Identification of three potential novel biomarkers for early diagnosis of acute ischemic stroke via plasma lipidomics

Metabolomic discoveries for early diagnosis and traditional Chinese medicine efficacy in ischemic stroke

Ischemic stroke (IS), a devastating cerebrovascular accident, presents with high mortality and morbidity. Following IS onset, a cascade of pathological changes, including excitotoxicity, inflammatory damage, and blood-brain barrier disruption, significantly impacts prognosis. However, current clinical practices struggle with early diagnosis and identifying these alterations. Metabolomics, a powerful tool in systems biology, offers a promising avenue for uncovering early diagnostic biomarkers for IS. By analyzing dynamic metabolic profiles, metabolomics can not only aid in identifying early IS biomarkers but also evaluate Traditional Chinese Medicine (TCM) efficacy and explore its mechanisms of action in IS treatment. Animal studies demonstrate that TCM interventions modulate specific metabolite levels, potentially reflecting their therapeutic effects. Identifying relevant metabolites in cerebral ischemia patients holds immense potential for early diagnosis and improved outcomes. This review focuses on recent metabolomic discoveries of potential early diagnostic biomarkers for IS. We explore variations in metabolites observed across different ages, genders, disease severity, and stages. Additionally, the review examines how specific TCM extracts influence IS development through metabolic changes, potentially revealing their mechanisms of action. Finally, we emphasize the importance of integrating metabolomics with other omics approaches for a comprehensive understanding of IS pathophysiology and TCM efficacy, paving the way for precision medicine in IS management.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.