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Metabolomic Estimation of the Diagnosis and Onset Time of Permanent and Transient Cerebral Ischemia

Molecular Neurobiology volume 55, pages 6193–6200 (2018)Cite this article

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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Author information

Author notes

  1. Serafí Cambray and Manuel Portero-Otin contributed equally to this work.

Affiliations

  1. Clinical Neurosciences Group, Institut de Recerca Biomèdica de Lleida (IRBLleida), Universitat de Lleida, Lleida, Spain

    Serafí Cambray, Laura Colàs-Campàs, Arabela Sanz, Ikram Benabdelhak & Francisco Purroy

  2. Department of Experimental Medicine, Universitat de Lleida-IRBLLEIDA, Lleida, Spain

    Manuel Portero-Otin & Mariona Jové

  3. Department of Vascular Surgery, Universitat de Lleida, Hospital Universitari Arnau de Vilanova de Lleida, Clinical Neurosciences Group IRBLleida, Lleida, Spain

    Nuria Torreguitart

  4. Stroke Unit, Department of Neurology, Universitat de Lleida, Hospital Universitari Arnau de Vilanova de Lleida, Avda Rovira Roure, 80, 25198, Lleida, Spain

    Ikram Benabdelhak & Francisco Purroy

  5. Institute of Neurological Sciences and Psychiatry, Hacettepe University, Ankara, Turkey

    Muge Yemisci, Turgay Dalkara & Buket Dönmez-Demir

  6. Department of Neurology, Faculty of Medicine, Hacettepe University, Ankara, Turkey

    Muge Yemisci, Turgay Dalkara & Buket Dönmez-Demir

  7. Molecular and Developmental Neurobiology Research Group, Universitat de Lleida – IRBLleida, Office 1.13, Lab. 1.06. Avda. Rovira Roure, 80, 25198, Lleida, Spain

    Joaquim Egea

Authors
  1. Serafí Cambray
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  2. Manuel Portero-Otin
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  3. Mariona Jové
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  4. Nuria Torreguitart
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  5. Laura Colàs-Campàs
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  7. Ikram Benabdelhak
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  11. Joaquim Egea
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  12. Francisco Purroy
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Contributions

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.

Corresponding authors

Correspondence to Joaquim Egea or Francisco Purroy.

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Conflict of Interest

The authors declare that they have no conflicts of interest.

Supplementary Material

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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

Keywords

  • Metabolomics
  • TIA diagnosis
  • Stroke onset time