Body Shape Index, Body Adiposity Index, and Body Roundness Index to Predict Cardiovascular Health Status

Background: Obesity and overweight, the ﬁfth noticeable reason for worldwide mortality, has been found directly related to cardiovascular illness and sudden death. This study aimed to evaluate anthropometric measurements including; a Body Shape Index (ABSI), Waist-Height Ratio (WHtR), Abdominal Volume Index (AVI) and Conicity Index (CI), the risk factors for cardiovascular diseases. Methods: This case-control study was conducted at BMSI, Jinnah Post Graduate Hospital from March 2019 to September 2020. Participants selected (n=105 adults, aged 30-50 years) were divided into three groups (35 each). Group A: patients with diabetes <5 years without microalbuminuria, Group B: patients > 5 years of diabetes with microalbuminuria and Group C: healthy individuals. All measurements were estimated twice. Data was analyzed by SPSS and the mean difference was found by ANOVA. Linear regression was applied to predict variables and p <0.05 was considered signiﬁcant. Results: The body mass index (BMI) among Group A, B and C was 25.1±0.04 kg/m 2 , 26.4±1.91 kg/m 2 and 23.7±1.9 kg/m 2 respectively. Statistically signiﬁcant ( p =0.000) mean difference for weight, BMI, WHtR, AVI and CI were observed among groups. A highly strong negative relationship between BMI with RPI and strong positive relationship of WHtR with AVI (r=0.887), BAI(r=0.929), CI(r=0.890), WWI(r=0.870), was found. However, highly strong positive relationship between ABSI with WWI and CI with WWI, was also observed. Conclusion: Predictors found related to cardiovascular health were BRI, BAI, and ABSI ( p =0.000). However, neither the BRI, ABSI nor BAI showed superior predictive power to WC, BMI, CI, WHtR and Conicity index.


INTRODUCTION
Obesity and overweight are the fifth noticeable reason for worldwide demise and are an expanding overall medical issue. In 2008, about 1400 million adults had been reported overweight while approximately 36% were fatty 1 . Obesity and overweight relate to an expanded danger of cardiovascular illnesses, Type 2 Diabetes mellitus, and sudden death [2][3] . Subsequently, recognition of obesity and overweight timely can prevent irreversible diseases like cardiovascular disease (CVD) 4 . In recent guidelines, Waist Circumference (WC) and Body Mass Index (BMI) is the utmost essential tool to detect obesity and overweight 5,6 . To be sure, an escalation in WC or BMI was demonstrated as the utmost risk to induce CVD 6 . In any case, past studies additionally exhibited that the differentiated limit of BMI is ideally not to be considered, and this could not recognize fat and appropriate weight 7 . WC has been demonstrated to be a decent indicator of abdominal fat, yet presently it is hazy how much the scope of WC relies upon body size 8,9 . This has prompted the possibility that joining customary procedures (for example stature, weight, BMI, or WC) can predict a considerable amount of body shape 10,11 . ABSI (A Body Shape Index), also depends on abdomen periphery (m), BMI (kg/m 2 ), and tallness (m) 11 . As indicated by the creators, an elevated ABSI connects with a more noteworthy part of abdominal fat and, by all accounts, is a great danger factor for sudden passing 12 . Different examinations have recommended that ABSI can anticipate the progression of diabetes mellitus.
A few investigations have projected that WHtR, can be a most excellent anthropometric boundary for predicting cardio-metabolic disease 13 . Be that as it may, it is not generally the best boundary. A few studies have displayed that WC was further connected with CHD and this is the key factor in Caucasians 12,13 . As of late, a few latest anthropometric boundaries have been projected. The abdominal volume index (AVI) is determined by utilizing the midsection boundary and hip, and one review has shown that it was a decent anthropometric device for assessing general abdominal volume 13 . The body adiposity index (BAI) is a complex term that depends on hip circumference and stature. It is separate from instinctive adiposity and generally, it can view as an upper limit of body adiposity. Notwithstanding, a few papers have revealed that BAI would not be considered to calculate adiposity. Importantly, BRI is one more complex measurement tool that depends upon WC and tallness. One paper has revealed that BRI can predict the early phase of CVD and a body shape index (ABSI) another tool to calculate adiposity and depend upon the abdominal, its periphery, BMI, and stature. Studies have displayed that ABSI is interconnected with hypertension and DM 14,15 . The Current study aimed to evaluate which anthropometric measurements among CHD patients can be utilized by specialists as early anthropometric risk factors.

METHODS
This case-control study was conducted at BMSI Jinnah Post Graduate Hospital from March 2019 to September 2020. A total of 105 adults who visited the diabetic clinic JPMC between the ages of 30-50 years were selected by non-probability purposive sampling. This study was approved by the institutional ethics review board. The consent of the patients was taken before adding them to the study. Participants were divided into three equal groups (35 patients in each group). Group A includes patients with diabetes less than or equal to 5 years without microalbuminuria, Group B includes patients with more than 5 years of diabetes with microalbuminuria and Group C includes healthy individuals. Variables were measured and data was collected by the researcher himself as per a predefined questionnaire-taking interview.
The questionnaire included socio-demographic variables, clinical and biophysical variables about smoking and its complete history. The Blood Pressure was estimated 3 times for every 10-min period by using a regular sphygmomanometer (HEM-907; Omron). Weight, stature, WC, and hip circumference were estimated by a normalized conventional method, by wearing light garments with no shoes. All measurements were estimated two times and on the off chance that the actions varied by > 0.5 cm or > 0.5 kg, individually, a 3rd estimation was taken finally. The normal of the 2 nearest estimations was taken in the examination. Approximately, 10hours of night fasting, serum glucose and lipid profile levels were estimated before breakfast utilizing a Vacutainer tube containing EDTA.
The Framingham hazard score was used which is a basic and universal tool that can assess levels of CAD for a single decade 10 . The FRS includes 6 coronary lethal factors that include age, sex, smoking habits SBP, TC, and HDL 16 . The ABSI was determined utilizing the recipe portrayed by Krakauer et al 17 . AVI was determined utilizing the accompanying recipe: AVI = [2 × (abdomen) 2 + 0.7 cm (midsection hip) 2]/1000. Hypertension was characterized as an SBP >140 mmHg, or potentially DBP > 90 mmHg, as indicated by the JNC-7 rules 18,19 . Diabetes mellitus is analyzed by utilizing WHO rules, FPG >126 mg/dl as well as being managed by taking anti-diabetic drugs 20 . Dyslipidemia was characterized as utilizing antidyslipidemic medicine or possessing at least one of the accompanying estimations: TG >1.7 mmol/L, TC > 5.2 mmol/L, HDL-C < 1.0 mmol/L and LDL-C > 3.4 mmol/L 21 .
Data was entered and analyzed by IBM SPSS v26.
Mean and Standard deviation was calculated for quantitative data. The mean comparison was done by one-way ANOVA. For determining the relationship of quantitative variables, Pearson's coefficient of correlation was applied. Linear regression was applied to make models for the prediction of quantitative variables. Variables. A p-value of less than 0.05 was considered significant.
The present study found a highly strong negative relationship between BMI with RPI(r=-0.910) while there was also a high strong positive relationship between WHtR with AVI(r=0.887), BAI(r=0.929), CI(r=0.890), WWI(r=0.870). The study also observed a highly strong positive relationship between ABSI with WWI(r=0.866) and CI with WWI(r=0.860) as shown in Table 2.  Prediction of different anthropometric measures was done by linear regression as shown in Figure 1 and detailed equations are presented in Table 3.