Waist Stature Ratio: A Measure of Adiposity and Body Fat Composition in Asian Indian

Arup Ratan Bandyopadhyay*, Kusum Ghosh, Diptendu Chatterjee

Department of Anthropology, University of Calcutta, 35, Ballygunge Circular Road, Kolkata – 700019, India

*Corresponding Author’s Email: abanthro@caluniv.ac.in

Abstract

Background: The imbalance between the energy ingested in food and expended can lead to obesity. It is regarded as one of the most prominent but ignored public health issues of today and threatens to inundate the health care resources through increasing clinical consequences and additionally as a f inancial burden. Hence, the identity of individuals with health dangers using easy, surrogate measures to estimate excess adiposity becoming very important. In this regard, the purpose of this study is to evaluate the incidence of obesity, considering commonly used obesity measures, and also to discern the best obesity predictor among the adult Bengalee f emales of West Bengal, India. Research Method: Participants included 210 healthy adult Bengalee women (mean age 43.06 ± 3.4 years). Following standard procedure, anthropometric measures were taken for height, weight, hip circumf erence, and waist circumf erence. Waist-to-hip and waist-to-stature ratios were then computed. A f at monitor was used to calculate body f at percentage. Results: Out of all the adiposity measures, Waist Circumf erenc e (r = 0.78, P<0.001), Hip Circumf erence (r = 0.74, P<0.001), and Waist Hip Ratio (r = 0.72, P<0.001), the results showed that Waist Stature Ratio had the largest positive connection (r = 0.88, P<0.001) with Percent Body f at. Conclusion: Theref ore, the current study indicated that among Asian Indian middle- aged women, WSR may be the most appropriate marker for PBF.

Keywords: Public Health; Obesity; Anthropometry; Waist Stature Ratio (WSR); Chronic Diseases

Introduction

An imbalance between the intake of energy f rom food and energy expenditure is what def ines obesity. Theref ore, the excess energy stored in f at cells which eventually leads to weight gain i.e., obesity (Bray, 2004). Currently, the World Health Organization (WHO) and many other national and international organizations have formally classif ied obesity as a disease (Müller et al., 2017). According to the Obesity Medicine Association, obesity is a multif actorial, relapsing, neurobehavioral condition that is persistent and recurrent. It is caused by abnormal f at mass physical forces and adipose tissue dysf unction, which can have detrimental ef f ects on one's metabolic, biomechanical, and psychosocial well-being (Bays et al., 2020). The World Health Organization (WHO, 2008) depicted obesity as one of the most advanced, conspicuously apparent, and often disregarded public health issues. The word "globesity" was coined to represent the rapidly spreading worldwide epidemic of overweight and obesity. Research (Meldrum et al., 2017; Venkatrao et al., 2021) has shown that obesity prevalence is approaching pandemic levels globally. WHO (WHO, 2020) projected that in 2016, over 650 million adults (13%) and over 1.9 billion adults (39%) globally were obese and overweight, respectively. However, the current trajectory of events (Kelly et al., 2008) suggests that by 2030, almost half of the population in this sector may be overweight or obese.

Many chronic diseases have excess obesity as a major risk f actor (Grey et al., 2011), and any disease whose risk is raised by obesity can be classif ied into one of two pathophysiological groups. The increased mass of f at itself is the f undamental cause of the impairments, which consist of the stigma of obesity and psychosocial issues (bad body picture perception, low self-esteem, depression, and reduced quality of lif e) and even osteoarthritis. In contrast, the second category included risks associated with metabolic abnormalities brought on by excess f at, such as diabetes mellitus, gallbladder disease, hypertension, cardiovascular disease, problems related to reproductive health (f rom inf ertility to subfertility, such as polycystic ovarian syndrome), and certain cancers (Bray, 2004). Besides this, obesity not only threatens to inundate the health care sources by increasing different clinical consequences, but it is also an economic burden. It debts for 2% to 7% of total healthcare costs, including some other which consists of decreased quality of lif e and productiveness loss along with disability-adjusted existence years (DALYs) (Chong et al., 2023) attributed to medical leave (WHO/FAO). Moreover, social risks, unemployment, and a decline in socioeconomic productivity are inked to it (Blüher, 2019). It seems improbable that India's health budget could cover the costs of treating the related consequences of obesity. Thus, to avoid both the early and late consequences of the disease, a nation like India needs to take the necessary measures for an early diagnosis.

In light of this, ef f ective diagnostic instruments to pinpoint those who are excessively obese have become crucial (Rakić et al., 2019). As a result, there is growing interest in concentrating on low-cost, easily measured anthropometric markers like height, weight, and circumf erences (Han et al., 2006). Several anthropometric measures have already been used to measure central and total adiposity, including Body mass index (BMI), waist circumf erence (WC), hip circumf erence (HC), waist-to-hip ratio (WHR), and waist stature ratio (WSR) (Bergman et al., 2011). Researchers (Janssen et al., 2004; Brambilla et al., 2013) noted that anthropometric measures for central obesity are marginally better predictors of elevated levels of health risk f actors in populations of all ages than overall obesity as measured by BMI. WC and WHR (Sahakyan et al., 2015) were used as proxies for central/visceral obesity whilst WSR is a proxy for visceral adipose tissue (Ashwell et al., 1996; Roriz et al., 2014). An ample number of studies (Hsieh et al., 1995; Shao et al., 2010; Ashwell and Gibson, 2016; Vasquez et al., 2019; Ashwell and Hsieh, 2005) have been conducted to compare the ef f ectiveness of the WSR versus BMI and other indices in identif ying individuals who are at risk of obesity and the exceptional metabolic syndrome that goes along with it. These studies have established that the WSR has several advantages over BMI, or even WC and WHR, when it comes to identif ying health risks in women and men, across various ethnic groups, and at all ages. Even though several previous meta-analyses (Vazquez et al., 2007; de Koning et al., 2007; Czernichow et al., 2011) f ailed to demonstrate a signif icant advantage of abdominal obesity indices over BMI or among the af orementioned indices that measure abdominal obesity. Hence, the crucial question is which anthropometric measure is the most straightf orward and accurate in suggesting "early health risk" of an individual as prevention of obesity and related non-communicable illnesses may not be f easible until obesity is accurately evaluated.

Objectives

Within this context, the current study aimed to assess the prevalence of obesity taking into account all regularly used obesity indicators and to identif y the most reliable obesity predictor among adult Bengalee f emales in West Bengal, India.

Research Methodology

The present study consisted of 210 adult Hindu caste Bengalee f emales residing Bally, Howrah, West Bengal. Inf ormed consent was obtained f rom each participant prior to the study and as well Institutional ethical clearance has been obtained (Ref No. CUIEC/02/15/2022-23). Measurements such as Height, Weight, HC, WC were taken f rom each participant using the standard protocol (Weiner and Lourie, 1981). BMI [WT (kg) / HT (m2)], WHR [WC (cm) / HC (cm)], and WSR [WC (cm) / HT (cm)] were calculated using the standard formulae (Fauziana et al., 2016; Choi et al., 2018; Morais et al., 2018. PBF was assessed by an OMRON body scanner with a scale following the instruction manual.

Results

Table 1 represents the general distribution of the age, anthropometric measurements, and body composition characteristics. Based on the BMI cut-of f value (table 2), the f requency of being overweight and obese was estimated to be 25.23% and 10%, respectively. Examination of the prevalence of obesity based on percent body f at (PBF), WC, WHR, and WSR as presented in Table 3 revealed that the f requency of obesity is higher according to WSR (46.19%) compared to PBF (42.38%) followed by WC (34.28%) and WHR (31.90%). In terms of predicting Percent Body Fat (PBF), Table 4's multiple regression analysis of central obesity measures showed that WSR had the strongest positive correlation (r = 0.88, p<0.001) with PBF when compared to other measures of adiposity, such as WC (r = 0.78, p<0.001), HC (r = 0.74, p<0.001), WHR (r = 0.72, p<0.001), and Conicity Index (CI) (r = 0.42, p<0.001).

Table 1: Age and Anthropometric Variables

Table 2: Obesity and overweight based on BMI categories (WHO, 2004)

Table 3: Obesity based on PBF, WC, WHR and WSR

Table 4: Stepwise Multiple Regression Analysis of Central Obesity Measures in Predicting Percent Body Fat

a Model 1, WSR; Model 2, WSR and CI; Model 3, WSR, CI and WC; Model 4, WSR, WC, HC, WHR, and CI.

Discussion

Obesity is a deadly disease of the 21st century and is a prime causative element for lots of diff erent other disorders (Ramachandran and Snehalatha, 2010). Several health ef f ects, higher prevalence, and the f inancial burden they impose make the prevention of obesity a primary public health priority. Theref ore, ef f ective interventions are needed to identif y people with excess adiposity (Rakić et al., 2019). The latest examination (Chong et al., 2023) pronounced overall obesity elevated in f emales f rom all socio-demographic statuses. Diff erent anthropometric measures like height, weight, and circumf erences have already been used for assessing overall and central adiposity (Bergman et al., 2011). However, because diff erent ethnic groups have varying body proportions, it is still important to determine which anthropometric measure is the best and most reliable for preventing obesity and related disorders. Theref ore, the goal of the current study was to ascertain the prevalence of obesity while taking into account widely used obesity measurements and to identif y the most ef f ective obesity predictors. The present discourse demonstrated various obesity indicators (BMI, WC, HC, WSR, WHR, and PBF) to measure excess body f at (Tables 2 and 3).

Additionally, a higher predisposition to be overweight and obese (35.23%) was noted among the population under study, despite the majority of participants (64.76%) having normal BMIs according to BMI classif ication (Table 2). This is even higher (17.45%) than another study conducted in the Bengalee population (Bhadra et al., 2005), may be because of the higher mean age of the studied population. As it has been shown in past research with increasing age Bengalee women put on weight and develop obesity (Sengupta et al., 2013). After Jammu & Kashmir and Uttar Pradesh, they have the third-highest rate of obesity in India (Zargar et al., 2000). When contrasting the current data with those f rom other Indian populations, it becomes clear that the high prevalence of overweight and obesity is in line with f indings f rom previous Indian studies (Gopinath et al., 1994; Visweswara et al., 1995; Gopalan, 1998). However, there aren't many studies on how percent body f at (PBF) relates to other anthropometric indices, ratios, and/or measurements.

Theref ore, the present study attempted to predict the best measures to discern PBF through stepwise multiple logistic regression (Table 4), and vindicated WSR was the best predictor for PBF among the aged Bengalee caste Hindu Population. Stated diff erently, WSR may serve as the exclusive, ideal, or most ef f ective anthropometric index to screen individuals who are at risk of obesity, especially those who have decreased other anthropometric parameters and are at increasing health risks which aligns with the f indings of a f ew earlier research (Shao et al., 2010; Kim et al., 2016). Moreover, both obesity and malnutrition need to be understood through the specif ic obesity measures that ef f ectively indicate ethnic specif ic disparity (O’Connell and Smith, 2016).

Conclusion

Waist Stature Ratio (WSR) can be an excellent, easy and reliable clinical predictor for obesity (PBF) in adult f emales of West Bengal.

Conflict of Interest

No conf lict of interest

Acknowledgement

ICSSR Major Research Project and University of Calcutta (BI 65- 8 & 9).

Authors are gratef ul to Dr. Sutapa Chowdhury, Associate Prof essor, Department of Anthropology, University of Calcutta for valuable suggestions and as well as to the participants of the present study.

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