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Review

Association of IL-10 and TNF-α Polymorphisms with Dental Peri-Implant Disease Risk: A Meta-Analysis, Meta-Regression, and Trial Sequential Analysis

1
Department of Prosthodontics, School of Dentistry, Kermanshah University of Medical Sciences, Kermanshah 6713954658, Iran
2
School of Medicine and Dentistry & Menzies Health Institute Queensland, Griffith University, Brisbane, QLD 4222, Australia
3
Department of Prosthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran 1983963113, Iran
4
Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah 6714415185, Iran
5
School of Medicine and Dentistry, Griffith University, Brisbane, QLD 4222, Australia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(14), 7697; https://doi.org/10.3390/ijerph18147697
Submission received: 16 June 2021 / Revised: 16 July 2021 / Accepted: 16 July 2021 / Published: 20 July 2021
(This article belongs to the Special Issue Periodontal and Peri-Implant Diseases)

Abstract

:
Genetic susceptibility has been reported to be an important risk factor for peri-implant disease (PID). The aim of this meta-analysis was to assess the association between TNF-α and IL-10 polymorphisms and PID susceptibility. The Web of Science, Cochrane Library, Scopus, and PubMed/Medline databases were searched for studies published until 12 April 2021. RevMan 5.3, CMA 2.0, SPSS 22.0, and trial sequential analysis software were used. Twelve studies were included in our analysis. The pooled ORs for the association of TNF-α (−308 G > A), IL-10 (−1082 A > G), IL-10 (−819 C > T), and IL-10 (−592 A > C) polymorphisms were 1.12, 0.93, 1.35, and 0.77 for allelic; 1.42, 0.95, 3.41, and 0.34 for homozygous; 1.19, 1.88, 1.23, and 0.49 for heterozygous, 1.53, 1.12, 1.41, and 0.39 for recessive; and 1.16, 1.87, 2.65, and 0.75 for dominant models, respectively, with all the estimates being insignificant. The results showed an association between TNF-α (−308 G > A) polymorphism and the risk of PID in patients of Asian ethnicity (OR = 1.59; p = 0.03). The present meta-analysis illustrated that TNF-α (−308 G > A), IL-10 (−1082 A > G), IL-10 (−819 C > T), and IL-10 (−592 A > C) polymorphisms were not associated with the risk of PID, whereas TNF-α (−308 G > A) polymorphism was associated with an elevated risk of PID in Asian patients.

1. Introduction

Despite the high survival rate and success of dental implants, it has long been known that osseointegrated implants may suffer from biological complications, collectively referred to as peri-implant disease (PID) [1]. PIDs are defined as inflammatory lesions of the tissue around the implant and include mucositis around the implant (inflammatory lesion confined to the mucosa around the implant) and peri-implantitis (an inflammatory lesion of the mucosa that affects the supporting bone with bone loss) [2]. A recent meta-analysis included peri-implantitis, implant failure, and marginal bone loss as PIDs [3]. A review study showed peri-implantitis in 28% and ≥56% of cases and in 12% and 43% of implant sites [4]. A systematic review suggested that the prevalence of peri-implantitis was approximately 22% (range: 1–47%) [5]. Another study found the prevalence of dental implant failures to be 11% in males and 9% in females; this prevalence was dependent on implant length, implant diameter, and bone quality [6]. Marginal bone loss (>0.5 mm) at implants was also recognized in 30% of cases and 16% of implant sites [7]. Evidence suggests that those who are aged more than 60 years, smokers, receiving head and neck radiation, postmenopausal, suffering from diabetes, and receiving hormone replacement therapy experienced significantly elevated implant failure in comparison with healthy patients [8].
Genetic susceptibility has been shown to be a significant risk factor for peri-implantitis, and there are numerous studies assessing this in different populations [9,10,11]. Gene polymorphisms refer to changes in DNA sequencing, such as the regulation of inflammatory mediators, primarily the gene promoter region, which can affect gene function and the progression of inflammatory diseases [12,13]. Polymorphisms of cytokines associated with the risk of PID, such as interleukin (IL)-1A [14], IL-1B [14,15], IL-6 [16,17], tumor necrosis factor-alpha (TNF-α) [17], and IL-10 [15,18] as an anti-inflammatory cytokine, could inhibit the production of proinflammatory cytokines and the induction of B lymphocyte proliferation as well as prevent the proliferation and activation of natural killer cells [19]. TNF-α is another anti-inflammatory cytokine that plays an important role in inflammatory processes [17]. The role of TNF-α in the destruction of bone around the implant has been suggested by researchers [20]. A meta-analysis [21] assessed the association of TNF-α (−308 G > A) and IL-10 (−1082 A > G) polymorphisms with the risk of implant failure by including two and three studies, respectively. Another meta-analysis [3] investigated the role of TNF-α (−308 G > A) polymorphism in PID using the data from six studies. Their results did not show any association between these polymorphisms and the risk of dental implant failure [21] and PID [3].
The aim of this study was to evaluate the association between TNF-α (−308 G > A), IL-10 (−1082 A > G), IL-10 (−819 C > T), and IL-10 (−592 A > C) polymorphisms and PID susceptibility with more studies and the addition of two new polymorphisms (IL-10 (−819 C > T) and IL-10 (−592 A > C)), meta-regression, and trial sequential analysis (TSA) compared to two previous meta-analyses.

2. Materials and Methods

2.1. Design

The preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines were used to report this study [22]. The PICO (patient/population, intervention, comparison, and outcomes) question was as follows: Is there an association between IL-10 and TNF-α polymorphisms and the risk of PID in patients with dental implants?

2.2. Literature Search Strategy

The Web of Science, Cochrane Library, Scopus, and PubMed/Medline databases were searched for studies published until 12 April 2021 without any restrictions. The searched terms were (“dental implant*” or “oral implant*” or “peri-implant disease*” or “implant loss” or “implant failure” or “peri-implantitis” or “periimplant” or “implant bone loss” or “failing implant”) and (“interleukin-10” or “IL-10” or “IL10” or “TNFA” or “TNF-α” or “TNF-alpha” or “TNFalpha” or “tumor necrosis factor-alpha” or “tumor necrosis factor alpha” or “TNFα” or “tumor necrosis factor-α”) and (“polymorphism*” or “allele” or “genotype*” or “variant*” or “SNP”). In addition, we manually checked the references of seminal articles related to the subject area to ensure that no potential articles were missed.

2.3. Eligibility Criteria

The studies were retrieved from the databases by one author (M.S.), and the duplicates and irrelevant studies were then excluded. The studies were considered relevant if they met the following eligibility criteria: (I) case–control design; (II) PID as the outcome of interest; (III) reporting TNF-α (−308 G > A), IL-10 (−1082 A > G), IL-10 (−819 C > T), or IL-10 (−592 A > C) polymorphisms with any genetic models; and (IV) having the required data to calculate the odds ratios (ORs) with 95% confidence intervals (CIs) for the genetic models. The studies were removed if they did not have the required data regarding genotype distributions or were animal studies, meta-analyses, review articles, letters to the editor, reported secondary data, and reported genotype distributions after treatment. The second author (L.J.) rechecked the relevant articles based on the eligibility criteria. Any disagreement between the two authors was resolved by discussion.

2.4. Data Extraction

One author (M.S.) independently extracted the data from each study and another author (J.T) rechecked them. The information retrieved from the studies included the first author’s name, publication year, ethnic group, control source, mean/median age and male/female ratio in the two groups (patients and controls), genotyping method, form of disease, number of patients or controls, the p-value of Hardy–Weinberg equilibrium (HWE) in controls, the quality score, and the distribution of genotypes in the two groups. If there was a disagreement between the authors, the problem was resolved by a short discussion.

2.5. Quality of Assessment

One author (L.J) distinguished the quality of each included article using the modified Newcastle–Ottawa Quality Assessment Scale questionnaire, which was used in a similar meta-analysis involving gene polymorphisms. It involves assigning scores ranging from 0–2 and 0–1 on five (representativeness of cases, ascertainment of case outcomes, ascertainment of controls, H–W equilibrium in controls, and association assessment) and two (description of follow-up and genotyping examination) criteria, respectively. A maximum total score of 12 was possible for each study [3].

2.6. Statistical Analyses

The Review Manager 5.3 (RevMan 5.3; the Cochrane Collaboration, the Nordic Cochrane Centre, Copenhagen, Denmark) was used to calculate crude odds ratio (OR) and 95% confidence interval (CI) showing the association between IL-10 and TNF-α polymorphisms and dental PID risk in the five genetic models. To evaluate the pooled OR significance, the Z test was applied with a p < 0.05. The Cochrane Q test and I2 statistic showed the heterogeneity (inconsistency in the polymorphism effect across primary studies). If there was a statistically significant heterogeneity (p < 0.1 or I2 > 50%), we used a random-effect model (DerSimonian and Laird method) [23], and if there was no significant heterogeneity, a fixed-effect model (Mantel–Haenszel method) [24] was used.
The chi-square test was used to calculate the p-value of HWE in the control group of each study, with p < 0.05 indicating a deviation from the HWE.
Subgroup, sensitivity, and meta-regression analyses were performed where possible depending on the number of studies available. The subgroup analysis for explanation of heterogeneity based on a priori hypothesis was done for TNF-α (−308 G > A) polymorphism according to the ethnicity, control source, disease form, and number of individuals.
The funnel plots were analyzed by the Egger’s and Begg’s tests (with p-values < 0.05 indicating statistically significant existence of the publication bias). To evaluate the stability of the pooled results, we used sensitivity analyses (“one study removed” and “cumulative analysis”) for TNF-α (−308 G > A) and IL-10 (−819 C > T) polymorphisms. The Comprehensive Meta-Analysis version 2.0 (CMA 2.0; Biostat Inc., Englewood, NJ, USA) was used for sensitivity analyses and assessing publication bias. A meta-regression was performed to check the effect of publication year and number of individuals on the pooled results of TNF-α (−308 G > A) polymorphism. SPSS version 22.0 software (IBM Corp. Release 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp) was used to calculate the results of meta-regression.
Each meta-analysis may create a false-positive or -negative conclusion [25]. Hence, TSA was conducted using TSA software (version 0.9.5.10 beta) (Copenhagen Trial Unit, Centre for Clinical Intervention Research, Rigshospitalet, Copenhagen, Denmark) to reduce these statistical errors [26]. Additionally, a threshold of futility was tested by TSA to earn a conclusion of no effect before reaching the information size. The required information size (RIS) based on an alpha risk of 5%, a beta risk of 20%, and a two-sided boundary type was computed. For those analyses where the Z-curve reached the RIS line or monitored the boundary line or futility area, it was considered that the studies had adequate sample size and their results were valid. Otherwise, it was assumed that the available information was inadequate and more evidence was needed.

3. Results

3.1. Study Selection

Through the electronic and manual search, 63 records were identified (Figure 1). After removing the duplicates, 30 records were screened, while 10 irrelevant records were removed. A total of 20 full-text articles were evaluated for possible inclusion, and 8 of them were deemed irrelevant and excluded with reasons (one animal study, two reviews, one reported gingival crevicular fluid level of TNF-α and not polymorphisms, two meta-analyses, one systematic review, and one reported implant failure after total hip arthroplasty). Finally, 12 studies were included in our analysis.

3.2. Quality Assessment

The quality score for the studies based on modified the Newcastle-Ottawa Scale (NOS) is shown in Table 1. The scores ranged from 8 to 10.

3.3. Study Characteristics

Out of the 12 studies [14,15,17,18,27,28,29,30,31,32,33,34], five [15,17,30,31,32] were reported in Caucasian, five [18,27,28,33,34] in mixed race, and two [14,29] in Asian ethnicities (Table 1). The control source was hospital-based in nine studies [17,18,27,28,29,31,32,33,34] and population-based in three studies [14,15,30]. The genotyping method in all studies was based on polymerase chain reaction (PCR). The form of PID in six [18,27,30,31,33,34], five [14,15,17,28,32], and one [29] studies were implant failure, peri-implantitis, and marginal bone loss, respectively.
Table 2 demonstrates the distribution of study population in the included studies based on the genotypes of TNF-α (−308 G > A), IL-10 (−1082 A > G), IL-10 (−819 C > T), and IL-10 (−592 A > C) polymorphisms. Ten [14,15,17,27,28,29,30,31,32,34] studies reported genotype prevalence of TNF-α (−308 G > A), four [17,18,30,33] reported IL-10 (−1082 A > G), three [15,18,30] reported IL-10 (−819 C > T), and two [15,18] reported IL-10 (−592 A > C) polymorphisms. Among the studies reporting TNF-α (−308 G > A) polymorphism, the control group in three studies [14,30,32] had a deviation from HWE. Among the studies reporting IL-10 (−819 C > T) polymorphism, one study [30] showed a deviation from HWE for the control group.

3.4. Pooled Analyses

The results of meta-analyses based on five genetic models for TNF-α (−308 G > A) polymorphism are shown in Table 3. The pooled ORs were 1.12 (95%CI: 0.90–1.39; p = 0.32; I2 = 43%), 1.42 (95%CI: 0.85–2.37; p = 0.18; I2 = 0%), 1.19 (95%CI: 0.87–1.63; p = 0.28; I2 = 0%), 1.53 (95%CI: 0.95–2.45; p = 0.08; I2 = 59%), and 1.16 (95%CI: 0.74–1.81; p = 0.52; I2 = 0%) for allelic, homozygous, heterozygous, recessive, and dominant models, respectively. The results showed that TNF-α (−308 G > A) polymorphism was not associated with PID risk.
The pooled ORs for allelic, homozygous, heterozygous, recessive, and dominant models of IL-10 (−1082 A > G) polymorphism were 0.93 (95%CI: 0.69–1.25; p = 0.61; I2 = 0%), 0.95 (95%CI: 0.51–1.79; p = 0.88; I2 = 0%), 1.88 (95%CI: 0.55–1.43; p = 0.62; I2 = 0%), 1.12 (95%CI: 0.74–1.68; p = 0.60; I2 = 35%), and 1.87 (95%CI: 0.36–2.11; p = 0.76; I2 = 56%), respectively (Table 4). The results showed that IL-10 (−1082 A > G) polymorphism was not associated with susceptibility to PID.
For allelic, homozygous, heterozygous, recessive, and dominant models of IL-10 (−819 C > T) polymorphism, the pooled ORs were 1.35 (95%CI: 1.00–1.82; p = 0.05; I2 = 37%), 3.41 (95%CI: 0.52–22.17; p = 0.20; I2 = 60%), 1.23 (95%CI: 0.80–1.90; p = 0.35; I2 = 0%), 1.41 (95%CI: 0.93–2.13; p = 0.10; I2 = 0%), and 2.65 (95%CI: 0.53–13.34; p = 0.24; I2 = 57%), respectively (Table 5). The results reported that there was no association between IL-10 (−819 C > T) polymorphism and susceptibility to PID.
Table 6 demonstrates the results for IL-10 (−592 A > C) polymorphism with data from two studies for C vs. A, CC vs. AA, AC vs. AA, CC + AC vs. AA, and CC vs. AA + AC genetic models. High heterogeneity was observed in all the models, the pooled ORs were 0.77 (95%CI: 0.18–3.31; p = 0.73), 0.34 (95%CI: 0.00–23.53; p = 0.62), 0.49 (95%CI: 0.03–9.22; p = 0.63), 0.39 (95%CI: 0.01–12.59; p = 0.60), and 0.75 (95%CI: 0.14–3.98; p = 0.73) for C vs. A, CC vs. AA, AC vs. AA, CC + AC vs. AA, and CC vs. AA + AC, respectively. The results showed that there was no association between IL-10 (−592 A > C) polymorphism and susceptibility to PID.

3.5. Subgroup Analysis

Subgroup analyses based on ethnicity, control source, disease form, and number of individuals were performed on the association between TNF-α (−308 G > A) polymorphism and PID risk (Table 7). The results showed that ethnicity was the only significant factor. Asian patients with TNF-α (−308 G > A) polymorphism had a significant elevated risk of PID than the controls (OR = 1.59; p = 0.03), whereas there was no significant association between the polymorphism and PID risk for Caucasian and mixed ethnicities.

3.6. Sensitivity Analysis

Sensitivity analyses were performed by removing studies with a deviation of HWE in their controls for both TNF-α (−308 G > A) and IL-10 (−819 C > T) polymorphisms (Table 8). In addition, “one study removed” and “cumulative analyses” were performed, and the results did not change for both the polymorphisms.

3.7. Meta-Regression

To check the effect of publication year and sample size on the pooled results of TNF-α (−308 G > A) polymorphism, meta-regression was conducted. The findings demonstrated that the publication year and sample size were not confounding factors on the association between TNF-α (−308 G > A) polymorphism and susceptibility to PID (Table 9).

3.8. Trial Sequential Analysis

For TNF-α (−308 G > A) and IL-10 (−1082 A > G) polymorphisms, the Z-curve did not reach the RIS line or cross the boundary line or enter futility area, establishing that the evidence was not enough for significant results and more information was needed. With regard to IL-10 (−819 C > T) polymorphism, the Z-curve exceeded the RIS line, confirming that there was enough evidence to conclude that that the IL-10 (−819 C > T) polymorphism was not associated with the PID risk (Figure 2).

3.9. Publication Bias

Funnel plots (Figure 3) along with Egger’s and Begg’s tests demonstrated that there was no publication bias for allelic (Egger’s p = 0.859 and Begg’s p = 0.834), homozygous (Egger’s p = 0.785 and Begg’s p = 0.452), heterozygous (Egger’s p = 0.667 and Begg’s p = 0.835), recessive (Egger’s p = 0.633 and Begg’s p = 0.929), and dominant (Egger’s p = 0.710 and Begg’s p = 0.881) models of TNF-α (−308 G > A) polymorphism.

4. Discussion

Dental implants provide a great treatment option for patients with missing teeth by replacing the root of the tooth with fixed permanent artificial tooth roots that are implanted into the jawbone matching the natural ones and supporting the prosthetic crowns [21].
The main results of the present meta-analysis showed that TNF-α (−308 G > A), IL-10 (−1082 A > G), IL-10 (−819 C > T), and IL-10 (−592 A > C) polymorphisms were not associated with PID risk. Out of TNF-α (−308 G > A), IL-10 (−1082 A > G), and IL-10 (−819 C > T) polymorphisms, the TSA confirmed the result of only IL-10 (−819 C > T) polymorphism, indicating the need for more evidence on TNF-α (−308 G > A) and IL-10 (−1082 A > G) polymorphisms. The TNF-α (−308 G > A) polymorphism had a significant elevated risk in Asian PID patients compared to controls. Moreover, the meta-regression confirmed that publication year and number of individuals were not confounding factors on the association between TNF-α (−308 G > A) polymorphism and PID susceptibility.
One research showed increased salivary TNF-α level in cases with peri-implant clinical condition, especially in patients with peri-implantitis [35]. Another research confirmed significantly higher serum level of TNF-α in peri-implantitis patients compared to controls, indicating the pivotal role of these cytokines in peri-implantitis [36]. Farhad et al. [37] concluded that IL-10 level increased in patients with PID compared to individuals with healthy peri-implant tissues, which was also confirmed by many other studies [38,39,40]. Differences in the level of two cytokines between PID patients and controls and the lack of association between the two polymorphisms and the risk of PID in our meta-analysis may indicate the influence of other genetic as well as environmental factors. Future studies might need to explore the influence of these factors.
A meta-analysis examined the association between smoking, radiotherapy, diabetes, and osteoporosis and the risk of dental implant failure [41]. Smoking [17,41,42,43] and radiotherapy [41] are considered the most significant risk factors for dental implant failure. It would be interesting to explore the role of these risk factors on the relationship between gene polymorphism and PID. However, we could not run a meta-regression analysis to assess the effect of these risk factors on the association between gene polymorphisms and PID risk due to unavailability of such data. Wilson and Nunn evaluated the effect of IL-1 polymorphism (smoking and age on dental implant failures) and found that smoking was the only strong risk factor for implant failure [44]. Feloutzis et al. observed similar findings suggesting that IL-1 genotype could further precipitate the detrimental effect of smoking on peri-implant bone loss [45]. Pathogenic bacteria, lack of oral hygiene, and alcohol consumption have also been reported as factors associated with peri-implantitis [42,43]. Research has also indicated the possible effect of systemic diseases on peri-implant bone loss, and most studies therefore recruit PID patients without any systemic diseases [46,47,48,49]. Most studies in our meta-analysis selected individuals who did not smoke or the smoking status was matched between two groups (patients and controls) [14,18,27,28,30,32,33] and without any systemic disease in both cases and controls [18,27,30,33].
Although research exploring the effect of several systemic, habitual, and clinical risk factors on the risk of PID is vast, the effect of genetic risk factors has not been well studied [50,51]. This meta-analyses evaluated TNF-α (−308 G > A) and IL-10 (−1082 A > G) polymorphisms [21] or TNF-α (−308 G > A) polymorphism [3] alone, and no association was observed between any of these polymorphisms and the risk of PID disease. In our meta-analysis, there was an association between TNF-α (−308 G > A) polymorphism and PID in Asian patients. We need to further explore the role of ethnicity on the association of the mentioned polymorphisms and PID risk, especially TNF-α (−308 G > A) polymorphism.
This meta-analysis had several limitations, namely (1) few studies and lack of subgroup analysis for IL-10 polymorphisms, (2) smaller sample sizes in some of the included studies, (3) inclusion of smokers as cases and controls in some studies, and (4) the studies that included populations from Asian ethnicity were both from China, meaning the results might not be representative of all Asian population. Lack of publication bias, stability of the pooled data, and the confirmation of the pooled results by TSA would be the important strengths of this meta-analysis.

5. Conclusions

The pooled analysis of the present meta-analysis showed that TNF-α (−308 G > A), IL-10 (−1082 A > G), IL-10 (−819 C > T), and IL-10 (−592 A > C) polymorphisms were not associated with PID risk, whereas TNF-α (−308 G > A) polymorphism was associated with a significant elevated risk of PID in patients of Asian ethnicity.

Author Contributions

Conceptualization, S.K.T. and J.T.; methodology, S.K.T. and M.S.; formal analysis, M.S.; investigation, L.J.; resources, M.S. and J.T.; data curation, P.C., J.T. and M.S.; writing—original draft preparation, L.J. and P.C.; and writing—review and editing, L.J., S.K.T. and J.T. All authors have read and agreed to the published version of the manuscript.

Funding

No financial support was received for this study.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

Acknowledgments

Santosh Kumar Tadakamadla acknowledges the support of National Health and Medical Research Council (Australia) through early career fellowship.

Conflicts of Interest

The authors have no conflict of interest regarding the publication of this study.

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Figure 1. Flow chart of the study selection.
Figure 1. Flow chart of the study selection.
Ijerph 18 07697 g001
Figure 2. Trial sequential analysis for the association between polymorphisms and dental peri-implant disease risk based on heterozygous model: (A) TNF-α (−308 G > A), (B) IL-10 (−1082 A > G), and (C) IL-10 (−819 C > T).
Figure 2. Trial sequential analysis for the association between polymorphisms and dental peri-implant disease risk based on heterozygous model: (A) TNF-α (−308 G > A), (B) IL-10 (−1082 A > G), and (C) IL-10 (−819 C > T).
Ijerph 18 07697 g002
Figure 3. Funnel plot analyses of five genetic models for the association between TNF-α (−308 G > A) polymorphism and peri-implant disease risk: (A) allelic, (B) homozygous, (C) heterozygous, (D) recessive, and (E) dominant.
Figure 3. Funnel plot analyses of five genetic models for the association between TNF-α (−308 G > A) polymorphism and peri-implant disease risk: (A) allelic, (B) homozygous, (C) heterozygous, (D) recessive, and (E) dominant.
Ijerph 18 07697 g003
Table 1. Characteristics of studies included in the analysis.
Table 1. Characteristics of studies included in the analysis.
First Author, Publication YearCountryEthnicityControl SourceCaseControlGenotyping MethodForm of DiseaseQuality Score
Number Mean/Median Age, YearSex (M/F)Number Mean/Median Age, YearSex (M/F)
Campos, 2004 [27]BrazilMixedHB2852.713/153843.218/20PCRImplant failure10
Cury, 2009 [28]BrazilMixedHB4951.115/344145.217/24PCRPeri-implantitis8
Lu, 2009 [29]ChinaAsianHB184714/4264815/11PCRMarginal bone loss8
Gurol, 2011 [30]TurkeyCaucasianPB16Range: 15–38-23Range: 15–38-ARMS-PCRImplant failure8
Pigossi, 2012 [18]BrazilMixedHB9255.137/5518553.164/121RT-PCRImplant failure8
Jacobi-Gresser, 2013 [31]GermanyCaucasianHB4151.118/236851.816/52PCRImplant failure8
Rakic, 2015 [32]SerbiaCaucasianHB18053.2102/7818949.499/90PCR-RFLPPeri-implantitis10
Petkovic-Curcin, 2017 [17]SerbiaCaucasianHB345826/8645844/20PCR-RFLPPeri-implantitis8
Ribeiro, 2017 [33]BrazilMixedHB29Range: 21–80-61Range: 21–80-ARMS-PCRImplant failure8
Broker, 2018 [34]BrazilMixedHB8152.930/511635152/111RT-PCRImplant failure9
He, 2020 [14]ChinaAsianPB14445.188/5617444.392/82PCRPeri-implantitis9
Saremi, 2021 [15]IranCaucasianPB5042.224/268940.443/46PCR-RFLPPeri-implantitis9
HB: hospital-based; PB: population-based; RT-PCR: real-time polymerase chain reaction; ARMS: amplification-refractory mutation system; RFLP: restriction fragment length polymorphism.
Table 2. Distribution of the genotypes of four polymorphisms.
Table 2. Distribution of the genotypes of four polymorphisms.
First Author, Publication YearTNF-α (−308 G > A)p-Value of HWE in Control
CaseControl
GGGAAAGGGAAA
Campos, 2004 [27]262032600.597
Cury, 2009 [28]3411431820.161
Lu, 2009 [29]126023300.746
Gurol, 2011 [30]11414190< 0.001
Jacobi-Gresser, 2013 [31]22172471920.962
Rakic, 2015 [32]1572031652130.026
Petkovic-Curcin, 2017 [17]1519568NA
Broker, 2018 [34]631601283221.000
He, 2020 [14]11311201461216< 0.001
Saremi, 2021 [15]41234418670.074
IL-10 (−1082 A > G)
AAAGGGAAAGGG
Gurol, 2011 [30]29431540.086
Pigossi, 2012 [18]3641156590240.412
Petkovic-Curcin, 2017 [17]6282539NA
Ribeiro, 2017 [33]61671124260.204
IL-10 (−819 C > T)
CCCTTTCCCTTT
Gurol, 2011 [30]01211191< 0.001
Pigossi, 2012 [18]3738118276190.824
Saremi, 2021 [15]22217533510.067
IL-10 (−592 A > C)
AAACCCAAACCC
Pigossi, 2012 [18]2438128777180.873
Saremi, 2021 [15]82616135530.067
Table 3. The results of meta-analyses based on five genetic models for TNF-α (−308 G > A) polymorphism.
Table 3. The results of meta-analyses based on five genetic models for TNF-α (−308 G > A) polymorphism.
Genetic ModelFirst Author, Publication YearCaseControlWeightOdds Ratio
EventsTotalEventsTotalM–H, Fixed, 95%CI
A vs. GCampos, 2004 [27] 2566763.3%0.43 [0.08, 2.23]
Cury, 2009 [28]199812827.0%1.40 [0.64, 3.09]
Lu, 2009 [29]6363521.4%3.27 [0.76, 14.04]
Gurol, 2011 [30]163219465.2%1.42 [0.57, 3.52]
Jacobi-Gresser, 2013 [31]2182231368.5%1.69 [0.87, 3.30]
Rakic, 2015 [32]263242738815.0%1.17 [0.67, 2.04]
Broker, 2018 [34]263603632423.3%0.62 [0.37, 1.06]
He, 2020 [14]512884434821.8%1.49 [0.96, 2.30]
Saremi, 2021 [15]8010015217814.5%0.68 [0.36, 1.30]
Subtotal (95%CI) 1376 1630100.0%1.12 [0.90, 1.39]
Total events 247 322
Heterogeneity: Chi2 = 14.03, df = 8 (p = 0.08); I2 = 43%
Test for overall effect: Z = 1.00 (p = 0.32)
AA vs. GGCampos, 2004 [27]026032 Not estimable
Cury, 2009 [28]012023 Not estimable
Lu, 2009 [29]4382337.9%1.82 [0.31, 10.66]
Gurol, 2011 [30]12040.8%9.00 [0.22, 362.48]
Jacobi-Gresser, 2013 [31]2242495.0%2.14 [0.28, 16.17]
Rakic, 2015 [32]3160316811.8%1.05 [0.21, 5.28]
Broker, 2018 [34]06321306.7%0.40 [0.02, 8.56]
He, 2020 [14]201231616247.6%1.77 [0.88, 3.58]
Saremi, 2021 [15]3438677120.2%0.51 [0.12, 2.15]
Subtotal (95%CI) 486 672100.0%1.42 [0.85, 2.37]
Total events 64 92
Heterogeneity: Chi2 = 4.30, df = 6 (p = 0.64); I2 = 0%
Test for overall effect: Z = 1.33 (p = 0.18)
GA vs. GGCampos, 2004 [27]2286386.7%0.41 [0.08, 2.21]
Cury, 2009 [28]11458399.2%1.25 [0.45, 3.52]
Lu, 2009 [29]6183262.3%3.83 [0.81, 18.09]
Gurol, 2011 [30]141519231.4%2.95 [0.30, 29.32]
Jacobi-Gresser, 2013 [31]1739196611.3%1.91 [0.84, 4.37]
Rakic, 2015 [32]201772118625.9%1.00 [0.52, 1.92]
Broker, 2018 [34]16793216024.0%1.02 [0.52, 1.99]
He, 2020 [14]111241215813.7%1.18 [0.50, 2.78]
Saremi, 2021 [15]121618225.4%0.67 [0.14, 3.19]
Subtotal (95% CI) 541 718100.0%1.19 [0.87, 1.63]
Total events 109 138
Heterogeneity: Chi2 = 6.60, df = 8 (p = 0.58); I2 = 0%
Test for overall effect: Z = 1.09 (p = 0.28)
AA + GA vs. GGCampos, 2004 [27]2286385.6%0.41 [0.08, 2.21]
Cury, 2009 [28]1549104111.0%1.37 [0.54, 3.49]
Lu, 2009 [29]6183266.3%3.83 [0.81, 18.09]
Gurol, 2011 [30]151619233.5%3.16 [0.32, 31.29]
Jacobi-Gresser, 2013 [31]1941216812.5%1.93 [0.87, 4.31]
Rakic, 2015 [32]231802418914.6%1.01 [0.55, 1.86]
Petkovic-Curcin, 2017 [17] 193486410.4%8.87 [3.25, 24.19]
Broker, 2018 [34]16793416214.0%0.96 [0.49, 1.86]
He, 2020 [14]311442817415.2%1.43 [0.81, 2.52]
Saremi, 2021 [15]465085897.0%0.54 [0.13, 2.26]
Subtotal (95%CI) 639 874100.0%1.53 [0.95, 2.45]
Total events 192 238
Heterogeneity: Tau2 = 0.30; Chi2 = 21.81, df = 9 (p = 0.009); I2 = 59%
Test for overall effect: Z = 1.75 (p = 0.08)
AA vs. GG + GACampos, 2004 [27]028038 Not estimable
Cury, 2009 [28]018026 Not estimable
Lu, 2009 [29]4492415.5%1.73 [0.30, 9.98]
Gurol, 2011 [30]1160231.0%4.55 [0.17, 118.99]
Jacobi-Gresser, 2013 [31]2412684.0%1.69 [0.23, 12.50]
Rakic, 2015 [32]318031897.9%1.05 [0.21, 5.28]
Broker, 2018 [34]07921624.5%0.40 [0.02, 8.51]
He, 2020 [14]201441617434.4%1.59 [0.79, 3.20]
Saremi, 2021 [15]3450678942.6%0.70 [0.32, 1.50]
Subtotal (95%CI) 605 810100.0%1.16 [0.74, 1.81]
Total events 64 92
Heterogeneity: Chi2 = 3.98, df = 6 (p = 0.68); I2 = 0%
Test for overall effect: Z = 0.64 (p = 0.52)
Table 4. Results on the association of the five genetic models of IL-10 (−1082 A > G) polymorphism with the risk of PID.
Table 4. Results on the association of the five genetic models of IL-10 (−1082 A > G) polymorphism with the risk of PID.
Genetic ModelFirst Author, Publication YearCaseControlWeightOdds Ratio
EventsTotalEventsTotalM–H, Fixed, 95%CI
G vs. AGurol, 2011 [30]173023449.1%1.19 [0.47, 3.04]
Pigossi, 2012 [18]7118413835864.5%1.00 [0.70, 1.44]
Ribeiro, 2017 [33]30587612226.5%0.65 [0.34, 1.22]
Subtotal (95%CI) 272 524100.0%0.93 [0.69, 1.25]
Total events 118 237
Heterogeneity: Chi2 = 1.68, df = 2 (p = 0.43); I2 = 0%
Test for overall effect: Z = 0.51 (p = 0.61)
GG vs. AAGurol, 2011 [30]46476.2%1.50 [0.16, 14.42]
Pigossi, 2012 [18]1551248962.3%1.13 [0.53, 2.42]
Ribeiro, 2017 [33]713263731.5%0.49 [0.13, 1.81]
Subtotal (95%CI) 70 133100.0%0.95 [0.51, 1.79]
Total events 26 54
Heterogeneity: Chi2 = 1.33, df = 2 (p = 0.51); I2 = 0%
Test for overall effect: Z = 0.15 (p = 0.88)
AG vs. AAGurol, 2011 [30]91115185.9%0.90 [0.13, 6.46]
Pigossi, 2012 [18]41779015579.7%0.82 [0.47, 1.43]
Ribeiro, 2017 [33]1622243514.4%1.22 [0.38, 3.97]
Subtotal (95% CI) 110 208100.0%0.88 [0.55, 1.43]
Total events 66 129
Heterogeneity: Chi2 = 0.36, df = 2 (p = 0.84); I2 = 0%
Test for overall effect: Z = 0.50 (p = 0.62)
GG + AG vs. AAGurol, 2011 [30]131519224.7%1.03 [0.15, 7.02]
Pigossi, 2012 [18]569211417969.2%0.89 [0.53, 1.49]
Petkovic-Curcin, 2017 [17]2329506115.2%0.84 [0.28, 2.56]
Ribeiro, 2017 [33]2834396410.9%2.99 [1.08, 8.25]
Subtotal (95%CI) 170 326100.0%1.12 [0.74, 1.68]
Total events 120 222
Heterogeneity: Chi2 = 4.64, df = 3 (p = 0.20); I2 = 35%
Test for overall effect: Z = 0.53 (p = 0.60)
GG vs. AA + AGGurol, 2011 [30]41542220.8%1.64 [0.34, 7.91]
Pigossi, 2012 [18]15922417944.1%1.26 [0.62, 2.54]
Ribeiro, 2017 [33]734266435.2%0.38 [0.14, 1.00]
Subtotal (95%CI) 141 265100.0%0.87 [0.36, 2.11]
Total events 26 54
Heterogeneity: Tau2 = 0.33; Chi2 = 4.50, df = 2 (p = 0.11); I2 = 56%
Test for overall effect: Z = 0.31 (p = 0.76)
Table 5. Meta-analyses of studies involving five genetic models of IL-10 (−819 C > T) polymorphism and the risk of PID.
Table 5. Meta-analyses of studies involving five genetic models of IL-10 (−819 C > T) polymorphism and the risk of PID.
Genetic ModelFirst Author, Publication YearCaseControlWeightOdds Ratio
EventsTotalEventsTotalM–H, Fixed, 95%CI
T vs. CGurol, 2011 [30]1426214210.1%1.17 [0.44, 3.11]
Pigossi, 2012 [18]6017211435466.3%1.13 [0.77, 1.66]
Saremi, 2021 [15]351003717823.6%2.05 [1.19, 3.55]
Subtotal (95%CI) 298 574100.0%1.35 [1.00, 1.82]
Total events 109 172
Heterogeneity: Chi2 = 3.17, df = 2 (p = 0.20); I2 = 37%
Test for overall effect: Z = 1.97 (p = 0.05)
TT vs. CCGurol, 2011 [30]111216.3%3.00 [0.06, 151.19]
Pigossi, 2012 [18]11481910151.2%1.28 [0.56, 2.97]
Saremi, 2021 [15]72915432.5%16.86 [1.96, 145.27]
Subtotal (95%CI) 78 157100.0%3.41 [0.52, 22.17]
Total events 19 21
Heterogeneity: Tau2 = 1.60; Chi2 = 4.99, df = 2 (p = 0.08); I2 = 60%
Test for overall effect: Z = 1.28 (p = 0.20)
CT vs. TTGurol, 2011 [30]121219201.6%1.92 [0.07, 51.03]
Pigossi, 2012 [18]38757615866.2%1.11 [0.64, 1.92]
Saremi, 2021 [15]2143358832.2%1.45 [0.69, 3.01]
Subtotal (95% CI) 130 266100.0%1.23 [0.80, 1.90]
Total events 71 130
Heterogeneity: Chi2 = 0.40, df = 2 (p = 0.82); I2 = 0%
Test for overall effect: Z = 0.93 (p = 0.35)
TT + CT vs. CCGurol, 2011 [30]131320211.5%1.98 [0.07, 52.16]
Pigossi, 2012 [18]49869517770.0%1.14 [0.68, 1.92]
Saremi, 2021 [15]2950368928.5%2.03 [1.01, 4.11]
Subtotal (95%CI) 149 287100.0%1.41 [0.93, 2.13]
Total events 91 151
Heterogeneity: Chi2 = 1.71, df = 2 (p = 0.43); I2 = 0%
Test for overall effect: Z = 1.63 (p = 0.10)
TT vs. CC + CTGurol, 2011 [30]11312120.5%1.67 [0.10, 29.18]
Pigossi, 2012 [18]11861917750.6%1.22 [0.55, 2.69]
Saremi, 2021 [15]75018928.9%14.33 [1.71, 120.16]
Subtotal (95%CI) 149 287100.0%2.65 [0.53, 13.34]
Total events 19 21
Heterogeneity: Tau2 = 1.18; Chi2 = 4.69, df = 2 (p = 0.10); I2 = 57%
Test for overall effect: Z = 1.18 (p = 0.24)
Table 6. Meta-analyses of association between IL-10 (−592 A > C) polymorphism and PID risk based on five genetic models.
Table 6. Meta-analyses of association between IL-10 (−592 A > C) polymorphism and PID risk based on five genetic models.
Genetic ModelFirst Author, Publication YearCaseControlWeightOdds Ratio
EventsTotalEventsTotalM–H, Random, 95%CI
C vs. APigossi, 2012 [18]6214811336450.8%1.60 [1.08, 2.38]
Saremi, 2021 [15]5810014117849.2%0.36 [0.21, 0.62]
Subtotal (95%CI) 248 542100.0%0.77 [0.18, 3.31]
Total events 120 254
Heterogeneity: Tau2 = 1.05; Chi2 = 19.07, df = 1 (p < 0.0001); I2 = 95%
Test for overall effect: Z = 0.35 (p = 0.73)
CC vs. AAPigossi, 2012 [18]12361810552.7%2.42 [1.02, 5.70]
Saremi, 2021 [15]1624535447.3%0.04 [0.00, 0.32]
Subtotal (95%CI) 60 159100.0%0.34 [0.00, 23.53]
Total events 28 71
Heterogeneity: Tau2 = 8.70; Chi2 = 13.45, df = 1 (p = 0.0002); I2 = 93%
Test for overall effect: Z = 0.50 (p = 0.62)
AC vs. AAPigossi, 2012 [18]38627716456.0%1.79 [0.99, 3.25]
Saremi, 2021 [15]2634353644.0%0.09 [0.01, 0.79]
Subtotal (95% CI) 96 200100.0%0.49 [0.03, 9.22]
Total events 64 112
Heterogeneity: Tau2 = 3.93; Chi2 = 7.11, df = 1 (p = 0.008); I2 = 86%
Test for overall effect: Z = 0.48 (p = 0.63)
CC + AC vs. AAPigossi, 2012 [18]50749518254.3%1.91 [1.08, 3.36]
Saremi, 2021 [15]4250888945.7%0.06 [0.01, 0.49]
Subtotal (95%CI) 124 271100.0%0.39 [0.01, 12.59]
Total events 92 183
Heterogeneity: Tau2 = 5.70; Chi2 = 10.16, df = 1 (p = 0.001); I2 = 90%
Test for overall effect: Z = 0.53 (p = 0.60)
CC vs. AA + ACPigossi, 2012 [18]12741818249.6%1.76 [0.80, 3.87]
Saremi, 2021 [15]1650538950.4%0.32 [0.15, 0.66]
Subtotal (95%CI) 124 271100.0%0.75 [0.14, 3.98]
Total events 28 71
Heterogeneity: Tau2 = 1.31; Chi2 = 9.75, df = 1 (p = 0.002); I2 = 90%
Test for overall effect: Z = 0.34 (p = 0.73)
Table 7. Subgroup analyses based on ethnicity, control source, disease form, and sample size for five genetic models of TNF-α (−308 G > A) polymorphism.
Table 7. Subgroup analyses based on ethnicity, control source, disease form, and sample size for five genetic models of TNF-α (−308 G > A) polymorphism.
Variable (N, N′)A vs. GAA vs. GGGA vs. GGAA + GA vs. GGAA vs. GG + GA
OR (95%CI), p, I2OR (95%CI), p, I2OR (95%CI), p, I2OR (95%CI), p, I2OR (95%CI), p, I2
All (9, 10)1.12 (0.90, 1.39), 0.32, 43%1.42 (0.85, 2.37), 0.18, 0%1.19 (0.87, 1.63), 0.28, 0%1.53 (0.95, 2.45), 0.08, 59%1.16 (0.74, 1.81), 0.52, 0%
Ethnicity
Caucasian (4,5)1.14 (0.82, 1.59), 0.44, 25%1.06 (0.43, 2.62), 0.89, 0%1.26 (0.79, 2.01), 0.34, 0%1.92 (0.76, 4.89), 0.17, 75%0.89 (0.47, 1.68), 0.72, 0%
Asian (2, 2)1.59 (1.05, 2.42), 0.03, 3% *1.77 (0.88, 3.58), 0.111.57 (0.75, 3.27), 0.23, 41%1.61 (0.95, 2.74), 0.08, 27%1.59 (0.79, 3.20), 0.19
Mixed (3, 3)0.77 (0.51, 1.16), 0.21, 40%1.17 (0.29, 4.81), 0.83, 0%0.97 (0.57, 1.64), 0.91, 0%0.97 (0.59, 1.62), 0.92, 0%1.14 (0.28, 4.63), 0.86, 0%
Control source
Hospital-based (6, 7)1.06 (0.79, 1.42), 0.68, 49%1.28 (0.50, 3.28), 0.61, 0%1.20 (0.84, 1.71), 0.32, 9%1.67 (0.89, 3.13), 0.11, 0.69%1.20 (0.47, 3.07), 0.70, 0%
Population-based (3, 3)1.20 (0.86, 1.68), 0.29, 50%1.48 (0.80, 2.74), 0.21, 39%1.17 (0.58, 2.36), 0.66, 0%1.33 (0.80, 2.21), 0.28, 6%1.14 (0.69, 1.90), 0.60, 37%
Disease form
Peri-implantitis (4, 5)1.19 (0.90, 1.59), 0.22, 25%1.39 (0.80, 2.42), 0.25, 0%1.06 (0.68, 1.65), 0.81, 0%1.60 (0.77, 3.35), 0.21, 75%1.13 (0.71, 1.81), 0.60, 0%
Implant failure (4, 4)0.98 (0.53, 1.83), 0.96, 57%1.63 (0.41, 6.40), 0.48, 0%1.22 (0.76, 1.96), 0.41, 17%1.20 (0.75, 1.91), 0.44, 26%1.39 (0.36, 5.39), 0.63, 0%
Marginal bone loss (1, 1)3.27 (0.76, 14.04), 0.11-3.83 (0.81, 18.09), 0.093.83 (0.81, 18.09), 0.09-
Number of individuals
>100 (5, 5)1.05 (0.71, 1.56), 0.81, 60%1.32 (0.76, 2.28), 0.32, 0%1.14 (0.80, 1.63), 0.46, 0%1.19 (0.87, 1.63), 0.28, 0%1.09 (0.68, 1.73), 0.73, 0%
≤100 (4, 5)1.37 (0.82, 2.28), 0.23, 8%2.46 (0.51, 11.85), 0.26, 0%1.39 (0.70, 2.77), 0.34, 27%2.37 (0.80, 7.03), 0.12, 68%2.18 (0.47, 10.06), 0.32, 0%
* p < 0.05.
Table 8. Sensitivity analyses removing the studies with a deviation of HWE in their controls for TNF-α (−308 G > A) and IL-10 (−819 C > T) polymorphisms.
Table 8. Sensitivity analyses removing the studies with a deviation of HWE in their controls for TNF-α (−308 G > A) and IL-10 (−819 C > T) polymorphisms.
Polymorphism (N, N′)AllelicHomozygousHeterozygousRecessiveDominant
OR (95%CI), p, I2OR (95%CI), p, I2OR (95%CI), p, I2OR (95%CI), p, I2OR (95%CI), p, I2
TNF-α (−308 G > A) (6, 7)1.02 (0.62, 1.66), 0.95, 54%0.95 (0.39, 2.35), 0.92, 0%1.24 (0.82, 1.85), 0.31, 12%1.61 (0.78, 3.32), 0.20, 70%0.84 (0.45, 1.60), 0.60, 0%
IL-10 (−819 C > T) (2)1.47 (0.82, 2.64), 0.19, 67%3.84 (0.30, 48.54), 0.30, 80%1.22 (0.78, 1.89), 0.38, 0%1.40 (0.92, 2.12), 0.11, 40%3.43 (0.30, 38.86), 0.32, 79%
Table 9. Meta-regression for TNF-α (−308 G > A) polymorphism based on publication year and sample size.
Table 9. Meta-regression for TNF-α (−308 G > A) polymorphism based on publication year and sample size.
Variable A vs. GAA vs. GGGA vs. GGAA + GA vs. GGAA vs. GG + GA
Year of publicationR0.2110.5220.2720.0750.585
Adjusted R2−0.0920.127−0.058−0.1190.210
p-value0.5860.2290.4790.8370.168
Number of individualsR0.2720.5580.4720.3370.566
Adjusted R2−0.0580.1730.1120.0030.185
p-value0.4790.1930.2000.3410.185
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Jamshidy, L.; Tadakamadla, S.K.; Choubsaz, P.; Sadeghi, M.; Tadakamadla, J. Association of IL-10 and TNF-α Polymorphisms with Dental Peri-Implant Disease Risk: A Meta-Analysis, Meta-Regression, and Trial Sequential Analysis. Int. J. Environ. Res. Public Health 2021, 18, 7697. https://doi.org/10.3390/ijerph18147697

AMA Style

Jamshidy L, Tadakamadla SK, Choubsaz P, Sadeghi M, Tadakamadla J. Association of IL-10 and TNF-α Polymorphisms with Dental Peri-Implant Disease Risk: A Meta-Analysis, Meta-Regression, and Trial Sequential Analysis. International Journal of Environmental Research and Public Health. 2021; 18(14):7697. https://doi.org/10.3390/ijerph18147697

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Jamshidy, Ladan, Santosh Kumar Tadakamadla, Parsia Choubsaz, Masoud Sadeghi, and Jyothi Tadakamadla. 2021. "Association of IL-10 and TNF-α Polymorphisms with Dental Peri-Implant Disease Risk: A Meta-Analysis, Meta-Regression, and Trial Sequential Analysis" International Journal of Environmental Research and Public Health 18, no. 14: 7697. https://doi.org/10.3390/ijerph18147697

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