• Muhammad Ghias
  • Khadija Irfan Khawaja
  • Faisal Masud
  • Salman Atiq
  • Muhammad Khalid Pervaiz


Background: Body mass index (BMI), derived by dividing weight (Kg) by the square of height(m), is a useful anthropometric parameter, with multiple applications. It is dependent uponaccurate measurement of its component parameters. Where measurement of height and weightwith calibrated instruments is not possible, other objective parameters are required to maintainaccuracy. Objectives: We aimed to propose an alternate prediction model for the estimation ofBMI based on statistical linear regression equation using hip and waist circumferences. Ourobjective was to ascertain the accuracy of estimated BMI when compared with observed BMI ofpatients, and to propose a model for BMI prediction which would overcome problems encounteredin the prediction of body mass index of critically ill or immobile patients, needed for applicationssuch as BMI based calculations in ventilation protocols in ICUs. Methods: This cross sectionalsurvey was done by reviewing hospital records of adult subjects of both genders (n=24,485;10,687 males and 13,798 females), aged 20 years and above, who were diagnosed with type 2diabetes. Two different prediction models were designed for males and females keepingmorphological and physiological differences in gender. The measured waist and hip circumferencevalues were used to estimate BMI. Results: Data analysis revealed a significant linear relationshipbetween BMI, waist and hip circumference in all categories [waist circumference (r=0.795,p=0.000), hip circumference (r=0.838, p=0.000)]. Estimated regression models for males andfemales were BMI= -10.71+0.212(hip cir)+0.170 (waist circumference); and BMI= -15.168+0.143(hip circumference)+0.30 (waist circumference) respectively. Conclusion: Estimation of BMIusing this prediction model based upon measured waist and hip circumferences, is an alternate andreliable method for the calculation of BMI.Keywords: Body mass index, BMI, waist circumference, hip circumference, Multiple LinearRegression model, Correlation, Diabetes mellitus


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