|Year : 2016 | Volume
| Issue : 1 | Page : 26-33
Prevalence of nonalcoholic fatty liver disease in an adult population in a rural community of Haryana, India
Anindo Majumdar1, Puneet Misra2, Sanjay Sharma3, Shashi Kant4, Anand Krishnan4, Chandrakant S Pandav5
1 Clinical Investigator Research Fellow, Centre for Chronic Disease Control, All India Institute of Medical Sciences, New Delhi, India
2 Additional Professor, All India Institute of Medical Sciences, New Delhi, India
3 Additional Professor, Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
4 Professor, All India Institute of Medical Sciences, New Delhi, India
5 Professor and Head, Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
|Date of Web Publication||23-Feb-2016|
Centre for Chronic Disease Control, 4th Floor, Plot No. 47, Sector 44, Institutional Area, Gurgaon - 122 002, Haryana
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Though nonalcoholic fatty liver disease (NAFLD) is increasingly becoming prevalent in the Indian population, knowledge regarding the burden and risk factors of NAFLD is limited, more so from rural areas. This study was thus conducted to estimate the prevalence of NAFLD among adults in a rural community of Haryana, India and to measure the association of diet, physical activity, and other selected risk factors with NAFLD. Materials and Methods: The present study was conducted in a rural community of Haryana, India among resident adults ≥35 years of age. Eight out of 28 villages were selected by probability proportion to size sampling. The number of eligible and consenting participants randomly selected from each village was 27. Out of 216 participants thus recruited, 184 participants reported for undergoing ultrasonography (USG) of the liver, anthropometry, blood pressure recording, and blood sample collection. Finally, 176 participants were analyzed. Results: Prevalence of NAFLD was 30.7%. There was no significant difference in the calorie intake and average total physical activity between participants with and without NAFLD. On multivariate analysis, hypertension [adjusted odds ratio (OR): 2.3, 95% confidence interval (CI): 1.1-5.0, P 0.03] and an increased waist circumference (adjusted OR: 4.9, 95% CI: 1.5-7.0, P < 0.001) were independently associated with NAFLD. A normal high-density lipoprotein (HDL) level was protective against NAFLD (adjusted OR: 0.4, 95% CI: 0.2-0.8, P 0.001). Conclusions: The high prevalence of NAFLD is already a public health problem, even in the rural parts of India. Urgent public health interventions are required to prevent its development by controlling the cardiometabolic risk factors associated with it.
Keywords: India, nonalcoholic fatty liver disease (NAFLD), prevalence, risk factors
|How to cite this article:|
Majumdar A, Misra P, Sharma S, Kant S, Krishnan A, Pandav CS. Prevalence of nonalcoholic fatty liver disease in an adult population in a rural community of Haryana, India. Indian J Public Health 2016;60:26-33
|How to cite this URL:|
Majumdar A, Misra P, Sharma S, Kant S, Krishnan A, Pandav CS. Prevalence of nonalcoholic fatty liver disease in an adult population in a rural community of Haryana, India. Indian J Public Health [serial online] 2016 [cited 2020 Oct 1];60:26-33. Available from: http://www.ijph.in/text.asp?2016/60/1/26/177295
| Introduction|| |
Nonalcoholic fatty liver disease (NAFLD) is probably the most common liver disorder in the world, affecting 2.8-24% of the general population.  It is also currently the most common cause of abnormal liver function tests and is recognized as a major cause of cryptogenic cirrhosis of liver.  It is principally a disease of middle age and old age but at present, there is no general consensus on whether there is any predilection for a specific gender.  The natural history of the disease is still unclear. In studies with paired biopsies (one biopsy performed after the period of follow-up), the disease progressed in 32-41%, remained stable in 34-50%, and improved in a minority of the patients with NAFLD. 
NAFLD is assuming higher importance now because of:
- The possible role in the development of cardiovascular disease;
- The association with diabetes and impaired glucose tolerance;
- The strong relationship with metabolic syndrome.
Early diagnosis and management of NAFLD is thus of vital importance.  Dietary modification and exercise constitute the mainstay of treatment. As the link to noncommunicable diseases is clear, there is a need to address this as a public health problem.
NAFLD rates are increasing in developing countries as well. Most of the studies examining the natural history of NAFLD in India and worldwide have been conducted in hospital settings with a small sample size and hence, the generalizability is questionable.  There is limited information regarding prevalence of NAFLD from India (including rural India). Thus, the objectives of the present study were to estimate the prevalence of NAFLD in adults in a rural community of India, and to measure the association of diet, physical activity, and other selected risk factors with NAFLD.
| Materials and Methods|| |
The present study was a community-based cross-sectional study conducted in the villages under the intensive field practice area of the Comprehensive Rural Health Services Project (CRHSP), Ballabgarh. Ballabgarh is situated 35 km southeast of New Delhi in the state of Haryana, India. A total of 28 villages are covered under its field practice area.
The study was conducted between October 2009 and March 2011. All individuals aged ≥35 years residing in the study area for at least 6 months were included in the study. Individuals taking ≥20 g/day or ≥140 g/week of alcohol for > 1 year, and seropositive for hepatitis B or hepatitis C (i.e., the presence of surface antigen of the hepatitis B virus or the presence of antibodies versus hepatitis C virus.) were excluded.
Based on a community-based study (by Mohan et al.  in South India), taking the prevalence to be 32% and absolute precision to be 10%, the sample size was calculated to be 87. Using a design effect of two, the sample size was 174. Considering a refusal rate of 20%, the final sample size was calculated to be 210. This was planned to be achieved by recruiting 27 individuals from each of the eight villages (selected out of a total of 28 villages by using probability proportional to size sampling technique).
For this, the population ≥35 years of age were obtained from the Health Management Information System (HMIS) of CRHSP, Ballabgarh, Haryana, India. Selection of 27 households was done by systematic random sampling. Within a household, the inclusion and exclusion criteria were applied to all persons ≥35 years of age available at the time of visit. Within a household, out of the respondents eligible and available at the house at the time of visit, one person was chosen randomly. If no eligible participant was found or the house was found to be locked, the house having the next higher number nearest to the locked house number was selected. This process was followed in the same manner till 27 respondents were recruited. Informed written consent was taken from the participants prior to administration of the study instrument. The study was approved by the Ethics Subcommittee of AIIMS, New Delhi, India.
On the first visit, information was collected by a face-to-face interview using a questionnaire (modified suitably after pretesting). The participants were asked to come to the primary health center (PHC)/subcenter on a specified date after an overnight fast (12 h) where physical examination, ultrasonography (USG), and blood sample collection were performed.
The first section of the questionnaire captured demographic information and also information regarding medical history and health-relevant behaviors, viz., alcohol consumption, smoking habits, and history of drug use. The second section obtained the dietary history using the 24-h dietary recall method. Calorie excess and deficit were calculated using recommended techniques. For example, calorie deficit for each participant was calculated by subtracting the calorie intake of a particular study participant from his/her total calorie requirement (based on age, gender, and type of work, i.e., sedentary, moderate, or heavy work).  The third section consisted of the Global Physical Activity Questionnaire (GPAQ) version 2.  Physical activity information was collected in three domains (namely, activity at work, travel to and from places, recreational activities) and information about sedentary behavior was obtained too. Metabolic equivalent (MET) values were applied to activity levels to calculate the total physical activity. 
Physical examination included measurements of body height, weight, blood pressure, waist circumference, and waist to hip ratio. For height and weight measurement, standard methods were followed. Body mass index (BMI) was calculated using the formula weight (kg)/height (m) 2 . The midpoint between the lowest rib and the iliac crest was taken as waist circumference. Hip circumference was recorded at the widest point of the hips using a non-elastic measuring tape (Seca, United Kingdom). Two measurements were made and the mean was taken. The waist-hip ratio for each participant was calculated.
Blood pressure was measured in a sitting position in the right arm using mercury sphygmomanometer (Pagoda, Elite Surgical Industries, New Delhi). If the difference between two readings was 10 mmHg or more, a third reading was taken. The reading having a lower value of the two was taken as blood pressure and was recorded to the nearest two mercury sphygmomanometer (Pagoda, Elite Surgical Industries, New Delhi).
Five mL of venous blood was collected from each participant taking universal precautions. It was then rapidly transported to the laboratory at CRHSP, Ballabgarh, Haryana, India within 30 min of collection after entering identifying details on the vials. Biochemical analysis for fasting blood glucose, total cholesterol, serum triglyceride, high-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (SGOT), alanine aminotransferase (SGPT), serum alkaline phosphatase (ALP), and serum total bilirubin were performed using ECHO automatic analyzer, Firmware version 3.13 (Embiel Ltd., Dangjeong-dong, Gunpo-si, Gyeonggi-do, South Korea). Commercially available enzymatic kits (made by Transasia Bio-Medicals Ltd. and ERBA Diagnostics, Mannheim, Baden-Württemberg, Germany) were used for the estimation of glucose, cholesterol, HDL cholesterol, and bilirubin. Test for hepatitis B and hepatitis C was performed in all the cases of fatty liver diagnosed by USG. Hepatitis B was tested using hepatitis B surface antigen (HBsAg) testing kit made by Diagnostic Enterprises (Hepacard). Hepatitis C was tested using anti-hepatitis C virus (HCV) testing kit (Instachk, InTec Products, Inc., P.R.C.).
USG was performed by the principal investigator MA after proper training in the Department of Radiodiagnosis, AIIMS. Training was focused only on identifying fatty changes in the liver and grading of fatty liver according to the standard diagnostic criteria. 
During the actual study, USG was performed on all participants using a portable ultrasound machine (MicroMaxx, SonoSite Inc., Washington, United States). The operational definition used for NAFLD in the present study was: "Any degree of fatty liver in the absence of alcohol intake ≥20 g/day or ≥140 g/week and not having hepatitis B or C on serology." As a means of maintaining quality assurance, ultrasonographic findings of 11 participants were cross-checked on the spot by SS. Four out of 11 participants had NAFLD and 100% agreement in diagnosis was there between MA and SS. All the images were saved and photographed. All images of fatty liver-positive cases and 10% of negative cases (randomly selected) were reviewed by SS (blinded for the findings) later. 100% agreement was there in the positive cases of NAFLD. One out of 12 negative cases was found to be a case of mild fatty liver by SS.
Test reports were distributed through health workers in sealed envelopes. All participants were given health education. Participants diagnosed to have fatty liver, diabetes, or hyperlipidemia were counseled regarding diet, physical activity, and other necessary lifestyle changes. Participants were referred to CRHSP, Ballabgarh Hospital (secondary care level hospital) when required.
Data were entered in Microsoft Excel 2007 and analysis was performed using Statistical Package for Social Sciences version 17.0 (SPSS Inc., Chicago). Proportions and means were calculated, along with 95% confidence interval (CI) and standard deviation (SD), respectively. Student's t-test was used to compare continuous variables and chi-square test was used to compare proportions among groups. P value <0·05 was considered to be significant.
Due to skewed distribution, variables such as calorie excess, average total physical activity, SGOT levels, SGPT levels, and resting time were transformed to natural logarithms. All of the above variables followed a normal distribution after log transformation. Bivariate analysis was performed using chi-square test (Fischer's exact test wherever applicable). For bivariate analysis, BMI,  hypertension status,  diabetes status,  lipid profile status,  waist circumference  and waist-hip ratio  were categorized according to standard guidelines. Multivariate analysis was performed using logistic regression to identify various risk factors (independent variables) of NAFLD (dependent variable). Variables, which were significantly associated (P < 0.25) in bivariate analysis were included in the multivariate analysis. As values for waist circumference and waist-hip ratio would be highly correlated (the latter is calculated from the former), only waist circumference was included in the regression equation. Adjusted odds ratio (OR) was calculated to measure the independent effect of the risk factors.
| Results|| |
[Figure 1] shows the flow of participants. The most common reason for refusal to participate was poor experience in the past of not getting reports after being tested in some of the projects (nine participants out of 24). Other reasons were perceived belief of having no disease, traveling to some other place, etc. During the study, 33 houses were found to be locked or with no one present during the visit. The number of houses with at least one member present at the time of visit but having no eligible participants was 14.
[Table 1] shows the sociodemographic characteristics of the study participants. Most of the population studied was young to middle-aged, females, married, and illiterate. Though 85.8% of the participants did primarily nonagricultural work, most of them were also involved in some form of agricultural work, either helping their family members or working as part-time laborers.
The study participants' illness profile reflected that 26.7% had hypertension (previously and newly diagnosed), 7.4% had diabetes mellitus (previously and newly diagnosed), 1.7% had cerebrovascular disease, 1.1% had coronary artery disease, and 4.5% had liver/gallbladder disease. Only participants having documentation of previous history of these abovementioned diseases were counted. 35.8% of participants were ever smokers, 9.1% were ever users of alcohol (less than the exclusion criteria). 54.5% participants had undergone at least one surgery in their lifetime. Most of these were family planning operation (tubal ligation) while others were hysterectomy, cholecystectomy, etc. 14.2% used the prescribed drugs continuously for the last 6 months for various health conditions.
The overall prevalence of NAFLD was 30.7%. Eleven out of 33 males (33.3%) had NAFLD and 43 out of 143 females (30.1%) had NAFLD. When only USG-diagnosed fatty liver was considered, 74.1% cases had mild fatty liver, 22.2% had moderate fatty liver, and only two out of 176 cases had severe fatty liver.
[Table 2] compares participants with NAFLD and without NAFLD. [Table 3] shows the effects of demographic, nutritional, and physical activity-related indicators on NAFLD. High SGOT (≥25 IU) values were present in around 72% of USG-diagnosed NAFLD cases and non-NAFLD cases. Similarly, high SGPT (≥25 IU) values were present in 83.3% of NAFLD cases and 82% of non-NAFLD cases. [Table 4] shows the effects of various physical and biochemical parameters on NAFLD.
|Table 3: Effects of demographic, nutritional, and physical activity-related indicators on NAFLD|
Click here to view
On multivariate analysis, hypertension (adjusted OR: 2.3, 95% CI: 1.1-5.0, P 0.03) and an increased waist circumference (adjusted OR: 4.9, 95% CI: 1.5-7.0, P < 0.001) were independently associated with NAFLD. A normal HDL level was protective against NAFLD (adjusted OR: 0.4, 95% CI: 0.2-0.8, P 0.001).
| Discussion|| |
In the present study, prevalence of NAFLD was 30.7% (33% in men and 30% in women, P 0.71), comparable to study by Mohan et al.  [32% prevalence in an urban South Indian population (men 35.1%, women 29.1%, P 0.14)] and Amarapurkar et al.  (prevalence of 16.6%). Another study conducted by Das et al.  in rural areas of West Bengal yielded a prevalence of 8.7% among the population greater than 18 years. This lower prevalence in a study by Amarapurkar et al. and Das et al. could be attributed to the lower age limit taken and different alcohol intake criteria used. Singh et al.  and Bajaj et al.  in hospital-based studies reported a prevalence of 24.5% and 32.2%, respectively. In the present study, during quality control one out of the 12 negative cases was a case of mild fatty liver, which points toward an even higher prevalence. The prevalence was high in settings outside India as well. Dassanayake et al.  reported a prevalence of 32.6% in the middle-aged in Sri Lanka. The prevalence ranged 23·4-46% in some other studies. ,,,,
NAFLD was significantly associated with hypertension (adjusted OR: 2.3, 95% CI: 1.1-5.0, P 0.03). Hypertension was also reported to be associated with NAFLD in other studies. ,,,, High serum cholesterol level was not an independent risk factor in the present study though was a recognized risk factor in previous studies. ,, Risk for NAFLD was significantly higher among participants who had a higher waist circumference (adjusted OR: 4.9, 95% CI: 1.5-7.0, P < 0.001). This was similar to the previous literature where significant associations of NAFLD with this parameter have been proved. ,,,,, A normal HDL cholesterol was found to be protective against NAFLD, similar to many studies conducted earlier.
In a previously conducted case-control study, the total calorie intake of NAFLD cases was significantly higher than the controls.  In our study, excess calorie intake and low physical activity were not significantly associated with NAFLD though the resting time was higher in participants with NAFLD than those without NAFLD.
Though liver biopsy is the gold standard diagnostic test for fatty liver, we did not use it as it is not known to be suitable for population-based studies because of attendant risks, expense, and uncertain benefits to asymptomatic patients. Sensitivity of USG in hospital-based studies ranges 60-94% and specificity ranges 88-95%. 
The present study was a community-based study. Only three out of previous five studies on prevalence from India, i.e., by Mohan et al.,  Amarapurkar et al.,  and Das et al.  were community-based. A single-trained investigator, a physician conducted the study with good quality control checks.
One limitation of the present study was that we selected a person above 35 years of age who was "available" during the house visit at daytime. In rural Haryana, which is agriculturally advanced, most males are involved in agriculture even though they have a different primary occupation. They leave very early in the morning and return late to their houses. Thus, during the study, most of the males could not be contacted further, in spite of efforts made to do so. Another reason was that because of the eligibility criteria for alcohol intake, 22 persons got excluded from the study, all of which were males. In spite of this, we think the results are fairly generalizable. This is because when males and females were analyzed separately for risk factors for NAFLD, there was not much of a difference between the two. For example, the mean age of males and females was 54.6 years (SD 11.7) and 51.7 years (SD 11.3), respectively. Similarly, the mean BMI for males was 21.8 kg/m 2 , whereas for females it was 22.8 kg/m 2 . Physical activity-wise also, not much difference was there, i.e., 103.7 for males and 91.8 for females. The prevalence of NAFLD (33% in men and 30% in women) was also similar in both genders, which corroborated with previous studies.
There could also be a possible underreporting of alcohol intake due to social desirability bias leading to overestimation of results, which means that some cases of NAFLD might actually have been cases of alcoholic fatty liver disease.
It can be concluded that the prevalence of NAFLD among adults (35 years and above) living in rural areas of Ballabgarh block, Haryana, India was 30.7%. Out of all these, most cases (74.1%) were of a mild variety, which indicates great scope for early intervention. Known cardiometabolic risk factors such as hypertension and central obesity were found to be independent risk factors for NAFLD. A normal HDL level had a protective effect.
There are important implications of the study. There is a need for further research regarding the burden and risk factors of NAFLD in India, especially in rural areas. Abdominal USG should be routinely used as a screening tool in those patients coming to health centers for any type of diagnostic USG, those having one or more components of metabolic syndrome, and in noncommunicable disease clinics. As a lot of patients undergo USG for different reasons, it is important for clinicians who routinely perform USG to take this as an opportunity for early detection of NAFLD.
We owe our sincere thanks to Dr. Naval K. Vikram., Additional Professor, Department of Medicine, AIIMS, New Delhi, India, for providing technical guidance and encouragement for conducting the study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]
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