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Body Shape Index in Comparison with Other Anthropometric Measures In Research agenda Body shape index in comparison with other anthropometric measures in prediction of total and cause-specific mortality Klodian Dhana,1 Maryam Kavousi,1 M Arfan Ikram,1,2,3 Henning W Tiemeier,1,4 Albert Hofman,1 Oscar H Franco1 ▸ Additional material is ABSTRACT and waist-to-hip ratio (WHR) separately in relation – published online only. To view Background The association of body mass index (BMI) to morbidity and mortality.7 9 While WC is sensi- please visit the journal online with mortality remains controversial among the middle- tive to height, WHtR is indifferent to body weight (http://dx.doi.org/10.1136/jech- 10 2014-205257). aged and elderly. Moreover, the contribution of other and fat distribution. In the measurement of 1 anthropometric measures to predict mortality is unclear. WHR, a disproportionately large hip circumference Department of Epidemiology, 10 Erasmus MC, University Methods We assessed the association of BMI, waist can hide the status of abdominal obesity. Medical Center Rotterdam, circumference (WC), waist-to-height ratio (WHtR), waist- Recently, a new anthropometric measure, a body The Netherlands to-hip ratio (WHR) and a body shape index (ABSI=WC/ shape index (ABSI), has been introduced.11 ABSI is 2 Department of Neurology, (BMI2/3×height1/2)) with total, cardiovascular and cancer based on WC, but is independent of height, weight Erasmus MC, University Medical Center Rotterdam, mortality by using Cox proportion hazard models among and BMI. Therefore, being independent of BMI, The Netherlands 2626 men and 3740 women from the prospective ABSI could shed light on elucidating the predictive 3Department of Radiology, population-based Rotterdam Study. Predictive ability of abdominal obesity that cannot be attribu- Erasmus MC, University performance was assessed through informativeness, ted to BMI alone. This new measure has been sug- Medical Center Rotterdam, c-statistic, integrated discrimination improvement (IDI), gested by Krakauer et al to predict mortality The Netherlands fi 11 4Department of Child and and continuous net reclassi cation improvement (cNRI). independently from BMI in the US population, 12 Adolescent Psychiatry, Erasmus Results During 22 years of follow-up, 3675 deaths from and recently in a European population. However, MC, University Medical Center all-causes, 1195 from cardiovascular disease, and 873 use of ABSI as a predictor of total and cause- Rotterdam, The Netherlands from cancer occurred. In the multivariable model, ABSI specific mortality has not yet been validated in an showed a stronger association with mortality compared elderly population, where the predictive ability of Correspondence to 13 14 Dr Maryam Kavousi, with BMI, WC, WHtR and WHR. HRs and CIs (95% CIs) traditional risk factors in prediction declines. Department of Epidemiology, for total mortality per 1 SD increase in ABSI were 1.15 We, therefore, sought to examine the predictive Erasmus MC, University (1.09 to 1.21) for men and 1.09 (1.04 to 1.14) for ability of ABSI in association with total and cause- Medical Center, P.O. Box women. For cardiovascular and cancer mortality, these specific (including cardiovascular disease (CVD) 2040, Rotterdam 3000 CA, The Netherlands; HRs (95% CI) were 1.18 (1.08 to 1.29) and 1.10 (0.99 and cancer) mortality in the population-based [email protected] to 1.22) for men, 1.04 (0.96 to 1.12) and 1.18 (1.07 to Rotterdam Study (RS). We also aimed to compare 1.30) for women, respectively. The models including ABSI the predictive performance of ABSI in association Received 18 November 2014 did not increase the c-statistics. Among men, in prediction with total and cause-specific mortality with those Revised 3 June 2015 of total mortality the model including ABSI was more from BMI, WC, WHtR and WHR. Accepted 17 June 2015 2 Published Online First informative (χ =26.4) and provided improvement in risk 9 July 2015 stratification (IDI 0.003, 95% CI 0.001 to 0.005; cNRI METHODS 0.13, 95% CI 0.06 to 0.21). Study population Conclusions In our population-based study, among The RS is a prospective population-based cohort different anthropometric measures, ABSI showed a study in the city of Rotterdam in the Netherlands. stronger association with total, cardiovascular and cancer The original RS cohort (RS-I) started in 1990 when mortality. However, the added predictive value of ABSI in all inhabitants aged 55 and over residing in the prediction of mortality was limited. Ommoord district of Rotterdam were invited to par- ticipate and 7983 (78.1%) were enrolled. For the present analysis, we excluded all participants INTRODUCTION without data for weight, height, waist or hip circum- Obesity is increasing globally and the association ference, and those who did not provide informed between body weight, morbidity and mortality has consent for follow-up data collection. This left a received widespread attention.1 Among different total of 6366 persons (2626 men and 3740 women) anthropometric measures, most of the studies have eligible for the analysis. A more detailed description focused on body mass index (BMI) in association of the RS can be found elsewhere.15 with morbidity and mortality.23While BMI is a widely accepted and an easily applicable measure of Assessment of anthropometric measurements obesity, its use has limitations. BMI depends only Anthropometrics were measured in the research on height and weight, and does not distinguish centre by trained staff. Height and weight were between the distribution of adipose tissue and measured with the participants standing without 4 To cite: Dhana K, muscle mass. Furthermore, focusing on BMI in shoes and heavy outer garments. WC was measured Kavousi M, Ikram MA, et al. relation to mortality has led to contradictory con- at the level midway between the lower rib margin J Epidemiol Community clusions.56A number of studies examined waist and the iliac crest, with participants in standing – Health 2016;70:90 96. circumference (WC), waist-to-height ratio (WHtR), position without heavy outer garments and with 90 Dhana K, et al. J Epidemiol Community Health 2016;70:90–96. doi:10.1136/jech-2014-205257 Research agenda emptied out pockets, breathing out gently. Hip circumference base model and then using each extended model. To compare was recorded as the maximum circumference over the buttocks. the predicted probabilities from the base model and each BMI was calculated as weight divided by height squared extended model, we computed the integrated discrimination (kg/m2), whereas WHtR and WHR were calculated as WC improvement (IDI),20 and the net reclassification improvement divided by height, and as WC divided by hip circumference, (NRI).21 Since well-established cut-off points for calculation of respectively. ABSI was defined as WC/(BMI2/3×height1/2) NRI across different risk categories for mortality are lacking, we expressing WC and height in metre.11 Information regarding calculated the continuous NRI (cNRI) for each participant. The the measurement of other risk factors is provided as online cNRI only takes into account the correct upward and downward supplementary material. reclassifications for individuals with and without an event (ie, mortality) and does not require risk stratification into categories. Assessment of mortality To deal with missing values for the covariates, we used mul- Data on total and cause-specific mortality were collected using an tiple imputation (n=5 imputations) with the Expectation automated follow-up system until 1 January 2011. Cardiovascular Maximization method in SPSS. For the informativeness, mortality was defined as mortality as a consequence of coronary c-statistic, IDI and NRI we used single imputed data set. heart disease, cerebrovascular disease, or other atherosclerotic Analyses were conducted by using SPSS software V.20 (IBM disease.16 Cancer mortality was defined as mortality attributed to SPSS Statistics for Windows, Armonk, New York: IBM Corp) malignant neoplasms (International Classification of Diseases, and the R statistical software (http://www.r-project.org), V.3.0.1 Tenth Revision, (ICD-10): C00–C97). The median follow-up for and its libraries “survcomp”, “nricens”, and “Hmisc”. the analyses was 15.93 years (IQR 8.74–18.01). RESULTS Statistical analysis Baseline characteristics Correlation between anthropometric variables was evaluated Table 1 presents the baseline characteristics of the study popula- with Pearson correlation analysis. Cox proportional hazards tion. Compared with men, women were slightly older, had regression models were used to estimate the HRs and 95% CIs higher mean values of total and HDL cholesterol, BMI and for the association between anthropometric measures and mor- WHtR; however, the mean values for ABSI, WC and WHR tality, separately for men and women.17 We initially adjusted the were lower among women. A larger proportion of women were models for age among men and women. For the main analysis, receiving antihypertensive treatment whereas a smaller propor- all models were adjusted for traditional risk factors including tion were current smokers. In our study, ABSI was not signifi- age, total and high-density lipoprotein (HDL) cholesterol, sys- cantly correlated with BMI but strongly correlated with WC, tolic blood pressure, treatment for hypertension, current WHtR, and WHR. The correlation coefficients of ABSI with smoking, and diabetes mellitus. Adjustments for confounders BMI, WC, WHtR, and WHR were: 0.002 (p=0.9), 0.600 were performed based on prior knowledge in published litera- (p<0.01), 0.600 (p<0.01) and 0.650 (p<0.01) in men and ture.11 We additionally adjusted the models for education, activ- −0.018 (p=0.268), 0.637 (p<0.01), 0.630 (p<0.01) and ities of daily living (as a proxy for physical activity), and marital 0.804 (p<0.01) in women, respectively. status (living or not living with a partner). To assess the performance of anthropometric measures in Associations with total mortality prediction of mortality, we developed several prediction models.
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