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. 2025 Aug 23;18:136. doi: 10.1186/s12920-025-02167-8

Association of IL-10, IL-6, and TNF-α polymorphisms with acute coronary syndrome risk: a systematic review, meta-analysis, and trial sequential analysis

Yang Luo 1,#, Shiqi Li 2,#, Yan Ding 3, Shasha Shi 4,✉
PMCID: PMC12374326  PMID: 40846944

Abstract

Inflammatory cytokines are pivotal in the pathophysiology of acute coronary syndrome (ACS) and have been associated with major adverse cardiovascular events. In this study, we aimed to assess the association of eight polymorphisms in IL-10, IL-6, and TNF-α with the risk of ACS through a systematic review, meta-analysis, and trial sequential analysis. A comprehensive literature review was conducted across multiple databases, including PubMed/Medline, Web of Science, Scopus, and Cochrane Library, up until August 8, 2024. The Review Manager 5.3 software was utilized to compute the effect sizes such as the odds ratio accompanied by a 95% confidence interval. Out of 1499 records identified from databases, sources, or electronic searches, 51 articles were included in qualitative and quantitative syntheses (meta-analysis). For IL-6 –174G > C, the allelic and homozygous models showed significant associations with ACS risk. IL-6 –572G > C showed no significant association across all genetic models. IL-10 polymorphisms (–592C > A, –819C > T, and –1082A > G) generally showed no significant associations. TNF-α –308G > A polymorphism showed significant associations in all models. TNF-α –1031T > C and –238G > A showed no significant associations, with varying degrees of heterogeneity. Results suggest that certain cytokine polymorphisms, notably IL-6 –174G > C and TNF-α –308G > A, may play a crucial role in increasing susceptibility to ACS. These associations are especially pronounced in certain ethnic groups, such as Asians and Arabs, highlighting the importance of considering genetic diversity in clinical assessments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-025-02167-8.

Keywords: Acute coronary syndrome, Cytokines, Interleukins, Polymorphism, Meta-analysis

Introduction

Acute coronary syndrome (ACS), a form of coronary heart disease, is responsible for one-third of all deaths in individuals over 35 years old [1]. While some types of coronary heart disease can be asymptomatic, ACS is always symptomatic [1]. ACS includes ST-Elevation myocardial infarction, non-ST-elevation myocardial infarction, and unstable angina [1–3]. Common risk factors include being 65 years or older, smoking, hypertension, diabetes mellitus, hyperlipidemia, a body mass index over 25 kg/m2, and a family history of premature coronary artery disease [4]. The most common symptom of ACS is chest discomfort at rest, affecting about 79% of men and 74% of women. However, around 40% of men and 48% of women present with nonspecific symptoms such as dyspnea, either alone or more commonly chest pain [5].

The field of genetics in cardiovascular disease and myocardial infarction has seen remarkable advancements over the past decade. For the first time, genetic risk scores comprising 47 known single-nucleotide polymorphisms associated with coronary artery disease have been linked to recurrent ACS after adjusting for multiple variables in a diverse ACS population [6]. This research also identifies genes involved in the destabilization and rupture of atherosclerotic plaques, suggesting potential targets for improved prevention and treatment strategies [7].

Atherosclerosis is an inflammatory disease that contributes to ACS due to an imbalance between inflammatory mediators and inhibitors [8]. Inflammation plays a crucial role in the development of heart diseases, particularly heart failure. Various inflammatory mediators are central to the progression of heart-related inflammatory conditions [9]. Three reviews have summarized recent evidence on the roles of proatherogenic and antiatherogenic immune networks in the pathogenesis of ACS, highlighting how immune responses and inflammation drive atherosclerosis progression, plaque instability, and adverse cardiovascular events [10–12].

A new paradigm of unbalanced cytokine-mediated inflammation is emerging, offering new diagnostic and therapeutic opportunities [13]. Studies have shown that an imbalance in cytokine release is present in ACS, significantly favoring pro-inflammatory effects [14]. Inflammatory cytokines play a crucial role in the pathophysiology of ACS and have been linked to major adverse cardiovascular events [15, 16].

There were just four previous meta-analyses to evaluate the associations between interleukin (IL)-6 –174G > C in 2016 [17], tumor necrosis factor-alpha (TNF-α) –308G > A in 2016 [18], IL-6 -174 G > C, and -572 G > C in 2014 [19], and IL-6 -572 G > C with myocardial infarction risk. Despite growing evidence on cytokine involvement in ACS, previous meta-analyses have only examined a few polymorphisms, with some findings remaining inconclusive. Moreover, recent studies have emerged, necessitating an updated and comprehensive analysis. To address this gap, we conducted a systematic review and meta-analysis, incorporating trial sequential analysis, to assess the association between eight polymorphisms in IL-6, IL-10, and TNF-α with ACS susceptibility.

Materials and methods

Study design

The systematic review and meta-analysis followed the protocols outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [20]. The research question, framed using the PECO (population, exposure, comparison, and outcome) model, was: Are IL-6, IL-10, and TNF-α polymorphisms correlated to ACS susceptibility in case–control studies? The systematic review and meta-analysis didn't register in any database.

Identification of articles

Two investigators (Y.L. and S.L.) independently performed a comprehensive literature review across multiple databases, including PubMed/Medline, Web of Science, Scopus, and Cochrane Library, up until August 8, 2024, without any restrictions to identify relevant studies. They reviewed the titles and abstracts of the identified articles and obtained the full texts of those that met the inclusion criteria. The search strategy included terms such as: ("acute coronary syndrome" or "ACS" or "myocardial infarction" or "acute myocardial infarction" or "AMI" or "unstable angina" or "non-ST-elevation myocardial infarction” or “NSTEMI” or “ST-elevation myocardial infarction” or “STEMI”) and ("interleukin-6″ or "IL-6″ or "IL6″ or "interleukin-10″ or "IL-10″ or "IL10″ or "Tumor Necrosis Factor" or "TNF-α" or "TNF alpha" or "TNF-a") and ("genotype*" or "allele*" or "variant*" or "polymorphism*"). To ensure no relevant study was missed, the reference lists of the articles were also examined. In case of any disagreement between the two authors, a third author (Y.D.) resolved it. Two authors (Y.L. and S.S.) independently collected the data from the studies included in the meta-analysis. In case of any discrepancies, they were resolved through mutual discussion.

Eligibility criteria

The inclusion criteria for our analysis were as follows: a) case–control studies to examine the polymorphisms of IL-6, IL-10, and TNF-α in ACS cases and control subjects. b) The diagnosis of ACS was done according to the American College of Cardiology (ACC) criteria [21] or based on standard laboratory findings, typical electrocardiographic changes, and confirmed by echocardiography and coronary angiography. c) The cases included in the studies should not have any other systemic diseases, and the controls should be either healthy individuals or those without any systemic disease (systemic diseases independently alter cytokine levels and effect on the heterogeneity and the bias of association of the polymorphisms and the risk of ACS). d) Both free full-text and subscription-based publications (we paid the fee for each subscription-based publication). Conversely, we excluded reviews, meta-analyses, studies without sufficient data, conference papers, studies including individuals with any systemic disease, and studies without a control group (see the studies of Imani et al. [22] and Azizi et al. [23]).

Quality score

Two authors (Y.L. and S.S.) independently evaluated the quality score of each study included in the meta-analysis. In case of any discrepancies, they were resolved through a short discussion. Quality score was evaluated by the Newcastle–Ottawa scale (NOS) [24]. It shows a rating system ranging from 0 to 9 scores, with scores equal to or greater than 7 considered high qualities.

Statistical analyses

The Review Manager 5.3 (RevMan 5.3) software was utilized to compute the effect sizes. These were presented as the odds ratio (OR) accompanied by a 95% confidence interval (CI), representing the prevalence of IL-6, IL-10, and TNF-α polymorphisms in patients with ACS and control subjects. A two-sided p-value of less than 0.05 is considered significant [25, 26]. For using random-effects or fixed-effect models, we used the cutoff of I2 in the studies of Nemati et al. [27] and Shakiba et al. [28]. We performed a subgroup analysis based on three variables.

The presence of publication bias was examined using a funnel plot and Egger’s regression test. Begg’s test was used to evaluate the likelihood of publication bias. The p-values from both Egger’s and Begg’s tests were calculated, with a 2-sided p-value less than 0.10 indicating the presence of publication bias [29, 30]. Meta-regression analysis, publication bias, and sensitivity analyses were performed using the Comprehensive Meta-Analysis version 3.0 (CMA 3.0) software.

To reduce the risk of drawing false-positive or negative conclusions from meta-analyses [31], a Trial Sequential Analysis (TSA) was performed. This analysis was conducted using the TSA software (version 0.9.5.10 beta) [32]. More information is located in the studies of Golshah et al. [33] and Mohammadi et al. [34].

Results

Study selection

Figure 1 provides the process of selecting studies for your systematic review and meta-analysis. 1491 records were identified from databases (PubMed, Web of Science, Cochrane Library, and Scopus). In addition, 8 records were identified from other sources or electronic searches. After removing duplicates, 983 records were screened. 914 records were excluded and then 69 articles were assessed for eligibility. Finally, 51 articles [35–85] were included in both qualitative and quantitative synthesis (meta-analysis).

Fig. 1.

Fig. 1

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PRISMA flowchart of the study selection

Characteristics of studies

Table 1 shows the characteristics of various studies investigating the association between IL-6 polymorphisms (–174G > C and –572G > C) and the risk of ACS. It includes data from diverse countries and ethnic groups, detailing the genotype distribution (GG, GC, and CC) among cases and controls. The studies vary in sample sizes and report Hardy–Weinberg equilibrium (HWE) p-values for control groups, with some showing significant deviations. This comprehensive data highlights the genetic diversity and potential influence of IL-6 polymorphisms on ACS risk across different populations. In this study, Asian ethnicity refers to people with ancestry from Asia (e.g., Kazakhstan, China, India, and Korea). Caucasian ethnicity refers to people from Europe and West Asia except for Arabs. The term "Arabs" generally refers to individuals who are part of an ethnic group originating from the Arab world, which spans Western Asia and North Africa, united by shared cultural traditions and the Arabic language. Latino refers to people living in Latin America. In one study conducted in the USA [74], over 94% of participants were identified as Caucasian; therefore, this study was categorized under the Caucasian ethnicity group. Supplementary 1 shows the distributions of genotypes for the polymorphisms.

Table 1.

Characteristics of studies reporting the association between IL-6 polymorphisms and the risk of acute coronary syndrome

IL-6 –174G > C (rs1800795)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Aimagambetova, 2016 [36] Kazakhstan Asian 81/100 STEMI 0.108 7
Bennermo, 2011 [38] Sweden Caucasian 356/378 MI 0.192 8
Bennet, 2003 [40] Sweden Caucasian 1157/1500 MI 0.803 9
Biswas, 2014 [42] India Asian 500/500 STEMI 0.072 7
Carvalho, 2016 [43] Brazil Latino 200/50 ACS 0.594 7
Chiappelli, 2005 [44] Italy Caucasian 66/53 MI 0.174 7
Coker, 2011 [46] Turkey Caucasian 167/235 MI 0.761 8
Georges, 2001 [54] France Caucasian 614/672 MI 0.345 8
Ghazouani, 2011 [56] Tunisia Arabs

215/406

112/406

MI

UA

 0.602 7
Grira, 2021 [57] Tunisia Arabs 108/400 ACS 0.599 7
Hashad, 2021 [58] Egypt Arabs 100/104 AMI 0.310 8
Kelberman, 2004 [62]

UK (North)

UK (South)

 Caucasian

229/244

278/317

 MI

0.863

0.317

 8
Licastro, 2004 [64] Italy Caucasian 138/97 MI 0.418 8
Lieb, 2004 [65] Germany Caucasian 1322/1023 MI 0.839 9
Nauck, 2002 [70] Germany Caucasian 1365/729 MI 0.739 9
Rosner, 2005 [74] USA Caucasian 522/2089 MI 0.823 8
Sadikova, 2018 [75] Russia Caucasian 286/301 MI 0.365 8
Srikanth Babu, 2012 [77] India Asian 651/432 ACS 0.451 7
Tsalamandris, 2022 [80] Greece Caucasian 250/272 UA 0.599 7
Vakili, 2011 [82] Iran Caucasian 450/450 AMI < 0.001 7
IL6 –572G > C (rs1800796)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Bennet, 2003 [40] Sweden Caucasian 1115/1435 MI 0.201 9
Coker, 2011 [46] Turkey Caucasian 167/235 MI < 0.001 8
Fragoso, 2010 [50] Mexico Latino 284/247 ACS 0.164 8
Fu, 2006 [52] China Asian 232/260 MI 0.039 7
Georges, 2001 [54] France Caucasian 611/665 MI 0.650 8
Kelberman, 2004 [62]

UK (North)

UK (South)

 Caucasian

223/241

282/306

 MI

0.376

0.420

 8
Nasibullin, 2014 [69] Russia Caucasian 225/257 MI 0.244 8
Park, 2007 [73] Korea Asian 166/168 AMI 0.823 8
Wei, 2005 [84] China Asian 128/152 MI 0.049 7
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The bold number means statistically significant datum (p < 0.05)

IL Interleukin, AMI Acute myocardial infarction, MI Myocardial infarction, ACS Acute coronary syndrome, UA Unstable angina, STEMI ST elevation myocardial infarction, HWE Hardy–Weinberg equilibrium (with a p-value for the control group)

Table 2 reports the characteristics of studies on the association between IL-10 polymorphisms (–592C > A, –819C > T, and –1082A > G) and the risk of ACS. It includes data from various countries and ethnicities, detailing genotype distributions (CC, CA, and AA for IL-10 –592C > A; CC, CT, and TT for IL-10 –819C > T; AA, AG, and GG for IL-10 –1082A > G) among cases and controls. Sample sizes vary, and HWE p-values are reported, with some studies showing significant deviations. This data highlights the genetic diversity and potential influence of IL-10 polymorphisms on ACS risk across different populations.

Table 2.

Characteristics of studies reporting the association between IL-10 polymorphisms and the risk of acute coronary syndrome

IL-10 –592C > A (rs1800872)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Biswas, 2014 [42] India Asian 500/500 STEMI 0.745 7
Cruz, 2013 [47] Mexico Latino 248/149 MI 0.864 8
Donger, 2001 [49] France Caucasian 987/954 MI 0.344 8
Fragoso, 2011 [51] Mexico Latino 389/302 ACS 0.151 8
Garcia-Garduño, 2024 [53] Mexico Latino 300/300 ACS 0.389 8
Koch, 2001 [63] Germany Caucasian 793/340 MI 0.977 8
Nasibullin, 2014 [69] Russia Caucasian 225/257 MI 0.505 8
Rosner, 2005 [74] USA Caucasian 522/2089 MI 0.027 8
Sadikova, 2018 [75] Russia Caucasian 286/301 MI 0.218 8
Srikanth Babu, 2012 [77] India Asian 651/432 ACS 0.126 7
IL-10 –819C > T (rs1800871)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Cruz, 2013 [47] Mexico Caucasian 149/248 MI 0.833 8
Donger, 2001 [49] France Caucasian 983/955 MI 0.407 8
Fragoso, 2011 [51] Mexico Latino 389/302 ACS 0.832 8
Garcia-Garduño, 2024 [53] Mexico Latino 300/300 ACS 0.389 8
Koch, 2001 [63] Germany Caucasian 793/340 MI 0.977 8
Wang, 2015 [83] China Asian 260/285 MI 0.182 7
IL-10 –1082A > G (rs1800896)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Aimagambetova, 2016 [36] Kazakhstan Asian 81/100 STEMI 0.023 7
Cruz, 2013 [47] Mexico Caucasian 149/248 MI 0.833 8
Donger, 2001 [49] France Caucasian 984/952 MI 0.943 8
Fragoso, 2011 [51] Mexico Latino 389/302 ACS 0.380 8
Garcia-Garduño, 2024 [53] Mexico Latino 300/300 ACS 0.409 8
Ianni, 2012 [61] Italy Caucasian 265/239 AMI < 0.001 7
Koch, 2001 [63] Germany Caucasian 793/340 MI 0.406 8
Lio, 2004 [66] Italy Caucasian 142/153 AMI 0.942 9
Lorenzová, 2007 [68] Czechia Caucasian 228/568 MI 0.083 8
O'Halloran, 2006 [71] Ireland Caucasian 1598/386 ACS 0.001 7
Srikanth Babu, 2012 [77] India Asian 651/432 ACS 0.079 7
Wang, 2015 [83] China Asian 260/285 MI 0.110 7
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The bold number means statistically significant datum (p < 0.05)

IL Interleukin, AMI Acute myocardial infarction, MI Myocardial infarction, ACS Acute coronary syndrome, UA Unstable angina, STEMI ST elevation myocardial infarction, HWE Hardy–Weinberg equilibrium (with a p-value for the control group)

Table 3 represents the characteristics of studies on the association between TNF-α polymorphisms (–308G > A, − 1031 T > C, and − 238G > A) and the risk of ACS. It includes data from various countries and ethnicities, detailing genotype distributions (GG, GA, and AA for TNF-α –308G > A; TT, TC, and CC for TNF-α − 1031 T > C; GG, GA, and AA for TNF-α − 238G > A) among cases and controls. Sample sizes vary, and HWE p-values are reported, with some studies showing significant deviations. This data highlights the genetic diversity and potential influence of TNF-α polymorphisms on ACS risk across different populations.

Table 3.

Characteristics of studies reporting the association between TNF-α polymorphisms and the risk of acute coronary syndrome

TNF-α –308G > A (rs1800629)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Abdulfattah, 2023 [35] Iraq Arabs 100/100 UA 0.011 6
Aimagambetova, 2016 [36] Kazakhstan Asian 81/100 STEMI 0.751 7
Antonicelli, 2005 [37] Italy Caucasian 293/310 AMI > 0.05 7
Bennet, 2006 [39] Sweden Caucasian 1167/1497 MI 0.368 9
Bernard, 2003 [41] France Caucasian

58/80

146/80

UA

MI

 0.798 7
Biswas, 2014 [42] India Asian 500/500 STEMI < 0.001 7
Chu, 2012 [45] China Asian 1020/420 MI 0.287 8
Dedoussis, 2005 [48] Greece Caucasian 199/200 ACS 0.801 8
Ghaderian, 2011 [55] Iran Caucasian 996/910 AMI 0.032 7
Grira, 2021 [57] Tunisia Arabs 108/400 ACS 0.569 7
Herrmann, 1998 [59]

Northern Ireland

France

 Caucasian

196/176

445/534

 MI 0.079 7
Hou, 2009 [60] China Asian 804/905 MI 0.524 7
Koch, 2001 [63] Germany Caucasian 793/340 MI 0.085 8
Nasibullin, 2014 [69] Russia Caucasian 225/257 MI 0.620 8
Padovani, 2000 [72] Brazil Latino 148/148 MI 0.399 8
Sadikova, 2018 [75] Russia Caucasian 286/301 MI 0.425 8
Srikanth Babu, 2012 [77] India Asian 651/432 ACS 0.519 7
Szabó, 2013 [78] Hungary Caucasian 118/384 MI < 0.001 6
Tobin, 2004 [79] UK Caucasian 547/505 AMI 0.986 9
Vaccarino, 2013 [81] Italy Caucasian 60/130 AMI 0.173 7
Zeybek, 2011 [85] Turkey Caucasian 143/213 MI < 0.001 6
TNF-α − 1031 T > C (rs1799964)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Bennet, 2006 [39] Sweden Caucasian 1145/1480 MI 0.421 9
Liu, 2009 [67] China Asian 299/202 UA 0.060 7
Sandoval-Pinto, 2016 [76] Mexico Latino 251/164 ACS 0.050 7
TNF-α − 238G > A (rs361525)
First author, publication year Country Ethnicity Cases/controls Disease type HWE NOS score
Bennet, 2006 [39] Sweden Caucasian 1150/1468 MI 0.324 9
Hou, 2009 [60] China Asian 504/1810 MI 0.133 7
Tobin, 2004 [79] UK Caucasian 505/547 AMI 0.701 9
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The bold number means statistically significant datum (p < 0.05)

TNF-α Tumor necrosis factor-alpha, AMI Acute myocardial infarction, MI Myocardial infarction, ACS Acute coronary syndrome, UA Unstable angina, STEMI ST elevation myocardial infarction, HWE Hardy–Weinberg equilibrium (with a p-value for the control group)

Pooled analyses

Supplementary 2 shows the forest plots for each polymorphism in each genetic model. Table 4 shows the forest plot analyses of the association between various cytokine polymorphisms and the risk of ACS. For IL-6 –174G > C, the allelic and homozygous models showed significant associations with ACS risk. IL-6 –572G > C showed no significant association across all genetic models. IL-10 polymorphisms (–592C > A, –819C > T, and –1082A > G) generally showed no significant associations. TNF-α –308G > A polymorphism showed significant associations in all models, with ORs ranging from 1.33 to 1.74 and high heterogeneity. TNF-α –1031T > C and –238G > A showed no significant associations, with varying degrees of heterogeneity. Overall, the data indicate that certain cytokine polymorphisms, particularly IL-6 –174G > C and TNF-α –308G > A, may be associated with increased ACS risk.

Table 4.

Summary of forest plot analyses reporting the association between cytokine polymorphisms and the risk of acute coronary syndrome

Polymorphism (N) Genetic model OR 95% CI p-value I2 Pheterogeneity
Min Max
IL-6 –174G > C (22) Allelic 1.12 1.02 1.25 0.02 77% < 0.00001
Homozygous 1.28 1.01 1.61 0.04 75% < 0.00001
Heterozygous 1.08 0.96 1.22 0.18 62% < 0.0001
Dominant 1.13 0.99 1.28 0.07 72% < 0.00001
Recessive 1.20 1.00 1.45 0.05 69% < 0.00001
IL6 –572G > C (10) Allelic 0.95 0.74 1.22 0.67 81% < 0.00001
Homozygous 0.70 0.33 1.46 0.34 72% 0.0002
Heterozygous 1.02 0.79 1.30 0.90 55% 0.02
Dominant 0.96 0.71 1.30 0.81 72% 0.0002
Recessive 0.83 0.55 1.26 0.38 64% 0.003
IL-10 –592C > A (10) Allelic 0.88 0.75 1.03 0.10 83% < 0.00001
Homozygous 0.79 0.57 1.09 0.15 78% < 0.00001
Heterozygous 0.86 0.72 1.04 0.12 74% < 0.0001
Dominant 0.85 0.69 1.04 0.11 81% < 0.00001
Recessive 0.85 0.67 1.07 0.17 65% 0.002
IL-10 –819C > T (6) Allelic 0.93 0.85 1.01 0.10 4% 0.39
Homozygous 0.86 0.70 1.05 0.14 0% 0.49
Heterozygous 0.94 0.84 1.06 0.31 0% 0.51
Dominant 0.92 0.82 1.03 0.16 3% 0.40
Recessive 0.89 0.74 1.07 0.20 0% 0.70
IL-10 –1082A > G (12) Allelic 0.91 0.77 1.08 0.30 86% < 0.00001
Homozygous 0.82 0.59 1.16 0.27 85% < 0.00001
Heterozygous 0.98 0.80 1.18 0.80 74% < 0.0001
Dominant 0.93 0.75 1.15 0.49 82% < 0.00001
Recessive 0.83 0.64 1.09 0.19 81% < 0.00001
TNF-α –308G > A (22*) Allelic 1.38 1.14 1.66 0.0007 88% < 0.00001
Homozygous 1.74 1.17 2.58 0.006 73% < 0.00001
Heterozygous 1.33 1.12 1.59 0.002 80% < 0.00001
Dominant 1.39 1.15 1.68 0.0006 85% < 0.00001
Recessive 1.55 1.08 2.23 0.02 68% < 0.00001
TNF-α − 1031T > C (3) Allelic 0.90 0.73 1.12 0.36 54% 0.11
Homozygous 0.71 0.38 1.32 0.28 58% 0.09
Heterozygous 0.97 0.73 1.29 0.84 58% 0.09
Dominant 0.93 0.72 1.20 0.56 53% 0.12
Recessive 0.71 0.37 1.35 0.29 61% 0.08
TNF-α − 238G > A (3) Allelic 0.89 0.73 1.07 0.22 0% 0.83
Homozygous 1.34 0.02 71.54 0.89 79% 0.03
Heterozygous 0.84 0.69 1.03 0.09 0% 0.72
Dominant 0.86 0.71 1.05 0.14 0% 0.88
Recessive 1.35 0.02 73.14 0.88 79% 0.03
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The bold number means statistically significant datum (p < 0.05). N Number of studies

IL Interleukin, TNF-α Tumor necrosis factor-alpha, OR Odds ratio, CI Confidence interval

*23 studies for the dominant model

Subgroup analysis

Table 5 reports the subgroup analysis reporting the association between cytokine polymorphisms and the risk of ACS based on three variables. The subgroup analysis reveals significant associations between cytokine polymorphisms and the risk of ACS across different ethnicities, disease types, and sample sizes. For IL-6 –174G > C, significant associations were found in Asians and Arabs, particularly in the allelic, homozygous, and recessive models, while no significant associations were observed in Caucasians and Latinos. IL-6 –572G > C showed significant associations in Asians and Latinos but not in Caucasians. IL-10 –592C > A was significantly associated with ACS in Caucasians and Latinos in the allelic and recessive models, respectively. IL-10 –1082A > G showed significant associations in Caucasians, particularly in the homozygous and recessive models. TNF-α –308G > A was significantly associated with ACS in Asians and Arabs across multiple genetic models, with the strongest associations observed in the homozygous and recessive models. The analysis by disease type indicated significant associations for IL-6 –174G > C and TNF-α –308G > A in MI and UA cases, respectively. Sample size analysis highlighted significant associations for IL-6 –174G > C, IL6 –572G > C, and TNF-α –308G > A in studies with both large (≥ 500) and small (< 500) sample sizes.

Table 5.

Subgroup analysis reporting the association between cytokine polymorphisms and the risk of acute coronary syndrome based on three variables

Polymorphism Variable Subgroup (N) Genetic model OR 95%CI p-value I2
Min Max
IL-6 –174G > C Ethnicity Asian (3) Allelic 1.66 1.44 1.91 < 0.00001 9%
Homozygous 5.11 1.42 18.38 0.01 56%
Heterozygous 1.33 0.89 1.99 0.16 69%
Dominant 1.62 1.20 2.20 0.002 53%
Recessive 4.89 1.12 21.30 0.03 66%
Caucasian (14) Allelic 1.04 0.94 1.14 0.45 69%
Homozygous 1.09 0.89 1.33 0.43 70%
Heterozygous 1.03 0.90 1.18 0.64 64%
Dominant 1.06 0.91 1.24 0.45 57%
Recessive 1.04 0.91 1.20 0.54 70%
Latino (1) Allelic 0.76 0.47 1.23 0.27 -
Homozygous 0.58 0.17 1.98 0.39 -
Heterozygous 0.73 0.38 1.40 0.35 -
Dominant 0.67 0.20 2.20 0.51 -
Recessive 0.71 0.38 1.32 0.28 -
Arabs (4) Allelic 1.29 1.05 1.59 0.01 0%
Homozygous 2.25 1.14 4.45 0.02 45%
Heterozygous 1.20 0.94 1.53 0.15 0%
Dominant 1.27 1.00 1.61 0.05 0%
Recessive 2.16 1.09 4.26 0.03 46%
Disease Type MI (17) Allelic 1.11 1.00 1.23 0.04 72%
Homozygous 1.19 0.96 1.49 0.12 69%
Heterozygous 1.10 0.96 1.25 0.16 65%
Dominant 1.12 0.98 1.29 0.09 70%
Recessive 1.13 0.94 1.34 0.19 61%
UA (2) Allelic 0.92 0.64 1.32 0.65 57%
Homozygous 0.76 0.46 1.25 0.28 23%
Heterozygous 0.86 0.55 1.34 0.50 55%
Dominant 0.87 0.54 1.41 0.57 64%
Recessive 0.89 0.56 1.42 0.62 0%
Sample size ≥ 500 (15) Allelic 1.15 1.03 1.29 0.01 81%
Homozygous 1.31 1.02 1.68 0.04 80%
Heterozygous 1.12 0.99 1.26 0.06 61%
Dominant 1.17 1.02 1.34 0.03 74%
Recessive 1.22 1.00 1.50 0.05 76%
< 500 (7) Allelic 1.03 0.80 1.33 0.81 60%
Homozygous 1.24 0.64 2.40 0.52 57%
Heterozygous 0.95 0.66 1.38 0.80 66%
Dominant 1.00 0.69 1.44 0.99 68%
Recessive 1.04 0.75 1.44 0.80 33%
IL6 –572G > C Ethnicity Asian (3) Allelic 0.70 0.58 0.86 0.0005 26%
Homozygous 0.32 0.11 0.94 0.04 52%
Heterozygous 0.44 0.23 0.86 0.02 15%
Dominant 0.39 0.21 0.74 0.004 43%
Recessive 0.69 0.55 0.88 0.003 11%
Caucasian (6) Allelic 1.10 0.75 1.62 0.61 84%
Homozygous 1.15 0.34 3.89 0.82 73%
Heterozygous 1.08 0.83 1.40 0.58 55%
Dominant 1.10 0.78 1.55 0.58 76%
Recessive 1.14 0.36 3.56 0.83 69%
Latino (1) Allelic 0.93 0.72 1.20 0.58 -
Homozygous 0.60 0.33 1.08 0.09 -
Heterozygous 1.33 0.92 1.92 0.12 -
Dominant 1.14 0.81 1.61 0.45 -
Recessive 0.52 0.30 0.90 0.02 -
Disease Type MI (9) Allelic 0.95 0.71 1.27 0.73 83%
Homozygous 0.69 0.28 1.74 0.44 74%
Heterozygous 0.97 0.73 1.28 0.81 56%
Dominant 0.92 0.65 1.31 0.65 75%
Recessive 0.91 0.56 1.46 0.69 66%
Sample size ≥ 500 (4) Allelic 0.92 0.79 1.07 0.29 0%
Homozygous 0.58 0.34 0.97 0.04 0%
Heterozygous 1.03 0.86 1.24 0.73 30%
Dominant 0.98 0.82 1.17 0.83 0%
Recessive 0.51 0.31 0.84 0.008 0%
< 500 (6) Allelic 0.96 0.60 1.53 0.87 89%
Homozygous 0.80 0.24 2.63 0.71 83%
Heterozygous 0.86 0.50 1.47 0.57 68%
Dominant 0.79 0.40 1.55 0.49 82%
Recessive 1.00 0.58 1.72 1.00 77%
IL-10 –592C > A Ethnicity Asian (2) Allelic 0.89 0.40 1.97 0.77 98%
Homozygous 0.87 0.21 3.58 0.85 97%
Heterozygous 0.76 0.27 2.13 0.60 96%
Dominant 0.80 0.25 2.52 0.70 97%
Recessive 1.00 0.44 2.28 0.99 93%
Caucasian (5) Allelic 0.91 0.84 1.00 0.04 0%
Homozygous 0.82 0.66 1.03 0.09 6%
Heterozygous 0.92 0.82 1.02 0.12 0%
Dominant 0.90 0.81 1.01 0.06 0%
Recessive 0.86 0.69 1.07 0.17 0%
Latino (3) Allelic 0.85 0.65 1.10 0.21 71%
Homozygous 0.71 0.44 1.13 0.15 63%
Heterozygous 0.91 0.73 1.13 0.39 43%
Dominant 0.84 0.59 1.19 0.33 65%
Recessive 0.73 0.57 0.93 0.01 38%
Disease Type MI (7) Allelic 0.95 0.83 1.09 0.48 69%
Homozygous 0.92 0.66 1.27 0.61 67%
Heterozygous 0.95 0.86 1.05 0.29 2%
Dominant 0.94 0.81 1.09 0.40 50%
Recessive 0.95 0.73 1.24 0.70 56%
Sample size ≥ 500 (8) Allelic 0.88 0.73 1.05 0.16 87%
Homozygous 0.78 0.53 1.13 0.18 83%
Heterozygous 0.87 0.70 1.08 0.21 80%
Dominant 0.85 0.67 1.08 0.18 85%
Recessive 0.84 0.64 1.09 0.19 72%
< 500 (2) Allelic 0.88 0.72 1.09 0.25 0%
Homozygous 0.84 0.52 1.35 0.46 0%
Heterozygous 0.83 0.62 1.11 0.22 0%
Dominant 0.84 0.63 1.10 0.20 0%
Recessive 0.91 0.59 1.42 0.69 0%
IL-10 –1082A > G Ethnicity Asian (3) Allelic 1.24 0.67 2.30 0.49 93%
Homozygous 1.92 0.49 7.54 0.35 93%
Heterozygous 1.07 0.68 1.70 0.76 75%
Dominant 1.19 0.63 2.26 0.59 89%
Recessive 1.77 0.55 5.62 0.34 91%
Caucasian (7) Allelic 0.81 0.65 1.01 0.06 88%
Homozygous 0.65 0.43 0.99 0.04 87%
Heterozygous 0.93 0.69 1.26 0.63 82%
Dominant 0.83 0.61 1.13 0.23 86%
Recessive 0.70 0.51 0.95 0.02 82%
Latino (2) Allelic 0.99 0.83 1.18 0.91 0%
Homozygous 1.00 0.67 1.48 0.99 0%
Heterozygous 0.98 0.78 1.23 0.85 0%
Dominant 0.98 0.79 1.22 0.88 0%
Recessive 1.01 0.69 1.47 0.98 0%
Disease Type MI (8) Allelic 0.91 0.70 1.18 0.48 90%
Homozygous 0.82 0.49 1.38 0.46 89%
Heterozygous 0.96 0.72 1.27 0.76 78%
Dominant 0.91 0.66 1.25 0.55 85%
Recessive 0.84 0.56 1.27 0.42 86%
Sample size ≥ 500 (9) Allelic 0.98 0.88 1.10 0.76 64%
Homozygous 0.96 0.78 1.18 0.69 57%
Heterozygous 1.06 0.90 1.25 0.47 62%
Dominant 1.03 0.88 1.20 0.72 62%
Recessive 0.91 0.76 1.09 0.32 56%
< 500 (3) Allelic 0.73 0.30 1.74 0.47 95%
Homozygous 0.63 0.09 4.46 0.64 93%
Heterozygous 0.66 0.30 1.45 0.30 84%
Dominant 0.61 0.22 1.73 0.35 91%
Recessive 0.80 0.15 4.16 0.79 92%
TNF-α –308G > A Ethnicity Asian (5) Allelic 1.60 1.10 2.32 0.01 89%
Homozygous 2.42 1.77 3.29 < 0.00001 44%
Heterozygous 1.53 1.06 2.21 0.02 84%
Dominant 1.64 1.09 2.47 0.02 88%
Recessive 1.98 1.46 2.69 < 0.00001 41%
Caucasian (14*) Allelic 1.21 0.98 1.50 0.08 85%
Homozygous 1.26 0.75 2.12 0.39 73%
Heterozygous 1.21 0.98 1.49 0.08 77%
Dominant 1.22 0.99 1.51 0.06 81%
Recessive 1.20 0.73 1.98 0.48 70%
Latino (1) Allelic 0.84 0.50 1.41 0.51 -
Homozygous 1.90 0.17 21.24 0.60 -
Heterozygous 0.75 0.42 1.33 0.32 -
Dominant 0.78 0.45 1.37 0.39 -
Recessive 2.01 0.18 22.45 0.57 -
Arabs (2) Allelic 2.60 1.56 4.36 0.0003 73%
Homozygous 5.03 2.92 8.65 < 0.00001 30%
Heterozygous 2.38 1.63 3.47 < 0.00001 0%
Dominant 2.88 2.03 4.09 < 0.00001 0%
Recessive 3.45 2.08 5.73 < 0.00001 44%
Disease Type MI (17**) Allelic 1.21 0.99 1.48 0.07 88%
Homozygous 1.38 0.87 2.19 0.17 73%
Heterozygous 1.19 0.99 1.43 0.07 79%
Dominant 1.21 1.00 1.48 0.05 84%
Recessive 1.32 0.85 2.05 0.21 71%
UA (2) Allelic 2.98 2.10 4.23 < 0.00001 36%
Homozygous 6.86 3.22 14.60 < 0.00001 0%
Heterozygous 2.14 1.29 3.54 0.003 0%
Dominant 2.95 1.87 4.66 < 0.00001 23%
Recessive 4.86 2.40 9.86 < 0.0001 0%
Sample size ≥ 500 (11***) Allelic 1.19 0.95 1.50 0.14 90%
Homozygous 1.33 0.80 2.22 0.27 82%
Heterozygous 1.24 1.01 1.51 0.04 81%
Dominant 1.24 0.99 1.54 0.06 86%
Recessive 1.23 0.78 1.95 0.37 78%
< 500 (11) Allelic 1.69 1.19 2.40 0.003 85%
Homozygous 2.86 1.96 4.18 < 0.00001 45%
Heterozygous 1.54 1.06 2.25 0.02 81%
Dominant 1.70 1.15 2.51 0.007 84%
Recessive 2.48 1.72 3.57 < 0.00001 38%
Open in a new tab

The bold number means statistically significant datum (p < 0.05). N: Number of studies

IL Interleukin, TNF-α Tumor necrosis factor-alpha, OR Odds ratio, CI Confidence interval, MI Myocardial infarction, MI Myocardial infarction, UA Unstable angina

* 15 studies for the dominant model. **18 studies for the dominant model. *** 12studies for the dominant model. *23 studies for the dominant model

The covariates ethnicity, disease type, and sample size significantly explain heterogeneity in the subgroup analysis. Ethnicity shows strong heterogeneity, with Asians and Arabs exhibiting notable associations (e.g., IL-6 –174G > C, Arabs: OR = 1.29, I2 = 0%), while Caucasians and Latinos show weaker effects. Disease type also influences variability; for example, myocardial infarction often has higher heterogeneity (e.g., IL-6 –174G > C, MI: I2 = 72%), while unstable angina shows larger effect sizes but lower variability. Sample size plays a key role, as smaller studies tend to have more variability and stronger effect sizes (e.g., TNF-α –308G > A, < 500 participants: OR = 1.69, I2 = 85%), while larger studies exhibit higher consistency but weaker effects. Overall, these covariates together underline the variability in the associations across populations and study designs.

Meta-regression analysis

The meta-regression analysis in Table 6 examines the association between cytokine polymorphisms and the risk of ACS based on publication year and sample size. For IL-6 –174G > C, the coefficients for publication year were positive but not statistically significant across all genetic models, indicating no strong temporal trend. The sample size also showed no significant association. IL-6 –572G > C similarly showed no significant associations with publication year or sample size. For IL-10 –592C > A, neither publication year nor sample size had significant coefficients, suggesting no clear influence of these variables. IL-10 –819C > T and IL-10 –1082A > G also showed no significant associations with publication year or sample size. However, TNF-α –308G > A showed significant positive associations with publication year across all genetic models, indicating an increasing trend over time, but no significant association with sample size. These results suggest that while most cytokine polymorphisms do not show significant temporal or sample size-related trends, TNF-α –308G > A may have an increasing association with ACS risk over time.

Table 6.

Meta-regression analysis reporting the association between cytokine polymorphisms and the risk of acute coronary syndrome based on two variables

Polymorphism Variable Genetic model Coefficient 95% Lower 95% Upper Z-value p-value
IL-6 –174G > C Publication year Allelic 0.0001 - < 0.0001 0.0002 1.81 0.0696
Homozygous 0.0002 - < 0.0001 0.0004 1.81 0.0700
Heterozygous 0.0001 - < 0.0001 0.0002 1.09 0.2739
Dominant 0.0001 - < 0.0001 0.0002 1.43 0.1536
Recessive 0.0001 - < 0.0001 0.0003 1.64 0.1017
Sample size Allelic - 0.0000 - 0.0002 0.0001 - 0.57 0.5665
Homozygous - 0.0001 - 0.0004 0.0002 - 0.75 0.4520
Heterozygous - 0.0000 - 0.0002 0.0001 - 0.38 0.7060
Dominant - 0.0000 - 0.0002 0.0001 - 0.43 0.6698
Recessive - 0.0001 - 0.0003 0.0001 - 0.67 0.5002
IL6 –572G > C Publication year Allelic - 0.0000 - 0.0002 0.0002 - 0.18 0.8559
Homozygous - 0.0002 - 0.0008 0.0003 - 0.76 0.4464
Heterozygous < 0.0001 - 0.0002 0.0002 0.36 0.7172
Dominant - 0.0000 - 0.0003 0.0002 - 0.12 0.9028
Recessive - 0.0001 - 0.0004 0.0002 - 0.54 0.5898
Sample size Allelic - 0.0000 - 0.0004 0.0004 - 0.10 0.9181
Homozygous 0.0001 - 0.0011 0.0013 0.17 0.8616
Heterozygous - 0.0001 - 0.0004 0.0003 - 0.41 0.6852
Dominant - 0.0000 - 0.0005 0.0004 - 0.10 0.9240
Recessive - 0.0000 - 0.0008 0.0008 - 0.03 0.9759
IL-10 –592C > A Publication year Allelic - 0.0001 - 0.0003 0.0001 - 1.28 0.1994
Homozygous - 0.0002 - 0.0005 0.0001 - 1.15 0.2491
Heterozygous - 0.0001 - 0.0003 0.0001 - 1.09 0.2772
Dominant - 0.0001 - 0.0003 0.0001 - 1.21 0.2266
Recessive - 0.0001 - 0.0004 0.0001 - 1.19 0.2325
Sample size Allelic 0.0001 - 0.0002 0.0003 0.54 0.5883
Homozygous 0.0001 - 0.0004 0.0006 0.49 0.6249
Heterozygous < 0.0001 - 0.0002 0.0003 0.35 0.7227
Dominant 0.0001 - 0.0002 0.0004 0.46 0.6440
Recessive 0.0001 - 0.0002 0.0005 0.58 0.5614
IL-10 –819C > T Publication year Allelic - 0.0000 - 0.0001 0.0001 - 0.04 0.9670
Homozygous < 0.0001 - 0.0002 0.0002 0.37 0.7107
Heterozygous - 0.0000 - 0.0001 0.0001 - 0.16 0.8724
Dominant - 0.0000 - 0.0001 0.0001 - 0.11 0.9159
Recessive < 0.0001 - 0.0001 0.0002 0.43 0.6706
Sample size Allelic - 0.0001 - 0.0002 0.0001 - 0.77 0.4385
Homozygous - 0.0002 - 0.0006 0.0001 - 1.29 0.1955
Heterozygous - 0.0000 - 0.0002 0.0002 - 0.31 0.7578
Dominant - 0.0001 - 0.0003 0.0002 - 0.49 0.6269
Recessive - 0.0002 - 0.0006 0.0001 - 1.24 0.2155
IL-10 –1082A > G Publication year Allelic - 0.0001 - 0.0002 0.0001 - 0.96 0.3350
Homozygous - 0.0002 - 0.0005 0.0002 - 1.03 0.3022
Heterozygous - 0.0001 - 0.0003 0.0001 - 0.79 0.4291
Dominant - 0.0001 - 0.0003 0.0001 - 0.98 0.3263
Recessive - 0.0001 - 0.0004 0.0001 - 1.00 0.3177
Sample size Allelic 0.0001 - 0.0002 0.0004 0.51 0.6070
Homozygous 0.0002 - 0.0004 0.0008 0.56 0.5773
Heterozygous 0.0001 - 0.0002 0.0005 0.78 0.4383
Dominant 0.0001 - 0.0002 0.0005 0.74 0.4621
Recessive 0.0001 - 0.0004 0.0006 0.41 0.6843
TNF-α –308G > A Publication year Allelic 0.0003 0.0001 0.0004 3.25 0.0012
Homozygous 0.0005 0.0001 0.0008 2.82 0.0048
Heterozygous 0.0002 0.0001 0.0004 2.85 0.0044
Dominant 0.0003 0.0001 0.0004 3.17 0.0015
Recessive 0.0004 0.0001 0.0007 2.47 0.0136
Sample size Allelic - 0.0002 - 0.0005 0.0001 - 1.54 0.1245
Homozygous - 0.0004 - 0.0010 0.0001 - 1.45 0.1461
Heterozygous - 0.0002 - 0.0004 0.0001 - 1.24 0.2156
Dominant - 0.0002 - 0.0005 0.0001 - 1.43 0.1532
Recessive - 0.0003 - 0.0009 0.0002 - 1.29 0.1959
Open in a new tab

The bold number means statistically significant datum (p < 0.05)

IL Interleukin, TNF-α Tumor necrosis factor-alpha

Sensitivity analysis

Both "cumulative" and "one-study-removed" analyses didn't change the pooled results for all polymorphisms, indicating that the findings are robust and not overly influenced by any single study or the order in which studies were added. We removed one study [82] with a deviation of HWE in controls for IL-6 –174G > C polymorphism and the new pooled analyses showed that this polymorphism doesn't associate with the risk of ACS based on all genetic models. This suggests that previous findings may have been driven by deviations in genotype distributions rather than a true genetic effect. We removed three studies [46, 52, 84] with a deviation of HWE in controls for IL6 –572G > C, but the previous pooled result significantly didn't change, reinforcing the robustness of this association. To remove one study [74] for IL-10 –592C > A significantly didn't change the previous pooled result, suggesting that these associations remain stable despite minor deviations in some studies. To remove three studies [36, 61, 71] for IL-10 –1082A > G, significantly didn't change the previous pooled result. To remove five studies [35, 42, 55, 78, 85] for TNF-α –308G > A, just heterogeneity was significantly reduced in the recessive model, indicating that HWE deviations in these studies contributed to variability in the pooled estimates. Therefore, HWE deviations appear to impact the results for some polymorphisms, particularly IL-6 –174G > C and TNF-α –308G > A, emphasizing the need for careful evaluation of these deviations to enhance the reliability of genetic association studies.

Trial sequential analysis

Supplementary 3 shows the TSA plots. Among two polymorphisms of IL-6, three polymorphisms of IL-10, and TNF-α –308G > A polymorphism, for the IL-6 –174G > C polymorphism, TSA results demonstrate that the Z-curve crossed the RIS line in allelic, homozygous, heterozygous, and dominant models, confirming sufficient sample size for reliable conclusions. Additionally, futility boundaries were crossed in allelic, homozygous, dominant, and recessive models, further solidifying the evidence. However, for IL-10 –819C > T polymorphism, the Z-curve only crossed futility boundaries in allelic and homozygous models, suggesting limited evidence and the need for further research to strengthen conclusions. For the TNF-α –308G > A polymorphism, the Z-curve crossed the conventional boundary for harm and trial sequential monitoring boundaries in multiple models, indicating strong evidence of harmful associations. However, in the recessive model, while the conventional boundary for harm was crossed, additional studies are required to confirm robustness. If TSA boundaries are not crossed, conclusions may remain suggestive but lack definitive statistical reliability, emphasizing the need for further investigations.

Publication bias

Supplementary 4 shows the funnel plots. The publication bias tests in Table 7 assess the association between cytokine polymorphisms and the risk of ACS using Egger’s and Begg’s tests. For IL-6 –174G > C, significant publication bias was detected in the homozygous and recessive models using Begg’s test (p = 0.0259 for both), but not with Egger’s test. Other polymorphisms showed no significant publication bias across all genetic models. Overall, the results suggest minimal publication bias for most cytokine polymorphisms, with some exceptions in specific genetic models for IL-6 –174G > C. Therefore, the reported associations may not fully reflect the true effect of the IL-6 –174G > C polymorphism on ACS risk, and caution should be exercised when interpreting these results.

Table 7.

The results of publication bias tests analysis reporting the association between cytokine polymorphisms and the risk of acute coronary syndrome

Polymorphism Genetic model p-value of Egger's test p-value of Begg's test
IL-6 –174G > C Allelic 0.3450 0.3820
Homozygous 0.1149 0.0259
Heterozygous 0.8581 0.5537
Dominant 0.6112 0.7996
Recessive 0.1020 0.0259
IL6 –572G > C Allelic 0.8003 0.7884
Homozygous 0.7526 0.7884
Heterozygous 0.1559 0.1797
Dominant 0.1807 0.1283
Recessive 0.5328 0.5312
IL-10 –592C > A Allelic 0.6344 0.6547
Homozygous 0.6575 0.9287
Heterozygous 0.7890 0.7884
Dominant 0.7360 0.7884
Recessive 0.3821 0.9287
IL-10 –819C > T Allelic 0.3752 0.3475
Homozygous 0.1884 0.4394
Heterozygous 0.6909 0.8509
Dominant 0.5773 0.3475
Recessive 0.8982 0.8509
IL-10 –1082A > G Allelic 0.6847 0.6807
Homozygous 0.7389 0.4928
Heterozygous 0.6175 0.7838
Dominant 0.5457 0.6805
Recessive 0.8659 1.0000
TNF-α –308G > A Allelic 0.3279 0.2253
Homozygous 0.9793 0.5537
Heterozygous 0.1850 0.1504
Dominant 0.2187 0.1780
Recessive 0.9450 0.5921
TNF-α − 1031 T > C Allelic 0.9274 0.6015
Homozygous 0.3783 0.1172
Heterozygous 0.5673 0.6015
Dominant 0.7899 0.6015
Recessive 0.2854 0.1171
TNF-α − 238G > A Allelic 0.1917 0.1171
Homozygous NA NA
Heterozygous 0.7264 0.6015
Dominant 0.8416 0.6015
Recessive NA NA
Open in a new tab

The bold number means statistically significant datum (p < 0.10)

NA "Not available"

Discussion

The analysis of cytokine polymorphisms and their association with ACS risk reveals that IL-6 –174G > C and TNF-α –308G > A are significantly associated with increased ACS risk, particularly in allelic and homozygous models. Subgroup analyses indicate that these associations vary by ethnicity, with significant findings in Asians and Arabs for IL-6 –174G > C and TNF-α –308G > A, but not in Caucasians. IL-10 polymorphisms generally show no significant associations with ACS risk. Meta-regression analysis suggests no significant temporal trends or sample size effects for most polymorphisms, except for TNF-α –308G > A, which shows an increasing association with ACS risk over time. Publication bias was detected for IL-6 –174G > C in certain models, but not for other polymorphisms. These findings highlight the importance of considering ethnicity, disease type, and sample size in genetic studies of ACS.

The relationship between immune-mediated inflammation and ACS is intricate, and the underlying mechanisms of ACS remain not fully understood. However, immune and inflammatory dysfunctions have been implicated in its pathogenesis [10]. Growing evidence shows that vascular inflammation plays a crucial role in the development of ACS, leading to its classification as an inflammation-related disease [10].

Cytokines, produced in various tissues, regulate the expression of numerous inflammatory molecules, leading to the destabilization and eventual rupture of vulnerable atheromatic plaques [86–88]. They also play a role in the pathophysiology of ACS by directly affecting myocardial contractility and apoptosis [86, 89]. Clinically, circulating cytokines serve as prognostic markers, predicting future coronary events in patients with advanced atherosclerosis and those who have experienced ACS [86, 90].

IL-6 has been implicated in the development of atherogenesis and the mediation of tissue damage [64, 91]. TNF-related pathways may contribute to the persistent inflammation characteristic of atherosclerosis, representing pathogenic loops active during plaque rupture and the development of ACS [92]. The inhibition of several proinflammatory factors, prevention of apoptosis in macrophages and monocytes post-infection, and promotion of the phenotypic switch of lymphocytes to the Th2 phenotype is crucial for the development and progression of atherosclerotic lesions, highlighting a potential regulatory role for IL-10 [93].

Accumulating evidence highlights the central role of inflammation in preclinical atherosclerosis, with ACS being a primary clinical manifestation [94]. This condition results from a chronic inflammatory process and disorders of lipid metabolism, influenced by genetic and environmental factors [95]. It is well-established that atherosclerosis is a progressive disease triggered by arterial wall injury and accelerated by inflammatory mechanisms and thrombotic factors, particularly in the context of ACS [96]. The involvement of genetic variants of proinflammatory cytokines and fibrinogen in cardiovascular diseases has not been fully elucidated [80]. Therefore, ACS represents the clinical manifestation of atherosclerotic plaque rupture, with inflammation playing a central role.

Deviation of allelic distributions from HWE may have contributed to between-study heterogeneity in the sensitivity analysis [25, 97, 98]. This meta-analysis demonstrated that removing studies with deviations from HWE significantly altered the pooled OR for the IL-6 –174G > C polymorphism. Additionally, HWE deviations could impact the heterogeneity of the TNF-α –308G > A polymorphism.

Stratified analyses revealed that certain polymorphisms showed stronger or weaker associations in specific ethnicities, highlighting the role of genetic background in ACS susceptibility. Variability in ACS classification and diagnostic criteria across studies may have contributed to inconsistencies in effect sizes and larger studies demonstrated more stable estimates, suggesting that sample size significantly impacts the reliability of genetic associations. Therefore, the results indicated that ethnicity, disease type, and sample size significantly influenced the association between IL-10, IL-6, and TNF-α polymorphisms and ACS risk.

Limitations: 1) Sample Size and Diversity: While significant associations were found, the sample sizes for certain polymorphisms and ethnic groups may still be insufficient to draw definitive conclusions. More extensive studies with larger and more diverse populations are needed. 2) Publication Bias: The detection of publication bias for IL-6 –174G > C in some models suggests that the results might be influenced by selective reporting. This could affect the reliability of the findings. 3) Heterogeneity: High heterogeneity observed in some analyses, particularly for TNF-α –308G > A, indicates variability in study results. This could be due to differences in study design, population characteristics, or other confounding factors. 4) Temporal Trends: Although TNF-α –308G > A showed an increasing association with ACS risk over time, the lack of significant temporal trends for other polymorphisms suggests that more longitudinal studies are needed to understand these dynamics fully. 5) Environmental Factors: The study primarily focuses on genetic factors, but environmental and lifestyle factors also play a crucial role in ACS risk. Future research should integrate these variables to provide a more comprehensive risk assessment. 6) Genetic Models: The study uses various genetic models to assess associations, but the choice of model can influence the results. Consistency across different models is essential for robust conclusions.

Conclusions

The findings suggest that specific cytokine polymorphisms, particularly IL-6 –174G > C and TNF-α –308G > A, can significantly associate with an increased risk of ACS. These associations are especially pronounced in certain ethnic groups, such as Asians and Arabs, highlighting the importance of considering genetic diversity in clinical assessments. From a biological standpoint, these polymorphisms may contribute to the dysregulation of immune and inflammatory pathways, enhancing our understanding of the molecular mechanisms driving ACS. Recognizing these genetic markers can serve not only as diagnostic tools to identify individuals at higher risk but also as potential therapeutic targets for intervention.

Clinical relevance and future directions

Clinically, the incorporation of cytokine polymorphism data could help refine personalized treatment strategies. By considering these genetic factors, healthcare providers can better predict the likelihood of ACS, adjust preventive measures, and tailor interventions, ultimately improving patient outcomes. Therefore, these polymorphisms offer a valuable opportunity for precision medicine, where genetic profiling could guide more effective and individualized cardiovascular care. The ethnic differences suggest that population-specific genetic screening could improve ACS risk stratification and early intervention strategies. Understanding these genetic influences may improve ACS prevention and treatment strategies, especially in populations with a high disease burden.

Future research should focus on expanding the sample sizes and including more diverse populations to confirm these associations and explore other potential polymorphisms. Additionally, longitudinal studies are needed to understand the temporal dynamics of these genetic associations and their interaction with environmental factors. Addressing publication bias and ensuring robust, reproducible results will be crucial for translating these genetic insights into clinical practice. Future research should focus on multiethnic cohort studies to validate these associations, functional studies to assess their impact on cytokine expression, and clinical trials to determine whether incorporating these genetic markers into risk prediction models improves patient outcomes. By integrating genetic insights into clinical practice, we can move toward more precise, ethnicity-informed ACS prevention and treatment strategies.

Supplementary Information

Supplementary Material 1. (66.7KB, docx)
Supplementary Material 2. (496.3KB, docx)
Supplementary Material 3. (2.7MB, docx)
Supplementary Material 4. (166.3KB, docx)

Acknowledgements

Not applicable.

Abbreviations

ACS

Acute coronary syndrome

STEMI

ST-elevation myocardial infarction

NSTEMI

Non-ST-elevation myocardial infarction

UA

Unstable angina

OR

Odds ratio

CI

Confidence acc

IL

Interleukin

TNF-α

Tumor necrosis factor-alpha

PRISMA

Preferred reporting items for systematic reviews and meta-analyses

PECO

Population, exposure, comparison, and outcome

NOS

Newcastle–Ottawa scale

HWE

Hardy–Weinberg equilibrium

TSA

Trial sequential analysis

ACC

American College of Cardiology

Authors’ contributions

Conceptualization, Y.L.; methodology, S.L. and Y.D.; validation, Y.L. and S.S.; formal analysis, S.L. and Y.D.; investigation, S.S.; resources, Y.L.; data curation, S.L.; writing-original draft preparation, S.S.; writing—review and editing, Y.L., S.L., Y.D. and S.S.; visualization, Y.L. and S.L.; supervision, Y.L.; project administration, Y.L. and S.L. All authors have read and agreed to the published version of the manuscript.

Funding

Not applicable.

Data availability

All data supporting the findings of this study are available within the paper and its supplementary information.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yang Luo and Shiqi Li equally contributed equally to this work.

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Data Availability Statement

All data supporting the findings of this study are available within the paper and its supplementary information.

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