Mariona Jové1, Gerard Mauri-Capdevila1, Idalmis Suárez1, Serafi Cambray1, Jordi Sanahuja1, Alejandro Quílez1, Joan Farré1, Ikram Benabdelhak1, Reinald Pamplona1, Manuel Portero-Otín1, Francisco Purroy2. 1. From NUTREN-Nutrigenomics Center (M.J., M.P.-O.), Department of Experimental Medicine (R.P.), Parc Científic i Tecnològic Agroalimentari de Lleida-Universitat de Lleida-IRBLleida, Lleida; Stroke Unit (G.M.-C., I.S., S.C., J.S., A.Q., I.B., F.P.), Department of Neurology, Universitat de Lleida, Hospital Universitari Arnau de Vilanova de Lleida, IRBLleida; and Laboratori Clinic (J.F.), Universitari Arnau de Vilanova de Lleida, IRBLleida, Spain. 2. From NUTREN-Nutrigenomics Center (M.J., M.P.-O.), Department of Experimental Medicine (R.P.), Parc Científic i Tecnològic Agroalimentari de Lleida-Universitat de Lleida-IRBLleida, Lleida; Stroke Unit (G.M.-C., I.S., S.C., J.S., A.Q., I.B., F.P.), Department of Neurology, Universitat de Lleida, Hospital Universitari Arnau de Vilanova de Lleida, IRBLleida; and Laboratori Clinic (J.F.), Universitari Arnau de Vilanova de Lleida, IRBLleida, Spain. fpurroygarcia@gmail.com.
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.
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 TIApatients 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 SRpatients but also early and late SR cases.
Authors: F Purroy; J Montaner; C A Molina; P Delgado; J F Arenillas; P Chacon; M Quintana; J Alvarez-Sabin Journal: Acta Neurol Scand Date: 2007-01 Impact factor: 3.209
Authors: F Purroy; I Suárez-Luis; G Mauri-Capdevila; S Cambray; J Farré; J Sanahuja; G Piñol-Ripoll; A Quílez; C González-Mingot; R Begué; M I Gil; E Fernández; I Benabdelhak Journal: Eur J Neurol Date: 2013-06-25 Impact factor: 6.089
Authors: Brett L Cucchiara; Steve R Messe; Lauren Sansing; Larami MacKenzie; Robert A Taylor; James Pacelli; Qaisar Shah; Scott E Kasner Journal: Stroke Date: 2009-05-21 Impact factor: 7.914
Authors: Susan Cheng; Svati H Shah; Elizabeth J Corwin; Oliver Fiehn; Robert L Fitzgerald; Robert E Gerszten; Thomas Illig; Eugene P Rhee; Pothur R Srinivas; Thomas J Wang; Mohit Jain Journal: Circ Cardiovasc Genet Date: 2017-04
Authors: M Mar Rodríguez; Daniel Pérez; Felipe Javier Chaves; Eduardo Esteve; Pablo Marin-Garcia; Gemma Xifra; Joan Vendrell; Mariona Jové; Reinald Pamplona; Wifredo Ricart; Manuel Portero-Otin; Matilde R Chacón; José Manuel Fernández Real Journal: Sci Rep Date: 2015-10-12 Impact factor: 4.379