Abstract WMP24: Plasma Proteomics Reveals Potential Biological Mechanisms Of Chronic Post-Stroke Depression

Introduction: Depression is common after stroke, and is a debilitating factor undermining recovery in approximately one third of stroke survivors. It is essential to understand the underlying mechanisms to develop better treatments. Such insight may come from identifying plasma proteins correlated with post-stroke depression. Previous work investigated inflammatory proteins. Methods: We recruited 85 subjects 5 months to 9 years after ischemic stroke, age >40, and able to perform cognitive testing. Mood was assessed with the Stroke Impact Scale (SIS3), transformed to a 100-point scale. Plasma was analyzed by O-link proteomics for 1011 proteins. Multivariable regression models were constructed to estimate SIS3 using proteomics and clinical data. Models were subject to bootstrapping for robustness, and cross-validation to ensure results were reported on subjects blinded during model training. Pearson correlation analysis identified linear associations between individual proteins and SIS3 scores. We also report differences in key proteins in subjects dichotomized into non-depressed (SIS3>63) or depressed (SIS3≤63) groups. Results: Proteomics results alone predicted SIS3 in multivariable models, and the best model also used age and time since stroke. A total of 180 proteins correlated significantly with SIS3. Plasma levels of IL-6 (p=0.0325), EGF (p<0.001) and TRIM5 (p=0.0011) were significantly elevated in subjects with post-stroke depression, while HPGDS was significantly reduced (p<0.001). There was no difference in plasma levels of IL-1ß (p=0.0830) or TNF (p=0.5287) between depressed and non-depressed subjects. Conclusions: We report that machine learning models can predict post-stroke mood from comprehensive plasma proteomics, and that age and time since stroke improves those models. Our findings also support other reports of elevated IL-6 in subjects with depression. We also identified proteins of interest including HPGDS (produces Prostaglandin D), EGF, or TRIM5 (upstream of NFkB) in post-stroke depression. Future studies are needed to replicate these findings, and studies in preclinical models may help uncover mechanistic relationships that could lead to new therapies.