# Association between Toll-Like Receptor 4 and Occurrence of Type 2 Diabetes Mellitus Susceptible to Pulmonary Tuberculosis in Northeast China

Authors: Yuze Li; Dianzhong Li; Jinfeng Zhang; Shurui Liu; Haijun Chen; Kun Wu
Journal: Stem Cells International
 (2016)

---

## Abstract

The purpose of this study is to explore why type 2 diabetes mellitus (T2DM) patients are susceptible to pulmonary tuberculosis through detection of serum Toll-like receptor 4 (TLR4), an important immune-related receptor, especially in terms of content and TLR4 gene polymorphism. Patients with T2DM complicated by pulmonary tuberculosis (T2DMTB) were selected as the case group and T2DM patients without tuberculosis were selected as the control group. Forty patients in each group were randomly selected and their serum TLR4 levels were detected and compared. Determination of six sites of TLR4 gene polymorphism was carried out in 238 T2DMTB patients and 310 patients with T2DM, and results showed that the serum TLR4 content of the T2DMTB group was significantly lower than that of the T2DM group (p
<
0.05). The six sites of TLR4 gene polymorphism did not show significant associations with T2DMTB risk. No statistically significant differences in genotype distributions were observed between T2DMTB patients and patients with T2DM when studied using the recessive and dominant genetic models. How two diseases with contradictory nutritional statuses can occur in the same person is difficult to explain from environmental factors perspective alone. Future research should study the causes of T2DMTB from the perspective of genetics.


---

## Body

## 1. Introduction

The prevalence of type 2 diabetes mellitus (T2DM) complicated by tuberculosis (TB) (T2DMTB) is rapidly increasing, resulting in detrimental effects on the economy and human health.Clinical symptoms reveal that environmental factors play important roles in T2DM and TB. Because T2DMTB may be attributed to multiple causes, clinical experience indicates the traditional perspective that the two diseases are related in terms of nutrition. TB is associated with malnutrition, while T2DM is associated with excess nutrients. Thus, environmental factors, among which nutrition is one of the most important, alone, cannot explain the pathogenesis of two seemingly conflicting diseases occurring simultaneously in the same individual. In this case, genetic factors may be responsible for the development of T2DMTB [1–3], but we are not the first who are aware of host genetic factors that are important to determine the risk of type 2 diabetes mellitus complicated by tuberculosis. García-Elorriaga et al. [4] analyzed the association of inflammatory cytokine polymorphisms and T2DMTB in Mexico.Toll-like receptor 4 (TLR4), a pattern recognition receptor, can recognize pathogen-associated molecular patterns (PAMPs) [5] and exacerbate and release inflammatory cytokines, such as TNF-α, IL-1β, IL-6, and IFN-γ [6].Mycobacterium tuberculosispresents liposaccharides (LPS), a type of PAMP. When TLR4 recognizes the LPS ofM. tuberculosis, inflammatory cytokines are induced and released by the pathway of TLR4 [7–9]. This pathway is an important element in the innate immune system of the human body. TLR4, as an important mediator of inflammation, can recognize a variety of pathogens, including Gram-negative and Gram-positive bacteria [10]. Previous studies have shown that human TLR4 is a potentially important gene that may affect the onset of T2DM [11–13]. The receptor may be related to susceptibility to some infections that can induce diseases such as TBs [14–18]. To date, no study has yet reported the association between TLR4 gene polymorphisms and the risk of T2DMTB in the Chinese population.We believe that TLR4 gene polymorphisms are a good starting point for studying T2DMTB. To be able to establish preventive measures for high-risk people as far as possible, in this study, we investigate the relationship between TLR4 gene polymorphisms and T2DMTB risk in Northeast China.


## 2. Material and Methods

### 2.1. Study Subject and Sample Collection

We recruited 238 cases with T2DMTB as the case group and 310 cases with T2DM as the control group. T2DMTB patient information was obtained from the Fourth Medical Ward, Heilongjiang Province Tuberculosis Control Center, from September 2013 to June 2015. T2DM patient information was obtained from the Second Department of Outpatient Services, Heilongjiang Province Tuberculosis Control Center, from September 2013 to November 2014.Subjects with a history of blood-transmitted diseases, such as AIDS, hepatitis B, hepatitis C, or other endocrine diseases, were excluded from this study. Blood samples were taken from the patients, and DNA was obtained and stored in 193 K. Both doctors and study subjects provided consent to participate in this work, and the Ethics Committee of Harbin Medical University approved of this research.


### 2.2. Immunohistochemical Method

To determine whether serum TLR4 levels were consistent with the gene expression level, 40 serum samples were collected from the T2DMTB group and T2DM group, respectively, according to the principle of random. Serum TLR4 contents were detected by using an immunohistochemical method following the manufacturer’s instructions (Beijing Cheng Lin Biological Technology Co., Ltd.).


### 2.3. Tag SNP Selection and Genotyping

Tag SNPs of TLR4 gene were evaluated and selected by using the HapMap database (https://hapmap.ncbi.nlm.nih.gov/) with the following criterion: a minor allele frequency (MAF) > 0.05 in the Chinese Han population in the National Center of Biotechnology Information Database; linkage disequilibrium (LD) blocks were established by using Chinese LD maps and r
2
>
0.8. Six tag SNPs representing the genetic information of TLR4 were selected for genotyping: rs1927914 located in the promoter region; rs11536879, rs1927911, and rs1927907 located in the intron region; and rs11536889 and rs7873784 located in the 3′-UTR region.According to the manufacturer’s instructions, genomic DNA was extracted from peripheral blood using a Tiangen DNA Blood Mini kit (Tiangen Biotech Co., Ltd., Beijing, China). All SNPs were genotyped using fluorogenic 5′-nuclease assay (TaqMan SNP Genotyping Assay, Applied Biosystems, Foster City, CA, USA). For quality control, 20% of all of the samples were performed in delicate form randomly for each SNP. The concordance rate of these repeated samples was 100%.


### 2.4. Statistical Analysis

Numerical data are expressed as mean ± SD. Student’st-test was performed to analyze differences between the T2DMTB and T2DM groups.Fisher’s exact test was used to evaluate the Hardy-Weinberg equilibrium (HWE) in the subjects. The chi-square test or Fisher’s exact test was used to identify statistical differences in the distributions of clinic pathological characteristics. Univariate and multivariate unconditional logistic regression were used to estimate crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs), all of which measured the associations between the risk factors of T2DMTB compared with T2DM. Two-sidedp
<
0.05 was considered as statistically significant. Statistical analysis was carried out by using SAS software (version 9.1.3, SAS Institute, Cary, NC).


## 2.1. Study Subject and Sample Collection

We recruited 238 cases with T2DMTB as the case group and 310 cases with T2DM as the control group. T2DMTB patient information was obtained from the Fourth Medical Ward, Heilongjiang Province Tuberculosis Control Center, from September 2013 to June 2015. T2DM patient information was obtained from the Second Department of Outpatient Services, Heilongjiang Province Tuberculosis Control Center, from September 2013 to November 2014.Subjects with a history of blood-transmitted diseases, such as AIDS, hepatitis B, hepatitis C, or other endocrine diseases, were excluded from this study. Blood samples were taken from the patients, and DNA was obtained and stored in 193 K. Both doctors and study subjects provided consent to participate in this work, and the Ethics Committee of Harbin Medical University approved of this research.


## 2.2. Immunohistochemical Method

To determine whether serum TLR4 levels were consistent with the gene expression level, 40 serum samples were collected from the T2DMTB group and T2DM group, respectively, according to the principle of random. Serum TLR4 contents were detected by using an immunohistochemical method following the manufacturer’s instructions (Beijing Cheng Lin Biological Technology Co., Ltd.).


## 2.3. Tag SNP Selection and Genotyping

Tag SNPs of TLR4 gene were evaluated and selected by using the HapMap database (https://hapmap.ncbi.nlm.nih.gov/) with the following criterion: a minor allele frequency (MAF) > 0.05 in the Chinese Han population in the National Center of Biotechnology Information Database; linkage disequilibrium (LD) blocks were established by using Chinese LD maps and r
2
>
0.8. Six tag SNPs representing the genetic information of TLR4 were selected for genotyping: rs1927914 located in the promoter region; rs11536879, rs1927911, and rs1927907 located in the intron region; and rs11536889 and rs7873784 located in the 3′-UTR region.According to the manufacturer’s instructions, genomic DNA was extracted from peripheral blood using a Tiangen DNA Blood Mini kit (Tiangen Biotech Co., Ltd., Beijing, China). All SNPs were genotyped using fluorogenic 5′-nuclease assay (TaqMan SNP Genotyping Assay, Applied Biosystems, Foster City, CA, USA). For quality control, 20% of all of the samples were performed in delicate form randomly for each SNP. The concordance rate of these repeated samples was 100%.


## 2.4. Statistical Analysis

Numerical data are expressed as mean ± SD. Student’st-test was performed to analyze differences between the T2DMTB and T2DM groups.Fisher’s exact test was used to evaluate the Hardy-Weinberg equilibrium (HWE) in the subjects. The chi-square test or Fisher’s exact test was used to identify statistical differences in the distributions of clinic pathological characteristics. Univariate and multivariate unconditional logistic regression were used to estimate crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs), all of which measured the associations between the risk factors of T2DMTB compared with T2DM. Two-sidedp
<
0.05 was considered as statistically significant. Statistical analysis was carried out by using SAS software (version 9.1.3, SAS Institute, Cary, NC).


## 3. Results

### 3.1. Demographic Characteristics

A total of 548 people participated in this study, including 238 cases of T2MDTB and 310 cases of T2MD. Among the patients, 338 were male and 210 were female. The mean ages of T2DMTB and T2DM patients were 51.97 ± 11.87 years and 58.73 ± 11.46 years, respectively. The demographic characteristics of the patients are shown in Table1.Table 1
Demographic characteristics of the subjects.
Variable
T2DMTB
T2DM
Total
p value
Age
⩽50
104
68
172
<0.0001
50–60
73
108
181
60–70
45
80
125
⩾70
16
54
70
Gender
Male
167
171
338
0.0003
Female
71
139
210
BMI
⩽18.5
35
8
43
<0.0001
18.5–24
112
64
176
⩾24
91
238
329
Smoking
Yes
132
86
218
<0.0001
No
106
224
330
Drinking
Yes
108
94
202
0.0003
No
130
216
346
Glucose
<5.0
12
13
25
0.0857
5.0–7.2
48
88
136
>7.2
178
209
387
Insulin use
Use
112
196
308
<0.0001
No use
126
112
238
Unclear
0
2
2
Hypoglycemic drug use
Use
45
224
269
<0.0001
No use
193
80
273
Unclear
0
6
6


### 3.2. Serum TLR4 Levels of Patients with T2DMTB and Patients with T2DM

The serum TLR4 levels of subjects with T2DMTB were significantly lower than those of subjects suffering from T2DM (p
<
0.0001). No differences in the basic characteristics of the participants, including gender (p
=
0.260) and age (p
=
0.631), were observed (Table 2).Table 2
Serum TLR4 levels of patients with T2DMTB and T2DM.
Variable
T2DMTB
T2DM
p value
Gender (male/female)
40 (20/20)
40 (25/15)
0.260
Age (years)
51.17
±
6.70
51.85
±
6.20
0.631
TLR4 (ng/mL)
15.27
±
2.52
19.40
±
1.80
<0.0001


### 3.3. Distributions of SNP Genotypes between Patients with T2DMTB and Patients with T2DM

Genotype distributions did not show statistically significant deviations from HWE for all SNPs in this study (Table3).Table 3
Distributions of SNP genotypes and Hardy-Weinberg equilibrium.
T2DMTBNo. (%)
T2DMNo. (%)
p value
Hardy-Weinberg
χ
2
p value
rs7873784
GG
243 (86.17)
236 (83.69)
0.19
0.56
0.46
GC
35 (12.41)
45 (15.96)
CC
4 (1.42)
1 (0.35)
rs11536889
GG
176 (61.97)
181 (64.41)
0.83
0.81
0.37
GC
93 (32.75)
86 (30.61)
CC
15 (5.28)
14 (4.98)
rs1927914
TT
90 (31.91)
102 (36.17)
0.50
0.53
0.47
CT
145 (51.42)
140 (49.65)
CC
47 (16.67)
40 (14.18)
rs1927911
TT
97 (34.28)
96 (34.16)
0.48
2.92
0.09
CT
139 (49.12)
148 (52.67)
CC
47 (16.61)
37 (13.17)
rs1927907
GG
145 (51.24)
147 (52.31)
0.96
2.12
0.15
AG
123 (43.46)
119 (42.35)
AA
15 (5.30)
15 (5.34)
rs11536879
AA
230 (82.44)
215 (77.62)
0.20
1.04
0.31
AG
41 (14.70)
56 (20.22)
GG
8 (2.87)
6 (2.17)


### 3.4. Distribution of Allelic Genes of SNPs between Patients with T2DMTB and Patients with T2DM

The distributions of allelic genes of six SNPs of the TLR4 gene were not statistically significant between patients with T2DMTB and patients with T2DM. Details of these distributions are summarized in Table 4.Table 4
Distributions of allelic genes of SNPs between patients with T2DMTB and patients with T2DM.
SNP
Allelic genes
T2DMTB
T2DM
p value
No.
%
No.
%
rs7873784
G allele
521
92.36
517
91.67
0.74
C allele
43
7.64
47
8.33
rs11536889
G allele
445
78.35
448
79.72
0.61
C allele
123
21.65
114
20.28
rs1927914
T allele
325
57.62
344
60.99
0.43
C allele
229
42.38
220
39.01
rs1927911
T allele
333
58.83
340
60.50
0.59
C allele
233
41.17
222
39.50
rs1927907
G allele
413
72.97
413
73.49
0.89
A allele
153
27.03
149
26.51
rs11536879
A allele
501
89.78
486
87.73
0.30
G allele
57
10.22
68
12.27


### 3.5. SNPs and T2DMTB Risk

The SNPs of TLR4 were not significantly associated with T2DMTB risk. No significant differences in genotype distributions between patients with T2DMTB and patients with T2DM were observed using the recessive and dominant genetic models (Table 5). However, potential trends may provide useful information for future studies. In rs7873784, compared with the GG genotype, the GC genotype presented lower risks of T2DMTB (O
R
a
d
j
u
s
t
e
d
=
0.69, 95% CI: 0.42–1.14, p
=
0.15). In rs11536879, AG genotype carriers showed decreased risk of T2DMTB compared with the GG genotype (O
R
a
d
j
u
s
t
e
d
=
0.68, 95% CI: 0.43–1.08, p
=
0.10).Table 5
Associations between the SNPs of TLR4 and DMTB risk.
Genotype
Crude OR (95% CI)
p value
Adjusted OR (95% CI)∗
p value
rs7873784
GG
1.00
1.00
GC
0.76 (0.47–1.22)
0.25
0.69 (0.42–1.14)
0.15
CC
3.89 (0.43–35.01)
0.23
3.16 (0.33–30.74)
0.32
CC/(GG + GC)
4.04 (0.45–36.41)
0.21
3.34 (0.34–32.38)
0.30
(GC + CC)/GG
0.82 (0.52–1.31)
0.41
0.75 (0.46–1.22)
0.25
rs11536889
GG
1.00
1.00
GC
1.12 (0.78–1.60)
0.54
1.04 (0.71–1.51)
0.85
CC
1.11 (0.52–2.36)
0.79
1.08 (0.49–2.39)
0.85
CC/(GG + GC)
1.07 (0.51–2.26)
0.86
1.07 (0.49–2.34)
0.87
(GC + CC)/GG
1.12 (0.79–1.57)
0.53
1.04 (0.73–1.49)
0.82
rs1927914
TT
1.00
1.00
CT
1.17 (0.81–1.69)
0.39
1.25 (0.85–1.84)
0.25
CC
1.33 (0.80–2.21)
0.27
1.35 (0.80–2.29)
0.27
CC/(TT + CT)
1.21 (0.77–1.91)
0.42
1.18 (0.73–1.91)
0.49
(CT + CC)/TT
1.21 (0.85–1.71)
0.29
1.28 (0.89–1.84)
0.19
rs1927911
TT
1.00
1.00
CT
0.93 (0.65–1.34)
0.70
0.93 (0.63–1.36)
0.71
CC
1.26 (0.75–2.10)
0.38
1.22 (0.72–2.09)
0.46
CC/(TT + CT)
1.31 (0.82–2.09)
0.25
1.28 (0.79–2.08)
0.36
(CT + CC)/TT
1.00 (0.70–1.41)
0.98
0.99 (0.69–1.43)
0.96
rs1927907
GG
1.00
1.00
AG
1.05 (0.75–1.47)
0.79
1.03 (0.72–1.48)
0.86
AA
1.01 (0.48–2.15)
0.97
0.95 (0.44–2.05)
0.89
AA/(GG + AG)
0.99 (0.48–2.07)
0.98
0.93 (0.44–1.99)
0.85
(AG + AA)/GG
1.04 (0.75–1.45)
0.80
1.02 (0.72–1.44)
0.90
rs11536879
AA
1.00
1.00
AG
0.68 (0.44–1.07)
0.09
0.68 (0.43–1.08)
0.10
GG
1.25 (0.43–3.65)
0.69
1.35 (0.45–4.04)
0.59
GG/(AA + AG)
1.33 (0.46–3.89)
0.60
1.44 (0.48–4.31)
0.51
(AG + GG)/AA
0.74 (0.49–1.12)
0.16
0.75 (0.48–1.15)
0.19
Note. ∗Age, gender, BMI, smoking, drinking, insulin use, and hypoglycemic drug use were adjusted.


## 3.1. Demographic Characteristics

A total of 548 people participated in this study, including 238 cases of T2MDTB and 310 cases of T2MD. Among the patients, 338 were male and 210 were female. The mean ages of T2DMTB and T2DM patients were 51.97 ± 11.87 years and 58.73 ± 11.46 years, respectively. The demographic characteristics of the patients are shown in Table1.Table 1
Demographic characteristics of the subjects.
Variable
T2DMTB
T2DM
Total
p value
Age
⩽50
104
68
172
<0.0001
50–60
73
108
181
60–70
45
80
125
⩾70
16
54
70
Gender
Male
167
171
338
0.0003
Female
71
139
210
BMI
⩽18.5
35
8
43
<0.0001
18.5–24
112
64
176
⩾24
91
238
329
Smoking
Yes
132
86
218
<0.0001
No
106
224
330
Drinking
Yes
108
94
202
0.0003
No
130
216
346
Glucose
<5.0
12
13
25
0.0857
5.0–7.2
48
88
136
>7.2
178
209
387
Insulin use
Use
112
196
308
<0.0001
No use
126
112
238
Unclear
0
2
2
Hypoglycemic drug use
Use
45
224
269
<0.0001
No use
193
80
273
Unclear
0
6
6


## 3.2. Serum TLR4 Levels of Patients with T2DMTB and Patients with T2DM

The serum TLR4 levels of subjects with T2DMTB were significantly lower than those of subjects suffering from T2DM (p
<
0.0001). No differences in the basic characteristics of the participants, including gender (p
=
0.260) and age (p
=
0.631), were observed (Table 2).Table 2
Serum TLR4 levels of patients with T2DMTB and T2DM.
Variable
T2DMTB
T2DM
p value
Gender (male/female)
40 (20/20)
40 (25/15)
0.260
Age (years)
51.17
±
6.70
51.85
±
6.20
0.631
TLR4 (ng/mL)
15.27
±
2.52
19.40
±
1.80
<0.0001


## 3.3. Distributions of SNP Genotypes between Patients with T2DMTB and Patients with T2DM

Genotype distributions did not show statistically significant deviations from HWE for all SNPs in this study (Table3).Table 3
Distributions of SNP genotypes and Hardy-Weinberg equilibrium.
T2DMTBNo. (%)
T2DMNo. (%)
p value
Hardy-Weinberg
χ
2
p value
rs7873784
GG
243 (86.17)
236 (83.69)
0.19
0.56
0.46
GC
35 (12.41)
45 (15.96)
CC
4 (1.42)
1 (0.35)
rs11536889
GG
176 (61.97)
181 (64.41)
0.83
0.81
0.37
GC
93 (32.75)
86 (30.61)
CC
15 (5.28)
14 (4.98)
rs1927914
TT
90 (31.91)
102 (36.17)
0.50
0.53
0.47
CT
145 (51.42)
140 (49.65)
CC
47 (16.67)
40 (14.18)
rs1927911
TT
97 (34.28)
96 (34.16)
0.48
2.92
0.09
CT
139 (49.12)
148 (52.67)
CC
47 (16.61)
37 (13.17)
rs1927907
GG
145 (51.24)
147 (52.31)
0.96
2.12
0.15
AG
123 (43.46)
119 (42.35)
AA
15 (5.30)
15 (5.34)
rs11536879
AA
230 (82.44)
215 (77.62)
0.20
1.04
0.31
AG
41 (14.70)
56 (20.22)
GG
8 (2.87)
6 (2.17)


## 3.4. Distribution of Allelic Genes of SNPs between Patients with T2DMTB and Patients with T2DM

The distributions of allelic genes of six SNPs of the TLR4 gene were not statistically significant between patients with T2DMTB and patients with T2DM. Details of these distributions are summarized in Table 4.Table 4
Distributions of allelic genes of SNPs between patients with T2DMTB and patients with T2DM.
SNP
Allelic genes
T2DMTB
T2DM
p value
No.
%
No.
%
rs7873784
G allele
521
92.36
517
91.67
0.74
C allele
43
7.64
47
8.33
rs11536889
G allele
445
78.35
448
79.72
0.61
C allele
123
21.65
114
20.28
rs1927914
T allele
325
57.62
344
60.99
0.43
C allele
229
42.38
220
39.01
rs1927911
T allele
333
58.83
340
60.50
0.59
C allele
233
41.17
222
39.50
rs1927907
G allele
413
72.97
413
73.49
0.89
A allele
153
27.03
149
26.51
rs11536879
A allele
501
89.78
486
87.73
0.30
G allele
57
10.22
68
12.27


## 3.5. SNPs and T2DMTB Risk

The SNPs of TLR4 were not significantly associated with T2DMTB risk. No significant differences in genotype distributions between patients with T2DMTB and patients with T2DM were observed using the recessive and dominant genetic models (Table 5). However, potential trends may provide useful information for future studies. In rs7873784, compared with the GG genotype, the GC genotype presented lower risks of T2DMTB (O
R
a
d
j
u
s
t
e
d
=
0.69, 95% CI: 0.42–1.14, p
=
0.15). In rs11536879, AG genotype carriers showed decreased risk of T2DMTB compared with the GG genotype (O
R
a
d
j
u
s
t
e
d
=
0.68, 95% CI: 0.43–1.08, p
=
0.10).Table 5
Associations between the SNPs of TLR4 and DMTB risk.
Genotype
Crude OR (95% CI)
p value
Adjusted OR (95% CI)∗
p value
rs7873784
GG
1.00
1.00
GC
0.76 (0.47–1.22)
0.25
0.69 (0.42–1.14)
0.15
CC
3.89 (0.43–35.01)
0.23
3.16 (0.33–30.74)
0.32
CC/(GG + GC)
4.04 (0.45–36.41)
0.21
3.34 (0.34–32.38)
0.30
(GC + CC)/GG
0.82 (0.52–1.31)
0.41
0.75 (0.46–1.22)
0.25
rs11536889
GG
1.00
1.00
GC
1.12 (0.78–1.60)
0.54
1.04 (0.71–1.51)
0.85
CC
1.11 (0.52–2.36)
0.79
1.08 (0.49–2.39)
0.85
CC/(GG + GC)
1.07 (0.51–2.26)
0.86
1.07 (0.49–2.34)
0.87
(GC + CC)/GG
1.12 (0.79–1.57)
0.53
1.04 (0.73–1.49)
0.82
rs1927914
TT
1.00
1.00
CT
1.17 (0.81–1.69)
0.39
1.25 (0.85–1.84)
0.25
CC
1.33 (0.80–2.21)
0.27
1.35 (0.80–2.29)
0.27
CC/(TT + CT)
1.21 (0.77–1.91)
0.42
1.18 (0.73–1.91)
0.49
(CT + CC)/TT
1.21 (0.85–1.71)
0.29
1.28 (0.89–1.84)
0.19
rs1927911
TT
1.00
1.00
CT
0.93 (0.65–1.34)
0.70
0.93 (0.63–1.36)
0.71
CC
1.26 (0.75–2.10)
0.38
1.22 (0.72–2.09)
0.46
CC/(TT + CT)
1.31 (0.82–2.09)
0.25
1.28 (0.79–2.08)
0.36
(CT + CC)/TT
1.00 (0.70–1.41)
0.98
0.99 (0.69–1.43)
0.96
rs1927907
GG
1.00
1.00
AG
1.05 (0.75–1.47)
0.79
1.03 (0.72–1.48)
0.86
AA
1.01 (0.48–2.15)
0.97
0.95 (0.44–2.05)
0.89
AA/(GG + AG)
0.99 (0.48–2.07)
0.98
0.93 (0.44–1.99)
0.85
(AG + AA)/GG
1.04 (0.75–1.45)
0.80
1.02 (0.72–1.44)
0.90
rs11536879
AA
1.00
1.00
AG
0.68 (0.44–1.07)
0.09
0.68 (0.43–1.08)
0.10
GG
1.25 (0.43–3.65)
0.69
1.35 (0.45–4.04)
0.59
GG/(AA + AG)
1.33 (0.46–3.89)
0.60
1.44 (0.48–4.31)
0.51
(AG + GG)/AA
0.74 (0.49–1.12)
0.16
0.75 (0.48–1.15)
0.19
Note. ∗Age, gender, BMI, smoking, drinking, insulin use, and hypoglycemic drug use were adjusted.


## 4. Discussion

A previous study showed that patients with T2DM demonstrated four to eight times increased risk of tuberculosis compared to patients without T2DM. For instance, TB in T2DM patients was 5 times more prevalent than in non-T2DM patients in some regions in the USA, 5.4 times more prevalent than in non-T2DM patients in Australia, and 6.8 times more prevalent than in non-T2DM patients in Mexico [4].In the clinic, we discovered that T2DMTB patients present uneven changes in inflammatory cytokines such as TNF-α and IFN-γ. Clinical examination of simple TB yielded a positive IFN-γ test, but the results of nearly all patients with T2DMTB were negative. TLR4 is an important element of the endogenic immune system and it can induce and release inflammatory cytokines [5]. TLR4 is an important element of the innate immune system of the human body. Thus, we hypothesize that serum TLR4 exhibits obvious changes in T2DMTB patients. We examined TLR4 serum levels in patients with T2DMTB and patients T2DM to determine whether TLR4 is involved in the susceptibility of T2DM patients suffering from TB in Northeast China. While no study has yet proven that TLR4 polymorphisms are related to T2DMTB or T2DM, other studies [19–22] indicate that TLR4 polymorphisms present statistically significant differences between healthy people and patients with TB.Our study results are different from those of Wu et al. [23], especially in terms of the frequencies of the GG genotype of SNP rs7873784 in TLR4 (OR = 2.136; 95% CI: 1.312–3.478) and the CC genotype of rs3764879 in TLR8 (OR = 1.982; 95% CI: 1.292–3.042). It was also significantly higher in the TB group than in the healthy group. Arji et al. [24] found TLR4 interactions influencing protection against TB in Moroccan patients. The present work and that of Jahantigh et al. [25] both demonstrated no significant relation between TLR4 and TLR9 polymorphisms and TB. Sánchez et al. [26] reported that they did not find any association between TLR4 polymorphic variants. These findings suggest that the gene polymorphisms were not involved in any risk factor for pulmonary TB in the Colombian population [26]. The same result was obtained by Xue et al. [27], who discovered that these polymorphisms were rare in the Southeastern Chinese population and not linked to susceptibility to TB. Newport et al. [28] also have studied that, but result was that no association between TLR4 Asp299Gly and TB was observed.The result of our study showed that it was negative. The reasons behind this result are first the size of the sample which was not enough. The second one is the geographic regions and genetic factors, because we were focused on the people of Northeast China in our work just only.The purpose of this study is to determine why patients with T2DM easily develop pulmonary TB. Thus, we did not detect TLR4 gene polymorphisms in healthy cases and we did not compare changes in TLR4 gene polymorphisms among healthy cases, patients with T2DM, and patients with T2DMTB. Changes in TLR4 gene polymorphisms may have already occurred in most patients with T2DM. In this case, a false negative result may appear if a comparison was carried out between T2DM patients and patients suffering from T2DMTB only. Such a phenomenon would also confirm some reports that T2DM is characterized by chronic inflammation.Some studies have recently mentioned that the risk genotypes of rs1927914 are significantly linked with diabetic foot ulcers [29]; the research team of Singh [30] also found that the combined genotype risks of TLR4 SNPs rs10759931 (odds ratio [OR] 1.50, p
=
0.05) and rs1927914 (OR 1.48, p
=
0.05) were significantly linked to retinopathy in T2DM. These works support the idea that T2DM is a chronic inflammatory disease. It is possible that T2DM patients already had TLR4 gene polymorphism. Therefore, we considered these points as the third reason.Two groups of patients were included in our study. For each group, we concerned about the changing of TLR4 gene polymorphism and no changing of TLR4 levels in the blood. The reason of that was hidden behind the changing in the TLR4 gene, because serum TLR4 is a sensitivity index but not a specific index. Chronic inflammation could directly lead to changes in TLR4 in the blood or other gene polymorphisms that could lead to TLR4 changes in the blood. Our study was limited by considering just TLR4 gene polymorphisms. Other inflammatory gene polymorphisms that promote development of T2DMTB may exist.The results of this study reveal that TLR4 changes at the molecular level are insignificant because the changes at the gene level may be not significant. Our results also demonstrated no significant link between the six SNPs of TLR4 studied in this work and the susceptibility of patients with T2DMTB in Northeast China. Future studies may be performed to determine the causes of T2DMTB from the perspective of genetics. We aim to determine the marker gene polymorphism in T2DM and show how it can be complicated by TB.



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*Source: 8160318-2016-03-31.xml*