Metrics details. Adiposity is a major component of the metabolic syndrome MetS , low muscle strength has also been identified as a risk factor for MetS and for cardiovascular disease. We describe the prevalence of MetS and evaluate the relationship between muscle strength, anthropometric measures of adiposity, and associations with the cluster of the components of MetS, in a middle-income country.

MetS was defined by the International Diabetes Federation criteria. For each alteration high triglycerides, low HDLc, dysglycemia, or high blood pressure one point was conferred. Combining these anthropometric measures improved the prediction of metabolic alterations over either alone.

Metabolic syndrome MetS is associated with a higher risk of cardiovascular disease CVD mortality and total mortality [ 1 ]. The cluster of the metabolic alterations that comprise MetS includes dysglycemia, low HDL-c, increased triglycerides, and elevated blood pressure.

The accumulation of adiposity, predominantly in visceral tissue, is the cornerstone feature of the development of MetS [ 2 , 3 ] and the growing prevalence of obesity is considered a principal determinant of the increased prevalence of MetS, type 2 diabetes mellitus and CVD globally [ 4 , 5 , 6 , 7 ].

Indeed, in the Latin American population, abdominal obesity has the highest population attributable risk for a first acute myocardial infarction [ 8 ].

The Prospective Urban Rural Epidemiological PURE study, an epidemiological cohort study with more than The combination of obesity and low muscle strength is associated with an additive cardiovascular risk in high income countries [ 11 ], but there is a paucity of information in low-medium income countries.

The present study aimed firstly to establish the prevalence of MetS within Colombia, a middle-income country, using the PURE database. The PURE study design, coordinated by the Population Health Research Institute PHRI Hamilton, ON, Canada , was described previously [ 12 ].

In the case of Colombia, the protocol was approved by the Fundación Cardiovascular de Colombia ethics committee.

Participants were selected from both urban and rural communities from eleven departments across the country, allowing the collection of data from a sample that represents A three-phase survey was applied, in which the first and second phases consisted of selecting the communities involved, and the third phase of selecting the homes included within those communities.

A community was defined as the geographical area where a group of people with common characteristics lived. We considered a home rural if it was located more than 50 km from an urban center.

A home was selected if a family member was between the ages of 35—70 years old and if the individuals intended to stay in this household for the next 4 years.

Trained personnel made three attempts to contact a member of each household for door-to-door collection of information. We included all participants who completed and signed written consent. For each consenting participant, sociodemographic characteristics and cardiovascular risk factors were obtained.

Blood pressure, anthropometrics and handgrip strength were also measured. Triglycerides, total cholesterol and high-density lipoprotein cholesterol were estimated by enzymatic colorimetric method in an automatic analyzer Hitachi , Boehringer Mannheim and LDL-c was calculated.

For detecting dysglycemia, the enzymatic hexokinase method was applied to determine glucose levels in each sample. Individuals with a low educational level were those without schooling, primary schooling, or unknown academic history.

We considered smokers all those who consumed a daily tobacco product in the last 12 months and included those who reported having quit smoking in the last year. Never drinking was defined as self-reported abstinence, former drinking was defined as having ceased alcohol consumption for 1 year or more, and current drinking was defined as consumption of alcohol in the past year.

Blood pressure was taken with no smoking, physical activity, or food consumption during the previous 30 min and after the participant sat for 5 min. Anthropometric measurements were taken following the standardized protocol of the PURE study. Weight was measured using a digital scale with the participant lightly clothed with no shoes.

Height was measured to the nearest millimeter using a tape measure with the participant standing without shoes. Waist and hip circumferences were measured unclothed using a tape measure.

The WC was considered the smallest circumference between the costal margin and the iliac crest. The hip circumference was measured at the level of the greater trochanters. Handgrip strength was measured was evaluated on the individual's non-dominant hand using a Jamar dynamometer Sammons Preston, Bolingbrook, IL, USA , according to a standardized protocol [ 9 ].

Standing, the participant held the dynamometer at the side of the body with the elbow flexed at degree angle and was asked to squeeze the device as hard as possible for 3 s. This was repeated twice with 30 s rest between each attempt. Physical activity PA was evaluated using the International Physical Activity Questionnaire IPAQ.

IPAQ which assesses physical activity undertaken across a comprehensive set of domains, including leisure-time physical activity, domestic and gardening activities, work-related physical activity, transport-related physical activity.

These thresholds take into account that the IPAQ queries PA in multiple domains of daily life, resulting in higher median MET-minutes estimates than would be that estimated from considering leisure-time participation alone.

One point was conferred for each alteration of the cluster of MetS as defined by IDF elevated triglycerides, low HDL-c, dysglycemia, or high blood pressure , generating a score of 0 to 4 for each participant, a high score was considered if 2 or more points were achieved.

WC was not included in the calculation of our metabolic score as it was also an outcome variable. Descriptive statistics were computed for variables of interests and included absolute and relative frequencies of categorical factors.

Testing for differences in categorical variables was accomplished using the Chi-square test. Moreover, we used unconditional multivariate logistic regression models to assess the associations between anthropometric variables and handgrip strength, and the MetS score.

These analyses were adjusted for potential confounders, such as age, socioeconomic status, income and education level. We re-coded the anthropometric variables and handgrip strength into sex-specific tertiles and compared the risk of a higher MetS score in each tertile with the lowest category of risk reference group.

All statistical analysis was carried out using the R software version 3. The mean age was The overall prevalence of MetS was MetS was more frequent in women, people older than 50 years; it was also more frequent in individuals living in urban areas, former drinkers, and smokers.

The prevalence of MetS was higher in participants with a lower level of education compared with those with a high school or college degree. The percentage of subjects with MetS was lower in tertile 1 of BMI There were no significant differences in the prevalence of MetS across tertiles of HGS tertile 3: However, the prevalence of MetS Figure 1 shows the sex-specific distribution of the MetS scores.

The association between anthropometric variables and the risk of a higher MetS score is shown in Table 2. A higher WC was associated with a risk of a higher MetS score, with women and men in the tertile 3 of WC mean Participants in tertile 3 of BMI mean In women, lower HGS was associated with a significantly higher MetS score T3 vs.

In men, there were no significant differences in MetS score across HGS tertiles. The overall prevalence of MetS in this cohort of Colombian adults was A lower prevalence was reported by Higuita-Guitierrez in Colombian adults of which Aging is associated with an increase in adipose tissue and a decreased muscle mass [ 17 ], body composition changes which predispose to the development of metabolic alterations.

The prevalence of MetS was higher in women Lower educational level was associated with a higher prevalence of MetS Educational level is an indicator of social inequity, lower levels reflecting not only less schooling, but also a higher risk of unhealthy life habits, and lower access to employment and physical activity participation.

Social factors associated with MetS prevalence, should be further examined. We found that lower muscle strength and higher central adiposity as defined by waist circumference, were independently associated with a higher MetS score, representing a greater number of alterations of the components of the MetS cluster.

Our cross-sectional analysis showed a stronger association between a higher MetS score and WC than BMI, confirming previous studies showing that in Latin-American and Chinese population, WC is a stronger predictor of major cardiovascular events such as myocardial infarction or stroke than BMI, particularly in men [ 8 , 21 ].

Similarly, in diabetic Chinese adults, high visceral fat measured by a visceral adiposity index and WC were associated with a higher prevalence of diabetic kidney disease and CVD compared to BMI [ 22 ]. These findings may be related to the higher inflammatory load associated with visceral adipose tissue accumulation, and inflammation is considered a key factor associated with insulin resistance, MetS and CVD [ 23 , 24 ].

The low-grade pro-inflammatory state characterized by high C-reactive protein levels is observed in adults and youth in our population with high visceral adiposity [ 25 , 26 ]. However, the accumulation of visceral fat is not the only contributing factor in the development of a pro-inflammatory state.

The accumulation of cardiac fat is also associated with higher levels of pro-inflammatory cytokines such as IL-6, IL-1, TNF-α, and the expression of adipokine fatty acid-binding protein 4 FABP4 that are associated with the development of MetS and the extent of coronary artery disease [ 27 , 28 ].

Hence, overall fat measurement should not be underestimated. For example, in a cohort of 1, Italian children and adolescents However, BMI cannot discriminate between lean body mass and fat mass; hence, BMI is not necessarily an appropriate parameter of excessive adiposity.

Body fat distribution may be more valuable than overall adiposity in the prediction of metabolic alterations. This aligns with the concept of an obesity paradox whereby subjects with higher BMI levels were shown to have lower levels of cardiovascular events [ 30 ].

Obesity induced alterations in body composition include both an increase in adipose and in low-density lean tissue, without an increment in normal- lean density tissue, suggesting a fatty infiltration of muscular tissue [ 31 ].

Furthermore, studies in Colombian adults have demonstrated that individuals with a high BMI due to higher muscle mass have a lower risk of CVD than individuals with the same BMI due to elevated adipose mass [ 32 ]. This highlights that not only adipose tissue influences insulin action, other tissues such as muscle and hepatic tissue also affect this interaction.

Therefore, in our population, WC continues to be the most applicable, easy to perform anthropometric indicator of adiposity and predictor of metabolic alterations and CV risk. Furthermore, rather than a specific weight value, the cardiometabolic dysfunction produced by the adipose tissue's inflammation and its involvement in the muscle tissue should be managed.

Few studies have examined associations between strength, adiposity, and MetS or its components in adults in low and middle-income countries and considered its association with CVD and mortality [ 1 ].

The PURE study, a large international prospective cohort that included the present population, demonstrated an association between low HGS and CVD and all-cause mortality in the population as a whole [ 9 ]. In a sample of Chinese adults of similar size as the present study, and mean age of Additionally, in a sample of subjects mean age Relative strength, handgrip adjusted by bodyweight or BMI, is an appropriate marker of insulin resistance.

Several levels of evidence support the notion that muscle strength is protective, and more so than muscle mass [ 39 , 40 ].

Prospective studies have established that low muscle strength, typically characterized using handgrip dynamometry, is predictive of cardiometabolic risk and mortality, independent of aerobic fitness and physical activity [ 9 , 41 ].

Furthermore, intervention studies also consistently show benefits of strength training on components of MetS and other relevant markers of CVD risk, such as C-reactive protein [ 43 ]. This is particularly relevant in low and middle-income countries on the basis that in these regions 1 there are steeper increases in the burden of chronic disease in low and middle-income countries [ 45 ] 2 lower muscle strength is reported compared to high -income countries [ 9 ] and 3 the protective effect of muscle strength on cardiometabolic health may be accentuated in individuals with lower birth weight, an indicator or poorer early life nutrition and a more common phenotype in the lower socioeconomic status within middle-income countries [ 26 ].

Considering the association between MetS cluster metabolic alterations and CVD, our findings suggest that public health strategies should not only focus on adiposity but also identify and address lower muscular strength in our population [ 10 , 46 ].

Our study has the limitation of cross-sectional analyses, in that we demonstrated associations between adiposity, strength, and MetS in our population without establishing causality in these associations. We did not use body composition methods such as bioimpedance or dual-energy X-ray absorptiometry that estimate muscle and fat mass.

Therefore, quantifying relative muscle strength in an individual through the simple, quick and low-cost measurement of handgrip dynamometry in addition to the classic anthropometric measurements of adiposity i.

Having greater muscle strength could be a protective factor against the metabolic alterations that constitute this syndrome. Handgrip strength is also associated with frailty and other non-cardiometabolic related chronic physical and mental health outcomes [ 47 ], so from a clinical perspective it can also contribute to the wider a screening of patient health.

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BMC Pediatr. Article PubMed PubMed Central CAS Google Scholar. Samples were sent to a commercial laboratory Quest Diagnostics, Lyndhurst, New Jersey for analysis. Total cholesterol, HDL cholesterol, low-density lipoprotein cholesterol, glucose, and triglyceride levels were measured by using standard laboratory procedures.

We used the definition of metabolic syndrome described by de Ferranti et al BMI and waist circumference cutpoints established by Katzmarzyk et al 8 were applied to identify participants who had metabolic syndrome.

Table 1 displays the age- and ethnicity-specific cutpoints for BMI and waist circumference. We calculated means of the continuous variables and frequencies of the categorical variables and used the data to describe CVD risk factor characteristics of participants with and without metabolic syndrome.

We conducted χ 2 tests to determine whether the distributions of participants with and without metabolic syndrome varied from participants with BMI and waist circumference above and below the cutpoints 8. We performed logistic regression analysis to determine whether measurements exceeding the BMI and waist circumference cutpoints were predictive of metabolic syndrome.

We performed stepwise logistic regression to determine whether BMI or waist circumference was a stronger predictor of metabolic syndrome. All logistic regression analyses were adjusted for age and ethnicity.

Data were analyzed by using SAS version 9. No demographic differences existed between participants who did and who did not provide blood samples. Characteristics of the participants are presented in Table 2. This demographic composition was similar to the overall school population.

Most participants were approximately aged 14 years. Significant differences were found between girls with metabolic syndrome and girls without metabolic syndrome for triglyceride level, HDL cholesterol level, fasting blood glucose level, BMI, waist circumference, systolic blood pressure, and diastolic blood pressure Table 3.

Participants with a BMI above the cutpoint were more likely than participants with a BMI below the cutpoint to have metabolic syndrome adjusted odds ratio [AOR], Participants with a waist circumference above the cutpoint were more likely than participants with a waist circumference below the cutpoint to have metabolic syndrome AOR, Waist circumference significantly predicted metabolic syndrome AOR, BMI and waist circumference cutpoints previously used to identify CVD risk factor clustering were successful in identifying metabolic syndrome in adolescent girls.

Because waist circumference had a stronger predictive ability than did BMI in identifying girls with metabolic syndrome, it should be used to screen adolescent girls for metabolic syndrome.

Previous international studies of children and adolescents and American studies of prepubescent children 20 have demonstrated that high waist circumference or high intra-abdominal adiposity is a better predictor of metabolic syndrome than is BMI. A few studies have reported the association between clustering of risk factors for metabolic syndrome and BMI or waist circumference in American adolescents 21,22 , but none have examined metabolic syndrome in American adolescents.

Our results add to the body of knowledge on American adolescents by confirming that the waist circumference cutpoint identified risk for metabolic syndrome in adolescents and that a waist circumference that exceeded the cutpoint was a stronger predictor of metabolic syndrome in adolescents than was a BMI that exceeded the cutpoint.

Waist circumference may be a better predictor than BMI in detecting metabolic syndrome because of its association with increased visceral adipose tissue. Increased visceral adipose tissue is associated with insulin resistance, glucose intolerance, and abnormal lipid profiles and is an independent predictor for the development of type 2 diabetes in adults The metabolic activity of visceral fat can increase free fatty acid circulation, decrease insulin uptake by the liver, increase circulating insulin levels, and ultimately lead to glucose intolerance Furthermore, insulin resistance impairs the breakdown of triglycerides, which in turn stimulates the production of other atherogenic lipoproteins and decreases HDL cholesterol levels Although visceral adipose tissue increases the chance that adults will develop metabolic syndrome 25 , this relationship is more difficult to establish in adolescents.

Because of hormonal and maturational differences between adolescents and adults, adolescents tend to have lower visceral fat deposits than do adults However, visceral fat is acquired during maturation in proportion to the increase in general body fat, which may suggest that visceral fat accumulation is as relevant a risk factor in adolescence as it is in adulthood Thus, waist circumference as a measure of central adiposity may be a more specific and sensitive tool than BMI in identifying multiple elevated risk factors for CVD, and specifically metabolic syndrome, in adolescents.

The Katzmarzyk cutpoints and the metabolic syndrome definition include a measure of waist circumference. However, the Katzmarzyk cutpoints and the definition for metabolic syndrome are different because they were developed on the basis of 2 different outcomes.

For example, in terms of risk factor clustering, a year-old African American girl has a waist circumference cutpoint of In contrast, the waist circumference criterion used for the metabolic syndrome definition acts as a threshold to indicate the presence of a single risk factor Using the de Ferranti et al definition of metabolic syndrome 12 , the waist circumference criterion is identified as being above the 75th percentile determined by age and sex Thus, for a year-old African American girl, the waist circumference threshold is Meeting this criterion is insufficient to classify metabolic syndrome, as she has met only 1 of the 5 criteria for diagnosing metabolic syndrome.

Therefore, the cutpoints from Katzmarzyk et al 8 predict the point at which cardiovascular clustering is more likely to occur, whereas the de Ferranti et al definition for meeting the waist circumference cutpoint 12 only identifies 1 of the 5 criteria for meeting the definition of metabolic syndrome.

Multiple definitions of metabolic syndrome make it difficult to directly compare population prevalence among studies. However, Thus, the prevalence of metabolic syndrome in our sample is comparable, although slightly lower, to previous work done among overweight adolescents.

Therefore, we did not conduct a sensitivity or specificity analysis to evaluate optimal cutpoints. Future work should consider the use of the Katzmarzyk et al cutpoints relative to those determined from a sensitivity analysis in adolescent populations 8.

If the Katzmarzyk cutpoints are reasonably approximated, they may be useful tools for clinical screening for metabolic syndrome 8. Although BMI is easy to measure, the measurement requires using a calibrated scale, obtaining the height and weight of a participant, and performing a calculation.

Removal of shoes and heavy clothing is required by the participant. In contrast, waist circumference is easier to obtain because it is a single measurement, requiring only an inexpensive tape measure.

Obtaining the measurement poses minimal participant burden because clothing needs to be removed only from the abdominal area. Waist circumference is easy to measure, fast, cheap, and highly reproducible Therefore, the Katzmarzyk waist circumference cutpoints for adolescent girls 8 are preferred to the BMI cutpoints, both in their predictive ability and practical application, for screening for metabolic syndrome.

This sample included a high percentage of African American girls, which may limit the generalizability to other adolescents. However, a high percentage of young African American girls are overweight 31 , have reduced insulin sensitivity 32 , have at least 1 risk factor for metabolic syndrome 33 , and have type 2 diabetes Therefore, we could apply the Katzmarzyk cutpoints and established statistical associations in this at-risk population, which provided sufficient evidence of metabolic syndrome 8.

The narrow age range years of participants also limited the generalizability of our results. Both BMI and waist circumference cutpoints accurately detected metabolic syndrome in a predominantly African American sample of female adolescents. Waist circumference was a better predictor of metabolic syndrome than was BMI.

Therefore, health care practitioners should routinely measure waist circumference when screening adolescents. Corresponding Author: Sarah M.

Camhi, PhD, University of Maryland, College Park School of Public Health, Department of Kinesiology, HHP Building, College Park, MD Telephone: E-mail: smcamhi umd.

Author Affiliations: JoAnn Kuo, Deborah R. Young, University of Maryland, College Park School of Public Health, Department of Kinesiology, College Park, Maryland. Abbreviations: SD, standard deviation; BMI, body mass index.

b According to the international definition of childhood overweight and obesity Abbreviations: SD, standard deviation; MS, metabolic syndrome; HDL cholesterol, high-density lipoprotein cholesterol.

a Mean values adjusted for age and ethnicity. b P values calculated by using general linear modeling and adjusted for age and ethnicity. P values determine statistical significance between girls with and without MS. Abbreviations: MS, metabolic syndrome; HDL cholesterol, high-density lipoprotein cholesterol; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Participants rested in a seated position for 5 minutes before measurements were taken. Three blood pressure measurements were taken, with a 1-minute interval between measurements, and averaged. Three second peripheral pulse rates were also taken and averaged.

Samples were sent to a commercial laboratory Quest Diagnostics, Lyndhurst, New Jersey for analysis. Total cholesterol, HDL cholesterol, low-density lipoprotein cholesterol, glucose, and triglyceride levels were measured by using standard laboratory procedures.

We used the definition of metabolic syndrome described by de Ferranti et al BMI and waist circumference cutpoints established by Katzmarzyk et al 8 were applied to identify participants who had metabolic syndrome. Table 1 displays the age- and ethnicity-specific cutpoints for BMI and waist circumference.

We calculated means of the continuous variables and frequencies of the categorical variables and used the data to describe CVD risk factor characteristics of participants with and without metabolic syndrome. We conducted χ 2 tests to determine whether the distributions of participants with and without metabolic syndrome varied from participants with BMI and waist circumference above and below the cutpoints 8.

We performed logistic regression analysis to determine whether measurements exceeding the BMI and waist circumference cutpoints were predictive of metabolic syndrome.

We performed stepwise logistic regression to determine whether BMI or waist circumference was a stronger predictor of metabolic syndrome. All logistic regression analyses were adjusted for age and ethnicity.

Data were analyzed by using SAS version 9. No demographic differences existed between participants who did and who did not provide blood samples. Characteristics of the participants are presented in Table 2.

This demographic composition was similar to the overall school population. Most participants were approximately aged 14 years. Significant differences were found between girls with metabolic syndrome and girls without metabolic syndrome for triglyceride level, HDL cholesterol level, fasting blood glucose level, BMI, waist circumference, systolic blood pressure, and diastolic blood pressure Table 3.

Participants with a BMI above the cutpoint were more likely than participants with a BMI below the cutpoint to have metabolic syndrome adjusted odds ratio [AOR], Participants with a waist circumference above the cutpoint were more likely than participants with a waist circumference below the cutpoint to have metabolic syndrome AOR, Waist circumference significantly predicted metabolic syndrome AOR, BMI and waist circumference cutpoints previously used to identify CVD risk factor clustering were successful in identifying metabolic syndrome in adolescent girls.

Because waist circumference had a stronger predictive ability than did BMI in identifying girls with metabolic syndrome, it should be used to screen adolescent girls for metabolic syndrome.

Previous international studies of children and adolescents and American studies of prepubescent children 20 have demonstrated that high waist circumference or high intra-abdominal adiposity is a better predictor of metabolic syndrome than is BMI.

A few studies have reported the association between clustering of risk factors for metabolic syndrome and BMI or waist circumference in American adolescents 21,22 , but none have examined metabolic syndrome in American adolescents.

Our results add to the body of knowledge on American adolescents by confirming that the waist circumference cutpoint identified risk for metabolic syndrome in adolescents and that a waist circumference that exceeded the cutpoint was a stronger predictor of metabolic syndrome in adolescents than was a BMI that exceeded the cutpoint.

Waist circumference may be a better predictor than BMI in detecting metabolic syndrome because of its association with increased visceral adipose tissue. Increased visceral adipose tissue is associated with insulin resistance, glucose intolerance, and abnormal lipid profiles and is an independent predictor for the development of type 2 diabetes in adults The metabolic activity of visceral fat can increase free fatty acid circulation, decrease insulin uptake by the liver, increase circulating insulin levels, and ultimately lead to glucose intolerance Furthermore, insulin resistance impairs the breakdown of triglycerides, which in turn stimulates the production of other atherogenic lipoproteins and decreases HDL cholesterol levels Although visceral adipose tissue increases the chance that adults will develop metabolic syndrome 25 , this relationship is more difficult to establish in adolescents.

Because of hormonal and maturational differences between adolescents and adults, adolescents tend to have lower visceral fat deposits than do adults However, visceral fat is acquired during maturation in proportion to the increase in general body fat, which may suggest that visceral fat accumulation is as relevant a risk factor in adolescence as it is in adulthood Thus, waist circumference as a measure of central adiposity may be a more specific and sensitive tool than BMI in identifying multiple elevated risk factors for CVD, and specifically metabolic syndrome, in adolescents.

The Katzmarzyk cutpoints and the metabolic syndrome definition include a measure of waist circumference. However, the Katzmarzyk cutpoints and the definition for metabolic syndrome are different because they were developed on the basis of 2 different outcomes.

For example, in terms of risk factor clustering, a year-old African American girl has a waist circumference cutpoint of In contrast, the waist circumference criterion used for the metabolic syndrome definition acts as a threshold to indicate the presence of a single risk factor Using the de Ferranti et al definition of metabolic syndrome 12 , the waist circumference criterion is identified as being above the 75th percentile determined by age and sex Thus, for a year-old African American girl, the waist circumference threshold is Meeting this criterion is insufficient to classify metabolic syndrome, as she has met only 1 of the 5 criteria for diagnosing metabolic syndrome.

Therefore, the cutpoints from Katzmarzyk et al 8 predict the point at which cardiovascular clustering is more likely to occur, whereas the de Ferranti et al definition for meeting the waist circumference cutpoint 12 only identifies 1 of the 5 criteria for meeting the definition of metabolic syndrome.

Multiple definitions of metabolic syndrome make it difficult to directly compare population prevalence among studies. However, Thus, the prevalence of metabolic syndrome in our sample is comparable, although slightly lower, to previous work done among overweight adolescents. Therefore, we did not conduct a sensitivity or specificity analysis to evaluate optimal cutpoints.

Future work should consider the use of the Katzmarzyk et al cutpoints relative to those determined from a sensitivity analysis in adolescent populations 8.

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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The relationship between WC, weight, and BMI can be conceptualized by using simple geometric relationships that consider the body as a cylinder; WC is the cylinder's circumference, height is its length, and weight is a measure of mass.

Therefore, BMI provides information about body volume and mass, and WC provides information about body shape. In general, BMI and WC are highly correlated, typically with r values in the range of 0.

The relationships among WC, BMI, and adipose tissue compartments in primarily Caucasian and African-American men and women are shown in Table 2 These data demonstrate that both BMI and WC are strongly correlated with total body adipose tissue mass but that WC is a better predictor of IAAT than is BMI.

Assessment of WC provides a measure of fat distribution that cannot be obtained by measuring BMI. However, there is no standardized approach for measuring WC and different anatomical landmarks have been used to measure WC in different studies.

Moreover, the measurement site that provides the best correlation with disease risk and best reflects changes in abdominal adipose tissue mass has not been established. Nonetheless, the precision of WC measurement is high at any given landmark. Even self-measurement can be highly reproducible when performed by properly trained subjects, although self-measurement results in an underestimation of true WC.

Measurement of WC cannot determine the individual contributions of SAAT and IAAT to abdominal girth, which require imaging by MRI or CT. The value of these scanning techniques in clinical practice has not been determined.

It is not known whether the storage of an absolute or relative excess amount of triglycerides in abdominal fat depots is directly responsible for increased disease risk or whether such deposition is simply associated with other processes that cause risk, or both.

In addition, WC values provide a measure of both SAAT and IAAT masses. Therefore, the relationship between WC and cardiometabolic risk cannot determine whether risk is associated with SAAT, IAAT, or both. The mechanism s responsible for the relationship between excess abdominal fat distribution and cardiometabolic disease is not known, but several hypotheses have been proposed.

One of the earliest hypotheses that implicated IAAT as a metabolic risk factor suggested that activation of the central nervous system—adrenal axis by environmental stressors caused both the preferential deposition of adipose tissue in the trunk and the cardiovascular and metabolic disorders associated with that deposition Excessive ectopic fat accumulation then causes metabolic dysfunction in those organs.

In fact, increased intrahepatic fat is associated with dyslipidemia and hepatic insulin resistance 23 , and increased intramyocellular fat is associated with skeletal muscle insulin resistance In this paradigm, IAAT is primarily a marker of the magnitude of overflow of fatty acids from subcutaneous depots.

Therefore, increased WC could be a discernible marker of a system-wide impairment in energy storage regulation, in which an increase in IAAT reflects a reduced capacity for energy storage in other adipose tissues. A third hypothesis proposes a direct effect of omental and mesenteric adipose tissue depots on insulin resistance, lipoprotein metabolism, and blood pressure.

Metabolic products of omental and mesenteric adipose tissue depots are released into the portal vein, which provides direct delivery to the liver. Lipolysis of omental and mesenteric adipose tissue triglycerides release free fatty acids that can induce hepatic insulin resistance and provide substrate for lipoprotein synthesis and neutral lipid storage in hepatocytes.

In addition, specific proteins and hormones produced by omental and mesenteric adipose tissue, such as inflammatory adipokines, angiotensinogen, and cortisol generated by local activity of 11 β-hydroxysteroid dehydrogenase , can also contribute to cardiometabolic disease.

A fourth hypothesis is that genes that predispose to preferential deposition of fat in abdominal depots independently cause cardiometabolic disease.

These hypotheses are not mutually exclusive, and it is possible that all, and other unknown mechanisms, are involved in the association between abdominal fat mass and adverse metabolic consequences. The importance of WC in predicting cardiometabolic risk factors e.

Specific relative risks between WC and these outcomes vary, depending on the population sampled and the outcome measured. The relationship between WC and clinical outcome is consistently strong for diabetes risk, and WC is a stronger predictor of diabetes than is BMI. The relative risk of developing diabetes between subjects in the highest and lowest categories of reported WC often exceeds 10 and remains statistically significant after adjusting for BMI.

These data demonstrate that WC can identify persons who are at greater cardiometabolic risk than those identified by BMI alone. Values for WC are also consistently related to the risk of developing CHD, and the relative risk of developing CHD between subjects in the highest and lowest categories of WC ranges from 1.

Values for WC are usually strongly associated with all-cause and selected cause-specific mortality rates. Data from several studies support the notion that WC is an important predictor of diabetes, CHD, and mortality rate, independent of traditional clinical tests, such as blood pressure, blood glucose, and lipoproteins 7 , However, there is not yet a compelling body of evidence demonstrating that WC provides clinically meaningful information that is independent of well-known cardiometabolic risk factors.

WC is an important predictor of health outcomes in men and women; Caucasians, African Americans, Asians, and Hispanics; and adults of all age-groups. In fact, the relationship between WC and health outcome changes much less with increasing age than does the relationship between BMI and health outcome The shape of the relationship between WC and health outcomes e.

Data from most studies suggest that the shape of the relationship between WC and health outcome lends itself to identifying clinically meaningful cut point values because risk often accelerates monotonically above, and can be relatively flat below, a specific WC value.

Optimum WC cut points will likely vary according to the population studied, the health outcome of interest, and demographic factors. Data from most clinical weight loss and exercise training trials have shown that reductions in WC occur concomitantly with reductions in obesity-related cardiometabolic risk factors and disease.

However, these results do not prove that the reduction in WC was responsible for the beneficial effect on health outcome. Additional studies are needed to evaluate the effect of decreasing WC on cardiometabolic outcomes.

The panel concluded that determining whether waist circumference should be measured in clinical practice depends on the responses to the following four key questions:.

Health care personnel and even patients themselves, who are given appropriate training in technique, can provide highly reproducible measurements of WC in men and women. However, it is not know whether measurement of one anatomical site is a better indicator of cardiometabolic risk than measurement at other sites.

Does waist circumference provide: a good prediction of diabetes, CHD, and mortality rate? Answer: Yes ; b incremental value in predicting diabetes, CHD, and mortality rate above and beyond that provided by BMI?

Answer: Yes ; c sufficient incremental value in these predictions above and beyond that offered by BMI and commonly evaluated cardiometabolic risk factors, such as blood glucose concentration, lipid profile and blood pressure?

Answer: Uncertain. Data from many large population studies have found waist circumference to be a strong correlate of clinical outcome, particularly diabetes, and to be independent of BMI.

In addition, data from a limited number of studies demonstrates that WC remains a predictor of diabetes, CHD, and mortality rate, even after adjusting for BMI and several other cardiometabolic risk factors. Additional studies are needed to confirm that WC remains an independent predictor of risk.

Answer: Yes. It is not known what portion of subjects who had a large WC would have been identified as having increased cardiometabolic risk based on findings from a standard medical evaluation.

Answer: Probably not. Measurement of WC in clinical practice is not trivial, because providing this assessment competes for the limited time available in a busy office practice and requires specific training to ensure that reliable data are obtained.

Therefore, waist circumference should only be measured if it can provide additional information that influences patient management. Based on NHANES III data, However, it is likely that different WC cut point values could provide more useful clinical information.

For example, an analysis of data obtained from the NHANES III and the Canadian Heart Health Surveys found that BMI-specific WC cut points provided a better indicator of cardiometabolic risk than the recommended WC thresholds For normal-weight BMI Waist circumference provides a unique indicator of body fat distribution, which can identify patients who are at increased risk for obesity-related cardiometabolic disease, above and beyond the measurement of BMI.

Therefore, the clinical usefulness of measuring WC, when risk is based on the currently accepted guidelines, is limited. However, WC measurement can sometimes provide additional information to help the clinician determine which patients should be evaluated for the presence of cardiometabolic risk factors, such as dyslipidemia, and hyperglycemia.

In addition, measuring WC can be useful in monitoring a patient's response to diet and exercise treatment because regular aerobic exercise can cause a reduction in both WC and cardiometabolic risk, without a change in BMI Further studies are needed to establish WC cut points that can assess cardiometabolic risk, not adequately captured by BMI and routine clinical assessments.

Nonetheless, it should be possible to determine more useful WC cut points than are currently recommended, by carefully reviewing published data and reevaluating datasets available from existing population studies. These additional analyses will define the future role of WC measurement in clinical practice.

Adapted from reference Data are correlation coefficients. has received research grants from Frito-Lay and OMP; has served as a consultant to Kraft Foods, Pfizer, Bristol-Myers Squibb, and Bio Era; and has received financial support from Lilly, Pfizer, Merck Pharmaceutical Company, Unilever, Coca-Cola, General Mills, International Life Sciences Institute, GlaxoSmithKline, OMP, Jansen Pharmaceuticals, and Frito-Lay.

has received research grants from Sanofi-Aventis, Merck, and Takeda for clinical trials; has served as a consultant to Sanofi-Aventis, Amylin Pharmaceuticals, EnteroMedics, Dannon-Yakult, and Merck Pharmaceutical Company. is an employee of Merck Pharmaceutical Company. A table elsewhere in this issue shows conventional and Système International SI units and conversion factors for many substances.

The costs of publication of this article were defrayed in part by the payment of page charges. C Section solely to indicate this fact.