bioRxiv preprint doi: https://doi.org/10.1101/710970; this version posted July 22, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Title 2 Comparative assessment of methods for determining adiposity and a model for 3 obesity index 4 5 Running title: 6 Indices for assessment and modelling of obesity 7 8 David Adedia1, Adjoa A. Boakye2, Daniel Mensah3, Sylvester Y. Lokpo4, Innocent Afeke4, Kwabena O. 9 Duedu2* 10 1 Department of Basic Sciences, School of Basic & Biomedical Sciences, University of Health and 11 Allied Sciences, Ho, Ghana 12 2 Department of Biomedical Sciences, School of Basic & Biomedical Sciences, University of Health 13 and Allied Sciences, Ho, Ghana 14 3 Department of Nutrition and Dietetics, School of Allied Health Sciences, University of Health and 15 Allied Sciences, Ho, Ghana 16 4 Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health 17 and Allied Sciences, Ho, Ghana 18 19 *Corresponding Author: Department of Biomedical Sciences, University of Health and Allied Sciences, 20 PMB 31. Ho VH-0194-2524. Ghana. 21 Email: [email protected] or [email protected] Tel: +233545017098 22 23 24 Competing Interests: No competing interests declared. The African Partnership for Chronic 25 Disease Research (APCDR), Cambridge, UK provided postdoctoral fellowship funding to 26 KOD. 1 bioRxiv preprint doi: https://doi.org/10.1101/710970; this version posted July 22, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 27 Abstract 28 Background: Obesity is increasingly becoming a pandemic considering the many risks it pose to other 29 disease conditions. Obesity is largely a measure of adiposity, however, adiposity is not centralized in 30 the human body. This makes it difficult for any single method to adequately represent obesity and by 31 extension the risks specific areas of adipose accumulation pose to specific disease conditions. 32 Subjects/Methods: We evaluated the prevalence of obesity in a cohort of Ghanaian women using the 33 body mass index (BMI) and further sought to evaluate how it compares to other methods of estimating 34 adiposity and the suitability of any particular methods representing obesity in general. We used 35 anthropometry and bioimpedance derived measures of adiposity and derived other indices such as the 36 abdominal volume index (AVI), body adiposity index (BAI) and conicity index (CI). 37 Results: Waist and hip circumference, body fat (%BF) and visceral fat (VF) were positively correlated 38 to BMI whereas skeletal muscle mass was negatively correlated. Physical activity indices did not show 39 any significant correlation with BMI. Prevalence of obesity was 16% and 31% using BMI and %BF 40 respectively. Receiver operating characteristic analysis showed that whereas BMI is effective in 41 predicting underweight, normal weight and obesity it was a poor predictor of overweight. 42 Conclusions: There was also no single measure that could adequately predict obesity as an 43 accumulation fat. Hence, we developed and propose a model as a factor of BAI, %BF, VF and BMI. 44 This model should correctly represent a person’s adiposity status and hence should be evaluated in 45 large cohort studies. 46 47 Keywords: Body mass index, Waist-to-hip ratio, Conicity index, Obesity, Adiposity index, Abdominal 48 volume index 49 50 2 bioRxiv preprint doi: https://doi.org/10.1101/710970; this version posted July 22, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 51 Introduction 52 Chronic diseases such as diabetes, hypertension, and metabolic syndrome are rapidly taking over as 53 the major causes of morbidity and mortality in sub-Saharan Africa [1, 2]. The chronic disease burden is 54 attributed to lifestyle changes such as diet, tobacco use and urbanization [2]. In sub-Saharan Africa, 55 the prevalence of infectious diseases such as malaria, HIV, tuberculosis and neglected tropical 56 diseases remains sturdy thereby inflicting a heavy blow on health systems [3, 4]. With the rapidly 57 increasing prevalence of chronic diseases, the health systems will be affected by the rise in infectious 58 diseases co-existing with chronic diseases such as diabetes, hypertension, and metabolic syndrome 59 [5]. Many health systems in the region are under-funded and under-resourced, hence, the chronic 60 disease burden if not nipped in the bud could potentially crash them [6, 7]. 61 Obesity is a widely reported risk factor for chronic diseases such as diabetes, cardiovascular disease 62 and some cancers and recent years have witnessed an alarming increase in the incidence of obesity 63 worldwide [8]. Due to the high health risk associated with obesity, it is important that methods that 64 accurately determines obesity are developed and used. BMI, the ratio of body weight in kilograms to 65 the height in meters squared, has been used to measure obesity for a long while especially in resource 66 limited settings however, BMI measurement does not differentiate between lean and fat mass thus 67 leading to misclassification in some instances. Hence methods that measure direct body fat composition 68 may represent the best standards for determining obesity. 69 Recent advances in technology have resulted in the development of various tools for measuring 70 adiposity among others. For example, methods like X-ray absorptiometry (DEXA), magnetic resonance 71 imaging (MRI) and bioelectrical impedance analysis (BIA) are available to assess the relative body 72 composition and adiposity. Of these, the BIA methods are relatively cheaper, simple and well adapted 73 for resource-limited settings [9]. The types of BIA instruments have been increasing over time. These 74 instruments can report over 20 parameters on the full body composition including body segment 75 analysis (left arm, right arm, trunk, left leg and right leg), body fat percentage and mass, fat free mass, 76 visceral fat, muscle mass, total body water and body water percentage, among others. However, in 77 many health centres across Ghana lack of the availability of these devices has resulted in the continual 78 use of BMI to predict obesity. 79 According to the 2016 Global report on diabetes [10], the prevalence of obesity in Ghana were 4.8% 80 and 10.9% (males 4.9% and females 16.8%) respectively. Alarmingly, the prevalence of overweight 81 was 30.8% (males 21.5% and females 39.9%). The primary method for assessing obesity in Ghana is 82 by the BMI method. It has been reported that compared to white Caucasians and other ethnic groups, 83 the South Asian Population have higher amounts of body fats despite having similar or lower 84 anthropometric values [11, 12]. No studies have compared that of Ghanaians in general or among the 85 different ethnic groups in Ghana. As a starting point we sought to (1) compare anthropometry derived 86 adiposity measures and BIA measurements and (2) to determine how accurately different 87 anthropometric measures of obesity can diagnose obesity. Accurate information on fat and other body 88 composition measures will benefit dieticians and other professionals who assist individuals in weight 89 modification programmes. 3 bioRxiv preprint doi: https://doi.org/10.1101/710970; this version posted July 22, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 90 91 Materials and Methods 92 Study Design and Population 93 We conducted a retrospective analysis comparing the prevalence of obesity and associations between 94 adiposity measures among a cross-section of women in Ho, Ghana. The data was collected as part of 95 a community-based Healthy Eating Advocacy Drive (HEAD) outreach conducted between May and 96 December 2016. Data on anthropometric characteristics included Age, Height (m), Weight (kg), Hip 97 Circumference (cm), Waist Circumference (m), Skeletal Muscle (SM, %), Body fat (BF, %), Visceral Fat 98 (VF) and the Resting metabolism rate (RMR). Body Mass Index (BMI) (kg/m2), Waist-to-Hip Ratio, Body 99 adiposity index, Abdominal volume index, Visceral adiposity index and Conicity index were derived from 100 the measurements as alternative methods of BMI for determining adiposity. The study was approved 101 by the research ethics committee of the University of Health and Allied Sciences. A standardized 102 questionnaire was used to collect data on the anthropometric measurements and others. 103 104 Physical activity and adiposity measurements 105 A standard questionnaire was used to obtain information regarding physical activity such as 106 engagement in sports and exercise, work, leisure, sleep and prescribed weight modification or 107 maintenance programmes. Data on behavioural activity related to alcohol consumption, smoking and 108 eating was also collected and characterized. 109 The Omron body composition monitor (Omron Healthcare Co., Ltd., Kyoto, Japan) was used to measure 110 weight to the nearest 0.1 kg without footwear. There was no adjustment for clothing. Age and gender 111 were inputted into analyser prior to measurements. The VF, SM, BF and RMR were obtained from the 112 BIA. WC and HC were measured using a measuring tape to the nearest 0.1 cm. The WC measurements 113 were taken at the level of the umbilicus with arms folded across the chest whereas the HC 114 measurements were taken at the maximum circumference over the buttocks.