A meta-analysis of the association of G915C, G800A, C509T gene polymorphism of transforming growth factor-β1 with diabetic nephropathy risk

Abstract

This meta-analysis was conducted to evaluate the association of transforming growth factor-β1 (TGF-β1) G915C, G800A, C509T gene polymorphism with the risk of diabetic nephropathy (DN). The association literatures were identified from PubMed, Cochrane Library, and CBM-disc (China Biological Medicine Database) on March 1, 2013, and eligible reports were recruited and synthesized. Seven reports were recruited into this meta-analysis for the association of TGF-β1 G800A, C509T, G915C gene polymorphism with DN risk. GG genotype, CC genotype, and C allele of TGF-β1 G915C were not associated with the DN risk (GG: OR = 0.84, 95% CI: 0.62–1.14, p = 0.27; CC: OR = 1.05, 95% CI: 0.50–2.22, p = 0.90; C allele: OR = 1.16, 95% CI: 0.88–1.51, p = 0.29). Furthermore, TGF-β1 G800A, C509T gene polymorphism was not associated with the DN risk. In conclusion, TGF-β1 G915C, G800A, and C509T gene polymorphism are not associated with the DN risk. However, more studies should be performed to confirm this relationship in the future.

• C509T
• diabetic nephropathy
• G800A
• G915C
• gene polymorphism
• meta-analysis
• transforming growth factor-β1

Introduction

Diabetic nephropathy (DN) is one of the important complications of both type 1 and type 2 diabetes, which is also associated with a poor life expectancy of patients with diabetes mellitus.1 DN is the major cause for end-stage renal disease, and susceptibility to DN has an inherent genetic basis as evidenced by familial aggregation and ethnic-specific prevalence rates.2,3 DN is a biologically heterogeneous disease containing many genetic and epigenetic alterations. There lacks a well-documented diagnostic approach for the DN risk; and the etiology of DN is not clear at present. Current evidence indicates that the gene polymorphism of some genes is associated with the susceptibility of DN.3–5

Gene polymorphism is an important factor taking part in the etiology of some diseases. The evidence from meta-analysis might be powerful compared with the individual investigation. There were some interesting studies performed to assess the association of the polymorphism of some genes with the onset of some diseases. Zhou et al.6 performed an interesting meta-analysis including 16 investigations to study the association of angiotensin-converting enzyme insertion/deletion (I/D) gene polymorphism and systemic lupus erythematosus (SLE)/lupus nephritis (LN) risk, and reported that D allele and DD homozygous were significant genetic molecular markers to predict SLE susceptibility, and DD genotype was a valuable marker to predict the LN risk. They also found that the D allele or DD homozygosity may become a significant genetic molecular marker for the onset of focal segmental glomerulosclerosis in Asian population.7 Zhu et al.8 conducted a meta-analysis and reported that the carnosinase D18S880 microsatellite polymorphism was associated with DN susceptibility, especially in the type 2 DM and the Caucasian population.

Transforming growth factor-β1 (TGF-β1) is known to be one of the major cytokines and plays an important role in regulating cellular processes including growth, differentiation, extracellular matrix formation, and immune suppression.9,10 The gene sites of G915C, G800A, C509T were the important mutation points of TGF-β1. Some epidemiologic studies investigating the association of TGF-β1 G915C, G800A, C509T gene polymorphism with the risk of DN were conducted in the past decades. There was no meta-analysis to evaluate the relationship between TGF-β1 G915C, G800A, C509T gene polymorphism and the risk of DN. This meta-analysis was conducted to investigate whether the TGF-β1 G915C, G800A, C509T gene polymorphism was associated with the risk of DM, by widely collected reported studies.

Materials and methods

Search strategy for the association of TGF-β1 G915C, G800A, C509T gene polymorphism with the risk of DN

The relevant studies were searched from the electronic databases of Pub Med, and Cochrane Library on March 1, 2013. The retrieval strategy of “(diabetic nephropathy OR diabetic renal disease) AND (TGF-β1 OR transforming growth factor-β1) AND (polymorphism OR variant)” was entered into these databases. The additional reports were identified through references cited in recruited articles.

Inclusion and exclusion criteria

Inclusion criteria

(1) The outcome had to be DN; (2) There had to be at least two comparison groups (case group vs. control group); (3) Investigation should provide the data of TGF-β1 G915C, G800A, C509T genotype distribution.

Exclusion criteria

(1) Review articles and editorials; (2) Case reports; (3) Preliminary result not on TGF-β1 G915C, G800A, C509T gene polymorphism or outcome; (4) Investigating the role TGF-β1 gene expression to disease; (5) If multiple publications for the same data from the same study group occurred, we only recruited the later paper into our final analysis.

Data extraction and synthesis

The following information from each eligible study was extracted independently by two investigators: first author’s surname, year of publication, ethnicity, genotyping methods, diabetes types, control source of the control group, and the number of cases and controls for TGF-β1 G915C, G800A, C509T genotypes. The results were compared and disagreement was resolved by discussion.

Statistical analysis

Cochrane Review Manager Version 5 (Cochrane Library, UK) was used to calculate the available data from each study. The pooled statistic was counted using the fixed effects model, but a random effects model was conducted when the p value of heterogeneity test was less than 0.1.7 Results were expressed with odds ratios (OR) for dichotomous data, and 95% confidence intervals (CI) were also calculated.11 p < 0.05 was required for the pooled OR to be statistically significant.12 I² was used to test the heterogeneity among the included studies. Sensitivity analysis was also performed according to source of the controls (healthy vs. hospital), diabetes types, and sample size of case (<100 vs. ≥100).

Results

Study characteristics

Seven studies13–19 reporting the relationship between TGF-β1 G915C, G800A, C509T gene polymorphism and DN susceptibility were included into this meta-analysis, two published in Chinese,17,19 and others13–16,18 in English. Five investigations14,15,17–19 were conducted for the association of TGF-β1 G915C gene polymorphism and DN risk, four15,16,18,19 for TGF-β1 G800A gene polymorphism, and four13,15,16,19 for C509T gene polymorphism (Table 1). Those seven investigations contained 1222 DN patients and 1187 controls. The average distribution frequency of C allele of TGF-β1 G915C in DN group was 11.58% and the average frequency in control group was 11.02%. The average distribution frequency of A allele of TGF-β1 G800A in DN group was 8.18% and the average frequency in control group was 7.92%. Furthermore, the average distribution frequency of T allele of TGF-β1 G800A in DN group was 33.37% and the average frequency in control group was 33.14%. The average distribution frequency of case group for C allele, A allele, T allele in TGF-β1 G915C, G800A, C509T was similar to that in control group (C allele: case/control = 1.05; A allele: case/control = 1.03; T allele: case/control = 1.01).

Table 1. General characteristics of the studies included in the meta-analysis.

Author, Year/Gene site	Ethnicity	Genotyping methods	Diabetes types	Cotrol source	Case			Control
G915C					GG	GC	CC	GG	GC	CC
Wei 2005	Asian	PCR-SSP, PCR-RFLP	Type 2	Hospital	82	8	1	84	7	1
Mcknight 2007	Caucasian	Taqman	Type 1	Hospital	219	50	3	298	65	4
Valladares-Salgado 2010	Mixed	PCR-RFLP	Type 2	Hospital	211	17	1	191	4	0
Rao 2011	Asian	PCR-SSP	Type 2	Hospital	30	5	10	34	7	12
El-Sherbini 2013	African	ARMS-PCR	Type 2	Hospital	39	10	0	40	10	0
G800A					GG	GA	AA	GG	GA	AA
Ng 2003	Caucasian	ASO-PCR	Type 1	Hospital	250	39	2	220	39	3
Wei 2005	Asian	PCR-SSP, PCR-RFLP	Type 2	Hospital	81	10	0	83	8	1
Mcknight 2007	Caucasian	Taqman	Type 1	Hospital	188	78	6	268	92	7
Valladares-Salgado 2010	Mixed	PCR-RFLP	Type 2	Hospital	213	15	0	189	11	1
C509T					CC	CT	TT	CC	CT	TT
Ng 2003	Caucasian	ASO-PCR	Type 1	Hospital	143	131	20	126	109	26
Wei 2005	Asian	PCR-SSP, PCR-RFLP	Type 2	Hospital	28	47	16	30	48	14
Mcknight 2007	Caucasian	Taqman	Type 1	Hospital	245	105	17	179	84	9
Buraczynska 2007	Caucasian	PCR-RFLP	Type 2	Healthy	83	118	44	60	76	31

Notes: PCR–RFLP: polymerase chain reaction-restriction fragment length polymorphism; ARMS-PCR: amplification refractory mutation system-polymerase chain reaction; ASO-PCR: allele-specific oligonucleotide-polymerase chain reaction; PCR-SSP: polymerase chain reaction-sequence specific primers.

Association of TGF-β1 G915C gene polymorphism with DN susceptibility

In this meta-analysis, we found that TGF-β1 G915C gene polymorphism was not associated with DN risk (GG: OR = 0.84, 95% CI: 0.62–1.14, p = 0.27; CC: OR = 1.05, 95% CI: 0.50–2.22, p = 0.90; C allele: OR = 1.16, 95% CI: 0.88–1.51, p = 0.29; Figure 1 and Table 2).

Figure 1. Association of TGF-β1 G915C gene polymorphism with DN susceptibility.

Table 2. Meta-analysis of the association of TGF-β1 G915C, G800A, C509T gene polymorphism with DN risk.

Gene sites and comparison	Studies number	Q test p value	Model selected	OR (95% CI)	p
G915C
GG versus GC + CC	5	0.22	Fixed	0.84(0.62,1.14)	0.27
CC versus GG + GC	5	0.96	Fixed	1.05(0.50,2.22)	0.90
C versus G	5	0.18	Fixed	1.16(0.88,1.51)	0.29
G800A
GG versus GA + AA	4	0.72	Fixed	0.93(0.72,1.19)	0.55
AA versus GG + GA	4	0.75	Fixed	0.80(0.34,1.87)	0.60
A versus G	4	0.70	Fixed	1.05(0.83,1.32)	0.69
C509T
CC versus CT + TT	4	0.96	Fixed	0.99(0.82,1.21)	0.95
TT versus CT + CC	4	0.46	Fixed	0.94(0.68,1.29)	0.71
T versus C	4	0.90	Fixed	0.99(0.86,1.15)	0.90

Association of TGF-β1 G800A gene polymorphism with DN susceptibility

In this meta-analysis, TGF-β1 G800A gene polymorphism was not associated with DN risk (GG: OR = 0.93, 95% CI: 0.72–1.19, p = 0.55; AA: OR = 0.80, 95% CI: 0.34–1.87, p = 0.60; A allele: OR = 1.05, 95% CI: 0.83–1.32, p = 0.69; Figure 2 and Table 2).

Figure 2. Association of TGF-β1 G800A gene polymorphism with DN susceptibility.

Association of TGF-β1 C509T gene polymorphism with DN susceptibility

In this meta-analysis, TGF-β1 C509T gene polymorphism was not associated with DN risk (GG: OR = 0.99, 95% CI: 0.82–1.21, p = 0.95; TT: OR = 0.94, 95% CI: 0.68–1.29, p = 0.71; T allele: OR = 0.99, 95% CI: 0.86–1.15, p = 0.90; Figure 3 and Table 2).

Figure 3. Association of TGF-β1 C509T gene polymorphism with DN susceptibility.

Sensitivity analysis

Sensitivity analysis for the relationship between TGF-β1 G915C, G800A, C509T gene polymorphism and DN risk was also performed according to the source of the controls (healthy vs. hospital). The control source of all the included studies for TGF-β1 G915C, G800A was from hospital source, and the results were the same as non-sensitivity analysis. The control source of one study for C509T was from healthy. The sensitivity analysis was performed, and we found that the results from healthy source or hospital source were similar to those from the non-sensitivity analysis (data not shown).

Sensitivity analysis according to diabetes types for the relationship between TGF-β1 G915C, G800A, C509T gene polymorphism and DN risk was also performed. We found that the results were also similar with the non-sensitivity analysis (data not shown).

Sensitivity analysis for the relationship between TGF-β1 G915C, G800A, C509T gene polymorphism and DN risk was also performed according to sample size of case (<100 vs. ≥100). We found that the results were also similar with the non-sensitivity analysis (data not shown).

Discussion

In this study, the average distribution frequency of C allele in DN group was 1.05-fold increase when compared with that in control group, and the average distribution frequency of A allele in DN group was 1.03-fold increase when compared with that in control group. Furthermore, T allele in case group was 1.01-fold increase when compared with that in control group. The average distribution frequency data indicated that there was no difference for C allele, A allele, and T allele distribution between cases and controls. The meta-analysis data also confirmed this conclusion.

In this meta-analysis, we found that TGF-β1 G915C, G800A, C509T gene polymorphism was not associated with the DN risk. There was no marked heterogeneity among the included studies. In the sensitivity analysis according to the source of the controls, diabetes types and the sample size of case, we found that the results were similar with those of non-sensitivity analysis. The results in our meta-analysis might be robust to some extent. The results from our meta-analysis might be robust to some extent. However, the number of included studies is a little small, and more studies should be performed to evaluate the relationship in the future.

There was one meta-analysis performed to investigate the association of TGF-β1 gene polymorphism and the risk of DN in the past. Jia et al.20 included nine studies with 1776 cases and 1740 controls to evaluate the relationship between TGF-β1 T869C gene polymorphism and DN susceptibility, and reported that C allele of T869C conferred a significantly increased risk of DN compared with T allele for allelic contrast. We speculated that the gene mutation of TGF-β1 T869C was associated with DN risk, but the TGF-β1 G915C, G800A, C509T not. However, more studies should be conducted to explore this relationship.

There were some meta-analyses to study the role of gene polymorphism in DN disease. Yang et al.21 performed a meta-analysis and the study supported that there was an association between MTHFR C677T polymorphism and DN risk, and MTHFR 677T variant contributed to increased risk of DN in Caucasian individuals with type 2 diabetes. Cui et al.22 conducted a systematic review and meta-analysis to explore the relationship between five GLUT1 gene single nucleotide polymorphisms and DN, and showed that XbaI, Enh2 and HaeIII SNPs, but not HpyCH4V SNP, in GLUT1 gene might be genetic susceptibility to DN. However, Wang et al.23 included 18 studies into their meta-analysis and reported that the Pro12Ala polymorphism in PPARgamma2 gene was not a risk factor for DN in type 2 diabetes. Those results indicated that the gene polymorphism of some gene sites was associated with DN risk, but others not.

Our meta-analysis indicated that there was an association between TGF-β1 G915C, G800A, C509T gene polymorphism and the DN risk. The outcome might be robust to some extent. However, those findings should be regarded cautiously because many other ingredients, such as small sample size of the included report, limited statistical power, heterogeneity of enrolled cases, variable study designs and different interventions, were closely related to affect the results.

In conclusion, the results in our study support that TGF-β1 G915C, G800A, C509T gene polymorphism was not associated with the DN risk. However, more association investigations are required to further clarify the role of the TGF-β1 G915C, G800A, C509T gene polymorphism in predicting the risk of DN.

Declaration of interest

The authors declare no competing interests.