Metabolomics predicts stroke recurrence after transient ischemic attack

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.

Figure 1. Study design and metabolomics profile of TIA patients

(A) Flow diagram of experiment design. Metabolomic profile of stroke recurrence (SR) and large-artery atherosclerosis (LAA) in TIA patients in cohort 1. (B) Heat map representation of hierarchical clustering of molecular features found in each sample. Each line of this graphic represents an accurate mass ordered by retention time, colored by its abundance intensity and baselining to median/mean across the samples. The scale from −10 blue (low abundance) to +10 red (high abundance) represents this normalized abundance in arbitrary units. Tridimensional partial least squares discriminant analysis (PLS-DA) graphs demonstrate that SR ([C] blue spots represent SR plasma samples; red ones represent non-SR samples) and TIA temporal patterns recurrence ([D] early recurrence [<90 days] is represented in blue spots, medium [>90 days and <1 any] in red, and late [>1 year] in brown) determine a plasma metabolome. (E) Tridimensional PLS-DA graphs show differences between patients with LAA. Blue spots represent LAA and red ones non-LAA plasma samples.

Figure 2. Plasma lysophosphatidilcholines as TIA recurrence biomarkers

(A) Lysophosphatidylcholine (LysoPC) (16:0) arose as a potential blood biomarker of stroke recurrence ([A] cohort 1; [B] cohort 2). **Indicates significant differences (p < 0.0001) by analysis of variance test with Tukey multiple comparison test. LysoPC(22:6) as a blood biomarker of large-artery atherosclerosis (LAA) ([C] cohort 1; [D] cohort 2). *Indicates significant differences by Student t test (at least p < 0.05). (E) The inclusion of LysoPC (20:4) levels to age, blood pressure, clinical features, duration of symptoms, and diabetes scale (ABCD2) and LAA score to receiver operating characteristic curve increase the predictive power of stroke recurrence (areas: ABCD2 = 0.646, p = 0.0.05; ABCD2 + LAA = 0.678, p = 0.001; ABCD2 + LAA + LysoPC(20:4) = 0.711, p < 0.001; integrated discrimination improvement (IDI) test for comparison of prediction models: p < 0.0001). (F) Kaplan-Meier estimates of the proportion of patients remaining free from any cerebral ischemic event. Red line indicates LysoPC(20:4) >1,14,000 MS counts; black line indicates LysoPC(20:4) <1,14,000 MS counts. MS = mass spectrometry.