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Published on: July 20, 2022
Screening metabolic features of stroke in atrial fibrillation
Haiyu Zhang1, Siqi Zhai2, Xiangyuan Gao2
1Key Laboratory of Cardiovascular Disease Acousto-Optic Electromagnetic Diagnosis and Treatment in Heilongjiang Province, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Insights
Atrial fibrillation (AF) stroke mechanisms were explored using metabolomics. New biomarkers were identified for predicting stroke risk in AF patients, improving early detection and treatment strategies.
Area of Science:
- Cardiovascular Medicine
- Metabolomics
- Biomarker Discovery
Background:
- Atrial fibrillation (AF) significantly increases stroke risk.
- Understanding AF-related stroke (AFS) mechanisms is vital for targeted therapies.
- Limited sample sizes have hindered previous mechanistic studies.
Purpose of the Study:
- To elucidate pathogenic mechanisms of AF-related stroke (AFS).
- To identify novel biomarkers for predicting stroke in AF patients.
- To enhance risk stratification and therapeutic strategies for AFS.
Main Methods:
- Collected plasma samples from 90 AF and 66 AFS patients.
- Performed untargeted metabolomics and Metabolite Set Enrichment Analysis (MSEA).
- Utilized Lasso regression for predictive model development and Weighted Gene Co-expression Network Analysis.
Main Results:
- Identified 12 significantly altered metabolic pathways between AF and AFS groups.
- Detected 51 differentially expressed metabolites (DEMs) in AFS plasma.
- Developed a 12-metabolite predictive model with an AUC of 0.96 for AFS prediction.
Conclusions:
- This study offers deep insights into AF-related stroke pathogenesis.
- Identified novel metabolic biomarkers for stroke risk assessment in AF.
- Findings pave the way for improved preventive and therapeutic interventions for AFS.
Background:
Atrial fibrillation (AF) is an independent risk factor for stroke, increasing the likelihood of stroke by three to five times. AF-related stroke (AFS) has a poor prognosis, making early prediction crucial. However, the underlying mechanisms have only been studied in limited samples. This lack of understanding of pathogenic pathways hampers the development of targeted therapies and patient risk stratification.
Results:
Our study increased the patient count to 90 individuals with AF and 66 with AFS. We collected plasma samples from these patients, detected 671 metabolites, and performed Metabolite Set Enrichment Analysis (MSEA), which revealed differences between the two groups. The results showed statistically significant variations in 12 metabolic pathways between the two groups. In plasma samples from AFS patients, we observed 25 up-regulated and 26 down-regulated differentially expressed metabolites (DEMs), according to the statistical significance criteria of fold change >1.2 or < 0.83, p < 0.05, and VIP score >1. The DEMs demonstrated enrichment in pathways involved in arachidonic acid, taurine, and hypotaurine metabolism, as well as other related processes. Furthermore, we established a predictive model based on 12 metabolites to determine their potential as biomarkers for predicting stroke in patients with AF using Lasso regression. The model attained a remarkable area under the curve of 0.96. Finally, we utilized Weighted Gene Co-expression Network Analysis to examine the clinical comorbidities in individuals with AF and AFS.
Significance:
This study provides an in-depth exploration of the basic pathogenic mechanisms involved in how AF leads to stroke in a large sample. In addition, it identifies new biomarkers that can be used to assess the likelihood of stroke and enhance outcomes in individuals with AF. These findings hold great promise for the development of more effective preventive and therapeutic strategies for stroke in patients with AF in the near future.
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