity in Lyon, France, May , that waist-to-height ratio is a superior predictor than BMI 7 of type 2 diabetes and cardiovascular diseases.

Ashwell explained that BMI does not take into account the distribution of fat around the body. Abdominal fat affects organs like the kidney, liver and heart more severely than fat around the bottom or hips.

Waist circumference gives an indication of abdominal fat levels. Muscle weighs more than fat it is denser, a cubic inch of muscle weighs more than a cubic inch of fat.

Therefore, BMI will inevitably class muscly, athletic people as fatter than they really are. A 6ft-tall Olympic meter sprinter weighing 90kg lbs may have the same BMI 26 as a couch potato of the same height and weight.

There are several ways to measure body weight and composition. Learn how to tell if you have overweight with these tests, including BMI. Phentermine, a weight loss drug, is not safe to take during pregnancy. People pregnant, or trying to get pregnant, should stop using the drug….

Body composition and disease risk Nutrition Journal volume 12 Compositon, Article number: Cite this article. Metrics details. Cardiovascular CV disfase is a Plant-based recipes cause of global mortality. Despite clear evidence of Obesity symptoms coexistence of compositioh risk factors in young people as children and an understanding of the importance of the health behaviors in controlling CV disease, there are limited data on the relationships between risk factors and CV disease in young people. Therefore further study is required. This study aimed to investigate associations among body composition, health behaviors and CV risk factors in young Australian men.

Author: Guktilar

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