The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Environmental risk factors for multiple sclerosis. Part I: The role of infection
Open accessAbstract
Although genetic susceptibility explains the clustering of multiple sclerosis (MS) cases within families and the sharp decline in risk with increasing genetic distance, it cannot fully explain the geographic variations in MS frequency and the changes in risk that occur with migration. Epidemiological data provide some support for the “hygiene hypothesis,” but with the additional proviso for a key role of Epstein–Barr virus (EBV) in determining MS risk. We show that whereas EBV stands out as the only infectious agent that can explain many of the key features of MS epidemiology, by itself the link between EBV and MS cannot explain the decline in risk among migrants from high to low MS prevalence areas. This decline implies that either EBV strains in low‐risk areas have less propensity to cause MS, or that other infectious or noninfectious factors modify the host response to EBV or otherwise contribute to determine MS risk. The role of infectious factors is discussed here; in a companion article, we will examine the possible role of noninfectious factors and provide evidence that high levels of vitamin D may have a protective role, particularly during adolescence. The primary purpose of these reviews is to identify clues to the causes of MS and to evaluate the possibility of primary prevention. Ann Neurol 2007;61:288–299
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Epidemiologic evidence for multiple sclerosis as an infection
Kurtzke, J. F. (1993) · Clinical Microbiology Reviews · doi:10.1128/cmr.6.4.382 · Cited by 472
Wallin, M. T., Page, W. F., & Kurtzke, J. F. (2004) · Annals of Neurology · doi:10.1002/ana.10788 · Cited by 224 · cited 1×
“Although a greater degree of underrecognition of MS in the southern states as compared with the northern states in the earlier cohorts cannot be completely excluded, it is highly unlikely that it accounted for a threefold difference among individuals of similar socioeconomic status(5) and access to health care.(21) In…”
Background
Cited by
Risk Alleles for Multiple Sclerosis Identified by a Genomewide Study
(2007) · New England Journal of Medicine · doi:10.1056/nejmoa073493 · Cited by 1,746
Proinflammatory T-cell responses to gut microbiota promote experimental autoimmune encephalomyelitis
Lee, Y. K., Menezes, J. S., Umesaki, Y., & Mazmanian, S. K. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000082107 · Cited by 1,323
Commensal microbiota and myelin autoantigen cooperate to trigger autoimmune demyelination
Berer, K., Mues, M., Koutrolos, M., Rasbi, Z. A., Boziki, M., Johner, C., Wekerle, H., & Krishnamoorthy, G. (2011) · Nature · doi:10.1038/nature10554 · Cited by 1,251
Interferons at age 50: past, current and future impact on biomedicine
Borden, E. C., Sen, G. C., Uze, G., Silverman, R. H., Ransohoff, R. M., Foster, G. R., & Stark, G. R. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2422 · Cited by 1,154
Meningeal inflammation is widespread and linked to cortical pathology in multiple sclerosis
Howell, O. W., Reeves, C. A., Nicholas, R., Carassiti, D., Radotra, B., Gentleman, S. M., Serafini, B., Aloisi, F., Roncaroli, F., Magliozzi, R., et al. (2011) · Brain · doi:10.1093/brain/awr182 · Cited by 858
Multiple sclerosis: risk factors, prodromes, and potential causal pathways
Ramagopalan, S. V., Dobson, R., Meier, U. C., & Giovannoni, G. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(10)70094-6 · Cited by 548
Nylander, A., & Hafler, D. A. (2012) · Journal of Clinical Investigation · doi:10.1172/jci58649 · Cited by 495
Animal models of multiple sclerosis: the good, the bad and the bottom line
Ransohoff, R. M. (2012) · Nature Neuroscience · doi:10.1038/nn.3168 · Cited by 379
The genetics of multiple sclerosis: SNPs to pathways to pathogenesis
Oksenberg, J. R., Baranzini, S. E., Sawcer, S., & Hauser, S. L. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2395 · Cited by 347
Neurodegenerative and neurological disorders by small inhaled particles
Heusinkveld, H. J., Wahle, T., Campbell, A., Westerink, R. H. S., Tran, L., Johnston, H., Stone, V., Cassee, F. R., & Schins, R. P. F. (2016) · NeuroToxicology · doi:10.1016/j.neuro.2016.07.007 · Cited by 331
Epstein–Barr virus as a leading cause of multiple sclerosis: mechanisms and implications
Bjornevik, K., Münz, C., Cohen, J. I., & Ascherio, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00775-5 · Cited by 246
Self-citationPeli1 promotes microglia-mediated CNS inflammation by regulating Traf3 degradation
Xiao, Y., Jin, J., Chang, M., Chang, J.-H., Hu, H., Zhou, X., Brittain, G. C., Stansberg, C., Torkildsen, Ø., Wang, X., et al. (2013) · Nature Medicine · doi:10.1038/nm.3111 · Cited by 184
Lucas, R. M., Norval, M., Neale, R. E., Young, A. R., de Gruijl, F. R., Takizawa, Y., & van der Leun, J. C. (2014) · Photochemical & Photobiological Sciences · doi:10.1039/c4pp90033b · Cited by 158
Innate, innate-like and adaptive lymphocytes in the pathogenesis of MS and EAE
Van Kaer, L., Postoak, J. L., Wang, C., Yang, G., & Wu, L. (2019) · Cellular & Molecular Immunology · doi:10.1038/s41423-019-0221-5 · Cited by 148
Neurotheranostics as Personalized Medicines
Kevadiya, B. D., Ottemann, B. M., Thomas, M. B., Mukadam, I., Nigam, S., McMillan, J., Gorantla, S., Bronich, T. K., Edagwa, B., & Gendelman, H. E. (2019) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2018.10.011 · Cited by 124
Abrahamyan, S., Eberspächer, B., Hoshi, M.-M., Aly, L., Luessi, F., Groppa, S., Klotz, L., Meuth, S. G., Schroeder, C., Grüter, T., et al. (2020) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2020-322941 · Cited by 119
The prevalence of multiple sclerosis in 3 US communities.
(2010) · PubMed Central · Cited by 75
Aloisi, F., Serafini, B., Magliozzi, R., Howell, O. W., & Reynolds, R. (2010) · Brain · doi:10.1093/brain/awq223 · Cited by 73
Theranostic implications of nanotechnology in multiple sclerosis: a future perspective.
Singh, A. V., Khare, M., Gade, W. N., & Zamboni, P. (2012) · Autoimmune Diseases · doi:10.1155/2012/160830 · Cited by 58
Metadata
- Authors
- Ascherio, A., & Munger, K. L.
- Venue
- Annals of Neurology
- Publisher
- Wiley
- Published
- 2007-04
- Volume
- 61
- Issue
- 4
- Pages
- 288-299
- ISSN
- 0364-5134 (ISSN-L)
- 1531-8249
- OpenAlex
- W2139113477
- Cited by
- 1,042
- References
- 2
- FWCI
- 38.12
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
