|Year : 2020 | Volume
| Issue : 2 | Page : 161-167
Is calorie intake the fundamental driver of noncommunicable diseases in India – A systematic review
Mitasha Singh1, Sunil Raina2, Mamta Parashar3, Ekta Gupta4, Shweta Goswami1, Manoj Kumar Gandhi5
1 Assistant Professor, Department of Community Medicine, ESIC MC and H, Faridabad, Haryana, India
2 Professor and Head, Department of Community Medicine, Dr. RP Government Medical College, Kangra, Himachal Pradesh, India
3 Assistant Professor, Department of Community Medicine, Lady Hardinge Medical College, New Delhi, India
4 Assistant Professor, Department of Community Medicine, Hamdard Institute of Medical Sciences, New Delhi, India
5 Medical Officer Cum Faculty, Regional Health and Family Welfare Training Centre, Kangra, Himachal Pradesh, India
|Date of Submission||20-Mar-2019|
|Date of Decision||20-Sep-2019|
|Date of Acceptance||18-Apr-2020|
|Date of Web Publication||16-Jun-2020|
Professor and Head, Department of Community Medicine, Dr. RP Government Medical College, Tanda, Kangra, Himachal Pradesh
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Nutrition epidemiology initially focused on few nutrients thought to be responsible for noncommunicable diseases (NCDs). The database of Indian Nutrition Survey is based majorly on calorie intake. Objective: The objective was to compare the change in the average calorie intake from 1990 to 2012 with the emerging epidemic of diabetes and hypertension (HTN) in India since 1990. Methods: A comprehensive search was made in National Library of Medicine's PubMed database and Google Scholar from March to August 2018, on the above-mentioned subjects. Reports of national surveys (National Sample Survey Office and National Nutrition Monitoring Bureau) were included for average calorie intake among different states from year 1990 onward. Region-wise search depicted by national nutrition surveys resulted in 277 and 587 abstracts on the prevalence of HTN and diabetes mellitus, respectively. There were 51 full-text articles and abstracts on the prevalence of HTN and diabetes from the above regions. Results: The average calorie intake per capita per day in the four zones of country in rural areas decreased from 1990 to 2012. An increasing trend in the prevalence of diabetes from rural areas was observed from 1994 to 2012. The per capita average calorie intake per day in urban areas from 1999 through 2011 in all zones except the eastern part of country was on rise. There was no consistent trend in the prevalence of HTN in any of the zones. Conclusion: It is not just an increase in calories, but a trade-off between the demand for calories and the demand for healthy lifestyles determines the prevalence of NCDs.
Keywords: Developing country, increasing burden of noncommunicable disease, role of calorie intake
|How to cite this article:|
Singh M, Raina S, Parashar M, Gupta E, Goswami S, Gandhi MK. Is calorie intake the fundamental driver of noncommunicable diseases in India – A systematic review. Indian J Public Health 2020;64:161-7
|How to cite this URL:|
Singh M, Raina S, Parashar M, Gupta E, Goswami S, Gandhi MK. Is calorie intake the fundamental driver of noncommunicable diseases in India – A systematic review. Indian J Public Health [serial online] 2020 [cited 2020 Sep 27];64:161-7. Available from: http://www.ijph.in/text.asp?2020/64/2/161/286808
| Introduction|| |
Diabetes mellitus (DM), hypertension (HTN), other cardiovascular diseases, chronic respiratory disease, cancer, and mental health conditions have been observed to be on rise in a developing country like India. Obesity and overweight are frequently associated with the presence of one or more of this noncommunicable diseases (NCDs). The years in which health loss from NCDs and injuries exceeded that from communicable, maternal, neonatal, and nutritional diseases (CMNND) varied widely between the states and regions ranging from 1986 to 2010. A nationwide detailed database is lacking for these diseases. However, prevalence data from various region-wise studies and national sample surveys give us an estimate. The India State-Level Disease Burden report describes the epidemiological transition in all the states of the country. In 1990, among all states, except Kerala, the disability-adjusted life years (DALYs) contributed by CMNNDs outweighed those caused by NCDs. However, by 2016, the DALYs in all states caused by NCDs were higher than those caused by CMNNDs.
Aside from the role of genetics, the dominant risk factors for NCDs are behavioral; they relate to unhealthy diets, inadequate physical activity, exposure to tobacco smoke (and air pollutants), and excessive alcohol use.
The quantity and quality of diet intake has been hypothesized to be associated with increasing prevalence of lifestyle diseases. India has transitioned from food-starved nation to food prosperous nation in the last two decades. Energy-dense Western diets have replaced traditional diets in many parts of the country. The database for calorie, protein, and fat consumption is not complete for every state in India. The National Sample Survey Office (NSSO) and National Nutrition Monitoring Bureau (NNMB) are the only agencies who have conducted surveys in selected regions of selected states of the country., Hence, the average calorie consumed by the population is the only available indicator for dietary intake of population. Diets in India are, however, extremely diverse due to various geographic, cultural, social, and economic factors, making it more appropriate to study subnational dietary patterns rather than a national average diet. In addition, the dietary determinants of NCDs are complex, and they cumulatively affect disease risk over decades. The epidemiologic investigations of these diseases have shown to focus upon on different food groups and evaluating the effect of the overall diet.
In this systematic review, we examined the reports on average calorie intake by Indian population and the studies on the prevalence of DM and HTN obtained from national surveys in different regions of India from 1990 onward. The aim of this study was to compare the change in average calorie intake from 1990 to 2012 with the emerging epidemic of DM and HTN in India.
| Materials and Methods|| |
We comprehensively searched the National Library of Medicine's PubMed database and Google Scholar from March to August 2018. We used combinations of medical subject headings (MESH) and free text words that included search terms such as calories intake, diet, rural, urban, states, and India. These were combined with search terms related to the prevalence, HTN, raised BP, high BP, diabetes, high blood sugar, DM, Non communicable diseases, India, and state. The filters included were English for the language category and humans for the study category. We identified articles eligible for further review by performing an initial screen of identified titles or abstracts. Full text and abstracts with relevant data were further reviewed.
Articles included in the review were cross-sectional or cohort; studies conducted among adult populations (≥18 years old) published after year 1991. The included studies were conducted between the years 1990 and 2012. Articles on the prevalence of diabetes and HTN were searched first and then were segregated region wise. Reports of national surveys (National sample survey office [NSSO] and NNMB) were included for average calorie intake among different states from 1990 onward.,,,,,,, The states or regions with reported average calorie intake were added in the MESH to search hypothesized outcome (DM and HTN) in that state [Box S1].
Average calorie intake was denoted per capita per day in kilocalories. HTN was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, and DM was defined as fasting blood sugar ≥126 mg/dl or postprandial blood sugar ≥200 mg/dl or on treatment. Articles were excluded if they were letters, abstracts without relevant data, conference proceedings, case series, trials, reviews, and meta-analysis; not conducted on humans; on special groups; and not community-based studies. In addition, articles aiming to study a comorbid condition with HTN or DM were excluded.
Three independent reviewers (EG, SG, and MKG) screened the titles and abstracts of the initially identified studies to determine whether they would satisfy the selection criteria. Any disagreements about selection were resolved through consensus or consultation with a third author (MP). Full-text articles were retrieved for the selected titles. Reference lists of the retrieved articles were searched for additional publications. The retrieved studies were assessed again by two independent authors (MS and SR) to ensure that they satisfied the inclusion criteria.
Quality assessment and data extraction
A critical appraisal checklist with few modifications was used for each article to assess quality by two authors (MS and SR) [Box S2]. A data collection form was designed prior to the implementation of the search strategy. The data collection form included questions on year of publication, design, study setting, geographic origin, inclusion and exclusion criteria, response rate, information on the reported variables, i.e., percentage of population with diabetes, percentage of hypertensive patients, and average calorie intake [Box S3]. We categorized the studies from different states in four zones of the country (north, south, east, and west). Further, the average calorie intake for a particular zone in a particular year of survey was calculated.
| Results|| |
The PubMed and Google Scholar search with different Boolean combinations initially identified 225,300 references, and a total of 404 abstracts were screened by inclusion criteria. Based on the search conducted according to the regions depicted by national nutrition surveys, 277 abstracts of prevalence of HTN and 587 on the prevalence of DM were screened. There were 51 full-text articles and abstracts on the prevalence of HTN and diabetes from the regions where the average calorie intake was available. Of these, 26 articles with relevant data were further shortlisted [Figure 1].
The average calorie intake per capita per day in four zones of country in rural areas has decreased from 1990 to 2012. The decline, however, was not continuous. There were surges in between in all the zones. In the years 1990, 1992, and 1996, majority of calorie intake surveys were conducted among the southern states of the country. NSSO in 1999–2000 represented of all the four zones and reported that the average calorie intake was highest in the north zone (2274.83 kcal) as compared to others. NNMB in 2001 reported the west zone (2068.50 kcal) as the leading intake of average calories per capita per day as compared to the east and south zones. NNMB did not include any north Indian state in 2001. NSSO in 2004–2005 again reported north zone (2081.22 kcal) as the leading consumer as compared to other zones. In 2009–2010, NSSO survey revealed that northern (2150.78 kcal) and eastern states (2150.33 kcal) on an average were almost equally consuming higher than other zones. The trend also shows that the average calorie intake decreased from 1990 to 2004–2005 and again increased in 2009–2010. According to NNMB, 2011, survey, eastern states were consuming the majority of calories (2017 kcal). The average intake again increased in 2012 as reported by the NSSO. It can be observed that calorie intake has been reported to be higher by the NSSO as compared to the NNMB [Table 1].
|Table 1: State-wise average calorie intake (kcal/per capita/day) of rural and urban India (1991-2012)|
Click here to view
Data for urban areas' calorie intake are available from 1999 onward conducted by the NSSO. There has been an increase in per capita average calorie intake per day from 1999 through 2011 in all zones except the eastern part of the country. There is a gradual increase in north Indian states in the decade. Punjab state in the north zone remained the highest consumer of calories except in 2004–2005 (where Jharkhand was the highest) and 2011–2012 (where Haryana was the highest). Among the representatives of eastern states of the country, West Bengal was the lowest consumer till 2009–2010 after this Assam was the lowest consumer in 2011–2012 [Table 1].
The reported prevalence of diabetes from different community-based studies of different states shows an overall increase in rural areas from 1994 to 2014,,,,,, [Table 2]. In rural North India, the prevalence in 1994 in Haryana was 3%, and this increased to 8.3% in 2008–2010 in rural Chandigarh. The prevalence as compared to other north states remained low in rural Kashmir (4.25%) and rural Jharkhand (3%). The scenario in South Indian states from 1998 onward showed a higher prevalence as compared to other regions (rural Kerala: 3.8% and 5.9% in 1998 and 1999, respectively). This further increased to 12.5% in 2007 in rural Kerala and 7.8% in rural Tamil Nadu in 2008–2010,,,,,, [Table 2].
|Table 2: State-wise prevalence of diabetes mellitus from rural and urban India|
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Studies on DM prevalence have been conducted in majority in urban areas. The increase is observed in all the four zones of the country. Studies from Jaipur, Rajasthan, from 2001 through 2007 reported a gradual increase in the prevalence of diabetes (8.6%–20.1%). Urban Srinagar observed an increase from 5.2% in 1994 to 6.05% in 2011. However, evidences from urban areas of Jammu and Kashmir (6.05%, 2011) and Himachal Pradesh (7.8%, 2011–12) reported lower diabetes prevalence as compared to other North Indian States. Southern states reported overall increased prevalence in urban areas from 1995 (Tamil Nadu, 11.6%), which further increased to 13.5% in 2000; 15.5% in 2003–2004, and decreased in 2008–2010 survey to 13.7%. Evidence from the west zone reported that Mumbai city in Maharashtra showed an increase in prevalence from 9.3% in 2000 to 10.9% in 2008–2010,,,,,,,,, [Table 2].
Based on inclusion and selection criteria from rural areas of the country, the prevalence of HTN has been reported since 2005. North Indian state of Himachal Pradesh from various studies did not observe a consistent trend in the prevalence of HTN. This was 49% in 2005–2007 and decreased to 35.9% in 2010 and further increased to 37.4% in 2011. The 2008–2010 INDIA-AB survey from rural Chandigarh and Jharkhand reported a prevalence of 20.4% and 22.2%, respectively. The prevalence from South Indian rural regions showed a gradual increase with 17.3% from Karnataka to 28% from Tamil Nadu in 2008–2010 and 32.5% from Kerala in 2008,,,,, [Table 3].
|Table 3: State-wise prevalence of hypertension from rural and urban India|
Click here to view
In urban areas, the search engine resulted in evidence since 1993 onward. A multicity survey on HTN was conducted in 1993–1996 and 2005–2007. In North India, the prevalence trend can be observed from different cities; 25.8% from Moradabad in Uttar Pradesh, which increased to 32.2% in Lucknow in 2003 and further 60% from Kanpur in 2005–2007. Urban Punjab in 2005–2007 survey reported 51.5% prevalence of HTN, which further decreased in 32.6% from Chandigarh city in 2008–2010, 35.9% from Amritsar in 2012, and 28.1% from urban Himachal Pradesh. The South Indian state of Kerala in different studies observed an increase from 1993 to 1996 (33.7%) to 2006 (47%) and further decline in 2008 (34.9%). Similar trend was observed in urban Maharashtra in the west zone,,,,,,, [Table 3].
| Discussion|| |
The review revealed that the average calorie intake per capita per day in the four zones of the country in rural areas has decreased from 1990 to 2012. The decline however has not been continuous with surges in between in all the zones.
The review also revealed that in urban areas, there had been a static trend in per capita average calorie intake per day from 1999 through 2011 in all zones except the eastern part of the country. However, the review reveals that the prevalence of diabetes from different community-based studies of different states shows an overall increase in rural areas from 1994 to 2012. Similar to this, gradual increase in HTN has been observed. The review therefore raises a point. This is the realization that the increasing burden of NCDs may be linked to some macro-level determinants and not increase in calorie intake.
Taken together, our findings suggest that potentially desirable effects of static calorie intake over the years on selected NCD risk factors may be outweighed by certain adverse ones, possibly including less healthful diets and lack of exercise. Our preliminary pathway analysis in particular indicates that we may need to think beyond total calorie and fat per capita supply as sole mediators of NCDs. Our findings seem particularly salient in view of the strong evidence on the steady increase in caloric intake around the world in the recent decades, and that overweight burden is significantly driven by the growing calorie consumption from energy-dense foods.,,
The question still remains as to what therefore drives the NCD burden in India. We think that a trade-off between the demand for calories and the demand for healthy lifestyles determines the prevalence of NCDs and not just an increase in calories. Importantly further, the type of calories consumed may also be a factor. The balance of this trade-off depends on the level of country income. India being a low- and middle-income country (LMIC), this trade-off is appears pronounced.
Several previous studies focusing on LMICs found a positive association between urbanization (or at least living in the city) and BMI, overweight, and obesity.,,, India has been a largely agriculture-based economy. However, the last few decades have seen a large-scale movement away from the agricultural society due to increasing urbanization.
According to the 2017 report from the International Food Policy Research Institute, poor-quality diets are now the leading cause of NCD worldwide. As per the sixth annual Global Food Policy report, the so-called nutrition transition has emerged as a key driver of NCDs in LMICs – especially overweight and obesity, diabetes, and cardiovascular disease. Therefore the fundamental driver of NCDs in India could have been the incorporation of unhealthy diet in the daily routine of our lives. Probably, time has also come when research on the possible role of endocrine disrupters such as pesticides need to be taken up for better understanding of the growing NCDs epidemic in India.
Countries across the globe are exploring to mitigate the negative effects of poor diets in urban areas – primarily targeting the food environment and behavioral change. A recent analysis shows that no low-income countries have yet introduced policies that aim to reduce consumption of sugar-sweetened beverages, although their consumption is increasing quickly in these countries. Importantly, the Global Food Policy report outlines that healthy diets will generally still are beyond the financial reach of the urban poor in LMICs despite policy initiatives. Therefore, beyond calories, action will need to go to initiating food policies that combat obesity and diabetes: reducing sedentary behavior; narrowing inequalities; and improving programs to identify, treat, and monitor residents with NCDs.
The search was majorly dependent on the nutrition reports available by national agencies. These agencies conducted survey in selected cities of selected states, hence limiting our search for the NCDs. Hence, the region-wise distribution cannot be a very correct picture of nutrition intake of the country.
| Conclusion|| |
The average calorie intake per capita per day in the four zones of the country in rural areas has decreased with surges in between from 1990 to 2012. In urban areas, there has been a static trend in per capita average calorie intake per day from 1999 through 2011 in all zones except the eastern part of the country. Simultaneously, the prevalence of diabetes and HTN from different community-based studies of different states shows an overall increase in rural areas. Hence, it is not the quantity but the quality of diet which could be the fundamental driver of NCDs in India.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3]