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

Multiple Sclerosis and Related Disorders
Ellen M MowryLisa F Barcellos

Abstract

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. 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. 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). While ...Continue Reading

Citations

Feb 8, 2021·Neurología : publicación oficial de la Sociedad Española de Neurología·M Vázquez-MarrufoG Izquierdo
Mar 28, 2021·Computers in Biology and Medicine·Morghan HartmannRuth Dobson
May 5, 2021·Computers in Biology and Medicine·Alberto MontolíoAmaya Pérez Del Palomar
Jun 6, 2021·Multiple Sclerosis and Related Disorders·Laura Edwards, Christopher Tench

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