Literature DB >> 30458730

Relationship between transforming growth factor-β1 and type 2 diabetic nephropathy risk in Chinese population.

Tianbiao Zhou1, Hong-Yan Li2, Hongzhen Zhong3, Zhiqing Zhong3.   

Abstract

BACKGROUND: Diabetes mellitus (DM) is divided into four different etiological categories: type 1 DM (T1DM), type 2 DM (T2DM), other specific types, and gestational DM. One severe complication of T2DM is type 2 diabetic nephropathy (T2DN). The possible association of serum transforming growth factor-β1 (TGF-β1) levels and the TGF-β1 T869C gene polymorphism with patient susceptibility to T2DN in Chinese population is unclear at present. This study was conducted to assess these relationships in Chinese population by a meta-analysis.
METHODS: Association reports were searched and pulled from the Cochrane Library, the China Biological Medicine Database (CBM), and PubMed on March 1, 2018, and eligible studies were selected and used for calculations. The results were expressed as weighted mean differences (MD) for continuous data. Odds ratios (OR) were used to express the results for dichotomous data. Additionally, 95% confidence intervals (CI) were calculated.
RESULTS: Forty-eight reports for the relationship between serum TGF-β1 levels and the risk of T2DN and 13 studies on the association of the TGF-β1 T869C gene polymorphism with susceptibility to T2DN in Chinese population were retrieved from this study. Serum TGF-β1 levels in the T2DM group were higher than those in the normal control group (MD = 17.30, 95% CI: 12.69-21.92, P < 0.00001). The serum TGF-β1 level in the T2DN group was significantly higher than that in the normal control group (MD = 70.03, 95% CI: 60.81-79.26, P < 0.00001;). The serum TGF-β1 level in the T2DN group was significantly higher than that in the T2DM group (MD = 56.18, 95% CI: 46.96-65.39, P < 0.00001). Serum TGF-β1 levels in T2DM patients with microalbuminuria were increased when compared with those in T2DM patients with normoalbuminuria. Furthermore, serum TGF-β1 levels in T2DM patients with macroalbuminuria were increased when compared with those in T2DM patients with microalbuminuria. The TGF-β1 T allele, TT allele and CC genotype were associated with T2DN susceptibility in Chinese population (T: OR = 0.74, 95% CI: 0.59-0.92, P = 0.007; TT: OR = 0.55, 95% CI: 0.31-0.96, P = 0.04; CC: OR = 1.38, 95% CI: 1.14-1.67, P = 0.001).
CONCLUSIONS: High levels of TGF-β1 are associated with susceptibility to T2DM, T2DN and the progression of proteinuria in T2DN patients in Chinese population. Further, the TGF-β1 T allele, and TT genotype were protective factors against the onset of T2DN and CC genotype was a risk factor for the susceptibility of T2DN in Chinese populations.

Entities:  

Keywords:  Diabetes mellitus (DM); Gene polymorphism; Meta-analysis; T869C; Transforming growth factor-β1; Type 2 diabetic nephropathy (T2DN)

Mesh:

Substances:

Year:  2018        PMID: 30458730      PMCID: PMC6247505          DOI: 10.1186/s12881-018-0717-3

Source DB:  PubMed          Journal:  BMC Med Genet        ISSN: 1471-2350            Impact factor:   2.103


Background

Transforming growth factor beta1 (TGF-β1) is one of the pro-fibrotic cytokines and is thought to be the primary mediator driving the progression of fibrosis, glomerulosclerosis and especially mesangial cell phenotype transformation in diabetic nephropathy (DN) [1, 2]. TGF-β1 directly stimulates the transcription of extracellular matrix (ECM). Increased TGF-β1 is reported to be associated with DN disease [3-5]. Gene polymorphisms of TGF-β1 can affect the activity of TGF-β1. The TGF-β1 T869C gene polymorphism is one of the most important gene polymorphisms that affects the protein expression of TGF-β1 [6]. Gene polymorphisms have been reported to be associated with some diseases [7-9]. However, there are conflicting reports on the association of the TGF-β1 T869C polymorphism with T2DN susceptibility [10-13]. Diabetes mellitus (DM), characterized by elevated levels of blood glucose, is a complex and heterogeneous, chronic metabolic disease [14]. DM is the leading cause of morbidity and mortality worldwide and is a major global health problem [15, 16]. DM is divided into four different etiological categories: type 1 DM (T1DM), type 2 DM (T2DM), other specific types, and gestational DM. The main characteristic of T2DM is insulin resistance, often followed by the failure of pancreatic β-cells. Recent data indicate that morbidity and mortality among diabetic patients are increased [14]. One severe complication of T2DM is type 2 diabetic nephropathy (T2DN), which is characterized by hypertension, albuminuria, and a progressive decline in glomerular filtration rate, developing into end-stage renal disease [17, 18]. There is increasing evidence showing that TGF-β1 takes part in the pathogenesis of T2DN [19-21]. In this study, we assessed the association between TGF-β1 levels and T2DN risk, and the association of the TGF-β1 T869C gene polymorphism with the susceptibility to T2DN in Chinese population, by a meta-analysis method.

Methods

Search strategy

The electronic databases of the Cochrane Library, the China Biological Medicine Database (CBM), and PubMed were searched on March 1, 2018, and relevant studies were retrieved. The retrieval strategy of “(transforming growth factor-β1 OR TGF-β1) AND (diabetic nephropathy OR diabetic kidney disease)” was entered and searched in these databases. Additional investigations were extracted from the references cited in articles retrieved in this search.

Inclusion and exclusion criteria

Inclusion criteria

(1) Each study had at least two comparison groups (case group vs. control group); (2) The outcome in patients had to be T2DN; (3) Each study should show data on the TGF-β1 level and/or the TGF-β1 T869C genotype distribution.

Exclusion criteria

(1) Editorials, review articles, case reports; (2) Study results not showing the TGF-β1 level or the TGF-β1 T869C gene polymorphism to disease; (3) Multiple publications from the same study group; (4) Study not conducted in Chinese population.

Data extraction and synthesis

The information was extracted from each eligible report by two authors independently: the surname of the first author, the publication year, the country of the study or ethnicity, the TGF-β1 levels, the number of patients or controls, and the number of subjects in case groups and control groups for TGF-β1 genotypes.

Statistical analysis

Cochrane Review Manager Version 5 software (Cochrane Library, UK) was used to calculate the available data from each investigation. The fixed effects model was used to calculate the pooled statistic. However, a random effects model was used to assess the relationship when the P value of the heterogeneity test was less than 0.1. The results were expressed as weighted mean differences (MD) for continuous data, and odds ratios (OR) were used to express the results for dichotomous data. Additionally, 95% confidence intervals (CI) were also counted. P < 0.05 was required for statistical significance for the pooled OR. I was used to test the heterogeneity among the included investigations. The Egger regression asymmetry test [22] and the Begg adjusted-rank correlation test [23] were used to test the publication bias, and P < 0.10 was considered significant.

Results

Study characteristics

Forty-five reports [24-68] were included for the meta-analysis of the relationship between TGF-β1 level and T2DN risk in Chinese population (Table 1). One report [67] was published in English and other reports were published in Chinese.
Table 1

General characteristics of the included studies for TGF-β1 levels in T2DN in this meta-analysis

First author, yearCountryAccording toCaseControl
UAER or UACRMeanSDNMeanSDN
Ju HB 2000ChinaNormoalbuminuria35.026.71423.958.0115
Microalbuminuria39.315.351823.958.0115
Macroalbuminuria58.589.561323.958.0115
Wang YJ 2002ChinaNormoalbuminuria147.0322.5734136.9737.9635
Macroalbuminuria170.6518.7431136.9737.9635
Li WM 2004ChinaNormoalbuminuria58.9111.034647.256.2248
Macroalbuminuria387.4582.064847.256.2248
Li ZJ 2004ChinaNormoalbuminuria14622361313640
Macroalbuminuria17219441313640
Jiang ZL 2005ChinaNormoalbuminuria428.343.729412.558.435
Microalbuminuria578.569.427412.558.435
Macroalbuminuria683.484.328412.558.435
Li ZZ 2005ChinaNormoalbuminuria4115.572710.045.3318
Microalbuminuria66.3518.041210.045.3318
Macroalbuminuria53.3115.641810.045.3318
Zhou Y 2005ChinaNormoalbuminuria31.1212.393029.410.6230
Microalbuminuria79.6315.963029.410.6230
Macroalbuminuria136.621.453029.410.6230
Jing CY 2005ChinaNormoalbuminuria31.1614.233124.5812.6120
Microalbuminuria48.218.32524.5812.6120
Macroalbuminuria62.1221.32324.5812.6120
Wei YS 2005ChinaNR41.5710.559125.467.88105
Li HP 2006ChinaNormoalbuminuria147.0220.57108131.963.84120
Macroalbuminuria170.6417.72132131.963.84120
Tao SP 2006ChinaNormoalbuminuria14723281323625
Macroalbuminuria17218341323625
Meng T 2006ChinaNormoalbuminuria217.71262884.523.430
Microalbuminuria288.2109.42484.523.430
Macroalbuminuria345.5118.22284.523.430
Xie HF 2006ChinaNormoalbuminuria42.19.36035.98.130
Macroalbuminuria61.811.24535.98.130
Qian YX 2006ChinaNormoalbuminuria14622481313660
Macroalbuminuria17219231313660
Fu CX 2007ChinaNormoalbuminuria36.28.83434.48.235
Microalbuminuria69.412.83134.48.235
Du JW 2007ChinaNormoalbuminuria179.1613.132068.4731.7519
Microalbuminuria192.6657.252168.4731.7519
Macroalbuminuria582.04211.252068.4731.7519
Zhang WJ 2007ChinaNormoalbuminuria23.353.73620.353.740
Microalbuminuria41.314.34520.353.740
Macroalbuminuria55.286.84520.353.740
Lai X 2007ChinaNormoalbuminuria89.6528.332731.469.0743
Microalbuminuria121.0232.362131.469.0743
Macroalbuminuria211.6969.831731.469.0743
Lin YH 2007ChinaNormoalbuminuria97.2418.61958.3613.7223
Macroalbuminuria136.7523.482458.3613.7223
Zhang SF 2007ChinaMicroalbuminuria21.18820.87156.9918.5718
Macroalbuminuria13.6419.44166.9918.5718
Zhang WK 2008ChinaNormoalbuminuria23.310.13020.33.726
Microalbuminuria41.34.23820.33.726
Macroalbuminuria88.26.83220.33.726
Wang YP 2008ChinaNormoalbuminuria35.47.14432.56.835
Macroalbuminuria68.212.53232.56.835
Zhang SB 2008ChinaNR121.537.23655.216.830
Li QX 2008ChinaNormoalbuminuria31.99.722621.56.8920
Microalbuminuria49.614.782321.56.8920
Macroalbuminuria70.326.481821.56.8920
Feng SJ 2008ChinaNormoalbuminuria208.21102580.623.438
Microalbuminuria293.3118.52380.623.438
Macroalbuminuria263.5108.21880.623.438
Zhang HM 2008ChinaNormoalbuminuria32.5212.2440
Microalbuminuria43.6120.3748
Cao B 2009ChinaNormoalbuminuria31.25.63117.43.430
Microalbuminuria54.97.83417.43.430
Macroalbuminuria78.210.33017.43.430
Li QX 2009ChinaNormoalbuminuria31.99.722621.56.8920
Microalbuminuria49.614.782321.56.8920
Macroalbuminuria70.326.481821.56.8920
Yang YZ 2010ChinaNormoalbuminuria28.593.642521.073.4830
Macroalbuminuria43.124.622521.073.4830
Feng LM 2010ChinaNormoalbuminuria34.27.14032.86.435
Macroalbuminuria69.47.23232.86.435
Wu YJ 2010ChinaNG172.520.430125.414.628
Ye CF 2010ChinaNormoalbuminuria31.365.753726.545.7832
Macroalbuminuria58.699.873726.545.7832
Huang JW 2010ChinaNormoalbuminuria41.8510.382922.55.7530
Microalbuminuria79.5144.953222.55.7530
Macroalbuminuria118.1559.382822.55.7530
Chen D 2011ChinaNormoalbuminuria129.1627.083083.3230.5560
Microalbuminuria162.9798.583083.3230.5560
Macroalbuminuria563.46122.673083.3230.5560
Li QX 2011ChinaNormoalbuminuria31.99.722621.56.8920
Microalbuminuria49.614.782321.56.8920
Macroalbuminuria70.326.481821.56.8920
Zhou ZX 2011ChinaNormoalbuminuria33.128.165032.987.8350
Microalbuminuria49.2118.115632.987.8350
He Y 2012ChinaNormoalbuminuria147.0120.9848131.8236.0160
Macroalbuminuria172.3119.0642131.8236.0160
Zhang Y 2012ChinaNG154.87.0928122.846.315
Du ZC 2013ChinaNormoalbuminuria18.552.67208.974.08718
Microalbuminuria19.042.87208.974.08718
Macroalbuminuria18.123.17218.974.08718
Zhang WQ 2014ChinaNormoalbuminuria30.34.423224.522.8123
Microalbuminuria34.324.324124.522.8123
Macroalbuminuria58.315.161324.522.8123
Liu S 2014ChinaNormoalbuminuria76.83.13029.62.530
Microalbuminuria114.83.13029.62.530
Macroalbuminuria135.85.93029.62.530
Bao HL 2014ChinaMicroalbuminuria75.49.23371.211.136
Feng R 2015ChinaNormoalbuminuria7.582.11225.131.6330
Microalbuminuria11.893.33295.131.6330
Macroalbuminuria24.626.62355.131.6330
Lv C 2015ChinaNormoalbuminuria27.35.4513714.983.23131
Microalbuminuria51.85.7212214.983.23131
Macroalbuminuria72.976.056814.983.23131
Bi FC 2016ChinaNormoalbuminuria5.612.08211.791.6420
Microalbuminuria8.982.26201.791.6420
Macroalbuminuria11.391.61201.791.6420

NR: not report

General characteristics of the included studies for TGF-β1 levels in T2DN in this meta-analysis NR: not report Eight studies [12, 32, 69–74] reporting the association of the TGF-β1 T869C gene polymorphism with susceptibility to T2DN were included in this study. Two report [69, 74] were published using the English language and the other reports were published using Chinese. The data for the pooled OR were extracted (Table 2). Those 8 investigations contained 1018 patients with T2DN and 941 controls. The average distribution frequency of the TGF-β1 T allele in the T2DN group in Chinese patients was 38.15% and the average frequency in the control group was 44.72%. The average distribution frequency of the TGF-β1 T allele in the case group was lower than that in the control group in Chinese population (Case/Control = 0.85).
Table 2

General characteristics of the included studies on TGF-β1 T869C gene polymorphism with T2DN risk in Chinese population

CaseControl
Author, YearEthnicityCCCTTTtotalCCCTTTtotal
Wong, 2003Asian272655824241765
Wei, 2005Asian3148129121462592
Wei, 2008Asian94128582807214266280
Chen, 2010Asian6811846232306333126
Chai, 2009Asian191914522726558
Pan, 2007Asian373498034292487
Rao, 2011Asian14256451332853
Mou, 2011Asian88875180717336180
General characteristics of the included studies on TGF-β1 T869C gene polymorphism with T2DN risk in Chinese population

Association of the TGF-β1 level with T2DN risk

In this study, we found that the serum TGF-β1 level in the T2DM group was higher than in the normal control group (MD = 17.30, 95% CI: 12.69–21.92, P < 0.00001; Table 3 and Fig. 1). The serum TGF-β1 level in the T2DN group was higher than that in the normal control group (MD = 70.03, 95% CI: 60.81–79.26, P < 0.00001; Table 3 and Fig. 2). The serum TGF-β1 level in the T2DN group was higher than in the T2DM group (MD = 56.18, 95% CI: 46.96–65.39, P < 0.00001; Table 3 and Fig. 3). The serum TGF-β1 level in T2DM patients with microalbuminuria was increased compared to that in T2DM patients with normoalbuminuria (MD = 22.78, 95% CI: 16.88–28.68, P < 0.00001; Table 3). Furthermore, the serum TGF-β1 level in T2DM patients with macroalbuminuria was increased compared to that in T2DM patients with microalbuminuria (MD = 28.47, 95% CI: 21.28–35.66, P < 0.00001; Table 3).
Table 3

Meta-analysis of the association of TGF-β1 levels with T2DN risk in Chinese population

ContrastsStudies numberQ test P valueModel selectedMD (95% CI) P
DM vs. Control38<0.00001Random17.30(12.69,21.92)<0.00001
DN vs. Control44<0.00001Random70.03 (60.81,79.26)<0.00001
DM vs. DN37<0.00001Random56.18 (46.96,65.39)<0.00001
Microalbuminuria VS. Normoalbuminuria26<0.00001Random22.78(16.88,28.68)<0.00001
Macroalbuminuria VS. Microalbuminuria24<0.00001Random28.47 (21.28,35.66)<0.00001
Fig. 1

Association of TGF-β1 levels with T2DM susceptibility (T2DM vs. control)

Fig. 2

Association of TGF-β1 levels with T2DN susceptibility (T2DN vs. control)

Fig. 3

Association of TGF-β1 levels with T2DN susceptibility (T2DN vs. T2DM)

Meta-analysis of the association of TGF-β1 levels with T2DN risk in Chinese population Association of TGF-β1 levels with T2DM susceptibility (T2DM vs. control) Association of TGF-β1 levels with T2DN susceptibility (T2DN vs. control) Association of TGF-β1 levels with T2DN susceptibility (T2DN vs. T2DM)

Association between the TGF-β1 T869C gene polymorphism and T2DN susceptibility in Chinese population

In this meta-analysis, the TGF-β1 T allele, TT allele and CC genotype were associated with T2DN susceptibility in Chinese population (T: OR = 0.74, 95% CI: 0.59–0.92, P = 0.007; TT: OR = 0.55, 95% CI: 0.31–0.96, P = 0.04; CC: OR = 1.38, 95% CI: 1.14–1.67, P = 0.001; Fig. 4 and Table 4).
Fig. 4

Association of TGF-β1 T869C CC genotype with DN susceptibility

Table 4

Meta-analysis of the association of TGF-β1 T869C gene polymorphism with T2DN risk in Chinese population

Genetic contrastsStudies numberQ test P valueModel selectedOR (95% CI) P
CC vs. CT + TT80.74Fixed1.38 (1.14,1.67)0.001
TT vs. CT + CC8<0.00001Random0.55 (0.31,0.96)0.04
T vs. C80.01Random0.74 (0.59,0.92)0.007
Association of TGF-β1 T869C CC genotype with DN susceptibility Meta-analysis of the association of TGF-β1 T869C gene polymorphism with T2DN risk in Chinese population

Evaluation of publication bias

There were publication biases for DM vs. control (Egger P = 0.001, Begg P = 0; Fig. 5a), DN vs. control (Egger P = 0, Begg P = 0; Fig. 5b), DN vs. DM (Egger P = 0, Begg P = 0; Fig. 5c), microalbuminuria vs. normoalbuminuria (Egger P = 0.021, Begg P = 0; Fig. 5d), macroalbuminuria vs. microalbuminuria in Chinese population (Egger P = 0.051, Begg P = 0.042; Fig. 5e). Interestingly, there was no publication bias for the association of the TGF-β1 T869C gene polymorphism with T2DN susceptibility in Chinese population (Egger P = 0.627, Begg P = 1.000; Fig. 5f).
Fig. 5

Publication bias. a DM vs. control. b DN vs. control. c DN vs. DM. d microalbuminuria vs. normoalbuminuria. e macroalbuminuria vs. microalbuminuria. f the association of the TGF-β1 T869C gene polymorphism with T2DN susceptibility in Chinese population

Publication bias. a DM vs. control. b DN vs. control. c DN vs. DM. d microalbuminuria vs. normoalbuminuria. e macroalbuminuria vs. microalbuminuria. f the association of the TGF-β1 T869C gene polymorphism with T2DN susceptibility in Chinese population

Discussion

TGF-β1 can stimulate the transcription of extracellular matrix (ECM) proteins, and high levels of TGF-β1 are associated with ECM accumulation, fibrosis, and glomerulosclerosis. Glomerulosclerosis is one of most important characteristics of patients with T2DN. In this study, we performed the meta-analysis in Chinese population and found that serum levels of TGF-β1 in the T2DM group were higher than those in the normal control group. The serum TGF-β1 level in the T2DN group was higher than that in the normal control group or the T2DM group. Indeed, the levels of TGF-β1 in the T2DM group and the T2DN group were higher than those in the normal control group. The level of TGF-β1 in T2DN was higher than that in the other two groups. We also performed a subgroup analysis according to albuminuria levels. The serum TGF-β1 level in T2DM patients with microalbuminuria was increased over that in T2DM patients with normoalbuminuria, and the serum TGF-β1 level in T2DM patients with macroalbuminuria was increased over that in T2DM patients with microalbuminuria. This indicated that the more urine protein is, the more severe the kidney disease becomes. Qiao et al. [75] conducted a meta-analysis based on 26 studies with 1968 cases and 2100 controls to evaluate the association between the levels of serum TGF-β1, and urinary TGF-β1 in patients with DM or diabetic nephropathy (DN). They reported that the levels of serum and urinary TGF-β1 were significantly increased in T2DM and T2DN. Mou et al. [76] assessed 9 reports that included 264 patients and 227 healthy controls in a meta-analysis to study the relationship between serum TGF-β1 levels and the risk of diabetic nephropathy. Their study indicated that increased serum TGF-β1 levels in DM patients were associated with a high risk of renal involvement. The results from Qiao et al. and Mou et al. indicated that serum and urinary TGF-β1 were significantly increased in DM and DN. Our meta-analysis included 45 reports to study the relationship between TGF-β1 level and T2DN risk in Chinese population. Our study concludes that high levels of TGF-β1 are associated with the susceptibility to T2DM, T2DN, and the progression of proteinuria in T2DN patients in Chinese population. The association of the TGF-β1 T869C gene polymorphism with the risk of T2DN in Chinese population was also assessed. In this meta-analysis, we found that TGF-β1 T allele, and TT genotype were protective factors against the onset of T2DN in Chinese population and CC genotype was a risk factor for the susceptibility of T2DN in Chinese populations. There was no publication bias for this meta-analysis. The results might be robust to some extent. However, there were only eight studies included into for this meta-analysis in Chinese population and more number of studies should be conducted to confirm the validity of these conclusions in the future. In a previous study, Jia et al. [77] conducted a meta-analysis to evaluate the impact of the TGF-β1 T869C gene polymorphism on DN, and reported that the TGF-β1 T869C gene polymorphism was associated with an elevated risk of DN disease. However, this notable association was observed only in T2DM patients. Zhou et al. [78] conducted a meta-analysis and indicated that the TGF-β1 CC genotype was associated with T2DN risk, and that the TGF-β1 T allele and the CC genotype were associated with the susceptibility to T2DN. In this meta-analysis, we firstly conducted the meta-analysis in Chinese population and observed that the TGF-β1 T allele, TT genotype and CC genotype are associated with the susceptibility to T2DN in Chinese population. However, more studies are also needed to confirm this in the future. The conclusions of our meta-analysis are limited because of the nature of the studies we analyzed. The studies themselves had several limitations, such as publication bias (most of the included studies from Chinese populations), heterogeneity of enrolled cases, small sample sizes, varying levels of plasma protein in different studies and different samples, and different timelines. In this meta-analysis, we conducted a subgroup analysis to delete any study with small sample size (less than 100), and we found that in the meta-analysis of only the larger sample studies, the CC genotype was associated with T2DN susceptibility (data not shown). However, the TGF-β1 T869C gene polymorphism was not associated with T2DN susceptibility in the meta-analysis that included small sample size studies (data not shown). In this study, we also found that there were publication biases among the recruited investigations for the relationship between serum TGF-β1 levels and the risk of T2DN, and for the relationship between the TGF-β1 T869C gene polymorphism and the risk of T2DN.

Conclusions

In conclusion, this study indicated that the serum TGF-β1 level in T2DM patients with microalbuminuria was significantly increased over that in T2DM patients with normoalbuminuria in Chinese population. The serum TGF-β1 level in T2DM patients with macroalbuminuria was significantly increased over that in T2DM patients with microalbuminuria in Chinese population. Furthermore, the TGF-β1 T allele, TT genotype and CC genotype are associated with the susceptibility to T2DN in Chinese population. However, more association studies are required to confirm the relationships.
  30 in total

1.  Association of Transforming Growth Factor Beta-1 (TGF-β1) Genetic Variation with Type 2 Diabetes and End Stage Renal Disease in Two Large Population Samples from North India.

Authors:  Priyanka Raina; Ruhi Sikka; Ramandeep Kaur; Jasmine Sokhi; Kawaljit Matharoo; Virinder Singh; A J S Bhanwer
Journal:  OMICS       Date:  2015-04-14

2.  [Infect of pingshen decoction on serum HGF, Cys C and TGF-beta1 diabetic nephropathy in early stage].

Authors:  Hui-Lan Bao; Shang-He Ye; Shi-Xian Lou; Xiao-Wen Lu; Xiang-Feng Zhou
Journal:  Zhongguo Zhong Yao Za Zhi       Date:  2014-03

3.  Association of transforming growth factor-beta (TGF-beta) T869C (Leu 10Pro) gene polymorphisms with type 2 diabetic nephropathy in Chinese.

Authors:  Teresa Yuk Hwa Wong; Peter Poon; Kai Ming Chow; Cheuk Chun Szeto; Man Kuen Cheung; Philip Kam Tao Li
Journal:  Kidney Int       Date:  2003-05       Impact factor: 10.612

4.  Operating characteristics of a rank correlation test for publication bias.

Authors:  C B Begg; M Mazumdar
Journal:  Biometrics       Date:  1994-12       Impact factor: 2.571

5.  Association of Chinese medicine constitution susceptibility to diabetic nephropathy and transforming growth factor-β1 (T869C) gene polymorphism.

Authors:  Xin Mou; Wen-hong Liu; Dan-yang Zhou; Ying-hui Liu; Yong-bin Hu; Guo-ling Ma; Cheng-min Shou; Jia-wei Chen; Jin-xi Zhao
Journal:  Chin J Integr Med       Date:  2011-09-11       Impact factor: 1.978

6.  Effect of TET2 on the pathogenesis of diabetic nephropathy through activation of transforming growth factor β1 expression via DNA demethylation.

Authors:  Liling Yang; Qian Zhang; Qiong Wu; Yi Wei; Jiawei Yu; Jiao Mu; Jun Zhang; Wei Zeng; Bing Feng
Journal:  Life Sci       Date:  2018-04-27       Impact factor: 5.037

Review 7.  Human gene copy number variation and infectious disease.

Authors:  Edward J Hollox; Boon-Peng Hoh
Journal:  Hum Genet       Date:  2014-06-05       Impact factor: 4.132

Review 8.  Changes of transforming growth factor beta 1 in patients with type 2 diabetes and diabetic nephropathy: A PRISMA-compliant systematic review and meta-analysis.

Authors:  Yong-Chao Qiao; Yin-Ling Chen; Yan-Hong Pan; Wei Ling; Fang Tian; Xiao-Xi Zhang; Hai-Lu Zhao
Journal:  Medicine (Baltimore)       Date:  2017-04       Impact factor: 1.889

9.  Short-term effects of lovastatin therapy on proteinuria of type 2 diabetic nephropathy: A clinical trial study.

Authors:  Alireza Sadighi; Javid Safa; Amir-Mansour Vatankhah; Sona Ghorashi; Aida Aharilahagh; Sina Davari-Farid; Ourmaan Nezami-Nargabad; Mohammad Naghavi-Behzad; Reza Piri; Parinaz Pishahang; Savalan Babapoor-Farrokhran; Sanam Fakour; Nastaran Ghodratnezhad-Azar
Journal:  Niger Med J       Date:  2016 Sep-Oct

Review 10.  Diabetes Mellitus and Ischemic Heart Disease: The Role of Ion Channels.

Authors:  Paolo Severino; Andrea D'Amato; Lucrezia Netti; Mariateresa Pucci; Marialaura De Marchis; Raffaele Palmirotta; Maurizio Volterrani; Massimo Mancone; Francesco Fedele
Journal:  Int J Mol Sci       Date:  2018-03-10       Impact factor: 5.923

View more
  6 in total

1.  Protective Effects of Ethanolic Extract from Rhizome of Polygoni avicularis against Renal Fibrosis and Inflammation in a Diabetic Nephropathy Model.

Authors:  Jung-Joo Yoon; Ji-Hun Park; Yun-Jung Lee; Hye-Yoom Kim; Byung-Hyuk Han; Hong-Guang Jin; Dae-Gill Kang; Ho-Sub Lee
Journal:  Int J Mol Sci       Date:  2021-07-05       Impact factor: 5.923

Review 2.  Relationship between TGF-β1 + 869 T/C and + 915 G/C gene polymorphism and risk of acute rejection in renal transplantation recipients.

Authors:  Hong-Yan Li; Tianbiao Zhou; Shujun Lin; Wenshan Lin
Journal:  BMC Med Genet       Date:  2019-06-25       Impact factor: 2.103

3.  The UK Chinese population with kidney failure: Clinical characteristics, management and access to kidney transplantation using 20 years of UK Renal Registry and NHS Blood and Transplant data.

Authors:  Katie Wong; Fergus J Caskey; Anna Casula; Yoav Ben-Shlomo; Pippa Bailey
Journal:  PLoS One       Date:  2022-02-28       Impact factor: 3.240

Review 4.  IL-1β Implications in Type 1 Diabetes Mellitus Progression: Systematic Review and Meta-Analysis.

Authors:  Fátima Cano-Cano; Laura Gómez-Jaramillo; Pablo Ramos-García; Ana I Arroba; Manuel Aguilar-Diosdado
Journal:  J Clin Med       Date:  2022-02-27       Impact factor: 4.241

Review 5.  Biochemical composition of the glomerular extracellular matrix in patients with diabetic kidney disease.

Authors:  María M Adeva-Andany; Natalia Carneiro-Freire
Journal:  World J Diabetes       Date:  2022-07-15

6.  Identification of Key Genes Involved in Diabetic Peripheral Neuropathy Progression and Associated with Pancreatic Cancer.

Authors:  Liumeng Jian; Guangda Yang
Journal:  Diabetes Metab Syndr Obes       Date:  2020-02-19       Impact factor: 3.168

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.