Metabolomics Predicts Neuroimaging Characteristics of Transient Ischemic Attack Patients

Abstract

Background: Neuroimaging is essential for the diagnosis and prognosis of transient ischemic attack (TIA). The discovery of a plasmatic biomarker related to neuroimaging findings is of enormous interest because, despite its relevance, magnetic resonance diffusion weighted imaging (DWI) is not always available in all hospitals that attend to TIA patients.

Methods: Metabolomic analyses were performed by liquid chromatography coupled to mass spectrometry in order to establish the metabolomic patterns of positive DWI, DWI patterns and acute ischemic lesion volumes. We used these methods with an initial TIA cohort of 129 patients and validated them with a 2nd independent cohort of 152 patients.

Findings: Positive DWI was observed in 115 (40.9%) subjects and scattered pearls in one arterial territory was the most frequent lesion pattern (35.7%). The median acute ischemic lesion volume was 0.33 (0.15-1.90)cm³. We detected a specific metabolomic profile common to both cohorts for positive DWI (11 molecules including creatinine, threoninyl-threonine, N-acetyl-glucosamine, lyso phosphatidic acid and cholesterol-related molecules) and ischemic lesion volume (10 molecules including lysophosphatidylcholine, hypoxanthine/threonate, and leucines). Moreover lysophospholipids and creatinine clearly differed the subcortical DWI pattern from other patterns.

Interpretation: There are specific metabolomic profiles associated with representative neuroimaging features in TIA patients. Our findings could allow the development of serum biomarkers related to acute ischemic lesions and specific acute ischemic patterns.

Keywords: Diffusion magnetic resonance imaging; Metabolomics; TIA.

Fig. 1

Patient inclusion chart.

Fig. 2

Association of DWI with differential metabolomic patterns. Upper panels: heatmap showing major metabolites differentiating plasma metabolomic profiles from DWI positive versus non DWI positive patients, both in the discovery cohort (A) and in the validation cohort (B). A PLSDA model could be designed with a high accuracy, as shown in the lower panels, both for the discovery cohort (A) and for the validation cohort (B).

Fig. 3

Imaging patterns of ischemia are associated with differential metabolomic profiles. PLSDA model showing association of different ischemia imaging patterns with specific metabolomic profiles, both for the discovery cohort (A) and for the validation cohort (B).