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Body Shape Index and Body Roundness Index: Two New Body Indices for Prediction of Multiple Metabolic Risk Factors in Southern China Adults

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Body Shape Index and Body Roundness Index: Two New Body Indices for Prediction of Multiple Metabolic Risk Factors in Southern China Adults Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 May 2020 1 Body Shape Index and Body Roundness Index: Two New Body Indices for Prediction of Multiple Metabolic Risk Factors in Southern China Adults Jinjian Xu1,3, Qiong Wu1, Yaohan Zhou1, Ziqi Jin1, Yimin Zhu1, 3* 1 Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] 2 Department of Respiratory, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Hangzhou 310020, Zhejiang, China; [email protected] 3 Department of Basic Medicine, Jiangxi Medical College, Shangrao 334000, Jiangxi, China; [email protected] * Correspondence: [email protected]; Tel.: +86-15990017182 © 2020 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 May 2020 2 Abstract: This purpose was to compare the ability of body shape index (ABSI) and body roundness index (BRI) with waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and body adiposity index (BAI) to predict metabolic risk. The cross-sectional study was conducted in 17,360 Chinese subjects (were aged 18-95 years old) who escaped cardiovascular disease (CVD) or diabetes. Biochemical and anthropometric variables were measured by trained staff. Receiver operating characteristic curve (ROC) and optimal cutoff values of obesity indices were recruited to compare the predictive ability for metabolic risk factors. The mean age of subjects was 53.7(13.1) years, 41.6 % were males. Within young group (<60 years), the areas under the curve (AUC) demonstrated that WC, BMI, WHR, WHtR and BRI were able to similarly predict high metabolic risk in males (0.74 vs. 0.74 vs. 0.73 vs. 0.73 vs. 0.73) and in females (0.73 vs. 0.73 vs. 0.71 vs. 0.73 vs. 0.73), while the approximate predictive ability were only acquired in males (0.73 vs. 0.73 vs. 0.70 vs. 0.73 vs. 0.73) within elder group (≥60 years). The optimal cut- off values of BRI for high metabolic risk were calculated in males (<60 y: 3.49 vs. ≥60 y: 3.46) and females (<60 y: 3.47 vs. ≥60 y: 3.60). Meanwhile, BRI displayed a strong prediction to elevated BP and elevated TG in males (AUC = 0.64; AUC = 0.70) and to elevated BP, elevated TG and elevated SUA in females (AUC = 0.67; AUC = 0.69; AUC =0.70). BRI was able to similarly predict high metabolic risk compared to WC, BMI, WHR, WHtR and BAI, while ABSI was not. Moreover, BRI revealed specific predictive ability for elevated BP, elevated TG and elevated SUA. Keywords: A body shape index; Body roundness index; Metabolic risk factors; Southern China adults Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 May 2020 3 1. Introduction Metabolic syndrome (MetS) consist of a group of metabolic risk factors, which plays a vital role in development of atherosclerotic heart disease, diabetes mellitus and cancer[1-3]. So far, over 33.9% of elderly Chinese reached abnormal metabolism and caused a large disease-related healthcare burden[4]. According to the International Diabetes Federation (IDF) in 2009, the general diagnosis criteria of metabolic syndrome consisted with obesity, elevated blood pressure (BP), elevated fasting plasma glucose (FPG), elevated triglyceride (TG) and reduced high-density lipoprotein cholesterol (HDL-C)[5]. Obesity played an important role in the development of cardiometabolic disorder[6]. The prevalence of obesity among adults was increasing rapidly worldwide; a similar rapid increase had reached epidemic levels in China, which poses heavy public health and economic burdens[7]. Obesity was mainly due to the disproportionate growth of adipose tissue and lean body mass which can lead to further morbidity and mortality from cardiovascular disease[8, 9]. The obesity was the most commonly used measure to assess the risk involved with adiposity-related metabolic derangements, whereas the use of more accurate methods such as dual-energy X-ray absorptiometry (DXA), hydrostatic weighing, bioelectrical impedance or even skinfold thickness were limited due to its complexity and/or cost[10-12]. More epidemiological studies have shown the anthropometric measures were accepted for the evaluation of obesity for their simplicity and usefulness[13-15]. The body mass index (BMI) has been long used as a diagnostic index of general obesity and could reflect the overall distribution of body fat[16]. Moreover, waist circumference (WC), body adiposity index (BAI), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) have been used to predict metabolic risk factors in numerous studies[17-20]. However, people have gradually found that traditional anthropometric indices failed to discriminate fat and muscle mass[21]. So, the controversy raised over which anthropometric parameter can best define obesity and predict metabolic risk[22]. Hence, researchers have explored new anthropometric indices in an attempt to improve above limitations[23]. Fortunately, previous study found that two new anthropometric indices named body shape index (ABSI) and body roundness index (BRI) were more associated with cardiovascular disease and mortality hazards than BMI and WC[24-26]. In this study, we conducted a population-based cross-sectional study to assess the ability of obesity indices for predicting multiple non-adipose metabolic risk factors in Chinese subjects. To further evaluate the importance of obesity factor in Chinese elderly people, we adopted seven anthropometric indices as well as more comprehensive metabolic risk factors, including elevated BP, elevated FPG, elevated TG, reduced HDL-C and elevated serum uric acid (SUA), in the present study. 2. Material and methods 2.1. Study population The cross-sectional data was extracted from the baseline records of Zhejiang Metabolic Syndrome Cohort initiated in Zhejiang Province from 2009 to 2012. A total of 22,649 participants (9,527 males and 13,122 females, mean age was 54.86±14.2 years) were recruited. Exclusion criteria: (1) severe infective diseases and malignancies in baseline; (2) patients with T2DM, CVD Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 May 2020 4 and Stroke in baseline; (3) less than 18 years old; (4) without biochemical and anthropometric data. A total of 17,360 (7,226 males and 10,134 females) participants met these criteria were finally included in this study. Additionally, inclusion criteria and selection flowchart for all samples shown in Figure 1. This study was approved by the Human Research Ethics Committee of the Zhejiang University, Zhejiang, China. All participants provided written informed consent before participation. 2.2. Measurements Height (cm), body weight (kg), waist circumference (WC; cm), hip circumference (HC; cm), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by trained assistants using same equipment. Height and weight were measured with the subjects wearing light clothing and no shoes. Height was recorded to the nearest 0.1 cm and body weight to the nearest 0.1kg. Body mass index (BMI) was computed as weight (kg) divided by height squared (m2). Girth of the midpoint between the lowest point of the rib and the upper edge of the iliac crest were calculated as waist circumference (WC). The length of the horizontal position of the hip protrusion was calculated as the hip circumference (HC) [14]. The measurements of WC and HC were taken to the nearest 0.1 cm. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were respectively calculated as WC divided by HC and WC divided by height. BAI was calculated as HC WC (cm)/height1.5 (m) minus 18[27]. ABSI was calculated using formula as ABSI = and BMI2/3Height1/2 (WC/(2π))2 BRI was calculated using formula as BRI = 364.2 − 365.5 × √1 − ( ). Sitting blood (0.5Height)2 pressure was measured 2 times after at least 20 minutes of rest using the standardized desktop sphygmomanometer. The average blood pressure derived from two measurement readings was used[26, 28]. After an overnight fast of at least 12 hours, blood samples of each participant were collected by venipuncture. EDTA blood was collected in pre-cooled tubes on ice, centrifuged at 4000 rpm for 15 min at 4℃. EDTA plasma and serum aliquots were stored at −80℃ until use. Fasting plasma glucose (FPG) was measured by the hexokinase method, SUA and lipid profile containing total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were measured by the enzymatic method using an Abbott Aeroset autoanalyzer. 2.3. Criteria for multiple metabolic risk factors According to the diagnosis criteria of MetS by International Diabetes Federation (IDF) in 2009[5], the comprehensive non-adipose metabolic risk factors included the following items: (1) elevated BP: systolic blood pressure (SBP) (≥ 130 mmHg) or diastolic blood pressure (DBP) (≥ 85 mm Hg), or ongoing antihypertensive medications; (2) elevated FPG: FPG≥ 5.6 mmol/L and/or diagnosis of type 2 diabetes, or ongoing anti-diabetic treatment; (3) elevated TG: TG≥ 1.7 mmol/L; (4) reduced HDL-C: HDL-C < 1.03 mmol/L in men and HDL-C < 1.29 mmol/L in women. In addition, we also enrolled: (5) elevated SUA: SUA > 420 μmol/L according to the diagnosis criteria of hyperuricemia in 2000[29]. The people with three or more risks were diagnosed as “high metabolic risk population” in present study. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 May 2020 5 2.4. Statistics The continuous variables were presented as means and standard deviations (SD), categorical variables as counts or percentages. The one-way analysis of variance and the chi-squared test were used to compare the baseline characteristics of participants stratified by number of risk factors.
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