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 »  Abstract
 » Introduction
 »  Materials and Me...
 » Results
 » Discussion
 » Conclusion
 »  References
 »  Article Figures
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 Table of Contents    
Year : 2023  |  Volume : 55  |  Issue : 2  |  Page : 76-88

Variation in normative values of major clinical biochemistry analytes in healthy reproductive-age women in India: A subset of data from a National Indian Council of Medical Research-Polycystic Ovary Syndrome task force study

1 Department of Endocrinology, Sher-i Kashmir Institute of Medical Sciences, Jammu and Kashmir, India
2 Department of Endocrinology and Metabolism, Institute of Post Graduate Medical Education and Research, West Bengal, India
3 Department of Obstetrics and Gynaecology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
4 Department of Operational Research, National Institute for Research in Reproductive and Child Health (NIRRCH), Mumbai, India
5 Department of General Medicine, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India
6 Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, Raipur, India
7 All India Institute of Medical Sciences, New Delhi, India
8 Department of Endocrinology, Osmania Medical College and Aster Prime Hospital, Hyderabad, India
9 Department of Endocrinology, Professor and Head, Government Medical College, Thiruvananthapuram, Kerala, India
10 Department of Obstetrics and Gynaecology, Maternal Health and Research Institute, Telangana, India
11 Indian Council of Medical Research, New Delhi, India
12 Department of Clinical Research, Sher-i Kashmir Institute Of Medical Sciences, Jammu and Kashmir, India

Date of Submission28-Sep-2022
Date of Decision04-May-2023
Date of Acceptance08-May-2023
Date of Web Publication03-Jun-2023

Correspondence Address:
Mohd Ashraf Ganie
Sher-i Kashmir Institute of Medical Sciences, Srinagar, Jammu and Kashmir
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijp.ijp_694_22

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 » Abstract 

OBJECTIVES: Clinical biochemistry reference intervals (RIs) play a crucial role in interpreting patient test results and making informed clinical decisions. Using data from an ongoing Indian Council of Medical Research-National task force study on healthy women, normative ranges for commonly analyzed biochemical analytes were established.
MATERIALS AND METHODS: A total of 13,181 women of reproductive age (18–40 years) were recruited from different urban and rural regions of the country, of which 9898 women signed an informed consent were included. Among these, women having features of hyperandrogenism, menstrual cycle irregularities, and comorbidities were excluded. RIs of 22 analytes were computed in the remaining 938 women controls. To estimate the 95% range of the reference distribution, the limits of the 2.5th percentile and the 97.5th percentile were used in the study.
RESULTS: Mean ± standard deviation of age and body mass index of participants was 30.12 ± 6.32 years and 22.8 ± 3.36 kg/m2 respectively. Centiles (2.5th–97.5th) of liver function parameters, lipid parameters, glycaemic parameters, and renal parameters are presented. No significant difference in analytes was observed in relation to the area of residence, and age groups except in albumin (P = 0.03). The distribution of most of the parameters was consistent with the various RI studies conducted in India as well as other countries.
CONCLUSION: This is the first study generating biochemical RIs data among a large representative sample of healthy reproductive-age women recruited using a robust design across the country. The resource may serve as a reference range for common biochemical analytes for future in this age group.

Keywords: Biochemical parameters, healthy women controls, Indian population, normative ranges

How to cite this article:
Ganie MA, Chowdhury S, Suri V, Joshi BN, Bhattacharya PK, Agarwal S, Malhotra N, Sahay R, Jabbar P K, Rozati R, Shukla A, Rashid H, Bashir R, Wani I, Nair A, Arora TK, Kulkarni B. Variation in normative values of major clinical biochemistry analytes in healthy reproductive-age women in India: A subset of data from a National Indian Council of Medical Research-Polycystic Ovary Syndrome task force study. Indian J Pharmacol 2023;55:76-88

How to cite this URL:
Ganie MA, Chowdhury S, Suri V, Joshi BN, Bhattacharya PK, Agarwal S, Malhotra N, Sahay R, Jabbar P K, Rozati R, Shukla A, Rashid H, Bashir R, Wani I, Nair A, Arora TK, Kulkarni B. Variation in normative values of major clinical biochemistry analytes in healthy reproductive-age women in India: A subset of data from a National Indian Council of Medical Research-Polycystic Ovary Syndrome task force study. Indian J Pharmacol [serial online] 2023 [cited 2023 Oct 4];55:76-88. Available from: https://www.ijp-online.com/text.asp?2023/55/2/76/378033

 » Introduction Top

Clinical biochemistry reference intervals (RIs), which are widely used in patient care and management and are essential benchmarks for clinical diagnosis, drug selection, disease risk assessment, disease staging, treatment monitoring, or use for research, refer to the range of concentration for a specific parameter in a group of individuals who are considered clinically healthy.[1],[2] Typically, the RIs are represented by 2.5 and 97.5th percentiles of a distribution acquired from a healthy populace.[1] In healthy individuals, several factors including drugs, vaccines, pathological state, and techniques used, may cause a significant variation in these parameters.[2],[3] RIs may also be markedly influenced by genetics, ethnic, and cultural factors, in addition to biological covariates such as age and gender.[4] Further hormonal variation during the life course also affects the biochemical tests.[5] The inconsistencies may also arise due to methodological differences and environmental factors such as geographical location, seasonal variations, dietary habits of that person, and sample processing that influence individuals from different studies.[6] The use of RIs outside the region of application could be misleading and may negatively affect clinical outcomes in patients.[7] Against the vast racial, ethnic, and environmental diversity, population-specific data generation is warranted.[8] The Clinical and Laboratory Standards Institute (CLSI) 2008 and the International Federation for Clinical Chemistry and Committee on RIs and Decision Limits provide guidelines for establishing population-specific RIs.[9],[10]

Many European, American, and Asian countries have established their national laboratory values for various biochemical analytes.[11],[12],[13] Recent studies from Africa, Saudi Arabia, and Egypt reported that significant variation in biochemical parameters compared to standard reference ranges.[7],[14],[15] Data from previous studies from India were mainly limited to subregional/regional or hospital-based settings,[16],[17],[18],[19],[20],[21],[22] though fragmented, has also shown variation of biochemical analytes among Indian populations when compared to RIs corroborated in western cohorts. [20,22-26] Thus, studies on a nationally representative sample of apparently healthy Indian women of reproductive age are largely unavailable. Reproductive age is a critical period in women's life whereby women are in continuous dynamics of physical, psychological, and social changes.[27] They have unique risks for many hormonal, reproductive, and metabolic maladies that might precipitate into long-term consequences and avoidable deaths. As reference values are needed by clinicians to interpret measurement results, they should accurately reflect a specified population that is comparable to the patients undergoing treatment who are coming in for investigation. Considering that women in India typically spend a considerable proportion of their lives in their reproductive years, which often marks their first dealing with healthcare, it is crucial to have reliable population-specific normative data with a clear definition, appropriate measurement, and inference.[28] Therefore, in the current study, the biochemical data of apparently healthy reproductive-age women representative of all regions of the country were collected, with the objective of meeting the demand for baseline reference laboratory ranges that can aid in the monitoring of physiological or pathological changes. These normative data from India are the first of its kind and can conveniently be used to draw comparisons and generate RIs for reproductive-age women in India.

 » Materials and Methods Top

This substudy comes from a multicentre investigation that was carried out to gauge polycystic ovary syndrome (PCOS) prevalence in the local population and the comprehensive methodology has previously been published elsewhere.[29]

Study design

The present study is multi-centric, community-based, and cross-sectional in design.

Study subjects

In this study, women aged 18–40 years from nine distinct regions across India were screened using a multistage sampling technique and the study included the following urban and rural districts: (1) Jammu and Kashmir (Urban: Srinagar; Rural: Ganderbal), (2) Delhi (Urban: South Delhi; Rural: Faridabad), (3) Chandigarh (Urban and rural areas), (4) Chhattisgarh (Urban and rural areas of Raipur), (5) West Bengal (Urban: Kolkata; Rural: Burdwan), (6) Meghalaya (Urban: Shillong; Rural: East Khasi hills), (7) Telangana (Urban and rural areas of Rangareddy, Hyderabad), (8) Kerala (Urban and rural areas of Thiruvananthapuram), and (9) Maharashtra (Urban: Mumbai; Rural: Palghar).

The Institutional Ethics Committees (Reference number: IEC/SKIMS Protocol #107/2016) in each participating institute gave their approval for this study, which was carried out in accordance with the 1975 Helsinki Declaration. The research included women from community pools of respective sites who satisfied the following criteria: aged 18–40 years, permanent inhabitants of the region (more than a year), ready to participate, had written informed consent, and were not receiving drug/therapy. Women having features of hyperandrogenism, menstrual irregularities, pregnant or lactating women, those with comorbidities, and people with cognitive or physical disabilities who could not complete the questionnaire were all excluded from this study. Women who had used steroids, androgens, oral contraceptives, anti-epileptics, or medicines known to interact with glucose or lipid metabolism were also excluded. Physicians meticulously evaluated information from medical records to identify individuals who met the screening criteria. The initial screening form had three sections: biographical information, menstrual history and clinical data, and prior medical history of systemic disorders. A total of 13,181 women were screened, of which 9898 women signed an informed consent [Figure 1], and among them 2761 had hyperandrogenism and irregular menstrual cycles, and were excluded from the study.
Figure 1: Flow of study subjects in the study. LFT = Liver function test, KFT = Kidney function test, OGTT = Oral glucose tolerance test, DHEA-S = Dihydroepiandrosterone sulfate, LH = Luteinizing hormone, FSH = Follicle-stimulating hormone, SHBG = Sex-hormone binding globulin, OCP = Oral contraceptive pill, LDL = Low-density lipoprotein, TG = Triglyceride, HDL = High-density lipoprotein, IFG = Impaired fasting glucose, IGT = Impaired glucose tolerance

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Clinical examination

Among 7137 women participants, clinical examination (physical examination, height, weight, waist circumference, blood pressure measurement) was done in 5130 subjects. Among these, 675 patients with obesity (WHO)[30] and 64 having hypertension (JNC VIII),[31] were excluded from the study.

Laboratory investigations

Laboratory investigations (blood counts, liver function test [LFT], kidney function test [KFT], lipids, and oral glucose tolerance test) were done in 1937 subjects, and hormonal assessment (T3, T4, thyroid-stimulating hormone, Prolactin, Cortisol, dehydroepiandrosterone sulfate, luteinizing hormone, follicle-stimulating hormone, total testosterone, sex-hormone binding globulin, E2, C-peptide) in 1605 subjects. Among participants with abnormal parameters, hypercholesterolemia were reported in 3.6%, high triglycerides (TGs) (9.1%), low high-density lipoprotein (HDL) (1.98%), high low-density lipoprotein (LDL) (1.14%) as per the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (NCEP-ATP III) guideline,[32] impaired fasting glucose (1.4%), impaired glucose tolerance (IGT) (6.7%), type 2 diabetes mellitus (0.3%) as per ADA 2022 guideline,[33] and hyperuricemia (3.3%), and hence excluded [Figure 2].
Figure 2: Exclusion criteria based on biochemical test results. LDL = Low-density lipoprotein, TG = Triglyceride, HDL = High-density lipoprotein

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Collection of blood samples and biochemical analysis

Whole blood samples, up to a maximum of 5 mL, were collected on day 2–5 of spontaneous menstrual bleeding after 7–12 h fast followed by centrifugation at 3 rpm × 1000 rpm for 15 min to obtain serum and plasma. The standard commercially available kits and fully automated biochemical analyzers were used to determine the baseline parameters, including (fasting glucose, albumin, alkaline phosphatase [ALP], total protein, total bilirubin, alanine aminotransferase [ALT], and aspartate aminotransferase [AST], LFT, KFT, lipids), sodium (Na), potassium (K), calcium (Ca), uric acid (UA), urea, creatinine, LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), TGs, and total cholesterol (TC), following the manufacturer's instructions.

According to the manufacturer's specified criteria, the analyzer is calibrated on a daily basis at centers. Furthermore, we validated selected samples (10/site in the initial lot as per random number table sequence) at our central departmental laboratory (an ISO-15189 accredited medical Laboratory) for all parameters. The coefficient of variation of different analytes was 1.1%–4.5% using different methods and hence was within the acceptable limits.

Statistical analysis

The IBM SPSS Statistics, Version 26.0. IBM Corporations, (Armonk, New York, USA) was used to carry out the statistical analysis. Using the lower reference limit at the 2.5th percentile and the upper reference limit at the 97.5th percentile, the middle 95% of the reference distribution was computed to determine the 95% RIs. The data were reported as median with 2.5th–97.5th percentile since, according to the Kolmogorov–Smirnov test, they did not have a normal distribution. Statistical significance was defined as a P = 0.05.

 » Results Top

Social and demographic characteristics of study participants

After the complete evaluation of subjects, the data from 938 women were used for generating the RIs of each biochemical parameter. The mean age of women participants was 30.12 ± 6.32 years with 31 (25–35) years as median (interquartile range) age. The mean ± standard deviation (SD) of weight, height, and body mass index of the study population was 54.6 ± 8.89 kg, 154.7 ± 5.73 cm, and 22.8 ± 3.36 kg/m2. The average ± SD of systolic and diastolic blood pressure was 114 ± 10.9 mmHg, and 75 ± 6.9 mmHg, respectively. The study population was equally distributed as per the area of residence (Urban 50.4%; Rural 49.6%).

Normative ranges of various biochemical analytes

The identified 95% RIs, based on the 2.5, 5, 10, 25, 50, 75, 90, 95, and 97.5th percentiles, for biochemistry analytes including renal function parameters, liver function parameters, lipid parameters, and glucose parameters are presented as a summary in [Table 1]. The median and RIs (2.5th–97.5th) percentiles of selected biochemical parameters among the study population as per various age categories, and as per residence areas (urban or rural) are presented in [Supplementary Table 1] and [Supplementary Table 2], respectively.
Table 1: Median and reference intervals (percentiles) of selected biochemical parameters among the study population

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Glycaemic parameters

The median and 2.5th–97.5th percentiles of plasma glycaemic parameters among healthy study controls were 87 (76–99) mg/dL (fasting blood glucose), 127 (100–165) mg/dL (blood glucose, 1 h), 103 (84–134) mg/dL (blood glucose, 2 h).

Liver function parameters

The median and 2.5th–97.5th percentiles of AST, ALT, and ALP were 22 (8.9–37) IU/L, 19 (8.3–41.5) IU/L, and 84 (57–132) IU/L, respectively. In case of total bilirubin, total protein, albumin, and globulin, the median and 2.5th–97.5th percentiles were 0.7 (0.42–1.2) mg/dL, 7.5 (6.7–8.28) g/dL, 4.36 (3.7–5) g/dL, and 3.1 (2.3–3.9) g/dL, respectively.

Lipid parameters

The median and 2.5th–97.5th percentiles for lipid parameters were 159 (116–196) mg/dL for TC, 56 (50–75.6) mg/dL for HDL, 98.5 (79–146.9) mg/dL for TGs, 81.6 (35.6–120) mg/dL for LDL, and 19 (12–34) mg/dl for very LDL.

Renal function parameters

The median and 2.5th–97.5th percentiles of serum urea, creatinine, UA, Ca, and phosphate were 19 (13–31) mg/dL, 0.79 (0.61–1.1) mg/dL, 4.2 (2.9–5.78) mg/dL,

9.2 (8.4–10.5) mg/dL, and 3.7 (3–4.5) mg/dL, respectively. In case of Na and K, median and 2.5th–97.5th percentiles were 140 (134–146) and 4.3 (3.5–5.2) mmol/L, respectively.

No significant difference was observed between the groups when categorized as per age (18–25, 26–33, and 34–40 years), and area of residence (rural or urban) (Refer S2 and S3). However, on stratifying the data on the basis of age as shown in S2, we observed a significant difference in albumin (P < 0.05).

Comparison of reference intervals used in previous studies

[Table 2] presents the summary of normative ranges of different biochemical analytes of the current study in comparison with the RIs of previous studies in ethnically different population. There is a scarcity of published research on RIs specific to Indian populations for lipid, renal, and liver-related biomarkers. The RIs found in this investigation, however, were found to be largely consistent with previous studies conducted in India, as well as in culturally, ethnically diverse populations in Asia, the Middle East, and Western countries. In case of liver function parameters, most analytes like total bilirubin, total protein, globulin, AST, ALT, and ALP were on a higher side in West African regions (Nigerian and Ghana) than in Asian (especially Indian population) and western countries. In renal function parameters, creatinine, urea, phosphate, and K were in higher ranges in the Nigerian population. The lipid and glycaemic parameters, though similar across all the locations, varied [Supplementary Table 3].
Table 2: Reference intervals of different biochemical analytes in comparison with the reference intervals of previous studies in different population

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 » Discussion Top

The present study evaluated common biochemical parameters in a large sample of healthy reproductive-age women to arrive at their normative ranges. The reference ranges provide information about the range of normal values for a specific population and can be used as a tool to assess an individual's health status. The various commonly used biochemical parameters were studied in accordance with the guidelines of CLSI in an interval between 25% and 97.5% of the data distribution.[34] The main results of this study were that 15.8% of women of reproductive age who seemed healthy showed abnormalities in at least one blood lipid parameter, with no dissimilarity between urban and rural study sites. These findings are consistent with a previous study conducted as part of Indian Council of Medical Research-India Diabetes in which at least one lipid abnormality was observed in 79% of the study population comprising men and women above 20 years of age from three Indian states and one Union Territory.[35] Among all the lipids, the highest derangement was reported in TGs with 9.1% of study women presenting with hypertriglyceridemia. In the current study population, high cholesterol levels were reported in 3.6% women subjects, however, earlier studies have reported a higher prevalence of 25.4%–30.8% hypercholesterolemia among Indian females.[36],[37],[38],[39] Although, in these studies, wider age groups have been studied unlike ours. The undiagnosed derailed lipid parameters among apparently healthy individuals may explain the alarming increased incidence of cardiovascular disease (CVD) among young Indians adults and translates to high future economic and health burden as well.[40] Further, glucose dysmetabolism was also observed in the study subjects with impaired fasting glucose (1.4%), and IGT (6.7%). A hidden burden of IGT and prediabetes was also reported earlier among general Indian populations below 40 years of age by several studies.[41] In addition to this, we report un-detected type 2 Diabetes mellitus in 0.3% of our study population which in addition to the known diabetics, eventually contribute to the disease burden. This is of particular relevance among our study group as good glycaemic control is the first target for pregnant women or intending to conceive to avoid hyperglycemia and adverse pregnancy outcomes.[42] In addition, the results emphasize the importance of high-quality epidemiological data and large-scale screening of asymptomatic apparently healthy populations in India where most of the metabolic and CVD mortalities occurs.[43]

Several variations in the normative ranges of various biochemical indices were observed in this study as compared to standard ranges or reports from other studies. The normative range for fasting blood glucose was (76–99 mg/dL) as against (75–104 mg/dL) reported in a study carried out at four centers across India while 2 h blood glucose ranges were (84–134 mg/dL vs. 70–129 mg/dL).[19] While stratifying the basal glucose as per the age and area of residence, no significant difference was found among the groups in fasting and postprandial glucose levels, although a few studies have reported increasing tendency of glucose with age.[44]

A significant variation of serum lipid levels in different populations has been reported which may be attributed to differences in food habits, nutrition, lifestyle, and socioeconomic status. This study also found significant variations in normative ranges of TC, HDL-C, LDL-C, as coherent with previous studies from Punjab India[21] and Ghana, West Africa.[45] In our study, we found normative ranges of TC, TGs, and LDL among the study group match the laboratory ranges unlike an earlier study conducted among Assamese populations of India who reported a wider range of TC, LDL-C in age groups 20–80 years.[20] We could not find exact studies exclusively in reproductive-age women for comparisons, most of the parallels were drawn on the basis of gender. Only Sairam et al. presented the lipid parameters females <40 years of age, and when compared with our study, the TGs (51–183 mg/dL vs. 79–147 mg/dL), LDL (58–155 mg/dL vs. 36–120 mg/dL) and TC (114–233 mg/dl vs. 116–196 mg/dL) were reported to be higher in their population.[19] The normative ranges for HDL-C (28–66 mg/dL) in our population were on lower side as compared to that reported in the previous study (30–72.1 mg/dL) in Karnataka.[16] This difference may be attributed to the inclusion of females of a specific age group only in our study. The upper limit and median for blood TC, LDL-C, and HDL-C levels were not significantly different when the patients were divided by age. Comparing our study's results to NCEP ATP-III standards for all lipids, the 97.5th percentile was well within the normal range.[30]

The normative ranges of albumin, ALT, and urea, in our study population, were comparable as found studies conducted among nonpregnant young women in China,[46] the USA,[47] different regions of Ethiopia,[4],[12],[48],[49] Ghana,[11] and Zimbabwe.[50] Although we could not find an exclusive study among Indian females of similar age group, it was observed that normative ranges of creatinine (0.6–1.1 vs. 0.58–1.2 mg/dL), total bilirubin (0.30–1.00 vs. 0.320–1.30 mg/dL), AST (8.9–37 vs. 11.4–37 U/L), and ALT (8.3–41.5 vs. 7.2–35.7 U/L) were comparable to the ranges reported in Indian females aged 18–65 years from a laboratory database of a tertiary care hospital.[18] In addition, our results demonstrated no significant difference in the intervals of all the biochemical analytes except albumin among different age groups of the study population.

The majority of the normative ranges of biochemical parameters in the current study were in line with those found in other RI studies encompassing the diverse Indian population's culture and ethnic background as well as those found in Asian, Middle Eastern, and Western nations. In case of liver function parameters, West African areas generally had higher ranges of analytes than Asian (particularly Indian population) and western countries. Creatinine, urea, phosphate, and K were in also higher ranges in the Nigerian population. The results emphasize the importance of developing population-specific RIs for accurate utilization in medical settings.

Strengths and limitations

The study derives its robustness from its large sample size and multisite data collection, which together form a comprehensive and truly representative depiction of the entire nation. The subjects were proportionally taken from all 6 zones of the country and represented both rural and urban population. Limitations in the present study are data from a single gender and limited age group, and thus do not determine the normative ranges in males and outside the 18–40 years of age group in females. Other biochemical indices (amylase and lipase), were not undertaken and information on physical activity/exercise/smoking is missing. Although validation in selected samples was undertaken, it would not obviate the need of a single methodology and equipment.

 » Conclusion Top

We conclude that this is the first well-designed study generating biochemical data among a large sample of healthy reproductive-age women representative of the country. Keeping in view the lack of such data in the country, it may serve as a reference range for common biochemical analytes in this age group. Further study on similar designs among males and outside this range may complete the normative data in the country.

Polycystic ovary syndrome study group

The Indian Council of Medical Research-PCOS study group consists of Mohd. Ashraf Ganie, Bharati Kulkarni, Rohina Bashir, Amlin Shukla, Taruna Arora, Sarita Agrawal, Neena Malhotra, Rakesh Sahay, Puthiyaveettil Khadar Jabbar, Roya Rozati, Vanita Suri, Beena Joshi, Prasanta Kumar Bhattacharya, Aafia Rashid, Imtiyaz Ahmad Wani, V Sreenivas, Mukesh Srivastava, Abilash Nair, Parvaiz Koul, Shariq Masoodi Zafar Amin Shah, Ghulam Nabi Yatoo, Tabassum Parvez, Aashima Arora, Neena Khanna, Tulika Singh, Naseer Ahmed, Tariq Gojwari, Muzafar Wani, Seema Qayoom, Wahid Khan, Rama Walia, Dipankar De, Nitish Naik, Shyam Prakash, Nandita Gupta, Rajesh Sagar, Jai Bhagwan Sharma, Devasenathipathy Kandasamy, Eli Mohapatra, Narendra Kuber Bodhey, Sabah Siddiqui, Archana Singh, Gourisankar Kamilya, Mousumi Mukhopadhyay, Pradip Mukhopadhyay, Nehar Ranjan Sarkar, Alice Abraham Ruram, Jessy Abraham, Saroj Kumar Pati, Dibakar Sahu, Lokesh Kumar Singh, Manika Agrawal, Donboklang LynserHemangini Thakkar, Anushree Patil, Shrabani Mukherjee, and Ashok Anand, Wasia Showkat, Aruna Ramaiah, Sudha Bindu, Lakshman Rao Nadeem Ahmad, Malathi Ponnuru, Sujatha Rani, Sultan Rizwan Ahmed, Aleem Ahmed Khan Prabhakar Rao, Anuja Elizabeth George, Nirmala C, Gaurav thakur, Saba Noor, Jayasree Leelamma, Jayakumari Shaikh, Sharmeen, Gaivee Vinam Meshram, Shouvik Choudhury, Pieu Adhikary, Subhasish Pramanik Nithlesh Kumar, Rahul Harish, Mudasir Makhdoomi, Mudasir Fayaz, Nafeez Rehman, Neha Ravi, Siffali Chandrakar, Ajay Kumar, Gazala Hasan, Sudipta Banerjee, Himali Vadhan, Nitin Deshpande, Ram Babu, Rita Parab, Humaira Minhaj, Balaji Bhasker, Arya Suresh and Shaik Iqbal Ahmed.


The Indian Council of Medical Research, Government of India, provided financial assistance for the study under file no. 5/7/1337/2015-RBMH. The researchers are grateful to every volunteer participant in the investigation. The Department of Health Research, Government of India, supported Multi-disciplinary Research Unit, SKIMS, Srinagar, which provided the essential research facilities for conducting this study, is also acknowledged by the authors.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


 » References Top

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  [Figure 1], [Figure 2]

  [Table 1], [Table 2]


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