Bio-cognitive Cut-Points Differentiate Risk vs. No Risk for Prodromal Parkinson Cognition in Young Post-mTBI Veterans and Non-mTBI Controls

Blood-based (BB) biomarker investigations in Parkinson disease (PD) and in mild traumatic brain injury (mTBI) have substantially grown over the past decade. High risks for PD in young post-mTBI veterans have been inferred from medical record data using actuarial modeling. However, potential utility of BB biomarkers to quantify risks vs. no risk for PD in young post-mTBI veterans has not been established. Previously we reported post-mTBI veterans performed significantly below the standardized normative scores for their age and education level on specific domains of executive functioning, on par with senior aged individuals with early-stage PD. Here, we examined serum brain-derived neurotropic factor (BDNF), ubiquitin C-terminal hydrolase-L1 (UCH-L1), glial fibrillary acidic protein (GFAP), and S100 calcium-binding protein {beta} (S100B) in association with executive functioning outcomes in search of a bio-cognitive model suitable to differentiate risk from no risk for prodromal PD. A reference range of bio-cognitive cut-points were derived from Area Under the Curve (AUC) sensitivity and specificity methods. Our data revealed two bio-cognitive signatures with reference range cut-points when GFAP was paired with cognitive flexibility / attention scores, and when S100B was paired with categorical / semantic verbal memory scores. Both bio-cognitive signatures revealed prodromal PD risk vs.no-risk parameters that remained relevant for differentiating young veterans who had encountered a past mTBI and those who had not experienced a mTBI. Subjects with early-stage PD who had withstood a mTBI up to 10- to 40-years earlier were also differentiated from those who had no mTBI history. These results indicate the predictive utility of expanding the biomarker field to include reference ranges, cut-points, and specific cognitive domains to estimate risks for PD in a clinic setting. These preliminary data also add value in establishing a quantifiable bio-cognitive risk signature to identify prodromal PD risks in young adults prior to obvious cognitive and motor decline. While encouraging, these data require further follow-up with a larger sample size in a longitudinal design to validate these findings.