Lazaros Belbasis1, Vanesa Bellou1, Evangelos Evangelou2, John P A Ioannidis3, Ioanna Tzoulaki4. 1. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece. 2. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece; Department of Biostatistics and Epidemiology, Imperial College London, London, UK. 3. Department of Medicine, Stanford Prevention Research Center, Stanford, CA, USA; Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA; Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, USA. 4. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece; Department of Biostatistics and Epidemiology, Imperial College London, London, UK; MRC-PHE Centre for Environment and Health, Imperial College London, London, UK. Electronic address: i.tzoulaki@imperial.ac.uk.
Abstract
BACKGROUND: The cause of multiple sclerosis is believed to involve environmental exposure and genetic susceptibility. We aimed to summarise the environmental risk factors that have been studied in relation to onset of multiple sclerosis, assess whether there is evidence for diverse biases in this literature, and identify risk factors without evidence of biases. METHODS: We searched PubMed from inception to Nov 22, 2014, to identify systematic reviews and meta-analyses of observational studies that examined associations between environmental factors and multiple sclerosis. For each meta-analysis we estimated the summary effect size by use of random-effects and fixed-effects models, the 95% CI, and the 95% prediction interval. We estimated the between-study heterogeneity expressed by I(2) (defined as large for I(2)≥50%), evidence of small-study effects (ie, large studies had significantly more conservative results than smaller studies), and evidence of excess significance bias (ie, more studies than expected with significant results). FINDINGS: Overall, 44 unique meta-analyses including 416 primary studies of different risk factors and multiple sclerosis were examined, covering a wide range of risk factors: vaccinations, comorbid diseases, surgeries, traumatic events and accidents, exposure to environmental agents, and biochemical, infectious, and musculoskeletal biomarkers. 23 of 44 meta-analyses had results that were significant at p values less than 0·05 and 11 at p values less than 0·001 under the random-effects model. Only three of the 11 significant meta-analyses (p<0·001) included more than 1000 cases, had 95% prediction intervals excluding the null value, and were not suggestive of large heterogeneity (I(2)<50%), small-study effects (p for Egger's test >0·10), or excess significance (p>0·05). These were IgG seropositivity to Epstein-Barr virus nuclear antigen (EBNA) (random effects odds ratio [OR] 4·46, 95% CI 3·26-6·09; p for effect size=1·5 × 10(-19); I(2)=43%), infectious mononucleosis (2·17, 1·97-2·39; p=3·1 × 10(-50); I(2)=0%), and smoking (1·52, 1·39-1·66; p=1·7 × 10(-18;)I(2)=0%). INTERPRETATION: Many studies on environmental factors associated with multiple sclerosis have caveats casting doubts on their validity. Data from more and better-designed studies are needed to establish robust evidence. A biomarker of Epstein-Barr virus (anti-EBNA IgG seropositivity), infectious mononucleosis, and smoking showed the strongest consistent evidence of an association. FUNDING: None.
BACKGROUND: The cause of multiple sclerosis is believed to involve environmental exposure and genetic susceptibility. We aimed to summarise the environmental risk factors that have been studied in relation to onset of multiple sclerosis, assess whether there is evidence for diverse biases in this literature, and identify risk factors without evidence of biases. METHODS: We searched PubMed from inception to Nov 22, 2014, to identify systematic reviews and meta-analyses of observational studies that examined associations between environmental factors and multiple sclerosis. For each meta-analysis we estimated the summary effect size by use of random-effects and fixed-effects models, the 95% CI, and the 95% prediction interval. We estimated the between-study heterogeneity expressed by I(2) (defined as large for I(2)≥50%), evidence of small-study effects (ie, large studies had significantly more conservative results than smaller studies), and evidence of excess significance bias (ie, more studies than expected with significant results). FINDINGS: Overall, 44 unique meta-analyses including 416 primary studies of different risk factors and multiple sclerosis were examined, covering a wide range of risk factors: vaccinations, comorbid diseases, surgeries, traumatic events and accidents, exposure to environmental agents, and biochemical, infectious, and musculoskeletal biomarkers. 23 of 44 meta-analyses had results that were significant at p values less than 0·05 and 11 at p values less than 0·001 under the random-effects model. Only three of the 11 significant meta-analyses (p<0·001) included more than 1000 cases, had 95% prediction intervals excluding the null value, and were not suggestive of large heterogeneity (I(2)<50%), small-study effects (p for Egger's test >0·10), or excess significance (p>0·05). These were IgG seropositivity to Epstein-Barr virus nuclear antigen (EBNA) (random effects odds ratio [OR] 4·46, 95% CI 3·26-6·09; p for effect size=1·5 × 10(-19); I(2)=43%), infectious mononucleosis (2·17, 1·97-2·39; p=3·1 × 10(-50); I(2)=0%), and smoking (1·52, 1·39-1·66; p=1·7 × 10(-18;)I(2)=0%). INTERPRETATION: Many studies on environmental factors associated with multiple sclerosis have caveats casting doubts on their validity. Data from more and better-designed studies are needed to establish robust evidence. A biomarker of Epstein-Barr virus (anti-EBNA IgG seropositivity), infectious mononucleosis, and smoking showed the strongest consistent evidence of an association. FUNDING: None.
Authors: Tai Lim Kim; Gwang Hun Jeong; Jae Won Yang; Keum Hwa Lee; Andreas Kronbichler; Hans J van der Vliet; Giuseppe Grosso; Fabio Galvano; Dagfinn Aune; Jong Yeob Kim; Nicola Veronese; Brendon Stubbs; Marco Solmi; Ai Koyanagi; Sung Hwi Hong; Elena Dragioti; Eunyoung Cho; Leandro F M de Rezende; Edward L Giovannucci; Jae Il Shin; Gabriele Gamerith Journal: Adv Nutr Date: 2020-11-16 Impact factor: 8.701
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Authors: Katarina Tengvall; Jesse Huang; Cecilia Hellström; Patrick Kammer; Martin Biström; Burcu Ayoglu; Izaura Lima Bomfim; Pernilla Stridh; Julia Butt; Nicole Brenner; Angelika Michel; Karin Lundberg; Leonid Padyukov; Ingrid E Lundberg; Elisabet Svenungsson; Ingemar Ernberg; Sigurgeir Olafsson; Alexander T Dilthey; Jan Hillert; Lars Alfredsson; Peter Sundström; Peter Nilsson; Tim Waterboer; Tomas Olsson; Ingrid Kockum Journal: Proc Natl Acad Sci U S A Date: 2019-08-02 Impact factor: 11.205