title: Body mass index, waist circumference, hip circumference, waist-hip-ratio and waist-height-ratio: which is the better discriminator of prevalent screen-detected diabetes in a Cameroonian population?
abstract: BACKGROUND
  The link between measures of adiposity and prevalent screen-detected diabetes (SDM) in Africa has been less well investigated. We assessed and compared the strength of association and discriminatory capability of measures of adiposity including body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-hip-ratio (WHR) and waist-height-ratio (WHtR) for prevalent SDM risk in a sub-Saharan African population.
  
  
  METHODS
  Participants were 8663 adults free of diagnosed type 2 diabetes, who took part in the nationally representative Cameroon Burden of Diabetes (CAMBoD) 2006 survey. Logistic regression models were used to compute the odd ratio (OR) and 95% confidence interval (95%CI) for a standard deviation (SD) higher level of BMI (7.3), WC (12.5), HC (11.7), WHR (0.19) and WHtR (0.08) with prevalent SDM risk. Assessment and comparison of discrimination used C-statistic and relative integrated discrimination improvement (RIDI, %).
  
  
  RESULTS
  The adjusted OR and 95%CI for prevalent SDM with each SD higher adipometric variable were: 1.05 (0.98-1.13) for BMI, 1.30 (1.16-1.46) for WC, 1.18 (1.05-1.34) for HC, 1.05 (1.00-1.16) for WHR and 1.26 (1.11-1.39) for WHtR. C-statistic comparisons and RIDI analyses showed a trend toward a significant superiority of WC over other adipometric variables in multivariable models. Combining adiposity variables did not improve discrimination beyond multivariable models with WC alone.
  
  
  CONCLUSIONS
  WC was the best predictors and to some extent WHtR of prevalent SDM in this population, while BMI and WHR were less effective.
authors.affiliation: Section of International Health, Department of Community Medicine, University of Oslo, Norway; Health of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé 1, Yaoundé, Cameroon. Electronic address: vivimbanya@yahoo.com.
authors.forename: V N
authors.initials: VN
authors.lastname: Mbanya
authors.affiliation: Health of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé 1, Yaoundé, Cameroon; South African Medical Research Council Cape Town, South Africa; University of Cape Town, Cape Town, South Africa. Electronic address: akengne@georgeinstitute.org.au.
authors.forename: A P
authors.initials: AP
authors.lastname: Kengne
authors.affiliation: Health of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé 1, Yaoundé, Cameroon. Electronic address: jcmbanya@yahoo.co.uk.
authors.forename: J C
authors.initials: JC
authors.lastname: Mbanya
authors.affiliation: Section of International Health, Department of Community Medicine, University of Oslo, Norway. Electronic address: hussain.akhtar@medisin.uio.no.
authors.forename: H
authors.initials: H
authors.lastname: Akhtar
mesh_terms: D000328:Adult; D015992:Body Mass Index; D002163:Cameroon / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening; D015995:Prevalence; D012307:Risk Factors; D055105:Waist Circumference; D065927:Waist-Height Ratio; D049629:Waist-Hip Ratio
keywords: Adiposity; Cameroon; Prediction; Prevalent screen-detected diabetes; Risk marker