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Mult Scler Relat Disord. 2018 Aug;24:135-141. doi: 10.1016/j.msard.2018.06.009. Epub 2018 Jun 23.

Incorporating machine learning approaches to assess putative environmental risk factors for multiple sclerosis.

Author information

1
Johns Hopkins University, 600N. Wolfe Street, Pathology 627, Baltimore 21287, MD, USA. Electronic address: Emowry1@jhmi.edu.
2
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
3
University of California, Berkeley, CA, USA.
4
Kaiser Permanente Division of Research, Oakland, CA, USA.
5
Case Western Reserve University, Cleveland, OH, USA.
6
Karolinska Institutet at Karolinska University Hospital, Solna, Sweden.
7
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.

Abstract

BACKGROUND:

Multiple sclerosis (MS) incidence has increased recently, particularly in women, suggesting a possible role of one or more environmental exposures in MS risk. The study objective was to determine if animal, dietary, recreational, or occupational exposures are associated with MS risk.

METHODS:

Least absolute shrinkage and selection operator (LASSO) regression was used to identify a subset of exposures with potential relevance to disease in a large population-based (Kaiser Permanente Northern California [KPNC]) case-control study. Variables with non-zero coefficients were analyzed in matched conditional logistic regression analyses, adjusted for established environmental risk factors and socioeconomic status (if relevant in univariate screening),± genetic risk factors, in the KPNC cohort and, for purposes of replication, separately in the Swedish Epidemiological Investigation of MS cohort. These variables were also assessed in models stratified by HLA-DRB1*15:01 status since interactions between risk factors and that haplotype have been described.

RESULTS:

There was a suggestive association of pesticide exposure with having MS among men, but only in those who were positive for HLA-DRB1*15:01 (OR pooled = 3.11, 95% CI 0.87, 11.16, p = 0.08).

CONCLUSIONS:

While this finding requires confirmation, it is interesting given the association between pesticide exposure and other neurological diseases. The study also demonstrates the application of LASSO to identify environmental exposures with reduced multiple statistical testing penalty. Machine learning approaches may be useful for future investigations of concomitant MS risk or prognostic factors.

KEYWORDS:

Environmental exposure; Epidemiology; Gene-environment interaction; Keywords:; Multiple sclerosis; Occupational exposure

PMID:
30005356
DOI:
10.1016/j.msard.2018.06.009
[Indexed for MEDLINE]

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