TY - JOUR
T1 - Incorporating machine learning approaches to assess putative environmental risk factors for multiple sclerosis
AU - Mowry, Ellen M.
AU - Hedström, Anna K.
AU - Gianfrancesco, Milena A.
AU - Shao, Xiaorong
AU - Schaefer, Catherine A.
AU - Shen, Ling
AU - Bellesis, Kalliope H.
AU - Briggs, Farren B.S.
AU - Olsson, Tomas
AU - Alfredsson, Lars
AU - Barcellos, Lisa F.
N1 - Funding Information:
The study was funded by NIH/NIEHS R01 ES017080 , NIH/NINDS R01 NS049510 , and NIH/NIAID R01AI076544 to LFB. EMM was funded by NIH/NINDS K23 NS067055 and is funded by the National MS Society and receives research funding from PCORI, Biogen Idec, and Sanofi Genzyme, as well as free medication for a trial from Teva Neuroscience; she receives royalties for editorial duties for UpToDate. EIMS was supported by the Swedish Research Council, the Swedish Council for Health, Working Life and Welfare, the Brain Foundation and the AFA insurance company.
Publisher Copyright:
© 2018
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
KW - Environmental exposure
KW - Epidemiology
KW - Gene-environment interaction
KW - Keywords:
KW - Multiple sclerosis
KW - Occupational exposure
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U2 - 10.1016/j.msard.2018.06.009
DO - 10.1016/j.msard.2018.06.009
M3 - Article
C2 - 30005356
AN - SCOPUS:85049584056
SN - 2211-0348
VL - 24
SP - 135
EP - 141
JO - Multiple Sclerosis and Related Disorders
JF - Multiple Sclerosis and Related Disorders
ER -