Genotype screening in human disease frequently results in the identification of sequence variations whose direct connection with occurrence of disease is often remains unclear. Extensive research work has elucidated now that many of these seemingly harmless changes are actually part of etiology of several diseases. DMT2 is a recognized metabolic disorder that has turned to a major health issue throughout the world. DMT2 is a complex polygenic disorder  illustrated by impaired insulin resistance, insulin secretion, and deregulation of lipid and protein metabolism with environmental and multiple genetic susceptibility [2,3].
Tumor necrosis factor (TNF) is a proinflammatory cytokine, which is responsible for the inflammation process. In addition, it reasons in upregulation of cell adhesion molecule expression, leukocyte recruitment, apoptosis induction, and monocyte chemo-attraction. It also is responsible for the amplification of the immune response through stimulation of the expression of various transcription factors, growth factors, and other inflammatory mediators . However, several studies have demonstrated that elevated levels of TNF-α production in both rodents and human subjects as a causative factor for insulin resistance and the pathogenesis of DMT2 [1,5–7]. This observation was further complemented with administration of exogenous TNF-α to animals to induce insulin resistance, whereas neutralization of TNF-α can improve insulin sensitivity [8–10]. This implies the association of TNF-α expression level with DMT2. For this reason, etiological studies of single nucleotide polymorphisms (SNPs) in promoter region of TNF-α have drawn significant attention as potential risk factor for DMT2 because it regulates the expression of the protein. The 308G>A (rs1800629) is one such mutation that is located at position 308 in the promoter region of the TNF-α gene and involves a substitution of adenine (A) for guanine (G). The 308G>A polymorphism was previously shown to be associated with the progression of impaired glucose tolerance in case of type-2 diabetes in Finns  and Asians [12,13]. Although there are some conflicts of findings within distinct ethnic groups [14–16] but confirmed using meta-analysis for Asian population . However, based on the literature review, research regarding allelic frequency of 308G>A polymorphism has not conducted on Bangladeshi population, which represents a significant ethnic group within Asia in terms of population size. Besides, correlation between age and 308G>A polymorphism has not been studied yet.
2. MATERIALS AND METHODS
2.1. Sample collection, biochemical test, and genomic DNA isolation
This study was performed in accordance with guidelines approved by the Institutional Animal, Medical Ethics, Biosafety and Biosecurity Committee (IAMEBBC) for Experimentations on Animal, Human, Microbes and Living Natural Sources (Memo number: 58/320/IAMEBBC/IBSc), Institute of Biological Sciences (IBSc), University of Rajshahi, Rajshahi, Bangladesh. A total of 657 individuals were accumulated in this study that was distributed into two groups: 330 non-diabetic controls and 327 DMT2 individuals. Peripheral blood samples were collected from clinically diagnosed DMT2 patients and non-diabetic individuals. Non-diabetic individuals served as control. Written informed consent from all the participating individuals was taken. Samples were randomly collected in EDTA.K3 vacuumed tubes from different parts of the country. A little part of the blood was used for genomic DNA isolation and the rest was used for selected blood parameter testing, i.e., HbA1c, triglyceride (Tg), and cholesterol. Genomic DNA was isolated from blood using Wizard Genomic DNA isolation Kit (Promega, USA) according to the manufacturer’s instructions. DNA concentration was measured by using UV spectrophotometer as well as agarose gel visualization. A further salt removal stage was performed using Sephacryl S-400 (GE Healthcare, USA) gel filtration.
2.2. High resolution melting (HRM) analysis and sequencing
A 110 bp sequence within TNF-α promoter region was targeted for HRM analysis. Primer sequences were as follows: TNF forward 5Ê¹-CCACAGACCTGGTCCCCA-3Ê¹ and TNF reverse 5Ê¹-GGTCTTCTGGGCCACTGACT-3Ê¹ (DNA Technology A/S, Denmark). PCR conditions were optimized as described previously  before HRM analysis and DNA concentration was again normalized based on qPCR Cq value. HRM was done using GoTaq qPCR master mix (2X) at 60°C for 40 cycles by using Eco™ qPCR system (Illumina, USA). However, each qPCR reaction mixture (10 μl) for HRM analysis comprised of 5 μl GoTaq® qPCR master mix (2X), 3 μl nuclease free water, 0.5 μl forward primer, 0.5 μl reverse primer, and 1 μl template DNA. qPCR was performed with the following cycling conditions: 95°C for 10 minutes, followed by 40 cycles of 95°C for 10 seconds, 60°C for 30 seconds, and 72°C for 15 seconds by using Eco™ qPCR system (Illumina, USA). PCR reaction specificity was confirmed by melt curve analysis at 95°C for 15 seconds, 55°C for 15 seconds, 95°C for 15 seconds. HRM results were analyzed by Eco™ software (Illumina, USA). Identified genotypes were further confirmed using sequencing (1st BASE, Malaysia). Sequencing primers TNF sequence forward 5Ê¹-TGC CCC AGT GGG GTC TGT GA-3Ê¹ and TNF sequence reverse 5Ê¹-AGC TTG TCA GGG GAT GTG GCG T-3Ê¹ were targeted for 750 bp sequence from both directions.
2.3. Statistical analysis
Statistical analysis was carried out using the statistical program SPSS (version 19, IBM Corporation, USA). Frequencies of genotypic difference were compared using the χ2 (Chi-square) test. One-way analysis of variance was utilized to compare the clinical features among groups. Fisher’s t-test was performed to check the correlation between age and genotypes.
3.1. Demographic characteristics
The potential physiological or pathophysiological association of TNF-α in lipid metabolism and in vivo insulin sensitivity has been previously shown [18,19]. Therefore, in this study, besides the allelic distribution of 308G>A we also investigated the common obesity associated risk factors within the population. Our analysis shows that the means of body mass index (BMI), HbA1c, Tg, and cholesterol among DMT2 patients were higher than control group (Table 1). The mean BMI was 28.85 ± 2.88 for control and 29.46 ± 2.57 for DMT2 (Table 1). Mean of HbA1c of diabetic individuals (50.20 ± 8.84) is higher than non-diabetic controls (37.36 ± 2.61) (Table 1). The means of Tg and cholesterol between patients and non-diabetic control are also higher (Table 1). According to the data (Table 1), the significant difference for BMI (p-value = 0.0049), HbA1c (p-value = <0.0001), Tg (p-value = <0.0001), and cholesterol (p-value = <0.0001) was detected between DMT2 and non-diabetic control.
3.2. HRM-based genotyping analysis
PCR amplicon was melted using HRM and unmatched melting curve was detected with appropriate optical detection system. A characteristic melting curve was generated between 81.5°C and 86.7°C and processed by integrated HRM analysis software installed in the platform. Mutations were identified and confirmed by comparing the pattern of our HRM melt curve with a well-established melt curve pattern reported previously . For each SNP, heterozygous genotype (G/A) resulted in an altered melting wider curve shape that was easily identified. Homozygous genotypes (A/A) were distinguished by Tm difference between wild type and the homozygous mutant. In this study, all 657 collected samples were distinctly genotyped using HRM methods (Fig. 1). Outcome of the HRM analysis is distributed as shown in Table 2. The HRM results were further confirmed by sequencing (Fig. 1). All the heterozygous (GA) and homozygous (AA) polymorphisms were sequenced from both direction but for GG variant and non-diabetic individuals random sequencing was done. The allele frequency of 308G>A SNP in the control group was within the Hardy–Weinberg equilibrium (0.00482) at 95% confidence level and associated with DMT2 (Table 2).
|Table 1: Demographic characteristics regarding the collected blood sample.|
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|Figure 1: HRM analysis for the genotypes of 308G>A (rs1800629) polymorphism. (A) normalized curve, (B) difference curve, and (C) sequencing result showing G/A genotype.|
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3.3. Correlation of 308G>A SNP association with early diabetes
Within 327 diabetic individuals, we subdivided the individuals into two age groups, i.e., <35 and >35. This was done based on DMT2 individual’s record of first time positively diagnosed for DMT2. Distribution of 308G>A SNP within these subgroups shows that 11 (nine heterozygous and two homozygous) individuals were under the age of 35, whereas only two individuals were within the over 35 age group (Table 3). Fisher’s t-test at the 95% confidence level provides a significant value of 0.008965 (Table 3). However, the independent analysis for either GA or AA genotype did not produce any significant difference at 95% confidence level.
DMT2 is a metabolic disorder that is illustrated by insulin resistance, which normally develops in elderly individuals. Studies in the recent past have observed a significant number of increases of the disease particularly in the developing countries possibly due to changes in life style and food habit [21–23]. Case-control studies often find the differences of allele frequency of genes between case and control patients that correlate to diseases . TNF-α is one such well-studied gene etiologically linked with several diseases, such as obstructive pulmonary disease, inflammatory bowel syndrome, sepsis susceptibility, pre-term birth, primary sclerosing cholangitis, as well as DMT2 [25–29]. In fact, inflammation is a commonly known feature of DMT2 with high levels of proinflammatory cytokines, involving IL-1, IL-6, and TNF-α. This is thought to be because TNF-α can impair insulin signal pathways and lead to B-cell annihilation, ultimately increased TNF-α deliberately plays a key role in the DMT2 proliferation . Besides, TNF-α is also largely present in adipose tissue of obese individual indicating its function in lipid metabolism [8,18,19]. Our analysis of demographic blood parameters related to obesity shows a significant difference (Table 1). Besides, our result showed significant frequency of 308G>A polymorphism within the DMT2 patients. The association of 308G>A polymorphism with DMT2 varies within ethnic groups [31–36], though this difference is often accuses the relative small size of the case-control study. Besides, the frequency of the 308G>A also varies within the same country once different ethnic group is considered. For example, the allelic frequency differs in India between the Punjabi and Bengali Hindu population [37,38]. Bengali population of India and Bangladeshi population are anthropometrically very similar though separated through international borderline. However, our frequency distribution analysis showed association between 308G>A and DMT2. This observation is also similar to other observations within Asian population as well as Indian population [32–35]. Recent trend of meta-analysis for error correction of case-control study could have been good but unfortunately there is no other data of TNF-α-308G>A allelic frequency in case of Bangladeshi population.
|Table 2: Distribution of allele frequency of 308G>A SNP.|
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|Table 3: Correlation of 308G>A SNP association with early diabetes.|
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DMT2 is a major cause of premature mortality in all over the world. In long term, this metabolic disorder exerts its effect through both micro-vascular and macro-vascular complications. For example, diabetic retinopathy within young adults is ultimately due to early proliferation of diabetes . Similar observation has been also found for chronic kidney disease, risk of foot ulcers, and limb amputation [40–43]. However, over the past two decades, occurrence of DMT2 has changed from being a mild disorder of old age to one of the major causes of morbidity and mortality affecting young and middle-aged people, even at the adolescents [44–46]. Understanding the etiology, early proliferation of DMT2 has implication for better clinical management, as well as research. There are few epidemiological studies on young adults with DMT2 diagnosed before age 25 years have been performed since last decade [47–50]. This lacks the etiology concerned with the diabetes within young adults. Interestingly, correlating the allelic frequency with age in diabetic patients has not yet been done, particularly for TNF-α-308G>A. Young diabetic adults are often found to be obese. More often this relates with physical inactiveness and high calorie diet. However, TNF-α protein is profoundly present in adipose tissue and associated with lipid metabolism . In case of TNF-α-308G/A polymorphism, it is well accepted that A allele increases the transcription of TNF-α while G allele is associated with low-level expression [51,52]. Therefore, we presume that TNF-α-308G/A polymorphism within Bangladeshi individual increases the obesity which indirectly helps to proliferate early diabetes. However, ethnic differences may attribute to this result, as well as some environmental factors may also affect the result [39,53,54].
Overall, our data showed that TNF-α-308G/A polymorphism is significantly involved with DMT2. Once we subdivided the DMT2 individual into two age groups, it was found that TNF-α-308G/A polymorphism causes early proliferation of DM type-2.
This research work was supported by World Bank Higher Education Quality Enhancement project (no. 2485). The authors would like to thank the HEPTA Diabetic Clinic for helping in blood sample collection.
Study design: AH; Molecular studies, data collection, and data analysis: DR, MSSU, BB, MMH, and MT; Manuscript preparation: AH and MMH.
CONFLICT OF INTEREST
The authors declared that they have no conflict of interest.
1. MÅ™ller S, Martin S, Koenig W, Hanifi-Moghaddam P, Rathmann W, Haastert B, et al. Impaired glucose tolerance is associated with increased serum concentrations of interleukin 6 and co-regulated acute-phase proteins but not TNF-α or its receptors. Diabetologia 2002;45(6):805–12.
2. Moon SS, Lee JE, Lee YS, Kim SW, Jeoung NH, Lee IK, et al. Association of pyruvate dehydrogenase kinase 4 gene polymorphisms with type 2 diabetes and metabolic syndrome. Diabetes Res Clin Pract 2012;95(2):230–6.
3. Wei WA, Peng WH, Lin LU, Zhang RY, Zhang Q, Wang LJ, et al. Polymorphism on chromosome 9p21.3 contributes to early-onset and severity of coronary artery disease in non-diabetic and type 2 diabetic patients. Chin Med J (Engl) 2011;124(1):66–71.
4. Adamis AP, Berman AJ. Immunological mechanisms in the pathogenesis of diabetic retinopathy. Semin Immunopathol 2008;30(2): 65–84.
5. Alexandraki K, Piperi C, Kalofoutis C, Singh J, Alaveras A, Kalofoutis A. Inflammatory process in type 2 diabetes: the role of cytokines. Ann N Y Acad Sci 2006;1084(1):89–117.
6. Gonzalez F, Thusu K, Abdel-Rahman E, Prabhala A, Tomani M, Dandona P. Elevated serum levels of tumor necrosis factor alpha in normal-weight women with polycystic ovary syndrome. Metabolism 1999;48(4):437–41.
7. Zinman B, Hanley AJ, Harris SB, Kwan J, Fantus IG. Circulating tumor necrosis factor-α concentrations in a native Canadian population with high rates of type 2 diabetes mellitus. J Clin Endocrinol Metab 1999;84(1):272–8.
8. Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science 1993;259(5091):87–91.
9. Lang CH, Dobrescu CO, Bagby GJ. Tumor necrosis factor impairs insulin action on peripheral glucose disposal and hepatic glucose output. Endocrinology 1992;130(1):43–52.
10. Van Der Poll T, Romijn JA, Endert ER, Borm JJ, Buller HR, Sauerwein HP. Tumor necrosis factor mimics the metabolic response to acute infection in healthy humans. Am J Physiol-Endocrinol Metabol 1991;261(4):E457–5.
11. Kubaszek A, Pihlajamäki J, Komarovski V, Lindi V, Lindström J, Eriksson J, et al. Promoter polymorphisms of the TNF-α (G-308A) and IL-6 (C-174G) genes predict the conversion from impaired glucose tolerance to type 2 diabetes: the Finnish Diabetes Prevention Study. Diabetes 2003;52(7):1872–6.
12. Liu ZH, Ding YL, Xiu LC, Pan HY, Liang Y, Zhong SQ, et al. A meta-analysis of the association between TNF-α-308G> A polymorphism and type 2 diabetes mellitus in Han Chinese population. PLoS One 2013;8(3):e59421.
13. Zhao Y, Li Z, Zhang L, Zhang Y, Yang Y, Tang Y, et al. The TNF-alpha-308G/A polymorphism is associated with type 2 diabetes mellitus: an updated meta-analysis. Mol Biol Rep 2014;41(1):73–83.
14. Furuta M, Yano Y, Ito K, Gabazza EC, Katsuki A, Tanaka T, et al. Relationship of the tumor necrosis factor-α− 308 A/G promoter polymorphism with insulin sensitivity and abdominal fat distribution in Japanese patients with type 2 diabetes mellitus. Diabetes Res Clin Pract 2002;56(2):141–5.
15. Hamann A, Mantzoros C, Vidalpuig A, Flier JS. Genetic variability in the TNF-α promoter is not associated with type II diabetes mellitus (NIDDM). Biochem Biophys Res Commun 1995;211(3):833–9.
16. Kim HR, Lee MK, Park AJ. The-308 and-238 polymorphisms of the TNF-α promoter gene in type 2 diabetes mellitus. Korean J Lab Med 2006;26(1):58–63.
17. Roy D, Hasan MM, Haque A. Mutation detection sensitivity of high resolution melting in clinical samples: a comparative study between formamide and dimethyl sulfoxide. J Adv Biotechnol Exp Ther 2019;2(2):51–4.
18. Beutler B, Cerami A. Cachectin (tumor necrosis factor): a macrophage hormone governing cellular metabolism and inflammatory response. Endocr Rev 1988;9(1):57–66.
19. Torti FM, Dieckmann B, Beutler B, Cerami A, Ringold GM. A macrophage factor inhibits adipocyte gene expression: an in vitro model of cachexia. Science 1985;229(4716):867–9.
20. You CG, Li XJ, Li YM, Wang LP, Li FF, Guo XL, et al. Association analysis of single nucleotide polymorphisms of proinflammatory cytokine and their receptors genes with rheumatoid arthritis in northwest Chinese Han population. Cytokine 2013;61(1):133–8.
21. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315(7109):629–34.
22. Mehta R, del-Moral ME, Aguilar-Salinas CA. Epidemiology of diabetes in the elderly. Rev Invest Clin 2010;62(4):305–1.
23. Wong VW, Wong GL, Tsang SW, Hui AY, Chan AW, Choi PC, et al. Genetic polymorphisms of adiponectin and tumor necrosis factor-alpha and nonalcoholic fatty liver disease in Chinese people. J Gastroenterol Hepatol 2008;23(6):914–21.
24. Kwon JM, Goate AM. The candidate gene approach. Alcohol Res Health 2000;24(3):164–8.
25. Hu GP, Peng GY, Hu JX, Ran PX. Association of tumor necrosis factor alpha 308 G/A gene promoter polymorphism with the presence of chronic obstructive pulmonary disease: a meta-analysis. Zhonghua Jie He He Hu Xi Za Zhi 2007;30(8):588–94.
26. López-Hernández R, Valdés M, Campillo JA, Martínez-Garcia P, Salama H, Salgado G, et al. Genetic polymorphisms of tumour necrosis factor alpha (TNF-α) promoter gene and response to TNF-α inhibitors in Spanish patients with inflammatory bowel disease. Int J Immunogenet 2014;41(1):63–8.
27. Menon R, Merialdi M, Betrán AP, Dolan S, Jiang L, Fortunato SJ, et al. Analysis of association between maternal tumor necrosis factor-α promoter polymorphism (–308), tumor necrosis factor concentration, and preterm birth. Am J Obstet Gynecol 2006;195(5):1240–8.
28. Mitchell SA, Grove J, Spurkland A, Boberg KM, Fleming KA, Day CP, et al. Association of the tumour necrosis factor α- 308 but not the interleukin 10-627 promoter polymorphism with genetic susceptibility to primary sclerosing cholangitis. Gut 2001;49(2):288–94.
29. Teuffel O, Ethier MC, Beyene J, Sung L. Association between tumor necrosis factor-α promoter-308 A/G polymorphism and susceptibility to sepsis and sepsis mortality: a systematic review and meta-analysis. Crit Care Med 2010;38(1):276–82.
30. Nishimura M, Obayashi H, Mizuta I, Hara H, Adachi T, Ohta M, et al. TNF, TNF receptor type 1, and allograft inflammatory factor-1 gene polymorphisms in Japanese patients with type 1 diabetes. Hum Immunol 2003;64(2):302–9.
31. Feng RN, Zhao C, Sun CH, Li Y. Meta-analysis of TNF 308 G/A polymorphism and type 2 diabetes mellitus. PloS One 2011;6(4):e18480.
32. Bo LI, Ning YU, Li-si TA, Jing-bo LI, Yan GU, YA-ping PA. A study of frequency of TNF alpha gene with type 2 diabetes mellitus with chronic periodontitis. Shanghai Kou Qiang Yi Xue 2011;20(2): 169–73.
33. Mukhopadhyaya PN, Acharya A, Chavan Y, Purohit SS, Mutha A. Metagenomic study of single-nucleotide polymorphism within candidate genes associated with type 2 diabetes in an Indian population. Genet Mol Res 2010;9(4):2060–8.
34. Shiau MY, Wu CY, Huang CN, Hu SW, Lin SJ, Chang YH. TNF-α polymorphisms and type 2 diabetes mellitus in Taiwanese patients. Tissue Antigens 2003;61(5):393–7.
35. Sobti RC, Kler R, Sharma YP, Talwar KK, Singh N. Risk of obesity and type 2 diabetes with tumor necrosis factor-α 308G/A gene polymorphism in metabolic syndrome and coronary artery disease subjects. Mol Cell Biochem 2012;360(1–2):1–7.
36. Yamashina M, Kaneko Y, Maesawa C, Kajiwara T, Ishii M, Fujiwara F, Taneichi H, Takebe N, Ishida W, Takahashi K, Masuda T. Association of TNF-α gene promoter C-857T polymorphism with higher serum LDL cholesterol levels and carotid plaque formation in Japanese patients with type 2 diabetes. Tohoku J Exp Med 2007;211(3):251–8.
37. Paine SK, Sen A, Choudhuri S, Mondal LK, Chowdhury IH, Basu A, et al. Association of tumor necrosis factor α, interleukin 6, and interleukin 10 promoter polymorphism with proliferative diabetic retinopathy in type 2 diabetic subjects. Retina 2012;32(6):1197–203.
38. Sikka R, Raina P, Matharoo K, Bandesh K, Bhatia R, Chakrabarti S, et al. TNF-α (g.− 308 G> A) and ADIPOQ (g.+ 45 T> G) Gene Polymorphisms in Type 2 Diabetes and Microvascular Complications in the Region of Punjab (North–West India). Curr Eye Res 2014;39(10):1042–51.
39. Fong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, et al. Retinopathy in diabetes. Diabetes Care 2004;27(suppl 1):s84–7.
40. Boulton AJ, Vinik AI, Arezzo JC, Bril V, Feldman EL, Freeman R, et al. Diabetic neuropathies: a statement by the American Diabetes Association. Diabetes Care 2005;28(4):956–62.
41. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 2006;3(11):e442.
42. Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H, WHO Multinational Study Group. Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia 2001;44(2):S14.
43. Roglic G, Unwin N, Bennett PH, Mathers C, Tuomilehto J, Nag S, et al. The burden of mortality attributable to diabetes: realistic estimates for the year 2000. Diabetes Care 2005;28(9):2130–5.
44. Brosnan CA, Upchurch S, Schreiner B. Type 2 diabetes in children and adolescents: an emerging disease. J Pediatr Health Care 2001;15(4):187–93.
45. Pinhas-Hamiel O, Dolan LM, Daniels SR, Standiford D, Khoury PR, Zeitler P. Increased incidence of non-insulin-dependent diabetes mellitus among adolescents. J Pediatr 1996;128(5):608–15.
46. Rosenbloom AL, Joe JR, Young RS, Winter WE. Emerging epidemic of type 2 diabetes in youth. Diabetes Care 1999;22(2):345–54.
47. Bell RA, Mayer-Davis EJ, Beyer JW, D'Agostino RB, Lawrence JM, Linder B, et al. Diabetes in non-Hispanic white youth: prevalence, incidence, and clinical characteristics: the SEARCH for Diabetes in Youth Study. Diabetes Care 2009;32(Supplement 2):S102–11.
48. Dabelea D, DeGroat J, Sorrelman C, Glass M, Percy CA, Avery C, et al. Diabetes in Navajo youth: prevalence, incidence, and clinical characteristics: the SEARCH for Diabetes in Youth Study. Diabetes Care 2009;32(Supplement 2):S141–7.
49. Kershnar AK, Daniels SR, Imperatore G, Palla SL, Petitti DB, Pettitt DJ, et al. Lipid abnormalities are prevalent in youth with type 1 and type 2 diabetes: the SEARCH for Diabetes in Youth Study. J Pediatr 2006;149(3):314–9.
50. Mayer-Davis EJ, Beyer J, Bell RA, Dabelea D, D'Agostino R, Imperatore G, et al. Diabetes in African American youth: prevalence, incidence, and clinical characteristics: the SEARCH for Diabetes in Youth Study. Diabetes Care 2009;32(Supplement 2):S112–22.
51. Kroeger KM, Carville KS, Abraham LJ. The -308 tumor necrosis factor-α promoter polymorphism effects transcription. Mol Immunol 1997;34(5):391–9.
52. Navarro JJ, Milena FF, Mora C, León C, Claverie F, Flores C, et al. Tumor necrosis factor-α gene expression in diabetic nephropathy: relationship with urinary albumin excretion and effect of angiotensin-converting enzyme inhibition. Kidney Int 2005;68:S98–102.
53. Haghani K, Bakhtiyari S. The study on the relationship between IRS-1 Gly972Arg and IRS-2 Gly1057Asp polymorphisms and type 2 diabetes in the Kurdish ethnic group in West Iran. Genet Test Mol Biomarkers 2012;16(11):1270–6.
54. Saberi H, Mohammadtaghvaei N, Gulkho S, Bakhtiyari S, Mohammadi M, Hanachi P, et al. The ENPP1 K121Q polymorphism is not associated with type 2 diabetes and related metabolic traits in an Iranian population. Mol Cell Biochem 2011;350(1–2):113–8.