Sage Journals: Discover world-class research

Abstract

Epidemiological studies have suggested a potential relationship between the transforming growth factor-β1 (TGF-β1) gene and ischemic stroke (IS) risk; however, the current results are inconsistent. Therefore, we performed this meta-analysis to assess the precise association between TGF-β1 polymorphisms and IS risk. Online databases were searched for themes related to TGF-β1 polymorphisms and ARE risk. Quantitative calculations of odds ratios (ORs) and confidence intervals (CIs) were performed using 5 genetic models of each variant locus. Heterogeneity tests, cumulative analyses, sensitivity analyses, and publication bias were conducted to examine statistical power. Moreover, changes in the secondary structure and minimum free energy (MFE) were explored using in silico analysis. Nineteen case-control studies were included in our meta-analysis on rs1800468 G>A, rs1800469 C>T, and rs1800470 T>C polymorphisms and IS risk. Overall, only a marginal association was found between the rs1800469 C>T polymorphism and IS risk (T vs C: OR = 1.12, 95%CI = 1.00-1.46, P = .05, I² = 77.0%). Otherwise, no significant association was observed between the rs1800468 G>A and rs1800470 T>C polymorphisms and IS risk in general and stratified analyses. Moreover, no significant changes in secondary structure and MFE were found in any of the 3 polymorphic loci. Current evidence cautiously suggests that TGF-β1 polymorphisms are not associated with IS susceptibility.

Keywords

transforming growth factor-β1 polymorphism ischemic stroke

Introduction

Ischemic stroke (IS) is the most common cerebrovascular disease and is caused by a thrombus, embolus, or hypoperfusion due to blood flow blockage and typically results in an insufficient or even interrupted blood supply to the local brain, causing brain cell damage or death, leading to permanent brain injury and loss of neurological function.¹ With the aging of the population, the number of patients with IS has increased to more than 11.92 million worldwide in 2016, accounting for approximately 87% of all stroke types.² IS is the leading cause of death and disability, has a major impact on the quality of life of patients, and places a great burden on the family and society, especially in developing countries.³ To the best of our knowledge, many pathophysiological studies have focused on IS. An increasing number of factors, such as hypertension, high cholesterol levels, smoking status, obesity, and unhealthy lifestyle, have been proven to be driving factors for IS. Moreover, in recent decades studies have shown that racial diversity and inheritance factors, such as genetic mutations and abnormal expression, are closely related to IS risks.⁴

Vascular damage, fibrosis, and lipid-rich atherosclerosis are the most important steps in IS development. Transforming growth factor-β (TGF-β) is a class of secretory polypeptide signal molecules that is not only an auxiliary factor for connective tissue growth but also a major factor affecting epithelial cell regeneration and the hematopoietic system. TGF-β modulates cell proliferation, cell migration, matrix synthesis, wound contraction, calcification, and the immune response, and is involved in all major steps of tissue reconstruction, repair, and other functions.

The human TGF-β1 gene is located on chromosome 19q13 and contains 7 exons that encode a 25-kDa peptide, expressed in endothelial and connective tissue cells. TGF-β1 constitutes a high proportion of TGF-β family members and is its most active member. Some studies have demonstrated that TGF-β1 plays a pivotal role in maintaining the balance between inflammation and fibrosis in atherosclerotic plaques.⁵ Recent research also indicated that TGF-β1 could suppress the expression of vascular smooth muscle cell (VSMC) proinflammatory genes by blocking both the STAT3 and NF-KB pathways and inhibiting the pathogenesis of atherosclerosis .⁶ Increasing evidence suggests that some single nucleotide polymorphisms (SNPs) in the TGF-β1 gene play an important role in the pathogenesis of several cardiovascular and cerebrovascular diseases, such as stroke, coronary heart disease, angina pectoris, and myocardial infarction. In 2006, Sie et al conducted the first case-control study on the association between TGF-β1 gene polymorphisms and IS risk and found that mutations in both the rs1800469 C>T and rs1800470 T>C loci might contribute to increased IS susceptibility.⁷ Since then, many studies have focused on the association between TGF-β1 polymorphisms and IS risk, but the results are either inconsistent or contradictory. Therefore, we conducted this meta-analysis of all related studies to further explore the association between TGF-β1 polymorphisms and IS risk.

Materials and Methods

This meta-analysis was conducted in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.⁸ All included information was collected from published studies and no ethical issues were involved.

Search Strategy

A comprehensive literature search was conducted in 5 online databases (PubMed, Embase, Web of Science, CNKI, and Wanfang) for all relevant studies published up to September 1, 2022 that focused on the association between TGF-β1 polymorphisms and IS risk using the following search terms: “transforming growth factor,” “transforming growth factor-β1,” “TGF-β1,” “rs1800468,” “rs1800469,” “rs1800470,” “polymorphism,” “variant,” “mutation,” “ischemic stroke,” “IS.” The relevant papers that were selected were further reviewed manually. Search strategy was used in PubMed for example:

#1 transforming growth factor

#2 transforming growth factor-β1

#3 TGF-β1

#4 rs1800468

#5 rs1800469

#6 rs1800470

#7 #1 OR #2 OR #3 OR #4 OR #5 OR #6

#8 polymorphism

#9 variant

#10 mutation

#11 #8 OR #9 OR #10

#12 ischemic stroke

#13 cerebral infarction

#14 #12 OR #13

#15 #7 AND #11 AND #14

Eligibility Criteria

All included studies in this meta-analysis met the following criteria: (1) observational studies involving the association between TGF-β1 polymorphisms and IS risk; (2) sufficient information regarding the genotypes both in the case and control groups, could be extracted to assess the crude odds ratios (ORs) and 95% confidence intervals (CIs); (3) published in English or Chinese; and (4) the largest or most recent samples on the same data were adopted when overlapping or duplicate samples were reported. Other studies were excluded when they were: (1) reviews, letters, or conference reports; (2) early publications or small sample studies with duplicate themes; (3) molecular biology research with animal models or cell lines; or (4) studies without sufficient data.

Data Extraction

All included studies were reviewed and the following information was extracted for each study by 2 independent investigators (Peng and Cheng): surname of first author, published year, country or region where the study was conducted, races of the research participants, control design, sample sizes of patients and controls, data of each genotype distribution and genotyping method.

Statistical Analysis

Crude ORs and 95% CIs were calculated to assess the statistical strength of the relationship between TGF-β1 polymorphisms [rs1800468 (−800 G>A), rs1800469 (−509 C>T), and rs1800470 (869 T>C)] and IS risk in the general population. Subgroup analyses were conducted based on the HWE status, race diversity, and genotyping methods with at least 3 studies on the same theme. Five genetic models, including allele contrast (M (mutative) versus W (wild)), co-dominant model (WM vs WW and MM vs WW), dominant model (WM+MM vs WW), and recessive model (MM vs WW+WM) were examined. Heterogeneity among the included studies was examined using Cochran's Q-test and the I² statistical method. A fixed-effects model was adopted when the value of the I² test was less than 40%; otherwise, a random-effects model was selected. Furthermore, meta-regression was conducted to explore the potential factors contributing to existing heterogeneity. Cumulative meta-analyses were conducted to assess change tendency in results with the addition of studies. Sensitivity analyses were also conducted to examine the stability of the results when studies were removed. Publication bias was examined using Egger‘s linear regression and Begg‘s funnel plots. Statistical analyses were performed using STATA software version 14.0 (Stata Corporation, College Station, TX, USA). A 2-sided P_value < .05 indicated statistical significance.

Bioinformatic Analysis

The secondary structure and variant effects of TGF-β1 polymorphisms were predicted using RNAsnp (http://rth.dk/resources/rnasnp/, accessed June 1, 2022).

Results

Study Characteristics

A total of 289 articles were identified based on the search criteria. The selection procedure is shown in Figure 1. During the subsequent steps, 277 articles were removed according to the eligibility criteria of duplicate checks, title and abstract screening, and text reviews. Finally, 12 articles reporting 19 independent case-control studies were included in this meta-analysis.^7,9–19 In general, 3 studies focused on the rs1800468 G>A polymorphism and IS risk,^7,9,10 8 studies focused on the 1800469 C>T polymorphism and IS risk ,^7,10–16 and 8 studies focused on the rs1800470 T>C polymorphism and IS risk.^{7,10,12–14,17–19} Moreover, only 2 articles were based on Caucasian participants, and the rest were based on Asian descendants. Furthermore, in terms of genotype distribution in the controls, only one study deviated from the HWE in the 1800469 C>T polymorphism. All extracted information is shown in Table 1.

Figure 1.

Statistical analysis of the association between TGF-β1 rs1800468 G>A polymorphism and IS risk in the dominant model. (A) ORs and 95% CIs; (B) cumulative analysis; (C) sensitivity analysis; (D) publication bias.

Table 1.

Characteristics of Included Studies on TGF-β1 Gene Polymorphisms and IS Risk.

First Author	Year	Country/Region	Racial	Source of Controls	Case	Control	Genotype Distribution						Genotyping Methods	P for HWE
First Author	Year	Country/Region	Racial	Source of Controls	Case	Control	Case			Control			Genotyping Methods	P for HWE
							rs1800468 (−800 G>A)
							GG	GA	AA	GG	GA	AA
Sie	2006	Netherlands	Caucasian	PB	312	6144	263	48	1	5096	1002	46	TaqMan	.671
Xie	2009	China	Asian	HB	247	167	227	16	4	152	13	2	PCR-RFLP	.012
Kumar	2017	India	Asian	HB	250	250	181	63	6	226	22	2	PCR- SNaPshot	.089
							rs1800469 (−509 C>T)
							CC	CT	TT	CC	CT	TT
Sie	2006	Netherlands	Caucasian	PB	309	6083	138	140	31	3076	2457	550	TaqMan	.063
Liu	2010	China	Asian	HB	100	100	0	61	39	0	75	25	PCR-RFLP	<.01
Liang	2010	China	Asian	PB	186	160	47	81	58	40	79	41	PCR-RFLP	.875
Peng	2011	China	Asian	HB	164	167	19	63	82	29	79	59	PCR-RFLP	.773
Tao	2012	China	Asian	PB	450	450	124	248	78	103	233	114	ARMS-PCR	.443
Kumar	2017	India	Asian	HB	250	250	157	86	7	193	55	2	PCR- SNaPshot	.368
Palm	2020	Germany	Caucasian	PB	457	793	204	197	56	353	357	83	PCR-RFLP	.603
Xie	2022	China	Asian	PB	96	110	14	26	56	17	44	49	PCR-RFLP	.186
							rs1800470 (869T>C)
							TT	TC	CC	TT	TC	CC
Sie	2006	Netherlands	Caucasian	PB	299	5888	109	142	48	2348	2720	820	TaqMan	.472
Kim-1	2006	Korea	Asian	PB	271	207	79	123	69	42	110	55	TaqMan	.335
Liang	2010	China	Asian	PB	186	160	44	82	60	38	88	34	PCR-RFLP	.203
Peng	2011	China	Asian	HB	164	167	34	70	60	46	86	35	PCR-RFLP	.656
Katakami	2011	Japan	Asian	HB	331	3462	68	170	93	935	1689	838	ASSP-PCR	.167
Tao	2012	China	Asian	PB	450	450	152	193	105	111	217	122	ARMS-PCR	.458
Kumar	2017	India	Asian	HB	250	250	164	79	7	215	33	2	PCR- SNaPshot	.560
Kim-2	2020	Korea	Asian	HB	449	447	124	223	94	103	226	104	TaqMan	.361

HWE in control.

Abbreviations: ASSP-PCR, allele-specific sense primers-polymerase chain reaction; ARMS-PCR, amplification refractory mutation system polymerase chain reaction; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; PB, population-based control; HB, hospital/healthy-based control.

Quantitative Analysis

TGF-β1 rs1800468 G>A Polymorphism and IS Risk

Three case-control studies involving 748 patients with IS and 6756 controls were used to determine the association between the F-β1 rs1800468 G>A polymorphism and IS risk. No significant association between this locus and IS risk was identified in the pooled data of the 5 genetic models (A vs G: OR = 1.40, 95%CI = 0.59-3.29, P = .44, I² = 90.8%; GA vs GG: OR = 1.41, 95%CI = 0.55-3.59, P = .47, I² = 90.1%; AA vs GG: OR = 1.35, 95%CI = 0.57-3.20, P = .50, I² = 30.1%; GA+AA vs GG: OR = 1.43, 95%CI = 0.57-3.63, P = .32, Figure 1A, I² = 90.8%; AA vs GG+GA: OR = 1.26, 95%CI = 0.53-3.03, P = .60, I² = 13.4%) (Table 2).

Table 2.

Summary ORs and 95% CI of TGF-β1 Gene Polymorphisms and Ischemic Stroke Risk.

Locus	N^a	OR	95% CI	P	I ²	OR	95% CI	P	I ²	OR	95% CI	P	I ²	OR	95% CI	P	I ²	OR	95% CI	P	I ²
−800 G>A (rs1800468)		A versus G				GA versus GG				AA versus GG				GA+AA versus GG				AA versus GG+GA
Total	3	1.40	0.59-3.29	.44	90.8	1.41	0.55-3.59	.47	90.1	1.35	0.57-3.20	.50	30.1	1.43	0.57-3.63	.45	90.8	1.26	0.53-3.03	.60	13.4
-509 C>T (rs1800469)		T versus C				CT versus CC				TT versus CC				CT+TT versus CC				TT versus CC+CT
Total	8	1.21	1.00-1.46	.05	77.0	1.10	0.88-1.38	.39	56.5	1.22	0.84-1.76	.30	67.6	0.65	0.24-1.78	.40	98.0	1.31	0.95-1.82	.08	72.7
HWE-yes	7	1.20	0.97-1.47	.09	79.6	1.10	0.88-1.38	.39	56.5	1.22	0.84-1.76	.30	67.6	0.65	0.24-1.78	.40	98.0	1.25	0.89-1.76	.20	73.7
Ethnicity
Asian	6	1.28	0.94-1.74	.12	83.0	1.09	0.75-1.59	.64	64.1	1.31	0.70-2.47	.40	76.8	1.12	0.92-1.36	.25	23.8	1.44	0.88-2.35	.15	80.5
Genotyping
PCR series	7	1.23	0.97-1.55	.09	79.9	1.06	0.81-1.40	.66	58.2	1.24	0.78-1.97	.37	71.9	0.98	0.68-1.40	.90	77.6	1.37	0.93-2.02	.11	76.6
869T>C (rs1800470)		C versus T				TC versus TT				CC versus TT				TC+CC versus TT				CC versus TT+TC
Total	8	1.16	0.93-1.44	.18	87.0	1.04	0.75-1.44	.83	84.2	1.15	0.81-1.64	.43	77.6	1.11	0.79-1.55	.55	86.5	1.19	0.94-1.51	.15	66.0
Ethnicity
Asian	7	1.17	0.90-1.53	.24	88.8	1.03	0.69-1.53	.90	86.3	1.15	0.76-1.75	.52	80.1	1.10	0.73-1.66	.64	88.3	1.20	0.91-1.60	.20	70.7
Genotyping
TaqMan	3	0.94	0.77-1.15	.55	66.5	0.85	0.60-1.20	.36	68.8	0.88	0.59-1.31	.53	65.1	0.86	0.60-1.22	.39	73.6	0.99	0.81-1.21	.91	2.2
PCR series	5	1.36	0.95-1.94	.09	90.5	1.20	0.70-2.04	.51	88.6	1.47	0.84-2.59	.18	82.2	1.33	0.78-2.27	.30	89.9	1.42	0.96-2.10	.08	75.1

Abbreviations: PB, population-based; HB, healthy/hospital-based.

Numbers of comparisons.

Test for heterogeneity.

Cumulative (Figure 1B for dominant model) and sensitivity (Figure 1C for dominant model) analyses were conducted according to publication date and consistently showed a negative result when each study was added or deleted. Publication bias was evaluated and no significant asymmetrical patterns were found (A vs G: P = .72; GA vs GG: P = .78; AA vs GG: P = .16; GA+AA vs GG: P = .74, Figure 1D; AA vs GG+GA: P = .11).

TGF-β1 rs1800469 C>T Polymorphism and IS Risk

Eight case-control studies on the association between the TGF-β1 rs1800469 C>T polymorphism and IS risk involving 3003 IS patients and 14,558 controls were collected. In general, only a marginal association was found in allele contrast, but not in other models (T vs C: OR = 1.21, 95%CI = 1.00-1.46, P = .05, I² = 77.0%, Figure 2; CT vs CC: OR = 1.10, 95%CI = 0.88-1.38, P = .39, I² = 56.5%; TT vs CC: OR = 1.22, 95%CI = 0.84-1.76, P = .30, I² = 67.6%; CT+TT vs CC: OR = 0.65, 95%CI = 0.24-1.78, P = .40, I² = 98.0%, Figure 2A; TT vs CC+CT: OR = 1.31,95%CI = 0.95-1.82, P = .08, I² = 73.7%) (Table 2). In addition, similar negative results were found in the subgroup analysis based on HWE status, race diversity, and genotyping methods.

Figure 2.

Statistical analysis of the association between TGF-β1 rs1800469 C>T polymorphism and IS risk in the dominant model. (A) ORs and 95% CIs; (B) cumulative analysis; (C) sensitivity analysis; (D) publication bias.

Cumulative analyses according to publication date presented continuous negative results when new studies were added (Figure 2B for dominant model). Sensitivity analysis also indicated consistent results of negative results only when the study by Tao et al was deleted in the T versus C, TT versus CC and TT versus CC+CT models (Figure 2C for dominant model). Publication bias was evaluated, and no significant asymmetrical patterns were found (T vs C: P = 0.08; CT vs CC: P = .88; TT vs CC: P = .14; CT+TT vs CC: P = .45, Figure 2D; TT vs CC+CT: P = .06).

TGF-β1 rs1800470 T>C Polymorphism and IS Risk

Eight case-control studies involving 2392 patients with IS and 11 017 controls were included to determine the association between the TGF-β1 1800470 T>C polymorphism and IS risk. No significant association was found between the rs1800470 T>C polymorphism and the risk of IS (C vs T: OR = 1.16, 95%CI = 0.93-1.44, P = .18, I² = 87.0%; TC vs TT: OR = 1.04, 95%CI = 0.75-1.44, P = .83, I² = 84.2%; CC vs TT: OR = 1.15, 95%CI = 0.81-1.64, P = .43, I² = 77.6%; TC+CC vs TT: OR = 1.11, 95%CI = 0.79-1.55, P = .55, I² = 86.5%, Figure 3A; CC vs TT+TC: OR = 1.19, 95%CI = 0.94-1.51, P = .15, I² = 66.0%) (Table 2). No other significant associations were identified in the subgroup analysis based on HWE status, race diversity, and genotyping methods.

Figure 3.

Statistical analysis of the association between TGF-β1 rs1800470 T>C polymorphism and IS risk in the dominant model. (A) ORs and 95% CIs; (B) cumulative analysis; (C) sensitivity analysis; (D) publication bias.

Cumulative (Figure 3B for dominant model) and sensitivity (Figure 3C for dominant model) analyses were conducted according to publication date and always showed a negative result when each study was added or deleted. Publication bias was evaluated, and no significant asymmetrical patterns were found (C vs T: P = .19; CC vs CT: P = .93; CC vs TT: P = .36; TC+CC vs TT: P = .65, Figure 3D; CC vs TT+TC: P = .16).

Bioinformatics Analysis

According to the RTH data, the RNAsnp indicated that the TGF-β1 rs1800468 G to A allele substitution results in a minimum free energy (MFE) change from −171.20 to −169.30 kcal/mol (Figure 4); the rs1800469 C to T allele substitution does not result in any MFE change of −127.30 kcal/mol (Figure 5); and the rs1800470T to C allele substitution results in a MFE change from −183.70 to −185.90 kcal/mol (Figure 6). Such changes, which may result in thermodynamic alterations in the RNA secondary structure ensemble, were not statistically significant (P_rs1800468 = .08, P_rs1800469 = .94, P_rs1800470 = . 31).

Figure 4.

The predicting secondary structure of TGF-β1 rs1800468 G>A. (A) The secondary structure of wild-type sequence with G allele. (B) The secondary structure of mutant-type sequence with A allele.

Figure 5.

The predicting secondary structure of TGF-β1 rs1800469 C>T. (A) The secondary structure of wild-type sequence with C allele. (B) The secondary structure of mutant-type sequence with T allele.

Figure 6.

The predicting secondary structure of TGF-β1 rs1800470 T>C. (A) The secondary structure of wild-type sequence with T allele. (B) The secondary structure of mutant-type sequence with C allele.

Discussion

To our knowledge, stroke is still the leading cause of death and disability worldwide, resulting in an annual mortality rate of approximately 5.5 million.²⁰ IS is the most common category and represents more than 80% of stroke patients.^21,22 IS, which originates from thrombotic or embolic occlusion of the brain vessels, leads to neural dysfunction, brain impairment, and even death.²³ Epidemiological studies have identified several risk factors for IS, including aging, hypertension, high cholesterol, cigarette and alcohol consumption, diabetes, stress, obesity, and poor diet.

TGF-β1 is an important cytokine with multiple-functions and is involved in the formation and stability of fibrin clots, as well as the adhesion and aggregation of platelets, which are strongly upregulated in the central nervous system following chronic ischemia-induced brain damage.²⁴ TGF-β1 is a key modulator of osteogenic transdifferentiation of VSMCs, resulting in the transformation of vascular calcification.²⁵ Active TGF-β1 promotes osteoinductive signaling pathways by combining with a transmembrane receptor and initiates the phosphorylation of regulatory Smad2/3 proteins. These processes drives the transcription of osteogenic genes and upregulation of MMP expression, leading to arterial calcification,²⁶ which accelerates arterial aging, and increases the occurrence of arterial embolism.^27,28

After brain injury, active TGF-β1 promotes tissue regeneration and neurological recovery by regulating the TGF-β/Smad2/3 pathway, which is expected to stimulate both angiogenesis and tube formation in endothelial cells and inhibit neuronal cell injury.²⁹ In this complex process, IS is always accompanied by a severe inflammatory reaction, TGF-β1 is activated, and the abundant expression of TGF-β1 displays sustained anti-inflammatory effects on microglial cells by persistently inhibiting endogenous Toll-like receptor ligand-induced IκB .³⁰ TGF-β1 is an important inflammatory inhibitor that can inhibit the aggregation of leukocytes in the vascular wall, especially in inflammatory cells, such as macrophages and T cells. It can also inhibit the proliferation of VSMCs, secretion of inflammatory cell adhesion molecules by endothelial cells, and formation of foam cells to interrupt the pathological process of the vascular wall.^5,31 TGF-β1 can inactivate T cells, protect the inner walls of blood vessels, and maintain the stability of atherosclerotic plaques.³² Moreover, TGF-β1 combines with the extracellular matrix, causing collagen deposition in the vascular wall, decreased vascular elasticity, vascular remodeling, and elevated blood pressure, and participates in the pathological process of vascular development and remodeling in stroke patients.^33,34

Several studies have suggested that mutations in TGF-β1 are associated with susceptibility to IS. In 2006, Sie et al conducted the first case-control study and found that individuals with the rs1800469TT genotype had an increased IS risk in Dutch populations (OR = 1.29, 95%CI = 1.02-1.63, P = .03), but not in the other 2 loci.⁷ In 2011, Peng et al reported that the frequencies of the rs1800469 TT and rs1800470 CC genotypes were significantly higher in the cerebral infarction group and were associated with IS susceptibility in Chinese patients.¹³ In contrast, negative associations with IS were reported in the latest research both by Palm et al ¹⁵ on the rs1800469 locus in Germans and by Kim et al ¹⁹ on the rs1800470 locus in Koreans. These results are inconsistent with or contradict with comparable research studies published later.

In 2013, Peng et al published the first meta-analysis and did not find any significant association between the rs1800470 T>C polymorphism and IS susceptibility .³⁵ However, this meta-analysis only focused on the rs1800470 T>C locus and the results were based on 6 case-control studies. In 2016, Kumar et al conducted another brief meta-analysis on the rs1800469 T>C polymorphism and IS susceptibility in only 2 case-control studies and no significant association was reported.³⁶

We comprehensively collected all current evidence and performed a meta-analysis to assess the association between TGF-β1 polymorphisms and IS susceptibility. Overall, no significant association was observed between the rs1800468 G>A, rs1800469 C>T, and rs1800470 T>C polymorphisms and IS risk with the combined data. Based on the RTH database, bioinformatics analysis indicated that there were slight changes in the spatial secondary structure of the 3 SNP loci, but no significant difference in the thermodynamic MFE between allele changes was observed, which further indicated that mutation of the above loci did not substantially change susceptibility to IS.

However, some limitations of our meta-analysis need to be addressed. First, only Caucasians and Asians were included in our meta-analysis, and the pooled results are difficult to apply to other races. Second, the above 3 polymorphic loci were assessed independently; thus, the haplotype among these loci and the interactive effects with other genetic loci could not be examined. Third, some environmental factors, such as infection, unhealthy drinking or smoking habits, and sleep disorders, could not be examined simultaneously due to the limited original data in the collected studies. Finally, heterogeneity was observed in several genetic models in our meta-analysis, but no significant factors were identified that might have contributed to the heterogeneity in the meta-regression analysis. Fortunately, the following scientific methods were adopted to ensure the quality of our meta-analysis: (1) scientific retrieval strategies and systemic inclusion and exclusion criteria were used to identify all relevant studies; (2) rigorous statistical methods including analysis for sensitivity, cumulative and meta-regression analyses, and tests for publication bias were used to guarantee the current results; (3) stratified analysis based on HWE status, ethnic diversity, and genotyping method was conducted to comprehensively assess the potential relationships; and (4) bioinformatics analysis with RTH data was conducted to explore the underlying mechanism of the change in spatial secondary structure and MFE due to gene mutations.

Conclusion

In conclusion, our meta-analysis indicates that the TGF-β1 rs1800468 G>A, rs1800469 C>T, and rs1800470 T>C polymorphisms are not determining factors for IS susceptibility. Based on this information, the current results should be accepted cautiously, and more case-control studies with larger sample sizes worldwide are required to further investigate the potential relationship between TGF-β1 polymorphisms and IS susceptibility.

Footnotes

Author Contributions

Xiao-Qin Peng and Lan Zhou designed the study, searched the literatures, collected the data, evaluated the qualities and analyzed data. Xiao-Qin Peng and Fan Cheng wrote the manuscript. Lan Zhou reviewed and revised the manuscript. All authors edited the manuscript and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics and Patient Consent

All included information was collected from published studies and no ethics or patient consent issues were involved.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Lan Zhou

References

Salmeron

Maniskas

Edwards

, et al. Interleukin 1 alpha administration is neuroprotective and neuro-restorative following experimental ischemic stroke. J Neuroinflammation. 2019;16(1):222.

Saini

Guada

. Global epidemiology of stroke and access to acute ischemic stroke interventions. Neurology. 2021;97(20 Suppl 2):S6–s16.

Ding

Liu

. Global, regional, and national burden of ischemic stroke, 1990-2019. Neurology. 2022;98(3):e279‐e290.

Mehndiratta

Ryan

Cronin

Wozniak

Cole

. Differences in multiple risk factors between black and white individuals with young-onset ischemic stroke. Neurology. 2022;99(6):e560‐e564.

Toma

McCaffrey

. Transforming growth factor-β and atherosclerosis: Interwoven atherogenic and atheroprotective aspects. Cell Tissue Res. 2012;347(1):155‐175.

Gao

, et al. Transforming growth factor β1 suppresses proinflammatory gene program independent of its regulation on vascular smooth muscle differentiation and autophagy. Cell Signal. 2018;50:160‐170.

Sie

Uitterlinden

Bos

, et al. TGF-beta 1 polymorphisms and risk of myocardial infarction and stroke: The Rotterdam study. Stroke. 2006;37(11):2667‐2671.

Moher

Liberati

Tetzlaff

Altman

. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. J Clin Epidemiol. 2009;62(10):1006‐1012.

Xie

Zeng

Wang

. TGF-beta1-800G>A polymorphism and cerebral infarction in Chinese Han population. Zhong nan da xue xue bao Yi xue ban = J Cent South Univ Med Sci. 2009;34(12):1166‐1170.

10.

Kumar

Misra

Kumar

, et al. Transforming growth factor-β1 (C509T, G800A, and T869C) gene polymorphisms and risk of ischemic stroke in North Indian population: A hospital-based case-control study. Ann Indian Acad Neurol. 2017;20(1):5‐12.

11.

Liu

Jiang

Zhang

. The relationship between the serum level of TGF-β1, TGF-β1 gene C-509 T polymorphism and acute ischemic stroke. China Modern Med. 2010;17(15):9‐10.

12.

Liang

. Research on the relationship between the TGFβ1 SNPs and haplotypes and stroke in Han people in Changsha area. Master degree, Central South University, 2010.

13.

Peng

Zhan

Chen

. Association of transforming growth factor-β1 gene C-509T and T869C polymorphisms with atherosclerotic cerebral infarction in the Chinese: A case-control study. Lipids Health Dis. 2011;10:100.

14.

Tao

Chen

Cheng

Shan

. The haplotype of the TGFβ1 gene associated with cerebral infarction in Chinese. Can J Neurol Sci Le Journal Can des Sci Neurol. 2012;39(5):626‐631.

15.

Palm

Aigner

Pussinen

, et al. Association of a multigenetic pro-inflammatory profile with ischaemic stroke. Cerebrovasc Dis. 2020;49(2):170‐176.

16.

Xie

Liu

Shi

Liu

Liang

Deng

. Study on transforming growth factor-β1 promoter polymorphism C-509T in atherothrombotic cerebral infarction. J Mol Diagn Ther. 2022;14(2):189‐196.

17.

Kim

Lee

. The gene encoding transforming growth factor beta 1 confers risk of ischemic stroke and vascular dementia. Stroke. 2006;37(11):2843‐2845.

18.

Katakami

Kaneto

Osonoi

, et al. Transforming growth factor β1T868C gene polymorphism is associated with cerebral infarction in Japanese patients with type 2 diabetes. Diabetes Res Clin Pract. 2011;94(3):e57‐e60.

19.

Kim

Lee

, et al.

Are genetic variants associated with the location of cerebral arterial lesions in stroke patients?

Cerebrovasc Dis. 2020;49(3):262‐268.

20.

Donkor

. Stroke in the 21(st) century: A snapshot of the burden, epidemiology, and quality of life. Stroke Res Treat. 2018;2018:3238165.

21.

Bamford

Sandercock

Dennis

Burn

Warlow

. Classification and natural history of clinically identifiable subtypes of cerebral infarction. The Lancet. 1991;337(8756):1521‐1526.

22.

Zhou

Wang

Zeng

, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: A systematic analysis for the global burden of disease study 2017. The Lancet. 2019;394(10204):1145‐1158.

23.

Seong

Han

, et al. Target ischemic stroke model creation method using photoacoustic microscopy with simultaneous vessel monitoring and dynamic photothrombosis induction. Photoacoustics. 2022;27:100376.

24.

Lesné

Blanchet

Docagne

, et al. Transforming growth factor-beta1-modulated cerebral gene expression. J Cereb Blood Flow Metab. 2002;22(9):1114‐1123.

25.

Chiang

Chu

Chen

Lee

. MFG-E8 promotes osteogenic transdifferentiation of smooth muscle cells and vascular calcification by regulating TGF-β1 signaling. Commun Biol. 2022;5(1):364.

26.

Hecht

Freise

Websky

, et al. The matrix metalloproteinases 2 and 9 initiate uraemic vascular calcifications. Nephrol Dial Transplant. 2016;31(5):789‐797.

27.

Wang

Jiang

Monticone

Lakatta

. Proinflammation: The key to arterial aging. Trends Endocrinol Metab. 2014;25(2):72‐79.

28.

Yao

Long

. Association between Serum Gamma-glutamyl transferase and intracranial arterial calcification in acute ischemic stroke subjects. Sci Rep. 2019;9(1):19998.

29.

Zhang

Wei

Kilic

. Extracellular vesicles from hypoxia-preconditioned microglia promote angiogenesis and repress apoptosis in stroke mice via the TGF-β/Smad2/3 pathway. Cell Death Dis 2021;12(11):1068.

30.

Islam

Choudhury

Kigami

, et al. Sustained anti-inflammatory effects of TGF-β1 on microglia/macrophages. Biochim Biophys Acta Mol Basis of Dis. 2018;1864(3):721‐734.

31.

Feinberg

Jain

. Role of transforming growth factor-beta1/smads in regulating vascular inflammation and atherogenesis. Panminerva Med. 2005;47(3):169‐186.

32.

Grainger

. Transforming growth factor beta and atherosclerosis: So far, so good for the protective cytokine hypothesis. Arterioscler, Thromb, Vasc Biol. 2004;24(3):399‐404.

33.

Bobik

. Transforming growth factor-betas and vascular disorders. Arterioscler, Thromb, Vasc Biol. 2006;26(8):1712‐1720.

34.

Hinz

. The extracellular matrix and transforming growth factor-β1: Tale of a strained relationship. Matrix Biol. 2015;47:54‐65.

35.

Peng

Chen

, et al. Association between transforming growth factor-beta 1T869C polymorphism and ischemic stroke: A meta-analysis. PloS One. 2013;8(7):e67738.

36.

Kumar

Srivastava

Misra

Pandit

Prasad

. Association of transforming growth factor Beta-1-509C/T gene polymorphism with ischemic stroke: A meta analysis. Basic Clin Neurosci. 2016;7(2):91‐96.

Association Between Transforming Growth Factor-β1 Polymorphisms and Ischemic Stroke Susceptibility: A Meta-Analysis

Abstract

Keywords

Introduction

Materials and Methods

Search Strategy

Eligibility Criteria

Data Extraction

Statistical Analysis

Bioinformatic Analysis

Results

Study Characteristics

Quantitative Analysis

TGF-β1 rs1800468 G>A Polymorphism and IS Risk

TGF-β1 rs1800469 C>T Polymorphism and IS Risk

TGF-β1 rs1800470 T>C Polymorphism and IS Risk

Bioinformatics Analysis

Discussion

Conclusion

Footnotes

Author Contributions

Declaration of Conflicting Interests

Ethics and Patient Consent

Funding

ORCID iD

References