High prevalence of metabolic obesity in India: The ICMR-INDIAB national study (ICMR-INDIAB-23)

Materials & Methods

This cross-sectional survey was undertaken by the department of Epidemiology and Research Operations and Diabetes Complications, Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India. The study was approved by the Institutional Ethics Committee of the coordinating centres and individual States. Written informed consent was obtained from all study participants. The study was registered with the Clinical Trials Registry of India (CTRI/2019/03/018095).

Results

Figure 1 presents the prevalence of different subtypes of obesity in the study population. The most prevalent obesity subtype was MONO, accounting for 43.3 per cent [95% confidence interval (CI): 42.6-44.%], followed by MOO (28.3%; 95% CI: 27.7-28.9%), MHNO (26.6%; 95% CI: 26.0-27.2%) and MHO (1.8%; 95% CI: 1.6-2.0%). The obese subtypes (MHO and MOO) were more common in the urban areas (urban vs. rural: MHO: 2%; 95% CI: 1.5-2.4% vs. 1.7%; 95% CI: 1.5-2%; P=0.304; MOO: 39%; 95% CI: 37.2-40.8% vs. 22.8%; 95% CI: 22-23.7%; P<0.001). MOO was more common among females (male vs. female: 23.2%; 95% CI: 22.2-24.3% vs. 32.9%; 95% CI: 31.7-34.2%; P<0.001), whereas MHO was more common in males (male vs. female: 2.1%; 95% CI: 1.8-2.4% vs. 1.5%; 95% CI: 1.3-1.8%; P=0.011).

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Fig. 1.

Different subtypes of obesity in the Asian Indian population. MHNO, metabolically healthy non obese; MONO, metabolically obese non obese; MHO, metabolically healthy obese; MOO, metabolically obese obese.

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The non-obese subtypes were more common in the rural areas (rural vs. urban MHNO: 29.5%; 95% CI: 28.6-30.4% vs. 19.4%; 95% CI: 18-20.8%; P<0.001; MONO: 46%; 95% CI: 45-46.9% vs. 39.6%; 95% CI: 37.8-41.3%; P<0.001) and among males (males vs. females MHNO: 30%; 95% CI: 28.8-31.1% vs. 22.4%; 95% CI: 21.4-23.4%, P<0.001; MONO: 44.7%; 95% CI: 43.5-45.8% vs. 43.1%; 95% CI: 41.8-44.4%: P=0.079).

The State-specific prevalence of the subtypes of obesity is depicted in supplementary figure. MONO was the most common subtype in all States, with prevalence ranging from 34.8 per cent (95% CI: 27.7-41.9%) in Delhi to 56.7 per cent (95% CI: 52.6-60.8%) in Tripura. Supplementary table I shows the contribution of various components to metabolic obesity. Among the five components of metabolic obesity, HDL-C had the highest contribution (79.2%; 95% CI: 78.5-79.9%), followed by BP (69.5%; 95% CI: 68.7-70.2%), fasting blood glucose (57.7%; 95% CI: 56.9-58.6%), WC (55.7%; 95% CI: 54.9-56.6%), and triglycerides (42.8%; 95% CI: 42-43.7%). When analysing the combination of different components of metabolic obesity, two components were present in 42.6 per cent (95% CI: 41.8-43.4%) of individuals, three in 30.7 per cent (95% CI: 29.9-31.5%), four in 19.2 per cent (95% CI: 18.6-19.9%), and all five in 7.5 per cent (95% CI: 7.1-8%).

Supplementary Figure

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Table I presents the anthropometric and metabolic characteristics of the study population characterised by obesity subtypes. The MOO subtype was more prevalent in females, while other subtypes were more prevalent in males. Compared to the healthy reference group, i.e., MHNO, all other subtypes were older (except MHO), had significantly higher BMI, WC, systolic and diastolic BP (except MHO), fasting blood glucose (except MHO), HbA1c, triglycerides, and total and LDL cholesterol. As expected, the MOO subtype had the highest values of all these parameters. While physical inactivity and family history of diabetes were higher in the obese subtypes (MHO and MOO), smoking rates were higher in the non-obese subtypes (MHNO and MONO). Compared to the reference group (MHNO), MOO reported significantly lower intake of fruits and vegetables.

Figure 2 presents the age-specific prevalence of the various obesity subtypes. With increasing age, the prevalence of MHNO and MHO decreased, and that of MONO increased. The prevalence of MOO rose until the age of 60, after which it declined.

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Fig. 2.

Age-specific prevalence of subtypes of obesity in the study population.

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The risk of T2D, CAD and CKD among the various obesity subtypes, using MHNO as a reference group, is presented in table II. The risk of T2D was highest in MOO [Odds ratio (OR)=12.89; 95% CI: 9.54-17.43; P<0.001], especially among females, followed by MONO (OR=6.90; 95% CI: 5.10-9.34; P<0.001) after adjusting for confounders. Similarly, the risk of CAD was highest for MOO (OR=1.92; 95% CI: 1.27-2.91; P=0.002), followed by MONO (OR=1.77; 95% CI: 1.22-2.57; P=0.003) after adjusting for age, sex, education, physical activity, smoking, income, family history of diabetes and dietary nutrient densities (Carbohydrates %E, Fat %E, and Protein %E). Among both MONO and MOO, the risk for CAD was higher in males than in females. For CKD, after adjusting for confounders, MONO had the highest risk followed by MOO. Among MONO, females were shown to have a higher risk of CKD, while among MOO, males were found to have a higher risk. Sensitivity analysis with different BMI cut points (27.5 and 30 kg/m²) showed similar results with MONO and MOO having significantly higher risk for T2D, CAD and CKD (Supplementary Table II).

Discussion

In this nationally representative study of India, we show that (i) the largest proportion (43.3%) of the population in India is metabolically obese but with BMI<25 kg/m² (referred to as MONO), (ii) MONO is more prevalent in rural areas and among males, (iii) MOO showed highest risk for diabetes and CAD, while MONO showed the highest risk of CKD especially among females.

One of the simplest ways to identify obesity is by anthropometric measures, such as the BMI, WC, WHR, and WHtR. BMI is the most commonly used and is recommended by the International Obesity Task Force (IOTF) and the WHO²⁵. However, BMI does have certain drawbacks, as it cannot be used to assess body composition and cannot distinguish between lean and fat mass⁷. Therefore, a normal BMI may conceal the presence of excess body fat. WC, WHR, and WHtR are considered better predictors of health risks associated with obesity than BMI, as they provide information on fat distribution and central adiposity.

Studies worldwide have attempted classifications of individuals based on obesity and metabolic abnormalities. A systematic analysis of 27 prospective and cross-sectional studies revealed around 30 definitions for metabolic health²⁶. For instance, MONO was characterised by Lee et al²⁷ as having a BMI <25 kg/m² and at least two metabolic risk factors among high fasting glucose, hypertension, high triglycerides, low HDL, and HOMA-IR ≥90^th percentile. MONO was described by Goday et al²⁸ as having a normal BMI (18.5-25 kg/m²) and poor metabolic health as determined by the NCEP-ATP III guidelines (high fasting glucose, high triglycerides, low HDL-C, and high WC). MONO was defined as BMI<25 kg/m² who developed T2D during follow up by Eckel et al²⁹. Triglyceride glucose (TyG) index was suggested as a parameter for classification by Lee et al³⁰ who defined MONO as individuals with a normal BMI (18.5-25 kg/m²), a high metabolic syndrome score, an abnormal TyG index value, or a HOMA-IR score in the highest quartile. The use of varying criteria for defining metabolic abnormalities makes comparing results between different studies challenging. It may underlie the wide variations in reported prevalence rates of the MHO phenotype (ranging from 6 to 75%). The United States National Health and Nutrition Examination Survey (NHANES, 1999-2004) data revealed that among men and women of normal weight, 30.1 and 21.1 per cent, respectively, had abnormal metabolic status³¹.

Results from a study on 2,350 urban Asian Indian adults suggested that metabolic obesity had different clinical implications than generalised obesity³². The MONO phenotype is characterised by increased subcutaneous and visceral fat and insulin-resistant glucose metabolism. This makes the pancreas produce more insulin, exhausting it over time and reducing its ability to make insulin^33,34. It is therefore not surprising that individuals with MONO in our study were found to have an elevated risk of T2D. Studies have also suggested that the high prevalence rates of MONO may explain the increased risk for T2D in Asian Indians³⁵. Our study findings showed that MONO was also associated with elevated risk of CAD and CKD. Metabolic risk factors such as insulin resistance, hypertension, dyslipidaemia, and inflammation, common in MONO, might play a synergistic role in the development and progression of atherosclerosis, the underlying cause of CAD. Insulin resistance promotes a pro-inflammatory and pro-thrombotic State, fostering the formation of atherosclerotic plaques in the coronary arteries. Elevated triglycerides and decreased HDL-cholesterol further exacerbate atherosclerosis, and hypertension adds to the burden by accelerating endothelial dysfunction and vascular damage. Insulin resistance and associated hyperinsulinemia contribute to renal damage through glomerular hyperfiltration and inflammation. Dyslipidaemia exacerbates renal injury by promoting glomerulosclerosis and tubulointerstitial fibrosis, and hypertension contributes to renal damage via increased glomerular pressure and renal vascular injury. According to the Tehran Lipid Glucose Study (TLGS)³⁶, compared to individuals with normal weight and normal metabolic status, those with MHO and MONW (MONO) had a 23 and 43 per cent increased risk, respectively, of developing CKD.

Our findings that more than a third of individuals, even in the 20-29 yr age group, had MONO is a concerning trend given the long-term health risks associated with metabolic dysfunction. The differences in MONO and MOO prevalence between sexes are likely driven by the complex interplay of hormones and metabolism. They may have different health consequences, as females have proportionally more fat mass and males, more muscle mass. Differences in body shape and fat distribution between males and females may result in different patterns of abdominal fat accumulation and its relationship with cardiometabolic risk factors.

Based on studies^37-42 that examined the changes in metabolic phenotype associated with obesity over time, MHO is suggested to be a transient phase, which progresses to MOO subtype with time. The Coronary Artery Risk Development in Young Adults (CARDIA) cohort⁴³, with a mean follow up of 9.1 yr, reported that individuals with MHO had a significantly higher risk of coronary artery calcium development compared to MHNW individuals. Between 15 and 25 yr, up to 60 per cent of individuals with MHO transitioned to metabolically unhealthy obesity, suggesting that MHO serves as an intermediate phenotype between metabolically low- and high-risk obese individuals.

As individuals with MONO are at higher risk of adverse metabolic consequences, early identification is crucial for targeted interventions. However, when BMI is used as the sole measure for identifying obesity, individuals with MONO may not be detected, and thereby lose out on opportunities for early intervention and prevention of NCDs. While diagnostic tests like insulin resistance assessments and detailed lipid panels are available to identify individuals at risk, simpler and more accessible screening tools, such as WC, WHR, WHtR, and BP measurements, can help detect this unfavourable phenotype early. Raising awareness among healthcare professionals that a ‘normal’ body weight or BMI does not necessarily mean healthy will sensitise them to screen individuals for metabolic ill-health using other simple, readily available tools, particularly in rural and other resource-constrained settings. This will also enable early interventions in the form of lifestyle modifications with or without medication to reduce cardiometabolic risk. Regular monitoring of metabolic markers is also essential to monitor changes and appropriately modify interventions in these at-risk individuals. A multifaceted approach, involving national, regional, State, and district-level action and community participation, is needed to combat the obesity and cardiometabolic disease challenge. In India, national policies are being established to tackle obesity and cardiometabolic diseases. The Food Safety and Standards Authority of India (FSSAI) has introduced initiatives to regulate the advertising and labelling of unhealthy foods, particularly those high in fat, sugar, and salt. The National Programme for Prevention and Control of Non-Communicable Diseases (NP-NCD) focuses on early detection and prevention of NCDs through lifestyle interventions, including promoting physical activity, dietary modifications, and smoking cessation. Some Indian States have implemented targeted measures such as the ‘fat tax’ on high-calorie foods, including burgers, pizzas, and sugary drinks, to discourage unhealthy eating and encourage healthier food choices.

The strengths of our study include the survey of a large, community-based, nationally representative sample of men and women spanning a wide age range, as well as the standardised assessment of cardiometabolic risk factors. Some of the limitations of our study include the fact that it is cross-sectional, which prevents us from assessing causal implications or looking at long-term outcomes. For example, it is unclear whether MONO precedes disease onset or is a consequence; longitudinal studies are needed to confirm causality. While we have based the diagnosis of diabetes on CBG, which has a higher coefficient of variation than venous plasma, we have previously demonstrated a strong correlation between CBG and venous plasma estimations⁴⁴. Finally, different study phases were carried out at different time points, which was unavoidable due to sampling across a vast geographical terrain.

In conclusion, clear definitions are necessary to classify obese individuals across clinical and research studies. Individuals with MONO have a unique phenotype with adverse metabolic consequences. They represent a significant public health challenge in India as they constitute more than 43 per cent of the adult population, have an elevated risk of T2D, CAD, and CKD, and can only be identified through targeted and specific screening tests, as they do not have any of the easily identifiable phenotypic indicators of obesity. Weight-centric paradigms must shift to a more holistic understanding of metabolic health to empower individuals to make informed choices and take proactive steps toward a healthier future. In the clinical scenario, identifying individuals with MONO during a routine health check-up will help initiate suitable interventions before developing undesired outcomes, such as CAD and CKD.