Waist Circumference as a Vital Sign: Is It Ready for Prime Time?
- Over the past decade, for every body mass index (BMI) category, waist circumference (WC) has increased disproportionately. Using a single WC threshold for each BMI is inadequate in identifying the high-risk obese individuals at risk of developing cardiometabolic disease.
- A combination of BMI and WC better predicts future risk of cardiometabolic disease in the highest risk obesity phenotype than either anthropometric measure alone.
- Although addition of WC to cardiovascular risk models does not improve risk prediction, it is still a robust interventional therapeutic target to achieve risk reduction.
Obesity is a chronic disease characterized by increased accumulation of visceral fat that impairs physical and mental health. By 2030, about half of US adults will be obese, and a quarter will have severe obesity.1 Obesity is associated with several cardiometabolic disorders,2,3 thereby increasing the risk of premature morbidity and mortality.4,5
Body mass index (BMI) is traditionally used in the diagnosis of obesity and waist circumference (WC) is a surrogate marker of visceral obesity. A sizeable minority of obese patients do not develop cardiometabolic disorders, and they are referred to as having metabolically healthy obesity (MHO). BMI alone cannot differentiate between metabolically healthy versus unhealthy obese patients. Clinicians often rely on BMI to manage obesity-related health risks, but there is uncertainty about which anthropometric measure, BMI, or WC reliably predicts cardiovascular disease (CVD). An expert consensus statement6 reviewed the utility of WC in evaluating and managing overweight or obese patients. The following are their key points.
How has measurement of WC evolved over time?
In 1995, a seminal study proposed that a single measurement of WC (thresholds of ≥102 cm in men and ≥88 cm in women) could classify obesity in combination with BMI.7 The recommendation was adopted by The National Institutes of Health (NIH) and has been the cornerstone of several guidelines. Since then, evidence across populations has demonstrated a disproportionate increase in WC across each BMI category, independent of age, sex, and ethnicity.8-11 Relative WC increases have outstripped the expected BMI, and BMI alone is insufficient at detecting all MHO patients.8-11 Therefore, the authors recommended measuring WC along with BMI to improve standardization of obesity categories and CVD prediction.
Can WC independently and positively identify the high-risk obesity phenotype?
BMI and WC are independently associated with increased morbidity and mortality, and these findings are independent of age, sex, and ethnicity.12,13 Adults with high WCs are at greater risk for adverse outcomes than those with low WCs in the same BMI category.14-17 After adjustment for BMI, WC has a better predicting value for adverse CVD events.14,18-21 Despite this additional information provided by WC in risk stratification, recommended WC threshold values for all BMI categories remains unchanged.22 Experts do recommend that measuring WC along with BMI will improve the identification of high-risk patients.
Does WC improve prognostic performance of risk algorithms?
Although elevated WC is independently associated with morbidity, addition of WC to risk models, such as the Framingham risk score (FRS)23 or Pooled cohort equation24 does not improve risk prediction. Numerous studies25-27 have demonstrated that adding any new biomarkers only nominally improves the c-statistic scores of CV risk models, as non-modifiable risk factors constitute 63-80% of the prognostic performance of these models.27 Although adding WC does not improve the prognostic performance of risk models, abdominal obesity impacts downstream development of cardiometabolic risk factors.
Is there any dose-response relationship between lifestyle modifications and reduction in WC?
Energy restriction or increased energy expenditure leads to reduction in WC, thereby reducing risk factors with or without a corresponding reduction in BMI.28,29 Although it is intuitive that exercise reduces WC, multiple studies failed to show that increased exercise quantity or intensity is associated with a dose-dependent WC reduction.30-32 Nevertheless, the authors recommend that WC is a surrogate marker to determine the efficacy of lifestyle-based strategies.
How should one correctly measure WC, is there any discrepancy in the different measurement protocols?
The authors recommend following either the NIH guidelines33 (the superior border of the iliac crest) or the WHO guidelines34 (the midpoint between the lower border of the rib cage and the iliac crest) to measure WC. The absolute difference in WC obtained by both protocols is not significantly different in men but is different in women (1.8-2cm).35-37 Either self-measurement or technician-assisted WC measurement with a reusable or disposable tape is adequate since there is a strong correlation between the measurements. The protocol used for index measurement should also be used for subsequent measurements.
Does the traditional sex-specific WC threshold identify those at increased health risk, or is there a better WC threshold?
Sex-specific WC thresholds irrespective of BMI category were developed in White adults and were proposed to be a substitute of BMI.38 Thereby, people with normal BMI with larger WC might be perceived to have a lower risk. Using FRS, Ardern et al. developed a cross-validated BMI-specific WC threshold to predict risk.39 When compared with the traditional threshold, the current recommendation of using a single WC measurement is insufficient to identify those at higher risk across all BMI categories.40 Thus, the combination of BMI and WC could be better at predicting adverse events than either measure alone.
WC is a powerful predictor of long-term adverse outcomes; therefore, measuring WC should be part of routine physical exams. Including WC alongside BMI accurately identifies patients with obesity at high risk of events. Even a modest reduction in WC in each BMI category can result in a significant improvement in risk factors. As WC is a simple low-cost measurement, it is generalizable, and easily adoptable in our practice. Clinicians should incorporate these recommendations into their practice.
Prospective studies are needed that will identify efficacy of measuring age, sex, and ethnicity standardized WC to aid in identification and characterization of obese patients at high risk of developing poor outcomes. More research is needed to evaluate the impact of WC reduction by a combination of medical and lifestyle interventions on short- and long-term CVD outcomes.
The expert consensus support measuring WC along with BMI. Collaboration between clinicians, researchers, and patients is essential for the smooth adoption of the recommendations. Further research exploring the impact of the addition of WC to established risk models to improve their prognostic performance is warranted. Addressing some of these limitations should help align health professionals in reducing the future cardiometabolic risk in the highest risk obesity phenotype.
- Ward ZJ, Bleich SN, Cradock AL, et al. Projected U.S. state-level prevalence of adult obesity and severe obesity. N Engl J Med 2019;381:2440-50.
- Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract 2010;89:309-19.
- Longo M, Zatterale F, Naderi J, et al. Adipose tissue dysfunction as determinant of obesity-associated metabolic complications. Int J Mol Sci 2019;20:2358.
- Global BMIMC, Di Angelantonio E, Bhupathiraju S, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet 2016;388:776-86.
- Whitlock G, Lewington S, Sherliker P, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009;373:1083-96.
- Ross R, Neeland IJ, Yamashita S, et al. 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 2020;16:177-89.
- Lean ME, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ 1995;311:158-61.
- Albrecht SS, Gordon-Larsen P, Stern D, Popkin BM. Is waist circumference per body mass index rising differentially across the United States, England, China and Mexico? Eur J Clin Nutr 2015;69:1306-12.
- Janssen I, Shields M, Craig CL, Tremblay MS. Changes in the obesity phenotype within Canadian children and adults, 1981 to 2007-2009. Obesity (Silver Spring) 2012;20:916-19.
- Visscher TLS, Heitmann BL, Rissanen A, Lahti-Koski M, Lissner L. A break in the obesity epidemic? Explained by biases or misinterpretation of the data? Int J Obes (Lond) 2015;39:189-98.
- Walls HL, Wolfe R, Haby MM, et al. Trends in BMI of urban Australian adults, 1980-2000. Public Health Nutr 2010;13:631-38.
- Snijder MB, Dekker JM, Visser M, et al. Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn Study. Am J Clin Nutr 2003;77:1192-97.
- Song X, Jousilahti P, Stehouwer CD, et al. Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur J Clin Nutr 2013;67:1298-302.
- Cerhan JR, Moore SC, Jacobs EJ, et al. A pooled analysis of waist circumference and mortality in 650,000 adults. Mayo Clin Proc 2014;89:335-45.
- Després JP. Excess visceral adipose tissue/ectopic fat the missing link in the obesity paradox? J Am Coll Cardiol 2011;57:1887-89.
- Rexrode KM, Carey VJ, Hennekens CH, et al. Abdominal adiposity and coronary heart disease in women. JAMA 1998;280:1843-48.
- Zhang X, Shu XO, Yang G, et al. Abdominal adiposity and mortality in Chinese women. Arch Intern Med 2007;167:886-92.
- Bigaard J, Tjønneland A, Thomsen BL, Overvad K, Heitmann BL, Sørensen TIA. Waist circumference, BMI, smoking, and mortality in middle-aged men and women. Obes Res 2003;11:895-903.
- Coutinho T, Goel K, Corrêa de Sá D, et al. Central obesity and survival in subjects with coronary artery disease: a systematic review of the literature and collaborative analysis with individual subject data. J Am Coll Cardiol 2011;57:1877-86.
- Hong Y, Jin X, Mo J, et al. Metabolic syndrome, its preeminent clusters, incident coronary heart disease and all-cause mortality--results of prospective analysis for the Atherosclerosis Risk in Communities study. J Intern Med 2007;262:113-22.
- Zhang C, Rexrode KM, van Dam RM, Li TY, Hu FB. Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Circulation 2008;117:1658-67.
- Seidell JC. Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea. Eur J Clin Nutr 2010;64:35-41.
- Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837-47.
- Khera R, Pandey A, Ayers CR, et al. Performance of the pooled cohort equations to estimate atherosclerotic cardiovascular disease risk by body mass index. JAMA Netw Open 2020;3:e2023242.
- Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:928-35.
- Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-72.
- Pencina MJ, Navar AM, Wojdyla D, et al. Quantifying importance of major risk factors for coronary heart disease. Circulation 2019;139:1603-11.
- Ross R, Janssen I, Dawson J, et al. Exercise-induced reduction in obesity and insulin resistance in women: a randomized controlled trial. Obes Res 2004;12:789-98.
- Short KR, Vittone JL, Bigelow ML, et al. Impact of aerobic exercise training on age-related changes in insulin sensitivity and muscle oxidative capacity. Diabetes 2003;52:1888-96.
- Ross R, Hudson R, Stotz PJ, Lam M. Effects of exercise amount and intensity on abdominal obesity and glucose tolerance in obese adults: a randomized trial. Ann Intern Med 2015;162:325-34.
- Slentz CA, Aiken LB, Houmard JA, et al. Inactivity, exercise, and visceral fat. STRRIDE: a randomized, controlled study of exercise intensity and amount. J Appl Physiol (1985) 2005;99:1613-8.
- Slentz CA, Duscha BD, Johnson JL, et al. Effects of the amount of exercise on body weight, body composition, and measures of central obesity: STRRIDE--a randomized controlled study. Arch Intern Med 2004;164:31-39.
- NHLBI Obesity Education Initiative Expert Panel. The Practical Guide to the Identification, Evaluation and Treatment of Overweight and Obesity in Adults (nhlbi.hih.gov). 2000. Available at: https://www.nhlbi.nih.gov/files/docs/guidelines/prctgd_c.pdf. Accessed 03/15/2021.
- WHO Expert Committee. Physical status: the use and interpretation of anthropometry (WHO website). 1995. Available at: https://www.who.int/childgrowth/publications/physical_status/en/. Accessed 03/15/2021.
- Mason C, Katzmarzyk PT. Variability in waist circumference measurements according to anatomic measurement site. Obesity (Silver Spring) 2009;17:1789-95.
- Wang J, Thornton JC, Bari S, et al. Comparisons of waist circumferences measured at 4 sites. Am J Clin Nutr 2003;77:379-84.
- Matsushita Y, Tomita K, Yokoyama T, Mizoue T. Optimal waist circumference measurement site for assessing the metabolic syndrome. Diabetes Care 2009;32:e70.
- Janssen I, Katzmarzyk PT, Ross R. Body mass index, waist circumference, and health risk: evidence in support of current National Institutes of Health guidelines. Arch Intern Med 2002;162:2074-79.
- Ardern CI, Janssen I, Ross R, Katzmarzyk PT. Development of health-related waist circumference thresholds within BMI categories. Obes Res 2004;12:1094-103.
- Bajaj HS, Brennan DM, Hoogwerf BJ, Doshi KB, Kashyap SR. Clinical utility of waist circumference in predicting all-cause mortality in a preventive cardiology clinic population: a PreCIS Database Study. Obesity (Silver Spring) 2009;17:1615-20.
Keywords: Dyslipidemias, Metabolic Syndrome X, Obesity, Abdominal, Obesity, Body Mass Index, Waist Circumference, Cardiovascular Diseases, Intra-Abdominal Fat, Obesity, Morbid, Ethnic Groups, Prognosis, Mental Health, Consensus, Overweight, Life Style, Biological Markers, Morbidity, Energy Metabolism, Chronic Disease, Reference Standards, Phenotype, National Institutes of Health (U.S.), Algorithms, Risk Assessment, World Health Organization
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