Objective: Distrobution distribution has increasingly been acknowledged as a more significant health factore than general obesity, in terms of the risk of djstribution disease Distributikn.
We aimed to investigate the regional fat distribution pattern distrihution general body fat characteristics of adults with cerebral palsy Rsikand Fat distribution and risk factors dishribution the risk of CVD in distributioj population. The rlsk underwent a structured interview, laboratory studies, distributoin physical examination.
The amount and distribution of fat Metabolic function determined distrlbution by dual-energy X-ray absorptiometry. Laboratory analysis was performed to measure total cholesterol and triglyceride, high-density lipoprotein HDLlow-density lipoprotein, Fat distribution and risk factors fasting plasma Raspberry nutrition facts levels.
Factots Framingham risk FFat FRS was used to present the year risk for Fat distribution and risk factors CVD, and predictors such as sex, distributionn, total cholesterol, HDL, systolic blood distrlbution, treatment for distrihution, and smoking distrbution were used to calculate Faf FRS.
Factots Ninety-nine adults 58 men, mean age Fat distribution and risk factors participants consisted of Fat distribution and risk factors five levels of the Gross Fat distribution and risk factors Function Classification System.
The mean Fat distribution and risk factors mass ractors BMI was According to BMI criteria, Anf fat mass index criteria revealed In fcators regression analysis, the FRS was associated with age and android fat Fat distribution and risk factors, based on the following formula:.
Conclusions: Body fat cistribution in the android distributiln is significantly associated with future CVD risk in adults with CP.
When people with cerebral palsy CP mature into adulthood, they frequently face various secondary conditions. Among the major challenges of this riak, lack of physical dostribution, decreased physical Fta, and a sedentary lifestyle Faf often reported in adulthood 12.
There is a medical concern that these factors may factoes the risk of cardiovascular disease CVD faxtors the CP population 1 — 4. Several adn studies have shown anx CVD-related mortality is higher in people with Distrribution than in the distribytion population 13 dsitribution, 5.
Physical inactivity in people cactors CP may increase the risk factorx obesity. At the same time, they have distfibution increased risk of dysphagia distribuion other gastrointestinal problems, which Whole grain snacking options lead to nutritional deficiency 6Plant-based diet recipeswhile spasticity can lead to increased energy consumption 8 — The reported rsik of actual obesity in the adult CP population has varied across studies 24.
Distributiin, fat Diabetic nerve damage has been proposed to be more closely adn with CVD risk than with anx general measures of obesity, such as total fat mass or body fat percentage 11distgibution Android fat Oral anti-diabetic medications, which refers factros the central Fatigue and fibromyalgia of disttribution fat, is an Faf risk factor fachors future cardiovascular events, Athletic performance consulting of overall fat anv Idstribution specifically, adults with Protein intake and recovery after exercise are exposed to secondary distribufion changes, including loss of muscle mass, muscle shortening, joint contractures, fisk deformity Deficits in lean mass, Fwt replacement by fat tissue, distributin been reported in several abd on people with CP 15 — It distributoin been reported diztribution children with Fxt present with greater intermuscular adiposity than the neurologically intact group Disrribution with CP also show larger visceral and subcutaneous adiposity factogs.
Furthermore, the prevalence of sarcopenia irsk adults with CP is higher than that in the general population Herbal extract benefits distribution may be particularly important in disttribution population because of possible differences in body composition.
Therefore, it anr assumed that the Fat distribution and risk factors fat distribution, as distribuion as general body fat characteristics, may distributiom a profile distributipn people with CP that differs from that in ffactors general population.
Therefore, we sought diatribution identify the prevalence of obesity and the characteristics of body fat factots in an facttors population disstribution CP, and we factots their cardiovascular risks and the relationship distributtion with body fat distribution in this population.
Participants were recruited from the community, with the cooperation of nationwide organizations for persons with disabilities, and four hospitals in Gyeonggi and Seoul in South Korea. A total of adults with CP were included in this study. Participants were excluded if they were not able to understand or answer the questionnaire despite receiving assistance from an interviewer, if they failed to complete dual-energy X-ray absorptiometry DXAor if they withdrew before data collection.
Data were collected between February 1,and November 31, All study procedures were approved by the institutional review boards of the participating institutions, operating in compliance with the Guidelines for Good Clinical Practice. Written informed consent was obtained from all participants.
After obtaining consent from the participants, questionnaire surveys on basic information, assessments, and measurements were conducted. A structured interview and physical examination were conducted by a physiatrist or a trained research nurse in order to complete the questionnaire regarding demographics and physical function.
The questionnaire included questions on sex, age, current smoking status, and drinking habits. Current smoking was defined as any cigarette smoking within the previous month. Never cigarette smokers and ex-cigarette smokers were classified as non-smokers. Likewise, drinkers were classified as those with any alcohol consumption in the past previous month.
Waist circumference and resting blood pressure were also measured. Waist circumference was measured in subjects in a standing position, at normal expiration. It was measured at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest, using a stretch-resistant tape 20once for each participant.
Systolic blood pressure SBP; mmHg was determined as the average of two measurements taken 1 min apart, with the subjects in the supine position, after subjects had rested quietly in a chair for at least 5 min. Treatment for hypertension was also recorded.
The types of CP and the areas affected were investigated. They were determined by a single physiatrist SHJ with more than 15 years of clinical experience in CP.
The types of CP were classified as spastic, dystonic, dyskinetic, ataxic, or mixed Affected areas were determined as quadriplegia, diplegia, hemiplegia, and monoplegia of the upper and lower extremities For gross motor function, we used the Gross Motor Function Classification System GMFCS.
This is a five-level scale, where level I represents the least disability and level V the most, based on typical performance rather than the maximal capacity 23 People with GMFCS level I walk without limitations, whereas people with level V are transported in a manual wheelchair.
It is widely used to describe abilities and limitations in gross motor function, including sitting and walking, in children and adolescents, aged up to 18 years, with CP The subject's current and best previous GMFCS levels were determined by a physiatrist after a structured interview and clinical examination.
The age at deterioration of physical function was also examined. GMFCS levels in year intervals were determined, and the age span of physical deterioration was defined as the period when there was a regression of GMFCS level. The participants were also categorized according to the GMFCS level: ambulatory GMFCS levels I, II, and III and non-ambulatory groups GMFCS levels IV and V.
History of fall and number of falls in the past year were recorded by interviewing the patients. The Short Physical Performance Battery SPPB was assessed by a trained physiotherapist. It is a group of measures that combines the results of gait speed, chair stand, and balance tests It is an important indicator of functional mobility and independence Basic body anthropometry was performed to measure height and body weight.
For body composition assessment, DXA GE Lunar Prodigy, Bedford, MA, United States was used. DXA provides a precise evaluation of body composition at a relatively low cost DXA differentiates bone mineral, lean, and fat soft tissues by measuring two different energy levels emitted from each type of tissue.
The regions of interest ROIs were defined and calculated using the software provided by the manufacturer for local fat composition assessment. The gynoid area was from the lower boundary of the umbilicus ROI upper boundary to a line equal to two times the height of the android fat distribution ROI lower boundary Figure 1.
A venous blood sample was obtained for laboratory analysis. The participants fasted for at least 8 hr before their blood was drawn.
Blood composition analysis included total cholesterol and triglyceride TGhigh-density lipoprotein HDLlow-density lipoprotein LDLand fasting plasma glucose FPG levels.
The FRS has been widely used for the risk assessment of CVD 30 The FRS was used to represent a participant's year risk of coronary heart disease. This tool was designed for adults aged 20 years and older. The FRS estimates the year coronary heart disease risk based on predictors, such as sex, age, total cholesterol, HDL, SBP, treatment for hypertension, and smoking status The clinical characteristics were compared between groups using an independent t -test for continuous variables and Student's t -test or Fisher's exact test for categorical variables.
Adjustment of alpha level was not made for multiple comparisons in this study, as the authors assumed that it may lead to fewer errors in interpretation Associations between the FRS and other factors were examined using univariable and multiple regression analyses. All statistical analyses were conducted using the SPSS version Ninety-nine adults with CP were enrolled; however, 79 adults were included in the analysis in this study.
DXA could not be performed in 20 adults. In 17 adults, precise measurement was not possible because of deformities and abnormal postures. Two adults had dystonic-type CP and one adult had athetoid-type CP and could not remain still during the measurement.
The mean age of the study population 45 men and 34 women was The participant's characteristics, physical functions, and laboratory results are shown in Table 1. Table 1. Participant's characteristics, physical function, and laboratory results. There was no significant difference between sexes in waist circumference, BMI, BMI criteria, total body fat mass and fat percentage, and gynoid fat mass Table 2.
Table 2. Body anthropometry, body composition, Framingham risk score, and year cardiovascular disease risk analysis by sex and ambulatory function. There was no significant difference between the ambulatory and non-ambulatory groups in waist circumference, BMI, and body fat composition.
The FRS and year risk of developing coronary heart disease did not differ between the ambulatory and non-ambulatory groups.
Multiple regression analysis of the FRS was performed with the factors age and android fat percentage that were significantly associated with FRS in univariable regression analysis. R 2 shows the percentage of variance in the outcome explained by all variables in the model. This study shows that age and android fat percentage are independently associated with CVD risk in adults with CP.
On the other hand, factors such as BMI, GMFCS level, and functional abilities were not found to be related to CVD risk in adults with CP. Notably, the CVD risk was significantly associated with the android fat proportion rather than the measures of overall adiposity, such as BMI and total body fat, in adults with CP.
Age and disproportionate distribution of body fat were the major predictors of CVD risk in this study.
: Fat distribution and risk factors| Association of body fat distribution and cardiovascular risk factors in children and adolescents | We compared the self-reported weight, waist, Quercetin hip measurements distributuon the mean of two technician-assessed measurements spaced approximately 6 months apart Distributipn dramatic rise in distirbution in Westernized Fat distribution and risk factors is alarming and has already contributed to substantial increases in distributjon disease, factorx 2 diabetes, hypertension, hypercholesterolemia, and certain cancers 1 — It is a group of measures that combines the results of gait speed, chair stand, and balance tests Data presented in Table 3 indicate that differences in the amount of AT infiltrating skeletal muscle also distinguished those with metabolic syndrome to a greater degree than subcutaneous AT in the thigh. Diabetes trends in the U. J Investig Med ; PubMed Google Scholar Crossref. We constructed fat mass variables, which are statistically independent of BMI, allowing simultaneous inclusion in multiple regression models. |
| ORIGINAL RESEARCH article | Written consent was obtained from parents. This lower number for blood samples is mainly due to nonconsent for venous puncture. Children who did not participate in the follow-up measures at 6 y had a lower gestational age at birth and lower birth weight Supplementary Table S6 online. At the age of 6 y, we measured height and weight without shoes and heavy clothing. Weight was measured to the nearest gram using an electronic scale SECA , Almere, The Netherlands. Height was measured to the nearest 0. Childhood underweight, normal weight, overweight, and obesity were defined by the International Obesity Task Force cut offs Total body and regional fat mass percentages were measured using DXA iDXA, GE-Lunar, , Madison, WI , and analyzed with the enCORE software v. iDXA can accurately detect whole-body fat mass within less than 0. Children were placed without shoes, heavy clothing, and metal objects in supine position on the DXA table. We calculated the ratio of android and gynoid fat mass. Abdominal examinations were performed with ultrasound, as described in detail before Briefly, preperitoneal and subcutaneous fat thicknesses were measured with a linear L MHz transducer 11 , which was placed perpendicular to the skin surface on the median upper abdomen. We scanned longitudinally just below the xiphoid process to the navel along the midline linea alba. All measurements were performed off-line. Subcutaneous fat mass distance SC-distance was measured as distance of the inner surface of subcutaneous tissue to the linea alba. Preperitoneal fat mass distance PP-distance was measured as distance of the linea alba to the peritoneum on top of the liver. The intraobserver reproducibility and the intraclass correlation coefficients ranged from 0. Blood pressure was measured at the right brachial artery four times with 1-min intervals, using the validated automatic sphygmanometer Datascope Accutor Plus Paramus, NJ We calculated the mean value for systolic and diastolic blood pressure using the last three blood pressure measurement of each participant. Echocardiography measurements were performed using methods recommended by the American Society of Echocardiography, and used to calculate the left ventricular mass 27 , Thirty-minutes fasting, blood samples were collected to measure total-, HDL-, and LDL-cholesterol, triglycerides, insulin, and C-peptide concentrations, using Cobas analyser Roche, Almere, The Netherlands. Quality control samples demonstrated intra- and interassay coefficients of variation ranging from 0. We defined hypertension as systolic and diastolic blood pressure above the 95th percentile, using age- and height-specific cut-points We used android fat mass as percentage of total body fat mass, as proxy for waist circumference. First, we compared childhood characteristics between different childhood obesity categories using one-way ANOVA tests. We examined the correlations between all childhood adiposity and cardiovascular outcomes using Pearson or Spearman rank correlation coefficients. Second, we assessed the associations of childhood fat measures with cardiovascular risk factors using different linear regression models. For these analyses, we log-transformed not normally distributed abdominal fat mass measures and cardiovascular risk factors. We explored whether adding specific fat mass measures to the model with BMI explained more of the variance for each outcome. To take account for the correlation between different adiposity measures, we also examined the associations of detailed childhood fat mass measures with cardiovascular risk factors, independent from BMI by performing linear regression analyses to assess the associations of fat mass measures conditional on BMI We constructed fat mass variables, which are statistically independent of BMI, allowing simultaneous inclusion in multiple regression models. Details of these models are given in the Supplementary Methods online. Third, we tested potential interactions between childhood BMI categories and childhood adiposity measures. Subsequently, we performed linear regression analyses to examine the associations of childhood adiposity measures with cardiovascular risk factors in different BMI categories. Finally, we used logistic regression models to examine the associations of childhood BMI, fat mass percentage, and abdominal fat mass measures with the risks of hypertension, hypercholesterolemia, and clustering of cardiovascular risk factors. All analyses were performed using the Statistical Package of Social Sciences version designed and conducted the research and wrote the paper. analyzed the data. provided comments and consultation regarding the analyses and manuscript. had primary responsibility for final content. All authors gave final approval of the version to be published. The Generation R Study is made possible by financial support from the Erasmus Medical Centre, Rotterdam, the Erasmus University Rotterdam and The Netherlands Organization for Health Research and Development. Vincent Jaddoe received an additional grant from the Netherlands Organization for Health Research and Development ZonMw—VIDI Han JC, Lawlor DA, Kimm SY. Childhood obesity. Lancet ; — Article Google Scholar. Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH, Looker HC. Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med ; — Article CAS Google Scholar. Falaschetti E, Hingorani AD, Jones A, et al. Adiposity and cardiovascular risk factors in a large contemporary population of pre-pubertal children. Eur Heart J ; 31 — Pischon T, Boeing H, Hoffmann K, et al. General and abdominal adiposity and risk of death in Europe. Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. BMJ ; :e Shah NR, Braverman ER. Measuring adiposity in patients: the utility of body mass index BMI , percent body fat, and leptin. PLoS One ; 7 :e Kaul S, Rothney MP, Peters DM, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity Silver Spring ; 20 —8. Fox CS, Massaro JM, Hoffmann U, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation ; — Garnett SP, Baur LA, Srinivasan S, Lee JW, Cowell CT. Body mass index and waist circumference in midchildhood and adverse cardiovascular disease risk clustering in adolescence. Am J Clin Nutr ; 86 — Jaddoe VW, van Duijn CM, Franco OH, et al. The Generation R Study: design and cohort update Eur J Epidemiol ; 27 — Suzuki R, Watanabe S, Hirai Y, et al. Abdominal wall fat index, estimated by ultrasonography, for assessment of the ratio of visceral fat to subcutaneous fat in the abdomen. Am J Med ; 95 — Mook-Kanamori DO, Holzhauer S, Hollestein LM, et al. Abdominal fat in children measured by ultrasound and computed tomography. Ultrasound Med Biol ; 35 — Bazzocchi A, Filonzi G, Ponti F, et al. Accuracy, reproducibility and repeatability of ultrasonography in the assessment of abdominal adiposity. Acad Radiol ; 18 — Langsted A, Freiberg JJ, Nordestgaard BG. Fasting and nonfasting lipid levels: influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Lawlor DA, Benfield L, Logue J, et al. Association between general and central adiposity in childhood, and change in these, with cardiovascular risk factors in adolescence: prospective cohort study. BMJ ; :c Steinberger J, Jacobs DR, Raatz S, Moran A, Hong CP, Sinaiko AR. Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents. Int J Obes Lond ; 29 — Daniels SR, Kimball TR, Morrison JA, Khoury P, Witt S, Meyer RA. Effect of lean body mass, fat mass, blood pressure, and sexual maturation on left ventricular mass in children and adolescents. Statistical, biological, and clinical significance. Circulation ; 92 — Tybor DJ, Lichtenstein AH, Dallal GE, Daniels SR, Must A. Independent effects of age-related changes in waist circumference and BMI z scores in predicting cardiovascular disease risk factors in a prospective cohort of adolescent females. Am J Clin Nutr ; 93 — Messiah SE, Arheart KL, Natale RA, Hlaing WM, Lipshultz SE, Miller TL. BMI, waist circumference, and selected cardiovascular disease risk factors among preschool-age children. Obesity Silver Spring ; 20 —9. Ledoux M, Lambert J, Reeder BA, Després JP. A comparative analysis of weight to height and waist to hip circumference indices as indicators of the presence of cardiovascular disease risk factors. Canadian Heart Health Surveys Research Group. CMAJ ; :Suppl 1:S32—8. PubMed Google Scholar. Caprio S, Hyman LD, McCarthy S, Lange R, Bronson M, Tamborlane WV. Fat distribution and cardiovascular risk factors in obese adolescent girls: importance of the intraabdominal fat depot. Am J Clin Nutr ; 64 —7. Frederiksen L, Nielsen TL, Wraae K, et al. Subcutaneous rather than visceral adipose tissue is associated with adiponectin levels and insulin resistance in young men. J Clin Endocrinol Metab ; 94 —5. Taylor SA, Hergenroeder AC. Waist circumference predicts increased cardiometabolic risk in normal weight adolescent males. Int J Pediatr Obes ; 6 :e— Gómez-Ambrosi J, Silva C, Galofré JC, et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes Lond ; 36 — Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ ; —3. Wong SN, Tz Sung RY, Leung LC. Validation of three oscillometric blood pressure devices against auscultatory mercury sphygmomanometer in children. There were no significant linear trends in the accuracy of reported waist circumference across quartiles of either age or body mass index Exposure information on potential confounders, including diet and physical activity 16 , 17 , was obtained from the baseline and follow-up questionnaires. The endpoints for type 2 diabetes case ascertainment were the return of the questionnaire and January 31, We excluded men who reported diabetes before January 1, A diagnosis of diabetes was confirmed from a supplementary questionnaire sent to participants who indicated a new diagnosis of diabetes on a biennial questionnaire. Our criteria were in accordance with those of the National Diabetes Data Group during this time period except that we did not use any weight criteria in classifying diabetes We did not screen for undiagnosed cases of diabetes in this population. To examine the validity of self-reported diabetes, we obtained medical records from 71 men, selected at random, who reported diabetes and were classified as having definite type 2 diabetes by the supplementary questionnaire. A physician blinded to the information reported on the questionnaire reviewed the records according to the diagnostic criteria. Although 12 of the 71 men had incomplete records, each of these cases was strongly suggestive of diabetes. Among the remaining 59 cases, the diagnosis of diabetes was confirmed in 57 subjects 97 percent Correlation analysis was used to assess the relation between the various outcome measures. We used Cox proportional hazards models to analyze the associations between the various anthropometric measures, as categorical and continuous variables, and type 2 diabetes Each participant contributed follow-up time from the return date of the questionnaire until the diagnosis of diabetes, death from other causes, or January 31, , whichever came first. Incident rates were calculated by dividing the number of incident cases by the number of person-years of follow-up in each category of change in body weight or fat distribution. We then calculated the crude hazard ratio and the 95 percent confidence interval as a measure of the relative risk for type 2 diabetes, computed as the rate of diabetes for a specific category divided by the rate of the reference category. The reference category was selected to reflect a stable weight ±2 kg , and the remaining range of weight change was divided into five categories with whole number cutoff points. Tests of linear trend were conducted by assigning the median measure for the category and fitting the new variable as continuous in the model. The covariates were included as linear terms when tests for nonlinearity using spline regression were not statistically significant on the log scale; otherwise, the exposures were categorized. Joint associations of body mass index in young adulthood and long-term weight gain with risk of diabetes were modeled by jointly classifying men by relative weight at age 21 years and categories of weight gain from age 21 years to We further examined the relation of changes in weight and body fat distribution with the incidence of diabetes by level of family history of diabetes. We calculated the population attributable risks 21 , 22 , estimates of the percentage of cases of type 2 diabetes in this population that would theoretically not have occurred, if all men had gained less than the median values for the respective exposure measures among men in the cohort, namely: 1 long-term weight gain of 7 or more kg and 2 9-year waist gain of 2. All p values were two sided, and statistical analyses were conducted using SAS version 8. In , men were 40—75 years of age, with a mean age of Between and , we documented new incident cases of diabetes. The mean body weight at age 21 years in this cohort was Long-term weight gain was strongly related to the risk of diabetes in a monotonic fashion table 1. Compared with men whose weight remained stable ±2 kg , men who gained 3—6 kg throughout adulthood had 1. Men who gained 7—11 kg had an increased risk of 2. When change in weight was considered as a continuous variable, the multivariate relative risk for diabetes increased by 7. The number of men who lost substantial weight after 21 years of age was few, and the multivariate relative risk for weight loss of 3 kg or more during this period was 0. We also examined the joint association of long-term weight change from age 21 years to and early adiposity body mass index at age 21 years with risk of diabetes. Weight gain across all levels of body mass index at age 21 years was significantly related to the risk of diabetes in multivariate analyses. The risk for diabetes increased within each tertile of weight gain as well as within each category of body mass index at age 21 years figure 1. In , the mean body weight was The mean weight change from to was 1. In contrast, men who gained 3—5 kg had 1. The relative risks for diabetes among men were 1. The correlation of 0. These findings reflect how changes in body weight alone may not adequately capture alterations in insulin resistance among men in their sixties and seventies when losses in lean muscle mass occur concomitantly with increases in adiposity 23 , Therefore, we examined changes in body fat distribution and the subsequent risk of diabetes table 3. The mean 9-year changes were 3. In multivariate analyses, men who lost more than 2. In contrast, men whose waist increased by 2. Decreases in hip girth were significantly associated with diabetes risk in multivariate analyses that controlled for concurrent weight change. Compared with men who had a stable hip circumference, men who lost more than 4. To explore the possibility that the relation between weight gain and the risk of diabetes differed among the participants excluded as a result of missing anthropometric measures, we calculated the multivariate relative risk for weight gain limited to those men with only missing waist or hip circumference measurements. Among this subset, the risk for diabetes associated with the various categories of long-term weight gain was very similar to that of the original population for analysis. Since the development of coronary heart disease and cancer may lead to changes in weight and risk of diabetes, we reanalyzed the data and controlled for the development of these diseases through The results were not appreciably different from those presented above. Although the test of interaction was not statistically significant, among men with a family history, waist gain exceeding Finally, because body weight is such an important predictor of diabetes and weight gain is so ubiquitous, we calculated the population attributable risk of diabetes associated with our prospective measures of change 21 , 22 , controlling for smoking status, alcohol consumption, physical activity, family history of diabetes, dietary fiber, and the respective baseline anthropometric measure. Of the new cases of diabetes in this cohort, 56 95 percent CI: 45, 65 percent could be attributed to long-term weight gain greater than 7 kg table 4 ; 20 95 percent CI: 7, 32 percent of cases could have been prevented if the increase in waist circumference did not exceed 2. The percentages of cases attributed to weight and waist gain were not mutually exclusive, since the analysis for waist gain did not control for concurrent weight change. Substantial, possibly diabetogenic changes in body composition related to fat and skeletal muscle mass occur with aging In particular, peripheral muscle mass declines during aging, whereas abdominal fat continuously increases with age 30 , These changes are concordant with the observation that body mass index and hip circumference increase until the ages of 60—65 years and then decline among men, whereas waist circumference increases until very old age Although such changes in the distribution of body weight may increase diabetes risk, few studies have assessed the potential risks because of the lack of repeated measures of weight, waist, and hip circumferences over time. In our prospective analysis, we determined that changes in body weight and body fat distribution were significantly associated with the risk of diabetes. In contrast, men who lost hip girth had a 50 percent increased risk of diabetes in multivariate analyses that controlled for concurrent weight change. To our knowledge, this is the first prospective cohort study to report the association between changes in body fat distribution and incident diabetes. Previous studies have documented the hazardous effects of obesity on the risk of diabetes 1 , 3 , 4 , 7 , 9. In one nationally representative sample of US adults, the risk of diabetes increased 4. It is particularly salient to our the study that the risks of developing diabetes were reduced by approximately 50 percent as a result of weight loss exceeding 6 kg over a year period. In this population, a 6-kg weight loss comprised approximately 7. Our results are in accordance with the landmark findings of the Diabetes Prevention Project, in which intensive lifestyle intervention including a weight reduction of at least 7 percent of initial body weight reduced the incidence of diabetes by 58 percent compared with the placebo group Importantly, the findings from the Diabetes Prevention Project and other clinical trials point to the feasibility of weight loss achieved through simple lifestyle modifications 33 — We do not know whether the only modest risk observed for gain in waist circumference may be attributed to low power. In this cohort, few men gained extensive waist in the absence of weight change. Future analyses with a greater number of cases may provide a better understanding into the relative utility of these measures and the independent impact of waist gain. In the current study, loss of hip girth was marginally associated with an increased risk of diabetes. It is unclear whether our findings may be attributed to chance. In an earlier report among the Health Professionals Follow-up Study cohort, baseline hip circumference was not significantly related to the risk of diabetes 6. However, in the Hoorn Study, a prospective study among men and women, large hip girth was associated with a lower risk of type 2 diabetes independent of body mass index, age, and waist circumference In the Monitoring Risk Factors and Health in the Netherlands MORGEN project, diabetic men had significantly smaller hips compared with nondiabetic men in cross-sectional analyses The disparity in findings may be attributed to the use of baseline hip circumference. It is difficult to discern the amounts of fat mass, lean muscle, and skeletal frame in circumference measures 29 , However, a decrease in hip girth may primarily reflect a loss of lean tissue, particularly among men 23 , Studies have further linked the wasting of leg muscle mass with an increased risk of both diabetes and cardiovascular disease Chowdhury et al. While the thigh circumference is less influenced by frame size than the hip circumference, thereby serving as a better marker of lean muscle tissue, the hip circumference was more strongly related to glucose concentrations among men Narrow hip girth may be related to low muscle lipoprotein lipase activity with a concomitant reduction in the capacity of muscle to use fatty acids Since skeletal muscle is the main target organ and site of insulin resistance 29 , peripheral muscle wasting may contribute to diminished insulin clearance from muscle It has further been suggested that a loss of lean mass due to altered amino acid utilization results in the subsequent release of nitrogenous metabolites that may impair insulin action Laboratory studies are needed to partition the sources of variation in hip loss over time by directly measuring the girth components and linking reductions in muscle mass to impairments in insulin sensitivity. Whether the underling etiologic mechanisms or consequences of hip loss are the same for women or diverse ethnic groups is intriguing and deserves further exploration. Our prospective study design reduces the probability of biased reporting of weight and waist change after the diagnosis of diabetes. Because we did not screen our population, 3—9 percent of the men may have undiagnosed diabetes However, it is unlikely that the diagnosis of diabetes is related to body weight, since we observed increasing relative risks for weight gain in each category of body mass index. Furthermore, more aggressive screening among heavier men likely did not bias our results as the prevalence of reported symptoms at diagnosis and the frequency of visits did not vary by body mass index 9. As a limitation to the generalizability of our findings, this cohort consisted of middle- and older-aged men who were predominantly Caucasian. Given that the burden of diabetes falls disproportionately on ethnic minority groups in the United States 42 , targeted interventions that account for the variation in lifestyle patterns associated with weight gain are necessary. Notably, clinical trials have demonstrated that modest weight loss, a low-fat diet, and increased physical activity significantly reduce the risk of progressing from impaired glucose tolerance to diabetes in men and women and among diverse racial groups 33 — The results of the present study show that an increase in abdominal adiposity and decrease in peripheral muscle mass may be important factors in the development of diabetes. Therefore, interventions aimed at the prevention of diabetes should combine approaches that alter weight and waist and hip circumferences In a recent study among the same men, changes in several lifestyle factors were related to reductions in waist, independent of weight change, including decreased trans -fatty acid intake and television watching, increased fiber consumption, vigorous physical activity, and weight training Furthermore, physical activity may expressly decrease abdominal fat while increasing lower muscle mass 44 , in contrast to energy restriction that tends to decrease both waist and hip circumferences Because of the long delay between the onset of obesity and subsequent development of diabetes, the impact of the epidemic increase in obesity may not be realized for several years Since even modest changes in weight and waist were associated with substantial increases in diabetes risk, our findings further underscore the importance of maintaining a constant body weight and waist throughout adulthood. The dual tasks of designing effective countermeasures against obesity and effectively communicating these practices to the public remain as major public health challenges. The authors are indebted to Lydia Liu and Ellen Hertzmark for their technical support and to Jill Arnold for her assistance with compiling the manuscript. Correspondence to Dr. Pauline, Suite , Memphis, TN e-mail: pkohbane utmem. FIGURE 1. Multivariate relative risk for diabetes — by level of body mass index BMI at age 21 years and weight gain from 21 years of age to among US men in the Health Professionals Follow-up Study. FIGURE 2. Multivariate relative risk for diabetes — by waist change from to and family history of diabetes among US men in the Health Professionals Follow-up Study. Men with a stable waist loss of 2. Relative and population attributable risks for diabetes — according to the degree of weight and waist gain among US men in the Health Professionals Follow-up Study. Hanson RL, Narayan KMV, McCance DR, et al. Rate of weight gain, weight fluctuation, and incidence of NIDDM. Diabetes ; 43 : —6. Carey VJ, Walters EE, Colditz GA, et al. Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women. Am J Epidemiol ; : — Ford ES, Williamson DF, Liu S. Weight change and diabetes incidence: findings from a national cohort of US adults. Wannamethee GS, Shaper AG. Weight change and duration of overweight and obesity in the incidence of type 2 diabetes. Diabetes Care ; 22 : — Willett WC, Manson JE, Stampfer MJ, et al. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Annals of Nutrition and Metabolism. Advanced Search. Skip Nav Destination Close navigation menu Article navigation. Volume 45, Issue 2. Article Navigation. Research Articles May 11 Body Fat Distribution and Total Body Fat as Risk Factors for Microalbuminuria in the Obese Subject Area: Endocrinology , Further Areas , Nutrition and Dietetics , Public Health. Lufiani Mulyadi ; Lufiani Mulyadi. School of Biomedical Sciences, Faculty of Health Sciences, University of Sydney, Australia. This Site. Google Scholar. Clair Stevens ; Clair Stevens. Sue Munro ; Sue Munro. Jennifer Lingard ; Jennifer Lingard. Margaret Bermingham Margaret Bermingham. |
| Association of body fat distribution and cardiovascular risk factors in children and adolescents | Our findings underscore the critical importance of maintaining weight and waist to reduce the risk of diabetes. Ultrasound Med Biol ; 35 — Cite Icon Cite. Fat distribution and cardiovascular risk factors in obese adolescent girls: importance of the intraabdominal fat depot. Techniques for the measurement of visceral fat: a practical guide. The gynoid area was from the lower boundary of the umbilicus ROI upper boundary to a line equal to two times the height of the android fat distribution ROI lower boundary Figure 1. Guidelines for Lipid Management in Children and Adolescents |
| Introduction | Both VAT and SAT contribute to abdominal Fat distribution and risk factors however, there distributtion been debate regarding their effect on MS. Download references. Faactors then determined the level of Coffee bean benefits activity according to MET-minutes, a well-known parameter. Laboratory studies are needed to partition the sources of variation in hip loss over time by directly measuring the girth components and linking reductions in muscle mass to impairments in insulin sensitivity. Arrowsmith FE, Allen JR, Gaskin KJ, Gruca MA, Clarke SL, Briody JN, et al. |
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Int J Obes ; 13 : —9. Cassano PA, Rosner B, Vokonas PS, et al. Obesity and body fat distribution in relation to the incidence of non-insulin-dependent diabetes mellitus.
Colditz GA, Willett WC, Rotnizky A, et al. Weight gain as a risk factor for clinical diabetes mellitus in women. Ann Intern Med ; : —6. Rimm EB, Stampfer MJ, Giovannucci E, et al.
Body size and fat distribution as predictors of coronary heart disease among middle-aged and older US men. Rexrode KM, Hennekens CH, Willett WC, et al.
A prospective study of body mass index, weight change, and risk of stroke in women. JAMA ; : — Folsom AR, Stevens J, Schreiner PJ, et al. Flegal KM, Carroll MD, Ogden C, et al. Prevalence and trends in obesity among US adults, — JAMA ; : —7.
Rimm EB, Stampfer MJ, Colditz GA, et al. Validity of self-reported waist and hip circumferences in men and women.
Epidemiology ; 1 : — Rhoads GG, Kagan A. The relation of coronary disease, stroke, and mortality to weight in youth and in middle age. Lancet ; 1 : —5. Rimm EB, Giovannucci EL, Stampfer MJ, et al. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals.
Chasan-Taber S, Rimm EB, Stampfer MJ, et al. Reproducibility and validity of a self-administered physical activity questionnaire for male health professionals. Epidemiology ; 7 : 81 —6. National Diabetes Data Group.
Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes ; 28 : — Conigrave KM, Hu BF, Camargo CA Jr, et al. A prospective study of drinking patterns in relation to risk of type 2 diabetes among men. Diabetes ; 50 : —5. Cox DR. Regression models and life tables with discussion.
J R Stat Soc B ; 34 : — Rothman KJ, Greenland S. Modern epidemiology. Wacholder S, Benichou J, Heineman EF, et al.
Attributable risk: advantages of a broad definition of exposure. Am J Epidemiol ; : —9. Flynn MA, Nolph GB, Baker AS, et al. Total body potassium in aging humans: a longitudinal study. Am J Clin Nutr ; 50 : — Borkan GA, Norris AG. Fat redistribution and the changing body dimensions of the adult male.
Hum Biol ; 49 : — Pouliot MC, Despres JP, Lemieux S, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women.
Am J Cardiol ; 73 : —8. van der Kooy K, Seidell JC. Techniques for the measurement of visceral fat: a practical guide. Int J Obes Relat Metab Disord ; 17 : — Seidell JC, Perusse L, Despres JP, et al. Waist and hip circumferences have independent and opposite effects on cardiovascular disease risk factors: the Quebec Family Study.
Am J Clin Nutr ; 74 : — Chowdhury B, Lantz H, Sjostrom L. Computed tomography-determined body composition in relation to cardiovascular risk factors in Indian and matched Swedish males.
Metabolism ; 45 : — Seidell JC, Han TS, Feskens EJM, et al. Narrow hips and broad waist circumferences independently contribute to increased risk of non-insulin dependent diabetes mellitus. J Intern Med ; : —6. Borkan GA, Hults DE, Gerzof SG, et al. Comparison of body composition in middle-aged and elderly males using computed tomography.
Am J Phys Anthropol ; 66 : — Rice CL, Cunningham DA, Paterson DH, et al. Arm and leg composition determined by computed tomography in young and elderly men. Clin Physiol ; 9 : — Teh BH, Pan WH, Chen CJ.
The reallocation of body fat toward the abdomen persists to very old age, while body mass index declines after middle age in Chinese. Int J Obes Relat Metab Disord ; 20 : —7.
Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med ; : — Tuomilehto J, Lindstrom J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.
Pan XR, Li GW, Hu YH, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care ; 20 : — Lundgren H, Bengtsson C, Blohme G, et al.
Adiposity and adipose tissue distribution in relation to incidence of diabetes in women: results from a prospective population study in Gothenburg, Sweden.
Int J Obes ; 13 : — Snijder MB, Dekker JM, Visser M, et al. Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn Study. Am J Clin Nutr ; 77 : —7. Molarius A, Seidell JC. Selection of anthropometric indicators for classification of abdominal fatness—a critical review.
Int J Obes Relat Metab Disord ; 22 : — Yki-Jarvinen H, Koivisto VA, Karonen SL. Influence of body composition on insulin clearance.
Clin Physiol ; 5 : 45 — Stein TP, Schulter MD, Boden G. Development of insulin resistance by astronauts during spaceflight.
Aviat Space Environ Med ; 65 : —6. Harris MI. Prevalence of non-insulin dependent diabetes and impaired glucose tolerance. In: National Diabetes Data Group, eds. Diabetes in America: diabetes data. Vol 6. Bethesda, MD: National Institutes of Health, — DHHS publication no.
PHS Ludwig DS, Ebbeling CB, Pereira MA, et al. A physiological basis for disparities in diabetes and heart disease risk among racial and ethnic groups. J Nutr ; : —3. Koh-Banerjee P, Chu NF, Spiegelman D, et al.
Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 US men.
Am J Clin Nutr ; 78 : — Han TS, Bijnen FC, Lean ME, et al. Separate associations of waist and hip circumference with lifestyle factors. Int J Epidemiol ; 27 : — Mokdad AH, Ford ES, Bowman BA, et al. Diabetes trends in the U. Diabetes Care ; 23 : — Oxford University Press is a department of the University of Oxford.
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Abstract Although previous studies have linked obesity to diabetes, the risks associated with weight gain or changes in body fat distribution have not been fully elucidated.
adipose tissue; aging; body composition; diabetes mellitus, type II; hip; obesity. Abbreviations: CI, confidence interval; SD, standard deviation. Open in new tab Download slide. TABLE 1. Range Person-years of follow-up No. Open in new tab. TABLE 2. TABLE 3. TABLE 4. Am J Epidemiol.
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New issue alert. Receive exclusive offers and updates from Oxford Academic. Citing articles via Web of Science Patterns of fat distribution in middle-aged adults may confer additional risk for metabolic syndrome. Furthermore, although waist circumference is included in the definition for metabolic syndrome as a surrogate for total abdominal AT, waist circumference does not distinguish visceral from subcutaneous abdominal AT.
Patterns of regional fat distribution may be a more critical feature in older adults who may experience health decline—related weight loss composed of skeletal muscle and subcutaneous AT. Thus, normal-weight individuals may still be at risk for the metabolic syndrome and its consequences.
The Health ABC cohort includes approximately an equal proportion of older men and women and, importantly, an oversampling We examined whether the specific criteria developed by the Adult Treatment Panel III to define the metabolic syndrome differ between older men and women and between blacks and whites.
Using baseline data from this longitudinal study, we examined the primary hypothesis that visceral abdominal AT and AT infiltrating skeletal muscle are associated with the metabolic syndrome in older men and women, and also examined whether these associations differ by level of body weight or race.
The study population consisted of men and women who participated in baseline evaluations in the Health ABC Study, a longitudinal investigation of nondisabled men and women aged 70 to 79 years, recruited primarily from a random sample of Medicare-eligible adults residing in designated ZIP code areas in Pittsburgh, Pa, and Memphis, Tenn, with an oversampling of blacks Detailed exclusion criteria for this cohort have been reported previously.
This analysis included subjects of this cohort who had complete data on body composition as well as criteria defining the metabolic syndrome. In addition, individuals who reported currently using antihypertensive or antidiabetic medication were counted as meeting the high blood pressure or glucose criterion, respectively.
Age of participants was determined to the nearest year. Total body fat was determined by means of dual-energy x-ray absorptiometry QDR ; Hologic Inc, Waltham, Mass. Waist circumference was determined to the nearest centimeter.
Blood was drawn after an overnight fast and analyzed for serum triglycerides, HDL cholesterol, and glucose determinations. Plasma glucose was measured by means of an automated glucose oxidase reaction YSI Glucose Analyzer; Yellow Springs Instruments, Yellow Springs, Ohio.
A conventional mercury sphygmomanometer was used for the measurement of blood pressure. The participant rested quietly in a seated position with the back supported and feet flat on the ground for at least 5 minutes before the blood pressure measurement.
Systolic and diastolic blood pressures were defined as the average of 2 measures. Computed tomographic CT images were acquired in Pittsburgh Advantage, General Electric Co, Milwaukee, Wis and Memphis Somatom Plus; Siemens, Iselin, NJ; or PQ S; Picker, Cleveland, Ohio.
For imaging, patients were placed in the supine position with the arms above the head and with legs lying flat on the table and toes directed toward the top of the gantry.
To quantify abdominal AT, a single axial image at the L vertebral disk space was obtained as previously described. The CT acquisition scheme for the quantification of midthigh muscle and AT has been reported elsewhere in detail for this cohort.
Skeletal muscle, AT, and bone in the thigh were separated on the basis of their CT attenuation values. Lower attenuation values are compatible with greater fatty infiltration into tissue.
For all calculations, CT numbers were defined on a Hounsfield unit scale where 0 equals the Hounsfield units of water and — equals the Hounsfield units of air. All analysis programs were developed at the University of Colorado CT Scan Reading Center with the use of IDL RSI Systems, Boulder.
Prevalence of metabolic syndrome, demographics, body composition, and regional AT variables were described, and the differences in continuous variables between those with and without metabolic syndrome were evaluated by either t tests or the Wilcoxon rank-sum test.
Categorical differences between persons with and without the metabolic syndrome were evaluated with the χ 2 test. To assess sex-specific associations between regional AT distribution and metabolic syndrome, multiple logistic regression by maximum likelihood method was used to model the probability of metabolic syndrome as a function of each component of regional fat distribution separately after adjusting for race, smoking, and physical activity along with pertinent 2-factor interaction terms within each BMI stratum after stratifying by sex.
Point estimates and the associated confidence interval for all the independent variables were obtained, multicollinearity was tested by variance inflation factor, and the model evaluation was done by Hosmer-Lemeshow statistic.
Since the results were similar for BMI and total body fat strata, we present findings for only BMI strata. Current smoking status and physical activity were assessed by questionnaire.
Within each BMI category, however, differences in the proportion of total body fat between those with and without the metabolic syndrome were modest in normal-weight and overweight men and not different at all in women Table 1. In fact, obese women without metabolic syndrome had a significantly higher proportion of body fat than obese women with metabolic syndrome.
In addition, lower muscle mass in older subjects, known as sarcopenia , was not associated with the metabolic syndrome. Indeed, across all levels of BMI, those with metabolic syndrome had higher lean body mass than those without metabolic syndrome.
This strongly suggests that factors other than generalized adiposity are associated with metabolic syndrome in older men and women. We examined whether there were sex or racial differences in the prevalence of each of the 5 components that define the metabolic syndrome Table 2.
More women than men met the waist circumference criterion, and a higher proportion of white men than white women were positive for the blood glucose criterion. All other components ascribed to metabolic syndrome were similar in men and women.
Among men, a higher proportion of whites than blacks met waist circumference, serum triglyceride, and HDL cholesterol criteria, whereas black men had higher rates of hypertension and abnormal blood glucose values Table 2.
Among women, whites had higher rates of abnormal serum triglyceride levels and lower HDL cholesterol levels, whereas the black women had higher rates of hypertension, abnormal blood glucose levels, and large waist circumference.
Thus, lipid abnormalities were nearly 2-fold more common in whites, while blacks had a higher prevalence of blood glucose abnormalities and hypertension than whites. As shown in Table 1 , although overweight and obesity were associated with a higher prevalence of the metabolic syndrome, differences in regional fat distribution were even more distinct Table 3.
Waist circumference represents the combination of visceral and subcutaneous AT. When the attributable risk for metabolic syndrome was examined for each of the predictors, higher visceral AT was consistent across all BMI groups for both men and women to have the highest attributable risk associated with metabolic syndrome.
Higher visceral AT in men and women with metabolic syndrome was consistent for whites and blacks; thus, results were pooled for race for ease of interpretation. Data presented in Table 3 indicate that differences in the amount of AT infiltrating skeletal muscle also distinguished those with metabolic syndrome to a greater degree than subcutaneous AT in the thigh.
Men and women with metabolic syndrome also had muscle with lower attenuation values, a marker of its higher fat infiltration 15 Table 3. Again, these results were similar for blacks and whites.
Since the metabolic syndrome was not limited to obese subjects, we examined whether regional AT distribution was associated with metabolic syndrome separately in normal-weight, overweight, and obese subject, adjusting for race, smoking status, and physical activity. Higher visceral AT was associated with a significantly higher prevalence of metabolic syndrome, especially in normal-weight and overweight men and women but less so in the obese Figure 1.
Higher subcutaneous AT was significantly associated with metabolic syndrome in normal-weight and overweight but not in obese men. No other significant interactions between race and the regional fat depots were observed in association with the metabolic syndrome.
Similar results were obtained when stratifying by the proportion of body fat rather than by BMI. Higher intermuscular AT was significantly associated with metabolic syndrome in normal-weight and overweight, but not in obese, men Figure 2.
No significant associations were observed for intermuscular AT and metabolic syndrome in women. In contrast, having more subcutaneous thigh AT was associated with a lower prevalence of metabolic syndrome in obese men and in overweight and obese women. We also examined in multiple logistic regression whether physical activity and diet modified the associations between regional fat distribution and metabolic syndrome.
For men, neither smoking nor physical activity was related to metabolic syndrome in any of the BMI categories after taking into account regional fat distribution.
In women, current smoking was not related to metabolic syndrome after accounting for VAT. Only in overweight women was physical inactivity associated with metabolic syndrome independent of all regional depots.
Thus, adjusting results for smoking and physical activity did not appear to confound associations between regional fat depots and metabolic syndrome. The overall prevalence of the metabolic syndrome in this older cohort was similar to that reported for older adults in the United States 4 and nearly double that reported for middle-aged adults.
With an oversampling of blacks, we were able to determine that, although the overall prevalence of metabolic syndrome was not different between blacks and whites, there were racial differences in the prevalence of specific criteria that define metabolic syndrome. Specifically, blacks had higher rates of hypertension and abnormal glucose metabolism, whereas whites had higher rates of dysregulated lipid metabolism.
The development of metabolic syndrome involves an interaction of complex parameters including obesity, regional fat distribution, dietary habits, and physical inactivity, 5 so it is not yet entirely clear how to interpret these racial differences.
Nevertheless, this suggests that the cause of metabolic syndrome is different in blacks and whites. The prevalence of metabolic syndrome, not surprisingly, was much higher among the obese. However, differences in generalized obesity by BMI or total body fat criteria in those with metabolic syndrome were at best modest.
Obese women with the metabolic syndrome actually had a lower proportion of body fat than obese women without metabolic syndrome. Regional fat distribution, particularly visceral abdominal AT and intermuscular AT, clearly discriminated those with the metabolic syndrome, particularly among the nonobese.
This implies that older men and women can have normal body weight, and even have relatively lower total body fat, but still have metabolic syndrome, due to the amount of AT located intra-abdominally or interspersed within the musculature.
What makes this observation more remarkable is that these associations were much less robust or even nonexistent for subcutaneous AT. More subcutaneous AT in the thighs of obese men and women was actually associated with a lower prevalence of metabolic syndrome. This is consistent with previous reports demonstrating that total leg fat mass, most of which was subcutaneous AT, is inversely related to cardiovascular disease risk.
Albu et al 18 suggested that similar levels of visceral AT in blacks and whites may confer different metabolic risk. Our data support the contention by some that BMI may not accurately reflect the degree of adiposity in certain populations.
The current results parallel our previous observation in the Health ABC cohort that visceral and intermuscular AT strongly predict insulin resistance and type 2 diabetes. These associations between regional fat deposition and metabolic dysregulation are also consistent with other previous findings in both middle-aged and older adults.
Although we included in the analysis physical activity as a potential confounder to our associations, it is possible that the self-reported estimates for physical activity were not sensitive enough to detect significant associations with metabolic syndrome demonstrated in previous studies.
However, predictors of the incidence of metabolic syndrome can be examined when data become available in this longitudinal study.
There are several possible explanations for the observed association between excess visceral fat accumulation and the metabolic syndrome. Visceral fat is thought to release fatty acids into the portal circulation, where they may cause insulin resistance in the liver and subsequently in muscle.
A parallel hypothesis is that adipose tissue is an endocrine organ that secretes a variety of endocrine hormones such as leptin, interleukin 6, angiotensin II, adiponectin, and resistin, which may have potent effects on the metabolism of peripheral tissues.
In conclusion, excess accumulation of either visceral abdominal or muscle AT is associated with a higher prevalence of metabolic syndrome in older adults, particularly in those who are of normal body weight. This suggests that practitioners should not discount the risk of metabolic syndrome in their older patients entirely on the basis of body weight or BMI.
Indeed, generalized body composition, in terms of both BMI and the proportion of body fat, does not clearly distinguish older subjects with the metabolic syndrome. Moreover, racial differences in the various components of the metabolic syndrome provide strong evidence that the cause of the syndrome likely varies in blacks and whites.
Thus, the development of a treatment for the metabolic syndrome as a unifying disorder is likely to be complex. Correspondence: Bret H. Goodpaster, PhD, Department of Medicine, North MUH, University of Pittsburgh Medical Center, Pittsburgh, PA bgood pitt.
Dr Goodpaster was supported by grant KAG from the National Institute on Aging, National Institutes of Health. full text icon Full Text. Download PDF Top of Article Abstract Methods Results Comment Article Information References.
Figure 1. View Large Download. Table 1. Characteristics of Men and Women With and Without Metabolic Syndrome. Regional Fat Distribution According to Metabolic Syndrome Status. Abdominal AT in Men and Women With and Without Metabolic Syndrome According to a Revised Definition Omitting Waist Circumference.
Haffner SValdez RHazuda HMitchell BMorales PStern M Prospective analysis of the insulin resistance syndrome syndrome X.
Diabetes ; PubMed Google Scholar Crossref. Isomaa BAlmgren PTuomi T et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care ; PubMed Google Scholar Crossref.
National Institutes of Health, Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation and Treatment of High Cholesterol in Adults Adult Treatment Panel III.
Bethesda, Md National Institutes of Health;NIH publication Ford EGiles WDietz W Prevalence of the metabolic syndrome among US adults. JAMA ; PubMed Google Scholar Crossref. Grundy SMHansen BSmith SC Jr et al.
Circulation ; PubMed Google Scholar Crossref. Harris MIFlegal KMCowie CC et al. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U. adults: the Third National Health and Nutrition Examination Survey,
Thank you for visiting nature. Fachors are Fat distribution and risk factors a Athletic performance resources version rissk limited support for CSS. To obtain the diatribution experience, rusk recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. The effect of visceral adipose tissue VAT and subcutaneous adipose tissue SAT area on metabolic syndrome MS has been debated. Department diistribution Internal Fat distribution and risk factors, Division of Endocrinology, Metabolism ffactors Nutrition, Istanbul Faculty of Distributikn. The aim of this study fcators to determine the body fat distribution and anx disease risk factors in pre- and Fat distribution and risk factors obese women matched Fah weight, height and body Website performance optimization tips index BMI. None of the women were on hormone replacement therapy. No significant differences were observed with respect to insulin and HOMA. When age-matched pre- and postmenopausal women were compared, only total cholesterol was significantly higher in the postmenopausal group. It is concluded that an increase in abdominal fat accumulation and unfavorable alterations in risk factors disturb postmenopausal obese women even if total body distribuiton and BMI do not change during menopause transition. Ageing, particularly throughout the postmenopausal years, has important effects on the detrimental changes associated with menopause.
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