Neurotoxicant exposures and rates of Chronic Multisymptom Illness and Kansas Gulf War Illness criteria in Gulf War deployed women veterans

AIMS

This study analyzed deployment-related exposures and risk of Persian Gulf War Illness (GWI) in women veterans from the Veterans Affairs (VA) Cooperative Studies Program 585 Gulf War Era Cohort and Biorepository (GWECB CSP#585).

MAIN METHODS

We examined the associations between GW deployment-related exposures and case definitions for GWI in deployed GW women. Multivariate regression analyses controlling for demographic outcomes were performed.

KEY FINDINGS

Surveys were obtained from 202 GW deployed women veterans. Self-reported exposure to smoke from oil well fires as well as chemical and biological warfare were the only exposures significantly associated with the Center for Disease Control and Prevention (CDC) GWI criteria. Seventy-nine women were excluded from the rest of the analyses as they met Kansas GW illness exclusion criteria. Eligible women who self-reported deployment-related exposure to smoke from oil wells, pyridostigmine bromide (PB) pills, pesticide cream, pesticide treated uniforms, and insect baits were significantly more likely to meet the Kansas GWI criteria (n = 123) than those unexposed and exposures were related to Kansas symptom subdomain endorsements.

SIGNIFICANCE

These results suggest that women GW veterans reporting deployment related exposures of pesticide, oil well fire and PB pills are significantly more likely to meet the Kansas GWI criteria in this national cohort of GW women suggesting its utility in future studies. In addition, based on these results it appears that women exposed to particular toxicants during the war may benefit from more targeted treatment strategies dependent upon the mechanism of exposure of their toxicant induced outcomes.

Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning

Gulf War illness (GWI) refers to the multitude of chronic health symptoms, spanning from fatigue, musculoskeletal pain, and neurological complaints to respiratory, gastrointestinal, and dermatologic symptoms experienced by about 250,000 GW veterans who served in the 1991 Gulf War (GW). Longitudinal studies showed that the severity of these symptoms often remain unchanged even years after the GW, and these veterans with GWI continue to have poorer general health and increased chronic medical conditions than their non-deployed counterparts. For better management and treatment of this condition, there is an urgent need for developing objective biomarkers that can help with simple and accurate diagnosis of GWI. In this study, we applied multiple neuroimaging techniques, including T1-weighted magnetic resonance imaging (T1W-MRI), diffusion tensor imaging (DTI), and novel neurite density imaging (NDI) to perform both a group-level statistical comparison and a single-subject level machine learning (ML) analysis to identify diagnostic imaging features of GWI. Our results supported NDI as the most sensitive in defining GWI characteristics. In particular, our classifier trained with white matter NDI features achieved an accuracy of 90% and F-score of 0.941 for classifying GWI cases from controls after the cross-validation. These results are consistent with our previous study which suggests that NDI measures are sensitive to the microstructural and macrostructural changes in the brain of veterans with GWI, which can be valuable for designing better diagnosis method and treatment efficacy studies.