Education & Certifications
Doctor of Medicine, Wayne State University School of Medicine (2015)
Bachelor of Science, Wayne State University, Biological Sciences (2009)
Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses.
Brain, behavior, and immunity
Post-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was assessed with the Stroke Impact Scale mood questionnaire (SIS3). Machine learning multivariable regression models were constructed to estimate SIS3 using proteomics data, age, and time since stroke. We also dichotomized participants into better mood (SIS3 > 63) or worse mood (SIS3 ≤ 63) and analyzed candidate proteins. Machine learning models verified that there is indeed a relationship between plasma proteomic data and mood in chronic stroke, with the most accurate prediction of mood occurring when we add age and time since stroke. At the individual protein level, no single protein or set of proteins predicts mood. But by using univariate analyses of the proteins most highly associated with mood we produced a model of chronic post-stroke depression. We utilized the fact that this list contained many proteins that are also implicated in major depression. Also, over 80% of immune proteins that correlate with mood were higher with worse mood, implicating a broadly overactive immune system in chronic post-stroke depression. Finally, we used a comprehensive literature review of major depression and acute post-stroke depression. We propose that in chronic post-stroke depression there is over-activation of the immune response that then triggers changes in serotonin activity and neuronal plasticity leading to depressed mood.
View details for DOI 10.1016/j.bbi.2023.08.002
View details for PubMedID 37557961
- Self-report Does Not Align With Objective Assessments Of Memory And Fine Motor Functioning In Stroke Survivors LIPPINCOTT WILLIAMS & WILKINS. 2022
- Depression And Not Cognitive Ability Is Most Strongly Associated With Long-term Functional Outcomes Following Stroke. LIPPINCOTT WILLIAMS & WILKINS. 2022
- Higher White Blood Cell Count In The First Week After Stroke Predicts Worse Cognitive Outcomes In A Population With Smaller Ischemic Strokes LIPPINCOTT WILLIAMS & WILKINS. 2022
Monocyte chemoattractant protein-1 predicts outcome and vasospasm following aneurysmal subarachnoid hemorrhage
JOURNAL OF NEUROSURGERY
2008; 109 (1): 38–43
Despite efforts to elucidate both the molecular mechanism and the clinical predictors of vasospasm after aneurysmal subarachnoid hemorrhage (ASAH), its pathogenesis remains unclear. Monocyte chemoattractant protein-1 (MCP-1) is a chemokine that has been firmly implicated in the pathophysiology of vasospasm and in neural tissue injury following focal ischemia in both animal models and human studies. The authors hypothesized that MCP-1 would be found in increased concentrations in the blood and cerebrospinal fluid (CSF) of patients with ASAH and would correlate with both outcome and the occurrence of vasospasm.Seventy-seven patients who presented with ASAH were prospectively enrolled in this study between July 2001 and May 2002. Using an enzyme-linked immunosorbent assay, MCP-1 levels were measured in serum daily and in CSF when available. The mean serum and CSF MCP-1 concentrations were calculated for each patient throughout the entire hospital stay. Neurological outcome was evaluated at discharge or 14 days posthemorrhage using the modified Rankin Scale. Vasospasm was evaluated on angiography.The serum MCP-1 concentrations correlated with negative outcome such that a 10% increase in concentration predicted a 25% increase in the probability of a poor outcome, whereas the serum MCP-1 levels did not correlate with vasospasm. Concentrations of MCP-1 in the CSF, however, proved to be significantly higher in patients with angiographically demonstrated vasospasm.These findings suggest a role for MCP-1 in neurological injury and imply that it may act as a biomarker of poor outcome in the serum and of vasospasm in the CSF.
View details for DOI 10.3171/JNS/2008/109/7/0038
View details for Web of Science ID 000257201500008
View details for PubMedID 18593272