ArticleMetabolomics : Official journal of the Metabolomic Society2024
Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension.
Article in Metabolomics : Official journal of the Metabolomic Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Advances in metabolomics of biomarkers for ischemic stroke: From bench to clinic.Neural regeneration research · 2026Article
- Efficacy and comparative performance of machine learning models for stroke risk prediction in hypertensive patients: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Serum lipid profiling reveals characteristic lipid signatures associated with stroke in patients with leukoaraiosis.Scientific reports · 2024Article
- Association between dietary intake of niacin and stroke in the US residents: evidence from national health and nutrition examination survey (NHANES) 1999-2018.Frontiers in nutrition · 2024Article
- Genetic causality of lipidomic and immune cell profiles in ischemic stroke.Frontiers in neurology · 2024Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
introductionThe burden of stroke in patients with hypertension is very high, and its prediction is critical.
objectivesWe aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.
methodsThis 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.
resultsCo-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).
conclusionsWe 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.
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