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      Waist-to-height ratio as an indicator of ‘early health risk’: simpler and more predictive than using a ‘matrix’ based on BMI and waist circumference

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      1 , 2 , 3
      BMJ Open
      BMJ Publishing Group
      PUBLIC HEALTH

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          Abstract

          Objectives

          There is now good evidence that central obesity carries more health risks compared with total obesity assessed by body mass index (BMI). It has therefore been suggested that waist circumference (WC), a proxy for central obesity, should be included with BMI in a ‘matrix’ to categorise health risk. We wanted to compare how the adult UK population is classified using such a ‘matrix’ with that using another proxy for central obesity, waist-to-height ratio (WHtR), using a boundary value of 0.5. Further, we wished to compare cardiometabolic risk factors in adults with ‘healthy’ BMI divided according to whether they have WHtR below or above 0.5.

          Setting, participants and outcome measures

          Recent data from 4 years (2008–2012) of the UK National Diet and Nutrition Survey (NDNS) (n=1453 adults) were used to cross-classify respondents on anthropometric indices. Regression was used to examine differences in levels of risk factors (triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), TC: HDL, glycated haemoglobin (HbA1c), fasting glucose, systolic (SBP) and diastolic blood pressure (DBP)) according to WHtR below and above 0.5, with adjustment for confounders (age, sex and BMI).

          Results

          35% of the group who were judged to be at ‘no increased risk’ using the ‘matrix’ had WHtR ≥0.5. The ‘matrix’ did not assign ‘increased risk’ to those with a ‘healthy’ BMI and ‘high’ waist circumference. However, our analysis showed that the group with ‘healthy’ BMI, and WHtR ≥0.5, had some significantly higher cardiometabolic risk factors compared to the group with ‘healthy’ BMI but WHtR below 0.5.

          Conclusions

          Use of a simple boundary value for WHtR (0.5) identifies more people at ‘early health risk’ than does a more complex ‘matrix’ using traditional boundary values for BMI and WC. WHtR may be a simpler and more predictive indicator of the ‘early heath risks’ associated with central obesity.

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          Most cited references24

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          Waist circumference and not body mass index explains obesity-related health risk.

          The addition of waist circumference (WC) to body mass index (BMI; in kg/m(2)) predicts a greater variance in health risk than does BMI alone; however, whether the reverse is true is not known. We evaluated whether BMI adds to the predictive power of WC in assessing obesity-related comorbidity. Subjects were 14 924 adult participants in the third National Health and Nutrition Examination Survey, grouped into categories of BMI and WC in accordance with the National Institutes of Health cutoffs. Odds ratios for hypertension, dyslipidemia, and the metabolic syndrome were compared for overweight and class I obese BMI categories and the normal-weight category before and after adjustment for WC. BMI and WC were also included in the same regression model as continuous variables for prediction of the metabolic disorders. With few exceptions, overweight and obese subjects were more likely to have hypertension, dyslipidemia, and the metabolic syndrome than were normal-weight subjects. After adjustment for WC category (normal or high), the odds of comorbidity, although attenuated, remained higher in overweight and obese subjects than in normal-weight subjects. However, after adjustment for WC as a continuous variable, the likelihood of hypertension, dyslipidemia, and the metabolic syndrome was similar in all groups. When WC and BMI were used as continuous variables in the same regression model, WC alone was a significant predictor of comorbidity. WC, and not BMI, explains obesity-related health risk. Thus, for a given WC value, overweight and obese persons and normal-weight persons have comparable health risks. However, when WC is dichotomized as normal or high, BMI remains a significant predictor of health risk.
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            Comparison of Body Mass Index (BMI), Body Adiposity Index (BAI), Waist Circumference (WC), Waist-To-Hip Ratio (WHR) and Waist-To-Height Ratio (WHtR) as Predictors of Cardiovascular Disease Risk Factors in an Adult Population in Singapore

            Background Excess adiposity is associated with cardiovascular disease (CVD) risk factors such as hypertension, diabetes mellitus and dyslipidemia. Amongst the various measures of adiposity, the best one to help predict these risk factors remains contentious. A novel index of adiposity, the Body Adiposity Index (BAI) was proposed in 2011, and has not been extensively studied in all populations. Therefore, the purpose of this study is to compare the relationship between Body Mass Index (BMI), Waist Circumference (WC), Waist-to-Hip Ratio (WHR), Waist-to-Height Ratio (WHtR), Body Adiposity Index (BAI) and CVD risk factors in the local adult population. Methods and Findings This is a cross sectional study involving 1,891 subjects (Chinese 59.1% Malay 22.2%, Indian 18.7%), aged 21–74 years, based on an employee health screening (2012) undertaken at a hospital in Singapore. Anthropometric indices and CVD risk factor variables were measured, and Spearman correlation, Receiver Operating Characteristic (ROC) curves and multiple logistic regressions were used. BAI consistently had the lower correlation, area under ROC and odd ratio values when compared with BMI, WC and WHtR, although differences were often small with overlapping 95% confidence intervals. After adjusting for BMI, BAI did not further increase the odds of CVD risk factors, unlike WC and WHtR (for all except hypertension and low high density lipoprotein cholesterol). When subjects with the various CVD risk factors were grouped according to established cut-offs, a BMI of ≥23.0 kg/m2 and/or WHtR ≥0.5 identified the highest proportion for all the CVD risk factors in both genders, even higher than a combination of BMI and WC. Conclusions BAI may function as a measure of overall adiposity but it is unlikely to be better than BMI. A combination of BMI and WHtR could have the best clinical utility in identifying patients with CVD risk factors in an adult population in Singapore.
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              Relation of body mass index and waist-to-height ratio to cardiovascular disease risk factors in children and adolescents: the Bogalusa Heart Study.

              Several investigators have concluded that the waist-to-height ratio is more strongly associated with cardiovascular disease risk factors than is the body mass index (BMI; in kg/m(2)). We examined the relation of the BMI-for-age z score and waist-to-height ratio to risk factors (lipids, fasting insulin, and blood pressures). We also compared the abilities of these 2 indexes to identify children with adverse risk factors. Children aged 5-17 y (n=2498) in the Bogalusa Heart Study were evaluated. As assessed by the ability of the 2 indexes to 1) account for the variability in each risk factor and 2) correctly identify children with adverse values, the predictive abilities of the BMI-for-age z score and waist-to-height ratio were similar. Waist-to-height ratio was slightly better (0.01-0.02 higher R(2) values, P<0.05) in predicting concentrations of total-to-HDL cholesterol ratio and LDL cholesterol, but BMI was slightly better in identifying children with high systolic blood pressure (0.03 higher R(2), P<0.05) in predicting measures of fasting insulin and systolic and diastolic blood pressures. On the basis of an overall index of the 6 risk factors, no difference was observed in the predictive abilities of BMI-for-age and waist-to-height ratio, with areas under the curves of 0.85 and 0.86 (P=0.30) and multiple R(2) values of 0.320 and 0.318 (P=0.79). This similarity likely results from the high intercorrelation (R(2)=0.78) between the 2 indexes. BMI-for-age and waist-to-height ratio do not differ in their abilities to identify children with adverse risk factors. Although waist-to-height ratio may be preferred because of its simplicity, additional longitudinal data are needed to examine its relation to disease.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2016
                14 March 2016
                : 6
                : 3
                : e010159
                Affiliations
                [1 ]Ashwell Associates , Ashwell, Herts, UK
                [2 ]Honorary Senior Visiting Fellow, City University London, UK
                [3 ]Sig-Nurture Ltd , Guildford, Surrey, UK
                Author notes
                [Correspondence to ] Dr Margaret Ashwell; margaret@ 123456ashwell.uk.com
                Article
                bmjopen-2015-010159
                10.1136/bmjopen-2015-010159
                4800150
                26975935
                9512c73b-6039-4088-81ee-c4a98f67db81
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 2 October 2015
                : 6 January 2016
                : 29 January 2016
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