Unraveling the Complexities of Gulf War Illness: Insights from Symptom Clustering and Laboratory Analysis
Faculty Sponsors
Dr. David Quesada
Project Type
Event
Location
Alvin Sherman Library
Start Date
2-4-2025 12:30 PM
End Date
3-4-2025 12:00 PM
Unraveling the Complexities of Gulf War Illness: Insights from Symptom Clustering and Laboratory Analysis
Alvin Sherman Library
Gulf War Illness (GWI) is a chronic condition impacting a subset of veterans who served during the Gulf War of 1990–1991. Exposure to extreme stress, endocrine-disrupting chemical agents, and mild traumatic brain injuries (mTBI) has been implicated in the development of persistent and overlapping symptoms. Understanding the associations between symptoms and laboratory findings is critical for developing more effective therapeutic strategies.
In this study, observational analysis was conducted on a cohort of GWI veterans to explore potential biomarkers and symptom patterns. The dataset included self-reported health surveys (SF36, MFI-100), laboratory panels (cortisol stress tests, cytokine profiles, CD cells, lymphocytes, monocytes, etc.), and stratifying factors such as age, BMI, C-reactive protein (CRP), PTSD, and mTBI status. Box plots were utilized to visualize variable distributions, highlighting normal and abnormal ranges, while symptom clustering was assessed across stratified groups.
One-way ANOVA revealed significant differences in only a few variables. However, Pearson correlation matrices with hierarchical clustering suggested the presence of two to three potential GWI phenotypes within the cohort. Interestingly, mTBI appeared to have no statistically significant impact on outcomes, while PTSD showed marginal effects. Many veterans continued to experience persistent symptoms despite laboratory variables remaining within normal ranges. Further exploration of immune and cortisol responses suggested blunted yet non-pathological values. Analyses of immune and systemic senescence markers revealed only marginal associations. These results may reflect the influence of a small sample size or confounding factors such as comorbidities (e.g., diabetes, hypertension, etc.) that overlap with GWI symptoms.
