Discovery Proteomics Using Data Independent Acquisition-based Mass Spectrometry Nominate Biomarker Candidates of Atrial Fibrillation in Stroke
Wooyoung Jang1, Shubham Misra1, Sebastian Sanchez Herrera2, Victor lopez2, Pinar Caglayan2, Richa Sharma1, Guido Falcone1, Lauren Sansing1, Srikant Rangaraju1
1Department of Neurology, Yale University, 2Yale University
Objective:
To identify a plasma biomarkers signature that discriminates between IS/TIA patients with and without a history of AFib using quantitative proteomics.
Background:
Atrial fibrillation (AFib) is a major risk factor for ischemic stroke (IS), and its early diagnosis is critical to optimize secondary prevention and reduce recurrent stroke risk.
Design/Methods:
We used clinical and proteomics data of stroke patients in a prospective plasma repository (2014–2023, Yale University). Samples were collected within 24 hours of stroke onset. Plasma was depleted using High-Select Top 14 Protein Depletion Resin. We measured differentially abundant proteins (DAPs) between patients with and without AFib using data-independent acquisition-based mass spectrometry (DIA-MS). DIA-MS data were quantified using DIA-NN, log2 transformed, batch corrected using Combat function (sva package in R), imputed for peptides with ≤30% missing values using Perseus, and normalized by total area sum. DAPs were identified using ±1.5-fold change and p-value <0.05 (Welch’s t-test). We adjusted for clinical covariates in multivariable logistic regression. Regularized LASSO logistic regression was applied to identify a panel that discriminates AFib from no AFib, with its performance evaluated by 10-fold cross-validated receiver operating characteristic curves. Pathway analysis was conducted using clusterProfiler package.
Results:
Among 40 IS/TIA patients (mean age 68.7 years, 62.5% male), 14 had AFib and 26 had no AFib. DIA-MS quantified 19771 protein peptides, of which 7153 were analyzed after imputation. We identified 174 DAPs, and 63 remained significantly associated with AFib after adjustment for age, sex, and coronary artery disease (adjusted p < 0.05). LASSO regression identified a panel of 8 protein peptides that distinguished AFib from no AFib (cvAUC: 0.83, sensitivity: 71%, specificity: 85%). Pathways associated with coagulation and hemostasis were upregulated in AFib.
Conclusions:
Our study highlights the potential of plasma proteomics as a valuable tool for discovering protein biomarkers to discriminate IS/TIA patients with AFib compared to no AFib.
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