Journal of

Journal of Immunology and Geriatrics

Current Issue Volume No: 1 Issue No: 1

ISSN: Coming Soon
share this page

Research Article Open Access
  • Available online freely Peer Reviewed
  • A study on the association of ACE i/D gene polymorphism, Obesity, Blood pressure and susceptibility of type 2 diabetes mellitus among the Kurmis of West Bengal, India.

    Pranabesh Sarkar 1   Diptendu Chattrejee 1   Arup Ratan Bandyopadhyay 1  

    1Department of Anthropology, University College of Science, Technology & Agriculture, University of Calcutta, India

    Abstract

    Earlier studies reported significant association of obesity, hypertension and Type2 Diabetes Mellitus (T2DM). Genetic and many disease-associated alleles have been identified through GWAS and applied to T2DM and indicated roles of renin-angiotensin system (RAS) in insulin signaling pathway and insulin resistance has been well documented. Angiotensin converting enzyme (ACE) gene catalyzes the conversion of angiotensin I to angiotensin II and also inactive the vasodilatation and hence renin-angiotensin system (RAS) in insulin signaling pathway and insulin resistance has been reported. To best of the knowledge we are reporting for the first time regarding association of ACE gene polymorphism with body composition, physiological and metabolic variables among any endogamous ethnic group (Kurmis) from of West Bengal, Eastern India. To achieve the purpose, total 197 (male 99 and female 98) randomly selected apparently healthy unrelated adult individuals of Kurmi population of Purulia District, West Bengal, India were incorporated in the present study. Anthropometric variables, physiological variables (blood pressure) and metabolic variables (PP blood sugar) have been collected using standard techniques. Extracted genomic DNA was PCR amplified and genotyped to understand ACE gene I/D polymorphism. The result demonstrated significant (p<0.05) sexual dimorphism in PBF. MAP and PP blood sugar found to be in normal range among the Kurmis. ACE gene polymorphism showed no deletion of the Kurmis and hence, only the prevalence of ACE II (insertion-Insertion) genotype has been noticed. The present study vindicated on the basis of body composition in terms of fat patterning, physiological and metabolic variables and ACE gene polymorphism that there is very low or no risk of T2DM among the Kurmis of West Bengal, India.

    Author Contributions
    Received 16 May 2016; Accepted 19 Jun 2016; Published 27 Jun 2016;

    Academic Editor: Divey Manocha, Upstate Medical University, United States

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright ©  2017 Fernando Augusto Mardiros Herbella, et al.

    License
    Creative Commons License     This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.

    Citation:

    Pranabesh Sarkar, Diptendu Chattrejee, Arup Ratan Bandyopadhyay (2016) A study on the association of ACE i/D gene polymorphism, Obesity, Blood pressure and susceptibility of type 2 diabetes mellitus among the Kurmis of West Bengal, India.. Journal Of Immunology And Geriatrics - 1(1):1-8.

    Download as RIS, BibTeX, Text (Include abstract )

    DOI Coming Soon

    Introduction:

    Obesity is well recognized as a major risk factor for coronaryheart disease 1, type 2 diabetes 2 and hypertension 3. Diabetes Mellitus type 2 (DM2) is a metabolic disease that develops by a decrease in sensitivity of insulin receptors as an effect of the disruption certain metabolic functions in the processing of glucose. T2DM patients have, uncontrolled glucose levels, and commonly have problems with obesity and cardiovascular disease 4 has been debated 2 Epidemiological studies suggest that an excess deposition of fat in the abdominal region may be more predictive for the risk of myocardial infarction, stroke, and diabetes than in general obesity 5 Richard et al. 1995. Many technologies are available to measure adipose tissue and its distribution,such as the underwater weighing method, air-displacement plethysmography, dual-energy X-ray absorptiometry (DXA), computerized axial tomography (CAT) and magnetic resonance imaging (MRI). However, the use of these methods is limited,because of inaccessibility and the high cost of equipments.Thus, simple methods such as bioelectrical impedance analysis(BIA) and anthropometric methods are still the normin field studies and for public use 6.

    The number of individuals with diabetes is on the rise and the figure is expected to reach 552 million by 2030 owing to many factors such as population growth, aging, urbanisation, obesity, and physical inactivity 7. Although environmental factors are reasons often considered, it is important to keep in mind that environmental factors, by themselves, represent only a fraction of cases. Individuals with family history of T2DM are at higher risk of developing the disease as shown in familial studies 8 suggest strong genetic contribution.

    The genome-wide association studies (GWAS) allowing genetic and many disease-associated alleles have been identified through GWAS and applied to T2DM 9 However, the utilization of the candidate gene approach has been a pioneer and at a forefront of genetic association studies. Although, genome-wide significance level has limited the common and rare variants capturing, therefore, contribute to the missing heritability. Thus, candidate gene approach is still valuable inspite of the GWAS era. The roles of renin-angiotensin system (RAS) in insulin signaling pathway and insulin resistance has been well documented 10. The blockade of the system has been found to have beneficial effects in the prevention of T2DM 11. Angiotensin converting enzyme (ACE) gene is a potent vasoconstrictor and directly involved in the process of cell proliferation, differentiation, apoptosis and angiogenesis 12. Contemporary study 13 reported ACE gene catalyzes the conversion of angiotensin I to angiotensin II and also inactive bradykinin. Bradykinin is vasodialotor which bringing about its proteolysis 14. ACE gene is located on 17(17q13) and contains 26 exons. ACE gene has insertion-deletion (I/D) polymorphism at 287 bp region in intron 16 15, which leads to three genotype DD, DI and II,. Variable findings in different populations on the association between (I/D) polymorphism in the ACE gene and Diabetic nephropathy has encourage to be studied on this matter 16. Ethnic variation of ACE (I/D) polymorphism has been reported in earlier studies 17. The polymorphism of ACE gene has been raising the susceptibility of T2DM in addition with environmental factors. 18

    In this circumstance, the present study is an attempt to understand the body composition in terms of fat patterning and or obesity and distribution of some physiological variables, and its association with ACE gene polymorphism. To best of the knowledge we are reporting for the first time regarding association of ACE gene polymorphism with body composition physiological and metabolic variables among any endogamous ethnic group from Eastern India.

    MATERIALS AND METHODS

    A total of 197 randomly selected apparently healthy unrelated (free from any major disease ) adult individuals (male 99 and female 98) of Kurmi population of Purulia District, West Bengal, India were incorporated in the present study. Purulia is one of the severely under developed district of India in West Bengal. Anthropometric data were obtained using techniques 19. Blood pressure (Systolic blood pressures SBP and Diastolic blood pressure- DBP) were also taken following the standard technique 19 and classified in accordance with the Seventh Report of the Joint National Committee (JNC-7) recommendation 20. The mean arterial pressure (MAP) was derived (Equation: MAP = (2 x diastolic) +systolic / 3. Diastole counts twice as much as systole because 2/3 of the cardiac cycle is spent in diastole) to understand the average blood pressure in an individual as the average arterial pressure during a single cardiac cycle. Postprandial glucose (PP) Glucose level were measured, by “Advance Micro-draw” Glucometer (Hypoguard, Minneapolis, MN 55439, USA), strictly following instruction manual and classified following the American Diabetes Association criteria 21. Reliability of all machine measurement was checked on a regular basis.

    Majority of the participants were found to be involved different activities like agricultural labourer and or day labourer. Verbal informed consents were taken from the participants before the work. For obtaining socio-demographic information a specially prepared and pre-tested heart disease risk factor schedule containing information regarding socio demographic variables, physical activities, sleep duration etc. have been used. Derived measures regarding fat patterning and obesity (BMI, WHR etc.) were calculated using standard equations. Assessment of body composition in terms of fat patterning form different aspects of the body viz. PBF (Percent of body fat) using bioelectrical impedance analysis and other body composition variables for componential fat determination have also been done using body scanner (OMRON HBF- 300, OMRON Corporation, Tokyo, Japan). For ACE gene polymorphism genomic DNA was extracted from the buccal swab using standard technique 22 with slight modifications. PCR products were genotyped for I (Insertion) and D (Deletion) alleles of ACE gene using locus specific primer viz. Forward Primer-5′-CTG GAG ACC ACT CCC ATC CTT TCT-3′ Reverse Primer-5′-GAT GTG GCC ATC ACA TTC GTC AGA T-3′. PCR product was taken for 2.5% agar gel electrophoresis 15. Obtained data were doubly checked and analyzed using the SPSS (Version- 16.0). Pearson’s correlation has been done and cut off was set as p=0.05.

    The Studied Population

    The population for the present study is Kurmi, also known as the Kurmi- Mahato and Mahato, they are sometimes referred to as Koli-Kurmi, as they have been living with tribal of Chotonagpur plateau for centuries and have been instrumental in disseminating agricultural technology and craftsmanship among them. The Kurmis live in the North and South Divisions of Chotonagpur, Santhal parganas of Bihar, Mayurbhanj, Keonjhore, Sundargarh, Balasore and cuttack district of Orissa, Purulia, Bankura, midnapore, Burdwan, Birbhum, Nadia, 24 Parganas, north Bengal and tea garden of Assam 23 and observed a few endogamous subgroups such as Kurum, Adh-kurmi and Madhyam-kurmi and etc. The Kurmis of West Bengal also have surnames such as Mahato, Deb, Debsigha, Sinha, Pramanik and Majhi. They speak Kurmali at home and Bengali with others. They claim an equal social status with the Santhal, Bhumij, Kora, Mudi and others and maintain ceremonial friendship ties with them. 24.

    Results

    Distribution of Anthropometric and physiological variables (Table 1) revealed significant (p<0.05) sexual dimorphism in percent body fat (PBF) in terms of significantly (p<0.05) higher PBF among the females. However, BMI being one of the general adiposity traits demonstrated no significant sexual dimorphism and eventually, showed the general adiposity as calculated from BMI being below normal for both the males and females.

    Table 1. Distribution of anthropometric, physiological and metabolic variables of the studied population
    Variables Male (n=99)Mean ± SD Female (n=98)Mean ± SD
    Age(years) 45.13±13.86 56.00±11.00
    BMI 21.79±3.640 22.30±4.79
    PBF 27.07±7.035 36.81±6.91*
    SBP(mm/hg) 140.32±23.34 146.45±32.51
    DBP(mm/hg) 83.81±8.94 83.35±13.15
    Blood Glucose(mg/dl) 105.74±25.88 103.77±24.27
    Waist hip Ratio(WHR) 0.92±0.05 0.90±0.06

    *p<0.05

    No significant correlation has been revealed among the anthropometric, metabolic and physiological variables in the males (Table 2). However, significant correlation (p<0.01) was found (Table 3) between BMI and WHR and as well as BMI and PBF among the females (Table 3). Regression analysis (not shown) expressed 39% of variation of PBF to BMI, while WHR expressed about 21% variation among females However, no correlation for both the sexes have been noticed with physiological variable (MAP) and metabolic variable (PP blood sugar) and also found to be in normal range.

    Table 2. Distribution of correlations of different anthropometric and physiological variables among the males
    BMI WHR PBF BLOOD SUGAR (PP) MAP
    BMI 1 -0.006 0.295 0.102 0.136
    WHR -0.006 1 0.114 0.056 0.047
    PBF 0.295 0.114 1 0.042 0.000
    BLOOD SUGAR (PP) 0.102 0.056 0.042 1 0.072
    MAP 0.136 0.047 0.000 0.072 1

    Table 3. Distribution of correlations of different anthropometric and physiological variables among the females.
    BMI WHR PBF BLOOD SUGAR (PP) MAP
    BMI 1 0.458** 0.626** 0.189 0.195
    WHR 0.458** 1 0.240 0.109 0.064
    PBF 0.626** 0.240 1 0.205 0.252
    BLOOD SUGAR (PP) 0.189 0.109 0.205 1 0.081
    MAP 0.195 0.064 0.252 0.081 1

    **p< 0.01 level

    ACE gene polymorphism (Table 4) has been taken into consideration as potent vasoconstrictor and directly involved in the process of cell proliferation, differentiation, apoptosis and angiogenesis and the result demonstrated no deletion in both sexes. Therefore all the studied participants belong to II (Insertion-Insertion) genotype II.

    Table 4. Distribution of ACE gene genotype of the studied participants
    ACE gene polymorphism Male Female
    DD genotype 0 0
    DI genotype 0 0
    II genotype 99 98

    Discussion

    Current trend in Public Health research have created demand for relevant information from Biological Anthropology, in particular, and Social Sciences. The existing contributions reflect that Biological Anthropology can provide significant new theories and data that dramatically expand the understanding of different processes that known to affect public health. Furthermore, such work demonstrated the capacity to expand the understanding of “normal” human biology across the life course. Analysis of specific local contexts and patterns of temporal variation, using a socio-ecological and lifespan perspective, posed challenges to notion of panhuman biological uniformity and the search for a single descriptive frame work in human biology. Biological Anthropology integrates multiple level of analysis, from ultimate (evolutionary) to proximate (molecular) causes. T2DM once considered as a disease limited to developed counties, the occurrence of diabetes mellitus is increasing throughout the world, both in industrialized and in developing countries with the developing countries experiencing an accompanying epidemiological transition 25 Since the publication of Thompson (1929) 26 on the theory of demographic transition, the notion of epidemiological transition was introduced by Omran (1971) 27 to describe the changes in health, occurring during the demographic transition, where the largest burden of disease gradually shifts from infectious diseases to chronic, non-communicable diseases (NCDs) like T2DM. Subsequently it was revealed that T2DM as one of the major categories of NCDs is associated with a two- to four-fold excess risk of death from coronary heart disease (CHD) 28. Thus, considering epidemiological transition as an effect of demographic transition contemporary studies 29 recognized T2DM as much a disease of poor and disadvantaged people as it is of fat and unfit people. Association of T2DM and obesity 30 has also become an important health problem in developing countries particularly in India 31, which is currently experiencing a rapid epidemiological transition. Positive association of obesity with T2DM has been established repeatedly in many cross-sectional and prospective studies 32, 33, 34. Thus, in order to develop ethnic specific T2DM prevention strategies, the relationship of anthropometric measures to T2DM risk factors requires in depth elucidation. The genome-wide association studies (GWAS) allowing genetic and many disease-associated alleles have been identified through GWAS and applied to T2DM 9. Hence, eventual contemplation for the potential link between obesity, hypertension and insulin resistance genes could stand as prospective area of research on biomedical aspect concerning to public health. In this context, the present study reports no significant excess adiposity among the Kurmis. However, PBF was found to be significantly (p<0.05) higher among the females might be due postmenopausal age (56.00±11.00 years) and eventually corroborative with earlier studies 35, 36.

    With regard to blood pressure and PP blood sugar all the participants of Kurmis of the present study has been found to be in normal range. ACE I/D gene polymorphism revealed only II (insertion – insertion) genotypes among the Kurmis. Since the present study on Kurmis, is the maiden attempt, comparison of the results regarding anthropometric, physiological, metabolic and ACE gene polymorphism remained unresolved. The present study also vindicated on the basis of body composition in terms of fat patterning, physiological and metabolic variables and ACE gene polymorphism that the Kurmis might not be susceptible for T2DM. However, limitation of this study is the sample size.

    Acknowledgements:

    Authors are grateful to the participants of the present study. Partial finance from University of Calcutta (BI 8 & 9) and UGC Project PWH-048/14-15 is acknowledged.

    References

    1.PWF Wilson, D’Agostino R B, Levy D, Belanger A M, Silbershatz H. (1998) Prediction of Coronary Heart Disease Using Risk Factor Categories. , Circulation 97, 1837-47.
    2.Wang Y, Rimm E B, Stampfer M J, Willett W C, Hu F B. (2005) Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. , Am J Clin Nutr 81, 555-63.
    3.Ghosh J R, Bandyopadhyay A R. (2013) Central adiposity and the risk of hypertension in. , Asian Indian girls World J Pediatr 9, 256-60.
    4.Miranda-Massari J R, Gonzalez M J, Fernando A S, Cidre C, Paz I M et al. (2015) Metabolic Correction as a tool to improve diabetes type 2 management. , Bol Asoc Med P R: 54-9.
    5.Richard N B, Heymsfield S B, RocheAF. (1995) Human Body Composition and the Epidemiology of Chronic Disease. Obes Res. 3, 73-95.
    6.Chatterjee D, Bandyopadhyay A R. (2009) Non-communicable disease and health hazards in Bengali population: a major public health issue in India. , J. Hum. Ecol 25, 143-6.
    7.Whiting D R, Guariguata L, Weil C, Shaw J. (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and. , Diabetes. Res. Clin. Pract 94, 311-21.
    8.Doria A, M E Patti, C R Kahn. (2008) The emerging genetic architecture of type 2 diabetes. , Cell. Metab 8, 186-200.
    9.Sladek R, Rocheleau G, Rung J, Dina C, Shen L et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. , Nature 445, 881-85.
    10.Velloso L A, Folli F, Sun X J, White M F, Saad M J et al. (1996) Cross-talk between the insulin and angiotensin signaling systems. , Proc Natl Acad Sci U S A 93, 12490-5.
    11.Scheen A J. (2004) Prevention of type 2 diabetes mellitus through inhibition of the Renin-Angiotensin system. , Drugs 64, 2537-65.
    12.Leung P S. (2004) The peptide hormone angiotensin II: its new functions in tissues and organs. , Curr Protein Pept Sci 5, 267-73.
    13.Joyce-Tan S M, Zain S M, Abdul Sattar MZ, Abdullah N A. (2016) Renin-Angiotensin System Gene Variants and Type 2 Diabetes Mellitus: Influence of Angiotensinogen. , Journal of Diabetes Research 2161376. J. Diab Res. Article ID 216137, 10-1155.
    14.Sausa E O, Cojocaru D. (2011) Angiotensin-Convering Enzyme Insertion/Deletion Polymorphism In Type I Diabetic Nephropathy. Alexandru Ioan Cuza University of Iasi. 15-18.
    15.Sikdar M, Purkait P, Raychoudhuri P, Bhattacharya K S, Naidu M J et al. (2013) . ACE Gene Insertion/Deletion Polymorphism and Type-2 Diabetic Nephropathy in Eastern Indian Population .
    16.Shaikh R, Shahid M S, Mansoor Q, Ismail M, Azhar A. (2014) . Genetic variants of ACE (Insertion/ Deletion) and AGT (M268T) genes in patients with diabetes and nephropathy , Angiotensin. Aldosterone. Syst 15, 124-30.
    17.Sagnella G A, Rothwell M J, Onipinla A K, Wicks P D, Cook D G et al. (1999) A population study of ethnic variations in the angiotensin-converting enzyme I/D polymorphism: relationships with gender, hypertension and impaired glucose metabolism. , J Hypertens 17, 657-64.
    18.Yoshida H, Kuriyama S, Atsumi Y, Tomonari H, Mitarai T et al. (1996) Angiotensin I converting enzyme gene polymorphism in non-insulin dependent diabetes mellitus. , Kidney Int 50, 657-64.
    19.T G Lohman, A F Roche, Martorell R. (1988) Anthropometric Standardization Reference Manual. Chicago: Human kinetics Books.
    20.Chobanian A V, Bakris G L, Black H R, William C, Lee A et al. (2016) . the National High Blood Pressure Education Program Coordinating Committee Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure .
    21.American DiabetesAssociation Releases (2016) Standards of Medical Care in Diabetes - See more at: http://www.diabetes.org/newsroom/press-releases/2015/2016-standards-of-medical-care-in- (accessed 12.05.2016).
    22.Zayats T, Young L T, Mackey A D, Malecaze F, Calvas P et al. (2009) . Quality of DNA extracted from mouth washes PLoS ONE 4, 10-1371.
    23.H. (1915) The people of India. Thacker and Spink & Co (Calcutta & Shimla), W. Tahcker .
    24.K S Singh. (1998) Indian Communities. Anthropological Survey of India.
    25.Omran A R. (2005) The epidemiologic transition: a theory of the epidemiology of population change. , Milbank. Q 83, 731-57.
    26.Thompson W S. (1929) . , Population. Amer. J. Sociol 34, 959-975.
    27.A R Omran. (1971) The epidemiologic transition. A theory of the epidemiology of population change. , Milbank. Q 49, 509-538.
    28.Boccara F. (2004) Cohen A. , Heart 90, 1371-73.
    29.Santosa A, Wall S, Fottrell E. (2014) The development and experience of epidemiological transition theory over four decades: a systematic review. Glob Health Action doi:. 10-3402.
    30.Das U N, Rao A A. (2007) Gene expression profile in obesity and type 2 diabetes mellitus. , Lipid. Health Dis 14, 35.
    31.Frank B H. (2011) Globalization of Diabetes The role of diet, lifestyle, and genes. , Diabetes Care 34, 1249-57.
    32.Eeg-Olofsson K, Cederholm J, Nilsson P M, Zethelius B, Nunez L et al. (2009) Globalization of Diabetes The role of diet, lifestyle, and genes Diabetologia. 52, 65-73.
    33.C L Hwang, Y T Wu, C H. (2011) Effect of aerobic interval training on exercise capacity and metabolic risk factors in people with cardiometabolic disorders: a meta-analysis. Cardiopulm Rehabi. , Prev 31, 378-85.
    34.Rajkovic N, Zamaklar M, Lalic K, Lalic N M, Popovic L et al. (2012) OP3: Oxidized LDL as residual lipid risk marker in type 2 diabetes. Diabetes Metab.38:S98–S99. doi:, 10-1016.
    35.Ablove T, Binkley N, Leadley S, Shelton J, Ablove R. (2015) Body mass index continues to accurately predict percent body fat as women age despite changes in muscle mass and height. , Menopause 22, 727-30.
    36.A C Fortaleza, F E Rossi, Buonani C, C E Fregonesi, L M Neves et al. (2014) Total body and trunk fat mass and the gait performance in postmenopausal women. , Rev Bras Ginecol Obstet 36, 176-81.