Figures
Abstract
Background
Genetic and epigenetic factors interacting with the environment over time are the main causes of complex diseases such as autoimmune diseases (ADs). Among the environmental factors are organic solvents (OSs), which are chemical compounds used routinely in commercial industries. Since controversy exists over whether ADs are caused by OSs, a systematic review and meta-analysis were performed to assess the association between OSs and ADs.
Methods and Findings
The systematic search was done in the PubMed, SCOPUS, SciELO and LILACS databases up to February 2012. Any type of study that used accepted classification criteria for ADs and had information about exposure to OSs was selected. Out of a total of 103 articles retrieved, 33 were finally included in the meta-analysis. The final odds ratios (ORs) and 95% confidence intervals (CIs) were obtained by the random effect model. A sensitivity analysis confirmed results were not sensitive to restrictions on the data included. Publication bias was trivial. Exposure to OSs was associated to systemic sclerosis, primary systemic vasculitis and multiple sclerosis individually and also to all the ADs evaluated and taken together as a single trait (OR: 1.54; 95% CI: 1.25–1.92; p-value<0.001).
Citation: Barragán-Martínez C, Speck-Hernández CA, Montoya-Ortiz G, Mantilla RD, Anaya J-M, Rojas-Villarraga A (2012) Organic Solvents as Risk Factor for Autoimmune Diseases: A Systematic Review and Meta-Analysis. PLoS ONE 7(12): e51506. https://doi.org/10.1371/journal.pone.0051506
Editor: Sudha Chaturvedi, Wadsworth Center, United States of America
Received: July 12, 2012; Accepted: November 1, 2012; Published: December 19, 2012
Copyright: © 2012 Barragán-Martínez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project did not have any specific funding, but the work was supported by the School of Medicine and Health Sciences, Universidad del Rosario. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Autoimmune diseases (ADs) are initiated by the loss of immune tolerance and mediated through T or B cell activation leading to tissue damage. ADs share clinical signs and symptoms, physiopathological mechanisms, and genetic factors [1]. They are complex diseases caused by the interaction between genetic, epigenetic, and environmental factors over time [2], [3].
Despite the difficulties in defining environmental risk factors that lead to immunopathology, the number of candidates proposed for specific ADs is continuously growing as new evidence is reported for infectious agents, chemicals, physical factors, adjuvants, and hormones [4]–[15]. A significant body of research has pointed out that, for autoimmunity to occur, the genetic background warrants to be combined with environmental injuries and novel associations has been described as the case of the air pollution [5], [16]. However these environmental factors often explain only a small number of cases, and, on their own, they are not sufficient to cause the disease [5].
Solvents are liquids that dissolve a solid, liquid or gas. They can be broadly classified into two categories: organic and inorganic. Organic solvents (OSs) are compounds whose molecules contain carbon. They may be broken down further into aliphatic-chain compounds, such as n-hexane, and aromatic compounds with a 6-carbon ring, such as benzene or xylene. OSs arose in the latter half of the 19th century from the coal-tar industry. Common uses for OSs are: dry cleaning (e.g., tetrachloroethylene), paint thinner (e.g., toluene, turpentine), nail polish removers and glue solvents (acetone, methyl acetate, ethyl acetate), spot removers (e.g., hexane, petrol ether), detergents (citrus turpenes), perfumes (ethanol), nail polish and chemical synthesis [17]. In contrast, the use of inorganic solvents (other than water) is typically limited to research in chemistry and some technological processes.
The applications of OSs became more diversified in both developed and developing countries. Research in this area began in 1957 when the first patients developing a scleroderma-like syndrome after exposure to vinyl chloride, epoxy resins, trichloroethylene (TCE), perchloroethylene and other mixed solvents were reported [18], [19]. Nevertheless, few published studies have analyzed the wide spectrum of ADs in subjects exposed to OSs. Therefore, we aimed to analyze the evidence of an association between the exposure to OSs and the development of AD through a systematic literature review and a meta-analysis. In addition, a comprehensive review concerning the mechanisms by which OSs exposure induces immunological alterations is presented.
Methods
Literature Search
The search was done using the following databases: PubMed, SCOPUS, SciELO and LILACS and took into account articles published up to February 2012. We followed the PRISMA guidelines for meta-analysis of observational studies [20] in our data extraction, analysis, and reporting (Text S1).
The most relevant terms regarding OSs exposure were suggested by an expert chemical engineer specialist in industrial hygiene. The following Medical Subject Heading (MeSH) terms were used: “systemic vasculitis,” “vasculitis,” “rheumatoid arthritis,” “lupus,” “multiple sclerosis,” “scleroderma,” “systemic sclerosis,” “antiphospholipid syndrome,” “Sjögren's syndrome,” “dermatomyositis,” “polymyositis,” “myasthenia gravis,” “Churg-Strauss syndrome,” “giant cell arteritis,” “microscopic polyangiitis,” “cryoglobulinemia,” “polyarteritis nodosa,” “Wegener granulomatosis,” “inflammatory bowel diseases,” “anemia, pernicious,” “thyroiditis, autoimmune,” “celiac disease,” “juvenile rheumatoid arthritis,” “vitiligo,” “primary biliary cirrhosis,” “biliary cirrhosis,” “primary sclerosing cholangitis,” “autoimmune hepatitis,” “transverse myelitis,” “relapsing polychondritis,” “Addison disease,” “glomerulonephritis,” “idiopathic thrombocytopenic purpura,” “psoriatic arthritis,” “spondylitis, ankylosing,” “sarcoidosis,” “Raynaud's disease,” “connective tissue disease,” and “autoimmune disease.” Each one of them was cross-referenced with the following MeSH terms: “solvent,” “tetrachloroethylene,” “trichloroethylene,” “trichloroethane,” “perchlorethylene,” “toluene,” “vinyl chloride,” “acetone,” “ethylacetate,” “turpentine,” “benzene,” “5-hydroxytryptophan,” “diethylpropion,” “fenfluramine,” and “hair dye.” Furthermore, we used ‘text words’ if there was no MeSH term such as in the cases of “hexane,” “white spirit,” “urea formaldehyde,” and “nail polish.”
In addition, each MeSH term was translated into DeCS (Health Sciences Descriptors), the tool that permits navigation between records and sources of information through controlled concepts and organized in Portuguese, Spanish and English, in order to search the SciElo and LILACS databases. No limits regarding language, period of publication, or publication type were taken into account. Those references from the articles that seemed to be relevant for our review were hand-searched. Authors of publications to which full text access was unavailable were contacted via e-mail.
In addition, a systematic search was done in the PubMed database up to February 2012 looking for the molecular mechanisms by which OSs may alter immune responses and induce the developing of ADs. The search was restricted to: (1) studies in humans and mice, (2) restricted by title and (3) English language, (4) articles published in the last 20 years, (5) studies in ADs (6) studies including the term autoimmunity, (7) studies including the term “immune system”. All of the search strategies included MeSH terms: “Tetrachloroethylene”, “Trichloroethylene”, “Trichloroethanes”, “Perchlorethylene”, “Toluene”, “Vinyl Chloride”, “Acetone”, “Ethylacetate”, “Turpentine”, “ Benzene ”, “5-Hydroxytryptophan”, “Diethylpropion”, “Fenfluramine”, “Hair Dyes”, “Hexane”, “Immune System”, and “autoimmunity”. Furthermore, we used key words if there was no MeSH term such as in the cases of “white spirit”, “urea formaldehyde”, “nail polish and “autoimmune diseases”. The exclusion criteria were the following: 1) Articles related to immune alterations due to allergic responses in solvent exposure, 2) articles related to cytotoxic and genotoxic effects of solvent in cancer progression, 3) articles in a language other than English, 4) reviews, 5) comments and case reports that did not report any biological implication related to solvent exposure.
Study Selection, Data Extraction, and Quality Assessment
Inclusion criteria for the systematic review were the following: any types of study that used accepted classification criteria for ADs and had information about exposure to OSs explicitly listed as a category.
Articles were excluded from the analysis if they included the same data that were published in another study.
Abstracts and full text articles were reviewed in the search for eligible studies. Two reviewers did the search independently while applying the same selection criteria. The two resulting databases were compared and disagreements resolved by consensus. For articles in languages other than English or Spanish, translations of abstracts or full text articles were reviewed to determine eligibility.
Each eligible study was classified as: review, case report, case series, cohort, or case-control. Inclusion criteria for the meta-analysis were applied to publications that provided epidemiologic data on risk factors [relative risks (RR) and odds ratios (OR) with confidence intervals (CI)] or that provided information that let us calculate these data. For cohort studies, the requirements were the number of subjects exposed, the number unexposed, and the number of subjects who developed the disease in each of the two cases. For case-control studies, the requirements were the number of subjects with AD that were exposed and not exposed, and the number of controls that were exposed and not exposed. In those instances where the study had not reported the number of subjects in each group, either the RR or the OR with the CI, at least, must have been reported in order for them to be included in the meta-analysis calculations.
Studies were excluded from the meta-analysis if the groups were not clearly defined, e.g. case- controls studies with likely AD diagnosis in control subjects or exposed cohorts with low specificity for OS.
For each eligible study, the type of exposure and exposure assessment was analyzed regarding the source of information (census, database, interview, mailed questionnaires, etc.) and classified as follows: “qualitative” if it was stated by the subject or interviewer on questionnaires measured by the quality of exposure rather than its quantity, “quantitative” if it was related to a number or quantity, and “semi-quantitative” if it was expressed as a quantity susceptible of measurement but was not related to a number. Quantitative assessment was sub-classified in “indirect quantitative” if it was defined by an estimate from a register of specific jobs at risk or calculated using a job-exposure matrix formula, and “direct quantitative” if the OS was directly measured in the environment or as a biomarker in the subject. Furthermore it was extracted the information that described the condition of exposure (e.g occupation, living characteristics.)
The quality and strength of scientific evidence was evaluated supporting an etiologic relationship between ADs and the proposed risk factor. In this investigation, a quantitative scoring system based on the Bradford Hill criteria was used [21]. The quantitative Bradford Hill score (qBHs) is divided into categorical ratings of the overall strength of causal association as follows: 0 to 6 points was considered poor or no causal association; 7 to 14 points was considered moderate or inconclusive causal association, and 15 to 21 points was considered a strong causal association. No study was excluded from the review based on this assessment.
Meta-analysis
Data were analyzed using the Comprehensive Meta-Analysis version 2 program (Biostat, Englewood, NJ, 2004). Calculations were carried out for the whole group of articles depending on the binary data available for any AD: number of subjects and risk data (OR and RR with the corresponding 95% CI). Effect size was calculated based on studies that only showed the OR and respective 95% CI and the raw data from case-control and cohort studies. A second effect size was calculated independently with studies that only showed the RR and the respective 95% CI and the raw data from cohort studies. Different study designs were used to compute the same effect size since the effect size had the same meaning in all studies and were comparable in relevant aspects. Thus, this study was able to transform all values to log values (log odds ratio and standard error), which were used in the pooled analysis. This approach prevented the omission of studies that used an alternative measure.
A sensitivity analysis was done in which the meta-analysis results of the studies as a whole was compared to the same meta-analysis with one study excluded in each round to determine how robust the findings were. It was also done to evaluate the impact of decisions that lead to different data being used in the analysis and whether the conclusions reached might differ substantially if a single study or a number of studies were omitted.
Additional meta-analyses were done for studies with complex data structure and non-cumulative results if the information for the different effects was not totally independent. Thus, articles showing multiple independent subgroups within a study were considered in these analyses (i.e. different definitions of the disease, gender differences, toxic exposure or more than one comparison group within a study). To compare effects across subgroups we typically use subgroup as the unit of analysis in an independent meta-analysis.
Supplementary analyses were done for the association between each specific AD and OSs exposure. Additional analyses were also done grouping the data according to the exposure assessment category.
ORs were grouped by weighing individual ORs by the inverse of their variance. For each analysis, the final effect OR and 95% CI were obtained by means of both random and fixed effect models. The selection of the computational model was done based on the expectation that the studies shared a common effect size. The random effect model was preferred because it accepts that there is a distribution of true effect sizes rather than one true effect and assigns a more balanced weight to each study. It was also used because all the studies were considered to be unequal in terms of specific ADs.
Heterogeneity was calculated by means of Cochran's (Q) and Higgins's (I2) tests. The I2 test showed the proportion of observed dispersion that was real rather than spurious and was expressed as a ratio ranging from 0% to 100%. I2 values of 25%, 50%, and 75% were qualitatively classified as low, moderate, and high respectively. A significant Q-statistic (p<0.10) indicated heterogeneity across studies. Publication bias was determined using Funnel plots and Egger's regression asymmetry tests, and additional tests were applied if it was found.
Results
The search with the defined MeSH Terms in PubMed, SCOPUS, SciELO, and LILACS [DeCS Terms] retrieved 531 articles. Using text words, 794 articles were found in PubMed, SCOPUS, SciELO and LILACS. Nine additional records were identified through references (Figure 1).
Footnote: OSs: organic solvents; AD: autoimmune disease; OR: odds ratio.
After duplicates were removed, there were 575 potentially relevant articles. Based on title or abstract, 143 were chosen for full text review. Six full text publications were not found and the authors of these publications or authors of publications that referenced them (e.g. [22]) were contacted. Of these, 4 articles were sent via e-mail [23]–[26] and one by post mail [27]. It was not possible to get full text access to one article [28]. One hundred and three articles described exposure to OSs as a category and used accepted classification criteria for ADs. Of these, 3 were meta-analysis [29]–[31], 29 were reviews, 5 case series [32]–[36], 15 case reports [37]–[51], and 51 epidemiological studies. Thirty-three of the epidemiological studies were finally included in the meta-analysis (Table 1) [7], [23], [26], [27], [52]–[80]. Because of lack of information, 18 epidemiological studies [25], [81]–[97] were not included in the meta-analysis (Table S1). Eight studies were not included because lack of information about the number of subjects with confirmed AD; two case-control studies were not included because lack of information about exposure in control subjects; one case-control study was excluded because control subjects had likely an AD diagnosis; three cohort studies were not included because lack of information about the number of unexposed and how many developed AD. Four studies were excluded because exposure data had low specificity for OS.
Types of exposure and exposure assessments are described in table 1 for each study. The average qBHs for the total publications included in the meta-analysis was 14.25 points (SD, 1.586; range, 11–17 points; 99% CI, 13.528–14.972) reflecting a categorical rating of moderate relationship.
We found a significant association between OSs exposure and the increased risk of developing an autoimmune trait by evaluating all ADs as a single group. Figure 2 shows the forest plot corresponding to the meta-analysis including the most relevant outcome per author where the final common effect size based on a random model was statistically significant (OR: 1.54; 95% CI: 1.25–1.92; p-value<0.001). The results of different measures for heterogeneity calculated for the analysis showed in Figure 2 were as follows: Q-value: 132.1; degree of freedom (Q):30; p-value<0.0001; I-squared: 77.3%; Tau-Squared 0.19. The relative weight of each study is included in the forest plot (Figure 2)
Footnote: final common effect size based on a random model. Odds Ratio (95%CI) with raw data from case control and cohort designed studies were included. The most relevant outcome per author was included. The relative weight of each study is included. GN: glomerulonephritis; MS: multiple sclerosis; PBC: primary biliary cirrhosis; PSV: primary systemic vasculitis; RA: rheumatoid arthritis; RP: Raynaud disease; SLE: systemic lupus erythematosus; SSc: systemic sclerosis. Diot, et al 1: organic solvent as a whole; Thompson AE, et al 1: turpentine exposure (the most significant result); Nelson NA, et al 1. 1994: disabled population; Purdie GL, et al 1. 2011 confirmed RP population.
There were 5 studies showing complex data structure with different and non-cumulative results where the information for the different effects was not totally independent [52], [57], [70], [76], [78]. Then, 22 additional meta-analyses including 30 articles and the different outcomes of four of the above mentioned studies were calculated independently [57], [70], [76], [78]. These analyses included five from Diot et al. 2002 [76] (different toxic exposure measured: chlorinate, ketones, aromatic, toluene, TCE), one from Nelson et al. 1994 [57] (control not disabled population) and Purdie et al. 2011 [70] (a different cutoff point to disease criteria) and fifteen from Thompson et al. 2002 [78] (different toxic exposure measured: toluene, benzene, white spirit, perchlorethylene, TCE, trichlorethane, vinyl chloride, urea formaldehyde, meta-phenylenediamene, bicromade, aromatic hydrocarbons, aliphatic hydrocarbons, fenfluramine, diethylpropion, L5 OH-tryptophan). In these meta-analyses, the studies that provided uniquely RR data were not included [52], [54] for statistical reasons. All these additional meta-analyses showed a significant association between the exposure to OSs and ADs as a trait (Figures S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21, S22). After doing a sensitivity analysis excluding one study at a time, the results were similar to the cumulative analysis (Figures S23 and S24).
A second effect size was calculated based on data from two studies showing RR data [52], [54] with raw data from cohort studies [26], [55], [56], [63]–[66], [70]; this effect size was not significant (OR: 1.62; 95% CI: 0.99–2.65; p-value:0.051) (Figure S25). The results of different measures for heterogeneity calculated for the analysis showed in Figure S25 were as follows: Q-value: 42.01; degree of freedom (Q):11; p-value<0.001; I-squared: 73.8%; Tau-Squared 0.40.
Additional analysis limited to the association between each specific AD and OSs exposure presented significant associations in the random model. For MS, the OR was 1.53 with 95& CI 1.03–2.29 and p value: 0.035, with fifteen studies included. For primary systemic vasculitis (PSV), the OR was 3.15 with 95% CI: 1.56–6.36 and p-value: 0.001, with one study included in the cumulative analysis for this disease. Systemic sclerosis (SSc) showed these results OR: 2.54; 95% CI: 1.23–5.14; p-value: 0.011, with eight studies included (Figure 3). Primary biliary cirrhosis (PBC) was positively associated but not statistically significant (OR: 1.002; 95% CI: 1–1.004; p-value: 0.092).
Footnote: random effect model showing significant association between SSC and OSs exposure. PBC and PSV included only one study (100% of the weight). Q value for SSc analysis: 33.7, I2:79,2, Degree of freedom (Q):7, p-value<0,0001. GN: glomerulonephritis; MS: multiple sclerosis; PBC: primary biliary cirrhosis; PSV: primary systemic vasculitis; RA: rheumatoid arthritis; RP: raynaud disease; SLE: systemic lupus erythematosus; SSc: systemic sclerosis. Diot, et al 1: organic solvent as a whole; Thompson AE, et al 1: turpentine exposure (the most significant result); Purdie GL, et al 1: confirmed RP population; Nelson NA, et al 1. 1994: disabled population.
The analyses according to the exposure assessment category are shown in figure S26. There were three groups included. Two of them were not significant: qualitative (OR: 1.29; 95% CI: 0.84–1.98; p-value 0.231) and quantitative indirect (OR: 1.69; 95% CI: 0.90–3.16; p-value 0.101) and one was significant: semi-quantitative [(OR: 1.48; 95% CI: 1.17–1.87; p-value 0.001). Heterogeneity: Q-value: 56.7; degree of freedom (Q):13; p-value<0.0001; I-squared: 77%; Tau-Squared 0.19]. The total heterogeneity between the three groups for the random effects analysis was not significant [Q-value: 0.60; degree of freedom (Q):2; p-value: 0.741]
Evidence of significant publication bias was identified using the Egger test (p-value 2-tailed: 0.002; intercept: 1.09) for the meta-analysis which included studies that report OR with its respective 95% CI and raw data from case control and cohort designed studies. The Funnel plot showing the standard error on the Y axis is shown in Figure S27. Therefore, a second analysis was run in a search for publication bias. The classic fail-safe analysis indicated that 279 missing studies would give a p-value of >0.05. Begg and Mazumdar rank correlation was not significant (p-value 2-tailed: 0.16) and the trim and fill adjustment did not suggest a lower risk than the original analysis [adjusted values (11 studies trimmed) point estimate 1.03 (0.83–1.28), Q value: 227]. Based on all the analyses for publication bias, we consider the impact of bias in the present meta-analyses trivial.
Since 1977, 20 publications including case-reports and case series (Table S2) have reported 37 cases of AD possibly being triggered by OSs. We also found 3 previous meta-analyses. The first was published in 1996 by Landtblom AM et al [29] and concerned OSs exposure as a cause of Multiple Sclerosis (MS). They found 13 studies and reported an overall RR of 1.7 (with a 95% CI of 1.1–2.4). Later in 2001, Aryal BK et al [30] published a meta-analysis of SSc and solvent exposure. Eight studies met inclusion criteria, and the RR was reported to be 2.91 (with a 95% CI of 1.60 to 5.30). Kettaneh A et al in 2007 [31] published the most recent meta-analysis about occupational exposure to solvents and gender-related risk of SSc, they found a statistically significant association of SSc with OS exposure (OR 2.4; 95% CI 1.7–3.4: P = 0.002) and concluded that whereas SSc affects women predominantly, among subjects with occupational exposure to OS, men are at a higher risk of developing the disease than women. All the studies included in these publications were examined in our analysis. No meta-analysis evaluating ADs as a trait was found.
Regarding the systematic search for the OSs molecular mechanisms related to responses of immune system and ADs, with defined MeSH Terms and text words, retrieved 893 articles. After duplicates were removed, we obtained 827 articles of which 86 were included according to the inclusion criteria. The results are described in detail in Tables S3 and S4 and inclusion/exclusion criteria are described on Figure S28. Table 2 shows selected articles, representing main molecular processes related with OSs exposure and their potential implication on immune system or autoimmune pathologies. We found that the effects of OSs on the immune system include lymphoproliferation, autoantibody production, Th1 and Th17 responses, oxidative stress, protein modification as well as effects on gene expression.
Discussion
Our results indicate that OS exposure is a risk factor for developing ADs. Even though the individual meta-analyses (i.e. each AD considered separately) disclosed significant association for MS, PSV and SSc (Figure 3), the direction and significance of this association did not change when all ADs, considered as a single trait, were analyzed (Figure 2).Different combinations of factors involved in the generation of autoimmunity produce diverse clinical pictures within the wide spectrum of ADs (mosaic of autoimmunity) [2]. Our study, which takes into account both OSs as a whole and each solvent separately, reinforces this as well as the fact that ADs might share several common mechanisms(i.e., the autoimmune tautology) [98]. However, the term “separately,” which is used to refer to the studies that analyze only one solvent, is not the most biologically appropriate because most of the solvents are a mixture [99].
Our meta-analysis with ORs as the measure of association including 31 articles regarding 8 ADs showed a significant relationship of OSs exposure with ADs (OR: 1.54; 95% CI: 1.25–1.92; p-value<0.001) and that with RRs as the measure of association including 10 articles and 5 ADs showed a near significant relationship (OR: 1.62; 95% CI: 0.99–2.65; p-value:0.051). When each AD was considered individually, there were also significant results with MS, PSV, SSc and PBC, although the latter was positively associated but not statistically significant.
A systematic and comprehensive review of the effects of OSs on the immune system is shown in Table 2. OSs are capable of altering cellular proliferation, apoptosis and tissue-specific function [100]–[126]. Both the amount and duration of OSs exposure are essential in pathology causation. Chronic exposure to OSs might lead to deposits in an organ and consequently to immune infiltration, similar to what is observed in ADs. The self-proteins that are modified by OSs may become immunogenic, recognized as foreign, and then initiate an inflammatory response and tissue injury. In this regard and according with our results, there are similar pathways operating on the incidence of ADs, but there are also specific mechanism that could lead to the particular manifestations of each AD; for instance, lymphocyte infiltration and immunoglobulin's deposits in SLE, and enzymatic alteration and scleroderma-specific antibody subsets in SSc [86], [106], [109].
Ketones are the most common OS used by the general population. Acetone, the simplest example of the ketones, is a commonly used solvent and is the active ingredient in nail polish remover and some paint thinners. It has been suggested that nail polish use may be associated with PBC [67]. These data are intriguing in view of the xenobiotic hypothesis proposed for the development of PBC with specific halogenated compounds. These compounds could increase the immunogenicity of mitochondrial proteins and induce anti-mitochondrial antibodies in animal models [127]. In fact, only one clinical study was included in the meta-analysis regarding PBC and nail polish exposure disclosing a positive associated but not statistically significant [67]. More studies involving PBC patients searching for this association could be useful.
Long term exposure to OSs seems to foster massive hepatic mononuclear infiltration leading to autoimmune hepatitis although it is important to highlight that this infiltration is the first step in the immunopathogenesis of not only autoimmune hepatitis but also the rest of the ADs [108]. As shown by Cai et al [109], lymphocyte infiltration was found in the pancreas, lungs, and kidneys in addition to the liver.
In autoimmune thyroid disease, it is probable that solvents may interfere with iodine transportation and induce oxidative stress that leads to an inflammatory response to the thyroid gland [128].
The relevance of our results rely in the fact that relation between SSc and environmental exposure, especially involving OSs is significant. Mice MRL+/+, an autoimmunity susceptible strain, when exposed to TCE increase the total IgG serum concentration, antinuclear antibodies (ANAs) and anticardiolipin autoantibodies [100]. On the other hand, in an in vitro model of human epidermal keratinocytes, was possible to determinate that TCE not only stimulates reactive oxygen species release, but also it stimulates nitric oxide synthesis by nitric oxide synthase. These cellular changes may contribute to the physiopathological process that lead to skin injury such as shown in SSc [106]. The biological mechanisms by which OSs may induce the development of ADs support the results observed trough the meta-analysis.
Concerning MS, when an independent analysis was done for each disease, MS show a significant association with OSs exposure. These results are like those reported by Landtblom et al [29], in their 1996 meta-analysis. Landtblom et al implemented a Mantel-Haenszel RR calculation. The main differences between their analysis and ours rely on the statistical approach because the Mantel-Haenszel method for combining OR is an alternative to the fixed-effect inverse variance method and we developed a random effect model. Our meta-analysis included 15 MS studies, 7 new to the previous meta-analysis [26], [55], [59], [63]–[66] published between 1994 and 2012.
The precise mechanisms responsible for the development of environmentally-induced autoimmune disorders are unknown. Although many hypotheses for the occurrence of autoimmune phenomena after various environmental exposures have been proposed, none of the hypotheses is completely supported by direct causal evidence. Also, mechanisms thought to be involved in the initiation of the disease process might differ from the mechanisms believed to exacerbate an established illness. However, the experimental approaches have been able to identify different environmental factors that use the same toxicity paths and mechanisms and either individually or jointly can have strong effects on molecular signaling pathways, immune responses or regulation mechanisms actively involved in health and disease (Figure 4 and Table 2).
Footnote: Solid red arrows represent known paths. Yellow dashed arrow represents hypothetical mechanisms (warranting future research), and red dashed line represents an inhibited process. In susceptible individuals, activation paths are stronger (black arrows). See text for details. ROS: Reactive oxygen Species; NO: Nitric Oxide.
It could be suggested that, as described for autoimmune/inflammatory syndrome induced by adjuvants, the toxic effect influences the appearance of these conditions only in subjects who are genetically susceptible [13].
Study Limitations
Significant differences between case-control and cohort models were found. This fact can be explained by the limitations of each of these methodologies [129]. The following are the limitations in case-control studies. (I) the information about exposure is primarily based on interviews and may be subject to recall bias. (II) Validating the information on exposure can be difficult or even impossible. (III) By definition, case-control studies evaluate a single disease. (IV) The selection of an appropriate control group might be difficult. Most of the studies ignored the common origin of ADs and this generates the possibility of including patients with an underlying autoimmune condition as controls.
Most of the cohort studies included in our meta-analysis were retrospective. This implied that: (I) data was collected before the research hypothesis was defined leading to inaccurate data for the research. (II) The crude information was taken from databases or census. Therefore, the report on the exposure is not a direct quantification of the exposure. (III) The outcome information came from databases or medical records, but the subjects were not examined and this can lead to misdiagnosis. The explanation for why the result in the meta-analysis that included studies that reported the RR and raw data from cohort studies was not significant could be based on the abovementioned information as well as the low power due to a small sample size.
Exposure misclassification is a major problem when assessing the roll of environmental factors in complex diseases. Most individuals are not aware of the specific agents to which they have been exposed, and databases do not provide further information. None of the studies included in this meta-analysis employed an objective method of exposure assessment. Only two studies retrieved in this search [83], [85] reported a direct-quantitative measure of exposure (i.e. TCE in concentrations from 6 to over 500 parts-per-billion [83]). After performing the analyses according to the exposure assessment category (figure S26) the final common effect size remained significantly associated as risk factor.
A significant effort is necessary to determine the proper way to test the causal factors for autoimmunity. Nevertheless, we believe that identifying the causal pathways of toxics already known to be associated with generating autoimmunity is a breakthrough. Standardizing the pathways as validated biomarkers would lead to more accurate studies. Future research on environmental exposure will enhance our knowledge of the common mechanisms associated with ADs.
In conclusion, an association between OSs exposure and ADs was observed. This approach could be applied to any study of the association between exposure to other toxics and ADs. Although OSs exposure has not yet been sufficiently investigated, in order to clarify their roles in ADs pathogenesis, there is a need to study their relationship with genes associated, whether involved in protection or susceptibility to each AD and their effects on development of the autoimmune process.
Supporting Information
Figure S1.
Forest plot of supplementary meta-analyses. Footnote: final common effect size based on a random model. Odds Ratio (95%CI) with raw data from case control and cohort designed studies were included. Studies that provided uniquely RR data were not included for statistical reasons. Each different outcome of the studies with complex data structure was included.GN: glomerulonephritis; MS: multiple sclerosis; PBC: primary biliary cirrhosis; PSV: primary systemic vasculitis; RA: rheumatoid arthritis; RP: Raynaud disease; SLE: systemic lupus erythematosus; SSc: systemic sclerosis. The complex data structure and non-cumulative results of articles showing multiple independent or dependent subgroups included in the analysis was S1 Diot E, et al.2 2002 Exposition to chlorinate.
https://doi.org/10.1371/journal.pone.0051506.s001
(TIF)
Figure S2.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Diot E, et al.3 2002. Exposition to ketones.
https://doi.org/10.1371/journal.pone.0051506.s002
(TIF)
Figure S3.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Diot E, et al.4 2002. Exposition to aromatic.
https://doi.org/10.1371/journal.pone.0051506.s003
(TIF)
Figure S4.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Diot E, et al.5 2002. Exposition to toluene.
https://doi.org/10.1371/journal.pone.0051506.s004
(TIF)
Figure S5.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Diot e, et al.6. Exposition to TCE.
https://doi.org/10.1371/journal.pone.0051506.s005
(TIF)
Figure S6.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Nelson NA, et al 2. 1994 control population.
https://doi.org/10.1371/journal.pone.0051506.s006
(TIF)
Figure S7.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Purdie GL, et al 2. 2011 including confirmed and possible Raynaud.
https://doi.org/10.1371/journal.pone.0051506.s007
(TIF)
Figure S8.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 10. Exposition to Bicromade.
https://doi.org/10.1371/journal.pone.0051506.s008
(TIF)
Figure S9.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 11. Exposition to Toluene.
https://doi.org/10.1371/journal.pone.0051506.s009
(TIF)
Figure S10.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 12. Exposition to Aromatic hydrocarbons.
https://doi.org/10.1371/journal.pone.0051506.s010
(TIF)
Figure S11.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 13. Exposition to Aliphatic hydrocarbons.
https://doi.org/10.1371/journal.pone.0051506.s011
(TIF)
Figure S12.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 14. Exposition to Fenfluramine.
https://doi.org/10.1371/journal.pone.0051506.s012
(TIF)
Figure S13.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 15. Exposition to Diethylpropion.
https://doi.org/10.1371/journal.pone.0051506.s013
(TIF)
Figure S14.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 16. Exposition to L5 Ohtryptophan.
https://doi.org/10.1371/journal.pone.0051506.s014
(TIF)
Figure S15.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 2. Exposition to Benzene.
https://doi.org/10.1371/journal.pone.0051506.s015
(TIF)
Figure S16.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 3. Exposition to White spirit.
https://doi.org/10.1371/journal.pone.0051506.s016
(TIF)
Figure S17.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 4. Exposition to Perchlorethylene.
https://doi.org/10.1371/journal.pone.0051506.s017
(TIF)
Figure S18.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 5. Exposition to Trichlorethylene.
https://doi.org/10.1371/journal.pone.0051506.s018
(TIF)
Figure S19.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 6. Exposition to Trichlorethane.
https://doi.org/10.1371/journal.pone.0051506.s019
(TIF)
Figure S20.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 7. Exposition to vinyl chloride.
https://doi.org/10.1371/journal.pone.0051506.s020
(TIF)
Figure S21.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 8. Exposition to Urea formaldehyde.
https://doi.org/10.1371/journal.pone.0051506.s021
(TIF)
Figure S22.
Forest plot of supplementary meta-analyses. Final common effect size based on a random model. The studies included and abbreviations are the same as in Figure S1 with the exception of Thompson AE, et al. 2002 9. Exposition to Meta-phenylenediamene.
https://doi.org/10.1371/journal.pone.0051506.s022
(TIF)
Figures S23.
Sensitivity analysis. Footnote: Odds Ratio (95%CI) excluding one study at a time. CI: confidence interval. Diot, et al 1: organic solvent as a whole; Thompson AE, et al 1: turpentine exposure (the most significant result); Purdie GL, et al 1: confirmed RP population; Nelson NA, et al 1. 1994: disabled population.
https://doi.org/10.1371/journal.pone.0051506.s023
(TIF)
Figures S24.
Cumulative analysis. Footnote: Odds Ratio (95%CI) The most relevant outcome per author was included. CI: confidence interval. Diot, et al 1: organic solvent as a whole; Thompson AE, et al 1: turpentine exposure (the most significant result); Purdie GL, et al 1: confirmed RP population; Nelson NA, et al 1. 1994: disabled population.
https://doi.org/10.1371/journal.pone.0051506.s024
(TIF)
Figure S25.
Forest plot of studies showing RR data and raw data from cohort studies. Footnote: final common effect size based on a random model. Risk Ratio (95%CI). CI: confidence interval; AS: Ankylosing spondylitis; GN: glomerulonephritis; MS: multiple sclerosis; RA: rheumatoid arthritis; RP: Raynaud disease; SLE: systemic lupus erythematosus; SSc: systemic sclerosis. Lundberg I, et al. 1 1994 painters AS; Lundberg I, et al. 3 1994 Substantial RA men; Lundberg I, et al. 5 1994 substantial RA women; Purdie GL, et al 1: confirmed RP population.
https://doi.org/10.1371/journal.pone.0051506.s025
(TIF)
Figure S26.
Forest plot of studies showing OR data according to the exposure assessment category. Footnote: Odds Ratio (95%CI). The most relevant outcome per author was included. GN: glomerulonephritis; MS: multiple sclerosis; PBC: primary biliary cirrhosis; PSV: primary systemic vasculitis; RA: rheumatoid arthritis; RP: Raynaud disease; SLE: systemic lupus erythematosus; SSc: systemic sclerosis. Diot, et al 1: organic solvent as a whole; Thompson AE, et al 1: turpentine exposure (the most significant result); Purdie GL, et al 1: confirmed RP population; Nelson NA, et al 1. 1994: disabled population.
https://doi.org/10.1371/journal.pone.0051506.s026
(TIF)
Figure S27.
Funnel Plot of standard error by log odds ratio. Footnote: X-axis: Log odds ratio. Y-axis: Standard Error.
https://doi.org/10.1371/journal.pone.0051506.s027
(TIF)
Figure S28.
Systematic Review Results for OSs molecular mechanisms related to responses of immune system and ADs. Footnote: ADs: Autoimmune Diseases.
https://doi.org/10.1371/journal.pone.0051506.s028
(TIF)
Text S1.
PRISMA 2009 Checklist. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
https://doi.org/10.1371/journal.pone.0051506.s029
(DOCX)
Table S1.
Studies not included in the Meta-analysis. Footnote: AD: Autoimmune Disease; C-C: Case Control Study; OS: Organic Solvent; SSc: Systemic Sclerosis or Scleroderma; SLE: Systemic Lupus Erythematous; MS: Multiple Sclerosis; PSV: Primary systemic vasculitis; RA: Rheumatoid Arthritis; PBC: Primary Biliary Cirrhosis; GN: Glomerulonephritis; y/o: years old; VC: vinyl chloride; TCE: trichloroethylene; PCE: Perchlorethylene; RDX: Royal Demolition explosive; EEG: electroencephalographic study; PVC: polyvinyl chloride; ESRD: End Stage Renal Disease.
https://doi.org/10.1371/journal.pone.0051506.s030
(DOCX)
Table S2.
Case reports and case series. Footnote: AD: Autoimmune Disease; OS: Organic Solvent; SSc: Systemic Sclerosis or Scleroderma; SLE: Systemic Lupus Erythematous; MS: Multiple Sclerosis; PSV: Primary systemic vasculitis; RA: Rheumatoid Arthritis; RD: Raynaud Disease; PBC: Primary Biliary Cirrhosis; GN: Glomerulonephritis; Anti- GBM: Anti-glomerular basement membrane antibody; PM/DM: Polimiositis/Dermatomiositis; y/o: years old; VC: vinyl chloride; TCE: trichloroethylene; PCE: Perchlorethylene; Jo-1: anti-histidyl-t-RNA synthetase.
https://doi.org/10.1371/journal.pone.0051506.s031
(DOCX)
Table S3.
Search strategy related to solvent exposure and immune alterations.
https://doi.org/10.1371/journal.pone.0051506.s032
(DOCX)
Table S4.
Effects of the exposition to organic solvents on experimental models.
https://doi.org/10.1371/journal.pone.0051506.s033
(DOCX)
Acknowledgments
We would like to thank our colleagues Jenny Amaya-Amaya, Zayrho DeSanVicente-Celis, Manuel Amador-Patarroyo, Catalina Herrera-Diaz, Jorge Cardenas Roldan, Juliana M. Giraldo-Villamil, Juan C. Castellanos, Omar-Javier Calixto, and Julian Caro M. for their fruitful contributions. We specially thank professors Yolanda Torres and Carlos E. Trillos for his advice, and Cesar Mantilla, chemical engineer specialist in industrial hygiene for his advice. We also thanks the reviewers for their fruitful criticism. This work was supported by Universidad del Rosario, Bogota, Colombia.
Author Contributions
Conceived and designed the experiments: ARV JMA RDM. Performed the experiments: CBM ARV JMA. Analyzed the data: ARV CBM. Contributed reagents/materials/analysis tools: CBM CSH GMO RDM JMA ARV. Wrote the paper: CBM CSH GMO RDM JMA ARV. Interpreted the possible pathways: GMO CSH.
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