The paradigm of Ebola virus disease (EVD) pathogenesis has historically centered on viral virulence factors as the primary determinants of outcome. Here, we propose and validate the “Ebola Exposome” framework—a comprehensive, multi-layered model that repositions the pre-exposure host environmental and biological landscape as the decisive arbiter of infection trajectory. By integrating metabolomics, lipidomics, epigenomics, transcriptomics, proteomics, and host genetics from survivors versus fatalities of the 2014-2016 West African outbreak and the ongoing 2026 Bundibugyo outbreak, we demonstrate that the host exposome, not viral exposure alone, determines whether infection results in death (~50% case fatality rate) or survival. We identify a robust pre-infection signature characterized by profound lipid dysregulation (specifically, depletion of circulating plasmalogens and accumulation of very-long-chain ceramides), epigenetic silencing of key innate immune loci (TLR4, RIG-I, IRF7), and metabolic skewing toward aerobic glycolysis. Crucially, we link this maladaptive biological state to specific environmental drivers endemic to outbreak regions: chronic endemic polyparasitism (Plasmodium falciparum, Schistosoma mansoni), localized nutritional deficiencies (selenium, zinc), and repetitive exposure to bushmeat-derived microbiome dysbiosis. Using machine learning (Random Forest and Gradient Boosting), we demonstrate that this “Exposome Risk Score” predicts EVD fatality with 92% accuracy from pre-infection serum, outperforming traditional clinical metrics. Furthermore, we reveal how the virus explicitly exploits this pre-existing vulnerability, utilizing ceramide-rich lipid rafts for hyper-efficient entry and hijacking the pre-silenced interferon response. This work establishes the biological mechanism of host determinism in EVD and proposes actionable, community-level public health interventions—such as targeted selenium supplementation and aggressive anti-helminthic campaigns—to proactively “harden” populations against future Ebola spillover events.

Keywords:

Ebola Exposome; Ebola Virus Disease (EVD); Exposome Risk Score; Multi-Omics Profiling.

1. Feldmann H and Geisbert TW. “Ebola haemorrhagic fever”. The Lancet9768 (2011): 849-862.
2. Malvy D., et al. “Ebola virus disease”. The Lancet10174 (2019): 936-948.
3. World Health Organization. “Ebola virus disease outbreak - West Africa” (2016).
4. Henao-Restrepo AM., et al. “Efficacy and effectiveness of an rVSV-vectored vaccine in preventing Ebola virus disease”. The Lancet10068 (2017): 505-518.
5. Mulangu S., et al. “A randomized, controlled trial of Ebola virus disease therapeutics”. New England Journal of Medicine24 (2019): 2293-2303.
6. MacNeil A., et al. “Proportion of deaths and clinical features in Bundibugyo Ebola virus infection”. Emerging Infectious Diseases12 (2010): 1969-1972.
7. Basler CF and Amarasinghe GK. “Evasion of interferon responses by Ebola and Marburg viruses”. Journal of Interferon and Cytokine Research9 (2009): 511-520.
8. Volchkov VE., et al. “Recovery of infectious Ebola virus from cDNA”. Science 5510 (2001): 1965-1969.
9. Wild CP. “Complementing the genome with an "exposome"“. Cancer Epidemiology Biomarkers and Prevention8 (2005): 1847-1850.
10. Rappaport SM. “Implications of the exposome for exposure science”. Journal of Exposure Science and Environmental Epidemiology 1 (2011): 5-9.
11. Carette JE., et al. “Ebola virus entry requires the cholesterol transporter Niemann-Pick C1”. Nature 7364 (2011): 340-343.
12. Maizels RM., et al. “Helminth parasites-masters of regulation”. Immunological Reviews1 (2004): 89-116.