Discovery of a New Biomarker Pattern for Differential Diagnosis of Acute Ischemic Stroke Using Targeted Metabolomics

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Discovery of a New Biomarker Pattern for Differential Diagnosis of Acute Ischemic Stroke Using Targeted Metabolomics

Ruitan Sun^1*, Yan Li¹, Ming Cai¹, Yunfeng Cao² and Xiangyu Piao¹

• ¹Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
• ²Department of Instrumentation and Analytical Chemistry, Dalian Institute of Chemical Physics, Dalian, China

Stroke is one of the leading causes of disability all over the world. However, biomarkers for fast differential diagnosis of acute ischemic stroke (AIS) from vertigo or headache, remains lacking. Using a direct-infusion mass spectrometry method, it is possible to establish an efficient method for AIS differential diagnosis that requires only a few minutes. Thirty-eight clearly diagnosed AIS patients and 46 patients with a main complaint of vertigo were enrolled in this study. There was a total of 58 metabolites that were measured by our targeted metabolomics method, and the data were analyzed by pattern recognition algorithms. As a result, a clear classification between AIS and vertigo patients was achieved. Acylcarnitines are the major discriminating metabolites between the two groups. Arginine and its ratio, which is related to urea cycle metabolites, including arginine/ornithine and citrulline/arginine, also accounted for the classification. Interestingly, the levels of these metabolites were also found to be restored among recovering AIS patients (n = 11), which indicated that the metabolic alterations are possibly related to AIS development. Based on the characters from the data pattern reorganization, a novel biomarkers pattern was established using a binary logistic model, which contained arginine, arginine/ornithine, vaccenylcarnitine, and hydroxylbutyrylcarnitine. This biomarkers pattern achieved an area under the receiver operating characteristic curve of 0.89 for the differential diagnosis of AIS. Considering the efficiency and the diagnostic performance of the biomarkers pattern, our method has potential future use for the clinical application.

Introduction

Stroke is the second most common cause of disability worldwide, accounting for ~9% of deaths every year (1). It is also a serious health problem in China, where there are ~1 million new patients that are diagnosed with stroke each year (2), a number that is also constantly increasing. Stroke is typically classified into two basic subtypes: ischemic and hemorrhagic. Approximately 80% of strokes are ischemic, and are caused by a bloodstream blockage that leads to brain tissues ischemic damage (3). Computed tomography (CT) and magnetic resonance imaging (MRI) scans are common tools for stroke diagnosis. Although imaging diagnosis can provide direct clinical evidence of stroke, these scans are still time-consuming, which may delay the therapeutic window for thrombolytic therapy following a stroke by 3–4.5 h (4). In addition, it is difficult to differentiate the clinical symptoms of an acute ischemic stroke (AIS) from transient ischemic attacks (TIAs), that may also exhibit tissue lesions on MRI images (4). Therefore, it is necessary to identify novel biomarkers for AIS that can clinically provide efficient analytical tools.

Metabolomics are considered a powerful tool for the classification of diseases and the discovery of new biomarkers from a pool of small molecules (5). Previous metabolomic studies investigated the use of metabolic biomarkers for the diagnosis or the investigation of the pathological mechanisms of stroke using untargeted metabolomics to determine the metabolic features of ischemic stroke (IS) (6), amino acid signatures (7), AIS progression (8), and TIA differential diagnosis (9). Some metabolites were found to be related to AIS occurrence and development and have been studied by targeted metabolomics methods. These studies included the diagnosis of post-stroke cognitive impairment (10), the relationship between lysine and high-risk stroke patients (11). Considering the efficiency and high-throughput nature of the metabolomic methods, the typical liquid chromatography, coupled to mass spectrometry (LC/MS)- or gas chromatography (GC)/MS-based metabolomics platforms, is not suitable for fast diagnosis, due to the complicated and time-consuming procedures for sample collection, pre-treatment, and chromatographic analysis. Direct infusion mass spectrometry is a high-throughput method of targeted or untargeted analyses that can be performed within 1–2 min. Thus, it is possible to use this method to develop novel diagnostic panels for the fast identification of AIS in patients with the chief complaint of headache or vertigo. In this study, we present a metabolomics approach that is based on an LC/MS direct infusion method to identify a potential biomarker panel for the fast diagnosis of AIS and to differentiate it from other cerebral diseases.

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