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Organic Solvents as Risk Factor for Autoimmune Diseases: A Systematic Review and Meta-Analysis

  • Carolina Barragán-Martínez,

    Affiliation: Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia

  • Cesar A. Speck-Hernández,

    Affiliation: Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia

  • Gladis Montoya-Ortiz,

    Affiliation: Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia

  • Rubén D. Mantilla,

    Affiliation: Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia

  • Juan-Manuel Anaya,

    Affiliation: Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia

  • Adriana Rojas-Villarraga

    adrirojas@gmail.com

    Affiliation: Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia

Organic Solvents as Risk Factor for Autoimmune Diseases: A Systematic Review and Meta-Analysis

  • Carolina Barragán-Martínez, 
  • Cesar A. Speck-Hernández, 
  • Gladis Montoya-Ortiz, 
  • Rubén D. Mantilla, 
  • Juan-Manuel Anaya, 
  • Adriana Rojas-Villarraga
PLOS
x

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).

Conclusion

Exposure to OSs is a risk factor for developing ADs. As a corollary, individuals with non-modifiable risk factors (i.e., familial autoimmunity or carrying genetic factors) should avoid any exposure to OSs in order to avoid increasing their risk of ADs.

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).

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Figure 1. Systematic Review Results.

Footnote: OSs: organic solvents; AD: autoimmune disease; OR: odds ratio.

http://dx.doi.org/10.1371/journal.pone.0051506.g001

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)

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Figure 2. Forest plot of studies meta-analyzed: association between organic solvents and autoimmune disease as a trait.

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.

http://dx.doi.org/10.1371/journal.pone.0051506.g002

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).

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Figure 3. Forest plot of studies meta-analyzed grouping by comparison of specific autoimmune diseases.

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.

http://dx.doi.org/10.1371/journal.pone.0051506.g003

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.

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Table 2. Effects of the exposition to organic solvents on experimental models.

http://dx.doi.org/10.1371/journal.pone.0051506.t002

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).

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Figure 4. Potential molecular mechanisms implicated in solvent autoimmune disease development.

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.

http://dx.doi.org/10.1371/journal.pone.0051506.g004

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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi: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.

doi:10.1371/journal.pone.0051506.s028

(TIF)

Text S1.

PRISMA 2009 Checklist. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

doi: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.

doi: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.

doi:10.1371/journal.pone.0051506.s031

(DOCX)

Table S3.

Search strategy related to solvent exposure and immune alterations.

doi:10.1371/journal.pone.0051506.s032

(DOCX)

Table S4.

Effects of the exposition to organic solvents on experimental models.

doi: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.

References

  1. 1. Anaya JM (2010) The autoimmune tautology. Arthritis research & therapy 12: 147 doi:10.1186/ar3175.
  2. 2. Anaya JM, Corena R, Castiblanco J, Rojas-Villarraga A, Shoenfeld Y (2007) The kaleidoscope of autoimmunity: multiple autoimmune syndromes and familial autoimmunity. Expert review of clinical immunology 3: 623–635 doi:10.1586/1744666X.3.4.623.
  3. 3. Selmi C, Leung PSC, Sherr DH, Diaz M, Nyland JF, et al. (2012) Mechanisms of environmental influence on human autoimmunity: A national institute of environmental health sciences expert panel workshop. Journal of autoimmunity Available: http://www.ncbi.nlm.nih.gov/pubmed/22749494. Accessed 19 October 2012.
  4. 4. Rook G (2011) Hygiene Hypothesis and Autoimmune Diseases. Clinical reviews in allergy & immunology: 5–15. doi:10.1007/s12016-011-8285-8.
  5. 5. Youinou P, Pers J-O, Gershwin ME, Shoenfeld Y (2010) Geo-epidemiology and autoimmunity. Journal of autoimmunity 34: J163–7. doi: 10.1016/j.jaut.2009.12.005
  6. 6. Pigatto PD, Guzzi G (2010) Linking mercury amalgam to autoimmunity. Trends in immunology 31: 45–48 doi:10.1016/j.it.2009.12.004.
  7. 7. Finckh A, Cooper GS, Chibnik LB, Costenbader KH, Watts J, et al. (2006) Occupational silica and solvent exposures and risk of systemic lupus erythematosus in urban women. Arthritis and rheumatism 54: 3648–3654. doi: 10.1002/art.22210
  8. 8. Kiyohara C, Washio M, Horiuchi T, Tada Y, Asami T, et al. (2009) Cigarette smoking, N-acetyltransferase 2 polymorphisms and systemic lupus erythematosus in a Japanese population. Lupus 18: 630–638. doi: 10.1177/0961203309102809
  9. 9. Shoenfeld Y, Aharon-Maor A, Sherer Y (1997) Smoking and immunity: an additional player in the mosaic of autoimmunity. Scandinavian Journal of Immunology 45: 1–6. doi: 10.1046/j.1365-3083.1997.d01-366.x
  10. 10. Cooper GS, Gilbert KM, Greidinger EL, James JA, Pfau JC, et al. (2008) Recent advances and opportunities in research on lupus: environmental influences and mechanisms of disease. Environmental health perspectives 116: 695–702. doi: 10.1289/ehp.11092
  11. 11. Chang C, Gershwin ME (2010) Drugs and autoimmunity A contemporary review and mechanistic approach. Journal of autoimmunity 34: J266–75. doi: 10.1016/j.jaut.2009.11.012
  12. 12. Barbara G, Cremon C, Carini G, Bellacosa L, Zecchi L, et al. (2011) The immune system in irritable bowel syndrome. Journal of neurogastroenterology and motility 17: 349–359. doi: 10.5056/jnm.2011.17.4.349
  13. 13. Shoenfeld Y, Agmon-Levin N (2011) “ASIA” - autoimmune/inflammatory syndrome induced by adjuvants. Journal of autoimmunity 36: 4–8 Available: http://www.ncbi.nlm.nih.gov/pubmed/20708902. Accessed 13 March 2012.
  14. 14. Shoenfeld Y, Selmi C, Zimlichman E, Gershwin ME (2008) The autoimmunologist: geoepidemiology, a new center of gravity, and prime time for autoimmunity. Journal of autoimmunity 31: 325–330 Available: http://www.ncbi.nlm.nih.gov/pubmed/18838248. Accessed 13 April 2012.
  15. 15. Chighizola C, Meroni PL (2012) The role of environmental estrogens and autoimmunity. Autoimmunity reviews 11: A493–501 Available: http://www.ncbi.nlm.nih.gov/pubmed/22172713. Accessed 19 October 2012.
  16. 16. Farhat SC, Silva CA, Orione MA, Campos LM, Sallum AM, et al. (2011) Air pollution in autoimmune rheumatic diseases: a review. Autoimmun Rev 11: 14–21 doi:S1568-9972(11)00150-9 [pii] 10.1016/j.autrev.2011.06.008 [doi].
  17. 17. Gourley M, Miller FW (2007) Mechanisms of disease: Environmental factors in the pathogenesis of rheumatic disease. Nature clinical practice Rheumatology 3: 172–180. doi: 10.1038/ncprheum0435
  18. 18. Garabrant DH, Dumas C (2000) Epidemiology of organic solvents and connective tissue disease. Arthritis research 2: 5–15.
  19. 19. Miller FW, Alfredsson L, Costenbader KH, Kamen DL, Nelson LM, et al. (2012) Epidemiology of environmental exposures and human autoimmune diseases: Findings from a National Institute of Environmental Health Sciences Expert Panel Workshop. Journal of autoimmunity doi:10.1016/j.jaut.2012.05.002.
  20. 20. Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Bmj 339: b2535–b2535 doi:10.1136/bmj.b2535.
  21. 21. Lozano-Calderón S, Anthony S, Ring D (2008) The quality and strength of evidence for etiology: example of carpal tunnel syndrome. The Journal of hand surgery 33: 525–538 doi:10.1016/j.jhsa.2008.01.004.
  22. 22. Marrie RA (2004) Reviews Environmental risk factors in multiple sclerosis aetiology. Neurology 3: 709–718. doi: 10.1016/s1474-4422(04)00933-0
  23. 23. Casetta I, Granieri E, Malagu S, Tola MR, Paolino E, et al. (1994) Environmental risk factors and multiple sclerosis: a community-based, case-control study in the province of Ferrara, Italy. Neuroepidemiology 13: 120–128. doi: 10.1159/000110369
  24. 24. Garnier R, Bazire A, Chataigner D (2006) Sclérodermie et exposition professionnelle aux solvants organiques. Archives des Maladies Professionnelles et de l'Environnement 67: 488–504 doi:ADMP-06-2006-67-3-1250-3274-101019-200518826.
  25. 25. Hopkins RS, Indian RW, Pinnow E, Conomy J (1991) Multiple sclerosis in Galion, Ohio: prevalence and results of a case-control study. Neuroepidemiology 10: 192–199 Available: http://www.ncbi.nlm.nih.gov/pubmed/1745329. Accessed 9 January 2012.
  26. 26. Stenager E, Brønnum-Hansen H, Koch-Henriksen N (2003) Risk of multiple sclerosis in nurse anaesthetists. Multiple Sclerosis 9: 427–428. doi: 10.1191/1352458503ms941xx
  27. 27. Flodin U (1988) Multiple sclerosis, solvents, and pets. Archives of neurology 47: 128. doi: 10.1001/archneur.1988.00520300038015
  28. 28. Zachariae H, Bjerring P, Søndergaard KH, Halkier-Sørensen L (1997) Occupational systemic sclerosis in men. Ugeskrift For Laeger 159: 2687–2689.
  29. 29. Landtblom AM, Flodin U, Söderfeldt B, Wolfson C, Axelson O (1996) Organic solvents and multiple sclerosis: a synthesis of the current evidence. Epidemiology (Cambridge, Mass) 7: 429–433. doi: 10.1097/00001648-199607000-00015
  30. 30. Aryal BK, Khuder S, Schaub E (2001) Meta-analysis of systemic sclerosis and exposure to solvents. American journal of industrial medicine 40: 271–274. doi: 10.1002/ajim.1098
  31. 31. Kettaneh A, Al Moufti O, Tiev K, Chayet C, Tolédano C, et al. (2007) Occupational exposure to solvents and gender-related risk of systemic sclerosis: a metaanalysis of case-control studies. The Journal of rheumatology 34: 97–103.
  32. 32. Fernández J, Sanz-Gallén PNS (2010) Seguimiento de dos pacientes con glomerulonefritis iga mesangial con antecedentes de exposición a tóxicos (cadmio y disolventes orgánicos) Follow-up of two patients with mesangial IgA glomerulonephritis exposed to cadmium and organic solvents. 33: 309–314. doi: 10.4321/s1137-66272010000400007
  33. 33. Savige JA, Dowling J, Kincaid-Smith P (1989) Superimposed glomerular immune complexes in anti-glomerular basement membrane disease. American journal of kidney diseases: the official journal of the National Kidney Foundation 14: 145–153 Available: http://www.ncbi.nlm.nih.gov/pubmed/2757019. Accessed 9 January 2012.
  34. 34. Brautbar N, Richter ED, Nesher G (2004) Systemic vasculitis and prior recent exposure to organic solvents: report of two cases. Archives of environmental health 59: 515–517 Available: http://www.ncbi.nlm.nih.gov/pubmed/16425661. Accessed 9 January 2012.
  35. 35. Calvani N, Silvestris F, Dammacco F (n.d.) Familial systemic sclerosis following exposure to organic solvents and the possible implication of genetic factors. Annali italiani di medicina interna: organo ufficiale della Società italiana di medicina interna 16: 175–178 Available: http://www.ncbi.nlm.nih.gov/pubmed/11692907. Accessed 9 January 2012.
  36. 36. Petkova V, Nakova L, Matakeva M (1992) [A clinical observation of vinyl chloride-induced disease]. Problemi na khigienata 17: : 195–199. Available: http://www.ncbi.nlm.nih.gov/pubmed/1364541. Accessed 9 January 2012.
  37. 37. Reis J, Dietemann JL, Warter JM, Poser CM (2001) A case of multiple sclerosis triggered by organic solvents. Neurological sciences official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 22: 155–158. doi: 10.1007/s100720170015
  38. 38. Amaducci L, Arfaioli C, Inzitari D, Martinetti MG (1978) Another possible precipitating factor in multiple sclerosis: the exposure to organic solvents. Bollettino dellIstituto sieroterapico milanese 56: 613–617.
  39. 39. Magnavita N, Bergamaschi A, Garcovich A, Giuliano G (1986) Vasculitic Purpura in Vinyl Chloride Disease: A Case Report. Angiology 37: 382–388. doi: 10.1177/000331978603700508
  40. 40. Ohtsuka T (2009) Organic solvent-induced myopathy simulating eosinophilic fasciitis and/or dermatomyositis. The Journal of dermatology 36: 358–359. doi: 10.1111/j.1346-8138.2009.00653.x
  41. 41. Serratrice J, Granel B, Pache X, Disdier P, De Roux-Serratrice C, et al. (2001) A case of polymyositis with anti-histidyl-t-RNA synthetase (Jo-1) antibody syndrome following extensive vinyl chloride exposure. Clinical rheumatology 20: 379–382 Available: http://www.ncbi.nlm.nih.gov/pubmed/11642524. Accessed 9 January 2012.
  42. 42. Benzarti A, Amor AB, Euch DE, Mbazaa A, Osmen AB, et al. (2010) Sclérodermie systémique et exposition professionnelle aux solvants organiques «perchloréthylène». À propos d ’ un cas Systemic sclerosis and occupational exposure to organic solvents: “‘ Perchlorethylene ’”, a case. Occupational and Environmental Medicine 50: 501–508. doi: 10.1016/j.reval.2010.01.041
  43. 43. Hinnen U, Schmid-Grendelmeier P, Müller E, Elsner P (1995) [Exposure to solvents in scleroderma: disseminated circumscribed scleroderma (morphea) in a painter exposed to perchloroethylene]. Schweizerische medizinische Wochenschrift 125: 2433–2437 Available: http://www.ncbi.nlm.nih.gov/pubmed/8553031. Accessed 9 January 2012.
  44. 44. Garcia-Zamalloa AM, Ojeda E, Gonzalez-Beneitez C, Goni J, Garrido A (1994) Systemic sclerosis and organic solvents: early diagnosis in industry. Annals of the Rheumatic Diseases 53: 618. doi: 10.1136/ard.53.9.618-a
  45. 45. Bottomley WW, Sheehan-Dare RA, Hughes P, Cunliffe WJ (1993) A sclerodermatous syndrome with unusual features following prolonged occupational exposure to organic solvents. The British journal of dermatology 128: 203–206 Available: http://www.ncbi.nlm.nih.gov/pubmed/8457454. Accessed 9 January 2012.
  46. 46. Tibon-Fisher O, Heller E, Ribak J (1992) [Occupational scleroderma due to organic solvent exposure]. Harefuah 122: 530–532, 551 Available: http://www.ncbi.nlm.nih.gov/pubmed/1398326. Accessed 9 January 2012.
  47. 47. Brasington RD, Thorpe-Swenson AJ (1991) Systemic sclerosis associated with cutaneous exposure to solvent: case report and review of the literature. Arthritis & Rheumatism 34: 631–633. doi: 10.1002/art.1780340516
  48. 48. Karamfilov T, Buslau M, Dürr C, Weyers W (2003) [Pansclerotic porphyria cutanea tarda after chronic exposure to organic solvents]. Der Hautarzt; Zeitschrift für Dermatologie, Venerologie, und verwandte Gebiete 54: 448–452.
  49. 49. Pralong P, Cavailhes A, Balme B, Cottin V, Skowron F (2009) [Diffuse systemic sclerosis after occupational exposure to trichloroethylene and perchloroethylene]. Annales de dermatologie et de vénéréologie 136: 713–717. doi: 10.1016/j.annder.2008.10.043
  50. 50. Sparrow GP (1977) A connective tissue disorder similar to vinyl chloride disease in a patient exposed to perchlorethylene. Clinical and experimental dermatology 2: 17–22. doi: 10.1111/j.1365-2230.1977.tb01532.x
  51. 51. Czirják L, Pócs E, Szegedi G (1994) Localized scleroderma after exposure to organic solvents. Dermatology (Basel, Switzerland) 189: 399–401 Available: http://www.ncbi.nlm.nih.gov/pubmed/7873829. Accessed 9 January 2012.
  52. 52. Lundberg I, Alfredsson L, Plato N, Sverdrup B, Klareskog L, et al. (1994) Occupation, occupational exposure to chemicals and rheumatological disease. A register based cohort study. Scandinavian journal of rheumatology 23: 305–310. doi: 10.3109/03009749409099278
  53. 53. Fored CM, Nise G, Ejerblad E (2004) Absence of association between organic solvent exposure and risk of chronic renal failure: a nationwide population-based case-control study. Journal of the: 180–186. doi:10.1097/01.ASN.0000103872.60993.06.
  54. 54. Sesso R, Stolley PD, Salgado N, Pereira AB, Ramos OL (1990) Exposure to hydrocarbons and rapidly progressive glomerulonephritis. Brazilian journal of medical and biological research Revista brasileira de pesquisas medicas e biologicas Sociedade Brasileira de Biofisica et al 23: 225–233.
  55. 55. Flodin U, Landtblom A-M, Axelson O (2003) Multiple sclerosis in nurse anaesthetists. Occupational and environmental medicine 60: 66–68. doi: 10.1136/oem.60.1.66
  56. 56. Amaducci L, Arfaioli C, Inzitari D, Marchi M (1982) Multiple sclerosis among shoe and leather workers: an epidemiological survey in Florence. Acta neurologica Scandinavica 65: 94–103. doi: 10.1111/j.1600-0404.1982.tb03066.x
  57. 57. Nelson NA, Robins TG, White RF, Garrison RP (1994) A case-control study of chronic neuropsychiatric disease and organic solvent exposure in automobile assembly plant workers. Occupational and environmental medicine 51: 302–307. doi: 10.1136/oem.51.5.302
  58. 58. Grønning M, Albrektsen G, Kvåle G, Moen B, Aarli JA, et al. (1993) Organic solvents and multiple sclerosis: a case-control study. Acta Neurologica Scandinavica 88: 247–250. doi: 10.1111/j.1600-0404.1993.tb04229.x
  59. 59. Zorzon M, Zivadinov R, Nasuelli D, Dolfini P, Bosco A, et al. (2003) Risk factors of multiple sclerosis: a case-control study. Neurological sciences: official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 24: 242–247. doi: 10.1007/s10072-003-0147-6
  60. 60. Juntunen J, Kinnunen E, Antti-Poika M, Koskenvuo M (1989) Multiple sclerosis and occupational exposure to chemicals: a co-twin control study of a nationwide series of twins. British journal of industrial medicine 46: 417–419. doi: 10.1136/oem.46.6.417
  61. 61. Koch-Henriksen N (1989) An epidemiological study of multiple sclerosis. Familial aggregation social determinants, and exogenic factors. Acta neurologica Scandinavica Supplementum 124: 1–123.
  62. 62. Landtblom AM, Flodin U, Karlsson M, Palhagen S, Axelson O, et al. (1993) Multiple sclerosis and exposure to solvents, ionizing radiation and animals. Scandinavian Journal of Work, Environment & Health 19: 399–404. doi: 10.5271/sjweh.1455
  63. 63. Landtblom A-M, Tondel M, Hjalmarsson P, Flodin U, Axelson O (2006) The risk for multiple sclerosis in female nurse anaesthetists: a register based study. Occupational and Environmental Medicine 63: 387–389. doi: 10.1136/oem.2005.024604
  64. 64. Mortensen JT, Brønnum-Hansen H, Rasmussen K (1998) Multiple sclerosis and organic solvents. Epidemiology (Cambridge, Mass) 9: 168–171 Available: http://www.ncbi.nlm.nih.gov/pubmed/9504285. Accessed 9 January 2012.
  65. 65. Riise T, Moen BE, Kyvik KR (2002) Organic solvents and the risk of multiple sclerosis. Epidemiology (Cambridge, Mass) 13: 718–720 Available: http://www.ncbi.nlm.nih.gov/pubmed/12410015. Accessed 9 January 2012.
  66. 66. Riise T, Kirkeleit J, Aarseth JH, Farbu E, Midgard R, et al. (2011) Risk of MS is not associated with exposure to crude oil, but increases with low level of education. Multiple sclerosis (Houndmills, Basingstoke, England) 17: 780–787 doi:10.1177/1352458510397686.
  67. 67. Gershwin ME, Selmi C, Worman HJ, Gold EB, Watnik M, et al. (2005) Risk factors and comorbidities in primary biliary cirrhosis: a controlled interview-based study of 1032 patients. Hepatology 42: 1194–1202. doi: 10.1002/hep.20907
  68. 68. Lane SE, Watts RA, Bentham G, Innes NJ, Scott DGI (2003) Are environmental factors important in primary systemic vasculitis? A case-control study. Arthritis and rheumatism 48: 814–823. doi: 10.1002/art.10830
  69. 69. De Roos AJ, Cooper GS, Alavanja MC, Sandler DP (2005) Rheumatoid arthritis among women in the Agricultural Health Study: risk associated with farming activities and exposures. Annals of epidemiology 15: 762–770 doi:10.1016/j.annepidem.2005.08.001.
  70. 70. Purdie GL, Purdie DJ, Harrison A (2011) Raynaud's Phenomenon in medical laboratory workers who work with solvents. The Journal of rheumatology 38: 1940–1946. doi: 10.3899/jrheum.101129
  71. 71. Cooper GS, Parks CG, Treadwell EL, Clair EW (2004) Occupational Risk Factors for the Development of Systemic Lupus Erythematosus. Journal of Rheumatology Oct 31(10): 1928–1933.
  72. 72. Cooper GS, Wither J, Bernatsky S, Claudio JO, Clarke A, et al. (2010) Occupational and environmental exposures and risk of systemic lupus erythematosus: silica, sunlight, solvents. Rheumatology (Oxford, England) 49: 2172–2180. doi: 10.1093/rheumatology/keq214
  73. 73. Nietert PJ, Sutherland SE, Silver RM, Pandey JP, Knapp RG, et al. (1998) Is occupational organic solvent exposure a risk factor for scleroderma? Arthritis & Rheumatism 41: 1111–1118. doi: 10.1002/1529-0131(199806)41:6<1111::aid-art19>3.0.co;2-j
  74. 74. Bovenzi M, Barbone F, Betta A, Tommasini M, Versini W (1995) Scleroderma and occupational exposure Short Communications Scleroderma and occupational exposure. Health (San Francisco) 21: 289–292. doi: 10.5271/sjweh.40
  75. 75. Bovenzi M, Barbone F, Pisa FE, Betta A, Romeo L, et al. (2004) A case-control study of occupational exposures and systemic sclerosis. International Archives of Occupational and Environmental Health 77: 10–16. doi: 10.1007/s00420-003-0462-5
  76. 76. Diot E, Lesire V, Guilmot JL, Metzger MD, Pilore R, et al. (2002) Systemic sclerosis and occupational risk factors: a case-control study. Occupational and environmental medicine 59: 545–549. doi: 10.1136/oem.59.8.545
  77. 77. Maître A, Hours M, Bonneterre V, Arnaud J, Arslan MT, et al. (2004) Systemic sclerosis and occupational risk factors: role of solvents and cleaning products. The Journal of 31.
  78. 78. Thompson AE, Pope JE (2002) Increased prevalence of scleroderma in southwestern Ontario: a cluster analysis. Increased Prevalence of Scleroderma in Southwestern Ontario: A Cluster Analysis. Journal of Rheumatology 29.
  79. 79. Silman AJ, Jones S (1992) What is the contribution of occupational environmental factors to the occurrence of scleroderma in men? Annals of the rheumatic diseases 51: 1322–1324. doi: 10.1136/ard.51.12.1322
  80. 80. Czirják L, Bokk A, Csontos G, Lörincz G, Szegedi G (1989) Clinical findings in 61 patients with progressive systemic sclerosis. Acta dermatovenereologica 69: 533–536.
  81. 81. Black CM, Welsh KI, Walker AE, Bernstein RM, Catoggio LJ, et al. (1983) Genetic susceptibility to scleroderma-like syndrome induced by vinyl chloride. Lancet 1: 53–55. doi: 10.1016/s0140-6736(83)91578-7
  82. 82. Landtblom A, Wastenson M, Ahmadi A, Söderkvist P (2003) Multiple sclerosis and exposure to organic solvents, investigated by genetic polymorphisms of the GSTM1 and CYP2D6 enzyme systems. Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 24: 248–251 doi:10.1007/s10072-003-0148-5.
  83. 83. Kilburn KH, Warshaw RH (1992) Prevalence of symptoms of systemic lupus erythematosus (SLE) and of fluorescent antinuclear antibodies associated with chronic exposure to trichloroethylene and other chemicals in well water. Environmental Research 57: 1–9. doi: 10.1016/s0013-9351(05)80014-3
  84. 84. Souberbielle BE, Martin-Mondiere C, O'Brien ME, Carydakis C, Cesaro P, et al. (1990) A case-control epidemiological study of MS in the Paris area with particular reference to past disease history and profession. Acta neurologica Scandinavica 82: 303–310. doi: 10.1111/j.1600-0404.1990.tb03308.x
  85. 85. Hathaway JA, Buck CR (1977) Absence of health hazards associated with RDX manufacture and use. Journal of occupational medicine: official publication of the Industrial Medical Association 19: 269–272 Available: http://www.ncbi.nlm.nih.gov/pubmed/323432. Accessed 9 January 2012.
  86. 86. Povey A, Guppy MJ, Wood M, Knight C, Black CM, et al. (2001) Cytochrome P2 polymorphisms and susceptibility to scleroderma following exposure to organic solvents. Arthritis and rheumatism 44: 662–665 Available: http://www.ncbi.nlm.nih.gov/pubmed/11263781. Accessed 9 January 2012.
  87. 87. Albert DA, Albert AN, Vernace M, Sebastian JK, Hsia EC (2005) Analysis of a Cluster of Cases of Wegener Granulomatosis. JCR: Journal of Clinical Rheumatology 11: 188–193. doi: 10.1097/01.rhu.0000173234.33984.4a
  88. 88. Brogren CH, Christensen JM, Rasmussen K (1986) Occupational exposure to chlorinated organic solvents and its effect on the renal excretion of N-acetyl-beta-D-glucosaminidase. Archives of toxicology Supplement = Archiv für Toxikologie Supplement 9: 460–464 Available: http://www.ncbi.nlm.nih.gov/pubmed/3468929. Accessed 9 January 2012.
  89. 89. Goldman JA (1996) Connective tissue disease in people exposed to organic chemical solvents: systemic sclerosis (scleroderma) in dry cleaning plant and aircraft industry workers. Journal of clinical rheumatology: practical reports on rheumatic & musculoskeletal diseases 2: 185–190. doi: 10.1097/00124743-199608000-00005
  90. 90. Koischwitz D, Marsteller HJ, Lackner K, Brecht G, Brecht T (1980) [Changes in the arteries in the hand and fingers due to vinyl chloride exposure (author's transl)]. RöFo: Fortschritte auf dem Gebiete der Röntgenstrahlen und der Nuklearmedizin 132: 62–68 Available: http://www.ncbi.nlm.nih.gov/pubmed/6446500. Accessed 9 January 2012.
  91. 91. Sińczuk-Walczak H, Głuszcz M (1982) [Various aspects of the clinical and electroencephalographic studies in workers chronically exposed to vinyl chloride]. Medycyna pracy 33: 349–354 Available: http://www.ncbi.nlm.nih.gov/pubmed/7182717. Accessed 9 January 2012.
  92. 92. Hotz P, Thielemans N, Bernard A (1993) Serum laminin, hydrocarbon exposure, and glomerular damage. British journal of: 1104–1110.
  93. 93. Hsieh H-I, Chen P-C, Wong R-H, Wang J-D, Yang P-M, et al. (2007) Effect of the CYP2E1 genotype on vinyl chloride monomer-induced liver fibrosis among polyvinyl chloride workers. Toxicology 239: 34–44. doi: 10.1016/j.tox.2007.06.089
  94. 94. Jacob S, Héry M, Protois J-C, Rossert J, Stengel B (2007) Effect of organic solvent exposure on chronic kidney disease progression: the GN-PROGRESS cohort study. Journal of the American Society of Nephrology: JASN 18: 274–281 doi:10.1681/ASN.2006060652.
  95. 95. Jacob S, Hery M, Protois J-C, Rossert J, Stengel B (2007) New insight into solvent-related End Stage Renal Disease: occupations, products and types of solvents at risk. Occupational and environmental medicine Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2095352&tool=pmcentrez&rendertype=abstract. Accessed 30 December 2011.
  96. 96. Prince MI, Ducker SJ, James OF (2010) Case-control studies of risk factors for primary biliary cirrhosis in two United Kingdom populations. Gut 59: 508–512 doi:10.1136/gut.2009.184218.
  97. 97. Magnant J, de Monte M, Guilmot JL, Lasfargues G, Diot P, et al. (2005) Relationship between occupational risk factors and severity markers of systemic sclerosis. The Journal of rheumatology 32: 1713–1718.
  98. 98. Anaya J-M, Rojas-Villarraga A, García-Carrasco M (2012) The autoimmune tautology: from polyautoimmunity and familial autoimmunity to the autoimmune genes. Autoimmune diseases 2012: 297193 Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3362807&tool=pmcentrez&rendertype=abstract. Accessed 29 October 2012.
  99. 99. United States Department of Labor: Safety and Health Topics|Solvents (n.d.).
  100. 100. Khan MF, Kaphalia BS, Prabhakar BS (1995) Trichloroethene-induced autoimmune response in female MRL+/+ mice. Toxicology and applied 134: 155–160 doi:10.1006/taap.1995.1179.
  101. 101. Halmes NC, Perkins EJ, McMillan DC, Pumford NR (1997) Detection of trichloroethylene-protein adducts in rat liver and plasma. Toxicology letters 92: 187–194. doi: 10.1016/s0378-4274(97)00053-2
  102. 102. Cai P, Konig MF, Khan MF, Kaphalia BS, Ansari GA (2007) Differential immune responses to albumin adducts of reactive intermediates of trichloroethene in MRL+/+ mice. Toxicology and applied 220: 278–283 doi:10.1016/j.taap.2007.01.020.
  103. 103. Wang G, König R, Ansari GAS, Khan MF (2008) Lipid peroxidation-derived aldehyde-protein adducts contribute to trichloroethene-mediated autoimmunity via activation of CD4+ T cells. Free radical biology & medicine 44: 1475–1482 Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2440665&tool=pmcentrez&rendertype=abstract. Accessed 9 January 2012.
  104. 104. Wang G, Ansari GAS, Khan MF (2007) Involvement of lipid peroxidation-derived aldehyde-protein adducts in autoimmunity mediated by trichloroethene. Journal of toxicology and environmental health Part A 70: 1977–1985 Available: http://www.ncbi.nlm.nih.gov/pubmed/17966069. Accessed 9 January 2012.
  105. 105. Grune T, Michel P, Sitte N, Eggert W, Albrecht-Nebe H, et al. (1997) Increased levels of 4-hydroxynonenal modified proteins in plasma of children with autoimmune diseases. Free radical biology & medicine 23: 357–360. doi: 10.1016/s0891-5849(96)00586-2
  106. 106. Wang G, Cai P, Ansari GAS, Khan MF (2007) Oxidative and nitrosative stress in trichloroethene-mediated autoimmune response. Toxicology 229: 186–193 Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1945101&tool=pmcentrez&rendertype=abstract. Accessed 5 January 2012.
  107. 107. Griffin JM, Blossom SJ, Jackson SK, Gilbert KM, Pumford NR (2000) Trichloroethylene accelerates an autoimmune response by Th1 T cell activation in MRL +/+ mice. Immunopharmacology 46: 123–137. doi: 10.1016/s0162-3109(99)00164-2
  108. 108. Griffin JM, Gilbert KM, Lamps LW, Pumford NR (2000) CD4(+) T-cell activation and induction of autoimmune hepatitis following trichloroethylene treatment in MRL+/+ mice. Toxicological sciences: an official journal of the Society of Toxicology 57: 345–352. doi: 10.1093/toxsci/57.2.345
  109. 109. Cai P, König R, Boor PJ, Kondraganti S, Kaphalia BS, et al. (2008) Chronic exposure to trichloroethene causes early onset of SLE-like disease in female MRL +/+ mice. Toxicology and applied pharmacology 228: 68–75 Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2442272&tool=pmcentrez&rendertype=abstract. Accessed 9 January 2012.
  110. 110. Peden-Adams MM, Eudaly JG, Heesemann LM, Smythe J, Miller J, et al. (2006) Developmental immunotoxicity of trichloroethylene (TCE): studies in B6C3F1 mice. Journal of environmental science and health Part A, Toxic/hazardous substances & environmental engineering 41: 249–271 Available: http://www.ncbi.nlm.nih.gov/pubmed/16484062. Accessed 9 January 2012.
  111. 111. Blossom SJ, Doss JC (2007) Trichloroethylene alters central and peripheral immune function in autoimmune-prone MRL(+/+) mice following continuous developmental and early life exposure. Journal of immunotoxicology 4: 129–141 Available: http://www.ncbi.nlm.nih.gov/pubmed/18958721. Accessed 9 January 2012.
  112. 112. Blossom SJ, Gilbert KM (2006) Exposure to a metabolite of the environmental toxicant, trichloroethylene, attenuates CD4+ T cell activation-induced cell death by metalloproteinase-dependent FasL shedding. Toxicological sciences : an official journal of the Society of Toxicology 92: 103–114 Available: http://www.ncbi.nlm.nih.gov/pubmed/16641322. Accessed 7 December 2011.
  113. 113. Gilbert KM, Pumford NR, Blossom SJ (2006) Environmental contaminant trichloroethylene promotes autoimmune disease and inhibits T-cell apoptosis in MRL(+/+) mice. Journal of immunotoxicology 3: 263–267 doi:10.1080/15476910601023578.
  114. 114. Gilbert KM, Rowley B, Gomez-Acevedo H, Blossom SJ (2011) Coexposure to mercury increases immunotoxicity of trichloroethylene. Toxicological sciences: an official journal of the Society of Toxicology 119: 281–292 Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3023566&tool=pmcentrez&rendertype=abstract. Accessed 30 September 2011.
  115. 115. Lash LH, Putt DA, Hueni SE, Horwitz BP (2005) Molecular markers of trichloroethylene-induced toxicity in human kidney cells. Toxicology and applied pharmacology 206: 157–168 Available: http://www.ncbi.nlm.nih.gov/pubmed/15967204. Accessed 9 January 2012.
  116. 116. Palbykin B, Borg J, Caldwell PT, Rowles J, Papoutsis AJ, et al. (2011) Trichloroethylene induces methylation of the Serca2 promoter in H9c2 cells and embryonic heart. Cardiovascular toxicology 11: 204–214 doi:10.1007/s12012-011-9113-3.
  117. 117. Olsgard ML, Bortolotti GR, Trask BR, Smits JEG (2008) Effects of inhalation exposure to a binary mixture of benzene and toluene on vitamin a status and humoral and cell-mediated immunity in wild and captive American kestrels. Journal of toxicology and environmental health Part A 71: 1100–1108 Available: http://www.ncbi.nlm.nih.gov/pubmed/18569622. Accessed 9 January 2012.
  118. 118. Lan Q, Zhang L, Li G, Vermeulen R, Weinberg RS (2004) Hematotoxicity in workers exposed to low levels of benzene. Science 306: 1774–1776 doi:10.1126/science.1102443.
  119. 119. Emara AM, El-Bahrawy H (2008) Green tea attenuates benzene-induced oxidative stress in pump workers. Journal of immunotoxicology 5: 69–80 Available: http://www.ncbi.nlm.nih.gov/pubmed/18382860. Accessed 9 January 2012.
  120. 120. Seaton MJ, Schlosser P, Medinsky MA (1995) In vitro conjugation of benzene metabolites by human liver: potential influence of interindividual variability on benzene toxicity. Carcinogenesis 16: 1519–1527. doi: 10.1093/carcin/16.7.1519
  121. 121. Park H-J, Oh JH, Yoon S, Rana SVS (2008) Time Dependent Gene Expression Changes in the Liver of Mice Treated with Benzene. Biomarker insights 3: 191–201 Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2688356&tool=pmcentrez&rendertype=abstract. Accessed 9 January 2012.
  122. 122. Wichmann G, Mühlenberg J, Fischäder G, Kulla C, Rehwagen M, et al. (2005) An experimental model for the determination of immunomodulating effects by volatile compounds. Toxicology in vitro: an international journal published in association with BIBRA 19: 685–693 doi:10.1016/j.tiv.2005.03.012.
  123. 123. McDermott C, Allshire A, van Pelt F, Heffron JJA (2008) In vitro exposure of jurkat T-cells to industrially important organic solvents in binary combination: interaction analysis. Toxicological sciences: an official journal of the Society of Toxicology 101: 263–274 Available: http://www.ncbi.nlm.nih.gov/pubmed/17982160. Accessed 9 January 2012.
  124. 124. Hendriksen PJM, Freidig AP, Jonker D, Thissen U, Bogaards JJP, et al. (2007) Transcriptomics analysis of interactive effects of benzene, trichloroethylene and methyl mercury within binary and ternary mixtures on the liver and kidney following subchronic exposure in the rat. Toxicology and applied pharmacology 225: 171–188 Available: http://www.ncbi.nlm.nih.gov/pubmed/17905399. Accessed 9 January 2012.
  125. 125. Wang G, Wang J, Fan X, Ansari GAS, Khan MF (2012) Protein adducts of malondialdehyde and 4-hydroxynonenal contribute to trichloroethene-mediated autoimmunity via activating Th17 cells: dose- and time-response studies in female MRL+/+ mice. Toxicology 292: 113–122 doi:10.1016/j.tox.2011.12.001.
  126. 126. Shen T, Zhu Q-X, Yang S, Ding R, Ma T, et al. (2007) Trichloroethylene induce nitric oxide production and nitric oxide synthase mRNA expression in cultured normal human epidermal keratinocytes. Toxicology 239: 186–194 Available: http://www.ncbi.nlm.nih.gov/pubmed/17719164. Accessed 19 June 2011.
  127. 127. Rieger R, Gershwin ME (2007) The X and why of xenobiotics in primary biliary cirrhosis. Journal of autoimmunity 28: 76–84 doi:10.1016/j.jaut.2007.02.003.
  128. 128. Duntas LH (2008) Environmental factors and autoimmune thyroiditis. Nature clinical practice Endocrinology & metabolism 4: 454–460 Available: http://www.ncbi.nlm.nih.gov/pubmed/18607401. Accessed 22 November 2011.
  129. 129. Noordzij M, Dekker FW, Zoccali C, Jager KJ (2009) Study designs in clinical research. Nephron Clinical Practice 113: c218–c221 Available: http://www.ncbi.nlm.nih.gov/pubmed/19690439. Accessed 22 November 2012.