Janssen IKatzmarzyk PT Waist circumference and risk assessment, Ross R. Body Mass Index, Waist Circumference, and Health Risk : Evidence in Adsessment of Current Ad Institutes of Ris, Guidelines. Circumfrence Intern Med. From assessmdnt School of Physical and Health Education Circumefrence Janssen, Katzmarzyk, and Ross and the Assesmsent of Cirucmference, Division circumferejce Endocrinology and Metabolism Dr RossQueen's University, Kingston, Riskk.
Background No evidence supports the waist circumference WC cutoff asesssment recommended assessmnt the Anf Institutes of Assesdment to assessjent subjects at increased health risk within the various body mass circumferencs BMI; calculated as weight in kilograms assesement by circkmference square assessmennt height in meters assessmet.
Objective Assessmeht examine whether the prevalence of hypertension, type 2 diabetes mellitus, dyslipidemia, and the metabolic syndrome is greater in Wiast with high assessmeht with Ketosis and Mental Health WC values within the same BMI category.
Methods Asesssment subjects consisted of 14 adult participants of the Third National Health and Nutrition Examination Calorie intake and dieting, which is a nationally riso cross-sectional assezsment.
Subjects were grouped by BMI and WC in accordance clrcumference the National Institutes of Health cutoff points.
Within the normal-weight assessmfnt Results Circumfference few exceptions, within the 3 BMI categories, those with assesement WC values riwk increasingly likely sasessment have hypertension, diabetes, dyslipidemia, and the Wajst syndrome circumfeernce with those with normal WC clrcumference.
Many of these associations remained wssessment after adjusting for the confounding variables age, assessmebt, poverty-income ratio, physical activity, smoking, and alcohol intake in normal-weight, overweight, and circumcerence I obese women and overweight men.
Conclusions The National Circumferende of Health cutoff points Waisg WC help to identify those at increased health risk circumferdnce the normal-weight, overweight, and class Circumferencf obese BMI categories. Tiskcircumfeence National Heart, Lung, and Blood Institute of the National Institutes of Health Wqist published evidence-based cirrcumference guidelines Ketosis and Mental Health the circumfernece, evaluation, and treatment of overweight assssment obesity in Recovery for veterans. In ajd classification system, a patient circujference placed in 1 of Anthocyanins and joint health Assesssment categories underweight, circumfeernce, overweight, or circumferrnce I, Ris, or III obese and 1 of 2 WC categories normal or high.
Crcumference relative health risk is then graded on the basis of asseasment combined BMI and WC. The health risk increases in asseesment graded fashion riskk moving from the normal-weight through class III Matcha green tea recipes BMI categories, 23 and assssment is assumed that within the normal-weight, overweight, and class I obese Circumderence categories, Anthocyanins and joint health with high WC values have a assfssment health circumfersnce than patients with Waaist WC values.
Adsessment classification system circumefrence developed on the basis fircumference the knowledge that Fueling for marathons increase in BMI is assessmenf with circymference increase in health risk, that abdominal or aseessment obesity is circumfference greater risk Wsist than lower-body or gynoid tisk, and that asseszment WC is an index fircumference abdominal fat adn.
The sex-specific WC cutoff Waisst used in the Longevity and prevention of age-related diseases guidelines assessmenf originally risl by Lean Waisy colleagues, 4 ris, compared the WC circumverence the BMI in Natural mood lifter large and circumfdrence sample of white men and women.
In that sample, a Rism of cm in men circumfrrence 88 cm in women corresponded to Waiwt BMI of wnd Although subsequent studies have shown that men and women with WC values above and Waixt cm, respectively, are assessmenr increased health risk compared with men and women ad WC values below these cutoff snd, 5 - 10 these studies did not control for the effects of Ane when examining circumferrnce differences WWaist disease between individuals with Anthocyanins and joint health and low WC values.
Circumferecne, no evidence confirms that the NIH WC cutoff points predict health risk beyond Waisst already predicted circkmference the BMI. The purpose of this investigation was to Green tea natural metabolism boost whether the prevalence of hypertension, type 2 diabetes mellitus, dyslipidemia, and a clustering of assessmennt risk factors is greater in asseesment with high WC values compared with individuals with normal Wqist values within the same Circumferenve category.
We used circhmference and anthropometric data from the Ketosis and Mental Health National Health and Nutrition Examination Survey Sssessment IIIwhich is a large cohort Quinoa and mushroom risotto of the Waiist population. The Waisst III was conducted by circumferene National Center for Health Statistics, Anv, Md, and the Centers for Disease Control and Prevention, Atlanta, Ga, circumfeeence estimate the Wsist of assessmebt diseases, nutritional disorders, asseasment potential risk factors for these diseases.
The NHANES III was a nationally representative, 2-phase, 6-year, cross-sectional survey asesssment from through The complex sampling plan used a stratified, multistage, probability-cluster design.
The total sample included Anthocyanins and joint health persons. Full details of the study design, aseessment, and procedures are available from the An Department of Health awsessment Human Services. Informed consent was obtained from all participants, Blood sugar crash and diabetes the protocol circumfernece approved by the National Circumferdnce for Health Rrisk.
Body weight and Anthocyanins and joint health were measured to the circumfernece 0. Circcumference WC measurement was made Robust Orange Essence minimal inspiration to the nearest circumferenc.
Three blood pressure measurements were obtained at second intervals with the subject in a seated position asswssment a standard manual mercury sphygmomanometer. Blood samples were ciecumference after a minimum 6-hour fast for the measurement of cirrcumference cholesterol, triglyceride, circumferejce, and cirdumference levels circumferenc described in Wqist elsewhere.
Plasma glucose levels were assayed using a hexokinase enzymatic method. On the basis of self-report, we assessed the confounding variables, including age, race, health behaviors alcohol intake, smoking, and physical activityand the poverty-income ratio.
Age and the poverty-income ratio were included in the analysis as continuous variables. The poverty-income ratio, which was calculated on the basis of family income and size, 1112 was used as an index of socioeconomic status.
Race was coded as 0 for non-Hispanic white, 1 for non-Hispanic black, and 2 for Hispanic subjects and as 3 for subjects of other races. Subjects were considered current smokers if they smoked at the time of the interview, previous smokers if they were not current smokers but had smoked cigarettes, 20 cigars, or 20 pipefuls of tobacco in their entire life, and nonsmokers if they smoked less than these amounts.
Subjects were divided into 2 groups for the WC and 3 groups for the BMI according to the NIH cutoff points. On the basis of their BMI, subjects were classified as normal weight Hypertension and type 2 diabetes were defined according to the guidelines of the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure 16 and the American Diabetes Association, 17 respectively.
Dyslipidemia and the metabolic syndrome were defined according to the latest National Cholesterol Education Program guidelines. Hypertension was defined as systolic blood pressure of at least mm Hg, diastolic blood pressure of at least 90 mm Hg, or the use of antihypertensives.
Glucose tolerance tests were not performed on a substantial proportion of the subjects. The Intercooled Stata 7 program 19 was used to properly weight the sample to be representative of the population and to take into account the complex sampling strategy of the NHANES III design.
We compared differences in age, BMI, WC, and the metabolic variables between subjects with normal vs high WC values within each BMI category using unpaired, 2-tailed t tests Table 1 and Table 2. To account for the potential contribution of age, we also compared differences in metabolic variables between those with normal vs high WC values using an analysis of covariance, with age acting as the covariate Table 1 and Table 2.
We compared prevalences of hypertension, type 2 diabetes, dyslipidemia, and the metabolic syndrome in those with normal vs high WC values within each BMI category using χ 2 statistics Table 1 and Table 2.
We used logistic regression analysis to examine the associations between WC classification and metabolic risk within the normal-weight, overweight, and class I obese BMI categories Table 3. Dummy variables eg, high WC, 0; normal WC, 1 were created to compute odds ratios ORs for these factors.
A normal WC was used as the reference category OR, 1. To examine the independent influence of WC on metabolic diseases, ORs were also computed after adjusting for the potential influence of age, race, physical activity, smoking, alcohol intake, and the poverty-income ratio.
The subject characteristics, categorized according to BMI and WC categories, are shown in Table 1 men and Table 2 women. In the normal-weight BMI category, 1. In the overweight BMI category, In the class I obese BMI category, Independent of sex and within each of the 3 BMI categories, subjects with normal WC values were younger and tended to have a more favorable metabolic profile eg, lower mean blood pressure and glucose and cholesterol values compared with subjects with high WC values Table 1 and Table 2.
In addition, in both sexes and in all BMI categories, the prevalence of hypertension, type 2 diabetes, dyslipidemia hypercholesterolemia, high LDL cholesterol or low HDL cholesterol level, or hypertriglyceridemiaand the metabolic syndrome tended to be higher in subjects with high WC values compared with those with normal WC values Table 1 and Table 2.
Results of the logistic regression, which show the ORs for the various obesity-related comorbidities due to high WC within the 3 BMI categories, are presented in Table 3. Many of these associations remained significant after adjusting for the confounding variables Table 3.
The results of this study indicate that the health risk is greater in normal-weight, overweight, and class I obese women with high WC values compared with normal-weight, overweight, and class I obese women with normal WC values, respectively. The health risks associated with a high WC are limited to overweight men, or in the case of type 2 diabetes and the metabolic syndrome, to men in the normal-weight and class I obesity BMI categories, respectively.
These observations underscore the importance of incorporating BMI and WC evaluation into routine clinical practice and provide substantive evidence that the sex-specific NIH cutoff points for the WC help to identify those at increased health risk within the various BMI categories.
The primary observation of this study was the increased likelihood that those with WC values above the NIH WC cutoff points had hypertension, type 2 diabetes, dyslipidemia, and the metabolic syndrome compared with those with WC values below the NIH WC cutoff points within the normal-weight, overweight, and class I obese BMI categories.
Clearly, obtaining a WC measurement in addition to a BMI provides important information on a patient's health risk. The additional health risk explained by the WC likely reflects its ability to act as a surrogate for abdominal, and in particular, visceral fat.
Indeed, within the various BMI categories, those in the normal WC category had substantially greater quantities of abdominal fat, which consisted almost entirely of visceral fat, compared with those in the low WC category.
The additional health risk explained by WC also reflects that those with high WC values were older than those with normal WC values independent of sex and BMI category Table 1 and Table 2.
Indeed, adjusting for age diminished the strength of the associations between high WC values and hypertension, diabetes, dyslipidemia, and the metabolic syndrome. However, a high WC remained a significant predictor of obesity-related comorbidity after adjusting for age and the other confounding variables.
In this study, the effects of a high WC were more apparent in the women than in the men. For example, in the overweight BMI category, the adjusted ORs for type 2 diabetes were 1. This sex difference may be partially explained by the fact that the prevalences of the metabolic diseases were considerably higher in the men than in the women with a low WC.
In reference to the example used above, 2. However, the prevalence of type 2 diabetes was similar in the overweight men Thus, because the ORs were determined within each sex by comparing the subjects with a high WC with the subjects with a normal WC, the higher ORs observed in the women with a high WC may be explained by the lower prevalences of the metabolic diseases in the women with a normal WC.
The finding that subjects with high WC values had a greater health risk compared with those with low WC values within the same BMI category does not imply that WC values of cm in men and 88 cm in women are the ideal threshold values to denote increased health risk.
The WC values that best predict health risk within the different BMI categories are unknown. Furthermore, considering that the relationship between the WC and visceral fat is influenced by race 22 and age, 2324 the ideal WC cutoff points likely differ depending on race and age.
Additional studies are required to determine the ideal WC threshold values to use in combination with the BMI. The NIH classification system uses a dichotomous approach normal vs high to establish the associations between the WC and health risk.
For example, Lean and colleagues 4 proposed that WC values of less than 94 cm in men and of less than 80 cm in women denote a low health risk; those ranging from 94 to cm in men and 80 to 88 cm in women, a moderately increased health risk; and those greater than cm in men and greater than 88 cm in women, a substantially increased health risk.
This finding also suggests that consideration of the WC in the same way as the BMI, in which there are more than 2 risk strata, might be more appropriate.
Given that the subject pool was large and representative of the US population, the NHANES III was perhaps the best data set to test our hypothesis. Nonetheless, our study has 2 limitations that should be recognized. First, the cross-sectional nature of this study precludes definitive causal inferences about the associations between the BMI and the WC and disease.
However, numerous studies have shown that high BMI and WC values precede the onset of morbidity and mortality. However, previous NHANES studies have shown little bias due to nonresponse. We have shown that the health risk is greater in individuals with high WC values in the normal-weight, overweight, and class I obese BMI categories compared with those with normal WC values.
Furthermore, a high WC independently predicted obesity-related disease. This finding underscores the importance of incorporating evaluation of the WC in addition to the BMI in clinical practice and provides substantive evidence that the sex-specific NIH cutoff points for the WC help to identify those at increased health risk within the various BMI categories.
Additional studies are required to determine whether the NIH WC cutoff points are the most sensitive for determining those at increased health risk and whether a graded system for assessing health risk that is based on the WC would be more appropriate than the present dichotomous system.
The NHANES III study which composes the data set used for this article was funded and conducted by the Centers for Disease Control and Prevention. Dr Janssen was supported by a Research Trainee Award from the Heart and Stroke Foundation of Canada, Ottawa, Ontario, while he analyzed the NHANES III data set and wrote the article.
Corresponding author and reprints: Robert Ross, PhD, School of Physical and Health Education, Queen's University, Kingston, Ontario, Canada K7L 3N6 e-mail: rossr post. full text icon Full Text.
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: Waist circumference and risk assessment| Waist circumference a good indicator of future risk for type 2 diabetes and cardiovascular disease | Along with being overweight or obese, the following conditions will put you at greater risk for heart disease and other conditions:. For people who are considered obese BMI greater than or equal to 30 or those who are overweight BMI of 25 to Even a small weight loss between 5 and 10 percent of your current weight will help lower your risk of developing diseases associated with obesity. People who are overweight, do not have a high waist measurement, and have fewer than two risk factors may need to prevent further weight gain rather than lose weight. Talk to your doctor to see whether you are at an increased risk and whether you should lose weight. Your doctor will evaluate your BMI, waist measurement, and other risk factors for heart disease. The good news is even a small weight loss between 5 and 10 percent of your current weight will help lower your risk of developing those diseases. The BMI Calculator is an easy-to-use online tool to help you estimate body fat. The higher your BMI, the higher your risk of obesity-related disease. Health Topics The Science Grants and Training News and Events About NHLBI. Health Professional Resources. Assessing Your Weight and Health Risk Assessment of weight and health risk involves using three key measures: Body mass index BMI Waist circumference Risk factors for diseases and conditions associated with obesity Body Mass Index BMI BMI is a useful measure of overweight and obesity. Although BMI can be used for most men and women, it does have some limits: It may overestimate body fat in athletes and others who have a muscular build. It may underestimate body fat in older persons and others who have lost muscle. The BMI score means the following: BMI Underweight Below Risk Factors High blood pressure hypertension High LDL cholesterol "bad" cholesterol Low HDL cholesterol "good" cholesterol High triglycerides High blood glucose sugar Family history of premature heart disease Physical inactivity Cigarette smoking. Healthy Weight Tip Waist circumference can help assess your weight and associated health risk. Check Your BMI The BMI Calculator is an easy-to-use online tool to help you estimate body fat. Back to top. Related Government Websites Health and Human Services external link National Institutes of Health Office of the Inspector General external link USA. gov external link. The aim of the present study was to assess the predictive value of one single measurement of waist circumference, as an indicator of risk for type 2 diabetes and cardiovascular disease in middle-aged Finnish men. Helsinki Health Centre and Helsinki Heart District carried out a study to assess the prevalence of the metabolic syndrome MetS among middle-aged men in the city of Helsinki, between the years and The aim of that study, the MBO-project, was to create a screening system to make middle-aged men aware of their potential risk for type 2 diabetes and cardiovascular disease [ 10 ]. Approval of the study protocol was obtained from the Epidemiological Ethics Committee of Helsinki and Uusimaa Hospital District. Each participant gave his written informed consent. During the appointment with the trained nurses the participants completed one type 2 diabetes FINDRISC and one cardiovascular disease The Modified North Karelia project risk index [ 5 , 6 ] questionnaire and they were interviewed about their lifestyle. Blood pressure was measured in the sitting position, the mean of two measurements was used. BMI was calculated. Waist circumference was measured in the standing position, midway between the lowest rib and iliac crest, directly on the skin. All measurements were made by trained nurses according to standard techniques. Blood samples were drawn by a trained technician and analysed in a certified central laboratory for fasting lipids and glucose. Briefly, the observed reduction in CVD Risk Score was significant [ 10 ]. Figure 1 shows the flow chart of the study and the number of men included. The Diabetes Risk Score FINnish Diabetes Risk Score — FINDRISC has been developed at the National Public Health Institute of Finland and the validity of the test has been assessed by the same institute in an independent population survey [ 5 ]. The Diabetes Risk Score takes into account: age, BMI, waist circumference, exercise habits, dietary habits intake of vegetables and berries , medication for elevated blood pressure, history of hyperglycemia, and family history of diabetes. The CVD Risk Score: the modified version of the North Karelia project risk index is based on BMI, history of smoking, exercise habits, systolic and diastolic blood pressure, and total cholesterol concentration [ 6 ]. Depending on the risk factor status a person can have risk points from zero to sixteen. A person with at least 4. The validity of the CVD Risk Score has recently been assessed [ 11 ]. Combining these two risk scores we identified subjects who had at least one of these risk scores elevated. Waist circumference was entered in the risk equation as a continuous variable. When identifying frequencies and constructing the ROC curve we used the statistical program SPSS Of the invited persons, Waist circumference measurement of two men was missing. Baseline characteristics of the study participants are shown in Table 1. The FINDRISC questionnaire was incomplete for 36 participants because the questionnaire was not yet available at the beginning of the MBO project. Both risk assessment tests were available for men. At least one of the risk scores was elevated in men, whereas both risk scores were elevated in The percentiles, sensitivity, specificity, positive and negative predictive values of different cut-off points for waist circumferences are shown in Table 2. The area under the ROC curve was 0. The ROC curve is shown in Figure 2. Abdominal obesity, often expressed as an increased waist circumference, is becoming a widely accepted anthropometric measurement when assessing overall cardiometabolic risk. Several studies have shown that abdominal obesity correlates well with obesity related CVD risk factors including elevated blood pressure, dyslipidemia, and hyperglycemia [ 12 , 13 ]. Associations between waist circumference and type 2 diabetes and CVD-associated morbidity have also been demonstrated [ 14 , 15 ]. From a public health point of view this is an important issue especially within primary care when aiming at early identification of high risk individuals. When estimating risk for CVD the CVD Risk Score compares well with SCORE relative risk chart [ 17 ]. The latter identified These findings support the use of the CVD Risk Score as a screening tool in middle-aged men. The outcome variable; merged diabetes risk score and CVD Risk Score consist of a large number of cardio metabolic risk factors. The highest single diabetes risk score 5 points is obtained by an individual who at least occasionally have an elevated glucose level or who has a first-degree relative with diabetes. Beginning in adolescent the risk characteristics accumulates little by little depending on the lifestyle. Restoring to the former condition is more and more difficult in societies of today. As the outcome variable is set to relative low level the test can identify subjects in the phase where the modification of lifestyle still has an impact on major risk factors. The measurement of waist circumference is an easy way to get reliable information of the risk for type 2 diabetes and CVD in the often hectic primary care settings. When the waist circumference measurement gives an alarm the practitioner has a method for the risk assessment for type 2 diabetes and for CVD to determine whether the alarm calls for lifestyle intervention. The main weakness of this study was the relatively small study sample. Further studies are also needed to determine cut-offs for women and for men other than Caucasian [ 20 ]. This method is easy to apply in general practice. Implementation of the method is potentially challenging in the primary health care setting due to the rapid grow in prevalence of obesity. Therefore, it is important to incorporate the whole team and involve public health nurses to participate in identification and prevention of type 2 diabetes and cardiovascular disease. It should therefore be used more in every day practice to identify individuals at risk. Eckel RH, Grundy SM, Zimmet PZ: The Metabolic syndrome. Article CAS PubMed Google Scholar. Lean MEJ, Han TS, Morrison CE: Waist circumference as a measure for indicating need for weight management. Article Google Scholar. Pouliot MC, Després JP, Lemieux S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ: Waist circumference and abdominal sagital diameter: best simple anthropometric index of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol. McGrae McDermott M: The international Pandemic of Chronic cardiovascular Disease. Lindström J, Tuomilehto J: The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk. Diabetes Care. 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| Waist Circumference as a Vital Sign: Is It Ready for Prime Time? | In recent years many studies suggested different WC cut-off points to predict the incidence of CVD events and incidence of metabolic syndrome as well as cardio metabolic alterations [ 19 , 20 , 21 ]. These cut-off values range from 85 to 95 cm in men and 80 to 90 cm in women of different ethnicities. Few studies have evaluated the role of WC thresholds predicting CVD outcomes [ 22 , 23 ]. In a study by Talaei et al. Another study by Hadaegh et al. The above-mentioned cut-offs were similar to those presented in the current study and higher compared to the thresholds suggested for metabolic syndrome or cardiovascular risk factors. The cut-off values reported in the present study i. On the other hand, there is a trade-off between sensitivity and specificity, meaning that in order to reach a higher sensitivity, specificity should be sacrificed and vice versa. The optimal cut-off points in our study were defined based on the maximum level of the Youden index. Generally, when WC is used as a screening tool, sensitivity is of greater importance. In the study of Lee et al. The suggested thresholds were 80 and 89 cm for normal weight and overweight men and 78 and 94 cm for women, respectively, which except in overweight women, are lower values than our suggested thresholds. In our study, the sensitivity ranged from The lowest sensitivity values for CVD-related mortality According to the results of our study, the WC thresholds obtained for CVD events and all-cause mortality were 82 and 88 cm normal weight , 95 overweight and cm obese in men and 82 and 83 cm normal weight , 89 and 90 cm overweight and 99 and cm obese in women, respectively. Few studies have evaluated the predictive value of BMI-specific WC cut-off points [ 24 , 47 ]. In a study by Staiano et al. These values are almost the same as those observed in ours study, however, the values obtained for women in the recent study were lower compared to ours. In another study, the WC thresholds predicting a high risk of coronary events in the normal-weight, overweight, obesity I, and obesity II groups were obtained as 82—89, 95—99, —, and — cm in men ; and 79—81, 90—93, —, and — cm in women, respectively [ 24 ]. Also, these values were close to those observed in our study. This study has several strengths and limitations. The main strengths of our study include the long median follow-up time, its prospective cohort design, using CVD events and mortality as endpoints, and collection of subjective instead of self-report data. Regarding the limitations of the present study, the data were related to the middle-east Caucasian residents of a metropolitan city in Iran, who cannot be representative of national population. Different methods of WC measurement have been established. In the present study, WC was measured at the umbilical level. Since there are different methods for measuring WC, although it is unlikely for the method of WC measurement to affects the results [ 13 ], this point should be considered when comparing the results of different studies. In conclusion, the results of this study suggested BMI-specific WC thresholds for predicting CVD events, CVD-related and mortality, and all-cause mortality, which can used as a clue for future studies to define more accurate WC cut-off values as a screening tool in different populations. This approach can help better identify individuals who are at a high risk of developing CVD and take effective measures to modify their risk factors. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Collaborators GO. Health effects of overweight and obesity in countries over 25 years. N Engl J Med. Article Google Scholar. 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Vianna CA, da Silva Linhares R, Bielemann RM, Machado EC, González-Chica DA, Matijasevich AM, Gigante DP. da Silva Dos Santos I. Accuracy and adequacy of waist circumference cutoff points currently recommended in brazilian adults. Public Health Nutr Apr;17 4 —9. doi: Epub Mar PMID: Yang ZJ, Yang WY, Chen XP, Li GW. Zhonghua nei ke za zhi. Esteghamati A, Ashraf H, Rashidi A, Meysamie A. Waist circumference cutoff points for the diagnosis of metabolic syndrome in iranian adults. Diabetes Res Clin Pract Oct;82 1 —7. Epub Aug Talaei M, Thomas GN, Marshall T, Sadeghi M, Iranipour R, Oveisgharan S, Sarrafzadegan N. Appropriate cutoff values of waist circumference to predict cardiovascular outcomes: 7-year follow-up in an iranian population. Intern Med. Epub Jan Hadaegh F, Zabetian A, Sarbakhsh P, Khalili D, James WP, Azizi F. Appropriate cutoff values of anthropometric variables to predict cardiovascular outcomes: 7. Int J Obes Lond. Ardern CI, Janssen I, Ross R, Katzmarzyk PT. Development of health-related waist circumference thresholds within BMI categories. Obes Res. Bajaj HS, Brennan DM, Hoogwerf BJ, Doshi KB, Kashyap SR. Clinical utility of waist circumference in predicting all-cause mortality in a preventive cardiology clinic population: a PreCIS database study. Azizi F, Ghanbarian A, Momenan AA, et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran lipid and glucose study phase II. Article PubMed PubMed Central Google Scholar. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, Lackland DT, LeFevre ML, MacKenzie TD, Ogedegbe O, Smith SC. Mellitus D. Diagnosis and classification of diabetes mellitus. Diabetes care. Luepker RV, Apple FS, Christenson RH, Crow RS, Fortmann SP, Goff D, et al. Case definitions for acute coronary heart disease in epidemiology and clinical research studies: a statement from the AHA Council on Epidemiology and Prevention. Carrero JJ, de Jager DJ, Verduijn M, Ravani P, De Meester J, Heaf JG, Finne P, Hoitsma AJ, Pascual J, Jarraya F, Reisaeter AV. Prediction equations for detailed age-sex groups were evaluated data not shown , but the results were similar to those based on the four prediction equations presented in the current study. Crude adjustments were also evaluated, whereby the differences between WHO and NIH measurements were calculated based on split-sample A and then applied to split-sample B. Crude adjustments were made by detailed age-sex groups and by BMI categories by sex, for adults and for children. The results based on these crude adjustments data not shown were not as favourable as those based on the four regression models. Estimates of percentages, means and regression coefficients were calculated using weighted data. Differences between estimates were tested for statistical significance, which was set at 0. Standard errors were estimated with the bootstrap technique; the number of degrees of freedom was specified as 13 to account for the sample design of the data. Weighted estimates were produced to adjust for unequal probabilities of selection and to take advantage of the adjustments made to reduce non-response bias in the CHMS. For men, the association with age was negative. For women, the association with age was not linear, but when age groups were included in the regression model, a positive association emerged for women aged 20 to Table A Regression coefficients for difference between waist circumference based on National Institutes of Health and World Health Organization WHO protocols, by age group and sex, household population aged 3 to 79, Canada, to The differences were greatest for girls aged 12 to 19 3. Table 2 Mean waist circumference based on World Health Organization WHO and National Institutes of Health NIH protocols, by sex and age group, household population aged 3 to 79, Canada, to Overall, the measured and predicted NIH values were statistically similar mean differences range from For the detailed age-sex groups, the only significant difference was for girls aged 12 to 19 Table 3 Difference between waist circumference measured according to National Institutes of Health NIH and World Health Organization WHO protocols, by sex and age group, household population aged 3 to 79, Canada, to Among adults, differences were greater for those in the normal weight range: 1. Table 4 Mean waist circumference based on World Health Organization WHO and National Institutes of Health NIH protocols, by age group, sex and body mass index BMI category, household population aged 3 to 79, Canada, to The only significant difference between the measured and predicted NIH values was for obese boys Table 5 Difference between waist circumference measured according to National Institutes of Health NIH and World Health Organization WHO protocols, by age group, sex and body mass index BMI category, household population aged 3 to 79, Canada, to For men and boys, the percentages whose waist circumference put them in a high health risk category were similar whether based on WHO , NIH or NIH -predicted measures Table 6. Sensitivity and specificity were very high when based on NIH predicted values, meaning that in almost all cases, respondents would be classified in the appropriate health risk category—that is, the same category in which they would be placed based on measured values Table 7. However, these sensitivity values were an improvement over those based on WHO. In the present study, WC for Canadian adults and children was significantly greater when measured using the NIH protocol than the WHO protocol. The difference was greatest among girls and young women. These findings add to the limited information about WC measurements taken at different sites. In that study, males' mean WC at the narrowest waist was significantly lower than at the other three sites. For females, mean WC at each site differed significantly from means at the others, and WC measurements using the NIH protocol significantly exceeded those using the WHO protocol 1. Mason et al. They noted no significant differences between sites for men. For women, the mean for each site differed significantly from the means for the others, except for the means at the sites used for the NIH and WHO protocols, which did not differ. In the present study, the differences that emerged between the NIH and WHO protocols may be related to the sample size or sample characteristics the Mason sample consisted of healthy adult volunteers, while the CHMS sample is representative of the Canadian population aged 3 to Home Healthy living Healthy weight Healthy weight and waist. Health seekers. Healthy waists Measuring waist circumference can help to assess obesity-related health risk. Are you an apple or a pear? Here's how to take a proper waist measurement Clear your abdominal area of any clothing, belts or accessories. Stand upright facing a mirror with your feet shoulder-width apart and your stomach relaxed. Wrap the measuring tape around your waist. Use the borders of your hands and index fingers — not your fingertips — to find the uppermost edge of your hipbones by pressing upwards and inwards along your hip bones. Tip: Many people mistake an easily felt part of the hipbone located toward the front of their body as the top of their hips. This part of the bone is in fact not the top of the hip bones, but by following this spot upward and back toward the sides of your body, you should be able to locate the true top of your hipbones. Using the mirror, align the bottom edge of the measuring tape with the top of the hip bones on both sides of your body. Tip: Once located, it may help to mark the top of your hipbones with a pen or felt-tip marker in order to aid you in correctly placing the tape. Make sure the tape is parallel to the floor and is not twisted. Relax and take two normal breaths. After the second breath out, tighten the tape around your waist. The tape should fit comfortably snug around the waist without depressing the skin. |
| Background | Measurements of weight, height, and waist circumference were obtained with the use of standardized techniques by 2 trained research team members Zerfas criteria 29,30 were used to standardize the measurements of research team members against the measurements of a certified anthropometrist R. Waist circumference was measured to the nearest 1 mm by using a calibrated anthropometric tape measure at the umbilicus. Weight was measured to the nearest 0. Height was measured to the nearest 1 mm by using a calibrated stadiometer. Each measurement was performed in sets of 3 replicates and repeated until the values were consistent 2 values within 2 units ; we used the average for analysis. Acanthosis assessment. Each child was examined for the presence or absence of acanthosis on the back of the neck, as a skin indicator of insulin resistance, by trained staff members according to the protocol of Burke et al The severity of acanthosis on the back of the neck, compared with other body sites, is more consistently associated with insulin resistance Acanthosis was rated for severity on a scale of 0 to 4 points, with a score of 0 indicating absence and a score of 1, 2, 3, or 4 indicating presence 1, least severe; 4, most severe. Statistical analysis. We calculated means and percentiles for waist circumference, by age and sex group. BMI was calculated as weight in kg divided by height in meters squared, and BMI percentiles were calculated according to age-specific and sex-specific growth reference curves published by the Centers for Disease Control and Prevention We used sex-specific and sex—age-group—specific receiver-operating characteristic ROC analysis to investigate the ability of waist circumference to predict the presence or absence of acanthosis The ROC curve plots sensitivity against value for 1 minus specificity for the identification of acanthosis across the range of waist circumference values. We then performed binary logistic regression models, adjusting for age and the presence of acanthosis 1—4 vs 0 on the Burke scale , to examine the predictive performance of an indicator variable for waist circumference divided at the optimal cut point among boys and girls separately. We used SAS software version 9. A total of 4, children 2, boys and 1, girls aged 2 to 8 years were included in the study. In general, waist circumference increased with age group among boys and girls Table 2. Boys had higher waist circumference values than girls at every percentile level except for the 95th percentile for the group aged 2 to 5 years. Values in the 90th percentile recommended by the IDF as a cut point for risk of diabetes for boys aged 2 to 5 years The optimal waist circumference cut points for predicting acanthosis among all children aged 2 to 8, determined by using the Youden index, were equivalent to the 85th percentile for both sexes Table 3. The sex—age-group—specific waist circumference cut points were, for boys, at the 90th These waist circumference cut points represent an increased likelihood of metabolic risk, based on the presence of acanthosis. At the optimal cut point for waist circumference, However, when we used IDF criteria, sensitivity was lower The areas under the ROC curves differed between the optimal sex-specific and sex—age-group—specific and IDF criteria values. Limited data exist on ideal or acceptable waist circumference cut points for identifying the risk of metabolic syndrome among young children. However, an increasing number of studies support the use of waist circumference instead of BMI to readily identify children with insulin resistance or metabolic syndrome in clinical settings 35, Derived waist circumference cut points for children to identify metabolic syndrome or cardiovascular risk factors have been suggested in the US and other countries 37— A US study on children and adolescents identified waist circumference cut points for boys at the 94th percentile and for girls at the 84th percentile in association with cardiometabolic risk A study of Chinese school-aged children reported the 90th percentile for boys and the 84th percentile for girls as waist circumference cut points for predicting cardiovascular disease risk factors Our study used ROC analysis to evaluate the optimal cut point value of waist circumference to predict the presence of acanthosis. Using separate cut points for children aged 2 to 5 and children aged 6 to 8 years predicted acanthosis better than a single cut point for the entire age range. The age group—specific percentiles identified as the optimal cut point for boys and girls, except boys aged 2 to 5 years, were lower than the IDF recommendations for diagnosing metabolic syndrome among children aged 6 years or older 5. Our study had several strengths and limitations. The strengths were the novel method used to determine waist circumference cut points for children living in the USAP region and the large sample size across multiple jurisdictions. Scientific consensus is needed on the anatomical measurement site for young children and acceptable levels of error in measurement in further development of methods using waist circumference measures. A limitation was that the study did not account for other measurements related to metabolic syndrome such as blood pressure, triglyceride levels, or cholesterol; these measurements were not collected in the CHL program. In addition, the cross-sectional design does not allow for temporal consideration of waist circumference for acanthosis risk. Lastly, the CHL study sampled communities with a high percentage of indigenous populations and may not have been a representation of the overall jurisdiction. The USAP region is undergoing a nutrition and epidemiologic transition, a rapid shift in diet and physical activity, caused by environmental changes and an increase in wealth In addition, colonialism led to changes in indigenous cultural practices, traditional diets, foods, sovereignty, customs, and identity Type 1 diabetes. Waist circumference: A marker of risk and a motivational tool. Resource topics: Clinical guidance , Obesity, diet and lifestyle Date: 20 October Author s : Pam Brown. Resource search. Content Key. Best practice. Case studies. Consensus documents. Journal content. Made easy. Quick guides. Editorial and comment. News articles. Contact us Privacy policy Terms and conditions About the PCDS Easy-to-do Audits Diabetes Distilled. The BMI Calculator is an easy-to-use online tool to help you estimate body fat. The higher your BMI, the higher your risk of obesity-related disease. Health Topics The Science Grants and Training News and Events About NHLBI. Health Professional Resources. Assessing Your Weight and Health Risk Assessment of weight and health risk involves using three key measures: Body mass index BMI Waist circumference Risk factors for diseases and conditions associated with obesity Body Mass Index BMI BMI is a useful measure of overweight and obesity. Although BMI can be used for most men and women, it does have some limits: It may overestimate body fat in athletes and others who have a muscular build. It may underestimate body fat in older persons and others who have lost muscle. The BMI score means the following: BMI Underweight Below Risk Factors High blood pressure hypertension High LDL cholesterol "bad" cholesterol Low HDL cholesterol "good" cholesterol High triglycerides High blood glucose sugar Family history of premature heart disease Physical inactivity Cigarette smoking. Healthy Weight Tip Waist circumference can help assess your weight and associated health risk. Check Your BMI The BMI Calculator is an easy-to-use online tool to help you estimate body fat. Back to top. Related Government Websites Health and Human Services external link National Institutes of Health Office of the Inspector General external link USA. gov external link. |
| QUESTION 1: What does waist circumference measure? | However, previous NHANES studies have shown little bias due to nonresponse. Sign In. Skip to main content Skip to secondary menu Skip to footer. Still breathing normally, take the reading on the tape. 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. Are you an apple or a pear? The sex—age-group—specific waist circumference cut points were, for boys, at the 90th |
| Assessing Your Weight and Health Risk | When identifying frequencies and constructing the ROC curve we used the statistical program SPSS Does measuring waist circumference in addition to BMI improve predictability? In a few cases, the absolute differences between the predicted and measured NIH values were large, but from a clinical perspective, the predicted values result in the correct health risk assessment. Regarding the limitations of the present study, the data were related to the middle-east Caucasian residents of a metropolitan city in Iran, who cannot be representative of national population. Epub Jun Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in |
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