Background: Serum biomarkers may inform and improve care in traumatic brain injury (TBI). We aimed to correlate serum biomarkers with clinical severity, care path and imaging abnormalities in TBI,... Show moreBackground: Serum biomarkers may inform and improve care in traumatic brain injury (TBI). We aimed to correlate serum biomarkers with clinical severity, care path and imaging abnormalities in TBI, and explore their incremental value over clinical characteristics in predicting computed tomographic (CT) abnormalities.Methods: We analyzed six serum biomarkers (S100B, NSE, GFAP, UCH-L1, NFL and t-tau) obtained <24 h post-injury from 2867 patients with any severity of TBI in the Collaborative European NeuroTrauma Effectiveness Research (CENTER-TBI) Core Study, a prospective, multicenter, cohort study. Univariable and multivariable logistic regression analyses were performed. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals.Findings: All biomarkers scaled with clinical severity and care path (ER only, ward admission, or ICU), and with presence of CT abnormalities. GFAP achieved the highest discrimination for predicting CT abnormalities (AUC 0.89 [95%CI: 0.87-0.90]), with a 99% likelihood of better discriminating CT-positive patients than clinical characteristics used in contemporary decision rules. In patients with mild TBI, GFAP also showed incremental diagnostic value: discrimination increased from 0.84 [95%CI: 0.83-0.86] to 0.89 [95%CI: 0.87-0.90] when GFAP was included. Results were consistent across strata, and injury severity. Combinations of biomarkers did not improve discrimination compared to GFAP alone.Interpretation: Currently available biomarkers reflect injury severity, and serum GFAP, measured within 24 h after injury, outperforms clinical characteristics in predicting CT abnormalities. Our results support the further development of serum GFAP assays towards implementation in clinical practice, for which robust clinical assay platforms are required.(C) 2020 The Authors. Published by Elsevier B.V. Show less
Toledo, J.B.; Arnold, M.; Kastenmüller, G.; Chang, R.; Baillie, R.A.; Han, X.; ... ; Kaddurah-Daouk, R. 2017
IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our... Show moreIntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. Show less
