Abstract
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.
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.
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).
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).
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.
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).
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.
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).
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.
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.
Footnotes
Conflict of interest
The authors declare that they have no conflict of interest.
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