Waist circumference and weight control -
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The most basic method, and the most common, is the body mass index BMI. Doctors can easily calculate BMI from the heights and weights they gather at each checkup; BMI tables and online calculators also make it easy for individuals to determine their own BMIs.
Strengths Easy to measure Inexpensive Standardized cutoff points for overweight and obesity: Normal weight is a BMI between Strengths Easy to measure Inexpensive Strongly correlated with body fat in adults as measured by the most accurate methods Studies show waist circumference predicts development of disease and death Limitations Measurement procedure has not been standardized Lack of good comparison standards reference data for waist circumference in children May be difficult to measure and less accurate in individuals with a BMI of 35 or higher Waist-to-Hip Ratio Like the waist circumference, the waist-to-hip ratio WHR is also used to measure abdominal obesity.
Strengths Convenient Safe Inexpensive Portable Fast and easy except in individuals with a BMI of 35 or higher Limitations Not as accurate or reproducible as other methods Very hard to measure in individuals with a BMI of 35 or higher Bioelectric Impedance BIA BIA equipment sends a small, imperceptible, safe electric current through the body, measuring the resistance.
Strengths Accurate Limitations Time consuming Requires individuals to be submerged in water Generally not a good option for children, older adults, and individuals with a BMI of 40 or higher Air-Displacement Plethysmography This method uses a similar principle to underwater weighing but can be done in the air instead of in water.
Strengths Relatively quick and comfortable Accurate Safe Good choice for children, older adults, pregnant women, individuals with a BMI of 40 or higher, and other individuals who would not want to be submerged in water Limitations Expensive Dilution Method Hydrometry Individuals drink isotope-labeled water and give body fluid samples.
Strengths Accurate Allows for measurement of specific body fat compartments, such as abdominal fat and subcutaneous fat Limitations Equipment is extremely expensive and cannot be moved CT scans cannot be used with pregnant women or children, due to the high amounts of ionizing radiation used Some MRI and CT scanners may not be able to accommodate individuals with a BMI of 35 or higher References 1.
and Y. was appointed and tasked with writing the first draft. High-quality published literature that became available after the initial face-to-face meeting through June was identified by all authors and reviewed by the executive writing group for inclusion in the manuscript.
The first author coordinated the final preparation and submission of the Consensus Statement after the group achieved consensus and approved its content.
The importance of body fat distribution as a risk factor for several diseases for example, CVD, hypertension, stroke and T2DM and mortality has been recognized for several decades. These classifications were later interpreted by Ahmed Kissebah and colleagues as upper versus lower body fat accumulation as reflected by a high or low waist—hip circumference ratio WHR , respectively The upper and lower body fat accumulation phenotypes were based on body morphology as assessed by external anthropometric measures such as skinfolds and circumferences.
The WHR increased in popularity when epidemiologists in the USA and Sweden showed that WHR, separately or in combination with BMI, was associated with increased risk of death, CVD and T2DM 19 , 20 , 21 , 22 , findings that were subsequently confirmed in many studies.
However, later evidence indicated that, compared with the WHR, waist circumference alone was more strongly associated with the absolute amount of intra-abdominal or visceral fat, the fat depot that conveys the strongest health risk 23 , Furthermore, when a ratio such as WHR is used to follow changes in regional adipose depots, the utility of the ratio is limited when both the numerator and denominator values change in response to treatment.
Consequently, the combination of WHR and BMI for assessing obesity risk were replaced by single threshold values for waist circumference alone Although the use of these specific waist circumference values to identify white adults with abdominal obesity remains a cornerstone of obesity guidelines worldwide, we present evidence to challenge the supportive rationale and provide evidence in support of alternative waist circumference values to be used in concert with BMI.
As an alternative to measurements of waist circumference, the WHR or waist—thigh circumference ratio, Margaret Ashwell and others proposed the waist—height ratio as a measure of abdominal obesity 26 , Compared with the previous measurements, the waist—height ratio shows similar and sometimes slightly stronger associations with the risk of CVD or T2DM 28 , An explanation for why adding height increases the prediction of disease risk might be because short stature is associated with increased risk of CVD In growing children and adolescents, the waist—height ratio could be more useful for the classification of abdominal obesity than waist circumference alone.
However, in fully grown adults, the waist—height ratio is less useful as height is generally fixed and the value can only be altered by changes in waist circumference.
Moreover, height is only marginally associated with waist circumference For the assessment of the effectiveness of lifestyle changes in adults, waist circumference might be preferred as a simple tool. Other alternatives to waist circumference have included the conicity index 32 and the abdominal obesity index 33 , but they are, at best, only slightly better predictors of disease risk than waist circumference alone.
Despite a strong association between waist circumference and BMI at the population level, emerging evidence suggests that, across populations, waist circumference might be increasing beyond what is expected according to BMI.
In other words, the phenotype of obesity might be changing over time to one that reflects an increase in abdominal adiposity For example, Ian Janssen and colleagues examined the changes in waist circumference for a given BMI over a year period in a Canadian sample Notably, for a given BMI, Canadians had a larger waist circumference in compared with Specifically, the researchers observed a waist circumference that was greater by 1.
Similarly, Sandra Albrecht and colleagues examined the secular changes in waist circumference in the USA — , England — , China — and Mexico — 36 and reported statistically significantly increased waist circumference values relative to BMI in all countries studied and in most subpopulations.
These observations are consistent with those of Tommy Visscher and colleagues, who performed an extensive review and concluded that the majority of the evidence suggests a trend in which the relative increases in waist circumference were larger than the relative increases in BMI This observation is seemingly independent of age, sex and ethnicity, as few groups failed to demonstrate the general trend of secular waist circumference increasing beyond that expected by BMI Fig.
The failure of BMI to detect such an increase in abdominal obesity confirms the limitations of BMI alone to identify the phenotype of obesity that conveys the greatest health risk. Changes in the prevalence of abdominal obesity measured using waist circumference and general obesity measured using BMI measured in different studies during the time period indicated on the x axis.
However, Xi et al. In addition, Barzin et al. Years given for example, — indicate the years in which data were collected. F, female; M, male. Data are from refs 37 , , , , , , , , , Although the prevalence of obesity measured by BMI might have plateaued in some countries, the prevalence of abdominal obesity as measured by waist circumference is generally increasing.
The lack of inclusion of waist circumference in global obesity surveillance might inadequately characterize the health risk associated with the global obesity prevalence, as it seems that the prevalence of abdominal obesity is increasing.
Current obesity prevalence trends based on BMI alone should be interpreted with caution. We recommend that serious consideration should be given to the inclusion of waist circumference in obesity surveillance studies. It is not surprising that waist circumference and BMI alone are positively associated with morbidity 15 and mortality 13 independent of age, sex and ethnicity, given the strong association between these anthropometric variables across cohorts.
However, it is also well established that, for any given BMI, the variation in waist circumference is considerable, and, in any given BMI category, adults with higher waist circumference values are at increased adverse health risk compared with those with a lower waist circumference 38 , 39 , This observation is well illustrated by James Cerhan and colleagues, who pooled data from 11 prospective cohort studies with , white adults from the USA, Australia and Sweden aged 20—83 years This finding is consistent with that of Ellen de Hollander and colleagues, who performed a meta-analysis involving over 58, predominantly white older adults from around the world and reported that the age-adjusted and smoking-adjusted mortality was substantially greater for those with an elevated waist circumference within normal weight, overweight and obese categories as defined by BMI The ability of waist circumference to add to the adverse health risk observed within a given BMI category provides the basis for the current classification system used to characterize obesity-related health risk 8 , Despite the observation that the association between waist circumference and adverse health risk varies across BMI categories 11 , current obesity-risk classification systems recommend using the same waist circumference threshold values for all BMI categories We propose that important information about BMI and waist circumference is lost when they are converted from continuous to broad categorical variables and that this loss of information affects the manner in which BMI and waist circumference predict morbidity and mortality.
Specifically, when BMI and waist circumference are considered as categorical variables in the same risk prediction model, they are both positively related to morbidity and mortality However, when BMI and waist circumference are considered as continuous variables in the same risk prediction model, risk prediction by waist circumference improves, whereas the association between BMI and adverse health risk is weakened 10 , Evidence in support of adjusting waist circumference for BMI comes from Janne Bigaard and colleagues who report that a strong association exists between waist circumference and all-cause mortality after adjustment for BMI Consistent with observations based on asymptomatic adults, Thais Coutinho and colleagues report similar observations for a cohort of 14, adults with CVD who were followed up for 2.
The cohort was divided into tertiles for both waist circumference and BMI. In comparison with the lowest waist circumference tertile, a significant association with risk of death was observed for the highest tertile for waist circumference after adjustment for age, sex, smoking, diabetes mellitus, hypertension and BMI HR 1.
By contrast, after adjustment for age, sex, smoking, diabetes mellitus, hypertension and waist circumference, increasing tertiles of BMI were inversely associated with risk of death HR 0. The findings from this systematic review 44 are partially confirmed by Diewertje Sluik and colleagues, who examined the relationships between waist circumference, BMI and survival in 5, individuals with T2DM over 4.
In this prospective cohort study, the cohort was divided into quintiles for both BMI and waist circumference. After adjustment for T2DM duration, insulin treatment, prevalent myocardial infarction, stroke, cancer, smoking status, smoking duration, educational level, physical activity, alcohol consumption and BMI, the HR for risk of death associated with the highest tertile was 2.
By contrast, in comparison with the lowest quintile for BMI adjusted for the same variables, with waist circumference replacing 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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Zhang, X. Zhang, He, Y. Zheng, Sun, Qi, Pan. Critical revision of the manuscript for important intellectual content: Yuan, Chen, Wang, Jiang, Zhou, X. Wu, Yang, Min, C. Zheng, Sun, Qi, Hemler, S. Wu, T. Wu, Pan.
Statistical analysis: Yuan, K. Zheng, Wang, Jiang, Ding, Yang, Min, Y. Zheng, S. Administrative, technical, or material support: Chen, Zhou, X. Wu, Yang, Li, X. Zhang, He, Sun, Hemler, Pan. Conflict of Interest Disclosures: None reported. Wu is supported by grant , , and from the National Natural Science Foundation of China, grant YFC from the National Key Research and Development Program of China, and grant kfyXMBZ from the Fundamental Research Funds for the Central Universities.
Dr Pan is supported by grant form the National Natural Science Foundation of China, grant YFC from the National Key Research and Development Program of China, and grant GCRC from the Fundamental Research Funds for the Central Universities. Dr Yuan is supported by grant XXJS from the Fundamental Research Funds for the Central Universities.
Additional Contributions: We thank all the study participants and project staff of the Dongfeng-Tongji cohort and the Kailuan study for providing data. full text icon Full Text. Download PDF Comment. Top of Article Key Points Abstract Introduction Methods Results Discussion Conclusions Article Information References.
Adjusted Hazard Ratios for All-Cause Mortality Based on the Joint Changes in Weight and Waist Circumference. View Large Download. Table 1. Basic Characteristics of the Participants by Weight and Waist Circumference Change. Table 2. Associations Between Weight Change Categories and All-Cause Mortality.
Table 3. Associations Between Waist Circumference Change Categories and All-Cause Mortality. Supplementary Methods eFigure 1. Flow Chart of Participant Recruitment From the DFTJ Cohort and Kailuan Study eFigure 2.
The Restricted Cubic Spline for the Association Between the Changes in Weight and All-Cause Mortality in the DFTJ Cohort and Kailuan Study eFigure 3. The Restricted Cubic Spline for the Association Between the Changes in Waist Circumference and All-Cause Mortality in the DFTJ Cohort and Kailuan Study eFigure 4.
Adjusted Hazard Ratios for All-Cause Mortality Based on the Joint Changes in Weight and Waist Circumference in DFTJ Cohort and Kailuan Study eFigure 5. Associations Between Weight Change Categories and All-Cause Mortality, Stratified by Age, Sex, BMI, Waist Circumference, Diabetes, and Physical Activity at Baseline eFigure 6.
Associations Between Waist Circumference Change Categories and All-Cause Mortality, Stratified by Age, Sex, BMI, Waist Circumference, Diabetes, and Physical Activity at Baseline eFigure 7.
Adjusted Hazard Ratios for All-Cause Mortality Based on the Joint Changes in Weight and Waist Circumference Random-effects Meta-analyses eFigure 8.
Adjusted Hazard Ratios for All-Cause Mortality Stratified by Age Based on the Joint Changes in Weight and Waist Circumference eFigure Adjusted Hazard Ratios for All-Cause Mortality Stratified by Sex Based on the Joint Changes in Weight and Waist Circumference eFigure Adjusted Hazard Ratios for All-Cause Mortality Stratified by Baseline BMI Based on the Joint Changes in Weight and Waist Circumference eFigure Adjusted Hazard Ratios for All-Cause Mortality Stratified by Baseline Waist Circumference Based on the Joint Changes in Weight and Waist Circumference eFigure Adjusted Hazard Ratios for All-Cause Mortality Stratified by Physical Activity Level Based on the Joint Changes in Weight and Waist Circumference eFigure Adjusted Hazard Ratios for All-Cause Mortality Stratified by Diabetes Based on the Joint Changes in Weight and Waist Circumference eTable 1.
Other Variables in the Associations Between Weight Change Categories and All-Cause Mortality eTable 2. Other Variables in the Associations Between Waist Circumference Change Categories and All-Cause Mortality eTable 3.
Basic Characteristics of the Participants by the Joint Categories of Weight and Waist Circumference Change eTable 4. Associations Between Weight Change Categories and All-Cause Mortality Among Never Smokers eTable 5. Associations Between Waist Circumference Change Categories and All-Cause Mortality Among Never Smokers eTable 6.
Associations Between Weight Change Categories and All-Cause Mortality Random-effects Meta-analyses eTable 7. Associations Between Waist Circumference Change Categories and All-Cause Mortality Random-effects Meta-analyses eTable 8. Associations of Baseline Characteristics With Weight Change and Waist Circumference Change eTable 9.
Associations of Baseline Characteristics With Joint Changes in Weight and Waist Circumference eReferences. Barzi F, Woodward M, Czernichow S, et al. The discrimination of dyslipidaemia using anthropometric measures in ethnically diverse populations of the Asia-Pacific Region: the Obesity in Asia Collaboration.
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Obese Canadians are Nut-Free Options times more likely to have diabetes, icrcumference than three times as Waist circumference and weight control to have high blood pressure and more than two Waist circumference and weight control more likely to have heart disease than those with Waixt healthy circumferencee. However, simply knowing your Waist circumference and weight control is not contorl to know Waistt health risk. Did you know that you can have a healthy weight, but still be at increased risk? How our bodies store excess weight specifically fat can negatively impact our health. 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. Measuring waist circumference can help to assess obesity-related health risk. Even at a healthy weight, excess fat carried around the waist can increase your risk of high blood pressure, high [blood] cholesterol, heart disease and type-2 diabetes.
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