Magnitude of Obesity - World including India

The 2016 Global Nutrition Report states that the rates of overweight/obese people are rising in every region of the world and in nearly every country. The global adult obesity prevalence is 13% as against 4.9% in India, and for overweight, the prevalence is 39% global and 22% for India, virtually indicating that one in four Indians has a weight problem of abundance. The World Health Organization (WHO) has declared obesity as the largest global chronic health problem in adults, which is increasingly turning into a more serious problem than malnutrition. Obesity is a gateway to ill health, and it has become one of the leading causes of disability and death, affecting not only adults but also children and adolescents worldwide. In 2014, more than 1.9 billion adults (18 years and older) were overweight. Of these, over 600 million were obese. Forty-two million children under the age of 5 were overweight or obese in 2013.³

Magnitude of obesity in India

In India, different geographical areas were studied for the prevalence of obesity. It was found that the prevalence of general obesity (GO) was 24.6%, 16.6%, 11.8%, and 31.3% among residents of Tamil Nadu (TN), Maharashtra (MH), Jharkhand (JH), and Chandigarh, respectively. The prevalence of AO was 26.6%, 18.7%, 16.9%, and 36.1% among residents of TN, MH, JH, and Chandigarh, respectively.

The survey conducted by National Family Health Survey-3 (NFHS-3) in India also found that obesity (BMI ≥25 kg/m²) was more prevalent in urban areas and in higher socioeconomic groups compared to rural areas, especially among women (Men - urban: 15.9 vs. rural: 5.6%; Women - urban: 23.5 vs. rural: 7.2%).⁴

A similar study from Jaipur called the “The Jaipur Heart Watch Study (I–IV)” showed that generalized and AO were significantly higher among the urban compared to the rural population.⁵ Pandey et al. studied middle-aged women in four urban and five rural areas and found that the prevalence of obesity in urban vs. rural was 45.6% vs. 22.5% and AO was 44.3% vs. 13.0%, respectively.⁶

In the Indian Council of Medical Research–India Diabetes (ICMR–INDIAB) study, it was found that women had higher mean BMI values than men (Urban: women: 23.6 vs. men: 22.7 kg/m² and Rural: women: 21.2 vs. men: 20.9, kg/m²). However, mean WC values were higher in men than in women (Urban: women: 77.4 vs. men: 83.6 cm; Rural: women: 71.7 vs. men: 78.1 cm).

The Chennai Urban Rural Epidemiology Study (CURES) conducted in Chennai city of TN reported age-standardized prevalence of GO to be 45.9%, while that of AO was 46.6%. Isolated GO was found in 9.1% while isolated AO was reported in 9.7%.⁷

Magnitude of overweight in India

It was observed in ICMR–INDIAB study that overweight was more prevalent in urban population than rural population (Chandigarh region - 18.9 vs. 14.8%; TN region - 16.5 vs. 14.6%; JH - 12.4 vs. 5.7%; and MH region - 13.6 vs. 10.3%).

Magnitude of childhood obesity in India

The Centre for Disease Control has published tables for determining obesity in children. For children and adolescents (age 2–19 years), the BMI value is plotted on the Center for Disease Control (CDC) growth charts to determine the corresponding BMI-for-age percentile.

• Overweight is defined as a BMI at or above the 85th percentile and lower than the 95th percentile

• Obesity is defined as a BMI at or above the 95th percentile.⁸

In a meta-analysis of 9 studies done by Midha et al., it was estimated that the prevalence of obesity in children <18 years of age was 3.39%, and the prevalence of overweight was 12.64%.⁹

Magnitude of diabetes in India

The burden of T2DM is high and increasing globally, and in developing economies like India, it is mainly fuelled by the increasing prevalence of overweight/obesity and unhealthy lifestyles. One estimate of International Diabetes Federation (IDF) in 2019 showed that 77 million individuals had diabetes in India, which is expected to rise to over 134 million by 2045.¹⁰ Approximately 57% of these individuals remain undiagnosed. The prevalence of diabetes in India has risen from 7.1% in 2009 to 8.9% in 2019.¹¹ As per the National Family Health Survey (NFHS)-5 data in the 22 States/UTs, around 16.8% of the male adult population and 14.6% of the female adult population, on average, are estimated to be diabetic.¹²

Diabetes in children and adolescents

T2DM, which earlier used to be associated with adults, is now observed in children and adolescents as well. In a study done across various hospital and clinic-based registries, reviewed by Chowdhury and Ghosh, it was suggested that the percentage of type 1 diabetes in children is showing a declining trend. This implies that the prevalence of T2DM in children is rising.¹³ The earliest report of T2DM in children was from Madras (Chennai). In this study, 18 children with insidious onset of diabetes at age ≤15 years, response to oral glucose-lowering drugs (preserved C peptide levels [≥ 1.82 pg/mL]) and absence of decarboxylase 65 (glutamic acid decarboxylase 65) antibodies were reported. T2DM in children is gradually being reported across the country, though the prevalence is not as high as in Asian countries such as Japan. At referral centers such as Lucknow and Chennai, the proportion of children with T2DM is reported as 12% and 26.7%, respectively.¹⁴

A large project (n = 1519) that was carried out in children aged 6–11 years from Chennai, found an overall prevalence of 3%–7% of glucose intolerance (4.2% in girls, 3.2% in boys; P < 0.001). The prevalence of dysglycemia, as assessed by the oral glucose tolerance test, was 12.7% in girls with AO. Upon multivariate analysis, a family history of diabetes was found to have a significant association with glucose intolerance in girls (odds ratio [OR], 4.11; 95% confidence interval [CI], 1.28–13.22; P = 0.018). Homeostasis model assessment-insulin resistance (HOMA-IR) was significantly associated with glucose intolerance in both boys (OR, 5.19; 95% CI, 1.54–17.44; P = 0.008) andgirls (OR, 11.22; 95% CI, 4.19–30.05; P < 0.001).¹⁵ The Comprehensive National Nutrition Survey, the first-ever nationally represented nutrition survey of children and adolescents in India used a sample size of 17,865 adolescent boys and 17,965 adolescent girls. The prevalence of pre-diabetes/diabetes was 12.3% and 8.4% among adolescent boys and girls in India, respectively. BMI and subscapular skinfold thickness were the two most important predictors of pre-diabetes/diabetes among adolescents. Further, physical activities show a negative association with pre-diabetes/diabetes. Moreover, interaction models in the present study clearly reveal the fact that adolescent girls were less likely to suffer from pre-diabetes/diabetes than adolescent boys. Additionally, it was found that the prevalence of pre-diabetes/diabetes was high among adolescent girls from lower socioeconomic strata.^16,17 A recent study conducted in Kerala included 607 children in the study. 56.3% were males. Prevalence of pre-diabetes was 20.4% with a combination of Oral Glucose Tolerance Test (OGTT), Fasting Blood Sugar (FBS), and Hemoglobin A1C (HbA1c) tests. Prevalence by OGTT was 5.9%, HbA1c 4.1%, and FBS was 17.1%. A higher prevalence of pre-diabetes was associated with male gender, frequent consumption of junk foods, decreased physical activities, overweight, obesity, and high waist-to-hip ratio. A combination of FBS with Glucose Tolerance Test (GTT) and FBS with HbA1c had better sensitivity and specificity when compared to a combination of OGTT with HbA1c.¹⁸ In another multicentric study on school children of MH, Gujarat, Chhattisgarh, Assam, TN, and Punjab from 40 selected schools, data on 14,339 subjects (7413 boys) were analyzed. Prevalence of obesity was 5.8%, and overweight was 10.6%. Overall, 1% had low (<54 mg/dL), 93.7% in the reference range (70–130 mg/dL), and 5.3% had elevated Random Blood Glucose (RBG) (>130 mg/dL). With increasing mean BMI, there was an increase in RBG concentrations. Children from TN were more likely to have RBG outside the reference range compared to other regions (P < 0.05). Assam and Punjab had the highest prevalence of RBG and BMI within the reference range. Energy intake partly explained regional variations. Multivariate analysis showed male gender, urban residency, age >10 years (girls) and 13 years (boys), and overweight or obesity were predictive of pre-diabetes.¹⁹

Diet as a risk factor for obesity:

Childhood obesity is an important public health issue worldwide. Urbanization, sedentary lifestyle, and changes in food habits are the chief reasons behind this pandemic. In a small proportion of children, obesity is the result of endocrine, syndromic, or monogenic causes. Production of high-fat, sugar and/ or salt-containing foods, use of complex marketing and advertising practices, aggressive sales tactics, and confusing food labeling restrict the consumers’ ability to make rational and healthy choices.⁴⁴

Satija et al conducted a cross-sectional study to identify dietary patterns in an Indian population and assess their relationship with anthropometric risk factors. The participants were rural-to-urban migrants and urban non-migrant factory workers, their rural and urban resident siblings, and their co-resident spouses. Three dietary patterns were identified: “cereals–savory foods” (cooked grains, rice/rice-based dishes, snacks, condiments, soups, nuts), “fruit–veg–sweets–snacks” (western cereals, vegetables, fruit, fruit juices, cooked milk products, snacks, sugars, sweets), and “animal-food” (red meat, poultry, fish/seafood, eggs). In adjusted analysis, positive-graded associations were found between the “animal-food” pattern and both anthropometric risk factors. Moderate intake of the “cereals–savory foods” pattern was associated with reduced odds of obesity and central obesity.⁴⁵

Ganguly et al performed a cross-sectional study in 701 women (aged 35 years and above) selected by cluster sampling from 12 different wards of the Kolkata Municipal Corporation (Kolkata, India). The following three major dietary patterns were identified: the “vegetable, fruits, and pulses” pattern (characterized by higher intakes of dark-yellow and green leafy vegetables, sweets, fruits, pulses, nuts, poultry and eggs, and lower intake of mustard oil); the “hydrogenated and saturated fat and vegetable oil” pattern (characterized by higher intakes of butter, hydrogenated oil, ghee, vegetable oil, mustard oil, condiments, sweets, fish, high-fat dairy, and refined grain); the “red meat and high-fat dairy” pattern (characterized by higher intakes of red meat, high-fat dairy products, whole grain, high-energy drinks and condiments, and lower intakes of fish, refined grain, and low-fat dairy products). The hydrogenated and saturated fat and vegetable oil pattern was positively associated with BMI, WC. In this Bengali population, these three major dietary patterns were observed, and the dietary patterns were independently associated with BMI, WC, and serum TC concentrations in women.⁴⁶

Gulati et al performed a randomized controlled trial on 122 overweight/obese men and women administered either a high protein meal replacement (HPMR) or a control diet after two weeks of diet and exercise run-in. Body weight, WC, percentage body fat, fasting blood glucose, post-oral glucose tolerance test (post-OGTT) blood glucose, fasting and post-OGTT serum insulin, lipid profile, high-sensitivity C-reactive protein, kidney function, and hepatic aminotransferases were assessed before and after the intervention. The findings show that intervention with HPMR may lead to significant weight loss and improvement in obesity measures, metabolic, lipid, and inflammatory parameters, and hepatic transaminases in overweight/obese Asian Indians.⁴⁷

Agarwal et al investigated the prevalence of obesity and diabetes among adult men and women in India consuming different types of vegetarian diets compared with those consuming non-vegetarian diets. Mean BMI was lowest in pesco-vegetarians (20.3 kg/m²) and vegans (20.5 kg/m²) and highest in lacto-ovo vegetarian (21.0 kg/m²) and lacto-vegetarian (21.2 kg/m²) diets. In this large, nationally representative sample of Indian adults, lacto-, lacto-ovo, and semi-vegetarian diets were associated with a lower likelihood of diabetes. These findings may assist in the development of interventions to address the growing burden of overweight/obesity and diabetes in Indian population.⁴⁸

Physical inactivity as a risk factor for obesity:

Patil et al conducted a cross-sectional study among adult patients attending the health centers of Indira Gandhi Government Medical College. Out of 200 study subjects, 59% were having a sedentary lifestyle, 27% were having a moderately active lifestyle, and 14% were having a vigorously active lifestyle. A statistically significant increasing trend for sedentary study subjects (P = 0.002) with age was found. Those who reported sedentary physical activity levels (PALs) had 3.42 odds of being hypertensive, 7.44 times odds of being diabetic, three times odds of being overweight, and 2.41 odds of being obese. The adults in urban areas had significantly less PALs when compared to rural areas. Higher levels of sedentary lifestyle were found in urban areas compared to rural areas. As the age increased, the sedentary lifestyle was having a significantly increasing trend. Physical activity was associated with the diabetic status, hypertensive status, and obesity status.⁴⁹

Singh et al conducted a cross-sectional study to find out the prevalence of overweight, obesity, undernutrition, and physical activity status in the urban populations of India. Cross-sectional surveys were conducted in 6–12 urban streets in each of the five cities across five different regions of India using a common study protocol and criteria of diagnosis. Obesity, overweight, central obesity, and sedentary behavior coexist with undernutrition and have become a public health problem in all the five cities of India. The prevalence of obesity and sedentary behavior was significantly greater in Trivandrum, Calcutta, and Bombay compared to Moradabad and Nagpur. Sedentary behavior was significantly associated with obesity compared to non-obese subjects in both sexes, which may be due to greater economic development in metro cities.⁵⁰

Arora et al conducted a cross-sectional study in eight schools (Private and Government) of Delhi in the year 2006. Participants: 1814 students from 8th and 10th grades. A total of 944 (52.0) private schools defined as belonging to the middle-to-upper income stata and 870 (48.0) government schools defined as belonging to the low-to-middle income group were taken in the study by MYTRI end-line survey in 2006 (Project MYTRI -Mobilizing Youth for Tobacco-Related Initiatives in India). Daily breakfast consumption is associated with less overweight and obesity and with healthier dietary and physical activity-related behaviors among urban Indian students.⁵¹

Diabetes mellitus:

India has the second largest number of people with diabetes in the world. Its increased incidence is driven by a combination of factors – rapid urbanization, sedentary lifestyles, unhealthy diets, tobacco use, and increasing life expectancy. According to WHO, India had 69.2 million people living with diabetes in 2015, and nearly 98 million may have T2DM by 2030. In 2019, diabetes was the ninth leading cause of death, with an estimated 1.5 million deaths directly caused by diabetes. NFHS-5 data revealed that the prevalence of diabetes in men is 15.6 % and in women it is 13.5 %.^12,52

Diet as a risk factor for DM in India:

The etiology of diabetes is believed to be multifactorial, nevertheless increase in the prevalence of T2DM in most populations has probably been driven by modifiable risk factors, including a sedentary lifestyle and/or lack of exercise and unhealthy diets like increased intake of refined grains, fat, sugar, and sweetened beverages and decreased intake of fruits and vegetables.⁵³

Nagarathna et al. conducted across seven zones of India, studying the association of dietary factors such as milk products, meat, and junk foods with fasting blood glucose and postprandial blood glucose in 12,500 individuals. In this multicentre cluster randomized controlled study covering 60 states and four union territories; 17,285 individuals were surveyed for dietary factors. The findings of the present study revealed that regular ingestion of excessive milk, meat, and junk foods has a substantiated effect on the blood glucose levels of individuals, particularly in the case of DM. The analysis of the results showed that consumption (usual/often) of milk (OR = 7.180), meat (OR = 6.81), less fibers (OR = 17.77), and less fruits (OR = 14.71) was strongly and positively associated with fasting blood glucose (P < 0.001). The post-prandial blood glucose (PPBG) in diabetes individuals also had a strong positive association (P < 0.001) with consumption (usual/often) of meat (OR = 22.82) and milk (OR = 17.19). In pre-diabetes individuals, milk was significantly (P < 0.001) associated with fasting blood glucose (OR = 2.74). In non-diabetes individuals also, milk was significantly associated with PPBG (OR = 2.56). Consumption of junk food was associated significantly (P < 0.001) with the status of known diabetes (OR = 1.345) and known hypertension (OR = 1.247). In all, the consumption of milk, meat, less vegetables, less fruits, and junk food has a significant effect on the glycemic status and cholesterol levels, and also on the status of known diabetes and hypertension.⁵⁴

Dietary carbohydrates (quantity and quality) and diabetes risk:

Increased consumption of refined cereals and white rice accounting for 73% of daily calorie intake from white rice. Due to rapid industrialization and green revolution, there has been a large increase in the consumption of refined grains (polished rice, white flour, semolina) in the last few decades. White rice, in particular, has a high glycemic index (GI) and accounts for almost 50% of the total calories in the diet of the South Indian urban population and 73% of the daily calorie intake of the rural Indian population. Data from Sri Lanka showed that white rice accounted for about 73% of caloric intake, while in Bangladesh, rice and other cereals contributed to 80% of the total calories. Thus, it is important to encourage the consumption of low-GI and high-fiber foods in this population in order to reduce the dietary glycemic load. Making a simple change in diet, such as substituting brown rice as an alternative to polished white rice, may help to reduce the burden of T2DM in India and South Asia.⁵⁵

The quality of the carbohydrates is very important such that whole grains and fiber are better choices than refined grains, and that fiber intake should be at least as high in people with T2DM as recommended for the general population, that diets that have a higher glycemic index and load are associated with an increased risk of T2DM, and that there is a modest glycemic benefit in replacing foods with higher glycemic load with foods with a low glycemic load. However, debate continues about the independence of these effects from the intake of dietary fiber. Some evidence exists that consumption of potato and white rice may increase the risk of T2DM, but this is limited and further research is needed.⁵⁶

Sugar and sugar-sweetened beverages:

In South Asia, the energy obtained from sugar and sweeteners has considerably increased in recent times. In Nepal, it has increased from 4 g/capita/day in 1970 to 57 g/capita/day in 2010. A report in 2009 found that sugar intake among urban South Indians was mainly in the form of added sugar in hot beverages (tea and coffee), and contributed about 3.6% of the total glycemic load compared to refined cereal (white rice), which provided 66% of the glycemic load. However, recent data suggest an increase in the intake of sugar from sugar-sweetened beverages among Indians. Further, the intake of “total” sugar (traditional sugar + sugars from sugar-sweetened beverages) among Indians (25.0 kg/capita in 2011) exceeds the average global annual per capita consumption of 23.7 kg. Diet lower in sugar-sweetened beverages has been shown to reduce the risk of diabetes in India.⁵⁴

Dietary fats (quantity and quality) and diabetes risk: Evidence:

Visible fats and oils are the main contributors to daily calorie intake in Indians. However, the total intake of dietary fat among Indian urban adults (24% total calories) and rural Indians (13% total calories) appears to be within the recommended intake of 30% of total calories. Visible fats and oils contribute almost half of the total calories derived from fat in Indian diets. There has been a sharp increase in the intake of dietary fat in the last three decades, from 29 g/capita/day in 1970 to 45 g/capita/day in 1999 in the South Asian population. More worrying is the fact that the quality of dietary fat in the South Asian population comprises a low intake of monounsaturated fatty acid (MUFA) and n-3 polyunsaturated fatty acid (PUFA) and a high intake of fats such as saturated fatty acid (SFA), and trans-fatty acid (Trans-Fatty Acid (TFA); mostly related to the widespread use of vanaspati, a hydrogenated vegetable oil) showing an imbalance and association with increased risk for NCDs especially T2DM. Several studies have reported that consumption of SFA or TFA contributes to an increased risk of NCDs like T2DM. Fat-rich animal foods are the primary source of SFA in diets. The total meat consumption has increased significantly in all South Asian countries in the past two decades. Countries like India have doubled their intake of meat and poultry since 2000, but the actual quantity is still low (50 g/capita/day), compared with diets in, for example, the United States Average Intake (USAI) (128 g/capita/day).

Meat consumption in Pakistan has increased by 130%. Sri Lanka has also gradually increased the trend in meat consumption from 1992 to 2007. Studies have shown that South Asians consume an excess of n-6 PUFA. This has primarily occurred because traditional oils such as groundnut (a good source of MUFA) and sesame were replaced with oils that are high in n-6 PUFA, like sunflower and safflower oils, leading to an imbalance in the n-6/n-3 ratio. Studies have shown that this imbalance in n-6/n-3 ratio, owing to the consumption of these high-n-6 PUFA oils, is associated with an increased risk for metabolic syndrome.

Similar findings were reported by Misra et al. in 2009. TFAs are even more deleterious to health than SFAs, owing to the hydrogenation process, which converts liquid oils to solid fats like vanaspati and margarine. The consumption of vanaspati accounts for 50% of TFA use and it is predominantly consumed in South Asian countries like India and Pakistan.⁵⁷ For unprocessed red meat, the evidence of possible harm because of the development of T2DM is less consistent and of a smaller magnitude.^56,58

Low consumption of fruits and vegetables:

There is evidence to suggest that consumption of fruits and vegetables (≥5 servings or 400 g/day) is associated with a reduction in the risk of chronic diseases like T2DM and coronary heart disease. However, the intake of fruits and vegetables is far below the recommended levels in almost all South Asian countries.

The average per capita consumption of fruits and vegetables in India is around three servings/day, while it is 2.2 servings/day in Sri Lanka, 1.8 servings/day in Nepal, and one serving/day in Maldives. A study in South Indians showed an inverse association between intake of fruits and vegetables and blood pressure, WC, total cholesterol, and LDL-C concentrations. Another study showed that total dietary fiber intake was inversely associated with total cholesterol and LDL cholesterol levels in people with diabetes.⁵⁵

South Asian diets are high in carbohydrates derived from refined cereals like white rice and refined flour. There is also a high intake of fat, especially SFA and PUFA, mainly in the forms of n-6 (omega-6) PUFA and TFA, and a low intake of MUFA and n-3 PUFA (resulting in a higher ratio of n-6/n-3). The diet is also low in dietary fiber and several micronutrients (e.g., magnesium, calcium, and vitamin D), all of which may contribute to the increased risk for NCDs like T2DM. This shift from ancestors’ diets (high in animal protein, adequate in fiber, relatively low in carbohydrates, and limited animal fat) has resulted primarily from the changes in demography and socioeconomic status and “modernization.” Urbanization and rising income levels have also led to an increase in the consumption of milled and polished grains like rice and wheat rather than unpolished brown rice, corn, and millet. Further, urbanization has led to increased employment for women. This has promoted a shift from home-cooked traditional foods to pre-cooked convenience foods, as the mother has less time to cook.⁵³

Physical activity as a risk factor for diabetes mellitus:

Physical inactivity is an independent risk factor for T2DM, and current evidence suggests that adequate levels of physical activity may reduce the risk of T2DM by 27%. Modern technical gadgets and the use of motorized transport have reduced physical activity among children and young adults. In 2013, Ranasinghe et al.⁵⁹ reported that the overall prevalence of physical inactivity among the population of India was 19%–88%, followed by Pakistan (60%) and Sri Lanka (11%–32%). The recent Indian Council of Medical Research–India Diabetes (ICMR–INDIAB) study also reported that levels of physical inactivity were high (≈55%) among Asian-Indians. According to the American Diabetes Association (ADA), “physical activity” is defined as bodily movement produced by the contraction of skeletal muscle that requires energy expenditure in excess of resting energy expenditure, while “exercise” is defined as a subset of physical activity that is planned and structured and consists of repetitive bodily movement performed to improve or maintain one or more components of physical fitness.⁵⁵ Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ), which was developed by the WHO—in ICMR-(Indian Council of Medical Research–India Diabetes) study for both the rural and the urban population in India. However, in the Ranasinghe study (a review), a number of methods were used for defining physical activity [Table 2], the most common ones were the interviewer-administered questionnaire and PAL, or the interviewer-administered questionnaire or the GPAQ by WHO.⁵⁵

The role of physical inactivity in the diabetes epidemic:

Physical inactivity has been shown to be an important risk factor for most chronic diseases, including T2DM, and seems to increase the risk of T2DM independently of diet. A sedentary lifestyle over several years has been shown to be associated with increased risk for T2DM, CVD, and premature mortality. There is mounting epidemiological evidence that, in addition to reduced physical activity, “sedentary behavior”, defined as engaging in activities at the resting level of energy expenditure, which includes sleeping, sitting, lying down, computer time, and viewing television, also plays an important role in the etiology of T2DM.

European data suggests that even resistance training has the potential to improve muscle strength and endurance, enhance flexibility and body composition, decrease risk factors for CVD, and result in improved glucose tolerance and insulin sensitivity. In order to prevent T2DM in high-risk individuals (e.g., those with impaired glucose tolerance), the ADA and American College of Sports Medicine (ACSM) recommend at least 150 min/week (2.5 h/week) of moderate to vigorous physical activity [Table 2].⁵³

Patterns of physical activity in South Asians:

Wide variations in the prevalence of physical activity have been reported in countries of South Asia, namely Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. In 2004, it was reported that the rates of physical activity were 50%–75% less among South Asians compared to the general population living in the United Kingdom of Great Britain and Northern Ireland. The World Health Survey, a large cross-sectional study, was conducted by WHO in 51 countries in 2002 and 2003. This included countries that had a large proportion of the world’s population, with a wide geographical distribution across WHO regions, including several countries in the Southeast Asia Region. Physical activity was assessed using the International Physical Activity Questionnaire. The prevalence of physical inactivity for men in South Asian countries, including Bangladesh, India, Nepal, Pakistan, and Sri Lanka, ranged from 6.5% to 12.8%, while for women, it ranged from 9.7% to 27.3%.

Three studies on physical activity have been reported from Sri Lanka: the WHO STEPS (STEP wise approach to Surveillance) survey, one in the western province of Sri Lanka, and finally, a nationally representative study. The prevalence of inactivity was reported to be 25% in the WHO STEPS survey, 31.8% in the western province, and 11% in the national sample. Men were more inactive than women, and inactivity was reported to be 35.2% in urban adults and 27.6% in rural adults in the study from the western province of Sri Lanka. A study on physical activity conducted in Pakistan in 2010 reported a high prevalence of inactivity (60.1%). The WHO STEPS survey conducted in Bhutan in 2007, Maldives in 2011, and Nepal in 2013 reported inactivity levels of 58.6%, 45.9%, and 3.5% in these populations, respectively.

Several studies have been done in India, and the prevalence of inactivity ranged widely from 9.7% to 54.4%, probably reflecting the methodology used or the sample surveyed. The WHO STEPS survey done on 1359 males and 1469 females in a rural area in the Faridabad district of Haryana reported a very high rate of physical inactivity. Another study conducted by Sullivan et al.⁶⁰ in 2011 which assessed the PALs in migrant groups in India (n = 6447) showed that physical activity was highest in rural men, followed by migrants, and then urban men. Levels of sedentary behavior and television viewing were lower in rural residents, whereas these were similar among migrant and urban residents. In 2014, Anjana et al.⁶¹ assessed the pattern of physical activity in a community-based national survey, the ICMR–INDIAB study, in four areas of India (TN, MH, JH, and Chandigarh, representing the south, west, east, and north of India, respectively), using the GPAQ. Of the 14,227 individuals studied, 54.4% were inactive. Subjects were more inactive in urban areas compared to rural areas (65.0% vs. 50.0%). Thus, epidemiological studies from South Asian countries show that a large percentage of people in this region are inactive, with very few engaging in recreational physical activity; the explosive increase in the prevalence of T2DM in these countries may be attributed to this high percentage of inactivity.

A systematic review done by Horne and Tierney in 2012, on barriers to exercise and physical activity among older adults in South Asia, concluded that a lack of understanding about benefits, a communication gap with healthcare professionals, cultural beliefs, and a lack of culturally sensitive facilities are some of the barriers for physical activity. In a recent study conducted by Anjana and colleagues in 2015 for the Diabetes Community Lifestyle Improvement Program, the most frequent barriers to exercise perceived by men were “few places to exercise” and “tires me,” followed by “takes too much of my time” and “places to exercise are far away.” The reasons most frequently cited by women were “takes too much of my time,” followed by “few places to exercise,” and “takes time away from my family.” Thus, it is clear that these barriers to physical activity must be overcome if levels of physical activity in society are to be improved.⁵⁵

Diet and physical inactivity as risk factors for metabolic syndrome in India:

Metabolic syndrome is a constellation of interrelated risk factors characterized by the co-occurrence of hyperglycemia, hypertension, high triglyceride levels, and low HDL-C levels. Metabolic syndrome also increases the risk of type 2 DM, CVD by 2-fold and a 5-fold increase in mortality over a 5–10 years period. It is estimated that 20%–25% of the world’s adult population suffer from metabolic syndrome disorders. In India, the prevalence of metabolic syndrome is increasing exponentially, as determined by 33.5% overall, 24.9% in males and 42.3% in females. It is a mixed outcome of genetic, metabolic, dietary, physical activity-related, and some environmental factors. A dietary habit seems to play a prime importance in the treatment and prevention of metabolic syndrome.

Metabolic syndrome: Dietary risk factors:

In general, the probable dietary risk factors include a shift occurring toward increasing consumption of calorie-dense foods containing refined carbohydrates, fats with high intakes of saturated fatty acids, and low intakes of omega-3 fatty acids, red meats, and low fiber increases the risk of metabolic syndrome. Although people consume a diet which includes many types of nutrients, in a cross-sectional study performed on Indian participants by Verma et al at the Institute of Medical Sciences, Banaras Hindu University (BHU), found an association between important nutrients risk factors and the number of metabolic syndrome components. Unhealthy eating practices with intake of less than three servings of fruits and vegetables were seen more among the participants who had more than three risk factors than in the participants who had three risk factors. The odds of taking more than times junk foods/week were three times higher, and sweet dishes were 2.3 times higher among the participants who had four to five risk factors when compared to the participants who had three risk factors. Significantly higher proportion of participants (33.7%) were in the habit of taking flesh foods and eggs>four times/week among the participants who had four to five risk factors in comparison to 9.3% among participants who had three risk factors. High intake of sodium is a major risk factor for CVDs for participants who had four to five risk factors. Also potassium, vitamin A, and phosphorus intake were lower among participants who had four to five risk factors.⁶² In another study by Misra et al. assimilating from several nutritional surveys in various developing countries, it concluded that low intake of n-3 PUFAs (obtained from flaxseed, mustard, and canola oils) and long-chain (LC) n-3 PUFAs (obtained from fish and fish oils), and increased intake of SFAs (obtained from coconut oil, palm oil, and ghee [clarified butter]) increases the risk of metabolic syndrome. Data also showed that intake of MUFAs ranged from 4.7% to 16.4% in developing countries, and supplementing it from olive, canola, mustard, groundnut, and rice bran oils may reduce metabolic risk.⁵⁷ Radhika et al, in a cross-sectional study, evaluated the association of refined grains consumption with insulin resistance and metabolic syndrome in an urban South Indian population and found that the mean refined grain intake was 333 g/d (46.9% of total calories). Compared with participants in the bottom quartile, participants who were in the highest quartile of refined grain intake were significantly more likely to have metabolic syndrome (OR, 7.83; 95% CI, 4.72–12.99). Higher intake of refined grains was associated with insulin resistance and metabolic syndrome in this population of Asian Indians who habitually consume high-carbohydrate diets.⁶³

Metabolic syndrome: Physical activity risk factors:

WHO predicts that by 2030, India, with over 79 million diabetic subjects, will contribute to more than 20% of the world’s diabetic population, and MS is probably due to the rapid demographic transition experienced in India, coupled to a more sedentary lifestyle. In data drawn in Chennai Urban Population by Mohan et al, it predicted a significantly increased prevalence of most of the components of metabolic syndrome (diabetes P < 0.001, obesity P = 0.003, AO P < 0.001, and hypertension P < 0.001) with a decrease in physical activity. Subjects in the light-grade activity group also had 2.4 higher odds of coronary heart disease (P = 0.011), compared with the heavy-grade activity group. They concluded that less physical inactivity is associated with the components of metabolic syndrome.⁶⁴ In another multisite study in the urban Indian setup, the prevalence of metabolic syndrome was significantly greater in subjects with the highest vs. lowest categories of sedentary lifestyle (47 vs. 38%) (P < 0.05), where any active physical activity was considered with >30 min of work and leisure, or commute-related physical activity was classified as moderately active. Physical inactivity was associated with higher triglyceride levels in males and with higher hip circumference, diastolic blood pressure, triglycerides, and fasting plasma glucose in females.⁶⁵

Non-alcoholic fatty liver disease: Diet as a risk factor:

The prevalence of NAFLD among the general population in India ranges from 9% to 53%. NAFLD is a global problem growing in parallel to the epidemics of obesity and diabetes, with South Asians being particularly susceptible. Nutrition and behavior are important modifiers of the disease. In an Indian cross-sectional case–control study held by Vijay et al, it was found that NAFLD patients had significantly higher consumption of refined rice, animal fat, red meat, refined sugar, and fried foods, and had lower consumption of vegetables, pulses, nuts, seeds, and milk compared to controls.⁶⁶ The consumption of red meat, animal fat, nuts, and refined rice was positively associated with NAFLD diagnosis and the presence of fibrosis, whereas the consumption of leafy vegetables, fruits, and dried pulses was negatively associated. Fried food consumption was positively associated with NAFLD, on the other hand, boiled food consumption had a negative association. Increased consumption of animal fats was associated with diabetes, hypertension, and cardiovascular outcomes among those with NAFLD, whereas consumption of wholegrain rice was negatively associated with these clinical-related outcomes.

In other case–control study by Singh et al, it was found that dietary risk factors associated with NAFLD were non-vegetarian diet (meat/fish), fried food, spicy foods, and tea.⁶⁷ This study also revealed that urban middle-class men with a sedentary lifestyle had a greater risk of NAFLD, which may be due to high fat intake in them. In a North Indian case–control study held on obese children, daily consumption of soft drinks (60.4%) and fried chips (58.5%) was more in NAFLD group than without NAFLD group. Also, calorie intake was more in obese children but excess in NAFLD group.⁶⁸

In an Indian case–control study held by Bhatt et al., the mean dietary intakes of total energy, carbohydrate, protein, total fat, saturated fat, and total cholesterol were significantly higher, while intake of MUFAs and PUFAs was significantly lower in cases as compared to controls.⁶⁹ In another case–control study by Sathiaraj et al., the total calorie intake, percent of carbohydrate, and fat intake of NAFLD cases were significantly higher than controls. Decreased nutritional intake with restricted fat may constitute an important therapy in subjects with NAFLD.⁷⁰

Non-alcoholic fatty liver disease: Physical inactivity as a risk factor:

NAFLD has today emerged as the leading cause of liver disorder, and physical inactivity is one of the major causative factors. In an Indian case–control study held by Singh et al, a majority (90.2%) of NAFLD patients were found to be sedentary with more than four times of any kind of physical activity/week, and only 32.8% NAFLD patients undertook regular exercise (>4 times/week).⁶⁷ In other North Indian case–control study by Singh et al., the mean physical activity in a day (expressed as MET. Minutes) and total energy expenditure were significantly lower in cases than in controls (33.3 ± 3.6 vs. 36.2 ± 0.5, P ¼ 0.001 and 2707.6 ± 505.6 vs. 2904.3 ± 690.3, P = 0.02, respectively) signifying lower physical activity as a risk factor for NAFLD.⁶⁷ Also, in another study by Nath et al., NAFLD police trainees in a moderate-intensity exercise group and in a low-intensity exercise group were recruited for a 6-month physical training course. Subjects in the low-intensity exercise had higher triglycerides and lower HDLs than moderate-intensity exercise subjects. Also, hepatic steatosis regressed in 66.7% of the NAFLD subjects in the moderate-intensity exercise group but in only 26.3% of the low-intensity exercise NAFLD subjects (P = 0.030).⁷¹

Genetic factors of overweight and obesity in India:

Two recent reviews shed light on the genetic variants of obesity reported in Indians.^74,75 The reported variants are: FTO, MC4R, TNF-α, PPAR-γ, UCP1, UCP2, LPL, LEPR, AMD1, IL6, IL6R, APOE, ADIPOQ, DOK5, INSIG2, PBEF1, Myostatin, CXCR4, HHEX, IRX3, POMC, NGN3, FOXA2, FLJ3970, MTR, TCN, CHDH, CETP, LMNA, APOB, AGRP, TCF7L2, and THADA. None of these variants are India-specific or novel, as these studies were variant-specific validation studies for variants that had been identified through Genome-wide association studies (GWAS) or sequencing-based-genome-wide approaches in other ethnicities. A single GWAS study on 5973 Indian adults, with a replication of the findings on 1286 Indian adolescents, was reported by Giri et al. in 2020.⁷⁶ This study reported the identification of novel variants in SLC22A11 and ZNF45 associated with BMI in Indians. Identification of further novel, India-specific variants, which may provide further insights into Indian aspects of obesity, will be dependent on studies done on large sample sizes with genome-wide approaches.

Genetic factors of diabetes in India:

Similar to investigations into the genetics of obesity and of NAFLD in India, attempts have been made to evaluate associations of genetic variants previously identified through genome-wide discovery approaches in other ethnicities with T2DM in Indians through variant-specific validation studies. Through such studies, associations of variants in TCF7L2, SLC30A8, STRA6, KCNQ1, INS, INSR, and PP1G.G have been confirmed in the Indian population.^77–80 Tabassum et al. reported TMEM163 (coding for transmembrane protein 163) as a novel T2DM-associated gene from their GWAS study in select Indian populations in North (Delhi) and South (Chennai) India.⁸¹ Interestingly, a replication study conducted in a separate group of subjects from Northwest India belonging to the Indo-European linguistic group was unable to find this similar association of TMEM163 with T2DM indicating large within-country genetic disparity, even in geographically close population groups.⁸²

Genetic factors of hypertension in India:

The ACE insertion/deletion polymorphism has been reported to be associated with hypertension in Indians from various parts of the country in multiple studies.^83–85 Apart from ACE, variants in CYP11B2,⁸⁶ NOS3,⁸⁷ AGT,⁸⁸ FABP2, and GST⁸⁹ have also been reported to be associated with higher risk of hypertension in Indians.

Genetic factors of dyslipidemia and metabolic syndrome in India:

Since dyslipidemia is one of the conditions included within the metabolic syndrome, genetic variants associated with dyslipidemia and with metabolic syndrome usually overlap with each other and also with those associated with obesity and T2DM. Variants in FABP2 and APOC3,⁹⁰ TCF7L2,⁹¹ CETP,⁹² RETN,⁹³ TNF-α,⁹⁴ and ADIPOQ^95,96 have been reported to be associated with dyslipidemia and metabolic syndrome.

Genetic factors of NAFLD in India:

NAFLD involves progressively increasing deposition of fat in the liver, resulting in fatty liver, hepatic fibrosis, cirrhosis, and either liver failure or HCC, all without addiction to alcohol.⁹⁷ The prevalence estimate of NAFLD in 2022 is 32.4% worldwide and 38.6% in India.^98,99 NAFLD heritability is estimated to range between 20% and 70%⁶ and ∼25 genetic variants have been reported to be associated with NAFLD.¹⁰⁰

The identification of the genes associated with NAFLD is important, as it allows for the prevention and identification of drug pathways. The first genetic variant identified in the Dallas Heart Study participants, not associated with NAFLD directly but with heightened hepatic fat content and hepatic inflammation, was the non-synonymous rs738409 C/G variant in PNPLA3 (patatin-like phospholipase domain containing 3/ adiponutrin/calcium-independent phospholipase A2-epsilon) leading to I148M amino acid substitution.¹⁰¹ A gene coding for a protein of unknown function at that time, this report fuelled intense work towards elucidation of the coded protein’s function. PNPLA3 turned out to be a triglyceride hydrolase with predominant expression in the liver, with the I148M variant promoting hepatic triglyceride accumulation by inhibiting triglyceride hydrolysis.^102,103 Kozlitina et al. conducted an exome-wide association study and identified the rs58542926 variant (Glu167Lys) in TM6SF2 associated with liver fat content.¹⁰⁴ Similar to PNPLA3, TM6SF2 was a gene of unknown function at that time, but this report led to extensive investigations on the function of the transmembrane protein coded by it. Utilizing Tm6sf2 knockout mice that exhibited hepatic steatosis and hypocholesterolemia, even without any dietary interventions, Smagris et al. reported that TM6SF2 is required for mobilization of neutral lipids for very low-density lipoprotein assembly.¹⁰⁵ These examples provide justification for conducting future studies to identify novel genetic variants associated with NAFLD to understand the pathophysiology of NAFLD at a cellular and molecular level.

A further reason for studying the genetics of NAFLD is to develop ethnicity-appropriate risk-stratification. Large ethnicity-based disparities in heritability of NAFLD have been noted, with heritability of NAFLD more than double in Hispanics (33%) than in African Americans (14%).¹⁰⁶ The MBOAT7 rs641738 variant has been reported to be associated with the development and severity of NAFLD in individuals of European descent but not in Hispanic or African-Americans.¹⁰⁷ Clearly, ethnicity-appropriate appropriate risk-stratification seems to be the next logical step. However, comparable data for South Asian Indians are sparse and inadequate. Going beyond genetics, epigenetics of NAFLD is now increasingly coming under focus, especially in terms of changes in levels of microRNAs in plasma or DNA methylation in cell-free DNA in plasma that can act as markers of NAFLD progression or as early markers of development of Hepatocellular Carcinoma (HCC) in NAFLD patients who have progressed to steatohepatitis.¹⁰⁸ Similar work is lacking in India.

The few GWAS or locus-specific validation studies conducted in India point to associations of NAFLD with known variants [PPARγ, SREBP-2 1784 G/C, AGTR1, SAMM50 (rs3761472), and PNPLA3 (rs738409)] and a few novel variants [FAM161A (rs17513722), and promoter of IL27 (rs4788084)].^109–112 Of these studies, two studies used abdominal ultrasound-based diagnosis of NAFLD (162 NAFLD subjects) and genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) to assess the association of the PPARγ and SREBP-2 variants with NAFLD.^109,110 Zain et al. included biopsy-proven 144 NAFLD patients based on increased echogenicity (compared to renal cortex) on ultrasound with or without abnormal alanine transferase (ALT) and did genotyping with pre-designed TaqMan SNP genotyping assays to test association of AGTR1 variants with susceptibility to NAFLD¹¹¹ and Chatterjee et al used exome-wide sequencing to identify SNPs associated with hepatic fat content measured by Proton magnetic resonance spectroscopy (H1-MRS) in 244 subjects with abdominal ultrasound based diagnosis of NAFLD.¹¹² Some data suggest that there may be a variation in the genetic predisposition of NAFLD among Indians based on ancestry, with TM6SF2 being significantly associated in South Indian ancestry and PNPLA3 in Northeast Indian ancestry. Nonetheless, it should be emphasized that no single genetic polymorphism is sufficiently strong to account for NAFLD, and other additional risk factors are practically always present.

Genetics of obesity and lifestyle diseases in India:

Considering the large existing diversity amongst Indians, in terms of multiple ancestral populations and admixed groups¹¹³ as well as diversity of environmental factors such as dietary habits,¹¹⁴ future region-specific genetic and epigenetic studies with large sample sizes that incorporate simultaneous deep phenotyping of the subjects are the need of the hour, to improve our understanding of the modifiable factors affecting the onset and progression of obesity and associated lifestyle diseases, including T2DM, hypertension, metabolic syndrome, and NAFLD in India. Further, the exploration of biological function and associated consequences of the identified variants in a country-specific context will lead to the translation of this knowledge into improving prevention, diagnosis, and development of focused therapeutic approaches.

Other risk factors for NAFLD:

The pathogenesis of NAFLD is multifactorial and involves environmental and genetic risk factors. At its core, NAFLD is primarily a lifestyle disease attributable to excess calorie intake (increased intake of calorie-dense foods, dietary sugars, particularly fructose, trans-fats, and “junk” food) and reduced expenditure (a sedentary lifestyle). In this context, it is important to highlight that dietary constituents and cooking media vary greatly in different regions of India. Further, many Indians consume a purely vegetarian diet, which is relatively rare in Western countries. The influence of diet on NAFLD in Indians is relatively unexplored. Limited Indian data suggest that there may not be any difference between vegetarian and mixed diets on the risk of NAFLD.¹²³ Aerated soft drinks have been linked to an increased risk of NAFLD.¹²⁴

Insulin resistance is the main driver of NAFLD pathogenesis in the vast majority of patients. Globally, multi-ethnic studies have suggested that Indians are more predisposed to insulin resistance and its consequences, including NAFLD. However, insulin resistance is not universal and some studies from the Eastern coastal areas of the Indian subcontinent have documented the absence of insulin resistance in almost half of the included patients.^125,126 Given the central role of insulin resistance, it is not surprising that there is an intricate relationship between NAFLD and other metabolic syndrome components like obesity, T2DM, hypertension, and dyslipidemia. On the one hand, patients with NAFLD are more likely to have metabolic comorbidities. On the other hand, the increasing number of metabolic comorbidities increases the risk and severity of NAFLD. Indeed, NAFLD is often considered to be the hepatic component of metabolic syndrome. In the ongoing multi-centric study of the Indian consortium on NAFLD (ICON-D), metabolic syndrome was observed in 43%, and at least one metabolic risk factor was present in 93% of 3553 patients with NAFLD with the commonest being central obesity in 83%. Almost 90% of the patients were obese or overweight (BMI ≥ 23 kg/m²).¹²⁷

A vexing issue in the field of NAFLD is that although most patients with NAFLD are overweight or obese, around 10%–20% of patients are lean and have normal BMI.^128-132

This phenotype was first described in India and China and is now being recognized globally. Current evidence suggests that although these patients have less severe metabolic dysfunction than those with the classical NAFLD phenotype, they are metabolically unhealthy compared to individuals without NAFLD.^128,129 Further, the disease severity appears to be similar in non-lean and lean NAFLD patients in the Indian subcontinent.^130–132

The role of gut microbiome in lifestyle diseases:

The role of gut dysbiosis is being increasingly recognized in the pathogenesis of obesity and associated lifestyle diseases. In a small study with analysis of fecal samples from five people, each with lean, normal BMI, obese, and surgically treated obese Indians, Patil et al. reported higher levels of bacterial genus Bacteroides and of higher archeal density in obese Indians.¹³³ Faecali bacteriumprausnitzii levels were higher in fecal samples from 15 obese 11–14-year-old South Indian children compared to 13 normal-weight children.¹³⁴

Higher levels of Escherichia and Prevotella have been reported in fecal samples from 17 Indian T2DM subjects compared to higher levels of Faecalibacterium, Eubacterium, and Bifidobacterium in 13 non-diabetic subjects.¹³⁵ Dysbiosis of the gut microbiome was reported by a study from Pune in 2017 with fecal samples from 14 newly diagnosed T2DM subjects, 16 long-standing T2DM subjects, and 19 healthy controls, mean age 48–52 years¹³⁶ and another study from Hyderabad in 2021 with fecal samples from 25 T2DM, 28 T2DM with retinopathy, and 30 healthy controls, mean age 52–57 years.¹³⁷ Interestingly, of the 11 taxa, the Pune study found to be discriminatory for T2DM (Oscillospira, Faecalibacterium prausnitzii, Bifidobacterium, Bifidobacterium adolescentis, Prevotellacopri, Lachnospiraceae, Lactobacillus ruminis, Ruminococcaceae, Roseburia, Collinsellaaerofaciens, and Streptocoocus), the Hyderabad study found only two of them, Roseburia and Lachnospira significantly reduced in abundance in T2DM, thereby underlining the need for gut microbiome studies with larger sample sizes adequately representing different geographical regions of the country that differ vastly in dietary and other cultural habits with domino effect on the gut microbiome composition. In another recent study, Gaike et al. reported that the gut microbial diversity of newly diagnosed diabetics but not of pre-diabetics was different from healthy controls, and anti-diabetic treatment in long-standing diabetics tended to reverse some of these changes.¹³⁸ Akkermansia, Blautia, and Ruminococcus levels were significantly decreased, and Lactobacillus levels were increased in newly diagnosed diabetics compared to healthy controls. This dysregulation was recovered in long-standing diabetics. This study was done in Pune with fecal samples from 17 pre-diabetics, 11 newly diagnosed diabetics, 39 long-standing diabetics, and 35 healthy controls, with mean age ranging from 37 years for the healthy controls to 52 years for long-standing diabetics. In another recent study on Indians and Danes, fecal samples from 157 T2DM and 137 healthy control Indians were included.¹³⁹ They reported increased levels of two operational taxonomic units (OTUs) from the Lachnospiraceae family and decreased levels of Subdoligranulum and Butyricicoccus in T2DM subjects.

Relations between dyslipidemia or CVD and gut microbiota composition in Indians has not been explored. One study to date has explored these relations in 71 Assamese individuals, reporting 19 amplicon sequence variants (ASVs) were discriminatory between 37 subjects with normal (<120 mmHg) and 34 subjects with elevated (≥120 mmHg) systolic blood pressure (SBP).¹⁴⁰ Four ASVs from genus Prevotella, two ASVs from Megasphaera, and one ASV from Butyricicoccus, Prevotellaceae, Faecalibacterium, Lachnoclostridium, Howardella, and g-UCG04 had higher abundance in those with elevated SBP while three ASVs from genus Prevotella, two from Alloprevotella, and one from Streptococcus and g-UCG-05 had higher abundance in those with normal SBP.

Data from India on relations between the gut microbial composition and NAFLD are limited and indicate that small intestinal bacterial overgrowth, lipopolysaccharides, and toll-like receptor expression play a role in the pathogenesis of NAFLD.¹⁴¹ Further, in a proof of concept study, the administration of high-dose probiotics showed a beneficial effect in patients with NAFLD.¹⁴²