Next Article in Journal
Intraoperative Flow Cytometry Upon and Beyond the Cell Cycle: A Case Study of the Characterization of a Bone Metastasis
Previous Article in Journal
The Potential Role of Histone Modifications in Glioblastoma Therapy: Review Article
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JMP
Volume 4
Issue 4
10.3390/jmp4040019
jmp-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

IL10 Gene and Neurodegenerative Sclerosis: A Systematic Review and Meta-Analysis

by
Ismael Duarte Machado de Almeida
1,
Pedro Luiz de Paiva
1,
Kamilla de Faria Santos
1,
Angela Adamski da Silva Reis
1,2,* and
Rodrigo da Silva Santos
1,2,*
1
Laboratory of Molecular Pathology, Biological Sciences Institute, Federal University of Goiás, Goiânia 74690-900, GO, Brazil
2
Department of Biochemistry and Molecular Biology, Biological Sciences Institute, Federal University of Goiás, Goiânia 74690-900, GO, Brazil
*
Authors to whom correspondence should be addressed.
J. Mol. Pathol. 2023, 4(4), 213-224; https://0-doi-org.brum.beds.ac.uk/10.3390/jmp4040019
Submission received: 6 August 2023 / Revised: 22 September 2023 / Accepted: 27 September 2023 / Published: 29 September 2023
(This article belongs to the Special Issue State-of-the-Art in Neuropathology)
Browse Figures
Versions Notes

Abstract

:
Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are incurable degenerative scleroses with unclear etiology. Neuroinflammation is an important factor in the neurodegeneration characteristic of these diseases. Additionally, Interleukin 10 (IL10) can inhibit the synthesis of inflammatory cytokines and plays a protective role against neurodegeneration associated with neuroinflammation. Thus, we developed a systematic review and meta-analysis in order to clarify the relationship between polymorphisms in the IL10 gene and MS and/or ALS. We searched for observational studies in four international databases without time restrictions. Seventeen studies were added to the systematic review and six polymorphisms were observed: IL10-592 (rs1800872; C>A), IL10-819 (rs1800871; C>T), IL10-1082 (rs1800896; A>G), IL10-2763 (rs6693899; A>C), IL10-2849 (rs6703630; A>G) and IL10-3575 (rs1800890; A>T). In the meta-analysis, we used odds ratio (OR) and 95% confidence interval (CI) to evaluate the association of IL10-1082, IL10-819 and IL10-592 polymorphisms and MS. We found a positive association of MS with the IL10-1082 SNP in genotypic comparison (AG+GG vs. AA) (OR = 1.23; 95% CI = 1.01–1.51; p = 0.04). Our search did not find any article relating polymorphisms in the IL10 gene with ALS. Therefore, our analysis indicates a possible association of IL10 gene SNPs in the development and progression of MS.

1. Introduction

Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are neurodegenerative diseases. ALS is the most frequent of the motor neuron diseases, and it affects mostly people over 40 years old. On the other hand, MS is often related to disability in younger adults, affecting sensory and motor neurons. Both diseases are incurable, and together they affect a significant portion of the population worldwide [1,2,3].
Advanced age seems to be the main risk factor for ALS due to natural neuronal death associated with aging. However, the pathogenesis of ALS is influenced by environmental factors and genetic conditions associated with neurodegeneration and oxidative stress. As neurodegeneration progresses, the symptoms become apparent, which may include muscle weakness, spasms, dysphagia and weight loss [1,4,5,6].
Similarly, MS is correlated to multiple factors, such as age, gender, ethnicity, diet and genetics factors. MS is an autoimmune disease influenced mainly by human leukocyte antigen (HLA) genes, characterized by inflammation and demyelination of neurons in different parts of the central nervous system (CNS), which leads to different clinical presentations depending on the affected areas. Symptoms of MS include muscle weakness, speech impediments, blurred vision and cognitive impairment [7,8,9].
Neuroinflammation is an important factor in neurodegeneration. The constant stimuli from inflammatory mediators can increase neuron excitability, cause cell damage and dysfunction of the blood–brain barrier [10,11]. Thus, the neuron death that occurs in both ALS and MS has been associated with neuroinflammation [12,13,14]. In both cases, the inflammatory process is complex and involves microglial cells, astrocytes and resident leukocytes [12,15,16].
Interleukin 10 (IL10) is a cytokine produced by a homonymous gene located at 1q32.1 that can inhibit the synthesis of inflammatory cytokines by microglial cells and plays a protective role against neurodegeneration associated with neuroinflammation. Moreover, IL10 was associated with greater survival of neurons and regulation of neurogenesis in adult individuals [17].
Single nucleotide polymorphisms (SNPs) present in the promoter region of the IL10 gene are capable of negatively regulating the expression of this cytokine. This can lead to neuroinflammation and result in neurodegeneration [14]. Therefore, IL10 is a gene potentially associated with the risk of neurodegenerative diseases.
We hypothesized that some SNPs of the IL10 gene may confer risk for MS or ALS. Thus, we performed a systematic review that aimed to identify polymorphisms in the IL10 gene involved in the development of ALS or MS, to elucidate the pathophysiological mechanisms involved and assist in the elaboration of new diagnostic and prognostic criteria and personalized therapies. When applicable, we used meta-analysis to verify this association.

2. Materials and Methods

2.1. PROSPERO Record and Searching Strategy

This protocol was registered in the international Prospective Register of Ongoing Systematic Reviews (PROSPERO) with the number CRD42021284650 in 5 November 2021 following the guiding question “What are the implications of polymorphic variants in the interleukin 10 gene in the pathological mechanisms of Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS)?” Aiming at a better description of the data, we used the Preferred Report Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [18].
We conducted the database search between November 2021 and November 2022 in the Virtual Health Library (VHL), PubMed/NCBI, Scientific Electronic Library Online (SciELO) and Web of Science databases. Terms present in the Medical Subject Headings (MeSH) and Health Sciences Descriptors (DeCS) were selected for “amyotrophic lateral sclerosis”, “multiple sclerosis”, “Interleukin 10” and “Single Nucleotide Polymorphism”. The operators “AND” and “OR” were employed to isolate terms and expand the results found.

2.2. Study Screening, Quality Assessment and Data Extraction

We searched observational studies that investigated the involvement of IL10 SNPs in the pathophysiology of ALS and/or MS. For inclusion criteria, we defined observational studies in humans that address polymorphisms in the IL10 gene related to ALS and/or MS, studies fully available online and written in English, Portuguese or Spanish. For exclusion criteria, we defined duplicate records, studies in non-humans, SNPs in other genes and studies with unavailable data.
We used the Rayyan® platform [19] to organize the records found and for duplicate removal. Two independent reviewers performed the selection at all stages of the review. Firstly, we selected studies by titles and abstracts according to predefined inclusion and exclusion criteria. Subsequently, we read the full papers and excluded any unfitting material according to the exclusion criteria.
The Joanna Briggs Institute (JBI) [20] critical assessment tools were applied to each study design in order to assess the methodological quality. Only studies that responded positively to at least 70% of the questions were included and classified as low risk of bias. Discrepancies were resolved by consensus.
After the final selection, we extracted the data from the articles to Excel® spreadsheets. The following information was described: authors and year of publication; country of study; study design; sample size; gender; average age and the genotypic and allelic frequencies when it was possible.

2.3. Statistical Analysis

To develop the meta-analysis, the same polymorphism must be described in at least two different studies. The calculation of the odds ratio (OR) and 95% confidence interval (CI) was used, with the results presented in a forest plot. The ORs were evaluated in dominant (heterozygous + mutant genotypes vs. wild) and allelic (mutant vs. wild) genetic models for each SNP. The dominant model was preferred because it more clearly reflects the comparison of the minor or mutant allele with the major or wild-type allele. Additionally, we used Higgins inconsistency tests (I2) to evaluate heterogeneity between studies.
The selection of the model used in the meta-analysis was based on the results of the heterogeneity test among the studies. The fixed effect model (Mantel–Haenszel method) was applied when I2 < 25% (low heterogeneity), and the random effects model (DerSimonian–Laird method) was applied when I2 25–75% (moderate heterogeneity). Values > 75% were defined as high heterogeneity.
For the analysis of publication bias, Egger’s regression asymmetry test was used [21], and the results visually displayed in funnel plots. A p-value < 0.05 indicates possible publication bias. All statistical analyses were performed using the RStudio® software (version 4.1.0).

3. Results

3.1. Selection and Characteristics of Included Studies

We found 364 records; after removing duplicates, 248 articles remained. Subsequently, a total of 178 articles were selected by reading the titles and abstracts. Of the 158 articles excluded for not meeting the inclusion criteria, the main exclusion reasons were (1) unrelated to IL10; (2) unrelated to ALS or MS and; (3) wrong study design. After full-text reading, 17 articles with publication dates between 1999 and 2021 were included in this systematic review (Figure 1).
The articles relating the presence of polymorphisms in the IL10 gene and the progression or development of ALS/MS were read completely, and we identified articles related to MS only. Six SNPs were identified in those articles: IL10-592 (rs1800872; C>A), IL10-819 (rs1800871; C>T), IL10-1082 (rs1800896; A>G), IL10-2763 (rs6693899; A>C), IL10-2849 (rs6703630; A>G) and IL10-3575 (rs1800890; A>T). The IL10-1082 SNP was mentioned in 16 articles, while the other most frequently mentioned polymorphisms were IL10-819 and IL10-592.
Twelve case–control studies [22,23,24,25,26,27,28,29,30,31,32,33] and two cohort articles [34,35] on the association between IL10-1082 SNP and MS were identified. Six case–control studies [24,25,27,29,30,31] and one cohort article [34] were identified for the IL10-819 SNP, and five case–control studies [24,27,29,30,33] and one cohort [34] studies addressed the IL10-592 SNP. We also included one review [36] and two meta-analyses [37,38] among the selected articles (Table 1).
England, Germany, Norway, Spain, Finland, Italy, Greece, Poland and India had one study each. Five studies were performed in Iran and two in Bulgaria. Not all studies specified the ethnicities or nationalities of their population, and all studies were about MS. Our search did not find any article relating polymorphisms in the IL10 gene with ALS.
There was considerable variability in case sample size across studies, ranging from 55 to 336 individuals. Controls ranged from 85 to 454 subjects. The selected studies included a total of 2396 MS cases and 2770 controls. The percentage between genders could not be compared due to a lack of specification in some studies. However, all observational studies reported pairing between groups. In studies with known information, the case group showed a predominance of female patients. The mean age of MS cases ranged from 27 ± 8 to 49 ± 11.5 years.

3.2. Results of Individual Studies

In summary, most studies showed an association between IL10 polymorphisms and progression or susceptibility to MS. Mirowska-Guzel et al. [26], Mäurer et al. [32], Azarpira et al. [27], Pickard et al. [33], Myhr et al. [34] and Forte et al. [28] found no significant association for alleles or genotypes with the development of MS (p > 0.05). Data from Doncel et al. [30] indicate that IL10 is not a major susceptibility locus in MS but can have a minor role in the development of the disease.
However, Jong et al. [31] revealed that carrying the A allele for the IL10-2849 SNP is significantly associated with susceptibility for relapse–onset MS among non-HLA-DR2 carriers, providing further evidence that IL10 polymorphisms can be associated with MS susceptibility. Furthermore, Al-Naseri et al. [24] reported a significantly increased frequency of CC genotype (IL10-592 SNP) in patients and demonstrated that the G (IL10-1082), C (IL10-819) and C (IL10-592) haplotypes were associated with increased risk to develop MS.
Mihailova et al. [29] found an association of CC (IL10-819 SNP) genotype and discussed the possibility that both SNPs (IL10-592 and IL10-819) play a role in MS susceptibility. Shahbazi et al. [25] found the same association; the CC (IL10-819 SNP) genotype was significantly more frequent among MS patients compared to controls and was associated with a higher risk of developing the disease.
Shahbazi et al. [25] also showed that the GG (IL10-1082 SNP) genotype and the HLA-DRB1*15 allele were associated with susceptibility to MS. The polymorphisms studied were not associated with MS susceptibility in the article by Luomala et al. [35]; however, the results suggest that IL10 production levels may be a factor in the progression of MS. The AG genotype of the IL10-1082 polymorphism discussed by the authors is known to produce low levels of IL10.
Trenova et al. [23] reported that the AA genotype (SNP IL10-1082) may be a risk factor for susceptibility to relapsing–remitting MS (RRMS) in Bulgarian patients and that it may be associated with impaired cognitive functions in these patients. Data from Trenova et al. [23] contradict those shown by Asgharzadeh et al. [22] and suggested by Al-Naseri et al. [24], in which they identify a possible protective role of the AA genotype of IL10-1082 SNP in MS. As shown in the study, this contradiction may be related to environmental factors and population differences.
The data from the included studies in this review, when combined with the review by Ramakrishnan et al. [38], revealed that the IL10-592, IL10-819 and IL10-1082 SNPs might not be risk factors for the development of MS in Asian and Caucasian populations, and Karimabad et al. [36] show that there is no consensus among studies on the association between the IL10-592 SNP and MS.

3.3. Meta-Analysis

Only three of the six polymorphisms identified in this systematic review allowed meta-analysis to be performed: IL10-592, IL10-819 and IL10-1082 SNPs, applied only for MS. Regarding the other polymorphisms, it was not possible to reach the minimum number of two studies. The total number of subjects identified in each SNP was 358 cases and 431 controls for IL10-592, 1177 cases and 1415 controls for IL10-819, and 2100 cases and 2714 controls for IL10-1082.
The meta-analysis for the IL10-592 SNP and MS included four studies, and the results showed no association between the polymorphism and disease in both the genotypic (CA+AA vs. CC) (OR = 0.80; 95% CI = 0.37–1.72; p = 0.56) and allelic comparisons (A vs. C) (OR = 0.79; 95% CI = 0.44–1.42; p = 0.43) (Figure 2).
For the IL10-819 SNP and MS, seven studies were included, and the meta-analysis did not demonstrate an association between the polymorphism and the disease in the genotypic (CT+TT vs. CC) (OR = 0.88; 95% CI = 0.71–1.10; p = 0.25) and allelic comparisons (T vs. C) (OR = 0.89; 95% CI = 0.76–1.04; p = 0.14) (Figure 3).
Meta-analysis for the IL10-1082 SNP and MS included 13, studies and the forest plot revealed an association between the polymorphism and the disease in the genotypic comparison (AG+GG vs. AA) (OR = 1.23; 95% CI = 1.01–1.51; p-value = 0.04) but not in the allelic comparisons (G vs. A) (OR = 1.09; 95% CI = 0.98–1.21; p-value = 0.12) (Figure 4).
In this meta-analysis, the OR and 95% CI were calculated using the Mantel–Haenszel test, and the random effect model was applied for all comparisons due to the value found in the test of heterogeneity (I2). The analysis of publication bias for all SNPs found no significant publication bias according to funnel plot (Figure 5) and Egger’s test for allelic (IL10-592: p = 0.8561; IL10-819: p = 0.7025; IL10-1082: p = 0.4112) and genotypic comparisons (IL10-592: p = 0.9769; IL10-819: p = 0.1646; IL10-1082: p = 0.2883).

4. Discussion

There is still much to learn about the mechanisms behind neurodegenerative scleroses, as well as the influence of genetic factors on susceptibility and progression of these diseases. IL10 secretion may vary in humans according to the genetic composition of the IL10 locus, which may increase or decrease the serum levels of this cytokine [39,40]. Therefore, our study sought to assess the influence of IL10 gene polymorphisms on neurodegenerative scleroses, since polymorphisms in this gene could affect the balance of the immune system.
Neuroinflammation can cause undesirable effects, such as neuron damage, increased neuronal excitability and dysfunction of the blood–brain barrier [10,11]. Furthermore, this mechanism has been associated with the onset and progression of neurodegenerative diseases, including MS, ALS, Alzheimer’s and Parkinson’s disease [12,14,41].
Thus, due to its potent anti-inflammatory effect, IL10 has a protective role against neurodegeneration associated with neuroinflammation. Furthermore, IL10 induces the alternative microglial activation phenotype, which secretes more IL10, neurotrophic factors and other anti-inflammatory cytokines. In astrocytes, IL10 decreases pro-inflammatory signaling by inducing astrocyte inactivation [42,43,44,45]. Regarding ALS, studies in murine models (SOD1-G93A mice) show that overexpression of IL10 in microglial cells significantly delayed disease onset and increased survival in ALS, highlighting the importance of this cytokine in disease progression [46].
We did not find any work relating polymorphisms in the IL10 gene with ALS. However, our data indicate that the IL10-1082 SNP is associated with MS risk. This SNP is located in the promoter region of the IL10 gene and is possibly linked to cytokine production [47]. Of the six polymorphisms found, three (IL10-2763, IL10-2849 and IL10-3575) were described only by Jong et al. [31]. Therefore, the lack of studies relating these polymorphisms to the susceptibility and progression of neurodegenerative diseases is highlighted.
Similar to our study, Nikolopoulos et al. [37] performed a meta-analysis of the IL10-1082 and IL10-819 SNPs in MS. In a recessive model, the GG genotype (IL10-1082 SNP) was associated with MS. Furthermore, they also found that the T allele (IL10-819 SNP) was associated with decreased risk of MS (OR = 0.841). This result corroborates the association of the AG+GG genotype of the IL10-1082 SNP with MS found in our meta-analysis. Furthermore, in our meta-analysis, thirteen studies on the IL10-1082 SNP and seven studies on the IL10-819 SNP were included, while Nikolopoulos et al. [37] included only eight and five for their analysis, respectively. Nikolopoulos et al. [37] also reported difficulties regarding heterogeneity and scarcity of studies in ethnicities other than Caucasians.
Ramakrishnan et al. [38] performed another similar study, in which they found no association between IL10-1082 SNP and MS in any of the tested genetic models. Their findings regarding this SNP were different from ours, despite having a larger number of included studies. On the other hand, we found heterogeneity issues in our expanded database, which hindered our analysis.
As for the IL10-592 SNP, Ramakrishnan et al. [38] found that the AA genotype presents an OR of 0.76 when compared to CC individuals. Our analysis also did not associate IL10-592 SNP genotypes and alleles with MS. Ramakrishnan et al. [38] also investigated the association between MS and the IL10-819 SNP. Their analysis indicated that the CC genotype is negatively associated with MS risk (OR = 0.80) compared to the TT genotype, which could represent a protective factor. Our findings revealed an OR of 0.88 for the CT+TT genotypes, demonstrating the non-association with MS. However, the scarcity of studies on this particular SNP is still an obstacle to any robust association, highlighting the importance of further studies.
Trenova et al. [23] also found associations of the AG genotype of the IL10-1082 SNP and highlighted that this genetic variant may be associated with cognitive decline in Bulgarian patients with RRMS. Another study by Trenova et al. [48] suggest that pro-inflammatory cytokines IL17A and TNF-alpha in combination with IL10 decrease are involved in cognitive deterioration in RRMS, which corroborates to the hypothesis that low levels of IL10 in serum may cause risks when associated with other factors. Polymorphisms in the IL10 gene promoter have a direct impact on the serum levels of the cytokine. Several studies try to relate the low levels of this cytokine with the risk for several diseases and the high production with protective factors. For example, the AA genotype of the SNP IL10-1082 (rs1800896) is recognized for its association with low serum levels of the cytokine in the blood and has been statistically associated with the risk of prostate cancer [49].
Therefore, our meta-analysis shows an association in the genotypic comparison for the IL10-1082 SNP (OR = 1.23) with the risk of MS. Nevertheless, our data must be interpreted with caution. As described in the literature, it is still not possible to arrive at precise answers due to the low number of studies in the same populations. Additionally, due to the rarity of these neurodegenerative scleroses, research with larger samples is necessary to obtain more precise answers.
A large number of studies would also be necessary to perform analysis by subgroups (gender or ethnicities for example) or meta-regression, which would enable the obtaining of lower heterogeneity between studies. In our study, one obstacle was the lack of published data on specific characteristics of the participants, such as the exact number of males and females genotyped in each group (Table 1). Furthermore, the dominant model was preferred because it more clearly reflects the comparison of the minor or mutant allele with the major or wild-type allele; however, further analyses with different models of inheritance may clarify the relationship of SNPs with MS.
The study of genetic factors related to the susceptibility or progression of diseases helps to create tools for precision medicine and can lead to an improved quality of life for patients. It was not possible to fulfill the objective of verifying the relationship of polymorphisms in the IL10 gene with ALS due to the lack of studies. However, this fact reveals the need for research aimed at better investigating the immunogenetic mechanisms of ALS.
In conclusion, this systematic review and meta-analysis reveals that carriers of the AG+GG genotype of the IL10-1082 SNP have an approximately 1.23-fold higher risk for MS. Our results indicate that IL10 SNPs can represent susceptibility factors for MS. This study may provide important information about the immunogenetic mechanisms related to the susceptibility and progression of neurodegenerative diseases. Our results may contribute to the development of therapeutic technologies for MS as well as influence future genetic research, in addition to revealing the need for more research in ALS.

Author Contributions

Conceptualization, R.d.S.S. and A.A.d.S.R.; Methodology, I.D.M.d.A., P.L.d.P., K.d.F.S., R.d.S.S. and A.A.d.S.R.; Software, K.d.F.S., R.d.S.S. and A.A.d.S.R.; Validation, R.d.S.S. and A.A.d.S.R.; Formal Analysis, I.D.M.d.A., P.L.d.P. and K.d.F.S.; Investigation, I.D.M.d.A., P.L.d.P., K.d.F.S., R.d.S.S. and A.A.d.S.R.; Resources, R.d.S.S. and A.A.d.S.R.; Data Curation, I.D.M.d.A., P.L.d.P. and K.d.F.S.; Writing—Original Draft Preparation, I.D.M.d.A., P.L.d.P., R.d.S.S. and A.A.d.S.R.; Writing—Review and Editing, I.D.M.d.A., P.L.d.P., K.d.F.S., R.d.S.S. and A.A.d.S.R.; Supervision, R.d.S.S. and A.A.d.S.R.; Project Administration, R.d.S.S.; Funding Acquisition, R.d.S.S. and A.A.d.S.R. All authors have read and agreed to the published version of the manuscript.

Funding

Coordination for the Improvement of Higher Education Personnel (CAPES) with financial support to K.d.F.S. and the National Council for Scientific and Technological Development (CNPq) with financial support to I.D.M.d.A.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Motor Neuron Diseases Fact Sheet|NINDS. n.d. Available online: https://www.ninds.nih.gov/motor-neuron-diseases-fact-sheet (accessed on 2 September 2022).
  2. Multiple Sclerosis—NHS. n.d. Available online: https://www.nhs.uk/conditions/multiple-sclerosis/ (accessed on 23 September 2022).
  3. Walton, C.; King, R.; Rechtman, L.; Kaye, W.; Leray, E.; Marrie, R.A.; Robertson, N.; La Rocca, N.; Uitdehaag, B.; van der Mei, I.; et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult. Scler. 2020, 26, 1816. [Google Scholar] [CrossRef] [PubMed]
  4. Azevedo, R.M.; Pardinho, J.M.C.; dos Anjos, L.R.B.; da Silva Reis, A.A.; da Silva Santos, R. Painel genético: Mecanismos moleculares e fisiopatológicos da esclerose lateral amiotrófica (ELA). Ensino Assistência e Avaliação de Tecnologias Na Saúde 2019, 1, 388–405. [Google Scholar]
  5. Cervantes-Aragón, I.; Ramírez-García, S.A.; Baltazar-Rodríguez, L.M.; García-Cruz, D.; Castañeda-Cisneros, G. Genetic approach in amyotrophic lateral sclerosis. Gac. Med. Mex. 2019, 155, 475–482. [Google Scholar] [CrossRef] [PubMed]
  6. Oskarsson, B.; Gendron, T.F.; Staff, N.P. Amyotrophic Lateral Sclerosis: An Update for 2018. Mayo Clin. Proc. 2018, 93, 1617–1628. [Google Scholar] [CrossRef] [PubMed]
  7. Multiple Sclerosis|NINDS. n.d. Available online: https://www.ninds.nih.gov/health-information/disorders/multiple-sclerosis (accessed on 18 September 2022).
  8. Omraninava, M.; Mehranfar, S.; Vahedi, P.; Razi, B.; Imani, D.; Aslani, S.; Feyzinia, S. Association between IL7 Receptor Alpha (Il7ra) gene rs6897932 polymorphism and the risk of Multiple Sclerosis: A meta-regression and meta-analysis. Mult. Scler. Relat. Disord. 2021, 48, 102687. [Google Scholar] [CrossRef]
  9. Svejgaard, A. The immunogenetics of multiple sclerosis. Immunogenetics 2008, 60, 275–286. [Google Scholar] [CrossRef]
  10. Chen, L.; Deng, H.; Cui, H.; Fang, J.; Zuo, Z.; Deng, J.; Li, Y.; Wang, X.; Zhao, L. Inflammatory responses and inflammation-associated diseases in organs. Oncotarget 2018, 9, 7204–7218. [Google Scholar] [CrossRef]
  11. Ekdahl, C.T.; Claasen, J.H.; Bonde, S.; Kokaia, Z.; Lindvall, O. Inflammation is detrimental for neurogenesis in adult brain. Proc. Natl. Acad. Sci. USA 2003, 100, 13632–13637. [Google Scholar] [CrossRef]
  12. Cervellati, C.; Trentini, A.; Pecorelli, A.; Valacchi, G. Inflammation in Neurological Disorders: The Thin Boundary Between Brain and Periphery. Antioxid. Redox Signal. 2020, 33, 191–210. [Google Scholar] [CrossRef]
  13. Andrés-Benito, P.; Moreno, J.; Domínguez, R.; Aso, E.; Povedano, M.; Ferrer, I. Inflammatory Gene Expression in Whole Peripheral Blood at Early Stages of Sporadic Amyotrophic Lateral Sclerosis. Front. Neurol. 2017, 8, 546. [Google Scholar] [CrossRef]
  14. Tang, Y.; Le, W. Differential Roles of M1 and M2 Microglia in Neurodegenerative Diseases. Mol. Neurobiol. 2016, 53, 1181–1194. [Google Scholar] [CrossRef] [PubMed]
  15. Sochocka, M.; Diniz, B.S.; Leszek, J. Inflammatory Response in the CNS: Friend or Foe? Mol. Neurobiol. 2017, 54, 8071. [Google Scholar] [CrossRef] [PubMed]
  16. Li, K.; Li, J.; Zheng, J.; Qin, S. Reactive Astrocytes in Neurodegenerative Diseases. Aging Dis. 2019, 10, 664. [Google Scholar] [CrossRef] [PubMed]
  17. Ouyang, W.; O’Garra, A. IL-10 family cytokines IL-10 and IL-22: From basic science to clinical translation. Immunity 2019, 50, 871–891. [Google Scholar] [CrossRef]
  18. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, 71. [Google Scholar] [CrossRef]
  19. Ouzzani, M.; Hammady, H.; Fedorowicz, Z.; Elmagarmid, A. Rayyan—A web and mobile app for systematic reviews. Syst. Rev. 2016, 5, 210. [Google Scholar] [CrossRef]
  20. Joanna Briggs Institute. JBI Reviewer’s Manual. 2022. Available online: https://reviewersmanual.joannabriggs.org/ (accessed on 1 May 2023).
  21. Egger, M.; Davey, S.G.; Schneider, M.; Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997, 315, 629–634. [Google Scholar] [CrossRef]
  22. Asgharzadeh, M.; Sanajou, D.; Kafil, H.S.; Farhoudi, M.; Oskouei, D.S.; Khaki-Khatibi, F.; Gholizadeh, P. Interleukin-10 Promoter and the CCR5 Polymorphisms in Iranian Azari Population with Multiple Sclerosis. Iran. J. Immunol. 2021, 18, 241. [Google Scholar]
  23. Trenova, A.G.; Miteva, L.D.; Stanilova, S.A. Association between TNFA, IL10 and IL18 promoter gene variants and cognitive functions in patients with relapsing-remitting multiple sclerosis. J. Neuroimmunol. 2020, 347, 577357. [Google Scholar] [CrossRef]
  24. Al-Naseri, M.A.; Salman, E.D.; Ad’hiah, A.H. Association between interleukin-4 and interleukin-10 single nucleotide polymorphisms and multiple sclerosis among Iraqi patients. Neurol. Sci. 2019, 40, 2383–2389. [Google Scholar] [CrossRef]
  25. Shahbazi, M.; Abadi, J.S.A.; Roshandel, D.; Koochaki, M.; Amiri, H.; Kohansal, R.; Zamani, M. Combination of interleukin-10 gene promoter polymorphisms with HLA-DRB1* 15 allele is associated with multiple sclerosis. Indian J. Med. Res. 2017, 145, 746. [Google Scholar] [CrossRef] [PubMed]
  26. Mirowska-Guzel, D.; Gromadzka, G.; Mach, A.; Czlonkowski, A.; Czlonkowska, A. Association of IL1A, IL1B, ILRN, IL6, IL10 and TNF-α polymorphisms with risk and clinical course of multiple sclerosis in a Polish population. J. Neuroimmunol. 2011, 236, 87–92. [Google Scholar] [CrossRef] [PubMed]
  27. Azarpira, N.; Haghighi, A.B.; Pourjafar, M.; Shariat, A. Interleukin 10 gene polymorphism in Iranian patients with multiple sclerosis. Acta Neurol. Taiwan 2010, 19, 107–111. [Google Scholar] [PubMed]
  28. Forte, G.I.; Ragonese, P.; Salemi, G.; Scola, L.; Candore, G.; D’amelio, M.A.R.C.O.; Caruso, C. Search for genetic factors associated with susceptibility to multiple sclerosis. Ann. N. Y. Acad. Sci. 2006, 1067, 264–269. [Google Scholar] [CrossRef]
  29. Mihailova, S.; Ivanova, M.; Mihaylova, A.; Quin, L.; Mikova, O.; Naumova, E. Pro-and anti-inflammatory cytokine gene polymorphism profiles in Bulgarian multiple sclerosis patients. J. Neuroimmunol. 2005, 168, 138–143. [Google Scholar] [CrossRef]
  30. Doncel, A.M.; Rubio, A.; Arroyo, R.; de las Heras, V.; Martín, C.; Fernandez-Arquero, M.; de la Concha, E.G. Interleukin-10 polymorphisms in Spanish multiple sclerosis patients. J. Neuroimmunol. 2002, 131, 168–172. [Google Scholar] [CrossRef]
  31. de Jong, B.A.; Westendorp, R.G.; Eskdale, J.; Uitdehaag, B.M.; Huizinga, T.W. Frequency of functional interleukin-10 promoter polymorphism is different between relapse-onset and primary progressive multiple sclerosis. Hum. Immunol. 2002, 63, 281–285. [Google Scholar] [CrossRef]
  32. Mäurer, M.; Kruse, N.; Giess, R.; Toyka, K.V.; Rieckmann, P. Genetic variation at position–1082 of the interleukin 10 (IL10) promotor and the outcome of multiple sclerosis. J. Neuroimmunol. 2000, 104, 98–100. [Google Scholar] [CrossRef]
  33. Pickard, C.; Mann, C.; Sinnott, P.; Boggild, M.; Hawkins, C.; Strange, R.C.; Donn, R.P. Interleukin-10 (IL10) promoter polymorphisms and multiple sclerosis. J. Neuroimmunol. 1999, 101, 207–210. [Google Scholar] [CrossRef]
  34. Myhr, K.M.; Vågnes, K.S.; Marøy, T.H.; Aarseth, J.H.; Nyland, H.I.; Vedeler, C.A. Interleukin-10 promoter polymorphisms in patients with multiple sclerosis. J. Neurol. Sci. 2002, 202, 93–97. [Google Scholar] [CrossRef]
  35. Luomala, M.; Lehtimäki, T.; Huhtala, H.; Ukkonen, M.; Koivula, T.; Hurme, M.; Elovaara, I. Promoter polymorphism of IL-10 and severity of multiple sclerosis. Acta Neurol. Scand. 2003, 108, 396–400. [Google Scholar] [CrossRef] [PubMed]
  36. Karimabad, M.N.; Arababadi, M.K.; Hakimizadeh, E.; Daredori, H.Y.; Nazari, M.; Hassanshahi, G.; Kennedy, D. Is the IL-10 promoter polymorphism at position-592 associated with immune system-related diseases? Inflammation 2013, 36, 35–41. [Google Scholar] [CrossRef] [PubMed]
  37. Nikolopoulos, G.K.; Masgala, A.; Tsiara, C.; Limitsiou, O.K.; Karnaouri, A.C.; Dimou, N.L.; Bagos, P.G. Cytokine gene polymorphisms in multiple sclerosis: A meta-analysis of 45 studies including 7379 cases and 8131 controls. Eur. J. Neurol. 2011, 18, 944–951. [Google Scholar] [CrossRef] [PubMed]
  38. Ramakrishnan, V.; Husain, R.A.; Ahmed, S.S. Genetic predisposition of IL-10 promoter polymorphisms with risk of multiple sclerosis: A meta-analysis. J. Neuroimmunol. 2017, 306, 11–18. [Google Scholar] [CrossRef]
  39. Eskdale, J.; Gallagher, G.; Verweij, C.L.; Keijsers, V.; Westendorp, R.G.; Huizinga, T.W. Interleukin 10 secretion in relation to human IL-10 locus haplotypes. Proc. Natl. Acad. Sci. USA 1998, 95, 9465–9470. [Google Scholar] [CrossRef]
  40. Westendorp, R.G.; Langermans, J.A.; Huizinga, T.W.; Elouali, A.H.; Verweij, C.L.; Boomsma, D.I.; Vandenbrouke, J.P. Genetic influence on cytokine production and fatal meningococcal disease. Lancet 1997, 349, 170–173. [Google Scholar] [CrossRef]
  41. Riccio, P.; Rocco, R. Undigested Food and Gut Microbiota May Cooperate in the Pathogenesis of Neuroinflammatory Diseases: A Matter of Barriers and a Proposal on the Origin of Organ Specificity. Nutrients 2019, 11, 2714. [Google Scholar] [CrossRef]
  42. Lobo-Silva, D.; Carriche, G.M.; Castro, A.G.; Roque, S.; Saraiva, M. Balancing the immune response in the brain: IL-10 and its regulation. J. Neuroinflamm. 2016, 13, 297. [Google Scholar] [CrossRef]
  43. González, H.; Pacheco, R. T-cell-mediated regulation of neuroinflammation involved in neurodegenerative diseases. J. Neuroinflamm 2014, 11, 201. [Google Scholar] [CrossRef]
  44. Dá Mesquita, S.; Ferreira, A.C.; Sousa, J.C.; Correia-Neves, M.; Sousa, N.; Marques, F. Insights on the pathophysiology of Alzheimer’s disease: The crosstalk between amyloid pathology, neuroinflammation and the peripheral immune system. Neurosci. Biobehav. Rev. 2016, 68, 547–562. [Google Scholar] [CrossRef]
  45. González-Reyes, R.E.; Nava-Mesa, M.O.; Vargas-Sánchez, K.; Ariza-Salamanca, D.; Mora-Muñoz, L. Involvement of astrocytes in Alzheimer’s disease from a neuroinflammatory and oxidative stress perspective. Front. Mol. Neurosci. 2017, 10, 427. [Google Scholar] [CrossRef] [PubMed]
  46. Gravel, M.; Béland, L.C.; Soucy, G.; Abdelhamid, E.; Rahimian, R.; Gravel, C.; Kriz, J. IL-10 Controls Early Microglial Phenotypes and Disease Onset in ALS Caused by Misfolded Superoxide Dismutase 1. J. Neurosci. 2016, 36, 1031–1048. [Google Scholar] [CrossRef] [PubMed]
  47. Turner, D.M.; Williams, D.M.; Sankaran, D.; Lazarus, M.; Sinnott, P.J.; Hutchinson, I.V. An investigation of polymorphism in the interleukin-10 gene promoter. Eur. J. Immunogenet. 1997, 24, 1–8. [Google Scholar] [CrossRef] [PubMed]
  48. Trenova, A.G.; Slavov, G.S.; Draganova-Filipova, M.N.; Mateva, N.G.; Manova, M.G.; Miteva, L.D.; Stanilova, S.A. Circulating levels of interleukin-17A, tumor necrosis factor-alpha, interleukin-18, interleukin-10, and cognitive performance of patients with relapsing-remitting multiple sclerosis. Neurol. Res. 2018, 40, 153–159. [Google Scholar] [CrossRef]
  49. Wang, M.H.; Helzlsouer, K.J.; Smith, M.W.; Hoffman-Bolton, J.A.; Clipp, S.L.; Grinberg, V.; De Marzo, A.M.; Isaacs, W.B.; Drake, C.G.; Shugart, Y.Y.; et al. Association of IL10 and other immune response- and obesity-related genes with prostate cancer in CLUE II. Prostate 2009, 69, 874–885. [Google Scholar] [CrossRef] [PubMed]
Jmp 04 00019 g001
Figure 1. PRISMA flowchart detailing the selection of the articles included and excluded in this systematic review and meta-analysis. Adapted from [18].
Figure 1. PRISMA flowchart detailing the selection of the articles included and excluded in this systematic review and meta-analysis. Adapted from [18].
Jmp 04 00019 g001
Jmp 04 00019 g002
Figure 2. Forest plot for the genotypic and allelic comparison of IL10-592 SNP (CA+AA vs. CC and A vs. C). CI = confidence interval; OR = odds ratio. Al-Naseri et al., 2019 [24]; Azarpira et al., 2010 [27]; Mihailova et al., 2005 [29]; Myhr et al., 2002 [34].
Figure 2. Forest plot for the genotypic and allelic comparison of IL10-592 SNP (CA+AA vs. CC and A vs. C). CI = confidence interval; OR = odds ratio. Al-Naseri et al., 2019 [24]; Azarpira et al., 2010 [27]; Mihailova et al., 2005 [29]; Myhr et al., 2002 [34].
Jmp 04 00019 g002
Jmp 04 00019 g003
Figure 3. Forest plot for the genotypic and allelic comparison of IL10-819 SNP (CT+TT vs. CC, and T vs. C). CI = confidence interval; OR = odds ratio. Al-Naseri et al., 2019 [24]; Azarpira et al., 2010 [27]; Jong et al., 2002 [31]; Mihailova et al., 2005 [29]; Myhr et al., 2002 [34]; Pickard et al., 1999 [33]; Shahbazi et al., 2015 [25].
Figure 3. Forest plot for the genotypic and allelic comparison of IL10-819 SNP (CT+TT vs. CC, and T vs. C). CI = confidence interval; OR = odds ratio. Al-Naseri et al., 2019 [24]; Azarpira et al., 2010 [27]; Jong et al., 2002 [31]; Mihailova et al., 2005 [29]; Myhr et al., 2002 [34]; Pickard et al., 1999 [33]; Shahbazi et al., 2015 [25].
Jmp 04 00019 g003
Jmp 04 00019 g004
Figure 4. Forest plot for the genotypic and allelic comparison of IL10-1082 SNP (AG+GG vs. AA, and A vs. G). CI = confidence interval; OR = odds ratio. Al-Naseri et al., 2019 [24]; Asgharzadeh et al., 2021 [22]; Azarpira et al., 2010 [27]; Forte et al., 2006 [28]; Jong et al., 2002 [31]; Luomala et al., 2003 [35]; Maurer et al., 1999 [32]; Mihailova et al., 2005 [29]; Mirowska-Guzel et al., 2011 [26]; Myhr et al., 2002 [34]; Pickard et al., 1999 [33]; Shahbazi et al., 2015 [25]; Trenova et al., 2020 [23].
Figure 4. Forest plot for the genotypic and allelic comparison of IL10-1082 SNP (AG+GG vs. AA, and A vs. G). CI = confidence interval; OR = odds ratio. Al-Naseri et al., 2019 [24]; Asgharzadeh et al., 2021 [22]; Azarpira et al., 2010 [27]; Forte et al., 2006 [28]; Jong et al., 2002 [31]; Luomala et al., 2003 [35]; Maurer et al., 1999 [32]; Mihailova et al., 2005 [29]; Mirowska-Guzel et al., 2011 [26]; Myhr et al., 2002 [34]; Pickard et al., 1999 [33]; Shahbazi et al., 2015 [25]; Trenova et al., 2020 [23].
Jmp 04 00019 g004
Jmp 04 00019 g005
Figure 5. Funnel plots for the publication bias of the studies included in the meta-analysis. (A) Allelic comparison (A vs. C) for IL10-592 SNP and MS.; (B) genotypic comparison (CA+AA vs. CC) for IL10-592 SNP and MS; (C) allelic comparison (T vs. C) for IL10-819 SNP and MS; (D) genotypic comparison (CT+TT vs. CC) for IL10-819 SNP and MS; (E) allelic comparison (G vs. A) for IL10-1082 SNP and MS; (F) genotypic comparison (AG+GG vs. AA) for IL10-1082 SNP and MS.
Figure 5. Funnel plots for the publication bias of the studies included in the meta-analysis. (A) Allelic comparison (A vs. C) for IL10-592 SNP and MS.; (B) genotypic comparison (CA+AA vs. CC) for IL10-592 SNP and MS; (C) allelic comparison (T vs. C) for IL10-819 SNP and MS; (D) genotypic comparison (CT+TT vs. CC) for IL10-819 SNP and MS; (E) allelic comparison (G vs. A) for IL10-1082 SNP and MS; (F) genotypic comparison (AG+GG vs. AA) for IL10-1082 SNP and MS.
Jmp 04 00019 g005
Table 1. Clinical characteristics of the 17 studies included.
Table 1. Clinical characteristics of the 17 studies included.
StudyYearCountryMultiple SclerosisHealthy ControlsPolymorphisms Analyzed
nFemale (%)Mean Age (year)nFemale (%)Mean Age (year)
Pickard et al. [33]1999England18574.041.7 ± 10.6211NANAIL10-592, IL10-1082
Mäurer et al. [32]1999Germany181NANA85NANAIL10-1082
Myhr et al. [34]2002Norway16864.9NA87NANAIL10-592, IL10-819, IL10-1082
Jong et al. [31]2002Iran25159.349 ± 11.532147.339 ± 17IL10-819, IL10-1082IL10-2763, IL10-2849, IL10-3575
Doncel et al. [30]2002Spain30067.0NA357NANAIL10-592, IL10-819, IL10-1082
Luomala et al. [35]2003Finland11657.746 ± 10400NA46 ± 10IL10-1082
Mihailova et al. [29]2005Bulgaria5556.332.3 ± 9.586NANAIL10-592, IL10-819, IL10-1082
Forte et al. [28]2006Italy91NANA220NANAIL10-1082
Azarpira et al. [27]2010Iran11070.932.210051.033.41IL10-592, IL10-819, IL10-1082
Nikolopoulos et al. [37] *2011Greece------IL10-819, IL10-1082
Mirowska-Guzel et al. [26]2011Poland22473.138.11 ± 10.9419566.340.09± 10.19IL10-1082
Karimabad et al. [36] *2012Iran------IL10-592
Shahbazi et al. [25]2015Iran33671.127 ± 8454NA35.45 ± 6.9IL10-819, IL10-1082
Ramakrishnan et al. [38] *2017India------IL10-592, IL10-819, IL10-1082
Al-Naseri et al. [24]2019Iraq6866.133.3± 2.315877.832.7 ± 0.8IL10-592, IL10-819, IL10-1082
Trenova et al. [23]2020Bulgaria15971.740.08 ± 8.4815477.339.29 ± 9.454IL10-1082
Asgharzadeh et al. [22]2021Iran152NA32.18 ± 6.52242NA31.02 ± 7.44IL10-1082
* Review/meta-analyses. NA = Not available.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

de Almeida, I.D.M.; de Paiva, P.L.; Santos, K.d.F.; Reis, A.A.d.S.; Santos, R.d.S. IL10 Gene and Neurodegenerative Sclerosis: A Systematic Review and Meta-Analysis. J. Mol. Pathol. 2023, 4, 213-224. https://0-doi-org.brum.beds.ac.uk/10.3390/jmp4040019

AMA Style

de Almeida IDM, de Paiva PL, Santos KdF, Reis AAdS, Santos RdS. IL10 Gene and Neurodegenerative Sclerosis: A Systematic Review and Meta-Analysis. Journal of Molecular Pathology. 2023; 4(4):213-224. https://0-doi-org.brum.beds.ac.uk/10.3390/jmp4040019

Chicago/Turabian Style

de Almeida, Ismael Duarte Machado, Pedro Luiz de Paiva, Kamilla de Faria Santos, Angela Adamski da Silva Reis, and Rodrigo da Silva Santos. 2023. "IL10 Gene and Neurodegenerative Sclerosis: A Systematic Review and Meta-Analysis" Journal of Molecular Pathology 4, no. 4: 213-224. https://0-doi-org.brum.beds.ac.uk/10.3390/jmp4040019

Article Metrics

Back to TopTop