Baseline Bio-molecular Models to Predict Infectious Disease Susceptibility
Abstract
The goal of this project is to develop a biomolecular classifier of the baseline Ôstate of healthÕ (BHEALTH) for a given individual that enables an accurate prediction of future health vs. disease states for that person. We believe that there is an opportunity to enhance the ability to monitor the warfighter routinely and develop individual predictive models using their individual BHEALTH parameters to determine whether they are at risk for infection, fit for duty or in need of further evaluation for the possibility of incipient disease or other reasons for suboptimal performance. This study will be a proof of concept for the capability to develop a BHEALTH model for the warfighter and for the use of individual predictive modeling of health and for disease state susceptibility. In order to develop BHEALTH we will acquire high density, short-term longitudinal data from study subjects (with demographic characteristics similar to a military population). Concurrently we will acquire data relevant to stress, sleep disturbances, [biometric], and changes in performance and behavior. We will deliver a viral infection perturbation and correlate the BHEALTH conditions with the outcomes of this perturbation. Our hypothesis is that baseline temporal, circadian and individual health models will be predictive of subsequent manifestations of infectious disease following exposure. To acquire the data necessary to develop a model that describes the physiologic state of health (BHEALTH), we will ascertain and study a cohort from a well-defined, Ôfree livingÕ human population representative of the military demographic: a combination of undergraduate residents of college dormitories and other healthy individuals between the ages of 18-35 years of age. This population will be serially followed for 4 days with dense continuous and temporal data collection. As each individual monitored serves as their own comparative control, the data collected will be used as both the training set for biologic, biometric and self-reported markers for health vs. disease states and as the validation data sets for each individual. Subsequently all subjects will be enrolled in a viral challenge study with Human Rhinovirus Subtype 16 and dense temporal data collection will continue for an additional 4 days post challenge. Symptoms of infectious illness, stress, sleep disturbance, and changes in performance monitoring will be captured. This pilot study will provide data to determine whether circadian and baseline data models can be used for prediction of subsequent infectious disease events. The resulting multi-dimensional dataset from this study will be a rich resource for diverse analytics and will inform a definitive study or series of studies based on our hypothesis.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 25, 2019
- Source ID
- W911NF1510161
Entities
People
- Geoffrey S Ginsburg
Organizations
- Army Contracting Command
- Defense Advanced Research Projects Agency
- Duke University