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Download references. 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.

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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.

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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.

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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.

VO 2 peak The highest value of VO 2 that is, oxygen consumption attained during an incremental or other high-intensity exercise test. Iliac crest The superior border of the wing of the ilium. Rights and permissions Open Access This work is licensed under a Creative Commons Attribution 4.

About this article. Cite this article Ross, R. Copy to clipboard. This article is cited by Novel subgroups of obesity and their association with outcomes: a data-driven cluster analysis Saki Takeshita Yuichi Nishioka Yutaka Takahashi BMC Public Health Trends in prevalence of obesity and its association with hypertension across socioeconomic gradients in rural Yunnan Province, China Xia Wu Guohui Li Le Cai BMC Cardiovascular Disorders The association between body mass index and abdominal obesity with hypertension among South Asian population: findings from nationally representative surveys Rajat Das Gupta Ateeb Ahmad Parray Mohammad Rifat Haider Clinical Hypertension Knee biomechanics variability before and after total knee arthroplasty: an equality of variance prospective study Erik Kowalski Danilo S.

Catelli Mario Lamontagne Scientific Reports Combined maternal central adiposity measures in relation to infant birth size Emelie Lindberger Fredrik Ahlsson Inger Sundström Poromaa Scientific Reports Publish with us For Authors For Referees Submit manuscript.

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Close banner Close. Today, there are two methods of self-assessment that can give you a clearer picture of how your weight may be affecting your health — measuring your waistline and calculating your Body Mass Index BMI.

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After further adjustment for systolic blood pressure, HDL and total cholesterol, and diabetes, these direct associations were still significant among men but not among women.

In both genders, and in women particularly, the effect of obesity on CVD risk was partly mediated through systolic blood pressure, HDL and total cholesterol, and diabetes. Exclusion of the subjects who died during the first two years of follow-up did not affect the associations between the obesity indicators and the risk of CVD markedly.

When different measures of obesity were compared, WHR in men and BMI in women were slightly better predictors of CVD than others data not shown. The age- and study year-adjusted hazard ratios of CVD in men were 1. In women, the corresponding hazard ratios were 1. Adjustment for education, smoking, and physical activity did not change the hazard ratios markedly but after further adjustment for systolic blood pressure, HDL and total cholesterol, and diabetes, these associations became non-significant.

In these analyses, the subjects were classified into four categories: both active and non-obese the reference group , active but obese, inactive but non-obese, both inactive and obese. Obesity was defined either as BMI⩾30 or the highest quartile of waist circumference or WHR.

The joint associations of physical inactivity and waist circumference, and particularly WHR, were inconsistent. The results of the present large population-based prospective study demonstrate that both physical activity and general and abdominal obesity predict the risk of CVD among middle-aged men and women.

Physical activity has a strong protective effect on CVD risk and this association attenuated only slightly after the adjustment for other CVD risk factors. Whereas obesity increases the risk mainly through other risk factors, particularly among women. Exclusion of the subjects who died during the first two years of follow-up did not affect the results markedly.

Most studies, 4,9,14—18 but not all, 19 have indicated that overall obesity assessed by BMI is associated with increased risk of CHD or CVD incidence, and CHD or CVD mortality.

Abdominal obesity, assessed by WHR or waist circumference, has been found to be a better predictor of total, CHD, and CVD mortality than BMI in some population groups, 20,21 but the prospective data of the effects of abdominal obesity on the CVD incidence are still scant.

Some studies indicated higher death rates in the subjects with abdominal obesity who had an underweight a low BMI and high WHR than in those without abdominal obesity who were overall obese a high BMI and low WHR.

A recent review on guidelines for healthy weight by Willett et al. The most serious problem is called reverse causation, another major concern is the failure to control for smoking, and the third problem is the inappropriate control for other risk factors.

In the present study, we excluded the subjects with a history of CHD, stroke, and heart failure at baseline. We analysed the data also after exclusion of the early events, which did not change the results.

In the analyses, smoking status was considered as a confounding factor in the intermediate model, and the physiological effects of excess fattiness blood pressure, diabetes, and total and HDL cholesterol were considered as mediating factors and included in the final model.

Our results are consistent with the findings of a number of prospective studies about the strong inverse association of physical activity, physical fitness with incidence of CHD, stroke, and CVD. This may cause greater errors in estimates of overall physical activity particularly in women and persons from lower socioeconomic groups.

A few prospective studies have evaluated the joint associations of physical activity, physical fitness, and body weight with CVD mortality, and the data are especially scarce among women. The Aerobic Center Longitudinal Study found that low cardiorespiratory fitness was a strong and independent predictor of CVD mortality among men, independent of body composition and other CVD risk factors.

On the other hand, the lowest CVD mortality rates were found among those with more exercise and normal weight. Our finding also supports the hypothesis that the adequate level of either occupational or leisure time physical activity, or both, can protect against the premature CVD in overweight and obese individuals.

Weight reduction in obese people reduces the risk of death and CVD, 2 but it is well known that reducing weight is very difficult and, even at best, only a limited weight reduction may be achieved.

It seems that increased physical activity is useful in this respect. There are several strengths and limitations in our study. First, our study is population-based comprising a large number of both men and women from a homogeneous population.

The median follow-up, 9. Second, occupational physical activity was also included in the total physical activity. Third, we had data on standardised measurement of three different indicators of obesity, and a large number of other obesity-related risk factors, which may modify the association of obesity with the CVD risk.

A limitation of our study was the self-report of physical activity. Using a questionnaire to assess habitual physical activity is crude and imprecise.

Misclassification, particularly over-reporting of the amount of physical activity leads to an underestimation of effects of physical activity on CVD risk.

It has been shown that measured physical fitness predicts mortality slightly better than self-reported physical activity. Leisure-time physical activity has a direct, and physical activity at work has an inverse, association with socio-economic status. Even though the analyses were adjusted for education, unmeasured components of socio-economic status may strengthen the protective effect of leisure-time activity and weaken the protective effect of occupational activity.

Moreover, several risk factors, such as triglycerides and apolipoprotein B, are not available for the present analysis. These factors, however, are most probably mediators such as blood pressure, cholesterol, and diabetes in the obesity and physical activity-related CVD risk, and therefore, including them in the analyses should not have influenced the interpretation of the role of obesity and sedentary lifestyle on CVD risk.

In conclusion, our study confirmed that both physical inactivity and obesity are important risk factors for CVD. Physical inactivity had a strong and consistent independent association with the CVD risk.

The risk of CVD associated with obesity was partly mediated through other risk factors, such as blood pressure, blood lipid, and diabetes, in women particularly. All obesity indicators predicted the risk of CVD in men, but in women only BMI had an independent association after adjustment for the obesity-related risk factors.

Adjusted for age, study year, systolic blood pressure, total and high density lipoprotein cholesterol, education, smoking, and diabetes at baseline. reference group. Table 1. Baseline characteristics according to physical activity levels among the Finnish population by sex.

Table 2. Hazard ratios for risk of cardiovascular disease according to different levels of physical activity by sex a. Table 3. Hazard ratios for risk of cardiovascular disease according to different levels of body mass index, waist circumference, and waist-to-hip ratio by sex a.

This study was supported by grants from the Finnish Academy Grants , , , and Pate RR , Pratt M, Blair SN, et al.

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Cardiovascular risk factor changes in Finland, — Int J Epidemiol ; 29 : 49 Pajak A , Kuulasmaa K, Tuomilehto J, et al.

Geographical variation in the major risk factors of coronary heart disease in men and women aged 35—64 years. The WHO MONICA Project World Health Stat Q ; 41 : Hu G , Eriksson J, Barengo NC, et al.

Occupational, commuting, and leisure-time physical activity in relation to total and cardiovascular mortality among Finnish subjects with type 2 diabetes Circulation ; : Tuomilehto J , Hu G, Bidel S, et al. Coffee consumption and risk of type 2 diabetes mellitus among middle-aged finnish men and women JAMA ; : Keys A.

Seven countries: a multivariate analysis of death and coronary heart diseaseCambridge, MA: Harvard University Press; Cox DR. Regression models and life tables with discussion J Roy Statist Soc B ; 34 : NIH consensus development panel on physical activity and cardiovascular health. physical activity and cardiovascular health.

JAMA ; —6. Fogelholm M , Kukkonen-Harjula K. Does physical activity prevent weight gain—a systematic review Obes Rev ; 1 : 95 Wei M , Gibbons LW, Kampert JB, et al.

Low cardiorespiratory fitness and physical inactivity as predictors of mortality in men with type 2 diabetes Ann Intern Med ; : Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

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Fitness alters the association of BMI and waist circumference with total and abdominal fat. Obes Res ; 12 : — Wong SL, Katzmarzyk PT, Nichaman MZ, Church TS, Blair SN, Ross R. Cardiorespiratory fitness in associated with lower abdominal fat independent of body mass index.

Med Sci Sports Exerc ; 36 : — Hulens M, Vansant G, Lysens R, Claessens AL, Muls E, Brumagne S. Study of differences in peripheral muscle strength of lean versus obese women: an allometric approach. Int J Obes Relat Metab Disord ; 25 : — Rolland Y, Lauwers-Cances V, Pahor M, Fillaux J, Grandjean H, Vellas B.

Muscle strength in obese elderly women: effect of recreational physical activity in a cross-sectional study. Suni JH, Miilunpalo SI, Asikainen T-M, Laukkanen RT, Oja P, Pasanen ME et al.

Safety and feasibility of a health-related fitness test battery for adults. Phys Ther ; 78 : — Su C-Y, Lin J-H, Chien T-H, Cheng KF, Sung YT. Grip strength in different positions of elbow and shoulder.

Arch Phys Med Rehabil ; 75 : — CAS PubMed Google Scholar. Bosco C, Luhtanen P, Komi PV. A simple method for measurement of mechanical power in jumping. Eur J Appl Physiol Occup Physiol ; 50 : — Åstrand PO, Rhyming I.

A Nomogram for calculation of aerobic capacity physical fitness from pulse rate during submaximal work. J Appl Physiol ; 7 : — Keskinen K, Keskinen O, Takalo T, Häkkinen K. Comparison between straight measurement and two concurrent protocols to predict maximal oxygen uptake.

Med Sci Sports Exerc ; 34 : S Viljanen T, Viitasalo JT, Kujala UM. Strength characteristics of a healthy urban adult population. Eur J Appl Physiol ; 63 : 43— Meir R, Newton R, Curtis E, Fardell M, Butler B.

Physical fitness qualities of professional rugby league football players: determination of positional differences. J Strength Cond Res ; 15 : — Roberts MA, O'Dea J, Boyce A, Mannix ET.

Fitness levels of firefighter recruits before and after a supervised exercise training program. J Strength Cond Res ; 16 : — PubMed Google Scholar. Malina RM, Beunen GP, Claessens AL, Lefevre J, Vanden Eynde B, Renson R et al. Fatness and physical fitness of girls 7 to 17 years.

Obes Res ; 3 : — Hulens M, Vansant G, Claessens AL, Lysens R, Muls E. Predictors of 6-min walk test in lean, obese and morbidly obese women. Scand J Med Sci Sports ; 13 : 98— Jebb SA, More MS. Contribution of a sedentary lifestyle and inactivity to the etiology of overweight and obesity: current evidence and research issues.

Med Sci Sports Exerc ; 31 Suppl 11 : S—S Lafortuna CL, Resnik M, Galvani C, Sartorio A. Effects if non-specific vs individualized exercised training protocols on aerobic, anaerobic and strength performance in severely obese subjects during a short-term body mass reduction program.

J Endocrinol Invest ; 16 : — Visser M, Langlois J, Guralnik JM, Cauley JA, Kronmal RA, Robbins J et al. High body fatness, but not low fat-free mass, predicts disability in oler men and women.

The cardiovascular health study. Am J Clin Nutr ; 68 : — Wannamethee SG, Shaper AG, Whincup PH, Walker M. Overweight and obesity and the burden of disease and disability in elderly men. Int J Obes Relat Metab Disord ; 28 : — Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R.

Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr ; 75 : — Ross R, Dagnone D, Jones PJ, Smith H, Paddags A, Hudson R et al. Reduction in obesity and related comorbid conditions after diet-induced weight loss or exercise-induced weight loss in men.

A randomized, controlled trial. Ann Intern Med ; : 92— Aromaa A, Koskinen S eds. Health and Functional Capacity in Finland. Baseline Results of the Health Health Examination Survey. Shvartz E, Reibold RC. Aerobic fitness norms for males and females aged 6 to 75 years: a review.

Aviat Space Environ Med ; 61 : 3— MacAuley D, Mc Crum EE, Stott G, Evans AE, Gamble RP, Mc Roberts B et al. Levels of physical activity, physical fitness and their relationship in the Northern Ireland Health and Activity Survey. Int J Sports Med ; 19 : — Download references. This study was supported by a grant from the Scientific Committee for National Defence.

We are grateful to Matti Pasanen, MSc, The UKK Institute, for valuable statistical advice and for critical comments on our paper, and to Kari Keskinen, PhD, for sharing the results from Fitware validation study.

The UKK Institute for Health Promotion Research, Tampere, Finland. Research Unit, Pirkanmaa Hospital District, Tampere, Finland. The Training Division of The Defence Staff, Finnish Defence Forces, Helsinki, Finland. Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, Finland.

Research Institute of Military Medicine, Central Military Hospital, Helsinki, Finland. You can also search for this author in PubMed Google Scholar. Correspondence to M Fogelholm. Reprints and permissions. Fogelholm, M. et al. Waist circumference and BMI are independently associated with the variation of cardio-respiratory and neuromuscular fitness in young adult men.

Int J Obes 30 , — Download citation. Received : 04 June Revised : 12 October Accepted : 13 December Published : 24 January Issue Date : 01 June 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. European Journal of Clinical Nutrition Skip to main content Thank you for visiting nature. nature international journal of obesity original article article. Abstract Objective: To test two hypotheses: 1 cardiorespiratory CRF and neuromuscular NMF fitness is associated with body mass index BMI and waist circumference WC , independent of each other and of leisure-time physical activity; 2 individuals with high CRF and NMF have lower WC for a given BMI, compared with those with low CRF and NMF.

Design: Cross-sectional study. Setting: Men participating in refresher training organized by the Finnish Defence Forces. Participants: A total of men mean age Main outcome measures: Body mass index, WC, maximal oxygen uptake VO 2 max , height of vertical jump, number of push-ups and sit-ups during a 1-min test, static back extension endurance, isometric grip strength, self-reported leisure-time vigorous physical activity.

Conclusions: Despite stronger isometric grip strength, the functional muscle fitness of the upper body, trunk and lower extremities is impaired in individuals with abdominal obesity. Access through your institution. Buy or subscribe. Change institution. Learn more.

Figure 1. Figure 2. References Bouchard C, Shephard RJ. Google Scholar Evenson KR, Stevens J, Cai J, Thomas R, Thomas O. Article Google Scholar Bertoli A, Di Daniele N, Ceccobelli M, Ficara A, Girasoli C, De Lorenzo A.

Article CAS Google Scholar Wei M, Kampert JB, Barlow CE, Nichaman MZ, Gibbons LW, Paffenbarger Jr RS et al. Article CAS Google Scholar Williams PT. Article CAS Google Scholar Guralnic JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB.

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It is partially related to the fact that BMI in isolation is an insufficient biomarker of abdominal adiposity 4. By using the BMI, one must rely on the assumption that adipose tissue is distributed evenly over the body, which does not take into account the heterogeneity of regional body fat deposition The level of obesity must be considered in the risk stratification.

In a recent meta-analysis of 2. However, when analyzing separately, compared with normal body weight, grade 1 obesity Table 1 is associated with a risk of death with an HR of 0. The prognostic value of BMI needs to pay attention to the length of follow-up time.

There was a J-type association between BMI and sudden cardiac death, and the lowest risk was observed within the normal weight range. However, in studies with a longer follow-up period, the increased risk of low BMI was attenuated In other words, the obesity phenotype may change over time to reflect the increase in abdominal obesity.

For example, Ian Janssen et al. studied the changes in WC for a given BMI over 30 years in a Canadian sample It is worth noting that for a given BMI, Canadians had a larger WC in than in Similarly, Sandra Albrecht et al.

studied 36 long-term changes in WC in the United States — , the United Kingdom — , China — , and Mexico — and reported significant statistics academic significance in all countries and most subgroups, WC values have increased relative to BMI. Part of the reason why BMI cannot fully capture cardiometabolic risks is that BMI alone is an insufficient biomarker for the whole body.

More importantly, the central abdominal fat mass does not explain the extreme changes in intra-abdominal visceral fat mass and fat distribution between individuals Compared with BMI, WC has a higher predictive value for cardiovascular death Visceral adipose tissue VAT has been proved to be independently associated with elevated CVD risk Data from several past epidemiological studies 30 years of experience show that VAT is an independent sign of morbidity and mortality In some populations, WC is more predictive of overall mortality, coronary heart disease CHD.

However, prospective data on the impact of abdominal obesity on CVD incidence is still scarce. Many experimental studies support the potential connection between VAT and biological pathways that are important in the pathogenesis of multiple disease outcomes. Adipokines are biologically active molecules secreted by adipose tissue and are key components of these pathways, including inflammatory cytokines, angiogenic factors, lipid metabolites, and extracellular matrix components The secretion of adipokines among specific fat depots appears to be different 22 , and compared with subcutaneous adipose tissue SAT , VAT exhibits more pro-inflammatory and proangiogenic gene expression.

In addition, compared with SAT, small arteries in VAT are more likely to exhibit endothelial dysfunction 23 , indicating that VAT has a potentially toxic effect on the vasculature. Visceral adipocytes differ from subcutaneous adipocytes in that they release secreted proteins that are known or potential risk factors for CHD.

In at least one study, visceral fat expressed and released more plasminogen activator inhibitor-1, a fibrinolysis inhibitor, than subcutaneous fat Angiotensinogen is a potential blood pressure regulator and is also highly expressed in VAT.

There are currently multiple methods to assess body fat distribution. The most accurate method is costly and time-consuming. It is not suitable for large-scale population research. Since routine access to CT, MRI may be too expensive to be feasible for many clinicians, and the use of these methods to image visceral and ectopic fat has historically been reserved for research purposes, perhaps the most widely used, and these measurements are taken for WC.

Ashwell et al. were the first to show that there is a correlation between visceral fat mass and waist-to-hip ratio. However, compared with the waist-to-hip ratio, WC has a stronger correlation with visceral fat mass Obesity is an important driving factor for the development of type 2 diabetes.

Compared with BMI-matched non-type 2 diabetic patients, type 2 diabetic patients have a larger WC and more VAT 7. A patient with VAT or severe obesity and type 2 diabetes is susceptible to cardiovascular abnormalities and CVD; their simultaneous presence should further increase the risk of cardiovascular outcomes The underlying mechanisms may be ectopic, and visceral obesity is related to insulin resistance, this may partially mediate the link between obesity, type 2 diabetes, and cardiovascular risk.

Metabolic syndrome and insulin resistance have been recognized as risk factors for cardiovascular morbidity and mortality Studies have also shown that the presence of metabolic syndrome increases the risk of heart failure. The result of a cross-sectional study has shown that obesity and type 2 diabetes have an additive effect on left ventricular remodeling in normotensive patients Our results suggested that patients with higher levels of WC coexisted with diabetes had the worst outcome, the association is still significant after adjustment of other clinical confounders.

Our study still has some limitations: first, we calculate the WC at bassline, we did not reevaluate WC during the follow-up. However, previous studies showed that changes in WC were not significantly associated with mortality Second, all included patients were Asians, and the result may not apply to another population.

Patients with higher levels of WC coexisted with diabetes had the worst outcome. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Chinese PLA General Hospital. S-xW and PZ made substantial contributions to the conception or design of the work. ML contributed to the data collection, data interpretation, and critical review and drafting of the manuscript.

All authors read and approved the final manuscript. This work was supported by the National Key Research and Development Program of China YFC , the National Defense Science and Technology Innovation Special Zone Project ZD , and the Key Projects of Logistics Scientific Research Project of Chinese PLA 19BJZ The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

AUC, area under the curve; BF, body fat; BMI, body mass index; CV, cardiovascular; CVD, cardiovascular disease; CHD, coronary heart disease; DBP, diastolic blood pressure; T2DM, type 2 diabetes mellitus; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; IDI, integrated discrimination improvement; LDL-C, low-density lipoprotein cholesterol; MACEs, major adverse cardiovascular events; NRI, net reclassification index; SAT, subcutaneous adipose tissue; SBP, systolic blood pressure; SD, standard deviation; TC, total cholesterol; TG, triglyceride; VAT, visceral adipose tissue; WC, waist circumference.

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J Strength Cond Res ; 16 : — PubMed Google Scholar. Malina RM, Beunen GP, Claessens AL, Lefevre J, Vanden Eynde B, Renson R et al. Fatness and physical fitness of girls 7 to 17 years. Obes Res ; 3 : — Hulens M, Vansant G, Claessens AL, Lysens R, Muls E. Predictors of 6-min walk test in lean, obese and morbidly obese women.

Scand J Med Sci Sports ; 13 : 98— Jebb SA, More MS. Contribution of a sedentary lifestyle and inactivity to the etiology of overweight and obesity: current evidence and research issues. Med Sci Sports Exerc ; 31 Suppl 11 : S—S Lafortuna CL, Resnik M, Galvani C, Sartorio A.

Effects if non-specific vs individualized exercised training protocols on aerobic, anaerobic and strength performance in severely obese subjects during a short-term body mass reduction program. J Endocrinol Invest ; 16 : — Visser M, Langlois J, Guralnik JM, Cauley JA, Kronmal RA, Robbins J et al.

High body fatness, but not low fat-free mass, predicts disability in oler men and women. The cardiovascular health study.

Am J Clin Nutr ; 68 : — Wannamethee SG, Shaper AG, Whincup PH, Walker M. Overweight and obesity and the burden of disease and disability in elderly men. Int J Obes Relat Metab Disord ; 28 : — Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R.

Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr ; 75 : — Ross R, Dagnone D, Jones PJ, Smith H, Paddags A, Hudson R et al. Reduction in obesity and related comorbid conditions after diet-induced weight loss or exercise-induced weight loss in men.

A randomized, controlled trial. Ann Intern Med ; : 92— Aromaa A, Koskinen S eds. Health and Functional Capacity in Finland. Baseline Results of the Health Health Examination Survey. Shvartz E, Reibold RC.

Aerobic fitness norms for males and females aged 6 to 75 years: a review. Aviat Space Environ Med ; 61 : 3— MacAuley D, Mc Crum EE, Stott G, Evans AE, Gamble RP, Mc Roberts B et al.

Levels of physical activity, physical fitness and their relationship in the Northern Ireland Health and Activity Survey. Int J Sports Med ; 19 : — Download references.

This study was supported by a grant from the Scientific Committee for National Defence. We are grateful to Matti Pasanen, MSc, The UKK Institute, for valuable statistical advice and for critical comments on our paper, and to Kari Keskinen, PhD, for sharing the results from Fitware validation study.

The UKK Institute for Health Promotion Research, Tampere, Finland. Research Unit, Pirkanmaa Hospital District, Tampere, Finland. The Training Division of The Defence Staff, Finnish Defence Forces, Helsinki, Finland. Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, Finland.

Research Institute of Military Medicine, Central Military Hospital, Helsinki, Finland. You can also search for this author in PubMed Google Scholar. Correspondence to M Fogelholm.

Reprints and permissions. Fogelholm, M. et al. Waist circumference and BMI are independently associated with the variation of cardio-respiratory and neuromuscular fitness in young adult men.

Int J Obes 30 , — Download citation. Received : 04 June Revised : 12 October Accepted : 13 December Published : 24 January Issue Date : 01 June 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. European Journal of Clinical Nutrition Skip to main content Thank you for visiting nature. nature international journal of obesity original article article.

Abstract Objective: To test two hypotheses: 1 cardiorespiratory CRF and neuromuscular NMF fitness is associated with body mass index BMI and waist circumference WC , independent of each other and of leisure-time physical activity; 2 individuals with high CRF and NMF have lower WC for a given BMI, compared with those with low CRF and NMF.

Design: Cross-sectional study. Setting: Men participating in refresher training organized by the Finnish Defence Forces. Participants: A total of men mean age Main outcome measures: Body mass index, WC, maximal oxygen uptake VO 2 max , height of vertical jump, number of push-ups and sit-ups during a 1-min test, static back extension endurance, isometric grip strength, self-reported leisure-time vigorous physical activity.

Conclusions: Despite stronger isometric grip strength, the functional muscle fitness of the upper body, trunk and lower extremities is impaired in individuals with abdominal obesity. Access through your institution. Buy or subscribe. Change institution.

Learn more. Figure 1. Figure 2. References Bouchard C, Shephard RJ. In a study on more than 23 million Korean people, a linear association was noticed between WC and all-cause mortality in all BMI categories [ 40 ].

It is well-established that WC varies considerably within any BMI category and shows a notable correlation with health-related risk factors. In a pooled analysis of 11 studies on , subjects, mortality positively correlated with WC in each BMI category [ 11 ].

However, when adjusted for WC, mortality was lower in subjects with higher BMI [ 42 ]. The association of WC with CVD events and CVD-related mortality has also been established in other studies [ 43 , 44 , 45 ].

In another prospective cohort study, a higher WC predicted higher nonfatal and fatal CVD incidents [ 45 ]. Also, in a cohort study on more than 58, elderly subjects, a greater WC was associated with a higher relative risk of CVD mortality in any BMI category [ 44 ].

In recent years many studies suggested different WC cut-off points to predict the incidence of CVD events and incidence of metabolic syndrome as well as cardio metabolic alterations [ 19 , 20 , 21 ]. These cut-off values range from 85 to 95 cm in men and 80 to 90 cm in women of different ethnicities.

Few studies have evaluated the role of WC thresholds predicting CVD outcomes [ 22 , 23 ]. In a study by Talaei et al. Another study by Hadaegh et al.

The above-mentioned cut-offs were similar to those presented in the current study and higher compared to the thresholds suggested for metabolic syndrome or cardiovascular risk factors.

The cut-off values reported in the present study i. On the other hand, there is a trade-off between sensitivity and specificity, meaning that in order to reach a higher sensitivity, specificity should be sacrificed and vice versa.

The optimal cut-off points in our study were defined based on the maximum level of the Youden index. Generally, when WC is used as a screening tool, sensitivity is of greater importance. In the study of Lee et al. The suggested thresholds were 80 and 89 cm for normal weight and overweight men and 78 and 94 cm for women, respectively, which except in overweight women, are lower values than our suggested thresholds.

In our study, the sensitivity ranged from The lowest sensitivity values for CVD-related mortality According to the results of our study, the WC thresholds obtained for CVD events and all-cause mortality were 82 and 88 cm normal weight , 95 overweight and cm obese in men and 82 and 83 cm normal weight , 89 and 90 cm overweight and 99 and cm obese in women, respectively.

Few studies have evaluated the predictive value of BMI-specific WC cut-off points [ 24 , 47 ]. In a study by Staiano et al. These values are almost the same as those observed in ours study, however, the values obtained for women in the recent study were lower compared to ours. In another study, the WC thresholds predicting a high risk of coronary events in the normal-weight, overweight, obesity I, and obesity II groups were obtained as 82—89, 95—99, —, and — cm in men ; and 79—81, 90—93, —, and — cm in women, respectively [ 24 ].

Also, these values were close to those observed in our study. This study has several strengths and limitations.

The main strengths of our study include the long median follow-up time, its prospective cohort design, using CVD events and mortality as endpoints, and collection of subjective instead of self-report data.

Regarding the limitations of the present study, the data were related to the middle-east Caucasian residents of a metropolitan city in Iran, who cannot be representative of national population.

Different methods of WC measurement have been established. In the present study, WC was measured at the umbilical level. Since there are different methods for measuring WC, although it is unlikely for the method of WC measurement to affects the results [ 13 ], this point should be considered when comparing the results of different studies.

In conclusion, the results of this study suggested BMI-specific WC thresholds for predicting CVD events, CVD-related and mortality, and all-cause mortality, which can used as a clue for future studies to define more accurate WC cut-off values as a screening tool in different populations.

This approach can help better identify individuals who are at a high risk of developing CVD and take effective measures to modify their risk factors. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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