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. 2023 Jan 4;13:1021423. doi: 10.3389/fgene.2022.1021423

Association of methylenetetrahydrofolate reductase (MTHFR) rs1801133 (677C>T) gene polymorphism with ischemic stroke risk in different populations: An updated meta-analysis

Lili Zhao 1, Tao Li 1, Meijuan Dang 1, Ye Li 1, Hong Fan 1, Qian Hao 2, Dingli Song 3, Jialiang Lu 1, Ziwei Lu 1, Yating Jian 1, Heying Wang 1, Xiaoya Wang 1, Yulun Wu 1, Guilian Zhang 1,*
PMCID: PMC9845415  PMID: 36685916

Abstract

Background: Recently, increasing evidence has implicated methylenetetrahydrofolate reductase (MTHFR) gene mutation as a risk factor for ischemic stroke (IS) in the general population. However, studies have been inconclusive and lack evidence on specific populations. We aim to determine whether the rs1801133 (NC_000001.11 (MTHFR):g. 677C>T (p.Ala222Val) variant, we termed as MTHFR rs1801133 (677 C>T), is linked to an increased risk of IS in different age groups and ancestry groups.

Methods: The literature relevant to our study was found by searching the PubMed, Cochrane Library, Web of Science, EMBASE, and CNKI databases. A random effect model analysis was used to calculate the pooled odds ratio (OR) and 95% confidence interval (CI) to evaluate any possible association. We conducted a subgroup analysis based on the age and ancestry groups of the included populations.

Results: As of March 2022, 1,925 citations had been identified in electronic databases, of which 96 studies involving 34,814 subjects met our eligibility criteria. A strong link was found between IS and the MTHFR gene rs1801133 (677C>T) polymorphism in all genetic models [dominant genetic model (OR = 1.47; 95%CI = 1.33–1.61; p < 0.001), recessive genetic model (OR = 1.52; 95%CI = 1.36–1.71; p < 0.001), heterozygous model (OR = 1.36; 95%CI = 1.24–1.48; p < 0.001), homozygous model (OR = 1.82; 95%CI = 1.58–2.11; p < 0.001), and T allelic genetic model (OR = 1.37; 95%CI = 1.27–1.48; p < 0.001)]. Further subgroup analyses indicated that the MTHFR rs1801133 (677C>T) variant may increase the risk of IS in Asian, Hispanic, or Latin population, middle-aged, and elderly populations (p < 0.001).

Conclusion: Our results implied that mutation of the T allele of MTHFR rs1801133 (677C>T) could be a risk factor for IS. A significant association was found among Asian, Hispanic, or Latin population, middle-aged, and elderly people.

Keywords: polymorphism, ischemic stroke, meta-analysis, risk, MTHFR rs1801133 (677C>T)

1 Introduction

Ischemic stroke (IS) is an acute neurological deficit caused by vascular occlusion. It is one of the leading causes of death and disability worldwide (Francis et al., 2007; Malik and Dichgans, 2018; Phipps and Cronin, 2020) and is caused by a combination of environmental and genetic factors (Black et al., 2015; Prabhakaran et al., 2015; Malik et al., 2019). Several pathophysiological mechanisms are involved in the development of this condition. Hyperhomocysteinemia is reported to be independently associated with the risk of stroke (Linnebank et al., 2012). The 5,10-methylenetetrahydrofolate reductase (MTHFR) locus is mapped to chromosome 1 (1p36.3) which encodes for the dimeric proteins of 70–77 kDa subunits (Goyette et al., 1994). Folate metabolism is largely controlled by MTHFR, which catalyzes the conversion of 5,10-methylenetetrahydrofolate to 5-methylenetetrahydrofolate. 5-Methylenetetrahydrofolate provides a methyl group in the methylation reaction that transforms homocysteine into methionine (Figure 1), as well as the DNA methylation process (Brattström et al., 1998). Thus, the MTHFR enzyme activity is important for homeostasis of the serum homocysteine level.

FIGURE 1.

FIGURE 1

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Roles of methylenetetrahydrofolate reductase in homocysteine metabolism.

The previous study had demonstrated that approximately 40% of the intragenic coding CpG islands were hyper-methylated, which had a higher C>T mutation rate. Also, the amino acid sequence of a protein and individual phenotypes could be changed by C>T substitutions at the CpG contexts in the protein-coding regions (Youk et al., 2020). The rs1801133 variant (NC_000001.11 (MTHFR):g. 677C>T (p.Ala222Val), also named MTHFR rs1801133 (677C>T), is a common mutant in MTHFR. The replacement of C with T at nucleotide 677 results in converting alanine to valine amino acid residue in the enzyme (Sharp and Little, 2004). Missense mutations cause a 50%–60% decrease in enzyme activity in patients who have the homozygous variant (TT) (Rozen, 199 7), which contributes to hyperhomocysteinemia (Castro et al., 2004). In addition, reduction of the MTHFR enzymatic activity would cause deficiency of folate, which is also an independent risk factor of IS (Qin et al., 2020). Moreover, when folic acid o is inadequate, the removal of homocysteine would be affected, leading to hyperhomocysteinemia and forming a vicious cycle (Liew and Gupta, 2015). Thus, it is important to determine the association between MTHFR rs1801133 (677C>T) polymorphism and the risk of IS for primary and secondary prevention of IS.

Many researchers have examined the relationship between MTHFR rs1801133 (677C>T) polymorphism and IS risk. However, there have been no definitive conclusions because different populations were examined and inconsistent results were obtained (Herak et al., 2017; Jiménez-González et al., 2021; Huang et al., 2022). Two meta-analyses were performed separately in 2016 and 2019 that reported a correlation between MTHFR rs1801133 (677C>T) polymorphism and IS (Song et al., 2016; Chang et al., 2019). However, only 22 studies were included in Song et al. (2016). Since then, many studies have been conducted in different populations. Moreover, the meta-analysis by Song et al. focused on the general population and did not consider whether MTHFR rs1801133 (677C>T) polymorphism might have varying effects on the risk of IS in different populations. Furthermore, Guilin Chang et al.‘s study, published in 2019, included only nine studies on the elderly population and did not consider young and middle-aged IS patients (Chang et al., 2019). Therefore, past meta-analyses were updated to investigate whether MTHFR rs1801133 (677C>T) polymorphism and stroke risk are related across age and ancestry groups in this study.

2 Materials and methods

The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (Moher et al., 2009).

2.1 Literature search

A systematic search of the PubMed, EMBASE, Cochrane Library, Web of Science, and CNKI databases for relevant observational studies published until 15 March, 2022, was undertaken independently by two reviewers (Zhao and Li). We used the following search terms to identify eligible studies: (“methylenetetrahydrofolate reductase” OR “MTHFR” OR “C677T” OR “rs1801133”), (“ischemic stroke” OR “cerebral infarction” OR “stroke”), AND (“single nucleotide polymorphism” OR “SNP” OR “genetic polymorphism” OR “mutation” OR “variation”). We reviewed the full text of each study when abstracts and titles were insufficient to make a final determination regarding study inclusion. The reference lists of included studies and existing reviews were screened to identify additional eligible studies. Any disagreements in the study selection process were resolved by a third person (Dang).

2.2 Selection criteria

We included the studies according to the following inclusion criteria: 1) the full text could be searched in electronic databases; 2) the studies were case-control or cohort studies examining MTHFR rs1801133 (677C>T) and stroke susceptibility; 3) the study population was limited to patients diagnosed with stroke for the first time; 4) the MTHFR rs1801133 (677C>T) genotype frequency was provided; and 5) articles were published in English or Chinese. The main exclusion criteria included the following: 1) the studies were duplicate articles or non-original research (letters, commentaries, editorials, reviews, and meta-analyses); 2) the studies were case reports or involved animal experiments; 3) the genotype frequency of MTHFR rs1801133 (677C>T) was not provided; and 4) the p-value of the Hardy–Weinberg equilibrium (HWE) test was <0.05.

2.3 Data extraction and quality assessment

A pre-designed extraction form was used to extract the data. The extracted data included the name of the first author, type of stroke, publication date, ancestry groups, sample size (case and control), study design, mean or median age of the population, HWE, and the Newcastle–Ottawa scale score. Disagreements regarding data extraction were resolved by discussions among the two investigators (Zhao and Li), and a third reviewer (Dang) was consulted if necessary. The HWE p-value was also calculated using the genotypic frequencies of MTHFR polymorphisms, and the threshold of HWE deviation was set at 0.05. The quality of eligible publications was assessed using the Newcastle–Ottawa scale (Stang, 2010), and studies with a score of 7–9 were considered to be of good quality.

2.4 Statistical analysis

We used five genetic comparison models, the T allelic model (T vs. C), dominant model (TT + TC vs. CC), recessive model (TT vs. CC + TC), heterozygous model (TC vs. CC), and homozygous model (TT vs. CC), to estimate the relationship between MTHFR rs1801133 (677C>T) polymorphism and stroke susceptibility by calculating the odds ratio (OR) and 95% confidence interval (CI). The I2 statistic was used to evaluate heterogeneity among genetic comparison models, and the pooled OR was estimated via the Mantel-Haenszel random effect model. Heterogeneity between studies is indicated by an I2 > 50%. Moreover, the Bonferroni method was utilized to adjust for multiple comparisons to control the false positive error rate. As we performed multiple comparisons in this meta-analysis for 45 times, the p-value which was less than 0.05/50 (0.001) indicated statistical significance after Bonferroni correction. To determine the possible causes of heterogeneity, the ancestry groups (Asian, European, African, Hispanic, or Latin American (HLA), and other and not reported ancestries (ONR)) and the study population (young: <18 years; middle-aged: 18–60 years; elderly: >60 years) were analyzed in subgroups. In addition, we examined the impact of a single study on the pooled OR by performing sensitivity analyses on different genetic comparison models. Egger’s test, Begg’s test, and funnel plots were used to evaluate the potential publication bias in our study (Peters et al., 2006). Stata 17.0 was used to perform the statistical analysis of all genetic comparison models.

3 Results

3.1 Literature search and characteristics of the included studies

There were 1,925 articles initially identified after searching the databases. Among them, 692 articles were removed because of duplication, and 911 articles were removed after the titles and abstracts were screened. In total, 322 articles were further screened for eligibility by reviewing their full texts, and eventually, 96 articles that met the criteria were selected (Figure 2).

FIGURE 2.

FIGURE 2

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Flow chart of identification of eligible studies.

Of the 96 studies included, 95 were case-control studies, and one was a nested case-referent study. In total, 52 studies were conducted in the Asian population, 19 were in the European population, 1 was in the African population, 3 were in the Hispanic or Latin American population, and 21 were in other and not reported ancestry population. In total, 14 studies examined children, 27 examined middle-aged people, and 42 examined the elderly population. The other 13 studies examined the general population. The main features of the included studies are shown in Table 1.

TABLE 1.

Characteristics of studies included in the meta-analysis.

Author Year Disease Ancestry Case Control Age Design HWE NOS
CC CT TT CC CT TT
Huang et al., (2022) 2022 IS Asian 94 72 32 101 62 5 >18 Case-control 0.21 8
Salomi et al., (2021) 2021 IS Asian 69 32 4 171 40 4 32.7±7.7 Case-control 0.36 8
Cernera et al., (2021) 2021 IS European 64 140 76 138 206 85 8 Case-control 0.61 7
Jiménez-González et al., (2021) 2021 IS HLA 35 105 38 60 83 40 34.1±5.4 Case-control 0.27 8
Mazdeh et al., (2021) 2021 IS ONR 157 124 37 219 155 26 48±0.10 Case-control 0.84 7
Hou et al., (2018) 2018 IS Asian 1063 793 138 1427 988 150 68.6±12.1 Case-control 0.22 9
Mao and Han, (2018) 2018 IS Asian 73 189 27 82 100 16 68.42±13.26 Case-control 0.06 8
Li et al., (2017) 2017 IS Asian 71 134 95 106 110 45 64.2±13.2 Case-control 0.08 8
Kamberi et al., (2016) 2017 IS European 15 21 3 27 55 20 2.83–12.59 Case-control 0.40 8
Ma et al., (2017) 2017 IS Asian 36 106 94 88 183 119 Media 66.0 Case-control 0.27 7
Lu et al., (2017) 2017 IS Asian 31 97 89 41 109 73 NA Case-control 0.8 7
Jiang et al., (2017) 2017 IS Asian 36 52 18 40 54 12 57.8±10.7 Case-control 0.33 8
Kumar et al., (2016) 2016 IS Asian 161 84 5 183 65 2 52.83±12.5 Case-control 0.14 7
Vijayan et al., (2016) 2016 IS Asian 164 35 1 185 8 0 57.74±13.84 Case-control 0.77 8
Zhang et al., (2016) 2016 IS Asian 13 27 10 36 18 5 NA Case-Control 0.23 8
Herak et al., (2017) 2017 IS European 29 34 10 41 51 8 4.3(0.01–16.7) Case-control 0.15 7
Ranellou et al., (2015) 2015 IS European 16 28 7 26 36 8 37.3±8.0 Case-control 0.40 8
Wei et al., (2015) 2015 IS Asian 177 98 26 226 65 6 52.6±68.8 Case-control 0.60 8
Luo et al., (2015) 2015 IS Asian 55 269 388 52 299 423 65.2±13.9 Case-control 0.93 8
Lv et al., (2015) 2015 IS Asian 70 98 31 88 116 37 68.78 ± 10.63 Case-control 0.90 8
Zhou et al., (2014) 2014 IS Asian 160 270 112 242 308 104 Media 66 Case-control 0.72 8
Atadzhanov et al., (2013) 2014 IS African 15 8 0 96 20 0 54 ± 16 Case-control 0.31 8
Shi, (2014) 2014 IS Asian 18 36 31 33 35 14 77.89 ± 8.85 Case-control 0.38 8
Supanc et al., (2014) 2014 IS European 41 78 36 75 59 16 Middle-aged Case-control 0.40 8
Fekih-Mrissa et al., (2013) 2013 IS ONR 35 43 6 60 35 5 56.06 ± 12.5 Case-control 0.97 8
Zhang and Guo, (2012) 2012 IS Asian 9 19 12 10 20 10 39.25 ± 4.08 Case-control 1.00 8
Djordjevic et al., (2012) 2012 IS ONR 24 50 6 33 54 13 6.7 ± 4.9 Case-control 0.21 8
Djordjevic et al., (2012) 2012 IS ONR 39 23 11 59 47 14 40.3 ± 11.6 Case-control 0.33 88
Somarajan et al., (2011) 2011 IS Asian 137 65 5 129 54 5 54 ± 15.9 Case-control 0.82 8
Mohamed et al., (2011) 2011 IS Asian 69 60 21 85 48 9 61.0 ± 10.1 Case-control 0.53 8
Hultdin et al., (2011) 2011 IS ONR 172 114 27 401 306 59 55.0 ± 8.0 Nested 0.95 8
Salem-Berrabah et al., (2010) 2011 IS ONR 33 15 2 57 35 5 Mean 57.62 Case-control 0.90 7
Isordia-Salas et al., (2010) 2010 IS HLA 35 105 38 60 83 40 33.1 ± 5.8 Case-control 0.27 8
Giusti et al., (2010) 2010 IS European 130 240 131 572 529 110 Media 44.0 Case-control 0.437 7
Tatarskyy et al., (2010) 2010 IS ONR 73 87 23 50 45 5 64.6 ± 9.1 Case-control 0.20 8
Al-Allawi et al., (2009) 2009 IS ONR 26 30 14 27 20 3 Media 60 Case-control 0.78 7
Sun et al., (2009) 2009 IS Asian 45 36 38 49 37 10 >60 Case-control 0.45 7
Sabino et al., (2009) 2009 IS HLA 12 9 0 24 12 1 33.3 ± 16.3 Case-control 0.73 7
Biswas et al., (2009) 2009 IS Asian 24 32 2 48 10 0 <15 Case-control 0.47 7
Biswas et al., (2009)) 2009 IS Asian 67 49 4 90 30 0 Young Case-control 0.12 8
(Morita et al., 2009) 2009 IS European 5 6 4 48 37 5 Children Case-control 0.53 7
Herak et al., (2009) 2009 IS European 11 17 5 46 56 10 Children Case-control 0.22 7
Goracy et al., (2009) 2009 IS European 65 69 18 83 46 6 66.7 ± 12.5 Case-control 0.91 8
Zak et al., (2009) 2009 IS European 25 30 9 32 25 2 Children Case-control 0.27 8
Djordjevic et al., (2009) 2009 IS ONR 9 16 1 23 22 5 Children Case-control 0.94 8
Almawi et al., (2009) 2009 IS ONR 54 26 38 90 26 4 NA Case-control 0.23 7
Sawula et al., (2009) 2009 IS European 70 50 8 35 19 5 18–101 Case-control 0.31 8
Yue et al., (2010) 2009 IS Asian 8 19 35 14 11 5 67 ± 9 Case-control 0.29 8
Celiker et al., (2009) 2009 IS ONR 158 4 0 63 37 6 Mean 69.8 Case-control 0.85 8
Shi et al., (2008) 2008 IS Asian 23 45 29 20 45 34 Young Case-control 0.07 8
Moe et al., (2008) 2008 IS Asian 73 36 11 136 68 3 62.5 ± 1.1 Case-control 0.09 8
Gao et al., (2008) 2008 IS Asian 4 20 18 6 11 13 ≤45 Case-control 0.22 8
Zhang et al., (2008) 2008 IS Asian 49 116 80 74 140 68 63.7 ± 10.4 Case-control 0.91 8
Berge et al., (2007) 2007 IS European 29 125 182 25 141 163 Old Case-control 0.47 7
Nan et al., (2007) 2007 IS Asian 14 55 31 28 53 19 <45 case-control 0.49 8
Kim et al., (2007) 2007 IS Asian 81 113 43 66 119 38 61.18 ± 11.09 Case-control 0.21 8
Komitopoulou et al., (2006) 2006 IS European 36 46 8 46 39 18 5.55 ± 0.48 Case-control 0.06 8
Sazci et al., (2006) 2006 IS ONR 42 41 9 115 119 25 53.45 ± 9.21 Case-control 0.47 8
Li et al., (2006) 2006 IS Asian 235 184 35 190 128 16 65.20 ± 12.75 Case-control 0.34 8
Dikmen et al., (2006) 2006 IS ONR 75 57 14 32 21 2 63.4 ± 0.87 Case-control 0.52 8
Gao et al., (2006) 2006 IS Asian 30 49 21 32 44 24 61.08 ± 10.77 Case-control 0.25 8
Yanqun et al., (2006) 2006 IS Asian 49 73 40 49 41 10 55 ± 6 Case-control 0.74 7
Hermans et al., (2006) 2006 IS ONR 4 13 6 62 58 22 71.7 ± 9.3 Case-control 0.18 9
Pezzini et al., (2005) 2005 IS European 46 83 34 60 75 23 35.0 ± 7.5 Case-control 0.96 9
En et al., (2005) 2005 IS Asian 88 43 5 51 17 2 65 ± 10 Case-control 0.69 8
Yan et al., (2006) 2005 IS Asian 34 24 3 37 17 3 Old Case-control 0.58 8
Jinhuan et al., (2005) 2005 IS Asian 55 28 4 53 24 3 Mean 65 Case-control 0.89 8
Alluri et al., (2005) 2005 IS ONR 47 21 1 48 1 0 7–78 Case-control 0.94 8
Kawamoto et al., (2005) 2005 IS Asian 33 43 21 91 110 40 78 ± 8.3 Case-control 0.49 8
Yi et al., (2005) 2005 IS Asian 27 42 9 22 25 3 68.3 ± 7.6 Case-control 0.23 8
Lingling et al., (2004) 2004 IS Asian 26 18 3 12 15 5 67.16 ± 10.11 Case-control 0.93 8
Choi et al., (2003) 2003 IS Asian 62 97 36 73 100 25 61.4 ± 10.9 Case-control 0.30 7
Jin et al., (2004) 2003 IS Asian 21 59 14 40 49 11 69.68 ± 8.9 Case-control 0.48 8
Jin et al., (2004) 2003 IS Asian 15 23 21 15 11 7 NA Case-control 0.09 7
Yan et al., (2003) 2003 IS Asian 55 28 4 53 24 3 66.13 ± 12.54 Case-control 0.89 8
Li et al., (2002) 2002 IS Asian 58 65 20 97 49 8 40–90 Case-control 0.58 8
Chuanqing et al., (2002) 2002 IS Asian 24 35 10 25 35 7 62.3 ± 12.9 Case-control 0.30 8
Wenping et al., (2003) 2002 IS Asian 8 34 19 25 46 15 61–79 Case-control 0.43 8
Huang et al., (2002) 2002 IS Asian 11 25 13 16 24 10 55.5 ± 13.0 Case-control 0.85 8
Guangsen and Chongwen, (2002) 2002 IS Asian 40 47 15 37 47 16 65 Case-control 0.87 8
Akar et al., (2001) 2001 IS ONR 24 18 4 39 23 6 Children Case-control 0.34 7
Wu et al., (2001) 2001 IS Asian 23 40 14 92 113 24 61.4 ± 6.8 Case-control 0.21 8
Yaqin et al., (2001) 2000 IS Asian 15 23 21 15 11 7 NA Case-control 0.09 7
Zheng et al., (2000) 2000 IS Asian 43 62 10 62 45 15 Mean 59 Case-control 0.14 8
Cumming et al., (1999) 1999 IS ONR 41 6 1 42 6 0 7–36 Case-control 0.64 7
Harmon et al., (1999) 1999 IS European 74 73 27 86 78 19 >60 Case-control 0.83 7
Gaustadnes et al., (1999) 1999 IS European 97 88 22 545 449 90 25–68 Case-control 0.85 8
Akar et al., (1999) 1999 IS ONR 14 10 4 63 37 6 Children Case-control 0.85 8
Press et al., (1999) 1999 IS ONR 72 85 10 50 57 8 65 ± 8 Case-control 0.12 8
Lalouschek et al., (1999) 1999 IS ONR 35 37 9 32 40 9 64.76 ± 13.2 Case-control 0.50 8
Xinliang et al., (1999) 1999 IS Asian 10 45 25 58 32 20 60.2 ± 6.1 Case-control 0.48 8
De Stefano et al., (1998) 1998 IS European 28 27 17 65 98 35 Mean 33.9 Case-control 0.85 8
Kostulas et al., (1998) 1998 IS ONR 50 40 10 50 40 10 NA Case-control 0.63 7
Salooja et al., (1998) 1998 IS European 81 76 16 114 107 21 68 (22–94) Case-control 0.56 8
Nakata et al., (1998) 1998 IS Asian 19 23 6 35 51 19 30–80 Case-control 0.96 8
Markus et al., (1997) 1997 IS European 162 146 37 76 63 22 65.7 (10.5) Case-control 0.13 7
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3.2 Meta-analysis results

There were 34,814 participants (15,569 cases and 19,245 controls) in the 96 studies included in the meta-analysis.

3.2.1 Dominant model

The TT + CT genotype showed significant heterogeneity compared with the CC genotype in the dominant genetic model (I2 = 69.9%; p < 0.001) (Table 2). The MTHFR rs1801133 (677C>T) mutation significantly increased the IS risk under the dominant genetic model (OR = 1.47; 95%CI = 1.33–1.61; p < 0.001). In the ancestry subgroup analysis, the rs1801133 (677C>T) polymorphism of MTHFR was evidently linked to an increased risk of IS in Asian (OR = 1.59; 95%CI = 1.41–1.80; p < 0.001) and Hispanic or Latin population (OR = 1.93; 95%CI = 1.39–2.67; p < 0.001). MTHFR rs1801133 (677C>T) gene polymorphism was associated with IS susceptibility in all age groups except young populations (middle-aged: OR = 1.59, 95%CI = 1.33–1.90, and p < 0.001; elderly: OR = 1.34, 95%CI = 1.17–1.53, and p < 0.001).

TABLE 2.

Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of the association between C677T polymorphism and stroke in the dominant model.

Model OR (95% CI) PZ I2 PH
Dominant
All IS 1.47 (1.33–1.61) <0.001 a 69.9% <0.001
Ancestry
 Asian 1.59 (1.41–1.80) <0.001 a 67.2% <0.001
 European 1.35 (1.10–1.65) 0.004 69.9% <0.001
 African 2.56 (0.96–6.85) 0.061 — —
 Hispanic or Latin American 1.93 (1.39–2.67) <0.001 a 0.0% 0.824
 Other and not reported ancestries 1.20 (0.91–1.57) 0.190 76.4% <0.001
Age
 Young 1.46 (1.12–1.91) 0.005 52.2% 0.012
 Middle-aged 1.59 (1.33–1.90) <0.001 a 69.8% <0.001
 Elderly 1.34 (1.17–1.53) <0.001 a 71.3% <0.001
 NA 1.77 (1.30–2.41) <0.001 a 69.3% <0.001
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Bonferroni correction for multiple testing was applied (p-value threshold, 0.001);

PZ, p-value for the Z-test; PH, p-value for heterogeneity;

a

Association was still significant after Bonferroni correction for multiple testing.

3.2.2 Recessive model

The TT genotype showed significant heterogeneity compared with the CC + CT genotype in the recessive genetic model (I2 = 55.7%; p < 0.001) (Table 3). MTHFR rs1801133 (677C>T) polymorphism was associated with an increased risk of stroke under the recessive model (OR = 1.52; 95%CI = 1.36–1.71; p < 0.001). The ancestry subgroup analysis showed a significant difference in the Asian populations, with combined ORs of 1.58 (95% CI = 1.38–1.81; p < 0.001). The middle-aged and elderly people had an increased risk of stroke according to the subgroup analysis (middle-aged: OR = 1.55, 95%CI = 1.22–1.96, and p < 0.001; elderly: OR = 1.47, 95%CI = 1.28–1.68, and p < 0.001)

TABLE 3.

Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of the association between C677T polymorphism and stroke in the recessive model.

Model Or (95% CI) PZ I2 PH
Recessive
 All IS 1.52 (1.36–1.71) <0.001 a 55.7% <0.001
Ancestry
 Asian 1.58 (1.38–1.81) <0.001 a 47.1% <0.001
 European 1.48 (1.11–1.97) 0.007 73.1% <0.001
 African — — — —
 Hispanic or Latin American 0.96 (0.68–1.37) 0.839 0.0% 0.949
 Other and not reported ancestries 1.47 (1.05–2.05) 0.026 49.6% 0.005
Age
 Young 1.25 (0.81–1.92) 0.313 53.7% 0.009
 Middle-aged 1.55 (1.22–1.96) <0.001 a 57.5% <0.001
 Elderly 1.47 (1.28–1.68) <0.001 a 46.8% 0.001
 NA 1.99 (1.29–3.07) <0.001 a 64.7% 0.001
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Bonferroni correction for multiple testing was applied (p-value threshold 0.001);

PZ, p-value for the Z-test; PH, p-value for heterogeneity;

a

Association was still significant after Bonferroni correction for multiple testing.

3.2.3 Heterozygous model

The TC genotype showed significant heterogeneity compared with the CC genotype in the heterozygous genetic model (I2 = 60.4%; p < 0.001) (Table 4). There was an obvious association between MTHFR rs1801133 (677C>T) polymorphism and increased risk of stroke under the heterozygous model (OR = 1.36; 95%CI = 1.24–1.48; p < 0.001). In the subgroup analysis, MTHFR rs1801133 (677C>T) gene polymorphism was associated with stroke susceptibility in Asian (OR = 1.46; 95%CI = 1.30–1.63; p < 0.001), Hispanic or Latin populations (OR = 2.09; 95%CI = 1.50–2.95; p < 0.001), middle-aged (OR = 1.50; 95%CI = 1.27–1.78; p < 0.001), and elderly groups (OR = 1.23; 95%CI = 1.08–1.40; p = 0.001).

TABLE 4.

Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of the association between C677T polymorphism and stroke in the heterozygous model.

Model OR (95% CI) PZ I2 PH
Heterozygote
 All IS 1.36 (1.24–1.48) <0.001 a 60.4% <0.001
Ancestry
 Asian 1.46 (1.30–1.63) <0.001 a 58.2% <0.001
 European 1.27 (1.08–1.51) 0.005 50.4% 0.006
 African 2.56 (0.96–6.85) 0.061 — —
 Hispanic or Latin American 2.09 (1.50–2.95) <0.001 a 0.0% 0.825
 Other and not reported ancestries 1.10 (0.86–1.40) 0.453 67.6% <0.001
Age
 Young 1.44 (1.13–1.84) 0.003 38.7% 0.069
 Middle-aged 1.50 (1.27–1.78) <0.001 a 62.5% <0.001
 Elder 1.23 (1.08–1.40) 0.001 a 62.8% 0.001
 NA 1.48 (1.14–1.92) 0.003 50.2% 0.020
Open in a new tab

Bonferroni correction for multiple testing was applied (p-value threshold 0.001);

PZ, p-value for the Z-test; PH, p-value for heterogeneity;

a

Association was still significant after Bonferroni correction for multiple testing.

3.2.4 Homozygous model

The TT genotype showed significant heterogeneity compared with the CC genotype in the homozygous genetic model (I2 = 64.2%; p < 0.001) (Table 5). There was also a significant association between MTHFR rs1801133 (677C>T) polymorphism and an increased risk of stroke under this model (OR = 1.82; 95%CI = 1.58–2.11; p < 0.001). However, the stratification analysis results were similar to those of the recessive model. Significant correlation was detected between MTHFR rs1801133 (677C>T) polymorphisms and the increased risk of stroke in the Asian population (OR = 1.98; 95%CI = 1.66–2.37; p < 0.001). Furthermore, the middle-aged and elderly people had an increased risk of stroke in the subgroup analysis (middle-aged: OR = 1.92, 95%CI = 1.45–2.53, and p < 0.001; elderly: OR = 1.74, 95%CI = 1.44–2.11, and p < 0.001).

TABLE 5.

Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of the association between C677T polymorphism and stroke in the homozygous model.

Model Or (95% CI) PZ I2 PH
Homozygote
 All IS 1.82 (1.58–2.11) <0.001 a 64.2% <0.001
Ancestry
 Asian 1.98 (1.66–2.37) <0.001 a 59.4% <0.001
 European 1.65 (1.14–2.39) 0.008 79.3% <0.001
 African — — — —
 Hispanic or Latin American 1.60 (1.05–2.46) 0.030 0.0% 0.863
 Other and not reported ancestries 1.59 (1.09–2.32) 0.017 56.2% 0.001
Age
 Young 1.43 (0.88–2.33) 0.146 55.6% 0.006
 Middle-aged 1.92 (1.45–2.53) <0.001 a 62.7% <0.001
 Elder 1.74 (1.44–2.11) <0.001 a 62.3% <0.001
 NA 2.45 (1.47–4.01) 0.001 a 69.1% <0.001
Open in a new tab

Bonferroni correction for multiple testing was applied (p-value threshold 0.001);

PZ, p-value for the Z-test; PH, p-value for heterogeneity;

a

Association was still significant after Bonferroni correction for multiple testing.

3.2.5 Allelic model

The T allele showed significant heterogeneity compared with the C allele in the allelic genetic model (I2 = 75.7%; p < 0.001) (Table 6). There was an obvious association between MTHFR rs1801133 (677C>T) polymorphism and an increased risk of stroke under the allelic model (OR = 1.37; 95%CI = 1.27–1.48; p < 0.001). In the subgroup analysis, MTHFR rs1801133 (677C>T) gene polymorphism was associated with stroke susceptibility in Asian populations (OR = 1.46; 95%CI = 1.33–1.60; p < 0.001). The middle-aged and elderly people with T allele mutation had a higher risk of stroke (middle-aged: OR = 1.42, 95%CI = 1.24–1.64, and p < 0.001; elderly: OR = 1.30, 95%CI = 1.18–1.43, and p < 0.001).

TABLE 6.

Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of the association between C677T polymorphism and stroke in the allelic model.

Model Or (95% CI) PZ I2 PH
Allele
 All IS 1.37 (1.27–1.48) <0.001 a 75.7% <0.001
Ancestry
 Asian 1.46 (1.33–1.60) <0.001 a 72.6% <0.001
 European 1.29 (1.09–1.53) 0.003 79.5% <0.001
 African 2.51 (1.06–5.93) 0.036 — —
 Hispanic or Latin American 1.28 (1.05–1.57) 0.016 0.0% 0.981
 Other and not reported ancestries 1.26 (0.96–1.52) 0.116 81.4% <0.001
Age
 Young 1.31 (1.05–1.64) 0.016 65.5% <0.001
 Middle-aged 1.42 (1.24–1.64) <0.001 a 73.7% <0.001
 Elder 1.30 (1.18–1.43) <0.001 a 75.0% <0.001
 NA 1.70 (1.28–2.26) <0.001 a 80.6% <0.001
Open in a new tab

Bonferroni correction for multiple testing was applied (p-value threshold 0.001);

PZ, p-value for the Z-test; PH p-value for heterogeneity;

a

Association was still significant after Bonferroni correction for multiple testing.

3.3 Sensitivity analysis

A sensitivity analysis was conducted to compare the pooled ORs after individually excluding each included study. There was no significant change in the results (Supplementary Figure S1).

3.4 Publication bias

The funnel plot is shown in Figure 3. All research studies included in this study distributed above the funnel plots, which indicated that variability of the effect size was low and the results were reliable. The Egger’s funnel plots for these five models were basically symmetrical though Egger’s test, indicating there was publication bias in the dominant (p = 0.04) and heterozygous models (p = 0.03) (Table 7). Nonetheless, we did not find any publication bias in all genetic models using Begg’s tests. The correlation between the lnOR and its variance and the level of heterogeneity across studies might contribute to the discrepancy between Egger’s and Begg’s tests. Actually, Begg’s test is more robust and has the appropriate type I error rates despite the sample size, the number of included studies, and the level of heterogeneity. Furthermore, when the summary estimates are ORs or RRs and there is obvious heterogeneity between studies (Schwarzer et al., 2002; Peters et al., 2006), type I error rates for Egger’s test are higher than those for Begg’s test.

FIGURE 3.

FIGURE 3

Open in a new tab

Funnel plots of the (A) dominant model; (B) recessive model; (C) heterozygous model; (D) homozygous model; and (E) allelic model.

TABLE 7.

Publication bias of included studies.

IS Egger’s test Begg’s test
Co-efficiency Standard error p-value Z p-value
Dominant model 0.83 0.39 0.04 a 1.56 0.12
Recessive model 0.37 0.29 0.20 1.28 0.20
Heterozygote model 0.74 0.34 0.03 a 1.83 0.07
Homozygote model 0.28 0.36 0.43 1.18 0.24
Allele model 0.76 0.44 0.09 1.71 0.09
Open in a new tab
a

p < 0.05.

4 Discussion

This meta-analysis demonstrates associations between MTHFR rs1801133 (677C>T) genetic polymorphism and susceptibility to IS under all genetic models. Our results were consistent with a previous meta-analysis performed in 2016, wherein this polymorphism was found to be potentially involved in the development of IS (Song et al., 2016). This suggests the MTHFR C677T mutation is a genetic risk factor of IS, and primary and secondary prevention should be initiated in a timely manner in population with this mutation.

Several factors might explain the association of the MTHFR rs1801133 (677C>T) mutation and increased IS risk. Most importantly, the MTHFR rs1801133 (677C>T) mutation leads to decreased MTHFR activity and elevated homocysteine levels (Castro et al., 2004). Hyperhomocysteinemia is linked to the overproduction of free radicals (Xi et al., 2016), induction of oxidative stress (Esse et al., 2019; Tchantchou et al., 2021), endothelial injury (Salvio et al., 2021), coagulation, and lipid metabolism disturbance (Herrmann, 2001), which all contribute to the incidence of IS. Meanwhile, previous studies demonstrated that people with the MTHFR rs1801133 (677C>T) mutation show a poor response to homocysteine-lowering treatment (Qin et al., 2020). Furthermore, a meta-analysis published in the current year showed that MTHFR rs1801133 (677C>T) polymorphism is related to susceptibility to H-type hypertension (Liao et al., 2022), which is a traditional risk factor for IS.

We also conducted a subgroup analysis on the basis of age and ancestry of the study population to further understand the significance of the MTHFR rs1801133 (677C>T) mutation in various populations. The result of subgroup analysis showed increased IS risk in populations with the MTHFR rs1801133 (677C>T) mutation in middle-aged and elderly groups. It was consistent with the fact that older people were more susceptible to atherosclerosis, which was an important cause of IS. Previous studies had demonstrated that the MTHFR rs1801133 (677C>T) mutation increased the risk of IS in patients with large-artery atherosclerosis (Cui, 2016) and adults (Xin et al., 2009).

Another important finding of this meta-analysis was the stable association between IS risk and MTHFR rs1801133 (677C>T) polymorphism in Asian populations in all genetic models and Hispanic or Latin American population in dominant and heterozygous models. Similar trends were also found among other populations, although no statistically significant difference was found in some genetic models. This may be related to the following reasons: 1) the frequency of the MTHFR 677T gene variant differs among ethnic groups due to different genetic backgrounds. Previous studies indicated that the frequency of the MTHFR rs1801133 T allele was 24–40% in Europeans, 40% in Koreans, and 26–37% in Japanese (Shao et al., 2017); 2) MTHFR rs1801133 (677C>T) was associated with increased coronary heart disease only when the folate level was low (Klerk et al., 2002). Thus, various dietary habits and differences in folate intake may also contribute to this difference; 3) the difference in the power of included studies may be another cause of this result. Nonetheless, the results for these ancestry groups need to be interpreted with caution, and more high-quality studies are still required to explore the correlation between MTHFR rs1801133 (677C>T) polymorphism and IS risk in these ancestry groups.

This study has several strengths. First, we included the most recent and relevant studies in this meta-analysis. In addition, we further analyzed the association between MTHFR rs1801133 (677C>T) polymorphism and IS risk in different populations. Finally, this meta-analysis included high-quality observational studies using real-world data with a large number of patients.

However, this meta-analysis also has some limitations. First, the study was based on the secondary study-level data. Age groups were defined according to the mean or median age of study subjects, and some studies did not provide clear information on age. Thus, the age stratification of subgroups might not be accurate. Second, the findings of this meta-analysis were mainly based on case-control studies and lacked prospective research; therefore, they should be interpreted with caution.

5 Conclusion

Our findings showed that the MTHFR rs1801133 (677C>T) variant may contribute to an increased risk of IS. This association was statistically significant in the Asian and Hispanic or Latin American cohorts and showed a similar trend in the populations of other ancestries. For middle-aged and elderly people, MTHFR rs1801133 (677C>T) might be a promising biomarker for early detection and prediction of the prognosis of IS. However, high-quality, prospective studies are needed in the future.

Acknowledgments

The authors would like to thank all of our teammates for contributing to this work. They also thank Lisa Kreiner, PhD, from Liwen Bianji, (Edanz) (www.liwenbianji.cn/), for editing the English text of a draft of this manuscript.

Author contributions

All authors contributed to the data analysis and drafting or revision of the manuscript, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.

Funding

This work was supported by the National Natural Science Foundation of China under the Grant No. 81971116 and the Key Research and Development Program of Shaanxi under the Grant No. 2019ZDLSF01–04.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2022.1021423/full#supplementary-material

Click here for additional data file. (1.4MB, TIF)

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