Metabolomics predicts stroke recurrence after transient ischemic attack

Jové Font, Mariona; Mauri-Capdevila, Gerard; Suárez-Luis, Idalmis; Cambray Carner, Serafí; Sanahuja Montesinos, Jordi; Quilez Martínez, Alejandro; Farré, Joan; Benabdelhak Abbou, Ikram; Pamplona Gras, Reinald; Portero Otín, Manuel; Purroy Garcia, Francisco

doi:https://doi.org/10.1212/WNL.0000000000001093

View/Open

022340 (1.741Mb)

Issue date

2015

Author

Jové Font, Mariona

Mauri-Capdevila, Gerard

Suárez-Luis, Idalmis

Cambray Carner, Serafí

Sanahuja Montesinos, Jordi

Quilez Martínez, Alejandro

Farré, Joan

Benabdelhak Abbou, Ikram

Pamplona Gras, Reinald

Portero Otín, Manuel

Purroy Garcia, Francisco

Suggested citation

Jové Font, Mariona; Mauri-Capdevila, Gerard; Suárez-Luis, Idalmis; Cambray Carner, Serafí; Sanahuja Montesinos, Jordi; Quilez Martínez, Alejandro; ... Purroy Garcia, Francisco. (2015) . Metabolomics predicts stroke recurrence after transient ischemic attack. Neurology, 2015, vol. 84, núm. 1, p. 36-45. https://doi.org/10.1212/WNL.0000000000001093.

Abstract

Objective: To discover, by using metabolomics, novel candidate biomarkers for stroke recurrence (SR) with a higher prediction power than present ones. Methods: Metabolomic analysis was performed by liquid chromatography coupled to mass spectrometry in plasma samples from an initial cohort of 131

TIA patients recruited ,24 hours after the onset of symptoms. Pattern analysis and metabolomic profiling, performed by multivariate statistics, disclosed specific SR and large-artery atherosclerosis (LAA) biomarkers. The use of these methods in an independent cohort (162 subjects) confirmed the results obtained in the first cohort. Results: Metabolomics analyses could predict SR using pattern recognition methods. Low concentrations of a specific lysophosphatidylcholine (LysoPC[16:0]) were significantly associated with SR. Moreover, LysoPC(20:4) also arose as a potential SR biomarker, increasing the prediction power of age, blood pressure, clinical features, duration of symptoms, and diabetes scale (ABCD2) and LAA. Individuals who present early (,3 months) recurrence have a specific metabolomic pattern, differing from non-SR and late SR subjects. Finally, a potential LAA biomarker, LysoPC(22:6), was also described. Conclusions: The use of metabolomics in SR biomarker research improves the predictive power of conventional predictors such as ABCD2 and LAA. Moreover, pattern recognition methods allow us to discriminate not only SR patients but also early and late SR cases.

URI

http://hdl.handle.net/10459.1/56762

DOI

https://doi.org/10.1212/WNL.0000000000001093

Is part of

Neurology, 2015, vol. 84, núm. 1, p. 36-45

Collections

The following license files are associated with this item:

Creative Commons