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Abstract

Background

Abdominal obesities are better markers for predicting cardiovascular abnormalities than risk stratification based only on body mass index (BMI). We aimed to estimate the prevalence of abdominal adiposities using predictive equations for Asian Indian adults and to determine the prevalence of metabolically healthy subjects among those overweight/obese and with normal BMI.

Methods

A community-based survey was conducted among those aged 18–69 years in the district of Puducherry between February 2019 and February 2020. We surveyed 2,560 individuals selected through multi-stage cluster random sampling from urban and rural areas (50 wards and 50 villages, respectively) of the district. Anthropometric measurements, such as height, weight, waist circumference, and blood pressure were recorded from each participant. Fasting blood sample was collected from each alternate participant to estimate metabolic risk factors.

Results

Over four-fifths (85.6%; 95% CI: 84.2–86.9) and two-thirds (69.7%; 95% CI: 67.9–71.6) of the population in the district had high levels of intra-abdominal adipose tissue (IAAT) and total abdominal fat (TAF), respectively. Both the risk factors were significantly higher among women and urban population. About 43% (95% CI: 41–44.9) of the population had high abdominal subcutaneous adipose tissue (SCAT) with a significantly higher prevalence among the urban population. Among those overweight/obese (n = 773), almost all 99.4% (95% CI: 98.7–99.9) were metabolically unhealthy. Among subjects with normal BMI (n = 314), only about 2.9% (95% CI: 1.3–4.8) were metabolically healthy.

Conclusion

We highlight the substantially high prevalence of IAAT, TAF, and SCAT in the district of Puducherry. Almost all the study population was metabolically unhealthy irrespective of their BMI levels.

Lay Summary

The distribution of abdominal fat is a better predictor of cardiovascular abnormalities in an individual than the risk assessment based only on body mass index (BMI). We conducted a community-based cross-sectional survey to estimate the prevalence of abdominal adiposities using predictive equations for Asian Indian adults and determine the prevalence of metabolically healthy subjects among those overweight/obese and with normal BMI. We surveyed 2,560 adults aged 18–69 years in the district of Puducherry between February 2019 and February 2020. We recorded each participant’s anthropometric measurements, such as height, weight, waist circumference, and blood pressure and collected a fasting blood sample to assess their metabolic health status. Over four-fifths (85.6%) and two-thirds (69.7%) of the population in the district had high levels of intra-abdominal adipose tissue (IAAT) and total abdominal fat (TAF), respectively. Nearly half (43%) of the population had high abdominal subcutaneous adipose tissue (SCAT). Both the risk factors were substantially higher among women and the urban population. Among those overweight/obese, almost all (99.4%) were metabolically unhealthy; among those with normal BMI, only about 2.9% were metabolically healthy. From this study, we highlight the immediate need for population-based health promotion interventions, especially among women and urban residents of Puducherry district.

body mass index, fat distribution, metabolic syndrome, obesity, phenotype, waist circumference
Key messages

  • Over 85% of the population had high levels of intra-abdominal adipose tissue (IAAT).

  • Over two-thirds (69.7%) of the population had high levels of total abdominal fat (TAF).

  • Both IAAT and TAF were significantly higher among women and the urban population.

  • Among the overweight/obese population, almost all (99.4%) were metabolically unhealthy.

  • Among those with normal BMI, only 2.9% were metabolically healthy.

Introduction

Obesity is one of the significant public health problems in developing countries like India.1 As per the body mass index (BMI) classification for Asian Indians, an individual having BMI of ≥25 kg/m2 is characterized as obese.2 Determining obesity using BMI is a simple yet effective method of identifying people with high risk for cardiovascular disorders.3 However, emerging evidence suggests that abdominal obesity is a better marker for an adverse metabolic profile in an individual when compared with generalized obesity.4 Abdominal obesity has been shown to increase the risk for all-cause mortality in individuals irrespective of their BMI.5 Hence, estimating the abdominal fat distribution is more important than risk management based only on generalized obesity.6

Several studies have linked abdominal adiposities with metabolic perturbations, such as insulin resistance, type 2 diabetes mellitus (DM), and metabolic syndrome.6 Asian Indians have higher levels of overall adiposity, truncal subcutaneous adipose tissue (SCAT), and intra-abdominal adipose tissue (IAAT) compared to other ethnicities, making this population at greater risk for cardiovascular health outcomes.7

Abdominal obesities can be measured more precisely through imaging modalities, such as dual-energy X-ray absorptiometry (DXA), computerized tomography (CT), and magnetic resonance imaging (MRI).8 But these imaging techniques are expensive and therefore cannot be introduced into public health practice. In recent years, to calculate total abdominal fat (TAF), IAAT, and SCAT, several predictive equations have been developed and validated for the Asian Indian population using simple anthropometric measurements, such as height, weight, waist circumference (WC), and hip circumference (HC).9 Emerging evidence shows an absence of metabolic abnormalities in a subsection of obese individuals (metabolically healthy obese (MHO)), and the presence of metabolic abnormalities in those having normal BMI levels (metabolically unhealthy non-obese [MUNO]).10 Therefore, it is crucial to determine the prevalence of MHO and MUNO in a population so as to prevent the cardiovascular diseases (CVDs) in them through targeted public health interventions. With this backdrop, we conducted this study from February 2019 to February 2020 in the district of Puducherry among the general population aged 18–69 years with the following objectives:

  1. To estimate the prevalence of abdominal adiposities using predictive equations for Asian Indian adults.

  2. To determine the prevalence of metabolically healthy subjects among those overweight/obese and with normal BMI.

Methods

Study population

We conducted this study in the district of Puducherry in India. It is one of the four districts of the Union Territory of Puducherry and is located along the coastal areas of South India. The total population of the district is 0.95 million. It is one of the highly urbanized districts in the country, with more than two-thirds (68.33%) of its population residing in its urban areas.11 The district ranks seventh on Human Development Index and has high levels of literary (85.4%), life expectancy (68 years), and sex ratio (1,029) when compared with other Indian states.11–13

We conducted a survey for non-communicable disease (NCD) risk factors in the district of Puducherry using WHO STEPS guidelines from February 2019 to February 2020. The detailed methodology has already been published.14 Here we present the results of abdominal obesities and metabolic health status of the population.

Sample size and sampling method

A sample size of 2,560 was derived for the study using OpenEpi (Version 3.01) by considering 50% prevalence, 95% confidence interval, design effect of 1.5, and response rate of 90%. This sample was sufficient to provide a district-wide representative estimation of study results by gender (men/women) and residence (urban/rural) stratification. Furthermore, the total sample size obtained was divided between urban (n = 1,770) and rural (n = 790) areas in proportionate to urban–rural population distribution (70:30) in the district.

Participants were selected through a multi-stage, cluster random sampling method using Census 2011 as the baseline. We employed two-staged sampling in urban areas, and three staged sampling in rural. In the first sampling stage, we chose 100 clusters in urban and rural areas (50 clusters in each) out of the total 92 urban wards and 56 revenue villages of the district. From each of the selected urban wards, we chose one Census Enumeration Block (CEB) randomly in the second stage. In the third stage of urban areas and the second stage of the rural regions, we chose 36 and 16 households from each selected CEB and village, respectively. At each household, one adult individual aged between 18 and 69 years, having resided in the household for at least the last 6 months, was chosen randomly using the Kish technique.15

Data collection procedure and operational definitions

We used the WHO STEPS questionnaire (Version 3.2) for the survey after translating it to the local language (Tamil), validation, and pilot testing. Socio-demographic characteristics, such as gender, age, education, etc., and physical measurements, such as height, weight, WC, and HC were recorded from each individual.

We recorded physical measurements following the STEPS guidelines. Height and weight were measured using a stadiometer and electronic weighing scale, respectively, while the participant was in a standing position with light clothing. Height and weight were recorded at the nearest 0.1 cm and 100 g, respectively. Participants’ WC and HC were measured using SECA constant tape at the nearest 0.1 cm. We regularly calibrated the measurement equipment before and during the survey. At the time of survey, we excluded pregnant women from participation as pregnancy is associated with weight gain due to physiological changes. Blood pressure was measured while the participant was in a seated position. Three reading were taken using an electronic device (Omron HEM-7120, Omron Corporation, Kyoto, Japan) with 5 min of interval between each reading, and an average of the last two readings were taken to determine the blood pressure. The study questionnaire was converted into an electronic version and was loaded into the software ODK Collect (Version 1.25.1) for data collection.

From every alternate participant, a fasting blood sample of 5 ml was collected at the participant’s residence after their overnight fasting of 10–12 h to estimate fasting blood glucose, total cholesterol, triglycerides, low-density lipoprotein (LDL)-cholesterol, and high-density lipoprotein (HDL)-cholesterol. The blood samples were transported under a cold chain to the laboratory of the biochemistry department of the institute for further processing and estimation. Serum levels of fasting glucose, triglycerides, total cholesterol, LDL, and HDL cholesterols were estimated using AU680 Analyzer (Beckman Coulter Inc., Brea, CA, USA).

The abdominal adiposities, such as IAAT, SCAT, and TAF were estimated using predictive equations recommended for Asian Indians, as given in Table 1.16

Table 1.
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Predictive equations for estimation of abdominal adipose tissue.

Risk factorEquation
Intra-abdominal adipose tissue (IAAT) (cm2)−238.7 + 16.9 × age (years) + 934.18 × gendera + 578.09 × BMI (kg/m2) − 441.06 × HC (cm) + 434.2 × WC (cm)
2. Subcutaneous abdominal adipose tissue (SCAT) (cm2)−49,376.4 − 17.15 × age (years) + 1,016.5 × gendera + 783.3 × BMI (kg/m2) + 466 × HC (cm)
3.Total abdominal fat (TAF) (cm2)−47,657.00 + 1,384.11 × gendera +1,466.54 × BMI  + 416.10 × WC
Risk factorEquation
Intra-abdominal adipose tissue (IAAT) (cm2)−238.7 + 16.9 × age (years) + 934.18 × gendera + 578.09 × BMI (kg/m2) − 441.06 × HC (cm) + 434.2 × WC (cm)
2. Subcutaneous abdominal adipose tissue (SCAT) (cm2)−49,376.4 − 17.15 × age (years) + 1,016.5 × gendera + 783.3 × BMI (kg/m2) + 466 × HC (cm)
3.Total abdominal fat (TAF) (cm2)−47,657.00 + 1,384.11 × gendera +1,466.54 × BMI  + 416.10 × WC

BMI, body mass index; HC, hip circumference; WC, waist circumference.

aMen: 1; Women: 2.

Table 1.
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Predictive equations for estimation of abdominal adipose tissue.

Risk factorEquation
Intra-abdominal adipose tissue (IAAT) (cm2)−238.7 + 16.9 × age (years) + 934.18 × gendera + 578.09 × BMI (kg/m2) − 441.06 × HC (cm) + 434.2 × WC (cm)
2. Subcutaneous abdominal adipose tissue (SCAT) (cm2)−49,376.4 − 17.15 × age (years) + 1,016.5 × gendera + 783.3 × BMI (kg/m2) + 466 × HC (cm)
3.Total abdominal fat (TAF) (cm2)−47,657.00 + 1,384.11 × gendera +1,466.54 × BMI  + 416.10 × WC
Risk factorEquation
Intra-abdominal adipose tissue (IAAT) (cm2)−238.7 + 16.9 × age (years) + 934.18 × gendera + 578.09 × BMI (kg/m2) − 441.06 × HC (cm) + 434.2 × WC (cm)
2. Subcutaneous abdominal adipose tissue (SCAT) (cm2)−49,376.4 − 17.15 × age (years) + 1,016.5 × gendera + 783.3 × BMI (kg/m2) + 466 × HC (cm)
3.Total abdominal fat (TAF) (cm2)−47,657.00 + 1,384.11 × gendera +1,466.54 × BMI  + 416.10 × WC

BMI, body mass index; HC, hip circumference; WC, waist circumference.

aMen: 1; Women: 2.

The cut-offs used for determining High IAAT, SCAT, and TAF are as follows9:

  1. High IAAT (Asian Indians) = ≥135.3 cm2 (Men) and ≥75.73 cm2 (Women)

  2. High SCAT (Asian Indians) = ≥110.74 cm2 (Men) and ≥134.02 cm2 (Women)

  3. High TAF (Asian Indians) = ≥245.6 cm2 (Men) and ≥203.46 cm2 (Women)

We considered the absence of five key metabolic risk factors for CVDs, such as hyperglycaemia, hypertension (HTN), dyslipidaemia, central obesity, and metabolic syndrome (as per Harmonization criteria)17 in an individual as metabolically healthy. Therefore, any of these five metabolic risk factors among those overweight/obese and normal BMI individuals were regarded as metabolically unhealthy obese (MUO) and metabolically unhealthy non-obese (MUNO), respectively.

Dyslipidaemia was defined as the presence of at least one lipid abnormality: LDL cholesterol ≥100 mg/dl, or triglycerides ≥150 mg/dl, or total cholesterol >200 mg/dl, or HDL cholesterol <40 mg/dl (Men), <50 mg/dl (Women).18

Statistical analysis

Data cleaning and analysis were conducted using Statistical Package for Social Sciences version 22 (IBM Corp., Armonk, NY, USA). Continuous variables like age and categorical variables, such as high TAF, IAAT, and SCAT were presented using mean (SD)/median (IQR) and proportions, respectively. All estimates were presented with 95% CI. The difference in the prevalence between the subgroups was assessed by comparing the 95% CIs. Chi-square test was used to test the association of various metabolic risk factors between those with normal BMI and overweight/obesity. A P-value of ≤0.05 was considered statistically significant.

Results

In the survey, the response rate was 93.9% (2,405/2,560). The mean (SD) age of participants was 44.3 (14) years. More than two-thirds of the population belonged to urban areas (69.6%), nearly half had received at least a secondary level of education 1,167/2,405 (48.5%), and were employed 1,237/2,405 (51.4%). Socio-demographic details of participants are provided in Table 2.

Table 2.
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Demographic characteristics of study participants (N = 2,405).

VariablesMen (n = 1,086)
n (%)		Women (n = 1,319)
n (%)		Both gender (N = 2,405)
n (%)
Agea categories (in years)
 18–44538 (49.5)657 (49.8)1,205 (49.9)
 45–69548 (50.5)662 (50.2)1,210 (50.1)
Residence
 Urban749 (69)925 (70.1)1,674 (69.6)
 Rural337 (31)394 (29.9)731 (30.4)
Educational status
 No formal education67 (6.2)223 (16.9)290 (12)
 Less than primary121 (11.1)193 (14.6)314 (13)
 Primary education287 (26.4)347 (26.3)634 (26.4)
 Secondary/Higher Secondary374 (34.4)357 (27.1)731 (30.4)
 Graduation and above235 (21.6)198 (15)433 (18)
 Refused to answer2 (0.2)1 (0.1)3 (0.1)
Religion
 Hindu1,027 (94.6)1,243 (93.6)2,261 (94)
 Christian38 (3.5)55 (4.2)93 (3.9)
 Muslim21 (1.9)28 (2.1)49 (2)
 Others02 (0.2)2 (0.1)
Marital status
 Never married228 (21)90 (6.8)318 (13.2)
 Currently married831 (76.5)978 (74.1)1,809 (75.2)
 Divorced/Separated6 (0.6)20 (1.5)26 (1.1)
 Widowed21 (1.9)231 (17.5)252 (10.5)
Occupation
 Government employee42 (3.9)19 (1.4)61 (2.5)
 Non-government employee40.6 (37.4)184 (13.9)590 (24.5)
 Self employed403 (37.1)183 (13.8)586 (24.4)
 Student56 (5.2)29 (2.2)85 (3.5)
 Homemaker10 (0.9)796 (60.3)806 (33.5)
 Retired57 (5.2)14 (1.1)71 (3)
 Unemployed (able to work)49 (4.5)34 (2.6)83 (3.5)
 Unemployed (Unable to work)63 (5.8)60 (4.5)123 (5.1)
Body mass index (BMI)
 Underweight (≤18.5 kg/m2)90 (8.3)81 (6.1)171 (7.1)
 Normal (≥18.5–22.9 kg/m2)353 (32.5)350 (26.3)703 (29.2)
 Overweight (23–24.9 kg/m2)236 (21.7)187 (14.1)423 (17.6)
 Obesity (≥25 kg/m2)407 (37.5)701 (52.7)1,108 (46.1)
VariablesMen (n = 1,086)
n (%)		Women (n = 1,319)
n (%)		Both gender (N = 2,405)
n (%)
Agea categories (in years)
 18–44538 (49.5)657 (49.8)1,205 (49.9)
 45–69548 (50.5)662 (50.2)1,210 (50.1)
Residence
 Urban749 (69)925 (70.1)1,674 (69.6)
 Rural337 (31)394 (29.9)731 (30.4)
Educational status
 No formal education67 (6.2)223 (16.9)290 (12)
 Less than primary121 (11.1)193 (14.6)314 (13)
 Primary education287 (26.4)347 (26.3)634 (26.4)
 Secondary/Higher Secondary374 (34.4)357 (27.1)731 (30.4)
 Graduation and above235 (21.6)198 (15)433 (18)
 Refused to answer2 (0.2)1 (0.1)3 (0.1)
Religion
 Hindu1,027 (94.6)1,243 (93.6)2,261 (94)
 Christian38 (3.5)55 (4.2)93 (3.9)
 Muslim21 (1.9)28 (2.1)49 (2)
 Others02 (0.2)2 (0.1)
Marital status
 Never married228 (21)90 (6.8)318 (13.2)
 Currently married831 (76.5)978 (74.1)1,809 (75.2)
 Divorced/Separated6 (0.6)20 (1.5)26 (1.1)
 Widowed21 (1.9)231 (17.5)252 (10.5)
Occupation
 Government employee42 (3.9)19 (1.4)61 (2.5)
 Non-government employee40.6 (37.4)184 (13.9)590 (24.5)
 Self employed403 (37.1)183 (13.8)586 (24.4)
 Student56 (5.2)29 (2.2)85 (3.5)
 Homemaker10 (0.9)796 (60.3)806 (33.5)
 Retired57 (5.2)14 (1.1)71 (3)
 Unemployed (able to work)49 (4.5)34 (2.6)83 (3.5)
 Unemployed (Unable to work)63 (5.8)60 (4.5)123 (5.1)
Body mass index (BMI)
 Underweight (≤18.5 kg/m2)90 (8.3)81 (6.1)171 (7.1)
 Normal (≥18.5–22.9 kg/m2)353 (32.5)350 (26.3)703 (29.2)
 Overweight (23–24.9 kg/m2)236 (21.7)187 (14.1)423 (17.6)
 Obesity (≥25 kg/m2)407 (37.5)701 (52.7)1,108 (46.1)

aMean (SD) age: Men 44 (15) years; Women 44 (13) years.

Table 2.
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Demographic characteristics of study participants (N = 2,405).

VariablesMen (n = 1,086)
n (%)		Women (n = 1,319)
n (%)		Both gender (N = 2,405)
n (%)
Agea categories (in years)
 18–44538 (49.5)657 (49.8)1,205 (49.9)
 45–69548 (50.5)662 (50.2)1,210 (50.1)
Residence
 Urban749 (69)925 (70.1)1,674 (69.6)
 Rural337 (31)394 (29.9)731 (30.4)
Educational status
 No formal education67 (6.2)223 (16.9)290 (12)
 Less than primary121 (11.1)193 (14.6)314 (13)
 Primary education287 (26.4)347 (26.3)634 (26.4)
 Secondary/Higher Secondary374 (34.4)357 (27.1)731 (30.4)
 Graduation and above235 (21.6)198 (15)433 (18)
 Refused to answer2 (0.2)1 (0.1)3 (0.1)
Religion
 Hindu1,027 (94.6)1,243 (93.6)2,261 (94)
 Christian38 (3.5)55 (4.2)93 (3.9)
 Muslim21 (1.9)28 (2.1)49 (2)
 Others02 (0.2)2 (0.1)
Marital status
 Never married228 (21)90 (6.8)318 (13.2)
 Currently married831 (76.5)978 (74.1)1,809 (75.2)
 Divorced/Separated6 (0.6)20 (1.5)26 (1.1)
 Widowed21 (1.9)231 (17.5)252 (10.5)
Occupation
 Government employee42 (3.9)19 (1.4)61 (2.5)
 Non-government employee40.6 (37.4)184 (13.9)590 (24.5)
 Self employed403 (37.1)183 (13.8)586 (24.4)
 Student56 (5.2)29 (2.2)85 (3.5)
 Homemaker10 (0.9)796 (60.3)806 (33.5)
 Retired57 (5.2)14 (1.1)71 (3)
 Unemployed (able to work)49 (4.5)34 (2.6)83 (3.5)
 Unemployed (Unable to work)63 (5.8)60 (4.5)123 (5.1)
Body mass index (BMI)
 Underweight (≤18.5 kg/m2)90 (8.3)81 (6.1)171 (7.1)
 Normal (≥18.5–22.9 kg/m2)353 (32.5)350 (26.3)703 (29.2)
 Overweight (23–24.9 kg/m2)236 (21.7)187 (14.1)423 (17.6)
 Obesity (≥25 kg/m2)407 (37.5)701 (52.7)1,108 (46.1)
VariablesMen (n = 1,086)
n (%)		Women (n = 1,319)
n (%)		Both gender (N = 2,405)
n (%)
Agea categories (in years)
 18–44538 (49.5)657 (49.8)1,205 (49.9)
 45–69548 (50.5)662 (50.2)1,210 (50.1)
Residence
 Urban749 (69)925 (70.1)1,674 (69.6)
 Rural337 (31)394 (29.9)731 (30.4)
Educational status
 No formal education67 (6.2)223 (16.9)290 (12)
 Less than primary121 (11.1)193 (14.6)314 (13)
 Primary education287 (26.4)347 (26.3)634 (26.4)
 Secondary/Higher Secondary374 (34.4)357 (27.1)731 (30.4)
 Graduation and above235 (21.6)198 (15)433 (18)
 Refused to answer2 (0.2)1 (0.1)3 (0.1)
Religion
 Hindu1,027 (94.6)1,243 (93.6)2,261 (94)
 Christian38 (3.5)55 (4.2)93 (3.9)
 Muslim21 (1.9)28 (2.1)49 (2)
 Others02 (0.2)2 (0.1)
Marital status
 Never married228 (21)90 (6.8)318 (13.2)
 Currently married831 (76.5)978 (74.1)1,809 (75.2)
 Divorced/Separated6 (0.6)20 (1.5)26 (1.1)
 Widowed21 (1.9)231 (17.5)252 (10.5)
Occupation
 Government employee42 (3.9)19 (1.4)61 (2.5)
 Non-government employee40.6 (37.4)184 (13.9)590 (24.5)
 Self employed403 (37.1)183 (13.8)586 (24.4)
 Student56 (5.2)29 (2.2)85 (3.5)
 Homemaker10 (0.9)796 (60.3)806 (33.5)
 Retired57 (5.2)14 (1.1)71 (3)
 Unemployed (able to work)49 (4.5)34 (2.6)83 (3.5)
 Unemployed (Unable to work)63 (5.8)60 (4.5)123 (5.1)
Body mass index (BMI)
 Underweight (≤18.5 kg/m2)90 (8.3)81 (6.1)171 (7.1)
 Normal (≥18.5–22.9 kg/m2)353 (32.5)350 (26.3)703 (29.2)
 Overweight (23–24.9 kg/m2)236 (21.7)187 (14.1)423 (17.6)
 Obesity (≥25 kg/m2)407 (37.5)701 (52.7)1,108 (46.1)

aMean (SD) age: Men 44 (15) years; Women 44 (13) years.

The mean levels of TAF and IAAT were significantly higher among women and the urban population. The mean levels of SCAT were significantly higher among women 124.8 (118–131) cm2. Over four-fifths (85.6%; 95% CI: 84.2–86.9) and two-thirds (69.7%; 95% CI: 67.9–71.6) of the population in the district had high levels of IAAT and TAF, respectively. Both the risk factors were significantly higher among women and the urban population. About 42.9% (95% CI: 41–44.9) of the population had a high level of SCAT, with a significantly higher prevalence among the urban population (Table 3).

Table 3.
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Mean (confidence interval) and prevalence of various abdominal adiposities by age group, gender, and residence among the study participants (N = 2,405).

VariablesMean (confidence interval) of abdominal adiposities
Total abdominal fat (cm2)Intra-abdominal adipose tissue (cm2)Subcutaneous adipose tissue (cm2)
Age (in years)
 18–44296 (285–306)188 (182–193)109 (101–116)
 45–69303 (294–311)183 (179–187)120 (113–126)
Gender
 Men264 (255–273)162 (158–165)100 (93–107)
 Women324a (315–333)202a (197–207)124.8a (118–131)
Residence
 Urban312a (305–319)195a (192–199)118 (112–123)
 Rural268 (256–281)161 (155–168)107.9 (98–117)
Overall299 (293–306)185 (182–188)114.8 (109–119)
Mean (SD)299 (111)185 (59)108 (51–159)b
VariablesMean (confidence interval) of abdominal adiposities
Total abdominal fat (cm2)Intra-abdominal adipose tissue (cm2)Subcutaneous adipose tissue (cm2)
Age (in years)
 18–44296 (285–306)188 (182–193)109 (101–116)
 45–69303 (294–311)183 (179–187)120 (113–126)
Gender
 Men264 (255–273)162 (158–165)100 (93–107)
 Women324a (315–333)202a (197–207)124.8a (118–131)
Residence
 Urban312a (305–319)195a (192–199)118 (112–123)
 Rural268 (256–281)161 (155–168)107.9 (98–117)
Overall299 (293–306)185 (182–188)114.8 (109–119)
Mean (SD)299 (111)185 (59)108 (51–159)b
% (confidence interval) of abdominal adiposities
High total abdominal fatHigh intra-abdominal adipose tissueHigh subcutaneous adipose tissue
Age (in years)
 18–4468.4 (65.5–71)85.4 (83.5–87.4)41.3 (38.4–44.3)
 45–6971 (68.3–73.6)85.8 (83.9–87.8)44.5 (41.7–47.5)
Gender
 Men53.9 (50.9–56.8)73.4 (70.8–76)42.5 (39.8–45.5)
 Women82.7a (80.7–84.8)95.7a (94.6–96.7)43.2 (40.5–45.9)
Residence
 Urban73.4a (71.2–75.6)87.5a (85.9–88.9)45.3a (42.8–47.7)
 Rural61.1 (57.7–64.7)81.3 (78.5–84.1)37.5 (33.9–40.9)
Overall69.7 (67.9–71.6)85.6 (84.2–86.9)42.9 (41–44.9)
% (confidence interval) of abdominal adiposities
High total abdominal fatHigh intra-abdominal adipose tissueHigh subcutaneous adipose tissue
Age (in years)
 18–4468.4 (65.5–71)85.4 (83.5–87.4)41.3 (38.4–44.3)
 45–6971 (68.3–73.6)85.8 (83.9–87.8)44.5 (41.7–47.5)
Gender
 Men53.9 (50.9–56.8)73.4 (70.8–76)42.5 (39.8–45.5)
 Women82.7a (80.7–84.8)95.7a (94.6–96.7)43.2 (40.5–45.9)
Residence
 Urban73.4a (71.2–75.6)87.5a (85.9–88.9)45.3a (42.8–47.7)
 Rural61.1 (57.7–64.7)81.3 (78.5–84.1)37.5 (33.9–40.9)
Overall69.7 (67.9–71.6)85.6 (84.2–86.9)42.9 (41–44.9)

aIndicates statistically significant difference between the subgroups.

bMedian (IQR).

Table 3.
Open in new tab

Mean (confidence interval) and prevalence of various abdominal adiposities by age group, gender, and residence among the study participants (N = 2,405).

VariablesMean (confidence interval) of abdominal adiposities
Total abdominal fat (cm2)Intra-abdominal adipose tissue (cm2)Subcutaneous adipose tissue (cm2)
Age (in years)
 18–44296 (285–306)188 (182–193)109 (101–116)
 45–69303 (294–311)183 (179–187)120 (113–126)
Gender
 Men264 (255–273)162 (158–165)100 (93–107)
 Women324a (315–333)202a (197–207)124.8a (118–131)
Residence
 Urban312a (305–319)195a (192–199)118 (112–123)
 Rural268 (256–281)161 (155–168)107.9 (98–117)
Overall299 (293–306)185 (182–188)114.8 (109–119)
Mean (SD)299 (111)185 (59)108 (51–159)b
VariablesMean (confidence interval) of abdominal adiposities
Total abdominal fat (cm2)Intra-abdominal adipose tissue (cm2)Subcutaneous adipose tissue (cm2)
Age (in years)
 18–44296 (285–306)188 (182–193)109 (101–116)
 45–69303 (294–311)183 (179–187)120 (113–126)
Gender
 Men264 (255–273)162 (158–165)100 (93–107)
 Women324a (315–333)202a (197–207)124.8a (118–131)
Residence
 Urban312a (305–319)195a (192–199)118 (112–123)
 Rural268 (256–281)161 (155–168)107.9 (98–117)
Overall299 (293–306)185 (182–188)114.8 (109–119)
Mean (SD)299 (111)185 (59)108 (51–159)b
% (confidence interval) of abdominal adiposities
High total abdominal fatHigh intra-abdominal adipose tissueHigh subcutaneous adipose tissue
Age (in years)
 18–4468.4 (65.5–71)85.4 (83.5–87.4)41.3 (38.4–44.3)
 45–6971 (68.3–73.6)85.8 (83.9–87.8)44.5 (41.7–47.5)
Gender
 Men53.9 (50.9–56.8)73.4 (70.8–76)42.5 (39.8–45.5)
 Women82.7a (80.7–84.8)95.7a (94.6–96.7)43.2 (40.5–45.9)
Residence
 Urban73.4a (71.2–75.6)87.5a (85.9–88.9)45.3a (42.8–47.7)
 Rural61.1 (57.7–64.7)81.3 (78.5–84.1)37.5 (33.9–40.9)
Overall69.7 (67.9–71.6)85.6 (84.2–86.9)42.9 (41–44.9)
% (confidence interval) of abdominal adiposities
High total abdominal fatHigh intra-abdominal adipose tissueHigh subcutaneous adipose tissue
Age (in years)
 18–4468.4 (65.5–71)85.4 (83.5–87.4)41.3 (38.4–44.3)
 45–6971 (68.3–73.6)85.8 (83.9–87.8)44.5 (41.7–47.5)
Gender
 Men53.9 (50.9–56.8)73.4 (70.8–76)42.5 (39.8–45.5)
 Women82.7a (80.7–84.8)95.7a (94.6–96.7)43.2 (40.5–45.9)
Residence
 Urban73.4a (71.2–75.6)87.5a (85.9–88.9)45.3a (42.8–47.7)
 Rural61.1 (57.7–64.7)81.3 (78.5–84.1)37.5 (33.9–40.9)
Overall69.7 (67.9–71.6)85.6 (84.2–86.9)42.9 (41–44.9)

aIndicates statistically significant difference between the subgroups.

bMedian (IQR).

Among those overweight/obese (n = 773), almost all 99.4% (95% CI: 98.7–99.9) were MUO. Among subjects with normal BMI (n = 314), about 2.9% (95% CI: 1.3–4.8) were metabolically healthy.

In this study, almost all those with normal BMI and overweight/obesity were metabolically unhealthy. Hence, we further explored for any statistically significant difference in the prevalence of metabolic risk factors between those having normal BMI and overweight/obesity. We found that expect the prevalence of dyslipidaemia, the prevalence of high TAF, high IAAT, high SCAT, abdominal obesity, metabolic syndrome, DM, and HTN were significantly higher among those having overweight/obesity when compared with those having normal BMI. This indicated that the similar levels of metabolically unhealthy status among both the groups (normal BMI and overweight/obesity) were due to similar levels of dyslipidaemia among the groups (93.4% and 95.4%, respectively). On further investigation, we found that the comparable levels of dyslipidaemia between normal BMI and overweight/obese groups were primarily due to comparably high levels of raised LDL cholesterol and total cholesterol between the groups (Table 4).

Table 4.
Open in new tab

Association of various metabolic risk factors between those with normal BMI and overweight/obese in the district of Puducherry.

VariablesCategoriesNormal BMI (n = 703)
n (%)		Overweight/obese (n = 1,531)
n (%)		P-value
High TAFaPresent260 (37)1,415 (92.4)≤0.05
Absent443 (63)116 (7.6)
High IAATbPresent560 (79.7)1,387 (90.6)≤0.05
Absent143 (20.3)144 (9.4)
High SCATcPresent49 (7)983 (64.2)≤0.05
Absent654 (93)548 (35.8)
Abdominal obesityPresent361 (51.4)1,248 (81.5)≤0.05
Absent342 (48.6)283 (18.5)
Metabolic syndrome (normal BMI, n = 314; overweight/obese, n = 773)Present107 (34.1)484 (62.6)≤0.05
Absent207 (65.9)289 (37.4)
Diabetes mellitus (normal BMI, n = 319; overweight/obese, n = 776)Present70 (21.9)226 (29.1)≤0.05
Absent249 (78.1)550 (70.9)
Hypertension (normal BMI, n = 703; overweight/obese, n = 1,531)Present193 (27.5)589 (38.5)≤0.05
Absent510 (72.5)942 (61.5)
Dyslipidaemia (normal BMI, n = 316; overweight/obese, n = 775)Present295 (93.4)739 (95.4)0.18
Absent21 (6.6)36 (4.6)
Raised LDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present234 (74.1)611 (78.8)0.09
Absent82 (25.9)164 (21.2)
Raised total cholesterol (normal BMI, n = 321; overweight/obese, n = 777)Present91 (28.3)260 (33.5)0.09
Absent230 (71.7)517 (66.5)
Low HDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present179 (56.6)555 (71.6)≤0.05
Absent137 (43.4)220 (28.4)
Raised triglycerides (normal BMI, n = 321; overweight/obese, n = 777)Present61 (19)191 (24.6)≤0.05
Absent260 (81)586 (75.4)
VariablesCategoriesNormal BMI (n = 703)
n (%)		Overweight/obese (n = 1,531)
n (%)		P-value
High TAFaPresent260 (37)1,415 (92.4)≤0.05
Absent443 (63)116 (7.6)
High IAATbPresent560 (79.7)1,387 (90.6)≤0.05
Absent143 (20.3)144 (9.4)
High SCATcPresent49 (7)983 (64.2)≤0.05
Absent654 (93)548 (35.8)
Abdominal obesityPresent361 (51.4)1,248 (81.5)≤0.05
Absent342 (48.6)283 (18.5)
Metabolic syndrome (normal BMI, n = 314; overweight/obese, n = 773)Present107 (34.1)484 (62.6)≤0.05
Absent207 (65.9)289 (37.4)
Diabetes mellitus (normal BMI, n = 319; overweight/obese, n = 776)Present70 (21.9)226 (29.1)≤0.05
Absent249 (78.1)550 (70.9)
Hypertension (normal BMI, n = 703; overweight/obese, n = 1,531)Present193 (27.5)589 (38.5)≤0.05
Absent510 (72.5)942 (61.5)
Dyslipidaemia (normal BMI, n = 316; overweight/obese, n = 775)Present295 (93.4)739 (95.4)0.18
Absent21 (6.6)36 (4.6)
Raised LDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present234 (74.1)611 (78.8)0.09
Absent82 (25.9)164 (21.2)
Raised total cholesterol (normal BMI, n = 321; overweight/obese, n = 777)Present91 (28.3)260 (33.5)0.09
Absent230 (71.7)517 (66.5)
Low HDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present179 (56.6)555 (71.6)≤0.05
Absent137 (43.4)220 (28.4)
Raised triglycerides (normal BMI, n = 321; overweight/obese, n = 777)Present61 (19)191 (24.6)≤0.05
Absent260 (81)586 (75.4)

aTAF: total abdominal fat.

bIAAT: intra-abdominal adipose tissue.

cSCAT: subcutaneous adipose tissue.

Table 4.
Open in new tab

Association of various metabolic risk factors between those with normal BMI and overweight/obese in the district of Puducherry.

VariablesCategoriesNormal BMI (n = 703)
n (%)		Overweight/obese (n = 1,531)
n (%)		P-value
High TAFaPresent260 (37)1,415 (92.4)≤0.05
Absent443 (63)116 (7.6)
High IAATbPresent560 (79.7)1,387 (90.6)≤0.05
Absent143 (20.3)144 (9.4)
High SCATcPresent49 (7)983 (64.2)≤0.05
Absent654 (93)548 (35.8)
Abdominal obesityPresent361 (51.4)1,248 (81.5)≤0.05
Absent342 (48.6)283 (18.5)
Metabolic syndrome (normal BMI, n = 314; overweight/obese, n = 773)Present107 (34.1)484 (62.6)≤0.05
Absent207 (65.9)289 (37.4)
Diabetes mellitus (normal BMI, n = 319; overweight/obese, n = 776)Present70 (21.9)226 (29.1)≤0.05
Absent249 (78.1)550 (70.9)
Hypertension (normal BMI, n = 703; overweight/obese, n = 1,531)Present193 (27.5)589 (38.5)≤0.05
Absent510 (72.5)942 (61.5)
Dyslipidaemia (normal BMI, n = 316; overweight/obese, n = 775)Present295 (93.4)739 (95.4)0.18
Absent21 (6.6)36 (4.6)
Raised LDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present234 (74.1)611 (78.8)0.09
Absent82 (25.9)164 (21.2)
Raised total cholesterol (normal BMI, n = 321; overweight/obese, n = 777)Present91 (28.3)260 (33.5)0.09
Absent230 (71.7)517 (66.5)
Low HDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present179 (56.6)555 (71.6)≤0.05
Absent137 (43.4)220 (28.4)
Raised triglycerides (normal BMI, n = 321; overweight/obese, n = 777)Present61 (19)191 (24.6)≤0.05
Absent260 (81)586 (75.4)
VariablesCategoriesNormal BMI (n = 703)
n (%)		Overweight/obese (n = 1,531)
n (%)		P-value
High TAFaPresent260 (37)1,415 (92.4)≤0.05
Absent443 (63)116 (7.6)
High IAATbPresent560 (79.7)1,387 (90.6)≤0.05
Absent143 (20.3)144 (9.4)
High SCATcPresent49 (7)983 (64.2)≤0.05
Absent654 (93)548 (35.8)
Abdominal obesityPresent361 (51.4)1,248 (81.5)≤0.05
Absent342 (48.6)283 (18.5)
Metabolic syndrome (normal BMI, n = 314; overweight/obese, n = 773)Present107 (34.1)484 (62.6)≤0.05
Absent207 (65.9)289 (37.4)
Diabetes mellitus (normal BMI, n = 319; overweight/obese, n = 776)Present70 (21.9)226 (29.1)≤0.05
Absent249 (78.1)550 (70.9)
Hypertension (normal BMI, n = 703; overweight/obese, n = 1,531)Present193 (27.5)589 (38.5)≤0.05
Absent510 (72.5)942 (61.5)
Dyslipidaemia (normal BMI, n = 316; overweight/obese, n = 775)Present295 (93.4)739 (95.4)0.18
Absent21 (6.6)36 (4.6)
Raised LDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present234 (74.1)611 (78.8)0.09
Absent82 (25.9)164 (21.2)
Raised total cholesterol (normal BMI, n = 321; overweight/obese, n = 777)Present91 (28.3)260 (33.5)0.09
Absent230 (71.7)517 (66.5)
Low HDL cholesterol (normal BMI, n = 316; overweight/obese, n = 775)Present179 (56.6)555 (71.6)≤0.05
Absent137 (43.4)220 (28.4)
Raised triglycerides (normal BMI, n = 321; overweight/obese, n = 777)Present61 (19)191 (24.6)≤0.05
Absent260 (81)586 (75.4)

aTAF: total abdominal fat.

bIAAT: intra-abdominal adipose tissue.

cSCAT: subcutaneous adipose tissue.

Discussion

In this study, we report a considerably higher prevalence of high IAAT (85.6%), high TAF (69.7%), and high SCAT (42.9%) in the population of Puducherry district. Among those having overweight/obesity, almost all were metabolically unhealthy. And among those with normal BMI, only about 2.9% were metabolically healthy. On reviewing the literature, we observed scarce evidence from India on these parameters at the population level. However, a few studies from India have reported these parameters that were carried out in urban areas and hospital settings in the country.19–21

In this study, the prevalence of high IAAT and high TAF from urban Puducherry was 87.5% and 73.4%, respectively. This is higher when compared with the prevalence of high IAAT (70.8%) and TAF (69.3%) observed in urban areas of New Delhi. Furthermore, the prevalence of high SCAT (45.3%) among the urban population of the current study was lesser when compared with SCAT prevalence (67.8%) in the later study.20 It was interesting to note that although both the studies had a similar prevalence of abdominal obesity measured using WC, considerable variation was present in the prevalence of abdominal obesities (TAF, IAAT, and SCAT) between the study populations. This difference could be attributed to the established evidence that abdominal fat distribution varies across ethnicities, age groups, and gender distribution between the studies.6

The mean levels of TAF and IAAT from the current study were substantially higher compared to the study carried out in Southern India among non-diabetic individuals (TAF: 332 ± 135.8 cm2; IAAT: 119.5 ± 53.5 cm2).21 The differences could be due to the relatively healthy population in the latter study compared to the general population in the current study, where almost one-third (33.6%) and one-fourth (26.7%) of participants had HTN and DM, respectively.14

Another study conducted among patients having HTN in a tertiary care setting in Puducherry showed a significantly higher prevalence of high TAF (94%) and IAAT (81%) among women compared to men (TAF; 72%, IAAT; 50%).19 The difference in the study populations makes it difficult to interpret the prevalence obtained in the former study with the current study. But, a significantly higher prevalence of these parameters among women reported in the former study is in line with the current study findings.19 Furthermore, the former study reported a higher prevalence of high SCAT among men (81%) compared to women (71%), whereas it was marginally higher among women (43.2% vs. 42.5% among men) in the current study.19 The consistent findings substantiate the higher level of these abdominal fat parameters among women in Puducherry, indicating the need for early intervention in the form of screening for metabolic abnormalities to prevent the impending metabolic and cardiovascular disorders in the district of Puducherry.

In this study, almost all (99.4%) of those having obesity/overweight were metabolically unhealthy. A review of the literature showed wide variations in the definitions used to assess overweight/obesity and in defining which parameters constitute MUO, making comparing its prevalence between studies difficult. Studies carried out in America showed that the prevalence of MHO ranged from 4% to 9.7%22–25; the corresponding proportions evidenced from the studies done in European countries showed 1.1% to 6.6%.26–29 Studies from Korea showed that the prevalence of MHO ranges from 5.7% to 25.8%.30–32 In comparison, the prevalence of MHO in the current study (0.6%) is abysmally low. This disparity in the prevalence across studies could be primarily attributed to varied definitions of MHO and MUO used across studies. Secondly, differences in inclusion criteria, metabolic parameter cut-offs, and a broad spectrum of age groups (20–90 years) included in the previous studies could have led to comparatively lower prevalence in the studies compared. As obesity and metabolic risk factors increase with age, heterogeneity in age groups included across studies also adds to complexities in comparing the studies.

Considering the variations in the definitions of MHO and MUO in determining its prevalence, a study from China revealed that the prevalence of MHO ranged between 4.2% and 11.4% when five most commonly used definitions of MHO/MUO were adopted.33 With a lack of data on the prevalence of MHO among Asian Indians, future studies shall apply the standard and commonly used definitions in a single population to estimate the impact of various definitions on the prevalence of MHO/MUO in the population.

In this study, it was concerning to observe that almost all those with normal BMI levels were equally unhealthy as those with overweight/obesity. Further analysis to explain this observation revealed that the comparable levels of metabolically unhealthy status between the groups were primarily due to similar levels of the prevalence of dyslipidaemia. Except for this metabolic risk factor, all other risk factors studied were significantly higher among overweight/obese people, which is in line with other studies that assessed the prevalence of these metabolic risk factors among those having overweight/obesity and normal BMI levels.34,35 The non-significant difference in the prevalence of metabolically unhealthy individuals between the study groups could be mainly attributed to the substantially higher levels of dyslipidaemia in the study population irrespective of the BMI levels. When significantly higher levels of dyslipidaemia among overweight/obese people could be justified,35 the higher prevalence among those with normal BMI levels raises concerns indicating the increasing prevalence of cardiovascular risk in the population.

Strengths and limitations

The study’s major strength was that the results were derived from a representative sample of Puducherry district by utilizing standard data collection methodologies as prescribed by STEPS guidelines, which enhances the generalizability of study findings. The prevalence of high TAF, IAAT, and SCAT obtained in this study might carry a certain degree of bias, as these estimates were derived from physical measurements, such as height, weight, WC, and HC that could have endured investigator and measurement device-dependent variations. However, to minimize this bias, all investigators involved in data collection were trained adequately before data collection. All measurement devices were regularly calibrated before and periodically during data collection. Furthermore, as we have used predictive equations for determining abdominal obesities, there could be some degree of non-differential misclassification bias in the prevalence of various abdominal obesities estimated in this study. However, considering the high cost involved for the accurate measurement of abdominal obesities that require advanced imaging modalities, the estimates derived from these predictive equations serve as a more cost-effective approach for furthering metabolic research, inter-ethnicity comparisons of adiposities, and determining people with adverse metabolic profile in a population.

Conclusion

In this study, we highlight the high prevalence of various abdominal obesities, such as IAAT (85.6%), TAF (69.7%), and SCAT (42.9%). Almost whole (99.4%) of the study population was metabolically unhealthy irrespective of their BMI levels. With these findings, we highlight the poor state of metabolic health in the district of Puducherry and the immediate need for population-based health promotion interventions, especially among women and urban residents of the community, to prevent the increase in metabolic and cardiovascular disorders in the district.

Author contributions

PS, JPS, SL, ZB, and SSK conceptualized the study design and methodology. PS conducted the field data collection. All authors contributed to the analysis plan of the manuscript, and the analysis was carried out by PS. PS wrote the first draft of the manuscript with inputs from SKK and JPS. All authors reviewed the manuscript, provided information for data interpretation, and also approved its final version.

Funding

Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), India [Grant Number: JIP/Dean (R)/Intramural/Phs 1/2019-20] has funded this project through the intramural funding for PhD studies.

Conflict of interest

None declared.

Ethical approval

The study was approved by the Ethics Committee of Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER) (No: JIP/IEC/2018/0246).

Consent for publication

Not applicable.

Data availability

The data underlying this manuscript could be shared by the corresponding author upon reasonable request (email: [email protected]).

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