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 BMI , the 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 47 , 48 , Moreover, VAT is an established marker of morbidity 50 , 51 and mortality 24 , These findings provide a plausible mechanism by which lower values for BMI or hip circumference for a given waist circumference would increase adverse health risk.

When this process becomes saturated or in situations where adipose tissue has a limited ability to expand, there is a spillover of the excess energy, which must be stored in visceral adipose tissue as well as in normally lean organs such as the skeletal muscle, the liver, the pancreas and the heart, a process described as ectopic fat deposition.

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.

FFA, free fatty acid. This notion is reinforced by Jennifer Kuk and colleagues who reported that BMI is an independent and positive correlate of VAT in adults before adjustment for waist circumference; however, BMI is negatively associated with VAT mass after adjustment for waist circumference This study also reported that, after adjustment for waist circumference, BMI was positively associated with lower body subcutaneous adipose tissue mass and skeletal muscle mass.

These observations support the putative mechanism described above and, consequently, that the negative association commonly observed between BMI and morbidity and mortality after adjustment for waist circumference might be explained by a decreased deposition of lower body subcutaneous adipose tissue and muscle mass, an increased accumulation of visceral adiposity, or both.

In summary, the combination of BMI and waist circumference can identify the highest-risk phenotype of obesity far better than either measure alone. Although guidelines for the management of obesity from several professional societies recognize the importance of measuring waist circumference, in the context of risk stratification for future cardiometabolic morbidity and mortality, these guidelines limit the recommendation to measure waist circumference to adults defined by BMI to have overweight or obesity.

On the basis of the observations described in this section, waist circumference could be just as important, if not more informative, in persons with lower BMI, where an elevated waist circumference is more likely to signify visceral adiposity and increased cardiometabolic risk.

This observation is particularly true for older adults In categorical analyses, waist circumference is associated with health outcomes within all BMI categories independent of sex and age. When BMI and waist circumference are considered as continuous variables in the same risk prediction model, waist circumference remains a positive predictor of risk of death, but BMI is unrelated or negatively related to this risk.

The improved ability of waist circumference to predict health outcomes over BMI might be at least partially explained by the ability of waist circumference to identify adults with increased VAT mass. For practitioners, the decision to include a novel measure in clinical practice is driven in large part by two important, yet very different questions.

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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Physical status: the use and interpretation of anthropometry: report of a WHO Expert Committee WHO, The final sample was formed by non-obese individuals, who were divided into two groups according to the WC.

There were persons in the group of normal WC NWC , and persons in the group of high WC HWC Fig 1. The HSA involved a health interview, physical examination performed by a public health practitioner and a community nurse , and laboratory tests.

The data collected and the questions applied in the HSA are shown in Additional file 2 see Additional file 2. Based on the definition of metabolic syndrome [ 18 ], laboratory data of blood glucose, total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels were used.

The thresholds for laboratory parameters given by the ATP III or the WHO [ 19 ] are shown in Table 1. The public health professionals followed the WHO STEPS STEPwise approach to surveillance protocol to measure the WC, that is, the measurement is made at the approximate midpoint between the lowest rib and the iliac crest [ 20 ].

Blood pressure was measured by electronic blood pressure monitor after a five-minute rest. A chi-square test was performed to show the basic characteristics of the two groups. A multiple logistic regression model was used to define the association between the metabolic risk factors and abdominal obesity with adjustments for age and gender.

Metabolic risk factors were used as dependent variables, and abdominal obesity was defined as an independent variable. A p-value lower than 0. Statistical analysis was performed by using the SPSS The final sample involved non-obese subjects.

Out of them, subjects Both groups involved more women than men, and the majority of the patients were young 18—29 years or middle-aged 30—44 years Table 2. The levels of HDL and LDL cholesterol were not significantly different between the two groups.

According to the results of the multiple logistic regression models, older people had significantly higher odds for developing most of the metabolic risk factors. Subjects in the age group of 30—44 years were significantly more likely to have high systolic blood pressure, high fasting blood glucose, high total cholesterol, and high LDL cholesterol levels compared to the 18—29 year age group Table 3.

This significantly increased risk of the occurrence of the above mentioned metabolic risk factors was shown in the older age groups. An increased risk for high triglyceride level was observed in the 45—59 and 60—74 year old age groups compared to the youngest age group.

Subjects in the 45—59 and 60—74 year old age groups had significantly lower odds for low HDL cholesterol. In contrast, the risk increased in the oldest age group, although not significantly Table 3.

Regarding gender, males had significantly higher odds for having high systolic blood pressure, high fasting blood glucose, low HDL cholesterol, and high triglyceride levels Table 3. Subjects in the HWC group were more likely to have some metabolic risk factors compared to those in the NWC group.

If a patient had abdominal obesity, the odds for high systolic blood pressure, low HDL cholesterol, and high triglyceride levels increased significantly Table 3. On the contrary, the risk for high LDL level was lower in the HWC group compared to the NWC group, but it was not significant.

The odds of high total cholesterol level was nearly the same in the NWC and the HWC groups Table 3. In this cross-sectional study, we examined the association of abdominal obesity assessed by WC with the occurrence of metabolic risk factors in non-obese subjects. Our results showed a high prevalence of abdominal obesity Our study also presented a very high, positive correlation between BMI and WC, which is consistent with other studies [ 23 , 24 ].

Subjects with abdominal obesity were proved to have significantly higher prevalence of high systolic blood pressure, high fasting blood glucose, high total cholesterol, and high triglyceride levels than subjects in the NWC group. The risk for having high systolic blood pressure, high fasting blood glucose, high total cholesterol, and high LDL cholesterol levels was age-dependent, these levels were significantly higher in the age group of 30—44 years, and the risk was also significant in the older age groups.

In case of high triglyceride level, an increased risk was found in the age groups 45—59 and 60—74 years. In general, as individuals grow older, the body fat content increases, especially in the abdominal region, which may be the cause of the elevated metabolic risk [ 25 ].

Therefore, screening of abdominal obesity is crucial in the elderly. Furthermore, males were significantly more affected by most of the cardiometabolic parameters, hence it would be fundamental to measure WC particularly in males. Likewise, the logistic regression model indicated that patients with abdominal obesity had significantly higher odds for high systolic blood pressure, low HDL cholesterol, and high triglyceride levels.

Our results are in line with the findings of other researchers [ 26 , 27 ]. In the study of Okosun et al. According to the findings of Huang, WC is a better predictor of insulin resistance [ 28 ] and a better predictor of mortality [ 8 ] than BMI.

Furthermore, a strong association has also been documented between abdominal obesity, CVDs, and total mortality [ 27 ]. It is well-known that obesity is strongly related to metabolic, CV, and other diseases [ 29 ]. The health risks of abdominal obesity have already been recognized as well, although WC is still less commonly measured than BMI in the clinical practice [ 8 ].

It is of importance that some individuals with normal BMI are insulin resistant and have metabolic abnormalities, which might contribute to an abnormal fat distribution, especially abdominal obesity [ 30 ]. Obviously, there are differences in the recommendations regarding the screening of obesity in the world.

The Obesity Society Guidelines for Managing Overweight and Obesity in Adults do not recommend measuring the WC [ 32 ].

The U. Preventive Services Task Force USPSTF recommends the screening of all adults for obesity by the calculation of the BMI, although it also states that WC may be an acceptable alternative to BMI measurement in some patient subpopulations [ 33 ].

Nonetheless, Hungary has adapted the available evidence that measurement of WC is effective in the prevention of cardiometabolic disorders; thus, according to a decree [ 34 ] regarding screenings done by GPs and financed by the health insurance, measurement of WC is to be included in the physical examination over 21 years of age, and it should be repeated every 5 years.

Despite the existing legal support for screenings in primary health care, the cardiometabolic preventive services are used at much lower rates than recommended for the age group of 21—64 years, and it might contribute to the extremely high CV mortality in Hungary [ 35 ].

In a study performed in a relatively large sample subjects , cardiometabolic risk has been assessed in only The logistic regression model also showed that the risk for elevated LDL cholesterol level was lower in patients with abdominal obesity.

This is consistent with a study [ 37 ] in which it was found that high LDL cholesterol remained relatively unchanged with inreased WC. Possibly, the LDL cholesterol level was modified by cholesterol-lowering therapy, which primarily aims to decrease LDL cholesterol level it is one of the known limitations, see below.

Using the BMI alone to identify metabolic risks, several patients with increased risk but normal BMI would be missed. To measure WC is particularly important above 30 years of age and in males, irrespective of their BMI. The strengths of our study were the large sample size, the measurement of WC by trained health personnel, and the interpretation of the results of a new, innovative model programme in terms of screening in primary care in Hungary.

Our limitations involve that the study was not representative selection bias , and we could not determine cause-and-effect relationships due to the cross-sectional type of this study. Finally, not all laboratory parameters were available for each subject.

In this analysis, we applied the definition of ATP III to classify the patient as having abdominal obesity but data were not analyzed according to International Diabetes Federation IDF thresholds.

As we did not have information on which patients had cholesterol lowering therapy, we could not exclude them from the present analysis, although this therapy might modify the measured LDL cholesterol levels and model calculations based on them. Our results suggest that screening of abdominal obesity by applying a simple, professionally performed measurement of WC might be a suitable predictor of metabolic syndrome, potentially more practical than, e.

The prevention of metabolic syndrome and the possible outcomes, such as CVDs and diabetes, would be especially beneficial in the Hungarian population, where CVDs are the leading causes of death, and the morbidity of T2DM is continuously increasing.

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Children require a separate threshold of sex-specific WC norms relative to age, height, and stage of sexual maturity because of the normal increase in WC throughout childhood.

Waist circumference has a low intraobserver and interobserver error, and when adjusted for clothing, accuracy remains good. The global increase in obesity in children and adolescents increases the risk for T2DM and adult CVD as components of the metabolic syndrome.

The insulin resistance of obesity is considered to play a major role in the development of the metabolic syndrome. Studies in adults demonstrate that abdominal obesity and high fasting insulin levels are strong and independent predictors of later development of insulin resistance syndrome.

The present study is consistent with previous descriptions of the effects of fat distribution on risk factors for CVD in adolescents. A more central deposition of fat android pattern was associated with an elevation of triglyceride level, decreased high-density lipoprotein cholesterol level, increased systolic BP, and increased left ventricular mass.

Because HOMA-IR might be insufficiently precise for estimating insulin resistance, we also measured proinsulin levels. Elevated fasting concentrations of intact proinsulin have been reported to be markers of insulin resistance.

The use of acanthosis nigricans as a predictive marker of hyperinsulinemia has become a common practice. Previous studies have associated the presence of acanthosis nigricans with high insulin levels, thus identifying a subgroup believed to be at greater risk for T2DM.

Use of acanthosis nigricans as the sole indicator of hyperinsulinemia led physicians to miss the diagnosis in half of all children with significant hyperinsulinemia.

In our study, there was a significant correlation between WC and all the components of the metabolic syndrome. Multiple linear regression analysis using HOMA-IR as the dependent variable showed that WC and systolic BP were independent predictors for insulin resistance, when adjustment was made for other variables.

Insulin resistance was predicted by WC and systolic BP, which explained In adults, insulin resistance drives the processes underlying the metabolic syndrome. Visceral obesity may be an important risk factor for insulin resistance syndrome in children. Waist circumference serves as a readily available means to estimate abdominal obesity in the office setting.

Normative data specific for ethnic group need to be collected. The present study showed that children with abdominal obesity, as determined by WC, have increased metabolic risk factors for CVD and T2DM.

Because this study is cross-sectional, longitudinal studies will be needed to determine the significance of our observations. Correspondence: Valeria Hirschler, MD, Maipú 5A M, Capital Federal , Argentina vhirschler intramed.

Acknowledgment: We would like to acknowledge Arlan Rosenbloom, MD, and Janet Silverstein, MD, for help editing the manuscript.

Hirschler V , Aranda C , Calcagno MDL , Maccalini G , Jadzinsky M. Can Waist Circumference Identify Children With the Metabolic Syndrome? Arch Pediatr Adolesc Med. Artificial Intelligence Resource Center.

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Although guidelines for the management of obesity from several professional societies recognize the importance of measuring waist circumference, in the context of risk stratification for future cardiometabolic morbidity and mortality, these guidelines limit the recommendation to measure waist circumference to adults defined by BMI to have overweight or obesity.

On the basis of the observations described in this section, waist circumference could be just as important, if not more informative, in persons with lower BMI, where an elevated waist circumference is more likely to signify visceral adiposity and increased cardiometabolic risk.

This observation is particularly true for older adults In categorical analyses, waist circumference is associated with health outcomes within all BMI categories independent of sex and age.

When BMI and waist circumference are considered as continuous variables in the same risk prediction model, waist circumference remains a positive predictor of risk of death, but BMI is unrelated or negatively related to this risk.

The improved ability of waist circumference to predict health outcomes over BMI might be at least partially explained by the ability of waist circumference to identify adults with increased VAT mass. For practitioners, the decision to include a novel measure in clinical practice is driven in large part by two important, yet very different questions.

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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Nguyen-Duy, T. Visceral fat and liver fat are independent predictors of metabolic risk factors in men. Kuk, J. 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.