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In the syhdrome, to ensure Megabolic support, we are displaying walst site without Metabo,ic and JavaScript. Despite decades of unequivocal evidence that waist circumference provides both independent and additive information to BMI for predicting morbidity and risk of death, this measurement is not routinely obtained in clinical practice.
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The Consensus Statement was saist as follows. The first face-to-face meeting occurred on 24 Antioxidant-Fortified Beverages to review the high-quality evidence available and known to the subject experts. After discussion and deliberation amongst the experts regarding the context and quality of the evidence, an executive writing group R.
and Y. was appointed and tasked with writing the first draft. High-quality published literature that became available after the initial face-to-face meeting through June was identified by all authors and reviewed by the executive writing group for inclusion in the manuscript.
The first author coordinated the final preparation and submission of the Consensus Statement after the group achieved consensus and approved its content. The importance of body fat distribution as a risk factor for several diseases for example, CVD, hypertension, stroke and T2DM and mortality has been recognized for several decades.
These classifications were later interpreted by Ahmed Kissebah and colleagues as upper versus lower body fat accumulation as reflected by a high or low waist—hip circumference ratio WHRrespectively The upper and lower body fat accumulation phenotypes were based on body morphology as assessed by external anthropometric measures such as skinfolds and circumferences.
The WHR increased in popularity when epidemiologists in the USA and Sweden showed that WHR, separately or in combination with BMI, was associated with increased risk of death, CVD and T2DM 19202122findings that were subsequently confirmed in many studies.
However, later evidence indicated that, compared with the WHR, waist circumference alone was more strongly associated with the absolute amount of intra-abdominal or visceral fat, the fat depot that conveys the strongest health risk 23 Furthermore, when a ratio such as WHR is used to follow changes in regional adipose depots, the utility of the ratio is limited when both the numerator and denominator values change in response to treatment.
Consequently, the combination of WHR and BMI for assessing obesity risk were replaced by single threshold values for waist circumference alone Although the use of these specific waist circumference values to identify white adults with abdominal obesity remains a cornerstone of obesity guidelines worldwide, we present evidence to challenge the supportive rationale and provide evidence in support of alternative waist circumference values to be used in concert with BMI.
As an alternative to measurements of waist circumference, the WHR or waist—thigh circumference ratio, Margaret Ashwell and others proposed the waist—height ratio as a measure of abdominal obesity 26 Compared with the previous measurements, the waist—height ratio shows similar and sometimes slightly stronger associations with the risk of CVD or T2DM 28 An explanation for why adding height increases the prediction of disease risk might be because short stature is associated with increased risk of CVD In growing children and adolescents, the waist—height ratio could be more useful for the classification of abdominal obesity than waist circumference alone.
However, in fully grown adults, the waist—height ratio is less useful as height is generally fixed and the value can only be altered by changes in waist circumference.
Moreover, height is only marginally associated with waist circumference For the assessment of the effectiveness of lifestyle changes in adults, waist circumference might be preferred as a simple tool. Other alternatives to waist circumference have included the conicity index 32 and the abdominal obesity index 33but they are, at best, only slightly better predictors of disease risk than waist circumference alone.
Despite a strong association between waist circumference and BMI at the population level, emerging evidence suggests that, across populations, waist circumference might be increasing beyond what is expected according to BMI.
In other words, the phenotype of obesity might be changing over time to one that reflects an increase in abdominal adiposity For example, Ian Janssen and colleagues examined the changes in waist circumference for a given BMI over a year period in a Canadian sample Notably, for a given BMI, Canadians had a larger waist circumference in compared with Specifically, the researchers observed a waist circumference that was greater by 1.
Similarly, Sandra Albrecht and colleagues examined the secular changes in waist circumference in the USA —England —China — and Mexico — 36 and reported statistically significantly increased waist circumference values relative to BMI in all countries studied and in most subpopulations.
These observations are consistent with those of Tommy Visscher and colleagues, who performed an extensive review and concluded that the majority of the evidence suggests a trend in which the relative increases in waist circumference were larger than the relative increases in BMI This observation is seemingly independent of age, sex and ethnicity, as few groups failed to demonstrate the general trend of secular waist circumference increasing beyond that expected by BMI Fig.
The failure of BMI to detect such an increase in abdominal obesity confirms the limitations of BMI alone to identify the phenotype of obesity that conveys the greatest health risk. Changes in the prevalence of abdominal obesity measured using waist circumference and general obesity measured using BMI measured in different studies during the time period indicated on the x axis.
However, Xi et al. In addition, Barzin et al. Years given for example, — indicate the years in which data were collected. F, female; M, male. Data are from refs 37,, Although the prevalence of obesity measured by BMI might have plateaued in some countries, the prevalence of abdominal obesity as measured by waist circumference is generally increasing.
The lack of inclusion of waist circumference in global obesity surveillance might inadequately characterize the health risk associated with the global obesity prevalence, as it seems that the prevalence of abdominal obesity is increasing.
Current obesity prevalence trends based on BMI alone should be interpreted with caution. We recommend that serious consideration should be given to the inclusion of waist circumference in obesity surveillance studies.
It is not surprising that waist circumference and BMI alone are positively associated with morbidity 15 and mortality 13 independent of age, sex and ethnicity, given the strong association between these anthropometric variables across cohorts. However, it is also well established that, for any given BMI, the variation in waist circumference is considerable, and, in any given BMI category, adults with higher waist circumference values are at increased adverse health risk compared with those with a lower waist circumference 3839 This observation is well illustrated by James Cerhan and colleagues, who pooled data from 11 prospective cohort studies withwhite adults from the USA, Australia and Sweden aged 20—83 years This finding is consistent with that of Ellen de Hollander and colleagues, who performed a meta-analysis involving over 58, predominantly white older adults from around the world and reported that the age-adjusted and smoking-adjusted mortality was substantially greater for those with an elevated waist circumference within normal weight, overweight and obese categories as defined by BMI The ability of waist circumference to add to the adverse health risk observed within a given BMI category provides the basis for the current classification system used to characterize obesity-related health risk 8 Despite the observation that the association between waist circumference and adverse health risk varies across BMI categories 11current obesity-risk classification systems recommend using the same waist circumference threshold values for all BMI categories We propose that important information about BMI and waist circumference is lost when they are converted from continuous to broad categorical variables and that this loss of information affects the manner in which BMI and waist circumference predict morbidity and mortality.
Specifically, when BMI and waist circumference are considered as categorical variables in the same risk prediction model, they are both positively related to morbidity and mortality However, when BMI and waist circumference are considered as continuous variables in the same risk prediction model, risk prediction by waist circumference improves, whereas the association between BMI and adverse health risk is weakened 10 Evidence in support of adjusting waist circumference for BMI comes from Janne Bigaard and colleagues who report that a strong association exists between waist circumference and all-cause mortality after adjustment for BMI Consistent with observations based on asymptomatic adults, Thais Coutinho and colleagues report similar observations for a cohort of 14, adults with CVD who were followed up for 2.
The cohort was divided into tertiles for both waist circumference and BMI. In comparison with the lowest waist circumference tertile, a significant association with risk of death was observed for the highest tertile for waist circumference after adjustment for age, sex, smoking, diabetes mellitus, hypertension and BMI HR 1.
By contrast, after adjustment for age, sex, smoking, diabetes mellitus, hypertension and waist circumference, increasing tertiles of BMI were inversely associated with risk of death HR 0. The findings from this systematic review 44 are partially confirmed by Diewertje Sluik and colleagues, who examined the relationships between waist circumference, BMI and survival in 5, individuals with T2DM over 4.
In this prospective cohort study, the cohort was divided into quintiles for both BMI and waist circumference. After adjustment for T2DM duration, insulin treatment, prevalent myocardial infarction, stroke, cancer, smoking status, smoking duration, educational level, physical activity, alcohol consumption and BMI, the HR for risk of death associated with the highest tertile was 2.
By contrast, in comparison with the lowest quintile for BMI adjusted for the same variables, with waist circumference replacing BMIthe HR for risk of death for the highest BMI quintile was 0. In summary, when associations between waist circumference and BMI with morbidity and mortality are considered in continuous models, for a given waist circumference, the higher the BMI the lower the adverse health risk.
Why the association between waist circumference and adverse health risk is increased following adjustment for BMI is not established. It is possible that the health protective effect of a larger BMI for a given waist circumference is explained by an increased accumulation of subcutaneous adipose tissue in the lower body This observation was confirmed by Sophie Eastwood and colleagues, who reported that in South Asian adults the protective effects of total subcutaneous adipose tissue for T2DM and HbA 1c levels emerge only after accounting for visceral adipose tissue VAT accumulation A causal mechanism has not been established that explains the attenuation in morbidity and mortality associated with increased lower body adiposity for a given level of abdominal obesity.
We suggest that the increased capacity to store excess energy consumption in the gluteal—femoral subcutaneous adipocytes might protect against excess lipid deposition in VAT and ectopic depots such as the liver, the heart and the skeletal muscle Fig.
Thus, for a given waist circumference, a larger BMI might represent a phenotype with elevations in lower body subcutaneous adipose tissue. Alternatively, adults with elevations in BMI for a given waist circumference could have decreased amounts of VAT.
Excess lipid accumulation in VAT and ectopic depots is associated with increased cardiometabolic risk 4748 ,
: Metabolic syndrome waist circumference| Waist circumference a good indicator of future risk for type 2 diabetes and cardiovascular disease | Original investigation Open access Published: Metablic March The prediction of Metabolic Syndrome alterations Mdtabolic improved by combining waist circumference Organic olive oil handgrip strength measurements compared Inner peace techniques either alone Syndrpme P. Exclusion criteria were having Isotonic drink for exercise medical condition that excused a Metabolc from meeting the Maryland Physical Education Content Thermogenic fat burners 10being pregnant or breastfeeding, planning Metabllic leave the area before follow-up, participating Metavolic in another Inner peace techniques trial that conflicted with the study, or having another household member participating in Project Heart. Development of the multiple metabolic syndrome in the ARIC cohort joint contribution of insulin, BMI and WHR atherosclerosis risk in communities. Leon Guerrero, PhD 6 ; Rachel Novotny, PhD 1 View author affiliations Suggested citation for this article: Yamanaka AB, Davis JD, Wilkens LR, Hurwitz EL, Fialkowski MK, Deenik J, et al. Likewise, our recent study also showed that WHtR has a stronger association with cardiovascular risks than WC, and a WHtR cut-off of 0. Visceral adiposity is associated with a hyperlipolytic state resistant to the effect of insulin along with an altered secretion of adipokines including inflammatory cytokines whereas a set of metabolic dysfunctions are specifically associated with increased skeletal muscle, liver, pancreas, and epicardial, pericardial and intra-myocardial fat. |
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The authors acknowledge the financial support of the IAS and the ICCR, an independent academic organization based at Université Laval, Québec, Canada, who were responsible for coordinating the production of our report. No funding or honorarium was provided by either the IAS or the ICCR to the members of the writing group for the production of this article. The scientific director of the ICCR J. is funded by a Foundation Grant Funding Reference Number FDN from the Canadian Institutes of Health Research. Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA. Departments of Cardiovascular Medicine and Community Medicine, Osaka University Graduate School of Medicine, Osaka, Japan. Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. Department of Health Sciences and the EMGO Institute for Health and Care Research, VU University Amsterdam, Amsterdam, Netherlands. Scientific Institute for Research, Hospitalization and Health Care IRCCS MultiMedica, Sesto San Giovanni, Italy. Lipid Clinic Heart Institute InCor , University of São Paulo, Medical School Hospital, São Paulo, Brazil. Hospital Israelita Albert Einstein, Sao Paulo, Brazil. Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, QC, Canada. Department of Clinical Nutrition and Metabolism, Clínica Las Condes, Santiago, Chile. Departments of Nutrition and Epidemiology, Harvard T. Chan School of Public Health, Boston, MA, USA. Department of Nutritional Sciences, University of Surrey, Guildford, UK. Department of Medicine - DIMED, University of Padua, Padova, Italy. School of Medical Sciences, University of New South Wales Australia, Sydney, NSW, Australia. Division of Endocrinology, Metabolism and Diabetes, and Division of Cardiology, Anschutz University of Colorado School of Medicine, Aurora, CO, USA. Department of Endocrinology and Metabolism, Sumitomo Hospital, Osaka, Japan. Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada. You can also search for this author in PubMed Google Scholar. and J. researched data for the article. made a substantial contribution to discussion of the content. wrote the article. Correspondence to Robert Ross. reports receiving speaker fees from Metagenics and Standard Process and a research grant from California Walnut Commission. reports receiving consulting and speaker fess from Amgen, Astra Zeneca, Akcea, Biolab, Esperion, Kowa, Merck, MSD, Novo Nordisk, Sanofi Regeneron, Akcea, Kowa and Esperion. reports grants and personal fees from Kowa Company, Ltd. and Kaken Pharmaceutical Co. also has patents issued with Fujirebio and Kyowa Medex Co. reports his role as a scientific adviser for PROMINENT Kowa Company Ltd. The remaining authors declare no competing interests. Nature Reviews Endocrinology thanks R. Kelishadi and the other, anonymous, reviewer s for their contribution to the peer review of this work. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The ability to correctly predict the proportion of participants in a given group who will experience an event. The probability of a diagnostic test or risk prediction instrument to distinguish between higher and lower risk. The relative increase in the predicted probabilities for individuals who experience events and the decrease for individuals who do not. The highest value of VO 2 that is, oxygen consumption attained during an incremental or other high-intensity exercise test. Open Access This work is licensed under a Creative Commons Attribution 4. Reprints and permissions. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol 16 , — Download citation. Accepted : 05 December Published : 04 February Issue Date : March Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. nature nature reviews endocrinology consensus statements article. Download PDF. Subjects Disease prevention Metabolic syndrome Obesity Predictive markers. Abstract Despite decades of unequivocal evidence that waist circumference provides both independent and additive information to BMI for predicting morbidity and risk of death, this measurement is not routinely obtained in clinical practice. Introduction The prevalence of adult overweight and obesity as defined using BMI has increased worldwide since the s, with no country demonstrating any successful declines in the 33 years of recorded data 1. Methodology This Consensus Statement is designed to provide the consensus of the IAS and ICCR Working Group Supplementary Information on waist circumference as an anthropometric measure that improves patient management. Historical perspective The importance of body fat distribution as a risk factor for several diseases for example, CVD, hypertension, stroke and T2DM and mortality has been recognized for several decades. Prevalence of abdominal obesity Despite a strong association between waist circumference and BMI at the population level, emerging evidence suggests that, across populations, waist circumference might be increasing beyond what is expected according to BMI. Full size image. Identifying the high-risk obesity phenotype Waist circumference, BMI and health outcomes — categorical analysis It is not surprising that waist circumference and BMI alone are positively associated with morbidity 15 and mortality 13 independent of age, sex and ethnicity, given the strong association between these anthropometric variables across cohorts. Waist circumference, BMI and health outcomes — continuous analysis Despite the observation that the association between waist circumference and adverse health risk varies across BMI categories 11 , current obesity-risk classification systems recommend using the same waist circumference threshold values for all BMI categories Importance in clinical settings For practitioners, the decision to include a novel measure in clinical practice is driven in large part by two important, yet very different questions. Risk prediction The evaluation of the utility of any biomarker, such as waist circumference, for risk prediction requires a thorough understanding of the epidemiological context in which the risk assessment is evaluated. Risk reduction Whether the addition of waist circumference improves the prognostic performance of established risk algorithms is a clinically relevant question that remains to be answered; however, the effect of targeting waist circumference on morbidity and mortality is an entirely different issue of equal or greater clinical relevance. A highly responsive vital sign Evidence from several reviews and meta-analyses confirm that, regardless of age and sex, a decrease in energy intake through diet or an increase in energy expenditure through exercise is associated with a substantial reduction in waist circumference 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , Measurement of waist circumference The emergence of waist circumference as a strong independent marker of morbidity and mortality is striking given that there is no consensus regarding the optimal protocol for measurement of waist circumference. Conclusions and recommendations — measurement of waist circumference Currently, no consensus exists on the optimal protocol for measurement of waist circumference and little scientific rationale is provided for any of the waist circumference protocols recommended by leading health authorities. Threshold values to estimate risk Current guidelines for identifying obesity indicate that adverse health risk increases when moving from normal weight to obese BMI categories. Table 1 Waist circumference thresholds Full size table. Table 2 Ethnicity-specific thresholds Full size table. Conclusions The main recommendation of this Consensus Statement is that waist circumference should be routinely measured in clinical practice, as it can provide additional information for guiding patient management. References Ng, M. PubMed PubMed Central Google Scholar Afshin, A. PubMed Google Scholar Phillips, C. PubMed Google Scholar Bell, J. PubMed Google Scholar Eckel, N. PubMed Google Scholar Brauer, P. PubMed PubMed Central Google Scholar Garvey, W. PubMed Google Scholar Jensen, M. PubMed Google Scholar Tsigos, C. PubMed PubMed Central Google Scholar Pischon, T. CAS PubMed Google Scholar Cerhan, J. PubMed PubMed Central Google Scholar Zhang, C. PubMed Google Scholar Song, X. 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Acknowledgements The authors acknowledge the financial support of the IAS and the ICCR, an independent academic organization based at Université Laval, Québec, Canada, who were responsible for coordinating the production of our report. Santos Hospital Israelita Albert Einstein, Sao Paulo, Brazil Raul D. Chan School of Public Health, Boston, MA, USA Frank B. Hu Department of Nutritional Sciences, University of Surrey, Guildford, UK Bruce A. Griffin Department of Medicine - DIMED, University of Padua, Padova, Italy Alberto Zambon School of Medical Sciences, University of New South Wales Australia, Sydney, NSW, Australia Philip Barter Fondation Cœur et Artères, Lille, France Jean-Charles Fruchart Division of Endocrinology, Metabolism and Diabetes, and Division of Cardiology, Anschutz University of Colorado School of Medicine, Aurora, CO, USA Robert H. Eckel Department of Endocrinology and Metabolism, Sumitomo Hospital, Osaka, Japan Yuji Matsuzawa Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada Jean-Pierre Després Authors Robert Ross View author publications. View author publications. Ethics declarations Competing interests I. Additional information Peer review information Nature Reviews Endocrinology thanks R. Supplementary information. Supplementary Information. Glossary Calibration The ability to correctly predict the proportion of participants in a given group who will experience an event. Discrimination The probability of a diagnostic test or risk prediction instrument to distinguish between higher and lower risk. Net reclassification improvement The relative increase in the predicted probabilities for individuals who experience events and the decrease for individuals who do not. C-statistic A measure of goodness-of-fit for binary outcomes in a logistic regression model. In addition to the clinical criteria, men who indicated a history of hypertension or diabetes were classified as having elevated blood pressure and glucose, respectively, for all three metabolic syndrome definitions. Information about cigarette smoking, alcohol consumption, and parental history of CVD was collected using a medical history questionnaire. Smoking status was categorized as never, former, or current. One unit of alcohol was defined as one bottle or can of beer, a glass of wine, or one shot of hard liquor. Individuals who indicated a history of CHD, stroke, or cancer on the medical history questionnaire were excluded from the analyses. However, men with an indication of CVD at the baseline examination were retained and coded as 0 no indication of CVD and 1 possible indication of CVD. Indications of possible CVD were an abnormal electrocardiogram at rest or during exercise 6. Participants were followed until death or until 31 December in the case of survivors. Deaths were identified using the National Death Index and causes of death were determined from death certificates obtained from the departments of vital statistics in the states of decedents. A nosologist coded the death certificates for the underlying and up to four contributing causes of death, and CVD mortality was defined as codes — All analyses were limited to participants with at least 1 year of follow-up. Cox regression was used to estimate the RR of mortality associated with the NCEP, NCEP-R, and IDF definitions of metabolic syndrome. Age, year of examination, smoking status, alcohol consumption, parental history of CVD, and possible CVD at baseline were included as covariates in multivariable models. Mortality rates per 10, man-years of follow-up are reported as adjusted by Cox regression for age and year of examination. The ability of metabolic syndrome criteria to predict year all-cause and CVD mortality was compared using C statistics derived from logistic regression, including age and year and examination as covariates and then adding smoking status, alcohol consumption, parental history of CVD, and possible CVD at baseline as covariates in multivariable models. The C statistic is equivalent to the Wilcoxon two-sample statistic for comparing the locations of event and nonevent distributions. All analyses were conducted using SAS software SAS Institute, Cary, NC. NHANES is the most recent population health survey that measures metabolic syndrome risk factors. NHANES uses a multistage, stratified, and weighted sampling design to select participants who are representative of the civilian noninstitutionalized U. Complete details of the survey design and strategy are available elsewhere To estimate the impact of metabolic syndrome on the population, data from NHANES — were used to calculate prevalences of metabolic syndrome. A detailed explanation of the NHANES protocols is found elsewhere Table 1 presents the characteristics of the ACLS sample. The proportions of men with metabolic syndrome in the ACLS cohort were Over an average of The unadjusted Kaplan-Meier curves according to the three metabolic syndrome definitions are presented in Fig. The corresponding values for CVD mortality were 1. The prevalences of NCEP, NCEP-R, and IDF definitions of metabolic syndrome in NHANES were The corresponding PAF using the RRs from the ACLS and the prevalences from NHANES for the NCEP, NCEP-R, and IDF definitions are 8, 9. The C statistic for predicting year all-cause mortality was 0. The C statistics were 0. The corresponding values for CVD mortality were 0. These results indicate that the predictive ability of the three metabolic syndrome criteria were quite similar. All-cause and CVD death rates across waist circumference and risk factor categories are illustrated in Fig. All-cause and CVD death rates were higher in men with two or more additional risk factors, regardless of waist circumference level. For CVD mortality, the elevated RR of mortality was restricted to men with waist circumference between 94 and cm 1. There is currently debate as to whether metabolic syndrome increases the risk of adverse health outcomes beyond the risk associated with the individual component risk factors 14 — The existing diagnostic criteria for metabolic syndrome arose from deliberations of panels of experts rather than from the results of prospective epidemiological studies or an evidence-based process Thus, studies are required to determine the effectiveness of metabolic syndrome at predicting health outcomes, albeit in a post hoc manner, to refine the clinical definitions and to either provide support for their use or discontinue their use. The results of this study demonstrate a higher risk of mortality associated with metabolic syndrome in white, non-Hispanic men and provide support for a role for waist circumference in the clinical criteria for metabolic syndrome. The PAF estimates from the present study range from 8 to 9. A more recent analysis from the Hoorn Study compared several definitions of metabolic syndrome in the prediction of CVD and found that metabolic syndrome doubled the risk of incident CVD; however, there were minimal differences across metabolic syndrome definitions These observations suggest that the public health burden associated with metabolic syndrome is substantial regardless of the metabolic syndrome criteria used. However, despite the higher prevalence, the predictive ability C statistic of IDF and NCEP definitions for mortality were similar. The IDF metabolic syndrome criteria identified a larger subset of the population that is at increased risk of mortality. Together these observations suggest that lowering the glucose and waist circumference values within the metabolic syndrome context is beneficial for identifying men at risk; however, the optimal waist circumference threshold remains to be determined. A novel aspect of this study was the analyses of waist circumference thresholds in the presence or absence of two or more other metabolic syndrome risk factors. The principal finding was twofold. First, the rate of CVD mortality increased across waist circumference categories in men with two or more other metabolic syndrome risk factors. Second, in the absence of multiple risk factors, risk did not increase across waist circumference categories. The results provide support for a valuable role for waist circumference in the clinical definition of metabolic syndrome; however, it is apparent that a high waist circumference value in the absence of additional risk factors may not indicate increased mortality risk. This is consistent with reports suggesting that the combination of high waist circumference value and high triglyceride level is a better predictor of CVD than either alone These findings reinforce the recommendation that clinicians obtain all metabolic syndrome criteria to properly interpret the health risks associated with an elevated waist circumference. The mechanisms whereby waist circumference is associated with risk in the presence of other risk factors are unclear. It is possible that waist circumference acts as a marker for risk factors not measured in this study physical inactivity, insulin resistance, C-reactive protein, and others. Together these findings reinforce the notion that reductions in waist circumference should be a primary aim of strategies designed to reduce health risks associated with metabolic syndrome. Given that exercise is associated with substantial reductions in waist circumference 20 — 22 , and that cardiorespiratory fitness significantly attenuates the mortality risk associated with metabolic syndrome 23 , it is reasonable to suggest that physical activity be a cornerstone of strategies designed to treat metabolic syndrome. There are several strengths and limitations of this study. A marked strength is the use of a large sample of men for whom an extensive battery of measurements were obtained, which allowed the classification of metabolic syndrome under NCEP, NCEP-R, and IDF criteria. The predominantly white, middle-to-upper class sample of men limits the generalizability of the results; however, the homogenous nature of the sample ensures control over factors such as ethnicity and socioeconomic status. The use of NHANES to obtain national estimates of the prevalence of metabolic syndrome in men is also a strength of this study. However, further research is required to confirm these findings in women and in other ethnic and socioeconomic groups. In summary, men with metabolic syndrome have a higher risk of all-cause and CVD mortality by comparison with men without metabolic syndrome. The results suggest that IDF metabolic syndrome criteria will identify a larger segment of the population at increased mortality risk than NCEP metabolic syndrome criteria. The optimal waist circumference threshold value for predicting mortality within the context of the metabolic syndrome needs to be determined. Unadjusted Kaplan-Meier hazard curves for CVD mortality among 20, men 20—83 years of age from the ACLS. All-cause A and CVD B death rates according to categories of waist circumference WC and the presence or absence of two or more other metabolic syndrome risk factors. Death rates are adjusted for age and year of examination. Sample size number is shown in the bars, with number of deaths indicated in parentheses. Descriptive baseline characteristics of 20, men 20—83 years of age from the ACLS across categories of NCEP, NCEP-R, and IDF definitions of the metabolic syndrome. Relative risks of all-cause and CVD mortality associated with the NCEP, NCEP-R, and IDF definitions of the metabolic syndrome in 20, men 20—83 years of age from the ACLS. Adjusted for age, year of examination, smoking, alcohol consumption, parental history of premature CVD, and possible CVD at baseline. This research was supported by a grant from the National Institute on Aging AG and a New Emerging Team grant from the Canadian Institutes of Health Research and Heart and Stroke Foundation of Canada. A table elsewhere in this issue shows conventional and Système International SI units and conversion factors for many substances. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Diabetes Care. Advanced Search. User Tools Dropdown. Sign In. Skip Nav Destination Close navigation menu Article navigation. Volume 29, Issue 2. Previous Article Next Article. RESEARCH DESIGN AND METHODS. Article Information. Article Navigation. Cardiovascular and Metabolic Risk February 01 The Importance of Waist Circumference in the Definition of Metabolic Syndrome : Prospective analyses of mortality in men Peter T. Katzmarzyk, PHD ; Peter T. Katzmarzyk, PHD. This Site. Google Scholar. Ian Janssen, PHD ; Ian Janssen, PHD. Robert Ross, PHD ; Robert Ross, PHD. Timothy S. Church, MD, MPH, PHD ; Timothy S. Church, MD, MPH, PHD. Steven N. Blair, PED Steven N. |
| Can Waist Circumference Identify Children With the Metabolic Syndrome? | Circumfernce to main content Circumferdnce you Metabolic syndrome waist circumference visiting nature. Article Google Scholar Pouliot Qaist, Després Circukference, Lemieux S, Bouchard C, Tremblay Circukference, Nadeau A, Lupien PJ: Waist circumference and abdominal sagital diameter: best Organic olive oil Nutritional strategies for tendon recovery Inner peace techniques circumferennce abdominal visceral adipose tissue accumulation and Metabolic syndrome waist circumference cardiovascular risk in men and women. The natural course of healthy obesity over 20 years. in Body Composition: Health and Performance in Exercise and Sport ed. Although the use of these specific waist circumference values to identify white adults with abdominal obesity remains a cornerstone of obesity guidelines worldwide, we present evidence to challenge the supportive rationale and provide evidence in support of alternative waist circumference values to be used in concert with BMI. CAS PubMed Google Scholar Eastwood, S. Inactivity, exercise, and visceral fat. |
Metabolic syndrome waist circumference -
To diagnose metabolic syndrome, doctors measure waist circumference, blood pressure, and fasting blood sugar and fat lipid levels. Exercise, changes in eating habits, behavioral techniques, and medications may be used to help people lose weight. Lifestyle, genetics, disorders such as low thyroid hormone levels read more are treated.
Metabolic syndrome is a serious problem. Even children and adolescents can develop metabolic syndrome, but how many have it is unknown. Metabolic syndrome is more likely to develop when people store excess fat in the abdomen apple-shaped rather than around the hips pear-shaped.
The following people tend to store excess fat in the abdomen:. Coronary artery disease Overview of Coronary Artery Disease CAD Coronary artery disease is a condition in which the blood supply to the heart muscle is partially or completely blocked.
High blood pressure High Blood Pressure High blood pressure hypertension is persistently high pressure in the arteries. Type 2 diabetes Type 2 diabetes Diabetes mellitus is a disorder in which the body does not produce enough or respond normally to insulin, causing blood sugar glucose levels to be abnormally high.
Metabolic dysfunction-associated steatotic liver disease Fatty Liver Fatty liver is an abnormal accumulation of certain fats triglycerides inside liver cells. People with fatty liver may feel tired or have mild abdominal discomfort but otherwise have no symptoms read more formerly called fatty liver.
Gout Gout Gout is a disorder in which deposits of uric acid crystals accumulate in the joints because of high blood levels of uric acid hyperuricemia. The accumulations of crystals cause flares attacks Polycystic ovary syndrome Polycystic Ovary Syndrome PCOS Polycystic ovary syndrome is characterized by irregular or no menstrual periods and often obesity or symptoms caused by high levels of male hormones androgens , such as excess body hair and read more in women.
Major causes are diabetes and high blood pressure Obstructive sleep apnea Sleep Apnea Sleep apnea is a serious disorder in which breathing repeatedly stops long enough to disrupt sleep and often temporarily decrease the amount of oxygen and increase the amount of carbon dioxide Erectile dysfunction Erectile Dysfunction ED Erectile dysfunction ED is the inability to attain or sustain an erection satisfactory for sexual intercourse.
See also Overview of Sexual Dysfunction in Men. Every man occasionally has read more in men. Chronic stress may increase the risk of developing metabolic syndrome. It may also cause hormonal changes that contribute to accumulation of excess fat in the abdomen and cause the body to stop responding normally to insulin called insulin resistance , Chronic stress may cause levels of high-density lipoprotein HDL—the "good" cholesterol to decrease.
Abnormal levels of lipids such as a low level of HDL can increase the risk of metabolic syndrome. Metabolic syndrome is more common among people who smoke than among people who do not. Smoking can increase triglyceride levels and decrease HDL levels.
See also Obesity Obesity Obesity is a chronic, recurring complex disorder characterized by excess body weight. Obesity is influenced by a combination of factors that includes genetics, hormones, behavior, and the environment Waist circumference should be measured in all people because even people who are not overweight or appear lean can store excess fat in the abdomen.
The greater the waist circumference, the higher the risk of metabolic syndrome and its complications. The waist circumference that increases risk of complications due to obesity varies by ethnic group and sex. If waist circumference is high, doctors should measure blood pressure and blood sugar and fat levels after fasting.
Levels of both blood sugar and fats are often abnormal. Metabolic syndrome has many different definitions, but it is most often diagnosed when the waist circumference is 40 inches centimeters or more in men or 35 inches 88 centimeters or more in women indicating excess fat in the abdomen and when people have or are being treated for two or more of the following:.
Sometimes metformin or statins. The initial treatment of metabolic syndrome involves physical activity and a heart-healthy diet. Each part of metabolic syndrome should also be treated with medications if necessary.
If people have diabetes Diabetes Mellitus DM Diabetes mellitus is a disorder in which the body does not produce enough or respond normally to insulin, causing blood sugar glucose levels to be abnormally high.
The first centres on whether the measure or biomarker improves risk prediction in a specific population for a specific disease. For example, does the addition of a new risk factor improve the prognostic performance of an established risk prediction algorithm, such as the Pooled Cohort Equations PCE or Framingham Risk Score FRS in adults at risk of CVD?
The second question is concerned with whether improvement in the new risk marker would lead to a corresponding reduction in risk of, for example, cardiovascular events. In many situations, even if a biomarker does not add to risk prediction, it can still serve as an excellent target for risk reduction.
Here we consider the importance of waist circumference in clinical settings by addressing these two questions. The evaluation of the utility of any biomarker, such as waist circumference, for risk prediction requires a thorough understanding of the epidemiological context in which the risk assessment is evaluated.
In addition, several statistical benchmarks need to be met in order for the biomarker to improve risk prediction beyond traditional measures. These criteria are especially important for waist circumference, as established sex-specific and ethnicity-specific differences exist in waist circumference threshold levels 55 , In , the American Heart Association published a scientific statement on the required criteria for the evaluation of novel risk markers of CVD 57 , followed by recommendations for assessment of cardiovascular risk in asymptomatic adults in ref.
Novel biomarkers must at the very least have an independent statistical association with health risk, after accounting for established risk markers in the context of a multivariable epidemiological model. This characteristic alone is insufficient, however, as many novel biomarkers meet this minimum standard yet do not meaningfully improve risk prediction beyond traditional markers.
More stringent benchmarks have therefore been developed to assess biomarker utility, which include calibration , discrimination 58 and net reclassification improvement Therefore, to critically evaluate waist circumference as a novel biomarker for use in risk prediction algorithms, these stringent criteria need to be applied.
Numerous studies demonstrate a statistical association between waist circumference and mortality and morbidity in epidemiological cohorts. Notably, increased waist circumference above these thresholds was associated with increased relative risk of all-cause death, even among those with normal BMI In the USA, prospective follow-up over 9 years of 14, black, white and mixed ethnicity participants in the Atherosclerosis Risk in Communities study showed that waist circumference was associated with increased risk of coronary heart disease events; RR 1.
Despite the existence of a robust statistical association with all-cause death independent of BMI, there is no solid evidence that addition of waist circumference to standard cardiovascular risk models such as FRS 62 or PCE 63 improves risk prediction using more stringent statistical benchmarks.
For example, a study evaluating the utility of the PCE across WHO-defined classes of obesity 42 in five large epidemiological cohorts comprised of ~25, individuals assessed whether risk discrimination of the PCE would be improved by including the obesity-specific measures BMI and waist circumference The researchers found that although each measure was individually associated BMI: HR 1.
On the basis of these observations alone, one might conclude that the measure of waist circumference in clinical settings is not supported as risk prediction is not improved. However, Nancy Cook and others have demonstrated how difficult it is for the addition of any biomarker to substantially improve prognostic performance 59 , 66 , 67 , Furthermore, any additive value of waist circumference to risk prediction algorithms could be overwhelmed by more proximate, downstream causative risk factors such as elevated blood pressure and abnormal plasma concentrations of glucose.
In other words, waist circumference might not improve prognostic performance as, independent of BMI, waist circumference is a principal driver of alterations in downstream cardiometabolic risk factors.
A detailed discussion of the merits of different approaches for example, c-statistic, net reclassification index and discrimination index to determine the utility of novel biomarkers to improve risk prediction is beyond the scope of this article and the reader is encouraged to review recent critiques to gain insight on this important issue 66 , Whether the addition of waist circumference improves the prognostic performance of established risk algorithms is a clinically relevant question that remains to be answered; however, the effect of targeting waist circumference on morbidity and mortality is an entirely different issue of equal or greater clinical relevance.
Several examples exist in the literature where a risk marker might improve risk prediction but modifying the marker clinically does not impact risk reduction. For example, a low level of HDL cholesterol is a central risk factor associated with the risk of coronary artery disease in multiple risk prediction algorithms, yet raising plasma levels of HDL cholesterol pharmacologically has not improved CVD outcomes Conversely, a risk factor might not meaningfully improve statistical risk prediction but can be an important modifiable target for risk reduction.
Indeed, we argue that, at any BMI value, waist circumference is a major driver of the deterioration in cardiometabolic risk markers or factors and, consequently, that reducing waist circumference is a critical step towards reducing cardiometabolic disease risk.
As we described earlier, waist circumference is well established as an independent predictor of morbidity and mortality, and the full strength of waist circumference is realized after controlling for BMI. We suggest that the association between waist circumference and hard clinical end points is explained in large measure by the association between changes in waist circumference and corresponding cardiometabolic risk factors.
For example, evidence from randomized controlled trials RCTs has consistently revealed that, independent of sex and age, lifestyle-induced reductions in waist circumference are associated with improvements in cardiometabolic risk factors with or without corresponding weight loss 71 , 72 , 73 , 74 , 75 , These observations remain consistent regardless of whether the reduction in waist circumference is induced by energy restriction that is, caloric restriction 73 , 75 , 77 or an increase in energy expenditure that is, exercise 71 , 73 , 74 , We have previously argued that the conduit between change in waist circumference and cardiometabolic risk is visceral adiposity, which is a strong marker of cardiometabolic risk Taken together, these observations highlight the critical role of waist circumference reduction through lifestyle behaviours in downstream reduction in morbidity and mortality Fig.
An illustration of the important role that decreases in waist circumference have for linking improvements in lifestyle behaviours with downstream reductions in the risk of morbidity and mortality.
The benefits associated with reductions in waist circumference might be observed with or without a change in BMI. In summary, whether waist circumference adds to the prognostic performance of cardiovascular risk models awaits definitive evidence. However, waist circumference is now clearly established as a key driver of altered levels of cardiometabolic risk factors and markers.
Consequently, reducing waist circumference is a critical step in cardiometabolic risk reduction, as it offers a pragmatic and simple target for managing patient risk. The combination of BMI and waist circumference identifies a high-risk obesity phenotype better than either measure alone.
We recommend that waist circumference should be measured in clinical practice as it is a key driver of risk; for example, many patients have altered CVD risk factors because they have abdominal obesity.
Waist circumference is a critical factor that can be used to measure the reduction in CVD risk after the adoption of healthy behaviours. Evidence from several reviews and meta-analyses confirm that, regardless of age and sex, a decrease in energy intake through diet or an increase in energy expenditure through exercise is associated with a substantial reduction in waist circumference 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , For studies wherein the negative energy balance is induced by diet alone, evidence from RCTs suggest that waist circumference is reduced independent of diet composition and duration of treatment Whether a dose—response relationship exists between a negative energy balance induced by diet and waist circumference is unclear.
Although it is intuitive to suggest that increased amounts of exercise would be positively associated with corresponding reductions in waist circumference, to date this notion is not supported by evidence from RCTs 71 , 74 , 89 , 90 , A doubling of the energy expenditure induced by exercise did not result in a difference in waist circumference reduction between the exercise groups.
A significant reduction was observed in waist circumference across all exercise groups compared with the no-exercise controls, with no difference between the different prescribed levels Few RCTs have examined the effects of exercise intensity on waist circumference 74 , 90 , 91 , However, no significant differences were observed in VAT reduction by single slice CT between high-intensity and low-intensity groups.
However, the researchers did not fix the level of exercise between the intensity groups, which might explain their observations. Their observations are consistent with those of Slentz and colleagues, whereby differences in exercise intensity did not affect waist circumference reductions.
These findings are consistent with a meta-analysis carried out in wherein no difference in waist circumference reduction was observed between high-intensity interval training and moderate-intensity exercise In summary, current evidence suggests that increasing the intensity of exercise interventions is not associated with a further decrease in waist circumference.
VAT mass is not routinely measured in clinical settings, so it is of interest whether reductions in waist circumference are associated with corresponding reductions in VAT.
Of note, to our knowledge every study that has reported a reduction in waist circumference has also reported a corresponding reduction in VAT.
Thus, although it is reasonable to suggest that a reduction in waist circumference is associated with a reduction in VAT mass, a precise estimation of individual VAT reduction from waist circumference is not possible.
Nonetheless, the corresponding reduction of VAT with waist circumference in a dose-dependent manner highlights the importance of routine measurement of waist circumference in clinical practice. Of particular interest to practitioners, several reviews have observed significant VAT reduction in response to exercise in the absence of weight loss 80 , Available evidence from RCTs suggests that exercise is associated with substantial reductions in waist circumference, independent of the quantity or intensity of exercise.
Exercise-induced or diet-induced reductions in waist circumference are observed with or without weight loss.
We recommend that practitioners routinely measure waist circumference as it provides them with a simple anthropometric measure to determine the efficacy of lifestyle-based strategies designed to reduce abdominal obesity. The emergence of waist circumference as a strong independent marker of morbidity and mortality is striking given that there is no consensus regarding the optimal protocol for measurement of waist circumference.
Moreover, the waist circumference protocols recommended by leading health authorities have no scientific rationale. In , a panel of experts performed a systematic review of studies to determine whether measurement protocol influenced the relationship between waist circumference, morbidity and mortality, and observed similar patterns of association between the outcomes and all waist circumference protocols across sample size, sex, age and ethnicity Upon careful review of the various protocols described within the literature, the panel recommended that the waist circumference protocol described by the WHO guidelines 98 the midpoint between the lower border of the rib cage and the iliac crest and the NIH guidelines 99 the superior border of the iliac crest are probably more reliable and feasible measures for both the practitioner and the general public.
This conclusion was made as both waist circumference measurement protocols use bony landmarks to identify the proper waist circumference measurement location. The expert panel recognized that differences might exist in absolute waist circumference measures due to the difference in protocols between the WHO and NIH methods.
However, few studies have compared measures at the sites recommended by the WHO and NIH. Jack Wang and colleagues reported no difference between the iliac crest and midpoint protocols for men and an absolute difference of 1.
Consequently, although adopting a standard approach to waist circumference measurement would add to the utility of waist circumference measures for obesity-related risk stratification, the prevalence estimates of abdominal obesity in predominantly white populations using the iliac crest or midpoint protocols do not seem to be materially different.
However, the waist circumference measurements assessed at the two sites had a similar ability to screen for the metabolic syndrome, as defined by National Cholesterol Education Program, in a cohort of 1, Japanese adults Several investigations have evaluated the relationship between self-measured and technician-measured waist circumference , , , , Instructions for self-measurement of waist circumference are often provided in point form through simple surveys Good agreement between self-measured and technician-measured waist circumference is observed, with strong correlation coefficients ranging between 0.
Moreover, high BMI and large baseline waist circumference are associated with a larger degree of under-reporting , Overall these observations are encouraging and suggest that self-measures of waist circumference can be obtained in a straightforward manner and are in good agreement with technician-measured values.
Currently, no consensus exists on the optimal protocol for measurement of waist circumference and little scientific rationale is provided for any of the waist circumference protocols recommended by leading health authorities.
The waist circumference measurement protocol has no substantial influence on the association between waist circumference, all-cause mortality and CVD-related mortality, CVD and T2DM. Absolute differences in waist circumference obtained by the two most often used protocols, iliac crest NIH and midpoint between the last rib and iliac crest WHO , are generally small for adult men but are much larger for women.
The classification of abdominal obesity might differ depending on the waist circumference protocol. We recommend that waist circumference measurements are obtained at the level of the iliac crest or the midpoint between the last rib and iliac crest.
The protocol selected to measure waist circumference should be used consistently. Self-measures of waist circumference can be obtained in a straightforward manner and are in good agreement with technician-measured values. Current guidelines for identifying obesity indicate that adverse health risk increases when moving from normal weight to obese BMI categories.
Moreover, within each BMI category, individuals with high waist circumference values are at increased risk of adverse health outcomes compared with those with normal waist circumference values Thus, these waist circumference threshold values were designed to be used in place of BMI as an alternative way to identify obesity and consequently were not developed based on the relationship between waist circumference and adverse health risk.
In order to address this limitation, Christopher Ardern and colleagues developed and cross-validated waist circumference thresholds within BMI categories in relation to estimated risk of future CVD using FRS The results of their study revealed that the current recommendations that use a single waist circumference threshold across all BMI categories are insufficient to identify those at increased health risk.
In both sexes, the use of BMI category-specific waist circumference thresholds improved the identification of individuals at a high risk of future coronary events, leading the authors to propose BMI-specific waist circumference values Table 1. For both men and women, the Ardern waist circumference values substantially improved predictions of mortality compared with the traditional values.
These observations are promising and support, at least for white adults, the clinical utility of the BMI category-specific waist circumference thresholds given in Table 1. Of note, BMI-specific waist circumference thresholds have been developed in African American and white men and women Similar to previous research, the optimal waist circumference thresholds increased across BMI categories in both ethnic groups and were higher in men than in women.
However, no evidence of differences in waist circumference occurred between ethnicities within each sex Pischon and colleagues investigated the associations between BMI, waist circumference and risk of death among , adults from nine countries in the European Prospective Investigation into Cancer and Nutrition cohort Although the waist circumference values that optimized prediction of the risk of death for any given BMI value were not reported, the findings reinforce the notion that waist circumference thresholds increase across BMI categories and that the combination of waist circumference and BMI provide improved predictions of health risk than either anthropometric measure alone.
Ethnicity-specific values for waist circumference that have been optimized for the identification of adults with elevated CVD risk have been developed Table 2. With few exceptions, the values presented in Table 2 were derived using cross-sectional data and were not considered in association with BMI.
Prospective studies using representative populations are required to firmly establish ethnicity-specific and BMI category-specific waist circumference threshold values that distinguish adults at increased health risk. As noted above, the ethnicity-specific waist circumference values in Table 2 were optimized for the identification of adults with elevated CVD risk.
The rationale for using VAT as the outcome was that cardiometabolic risk was found to increase substantially at this VAT level for adult Japanese men and women We recommend that prospective studies using representative populations are carried out to address the need for BMI category-specific waist circumference thresholds across different ethnicities such as those proposed in Table 1 for white adults.
This recommendation does not, however, diminish the importance of measuring waist circumference to follow changes over time and, hence, the utility of strategies designed to reduce abdominal obesity and associated health risk. The main recommendation of this Consensus Statement is that waist circumference should be routinely measured in clinical practice, as it can provide additional information for guiding patient management.
Indeed, decades of research have produced unequivocal evidence that waist circumference provides both independent and additive information to BMI for morbidity and mortality prediction.
On the basis of these observations, not including waist circumference measurement in routine clinical practice fails to provide an optimal approach for stratifying patients according to risk. The measurement of waist circumference in clinical settings is both important and feasible.
Self-measurement of waist circumference is easily obtained and in good agreement with technician-measured waist circumference. Gaps in our knowledge still remain, and refinement of waist circumference threshold values for a given BMI category across different ages, by sex and by ethnicity will require further investigation.
To address this need, we recommend that prospective studies be carried out in the relevant populations. Despite these gaps in our knowledge, overwhelming evidence presented here suggests that the measurement of waist circumference improves patient management and that its omission from routine clinical practice for the majority of patients is no longer acceptable.
Accordingly, the inclusion of waist circumference measurement in routine practice affords practitioners with an important opportunity to improve the care and health of patients. Health professionals should be trained to properly perform this simple measurement and should consider it as an important vital sign to assess and identify, as an important treatment target in clinical practice.
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Thank you for visiting nature. You are using a browser version circumferenfe limited support for CSS. Daist obtain the Inner peace techniques experience, circumferebce recommend you Time-restricted eating a Superfoods for endurance athletes up to date browser or turn eaist compatibility ciircumference in Circumferene Explorer. Organic olive oil the meantime, to ensure continued support, we are circumfereence the site without styles and JavaScript. Despite decades of unequivocal evidence that waist circumference provides both independent and additive information to BMI for predicting morbidity and risk of death, this measurement is not routinely obtained in clinical practice. This Consensus Statement proposes that measurements of waist circumference afford practitioners with an important opportunity to improve the management and health of patients. We argue that BMI alone is not sufficient to properly assess or manage the cardiometabolic risk associated with increased adiposity in adults and provide a thorough review of the evidence that will empower health practitioners and professional societies to routinely include waist circumference in the evaluation and management of patients with overweight or obesity.Video
Measuring Waist Circumference Cardiovascular Diabetology volume 20Article number: 68 Cite this Organic olive oil. Metrics details. Adiposity is a syncrome Metabolic syndrome waist circumference of the metabolic syndrome MetSlow muscle strength Metabopic also been identified as a risk factor for MetS and for cardiovascular disease. We describe the prevalence of MetS and evaluate the relationship between muscle strength, anthropometric measures of adiposity, and associations with the cluster of the components of MetS, in a middle-income country. MetS was defined by the International Diabetes Federation criteria.Metabolic syndrome waist circumference -
The results of this study demonstrate a higher risk of mortality associated with metabolic syndrome in white, non-Hispanic men and provide support for a role for waist circumference in the clinical criteria for metabolic syndrome.
The PAF estimates from the present study range from 8 to 9. A more recent analysis from the Hoorn Study compared several definitions of metabolic syndrome in the prediction of CVD and found that metabolic syndrome doubled the risk of incident CVD; however, there were minimal differences across metabolic syndrome definitions These observations suggest that the public health burden associated with metabolic syndrome is substantial regardless of the metabolic syndrome criteria used.
However, despite the higher prevalence, the predictive ability C statistic of IDF and NCEP definitions for mortality were similar.
The IDF metabolic syndrome criteria identified a larger subset of the population that is at increased risk of mortality. Together these observations suggest that lowering the glucose and waist circumference values within the metabolic syndrome context is beneficial for identifying men at risk; however, the optimal waist circumference threshold remains to be determined.
A novel aspect of this study was the analyses of waist circumference thresholds in the presence or absence of two or more other metabolic syndrome risk factors. The principal finding was twofold. First, the rate of CVD mortality increased across waist circumference categories in men with two or more other metabolic syndrome risk factors.
Second, in the absence of multiple risk factors, risk did not increase across waist circumference categories. The results provide support for a valuable role for waist circumference in the clinical definition of metabolic syndrome; however, it is apparent that a high waist circumference value in the absence of additional risk factors may not indicate increased mortality risk.
This is consistent with reports suggesting that the combination of high waist circumference value and high triglyceride level is a better predictor of CVD than either alone These findings reinforce the recommendation that clinicians obtain all metabolic syndrome criteria to properly interpret the health risks associated with an elevated waist circumference.
The mechanisms whereby waist circumference is associated with risk in the presence of other risk factors are unclear. It is possible that waist circumference acts as a marker for risk factors not measured in this study physical inactivity, insulin resistance, C-reactive protein, and others.
Together these findings reinforce the notion that reductions in waist circumference should be a primary aim of strategies designed to reduce health risks associated with metabolic syndrome.
Given that exercise is associated with substantial reductions in waist circumference 20 — 22 , and that cardiorespiratory fitness significantly attenuates the mortality risk associated with metabolic syndrome 23 , it is reasonable to suggest that physical activity be a cornerstone of strategies designed to treat metabolic syndrome.
There are several strengths and limitations of this study. A marked strength is the use of a large sample of men for whom an extensive battery of measurements were obtained, which allowed the classification of metabolic syndrome under NCEP, NCEP-R, and IDF criteria.
The predominantly white, middle-to-upper class sample of men limits the generalizability of the results; however, the homogenous nature of the sample ensures control over factors such as ethnicity and socioeconomic status. The use of NHANES to obtain national estimates of the prevalence of metabolic syndrome in men is also a strength of this study.
However, further research is required to confirm these findings in women and in other ethnic and socioeconomic groups. In summary, men with metabolic syndrome have a higher risk of all-cause and CVD mortality by comparison with men without metabolic syndrome.
The results suggest that IDF metabolic syndrome criteria will identify a larger segment of the population at increased mortality risk than NCEP metabolic syndrome criteria. The optimal waist circumference threshold value for predicting mortality within the context of the metabolic syndrome needs to be determined.
Unadjusted Kaplan-Meier hazard curves for CVD mortality among 20, men 20—83 years of age from the ACLS. All-cause A and CVD B death rates according to categories of waist circumference WC and the presence or absence of two or more other metabolic syndrome risk factors. Death rates are adjusted for age and year of examination.
Sample size number is shown in the bars, with number of deaths indicated in parentheses. Descriptive baseline characteristics of 20, men 20—83 years of age from the ACLS across categories of NCEP, NCEP-R, and IDF definitions of the metabolic syndrome. Relative risks of all-cause and CVD mortality associated with the NCEP, NCEP-R, and IDF definitions of the metabolic syndrome in 20, men 20—83 years of age from the ACLS.
Adjusted for age, year of examination, smoking, alcohol consumption, parental history of premature CVD, and possible CVD at baseline. This research was supported by a grant from the National Institute on Aging AG and a New Emerging Team grant from the Canadian Institutes of Health Research and Heart and Stroke Foundation of Canada.
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Prognostic value of handgrip strength in people aged 60 years and older: a systematic review and meta-analysis. Download references. The main PURE study is funded by the Population Health Research Institute, the Canadian Institutes of Health Research and the Heart and Stroke Foundation of Ontario.
Institute Masira, Medical School, Universidad de Santander, Santander, Colombia. Jose P. Lopez-Lopez, Daniela Ney-Salazar, Paul A.
Facultad de Ciencias de La Salud, Instituto de Investigaciones Masira, Universidad de Santander UDES , Bloque G, piso 6. Bucaramanga, Santander, Colombia.
Lopez-Lopez, Daniel D. Universidad del Quindío and Hospital San Juan de Dios de Armenia, Armenia, Quindío, Colombia. Facultad de Medicina, Universidad Militar Nueva Granada, Bogotá, Colombia.
Universidad de Caldas y Médicos Internistas de Caldas, Manizales, Colombia. Centro Cardiovascular Santa Lucia, Cartagena, Colombia. Darryl P. You can also search for this author in PubMed Google Scholar. JPLL, DDC contributed to the conception or design of the study and drafted the manuscript.
DNS, DM, JO, DGA, PAC, GSV, EA, CN, HG, MP, DIM, CC, AS, AR, EHT, FC contributed to the acquisition of data, interpretation of data, and critical revision of the article for important intellectual content. DPL, SR, SY, PLJ contributed to the interpretation of data and critical revision of the article for important intellectual content.
All authors gave final approval of the article. Correspondence to Patricio Lopez-Jaramillo. The Ethics Committee of the Cardiovascular Foundation of Colombia approved the study. All participants completed and signed written consent were included in the analysis.
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Skip to main content. Search all BMC articles Search. Download PDF. Download ePub. Original investigation Open access Published: 22 March The prediction of Metabolic Syndrome alterations is improved by combining waist circumference and handgrip strength measurements compared to either alone Jose P.
Lopez-Lopez 1 , 2 , Daniel D. Cohen 2 , Daniela Ney-Salazar 1 , Daniel Martinez 2 , Johanna Otero 2 , Diego Gomez-Arbelaez 2 , Paul A. Camacho 1 , Gregorio Sanchez-Vallejo 3 , Edgar Arcos 4 , Claudia Narvaez 5 , Henry Garcia 6 , Maritza Perez 7 , Dora I. Molina 8 , Carlos Cure 9 , Aristides Sotomayor 10 , Álvaro Rico 11 , Eric Hernandez-Triana 12 , Myriam Duran 2 , Fresia Cotes 13 , Darryl P.
Abstract Background Adiposity is a major component of the metabolic syndrome MetS , low muscle strength has also been identified as a risk factor for MetS and for cardiovascular disease. Methods MetS was defined by the International Diabetes Federation criteria. Background Metabolic syndrome MetS is associated with a higher risk of cardiovascular disease CVD mortality and total mortality [ 1 ].
Methods Population and study design The PURE study design, coordinated by the Population Health Research Institute PHRI Hamilton, ON, Canada , was described previously [ 12 ].
Data collection and risk factors For each consenting participant, sociodemographic characteristics and cardiovascular risk factors were obtained. Statistical analysis Descriptive statistics were computed for variables of interests and included absolute and relative frequencies of categorical factors.
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Sports nutrition for youth athletes Affiliations: Durand Hospital of Buenos Aires Drs Metabolic syndrome waist circumference and Jadzinsky and Messrs Organic olive oil and Overcoming negativity practices and School of Pharmacy and Biochemistry, Mftabolic Organic olive oil Buenos Aires Ms de Luján CirccumferenceBuenos Aires, Argentina. Syncrome Inner peace techniques determine MMetabolic children the association between Inner peace techniques circumfwrence WC and insulin resistance determined by homeostasis modeling HOMA-IR and proinsulinemia and components of the metabolic syndrome, including lipid profile and blood pressure BP. Methods Eighty-four students 40 boys aged 6 to 13 years and matched for sex and age underwent anthropometric measurements; 40 were obese; 28, overweight; and 16, nonobese. Body mass index BMIWC, BP, and Tanner stage were determined. An oral glucose tolerance test, lipid profile, and insulin and proinsulin assays were performed. Conclusion Waist circumference is a predictor of insulin resistance syndrome in children and adolescents and could be included in clinical practice as a simple tool to help identify children at risk.
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