Metabolomics predicts stroke recurrence after transient ischemic attack

被引：103

作者：

Jove, Mariona ^{[1
]}

Mauri-Capdevila, Gerard ^{[3
]}

Suarez, Idalmis ^{[3
]}

Cambray, Serafi ^{[3
]}

Sanahuja, Jordi ^{[3
]}

Quilez, Alejandro ^{[3
]}

Farre, Joan ^{[4
]}

Benabdelhak, Ikram ^{[3
]}

Pamplona, Reinald ^{[2
]}

Portero-Otin, Manuel ^{[1
]}

Purroy, Francisco ^{[3
]}

机构：

[1] Univ Lleida, IRBLleida, Parc Cient & Tecnol Agroalimentari Lleida, NUTREN Nutrigenom Ctr, Lleida, Spain

[2] Univ Lleida, IRBLleida, Parc Cient & Tecnol Agroalimentari Lleida, Dept Expt Med, Lleida, Spain

[3] Univ Lleida, IRBLleida, Hosp Univ Arnau de Vilanova Lleida, Stroke Unit,Dept Neurol, Lleida, Spain

[4] Univ Arnau Vilanova Lleida, IRBLleida, Lab Clin, Lleida, Spain

来源：

NEUROLOGY | 2015年 / 84卷 / 01期

关键词：

C-REACTIVE PROTEIN; PHOSPHOLIPASE A(2); CEREBRAL-ISCHEMIA; RISK; LYSOPHOSPHATIDYLCHOLINE; BRAIN; INFLAMMATION; EXPRESSION; BIOMARKERS; INFARCTION;

D O I：

10.1212/WNL.0000000000001093

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

100204 [神经病学];

摘要：

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.

引用

页码：36 / 45

页数：10

共 38 条

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