Biomarkers for pressure injury risk following spinal cord injury: Development of a multi-scalar predictive model for personalized preventive health care

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Pressure injures (PrI) are a major secondary complication for far too many people with spinal cord injury (SCI). Development and/or recurrence of a PrI limits activities of daily living, often leading to hospitalization and even death. In addition to the devastating impact on affected individuals and their caregivers, PrI management has a significant effect on Veterans Heath Administration healthcare costs, which provides lifetime care for our Veterans with SCI. The proposed study will address the conundrum of why some Veterans with SCI suffer from a continuous cycle of recurring PrI, while others remain PrI free. The research strategy will build on the model developed by the Bogie lab of Biomarkers for Early Identification of Pressure Injury Risk (BEIPIR) for persons with SCI. BEIPIR unifies hierarchical relationships between clinical factors, health behaviors and muscle composition. We have shown that intramuscular adipose tissue (IMAT) is a critical clinically significant risk factor for PrI development. IMAT levels and accumulation rates vary greatly in this cohort. Some people exhibit rapid IMAT accumulation following SCI, while others do not. It is important to explain what is driving these changes. Our preliminary findings provide the basis for the central hypothesis: DNA variants predispose some individuals to increased deposition of IMAT following SCI, and resultant increased PrI risk. The proposed study will update the BEIPIR model by examining IMAT in conjunction with investigation of DNA variants associated with accelerated and/or higher levels of IMAT deposition. The TruSight™ One Expanded Sequencing panel (Illumina, San Diego CA) will be applied for Next Generation Sequencing of 50 existing blood samples from 38 persons with complete or incomplete SCI (AIS A-D) for whom gluteal muscle composition over time has already been evaluated. Genetic profile information, specifically DNA variants which are differentially active between persons with and without a history of PrI at a statistically significant level of p<0.05, will be selected and incorporated into the multi-scalar BEIPIR model for early identification of PrI risk. The updated BEIPIR model will be internally and externally validated to establish predicative efficacy. Internal validation of the BEIPIR model will be provided by testing the model with the genetic biomarkers identified. Split bootstrap procedures will be employed in order to derive stable estimations with low bias. A four year repeated measures study will be carried out to externally validate the BEIPIR model. A stratified study design will be employed to achieve a study cohort of 100 Veterans with SCI (AIS A-D) including participants with and without a history of PrI. Study participants will be recruited from Louis Stokes Cleveland VA Medical Center and the James J. Peters VA Medical Center (Site PI: Dr. Galea). Whole blood will be collected from study participants and DNA extracted prior to processing using the TruSight™ One Expanded Sequencing panel. Very low dose transverse pelvic region CT scans with contrast will be carried out based on our established protocol. Muscle composition and cross-sectional area will be determined using our established Hounsfield Unit scale classification protocol to determine relative lean muscle and IMAT content. 3D reconstruction will be applied to show IMAT distribution throughout the muscle. Study participants will be surveyed monthly by phone using our standardized skin status questionnaire to determine tissue health status. Incidences of tissue compromise or breakdown will be monitored and data applied to refine the BEIPIR model. Blood draw and CT scans will be repeated annually or when a PrI occurs. Longitudinal repeated measures of the de novo study cohort will be used to evaluate BEIPIR model performance and provide external validation of the model. Update and validation of the BEIPIR model will provide a clinical tool to optimize personalized care, recognizing that every person with SCI is an individual. Our proposed study has great potential to improve PrI risk assessment, enhance health status and quality of life for Veterans with SCI and reduce VHA costs. In the longer term, the BEIPIR model may provide the basis for development of a blood test kit for PrI risk. This research will also expand the de-identified genetic data publicly available for this underrepresented population.