==== Front Ann Saudi MedAnn Saudi MedAnnals of Saudi Medicine0256-49470975-4466King Faisal Specialist Hospital and Research Centre 2640979610.5144/0256-4947.2015.214asm-3-214Original ArticleN-acetyltransferase 1 and 2 polymorphisms and risk of diabetes mellitus type 2 in a Saudi population Al-Shaqha Waleed M. aAlkharfy Khalid M. bAl-Daghri Nasser M. cMohammed Abdul Khader c a College of Medicine, Al-Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia b Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia c Department of Biochemistry, College of Science, King Saud University, Riyadh, Saudi ArabiaCorrespondence: Dr. Khalid M. Alkharfy, Department of Clinical Pharmacy, College of Pharmacy, King Saud University, PO Box 2457, Riyadh 11451, Saudi Arabia, T: +966 1-467-7494, F: +966 1-467-7480, alkharfy@ksu.edu.saMay-Jun 2015 35 3 214 221 Copyright © 2015, Annals of Saudi Medicine2015This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.BACKGROUND AND OBJECTIVES There have been inconsistent reports on N-acetyltransferase (NAT) gene polymorphism in type 2 diabetes mellitus (T2DM), and data is particularly limited in the Arab population. Therefore, the main objective of this study was to identify whether the genetic polymorphisms of NAT1 and NAT2 play a role in susceptibility to T2DM in the Saudi population. DESIGN AND SETTING A population-based, prospective genetic association case-control study on a Saudi population. PATIENTS AND METHODS Whole blood, anthropometric measurements and biochemistry data were collected from 369 Saudi individuals (186 T2DM patients and 183 healthy controls). DNA was isolated from the blood. Polymorphism of NAT1 and NAT2 SNPs [NAT2*7B, rs1041983(C>T); NAT2*7, rs1799931(G>A); NAT2*6A, rs1799930(G>A); NAT2*5A, rs1799929(C>T); and NAT1*11A, rs4986988(C>T)] were evaluated by allelic discrimination using real-time PCR. RESULTS Subjects with T2DM had a significantly increased body mass index (BMI), waist circumference, systolic and diastolic blood pressure, glucose, triglycerides, and LDL-cholesterol compared with healthy controls (P<.05). The rs1799931(G>A) genotype was detected in the control population but not in the T2DM population (P<.001). The wild type (G) allele frequency was higher in T2DM than controls (P=.038). The mutant allele (A) in rs1799931(G>A) had a protective effect for T2DM (OR 0.32, 95% CI 0.16–0.62; P=.001). Regression analysis showed that BMI, systolic BP and triglycerides are potential risk factors for T2DM. CONCLUSION The genotypes as well as the individual alleles of rs1799931(G>A) differed significantly between the case and control populations. The variation in the data reported so far suggest that polymorphism of the NAT gene may vary among different geographical areas. Environmental or dietary factors may also contribute to disease manifestation. ==== Body Diabetes mellitus (DM) is a major health problem. Globally, its prevalence was estimated to increase from 4% in 1995 to 5.4% by the year 2025.1 The World Health Organization has predicted that most of the burden of this disease will occur in developing countries.2 In 2004, a national survey indicated that the prevalence of diabetes in Saudi Arabia is 23.7%. A more recent study showed that the prevalence of type 2 DM in Saudi Arabia is 17.7% and 16.4% in men and women, respectively.3,4 Heredity plays a significant, but variable, role in the etiology of DM. Both type 1 DM (T1DM) and type 2 DM (T2DM) show a familial predisposition, which indicates the involvement of genetic factors in determining susceptibility. Type 2 diabetes mellitus (T2DM) is known to be influenced by both genetic and environmental factors. Therefore, identification of loci that are associated of T2DM has been investigated. N-acetyltransferase 1 (NAT1) and N-acetyltransferase 2 (NAT2) family of enzymes catalyzes the acetylation of many aromatic and hydrazine drugs as well as many aromatic and heterocyclic amine carcinogens.5,6 This reaction can result in either detoxification (N-acetylation) or activation (O-acetylation) of these compounds.7,8 Both NAT1 and NAT2 are products of intronless protein-coding 870 base pair exons. NAT1 and NAT2 exhibit allelic variations and genetic polymorphisms associated with increased susceptibility toward the toxicity of drug and environmental diseases. Variant NAT1 and NAT2 alleles possess various combinations of single nucleotide polymorphisms (SNPs), deletions, and/or insertions.6 The frequency of NAT1 and NAT2 alleles, genotypes, and phenotypes differs markedly among ethnic groups.5 To date, 28 NAT1 and 66 NAT2 alleles have been identified in the human population.9 The coding region of the genes possess most of the NAT1 polymorphism, and all of the NAT2 polymorphisms. The substrate affinity, catalytic activity, protein degradation or stability changes as a result of the polymorphisms.6,10 A role for NAT2 gene polymorphism has been suggested in various types of cancer.11–13 Interestingly, the association between slow acetylator type of NAT2 and the risk of bladder cancer differ according different to geographical regions.14 Reports are inconsistent on NAT gene polymorphism in T2DM. Studies form Bosnia and Herzegovina and Turkey demonstrate an association between NAT2 variation and diabetes in their population.15,16 However, the results of a Jordanian study are inconsistent with those findings.17 A study from Saudi Arabia showed an association between the slow acetylator phenotype and T1DM while the other reported an association between the rapid acetylator phenotype and T1DM.18,19 The data on the association of T2DM and NAT genes are inconsistent among different populations so far. Therefore, we designed the our study with the objective of identifying whether the genetic polymorphisms of NAT1 and NAT2 play a role in susceptibility to T2DM in the Saudi population. More specifically, we studied the distribution of selected gene polymorphisms (NAT1*11, NAT2*5A, NAT2*6A, NAT2*7A/B, NAT2*14A), which represent a combination of rapid and slow acetylation genotypes, and their association with T2DM patients and normal controls. PATIENTS AND METHODS Study subjects Three-hundred-sixty-nine Saudi individuals (186 T2DM patients and 183 healthy controls) were enrolled in the study. These individuals were part of the Biomarkers Screening in Riyadh Project (Riyadh Cohort), a capital-wide epidemiologic study, taken from over 17 000 consenting Saudis coming from different primary health care centers (PHCCs) during the years 2009–2013. Controls were randomly selected from the master database of this project.”. We used a generalized questionnaire to collect demographic information, and a medical history was taken from all subjects. Those with co-morbidities that needed medical attention were excluded from the study. Written informed consent was obtained after orientation for the study. Ethical approval for the study was granted by the Ethics Committee of the College of Science Research Center, King Saud University, Riyadh, Saudi Arabia. Anthropometry and blood collection Participating subjects were requested to return to their respective PHCCs after an overnight fast (>10 hours) for anthropometry and blood withdrawal. Anthropometry included height (to the nearest 0.5 cm), weight (to the nearest 0.1 kg), waist and hip circumference using a standardized measuring tape in centimeters, systolic and diastolic blood pressure, and body mass index (BMI, calculated as kg/m2). Blood was taken from the subjects and placed immediately into a non-heparinized tube for centrifugation. Serum was separated and transferred to a pre-labeled plain tube, stored in ice, and delivered to the Biomarkers Research Center in King Saud University on the same day. Biochemical analysis Fasting serum samples were stored in a −20°C freezer prior to analysis. Fasting glucose, triglyceride, total and HDL-cholesterol levels were measured by chemistry auto-analyzer (Konelab, Vantaa, Finland) and concentrations of LDL-cholesterol were calculated using Friedwald’s formula. The inter- and intra-assay variabilities were 5.3% and 4.6%. DNA extraction Whole blood was collected in EDTA-containing tubes and genomic DNA was isolated using the blood genomic prep Mini Spin Kit (GE Healthcare Life Sciences, Piscataway, NJ, USA) according to manufacturer’s instructions. Briefly, this method uses a simple genomic DNA purification protocol that uses chemotropic agents to extract DNA from blood cells, denature protein components, and promote the selective binding of DNA to a column-based, silica membrane. The isolated genomic DNA was stored at −20°C until further analysis. Genotype analysis Five NAT1 and NAT2 SNPs [NAT2*7B, rs1041983(C>T); NAT2*7, rs1799931(G>A); NAT2*6A, rs1799930(G>A); NAT2*5A, rs1799929(C>T); and NAT1*11A, rs4986988(C>T)] were evaluated by allelic discrimination real-time polymerase chain reaction (PCR) using pre-designed TaqMan probes from Applied Bio-Systems (Foster City, CA, USA). Amplification reactions were performed in a volume of 10 μL containing 1X TaqMan genotyping Master Mix (Applied Biosystems), 1x mix of unlabeled PCR primers and TaqMan MGB probes, and 50 ng of template DNA. All amplification and detection was conducted on genomic DNA in 96-well PCR plates using a Bio-Rad CFX96 Real-Time PCR Detection System (Bio-Rad, Milan, Italy). Thermal cycling was initiated with a denaturation step of 10 min at 95°C, followed by 50 cycles of 15 s at 95°C and 90 s at 60°C. After PCR was completed, allelic discrimination was analyzed using the Bio-Rad CFX Manager Software (Version 1.6, Bio-Rad). Genotype assignment was determined by plotting the end point relative fluorescent units (RFU) for one fluorophore (allele 1 on the X-axis) against the RFU for the other fluorophore (allele 2 on the Y-axis) on the allelic discrimination plot (Figure 1) The genotypes were assigned using the whole data from the study, simultaneously. All PCR reactions were set up in a dedicated PCR area with dedicated PCR pipettes and reagents. For validation, about 10% of the samples were re-genotyped. The results were reproducible with no discrepancies in genotyping. Statistical analysis Data are expressed as mean (standard deviation) or number (percentage). A chi-square test or Fisher exact test was used for comparison of categorical variables. A χ2 test or analysis of variance was used to analyze the difference between continuous variables. Odds ratios (OR), 95% confidence intervals (CI) and corresponding P values for risk of T2DM were calculated by using logistic regression analysis. The most common genotype was used as the reference in the model. For the identification of potential risk factors for T2DM, the anthropometric variables that were significantly different between cases and controls were selected as covariates in the logistic regression model. All the statistical analyses were carried out using SPSS statistics software version 17.0 (SPSS Inc, Chicago IL, USA). Chi square with one degree of freedom was used to assess the departure from the Hardy-Weinberg equilibrium. A two-sided P value <.05 was considered significant. The sample size was estimated based on information from previously published studies. RESULTS For 186 T2DM patients and 183 healthy controls included in the analysis, the median age of all (369) subjects was 47 years (range 31–54 years) and females constituted about 50.7%. The anthropometric, clinical and biochemical features of individuals enrolled in the study are presented in Table 1. Subjects with T2DM had significantly increased BMI, waist circumference, systolic and diastolic blood pressure, glucose, triglycerides, and LDL-cholesterol levels compared with the healthy control subjects (P<.05). Taking overall study population into consideration, the TT genotype of SNP rs1041983, and AA genotype of rs1799930 was significantly associated with a low HDL count (P<.05), whereas the GA genotype was associated with a low glucose level for rs1799931 (P<.001; Table 2). The genotype distribution as well as single allele frequencies of rs1041983(C>T), rs1799930(G>A), rs1799931(G>A), rs1799929(C>T) and rs4986988(C>T) variants are summarized in Table 3. In general, there was no significant difference in the frequencies of the polymorphisms between the two groups. However, rs1799931(G>A) genotype was not detected in the T2DM population, a difference that was statistically significant as opposed to controls (P<.001). Furthermore, the wild type (G) allele frequency was higher in T2DM population than in the non-diabetic population (P=.038). Interestingly, the genotype frequencies of rs1041983 (C>T), rs1799929 (C>T), rs4986988 (C>T), rs1799930 (G>A) polymorphisms met the Hardy Weinberg Equilibrium (HWE) in both the control group as well as the T2DM subjects (Table 4). Whereas in the case of rs1799931 (G>A), the G and A allele frequencies deviate significantly from the HWE both in the T2DM group (P<.001) and controls (P=.002). BMI (OR 1.177, 95% CI 1.09–1.26; P<.001), systolic BP (OR 1.085, 95% CI 1.04–1.13; P<.001), and triglycerides (OR 1.163, 95% CI 1.18–2.26; P=.003) added significantly to the model and were potential risk factors for T2DM. However, diastolic BP, cholesterol, LDL, and BP did not add significantly to the model (Table 5). A logistic regression model for the genotype and allele provided a protective effect for mutant allele A in the SNP rs1799931(G>A) (OR 0.32, 95% CI 0.16–0.62; P=.001). The allele T in SNP rs1041983(C>T) did produce higher odds for the being the risk factor but the result was not statistically significant. Other genotypes and alleles also did not show any significant effect on T2DM risk (Table 6). DISCUSSION In this case-control study, we investigated whether the polymorphism of NAT1 and NAT2 genes plays role in the development of T2DM. Drug-metabolizing enzyme activity is altered in diseases such as cancers, infectious and inflammatory disease, and immune system disorders.16 The relevance of NAT genes in drug metabolism and disease susceptibility has been a central theme of pharmacogenetic research mainly because of the genetic variability among different human populations. The protein product of the NAT2 gene is capable of N-acetylation and O-acetylation which is implicated in the metabolism and detoxification of naturally occurring xenobiotics, including carcinogens and drugs.20 The acetylator phenotype is determined by studying the acetylation of a variety drugs such as isoniazid, sulfadimidine, dapsone or caffeine. Therefore, the acetylation capacity in humans has been linked to NAT2 gene polymorphisms, which alters susceptibility to cancer and other diseases including adverse drug reactions.21,22 Studies describing the association of NAT alleles with the T2DM are few and have been inconsistent in different populations and geographical regions. Only one of the SNPs of the NAT gene polymorphism (rs1799931 G>A) that we studied was significantly different between the cases and controls. The frequency of wild type genotypes was higher in the T2DM patients than the control group. Also, the individual allelic frequency of major alleles differed significantly in both groups with a protecting effect for the mutant allele (OR 0.32). However, another study showed no significant difference between the two groups for this SNP.15 A study from Japan also reported no significant increase in the prevalence of the slow acetylator phenotype in patients with diabetic nephropathy, suggesting that NAT gene polymorphism might not be a risk factor for diabetic nephropathy in diabetic patients.23 A previous study from Saudi Arabia showed significant differences in the proportion of rapid acetylators between T1DM and T2DM (P=.0436), and between the control and overall diabetic population (P=.024) or those with T1DM (P=.0028). There was also a significant association between rapid acetylator and T1DM.18 Mrozikiewicz et al found no relation between the fast acetylator genotypes (homozygous and heterozygous) and T1DM.24 Another study also found no relationship between NAT2 polymorphism and T2DM or its complications such as nephropathy and neuropathy.25 Furthermore, Hegele et al showed no difference in the genotype and allele frequencies of the NAT2 gene and T2DM.26 However, Yalin et al15 concluded that the NAT2 slow acetylator genotypes could be an important factor that determines DM in a Turkish population. The investigators, in a case-control study, reported that the NAT2 slow allele (especially *6A) was found to confer a 5-fold increased risk of T2DM.15 An increased risk for diabetes have also been shown in the NAT2*5A mutant genotype and the NAT2*14A heterozygous genotype, while the polymorphism in the NAT2*7A/B have not been associated with an increased risk. Another study in the population of Bosnia and Herzegovina reported that NAT2*5 polymorphism is significantly associated with a 2.4-fold increased risk for developing T2DM, while NAT2*6 polymorphism significantly decreases the risk of T2DM by 5-fold.16 In a more recent study, Irdshaid et al explored the association between NAT2 genotypes and T1MD and T2DM in the Jordanian population.17 They concluded that there is an excess of genotypes encoding intermediate acetylation in T2DM and an excess of slow acetylator genotypes in T1DM. They further showed that the NAT2*4/6 genotype is more prevalent in T2DM.17 Our study does not support the involvement of NAT2*5A or NAT2*6 in an increased risk of T2DM, and also does not indicate a lack of association of NAT2*7A/B with the disease phenotype. The departure from the Hardy Weinberg (HW) equilibrium is evident in the case of SNP rs1799931 (G>A) in the present study. Other studies have reported the frequencies of the wild type and mutant alleles of these SNPs are under the HW equilibrium.27 Deviation from HW equilibrium can also be caused by selection bias, which may have occurred in our study due to the small sample size, but is unlikely because of random sampling. Several studies have shown that anthropometric measures including estimates of body composition and BMI are significantly and positively associated with T2DM risk in both men and women independent of age and other individual characteristics.16,28,29 CONCLUSION Our study has demonstrated that genotypes as well as the individual allele of SNP rs1799931(G>A) significantly differs between the case and control populations. Further, allele A provided a protective effect from diabetes. The BMI, systolic BP and triglycerides are the potential risk factors for T2DM. The variation in the data reported so far suggests that polymorphisms of the NAT gene may vary among different geographical areas. It is also possible that some environmental or dietary factors may also contribute to disease manifestation. Therefore, further studies with larger sample sizes on these lines are warranted. Acknowledgments The authors would like thank Mr. Saud Azam for his valuable help in the statistical analysis and preparation of this manuscript. This study was funded by grant no. 321306 from the Deanship of Scientific Research of Al-Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia. Conflict of interest The authors declare that they have no conflict of interest. Figure 1 Allelic discrimination plot showing genotype assignment determined by plotting the end point RFU for one fluorophore -VIC (allele 1 on the x-axis) against the RFU for the other fluorophore-FAM (allele 2 on the y-axis). Table 1 Anthropometric and metabolic characterization of study subjects. Parameters T2DM (n=186) Control (n=183) P value Male/Female (n, %) 93 (50%)/93 (50%) 89 (48.6%)/94 (58.4%) .793 Age (years) 46.1 (5.4) 46.2 (3.5) .630 BMI (kg/m2) 31.8 (6.2) 26.5 (4.0) <.001 Systolic BP (mm Hg) 126.2 (13.3) 117.2 (9.3) <.001 Diastolic BP (mm Hg) 79.6 (8.7) 76.6 (6.9) <.001 Cholesterol (mmol/L) 5.4 (1.0) 5.2 (1.1) .099 HDL (mmol/L) 0.8 (0.3) 0.8 (0.3) .131 Triglyceride (mmol/L) 2.1 (0.9) 1.6 (0.8) <.001 Glucose (mmol/L) 9.8 (1.7) 5.1 (0.6) <.001 LDL-Cholesterol (mmol/L) 4.4 (0.9) 4.1 (1.0) .031 Waist 96.0 (21.8) 89.4 (15.2) .003 Hip 103.0 (24.7) 98.9 (16.1) .082 Values are represented as mean (standard deviation). T2DM, Type 2 diabetes mellitus; BMI, Body mass index; HDL, High density lipoprotein; LDL, Low density lipoprotein; P value <.05 was taken as significant. Table 2 Anthropometric and metabolic parameters by SNP genotype polymorphisms in the overall population (n=369). Genotype/Allele Age BMI (kg/m2) Systolic BP (mmHg) Diastolic BP (mmHg) Cholesterol (mmol/l) HDL (mmol/l) Triglyceride (mmol/l) Glucose (mmol/l) LDL-Cholesterol (mmol/l) Waist Hip rs1041983  CC 45.9 (4.6) 28.5 (6.2) 121.0 (11.8) 78.1 (8.4) 5.3 (1.1) 0.8 (0.3) 1.8 (0.9) 7.3 (2.6) 4.2 (1.0) 89.4 (17.5) 100.7 (19.8)  CT 46.3 (4.4) 29.5 (5.3) 121.5 (12.8) 77.3 (7.7) 5.3 (1.1) 0.8 (0.3) 1.9 (0.9) 7.5 (2.7) 4.1 (1.0) 94.9 (19.0) 99.3 (20.3)  TT 46.4 (5.0) 29.6 (6.5) 122.5 (12.4) 79.2 (7.2) 4.9 (0.9) 0.6 (0.3) 1.6 (0.9) 7.0 (2.6) 4.1 (1.0) 92.9 (19.5) 102.9 (20.3) P value .729 .253 .778 .386 .161 .029 .142 .556 .795 .117 .682 rs1799929  CC 45.8 (4.6) 29.8 (5.3) 123.4 (12.4) 78.8 (8.1) 5.1 (0.9) 0.7 (0.3) 1.8 (0.9) 7.4 (2.7) 4.1 (0.9) 93.9 (19.2) 102.5 (21.2  CT 46.6 (4.6) 28.6 (5.5) 120.8 (13.0) 77.4 (7.9) 5.3 (1.1) 0.8 (0.3) 1.8 (0.8) 7.5 (2.7) 4.2 (1.1) 90.4 (19.4) 97.9 (20.6)  TT 45.7 (4.4) 29.2 (7.3) 121.2 (10.6) 78.4 (7.9) 5.3 (0.9) 0.8 (0.3) 1.9 (1.0) 7.3 (2.6) 4.2 (0.9) 92.0 (14.6) 104.2 (14.9) P value .240 .298 .227 .350 .128 .246 .563 .752 .621 .363 .027 rs1799930  AA 46.2 (4.9) 29.4 (6.1) 123.7 (15.3) 80.0 (7.6) 4.9 (1.0) 0.6 (0.3) 1.5 (0.8) 7.4 (2.7) 4.1 (1.1) 96.6 (15.2) 107.3 (14.8)  GA 46.4 (4.4) 29.7 (5.4) 121.5 (11.8) 77.5 (7.4) 5.2 (1.1) 0.8 (0.3) 1.9 (0.9) 7.4 (2.7) 4.1 (1.1) 93.8 (19.4) 93.8 (19.4)  GG 45.9 (4.6) 28.8 (6.3) 121.0 (12.0) 78.0 (8.4) 5.4 (1.0) 0.8 (0.3) 1.8 (0.9) 7.4 (2.6) 4.3 (1.0) 89.4 (17.9) 89.4 (17.9) P value .562 .177 .526 .305 .057 .018 .132 .999 .308 .054 .138 rs1799931  AA 46.1 (5.2) 29.8 (5.9) 120.6 (12.3) 79.3 (10.3) 5.3 (0.7) 0.6 (0.2) 2.1 (1.1) 7.9 (2.6) 4.3 (0.6) 83.0 (24.3) 94.8 (26.2)  GA 46.5 (3.0) 26.5 (4.2) 119.5 (9.0) 79.7 (6.6) 5.2 (1.5) 0.7 (0.2) 1.5 (0.8) 5.0 (0.6) 4.2 (1.4) 88.6 (16.5) 98.4 (17.9)  GG 46.1 (4.7) 29.6 (6.0) 121.9 (12.6) 78.0 (8.1) 5.3 (1.0) 0.8 (0.3) 1.9 (0.9) 7.8 (2.7) 4.2 (1.0) 92.9 (18.4) 101.3 (19.6) P value .94 .056 .222 .530 .960 .415 .187 <.001 .886 .114 .438 rs4986988  CC 46.2 (4.5) 28.9 (6.0) 122.0 (12.7) 78.1 (1.1) 5.3 (1.1) 0.8 (0.3) 1.4 (0.9) 7.5 (2.6) 4.2 (1.0) 91.0 (19.1) 100.1 (19.1)  CT 46.0 (4.7) 29.4 (5.5) 120.5 (11.4) 77.9 (7.7) 5.1 (0.9) 0.7 (0.3) 1.8 (0.8) 7.1 (2.7) 4.0 (0.9) 93.6 (18.2) 100.0 (18.8)  TT 45.7 (4.4) 30.9 (7.7) 120.0 (10.9) 77.4 (7.9) 5.0 (1.2) 1.0 (0.3) 1.5 (0.9) 8.2 (3.0) 3.7 (1.1) 96.9 (16.4) 108.7 (39.4) P value .873 .497 .564 .950 .137 .112 .417 .268 .138 .388 .411 Values are represented as mean (standard deviation). BMI, Body mass index; HDL, High density lipoprotein; LDL, Low density lipoprotein; P value <.05 was taken as significant. Table 3 Genotype distribution frequencies of studied NAT1 and NAT2 SNPs. Genotype/Allele Frequency P value T2DM n (%) Control n (%) Genotype rs1041983 (C>T) 180 180  CC 85 (47.2) 91 (50.6) .755  CT 78 (43.3) 71 (39.4)  TT 17 (9.4) 18 (10) Allele  C 248 (68.8) 253 (70.2) .689  T 112 (31.1) 107 (29.7) Genotype rs1799929 (C>T) 186 180  CT 91 (48.9) 84 (46.7) .851  CC 53 (28.5) 51 (28.3)  TT 42 (22.6) 45 (25.0) Allele  C 197 (52.9) 186 (51.6) .729  T 175 (47.0) 174 (48.3) Genotype rs1799930 (G>A) 179 181  GG 91 (50.8) 94 (51.9) .919  GA 71 (39.7) 72 (39.8)  AA 17 (9.5) 15 (8.3) Allele  G 253 (70.6) 260 (71.8) .729  A 105 (29.3) 102 (28.2) Genotype rs1799931 (G>A) 185 154  GG 176 (95.1) 124 (80.5) <.001  GA 0 (0) 24 (15.6)  AA 9 (4.9) 6 (3.9) Allele  G 352 (95.1) 272 88.3)) .038  A 18 (4.8) 36 (11.6) Genotype rs4986988 (C>T) 186 181  CC 140 (75.3) 127 (70.2) .254  CT 39 (21.0) 50 (27.6)  TT 7 (3.8) 4 (2.2) Allele  C 319 (85.7) 304 (83.9) .502  T 53 (14.2) 58 (16.0) T2DM, Type 2 diabetes mellitus; P value <.05 was taken as significant. Table 4 Genotype, allele frequencies and Hardy-Weinberg chi-square in the NAT1 and NAT2 genes in case and control populations. Population Genotype frequency Allelic frequency HWE CC CT TT Wild type (C) Mutant (T) HW χ2 HW P rs1041983 C>T  T2DM 85 (47.2) 78 (43.3) 17 (9.4) 248 (68.8) 112 (31.1) 0.026 .883  Control 91 (50.6) 71 (39.4) 18 (10) 253 (70.2) 107 (29.7) 0.561 .454 rs1799929 C>T  T2DM 53 (28.5) 91 (48.9) 42 (22.6) 197 (52.9) 175 (47.0) 0.061 .805  Control 51 (28.3) 84 (46.7) 45 (25.0) 186 (51.6) 174 (48.3) 0.775 .379 rs4986988 C>T  T2DM 140 (75.3) 39 (21.0) 7 (3.8) 319 (85.7) 53 (14.2) 3.745 .053  Control 127 (70.2) 50 (27.6) 4 (2.2) 304 (83.9) 58 (16.0) 0.128 .721 GG GA AA Wild type (G) Mutant (A) HW χ2 HW P rs1799930 G>A  T2DM 91 (50.8) 71 (39.7) 17 (9.5) 253 (70.6) 105 (29.3) 0.333 .563  Control 94 (51.9) 72 (39.8) 15 (8.3) 260 (71.8) 102 (28.1) 0.054 .817 rs1799931 G>A  T2DM 176 (95.1) 0 (0) 9 (4.9) 352 (95.1) 18 (4.8) 185 <.001  Control 124 (80.5) 24 (15.6) 6 (3.9) 272 88.3)) 36 (11.6) 9.25 .002 HWE, Hardy-Weinberg equation; T2DM, Type 2 diabetes mellitus; HW P, Hardy–Weinberg P value; All the values are represented as n (%); d.f.=1 for all tests. P value <.05 was taken as significant. Table 5 Multiple logistic regression analysis of anthropometric and metabolic variables on T2DM. Parameters Odds ratio 95% Confidence interval P value Body mass index 1.177 1.09–1.26 <.001 Systolic blood pressure 1.085 1.04–1.13 <.001 Diastolic blood pressure 0.949 0.89–1.00 .067 Cholesterol 1.102 0.44–2.74 .835 Triglyceride 1.634 1.18–2.26 .003 LDL-Cholesterol 0.943 0.37–2.42 .902 Waist 1.007 0.99–1.03 .466 LDL, low density lipoprotein, P value <.05 was taken as significant. Table 6 Logistic regression analysis of different genotypes and alleles on T2DM. Genotype/Allele OR 95% CI P value rs1041983 (C>T)  CC Reference  CT 2.09 0.46–9.45 .338  TT 0.94 0.96–9.09 .955  C Reference  T 1.89 0.74–4.82 .182 rs1799929 (C>T)  CT Reference  CC 1.02 0.52–1.97 .964  TT 0.80 0.40–1.59 .525  C Reference  T 0.93 0.68–1.28 .654 rs1799930 (G>A)  GG Reference  GA 0.39 0.08–1.79 .225  AA 0.83 0.08–7.99 .869  G Reference  A 0.58 0.23–1.50 .262 rs1799931 (G>A)  GG Reference  GA 0.00 -- .998  AA 1.11 0.25–0.49 .889  G Reference  A 0.32 0.16–0.62 .001 rs4986988 (C>T)  CC Reference  CT 0.67 0.39–1.16 .148  TT 1.07 0.28–4.03 .924  C Reference  T 0.83 0.53–1.29 .417 OR, Odds ratio; CI, Confidence interval; P value <.05 was taken as significant. ==== Refs REFERENCES 1 Wild S Roglic G Green A Sicree R King H Global prevalence of diabetes: estimates for the year 2000 and projections for 2030 Diabetes Care 2004 27 5 1047 53 15111519 2 King H Aubert RE Herman WH Global burden of diabetes, 1995–2025 prevalence, numerical estimates, and projections Diabetes Care 1998 21 9 1414 31 9727886 3 Al-Nozha MM Al-Maatouq MA Al-Mazrou YY Al-Harthi SS Arafah MR Khalil MZ Diabetes mellitus in Saudi Arabia Saudi Med J 2004 25 11 1603 10 15573186 4 Al-Quwaidhi AJ Pearce MS Sobngwi E Critchley JA O’Flaherty M Comparison of type 2 diabetes prevalence estimates in Saudi Arabia from a validated Markov model against the International Diabetes Federation and other modelling studies Diabetes Research and Clin Pract 2014 103 3 496 503 5 Butcher NJ Boukouvala S Sim E Minchin RF Pharmacogenetics of the arylamine N-acetyltransferases Pharmacogenomics J 2002 2 1 30 42 11990379 6 Hein DW Doll MA Fretland AJ Leff MA Webb SJ Xiao GH Molecular genetics and epidemiology of the NAT1 and NAT2 acetylation polymorphisms Cancer Epidemiol Biomarkers Prev 2000 9 1 29 42 10667461 7 Hein DW Doll MA Rustan TD Gray K Feng Y Ferguson RJ Metabolic activation and deactivation of arylamine carcinogens by recombinant human NAT1 and polymorphic NAT2 acetyltransferases Carcinogenesis 1993 14 8 1633 38 8353847 8 Layton DW Bogen KT Knize MG Hatch FT Johnson VM Felton JS Cancer risk of heterocyclic amines in cooked foods: an analysis and implications for research Carcinogenesis 1995 16 1 39 52 7834804 9 Database of arylamine N-acetyltransferases (NATs) Available from http://nat.mbg.duth.gr/ accessed 15 April 2015 10 Zang Y Zhao S Doll MA States JC Hein DW The T341C (Ile114Thr) polyorphism of N-acetyltransferase 2 yields slow acetylator phenotype by enhanced protein degradation Pharmacogenetics 2004 14 11 717 23 15564878 11 Filiadis IF Georgiou I Alamanos Y Kranas V Giannakopoulos X Lolis D Genotypes of N-acetyltransferase-2 and risk of bladder cancer: a case-control study J Urol 1999 161 5 1672 5 10210437 12 El Desoky ES AbdelSalam YM Salama RH El Akkad MA Atanasova S von Ahsen N NAT2*5/*5 genotype (341TOC) is a potential risk factor for schistosomiasis-associated bladder cancer in Egyptians Ther Drug Monit 2005 27 3 297 304 15905799 13 van der Hel OL Peeters PH Hein DW Doll MA Grobbee DE Kromhout D NAT2 slow acetylation and GSTM1 null genotypes may increase postmenopausal breast cancer risk in long-term smoking women Pharmacogenetics 2003 13 7 399 407 12835615 14 Klimcáková L Habalová V Sivonová M Nagy V Salagovic J Zidzik J Effect of NAT2 gene polymorphism on bladder cancer risk in Slovak population Mol Biol Rep 2011 38 2 1287 93 20568013 15 Yalin S Hatungil R Tamer L Ates NA Dogruer N Yildirim H N-acetyltransferase 2 polymorphism in patients with diabetes mellitus Cell Biochem Funct 2007 25 4 407 11 16397907 16 Semiz S Dujic T Ostanek B Velija-Asimi Z Prnjavorac B Bego T Association of NAT2 polymorphisms with type 2 diabetes in a population from Bosnia and Herzegovina Arch Med Res 2011 42 4 311 17 21820610 17 Irshaid YM Abujbara MA Ajlouni KM El-Khateeb M Jarrar YB N-acetyltransferase-2 genotypes among Jordanian patients with diabetes mellitus Int J Clin Pharmacol Ther 2013 51 7 593 99 23587153 18 el-Yazigi A Johansen K Raines DA Dossing M N-acetylation polymorphism and diabetes mellitus among Saudi Arabians J Clin Pharmacol 1992 32 10 905 10 1447397 19 Evans DAP Paterson S Francisco P Alvarez G The acetylator phenotypes of Saudi Arabian diabetics J Med Genet 1985 22 6 479 83 4078878 20 Lee MS Su L Christiani DC Synergistic effects of NAT2 slow and GSTM1 null genotypes on carcinogen DNA damage in the lung Cancer Epidemiol Biomarkers Prev 2010 19 6 1492 97 20501762 21 Sabbagh A Darlu P SNP selection at the NAT2 locus for an accurate prediction of the acetylation phenotype Genet Med 2006 8 2 76 85 16481889 22 Hein DW Doll MA Accuracy of various human NAT2 SNP genotyping panels to infer rapid, intermediate and slow acetylator phenotypes Pharmacogenomics 2012 13 1 31 41 22092036 23 Neugebauer S Baba T Watanabe T Ishizaki T Kurokawa K The N-acetyltransferase (NAT) gene: an early risk marker for diabetic nephropathy in Japanese type 2 diabetic patients? 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