Introduction

Modernization and change in human lifestyle have proliferated the prevalence of obesity and related co-morbidities worldwide. Overweight or obesity is a complex multifactorial disease defined by excessive adiposity causing several non-communicable diseases (NCDs) including hypertension (1). Earlier, high adiposity was considered a major health problem among the developed countries and affluent societies. However, it has now reached the stage of global epidemic affecting even the middle-income and low-income countries. In the WHO European region, it has reached epidemic proportions affecting almost 60% of adults causing a world-wide state of alarm (1). Increased consumption of calorie-dense, less nutritive foods combined with reduced physical activity led to a rise in the incidence of obesity since the 1980s, both in developed and developing countries (2). According to World Health Organization, high body mass index (BMI) claims approximately 2.8 million lives each year (3), while elevated blood pressure (BP) accounts for about 7.1 million deaths worldwide, especially among middle-aged and elderly adults (4).

Developing countries like India, though known for the high prevalence of under nutrition, now see a considerable prevalence of overweight and obesity co-existing among the population (5,6). It is alarming to note that non-communicable diseases (NCDs), including obesity and hypertension, are the leading causes of death and are responsible for 74% of deaths worldwide, and 66% of deaths in India and the probability of premature mortality from NCDs was 22% (7). Further, based on the Global Burden of Disease Study (GBD), Gupta and Xavier (8) estimated that hypertension led to 1.6 million deaths and 33.9 million disability-adjusted life in 2015 and is the most important cause of mortality and disease burden in India.

Excess fat deposition and pulmonary hypertension (PH) are two conditions that frequently co-exist in clinical practice. A major upshot of being overweight includes a higher prevalence of hypertension and a cascade of cardiovascular diseases (CVDs). Several studies (9–12) have shown sex differences in BMI and BP, and the association between the two health indicators in various populations worldwide. While some studies (13,14) showed a higher prevalence of obesity among men than their women counterparts, other studies (15,16) have reported a higher prevalence among women. On the other hand, males were generally found to be more hypertensive than females (12,17).

Bakir et al. (18) reported that the published studies on obesity have shown that the prevalence of obesity varies significantly across the world, ranging between 15 percent to 60 percent among adults. Rural Indians are no exception in this secular trend due to nutritional transition and changes in lifestyle as a consequence of urbanization. Tribal areas in India are also seeing an increase in the prevalence of non-communicable diseases due to socio-economic changes and the adoption of modern lifestyles (19). A systematic review on hypertension in India by Anchala et al. (20) reported that the pooled prevalence of hypertension for rural North India, East India, West India, and South India was 14.5%, 31.7%, 18.1%, and 21.1%, respectively, while eastern India reported the highest prevalence equaling that of numbers seen in urban parts of India. Besides, there is a gap in the prevalence of undiagnosed and untreated hypertension in rural and urban areas, with education, co-morbidities, tobacco usage, social group, work status, BMI, religion, and physical activity being the significant contributors in expanding the rural-urban gap (21).

Northeast India, being a tribal-dominated area, is the least developed compared to other parts of India in terms of infrastructure, connectivity, and technology. The majority of this region's indigenous population still relies on the traditional agriculture-based economy. As such, a lower prevalence of lifestyle-related diseases may be expected. Yet, the National Family Health Survey (NFHS-4) 2015-2016 (22) reported the prevalence of hypertension in men to be 23.6% and 16.8% in females in Nagaland. The prevalence increased in the following report (23), becoming 28.7% in males and 22.3% in females. It may also be noted that, according to the Ministry of Health and Family Welfare (24), in northeast India, Nagaland has the third highest prevalence of hypertension among men (23.6%) and women (16.8%) aged between 15-49 years, with Sikkim having the highest prevalence in both male (30.9%) and female (18.4%). Previous studies (25,26) in this region have reported a high prevalence of undernutrition in different ethnic groups. Nevertheless, numerous recent findings in similar fields by other researchers have reported the prevalence of obesity and hypertension in both adults and adolescents among different tribal groups of the region (27–31). Hence, like other regions of the country, Northeast India also faces the double burden of undernutrition and overnutrition.

The health of an individual is significantly influenced by the environment they live in. Therefore, hypertension and obesity correlate to several modifiable socio-economic determinants, such as occupation, income, education, lifestyle, living standard, etc. According to Shahraki et al. (32), the possible explanations for obesity are educational level, multiple pregnancies, marital status, and lack of exercise. Similar observations of socio-economic and behavioral factors impacting obesity and hypertension are reported in various studies (33–35).

With these views and facts in mind, the current study was conducted with the following three-fold aims: (a) to report the prevalence of obesity and hypertension, (b) to assess the relationship between BMI and BP, and (c) to examine the effect of socio-economic and demographic factors on obesity and hypertension.

Samples and Methods

The Chakhesang Nagas

The Northeastern region in India is one of the most culturally diverse regions in the country, comprising of eight states viz., Arunachal Pradesh, Assam, Manipur Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura. There are over 145 tribal communities in this region alone (36). The Chakhesang Naga tribe is one of the major tribal communities in Nagaland state. They are geographically concentrated in the hills of the Phek district. They belong to the Mongoloid ancestry and the language spoken by them belongs to the Sino-Tibetan family (37).

According to the Census of India (38), the total population of the Chakhesang Nagas is 1,54.874, of which, 1,22,767 individuals reside in rural areas, while 32,107 people are located in urban areas. The Chakhesang families are nuclear to a large extent (97.5%), having an average family size of 5.2 individuals per family (39). The main occupation of the Chakhesang Nagas is agriculture. Sükrünye is the most important festival of the Chakhesang tribe, which is celebrated by performing various rituals for the purification and sanctification of the body and soul. Like other Naga tribes, the lineage system of the Chakhesang Nagas is patrilineal. Traditionally, the division of labor was based on sex and age. Men in this community were warriors, and their role was to protect the women, children and their village from potential neighboring enemies. Men were primarily involved in hunting, village administration, trade and business, while the role of women were household work, agriculture-related work, child rearing and food gathering practices (39). However, in the present age, although men may have a higher social status in society, women, too, have significant power in decision-making and managing finances in the family.

Study design and data collection

The present study is a cross-sectional study conducted among the Chakhesang adults from five villages of Phek district in Nagaland, Northeast India. Data were collected from a sample of 209 adults (102 females and 107 males) aged between 18 and 50. Care was taken to include only healthy participants in the study. Before data collection, verbal consent was obtained from the respective village Headmen. Written informed consent in both English and local dialect was obtained from each participant. Also, the purpose of the study and the principles of ethical standards according to Helsinki Declaration were carefully elaborated to the participants.

Anthropometric and physiological measurements

Data on height and weight was collected following appropriate techniques given by the International Society for the Advancement of Kinanthropometry (40). Data on systolic BP (SBP) and diastolic BP (DBP) were collected using a mercury sphygmomanometer. BP was measured with the participant in a sitting position. The participants were allowed to rest for about five minutes to recover from recent activities and apprehension. BP was then measured twice with a minimum gap of two minutes between the measurements, and the average was taken. Utmost care was taken to ensure that all the anthropometric and BP measurements were taken before a heavy meal.

Definitions

The nutritional status of the present population was classified using the criteria given by WHO (41) for Asian adults. Accordingly, the body mass index (BMI) was classified as follows- Underweight = ≤18.5 kg/m², Normal weight = 18.5-22.9 kg/m², Overweight = 23-27.4kg/m² and Obese = ≥27.5kg/m². BP was classified based on the classification of the Joint National Committee VII Report (42). Accordingly, BP categories were grouped as follows- Normal = <120/80 mmHg, Pre-hypertension = 120-139/80-89 mmHg, and Hypertension = ≥140/90 mmHg. In the present investigation, Hypertension I (140-159/90-99 mmHg) and Hypertension II (≥160/100 mmHg) were pooled together as Hypertension. Mean arterial pressure (MAP) was calculated using the formula- MAP = DBP + (SBP – DBP)/3. This formula, given by Haque and Zaritsky (43), assumes that one-third of the cardiac cycle is spent in the systole.

Demographic and socio-economic information

Socio-economic and demographic parameters included in the present study are, age, sex, marital status, family size, occupation, education, and income. Data on these parameters were collected by interviewing the participants using an interview schedule. Marital status was grouped into three categories: Married, Unmarried, and DSW (Divorced/Separated/Widowed). Family size was pooled into three categories viz., Small (1-3 members), Medium (4-6 members), and Large (>6 members). Occupation was categorized into three groups viz., Group I (Farmers and daily wagers), Group II (Government/private sector employees and entrepreneurs), and Group III (Homemakers, students and currently not working). Education levels were grouped into three levels viz., Level I (Matriculation and below), Level II (Post-matriculation to undergraduate), and Level III (Graduate and above). Lastly, income groups were classified based on per capita income following the percentile method given by United Nations (44). Suitably, the income groups for this population were classified as follows- Low-income group/LIG (<50^th percentile = ≤2500 INR), Middle-income group/MIG (50^th to 75^th percentile = 2501-5000 INR), and High-income group/HIG (>75^th percentile = >5000 INR).

Statistical analysis

This paper's statistical analyses were computed using the Statistical Package for Social Sciences (SPSS) software (version 26.0) for Windows. Descriptive statistics in the form of mean, standard deviation, and percentage were computed. The difference between two proportions was calculated using t-test and chi-square test. The difference between more than two proportions was calculated using the analysis of variance (ANOVA) test.

We utilized linear regression and logistic regression models to assess the relationship between nutritional status and BP. In the linear regression model, SBP, DBP, and MAP were dependent variables, while BMI was independent. For the binary logistic regression (BLR) model, the dummy variable was 1 for those who are hypertensive and 0 for those who are not. Finally, multivariate multiple regression (MMR) analysis was done to assess the effects of socio-economic and demographic factors on BMI and BP. In this model, all the dependent variables viz., BMI, SBP and DBP were considered simultaneously. All the covariates viz., age, sex, marital status, family size, occupation, education level, and income group were recorded as dummy variables. The dummy variables for these covariates are as follows-

• Age: 1 = Younger adults, 2 = Older adults
• Sex: 1 = Female, 2 = Male
• Marital status: 1 = Married (DSW included), 2 = Unmarried
• Family size: 1 = Small (1-5 members), 2 = Large (>5 members)
• Occupation: 1 = Labour based 2 = Non-labour based
• Education level: 1 = Lower (till matriculation), 2 = Higher (above matriculation)
• Income group: 1= LIG (≤5000 INR per capita), 2 = HIG (>5000 INR per capita)

In the MMR analysis, we used the general model (GM), including all tested independent variables, and the most parsimonious model (MPM), including only statistically significant independent variables from the GM.

Results

Socio-economic and demographic background

Background information on the socio-economic and demographic characteristics of the presently studied population is presented in Table 1. Concerning age, younger adults comprise 55.0% of the total sample, while older adults comprise 45.0%. The percentage frequencies of married, unmarried, and DSW individuals are 46.9%, 45.5%, and 7.7%, respectively. On the accounts of family size, medium family size (44.5%) was highest, followed by large family size (34.4%) and least by small family size (21.1%). Most of the people in this population are working as farmers and daily wagers (46.9%), which requires a lot of physical activity. Entrepreneurs, government, and private sector employees account for 32.5% of the total sample. In terms of education level, below matriculation (46.9%) was found to be the highest, followed by post-matriculation (32.5%), while about 20.6% were found to have graduated college. Furthermore, the percentage frequencies of LIG, MIG, and HIG, were 41.6%, 34.9%, and 23.4%, respectively.

Anthropometric and physiological characteristics

Descriptive statistics of anthropometric and BP measurements of the Chakhesang adults are illustrated in Table 2. Chakhesang males are significantly taller (164.71 cm Males vs. 154.63 cm Females) and heavier (65.03 kg Males vs. 55.98 kg Females) than their female counterparts (p<0.001). BMI was also higher among males (23.91 kg/m²) than females (23.41 kg/m²), albeit with an insignificant variation. Both SBP and DBP were found to be significantly (p<0.05) higher among Chakhesang males (131.43 mmHg SBP and 83.63 mmHg DBP) than their female counterparts (125.80 mmHg SBP and 79.70 mmHg DBP). Similarly, the mean MAP was higher in males (99.57 mmHg) than the females (95.07 mmHg) at p<0.05.

Prevalence of obesity and hypertension

Sex-wise distribution of the prevalence of overweight/obesity and hypertension among the Chakhesang adults is shown in Table 3. The overall prevalence of overweight and obesity are 42.6% and 13.4%, respectively. Males exhibited a higher prevalence of overweight (43.0% Males and 42.2% Females) and obesity (15.0% Males and 11.8% Females) as compared to the females, albeit insignificant variation. It is also interesting to see that the percentage frequency of underweight (3.8%) individuals in this population is minimal. Regarding BP, the overall prevalence of SBP hypertension was 22.5%, and that of DBP hypertension was 28.2%. Chakhesang males showed a significantly higher prevalence of SBP hypertension (27.1% Males vs. 17.6% Females) than their female counterparts (p<0.05). DBP hypertension also showed similar gender differences (31.8% Males and 24.5% Females), yet, this variation was statistically insignificant.

Association of nutritional status with blood pressure

Sex-wise distribution of BP in different categories of nutritional status among the Chakhesang adults is given in Table 4. In all the BP traits, the mean values in both sexes show an increasing trend as BMI rise. The ANOVA test used for assessing the association between BP values and nutritional status presented significant figures in all the traits considered. Further, the linear regression analysis (Table 5) revealed that a unit rise in BMI significantly increases SBP, DBP, and MAP by 2.32 mmHg, 1.82 mmHg, and 1.99 mmHg, respectively. The R² values explain about 14.6%, 23.7%, and 21.7% of high SBP, high DBP, and high MAP, respectively, as a consequence of an increase in BMI.

Furthermore, after adjusting for age and sex, results of the BLR analysis showing the relationship between hypertension and nutritional status is shown in Table 6. It is seen that, overweight individuals are at 2.52 times (systolic) and 2.24 times (diastolic) higher risk to develop hypertension than their normal weight counterparts (p<0.05). On the other hand, the odds of developing systolic hypertension by obese adults in this community is 7.34 as compared to the reference group (p<0.001). Likewise, obese BMI was found to pose 7.70 times increased risk for developing diastolic hypertension than normal BMI (p<0.001).

Impact of socio-demographic and economic factors on obesity and hypertension

Table 7 presents the results of the MMR analysis depicting the impact of socio-economic and demographic factors on nutritional status and BP through the GM, which includes all the tested variables, and the MPM, which includes only statistically significant variables. The GM shows that the variation that can be explained through the selected confounding variables was ~20.0% for BMI, ~18.7% for SBP, and ~20.2% for DBP. Age was found to be the most crucial confounder of BMI, SBP, and DBP (Table 7). More specifically, Chakhesang adults in the higher age group showed a higher prevalence of overweight/obesity and hypertension. Additionally, gender differences in BP were also observed, with the males showing a higher tendency to become hypertensive than the females in both SBP and DBP (p<0.05).

Further, nutritional status was significantly associated with income (p<0.05), occupation (p<0.001), and education (p<0.05) (Table 7). As evident from the positive β-coefficient values, higher BMI was associated with higher income and occupation not involving physical labour. In contrast, higher education was found to be a significant (p<0.05) protective factor for increased BMI, as seen from the negative β-coefficient value. From the MPM, it is seen that age (p<0.05), income level (p<0.05), occupation (p<0.001), and education level (p<0.05) can explain ~19.1% of the total variation in BMI. Concerning BP, education was found to have a significant negative impact on SBP (p<0.05) (Table 7). In other words, people of this community with higher education show lesser vulnerability to becoming hypertensive in SBP. At the same time, higher income was found to be significantly associated with higher DBP (p<0.05), as apparent from the positive β-coefficient value. Additionally, from the MPM, we see that age (p<0.001) and sex (p<0.05) alone can explain ~ 15.3% of the total variance in systolic hypertension. While the three confounding variables, viz., age (p<0.001), gender (p<0.05), and income level (p<0.05), can explain ~18.9% of the variation in DBP.

Discussion

Lifestyle-related NCDs are a growing worldwide phenomenon; therefore, it has become vital to understand their social, economic, behavioral, and demographic correlates at the community level. The present study attempts to examine the prevalence of obesity and hypertension in a tribal population in India, emphasizing their association with selected socio-economic and demographic parameters.

Our study depicts that Chakhesang males were significantly taller, heavier, and showed higher BP readings than their female counterparts. As a result, over-nourished nutritional status and high BP is more prevalent among the males than the females in this tribe. This finding on gender disparity among the Chakhesang adults aligns with results on other populations in India (14,45,46). On the other side, our finding on sex difference contradicts other literature that reported females to be more vulnerable to obesity and hypertension (16,19,47). Hence, gender variation in terms of obesity and hypertension is inconsistent as it may have to do with community-specific gender roles in different societies.

The prevalence of obesity (13.4%) and hypertension (25.4%, SBP and DBP combined) in this tribal community is relatively high. Recent studies in Northeast India have also reported the prevalence of obesity and hypertension in different tribal groups viz., Zou (28), Rengma (5), Mising (14), Khasis (30), Tangkhul (48), Rongmei (29), Hmar (49), and Angami (13). Thus, we see that the cases of obesity and hypertension are on the rise even among the neglected tribal populations in the region. This is probably because of the shift from an agrarian society to a more urban lifestyle.

Although the present study fails to examine the rural-urban dichotomy in the prevalence of obesity and hypertension, our findings suggest an appallingly high prevalence of these two NCDs among the Chakhesangs in a rural setting. Majority of the people in this community are overweight (56%) according to the Asian Indian BMI cut-off values given by WHO (41). Such a scenario is rare in the tribal population of India, especially in rural areas. For instance, the combined prevalence of overweight and obesity among rural Hmars was 24.4% (50), among rural Angamis was 25% (19), and rural Rongmei Nagas was 41% (29). Similarly, the prevalence of hypertension (25.5%) in the current study was also found to be very high compared to other rural populations of India. For instance, the prevalence of hypertension among rural Indian adults based on NFHS-4 data was 15.5% (45), among rural Khasi women was 8.65% (30), and among rural Hmars was 17.4% (50). Hence, the present study’s findings show a ridiculously high prevalence of both overweight and hypertension for a rural settlement compared to other rural populations in the country. A previous study by Longvah et al. (39) conducted among the Chakhesang women reported 12% of overweight and 18% of hypertension which is much lower as compared to the women of the present study (54% Overweight and 21.1% Hypertensive). In this connection, it may be noted that hypertension in low- and middle-income countries has increased between 1990 and 2020 in both urban and rural areas, but with a stronger trend in rural areas (51). Thus, our findings on incidences of high BP strongly supplements to such a secular trend.

The association of obesity and hypertension has much caught the attention of epidemiological researchers. Using various statistical tools, our study revealed that BP increased in both sexes with a rise in BMI. Thus, overweight and obese people in this community are more susceptible to becoming hypertensive than their normal-weight counterparts. This positive association of nutritional status with BP largely commensurate with the broader literature. Major researches which showed BMI as a critical indicator of hypertension risks were reported among American Adults (52), Chinese adults (53), and African and Asian adults (54). Contradicting these findings reported worldwide and in the present study, no significant association between BMI and BP was found among the Cameroonians (9) and eight tribal communities in India (55).

Our study reported age as one of the most critical confounders of BMI and BP. The MMR analysis shows older adults at higher risk of developing obesity and high BP than younger adults. More specifically, Chakhesang Nagas tend to become hypertensive and accumulate higher adiposity as age advances. This trend is true to a large extend even in other studies conducted by various researchers (13,48,56,57). However, few studies in India have reported no significant effect of age on BMI (28,58). Conversely, BP seems to universally increase with increment in age. This is because as one grows older, there are structural changes in the arteries, making them stiffer, thus causing BP to rise (59).

The impact of socioeconomic factors on adiposity and BP shows variant results in different populations. In developed countries, higher socioeconomic status (SES) is greatly associated with lower incidences of CVDs. For instance, cardiovascular health is worst in lower SES groups among the American (60) and Spanish (61) populations. However, in developing countries like India, higher SES is linked with higher CVDs, including obesity and hypertension as seen in numerous studies (33,47,62). Our findings depict that income, occupation and education were the important significant confounders of obesity and hypertension among the Chakhesang adults. Precisely, Chakhesang adults placed in the higher income group are more vulnerable to developing both obesity and high BP, while people involved in non-labor based occupation are more susceptible to becoming obese. People of this community involved in non-labor based occupations are mostly white-collar job holders and entrepreneurs having higher incomes than the agriculturalists and daily wagers. Perhaps, the people employed in government and private sectors perform lesser physical activity and can afford higher calorie food which may have accelerated the BMI and BP readings in this population. This suggests that, like majority of the works reported in India, cardiovascular health is worst in higher SES even among the Chakhesang tribe. A similar effect of income was reported in a multi-tribal population study in India (33), while Rengma et al. (5) found the association of both income and occupation on BMI among the Rengma Nagas of Assam, alike to the present findings. On the other hand, some recent studies (13,46) in the country couldn’t detect the impact of socio-economic factors on cardiovascular risks.

Education is widely considered to ameliorate the risk of CVDs in high-income countries (62). Although incomparable, the present population intriguingly shows a similar trend of education effect on BMI and BP with other higher-income countries like America (52), Spain (61), and Denmark (63), where hypertension risk is worst in people with low education. More precisely, education had a negative impact on nutritional status and BP, indicating that Chakhesang adults placed in the higher education level had lower BP than their counterparts who are placed in the lower education level. This is perhaps because Chakhesang adults who have higher education are more sensitized about the causes, risks, management, and treatment of hypertension. This finding on education in the present population contradicts the findings on other neighboring populations of Northeast India viz., the Misings tribe (14) and the Rengma tribe (5), and other communities from other parts of India (33,62).

Chakhesang Nagas are also well known for their high consumption of pork meat, high salt intake, and high consumption of locally brewed rice beer (hezo). Pork meat has a high content of fats, while hezo has a high content of carbohydrates and alcohol. Regular consumption of these two components by the people might have raised body fat levels leading to higher cases of obesity which ultimately spiked up the BP. A recent study by Tsukru and Ghosh (14) in the neighboring population of Mising tribe have reported a similar impact of pork fat consumption and traditionally processed rice beer on BMI and BP. Also, high sodium intake has been linked to hypertension yet the frequency of high salt intake has been on the rise in many populations worldwide (64). In this view, it is interesting to note that a previous study by Longvah et al. (39) on the same population revealed that the salt intake in this particular community was 8.3g/day which was higher than the WHO (64) recommended level of <5g/day. Such a high salt intake on a regular basis could have spiked the BP in the current study. However, the effects of extra salt intake along with high pork meat intake and hezo consumption on obesity and hypertension among the Chakhesang Nagas needs to be further investigated at the micro-nutrient level. Other indigenous communities in Northeast India too have shown a similar impact of extra salt intake on BP (33,47,65). Furthermore, Dixit and Dhakad (66) suggests that even a moderate reduction in its consumption in the general population would bring about a change in the health aspect reversing 8.5 million cardiovascular-related deaths within a time interval of ten years.

In this field of research, tribal populations in Northeast India furnish a fascinating epidemiological window, since previous studies (25,26) over the past have shown higher prevalence of undernutrition. Moreover, the prevalence of hypertension and obesity among the tribals has particularly intrigued the scientific community as it exhibits a transition from rural to urban lifestyle. It may also be noted that, Naga tribes are disadvantaged communities listed in the Indian Constitution under Article 342 by the Government of India (67). In the past, the Chakhesang tribe was by and large an agrarian society practicing primitive technology-based agriculture which required a lot of physical activities. But, with the uproar of modernization and urbanization processes, a lot have changed in this community. Many people are now engaged in white-collar jobs in the government and private sectors, entrepreneurship, and other income-generating activities like floriculture, farming, weaving, basketry, construction, daily wage labor, etc. Hence, as also evident from the impact of socio-economic factors in the present study, the high prevalence of overweight/obesity and hypertension among the Chakhesang adults can be attributed to urbanization, which led to a more sedentary lifestyle and changes in dietary patterns upon adoption of modern lifestyle.

Conclusion

The prevalence of overweight/obesity and hypertension in this tribal community is appallingly high for a rural setting. Age, gender, income, occupation, and education were found to be the most crucial socio-demographic determiners of obesity and hypertension among the Chakhesang adults. Transition from the traditional agrarian economy to non-agricultural economy, have improved the socio-economic position and lifestyle of this tribal community which has ultimately resulted in worsening their cardiovascular health. Additionally, improving the educational status and reducing the daily salt intake in this community might significantly reduce the risks of obesity and hypertension in this population. Hence, our findings call for robust screening, earliest detection, and awareness starting from rural household levels.

Limitations of the study

Our investigation was unable to assess the impact of dietary intake habits (specifically, high pork fat consumption and high salt intake in this tribal community) and behavioral factors (specifically, alcohol and tobacco consumption) which are considered to be the major contributors to cardiovascular risks.

Acknowledgements

The authors thank all the Chakhesang participants for their cooperation in the present study. NN and VT would like to thank Mr. Venuvoto Theluo and his parents for their unceasing hospitality, accommodation, help, and care rendered during our fieldwork. The authors are thankful to the Department of Anthropology, North-Eastern Hill University, for conducting the fieldwork.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Source of funding

The authors did not receive any funding for this study.

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