Abstract
Determining the time of stroke onset in order to apply recanalization therapies within the accepted therapeutic window and the correct diagnosis of transient ischemic attack (TIA) are two common clinical problems in acute cerebral ischemia management. Therefore, biomarkers helping in this conundrum could be very helpful. We developed mouse models of distal middle cerebral artery occlusion mimicking TIA and ischemic stroke (IS), respectively. Plasma samples were analyzed by metabolomics at 6, 12, 24, and 48 h post onset in order to find TIA- and time-related stroke biomarkers. The results were validated in a second experimental cohort. Plasma metabolomic profiles identified time after stroke events with a very high accuracy. Specific metabolites pointing to a recent event (< 6 h) were identified. A multivariate (partial least square discriminant analyses [PLS-DA]) model was also able to separate samples from TIA, IS, and sham events with high accuracy and to obtain specific metabolites for each time point. The combination of mice models of focal ischemia with plasma metabolomics allows the discovery of candidate biomarkers for the diagnosis and estimation of onset time of stroke and TIA diagnosis.
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S. Cambray—Design and conceptualization of the study. Analysis and interpretation of the data. Drafting and revising the manuscript for intellectual content.
M. Portero-Otín—Design and conceptualization of the study. Analysis and interpretation of the data. Drafting and revising the manuscript for intellectual content.
M. Jove—Design and conceptualization of the study. Analysis and interpretation of the data.
N. Torreguitart—Design and conceptualization of the study. Analysis and interpretation of the data.
L. Colàs-Campàs—Design and conceptualization of the study. Analysis and interpretation of the data.
A. Sanz—Design and conceptualization of the study. Analysis and interpretation of the data.
I. Benabdelhak—Design and conceptualization of the study. Analysis and interpretation of the data.
M. Yemisci, PhD—Design and conceptualization of the study. Revising the manuscript for intellectual content.
T. Dalkara, PhD—Design and conceptualization of the study. Revising the manuscript for intellectual content.
B. Dönmez-Demir—Design and conceptualization of the study.
J. Egea—Design and conceptualization of the study. Analysis and interpretation of the data. Drafting and revising the manuscript for intellectual content.
F. Purroy—Design and conceptualization of the study. Analysis and interpretation of the data. Drafting and revising the manuscript for intellectual content.
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Supl. Fig. 1
Comparison of differential metabolites through different time points in cohort 1 (A) and cohort 2 (B). Common metabolites though different time points of second cohort are showed in C, with 1-Monopalmitin present from 12 to 48 h, and with greatest coincidences between 6 h and 12 h (16 common metabolites). (JPEG 89.1 kb)
Supl. Fig. 2
PLS-DA model was used to identify SHAM and TIA animals from second cohort at different time points. Both groups were clearly identified at 6 h (A), 12 h (B), 24 h (C) and 48 h (D). (JPEG 54.7 kb)
Supl. Fig. 3
PLS-DA model was used to identify SHAM and TIA animals from cohort 1 and cohort 2 together at different time points. Both groups were clearly identified at 6 h (A), 12 h (B), 24 h (C) and 48 h (D).” (JPEG 98 kb)
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Cambray, S., Portero-Otin, M., Jové, M. et al. Metabolomic Estimation of the Diagnosis and Onset Time of Permanent and Transient Cerebral Ischemia. Mol Neurobiol 55, 6193–6200 (2018). https://doi.org/10.1007/s12035-017-0827-5
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DOI: https://doi.org/10.1007/s12035-017-0827-5