Atrial Fibrillation and Stroke Symptoms in the REGARDS Study

Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning

Background

Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF.

Methods

We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS.

Results

Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4.

Conclusion

In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation

OBJECTIVE

Accurate prediction of survival prognosis is helpful to guide clinical decision-making. The aim of this study was to develop a model using machine learning techniques to predict the occurrence of composite thromboembolic events (CTEs) in elderly patients with atrial fibrillation(AF). These events encompass newly diagnosed cerebral ischemia events, cardiovascular events, pulmonary embolism, and lower extremity arterial embolism.

METHODS

This retrospective study included 6,079 elderly hospitalized patients (≥ 75 years old) with AF admitted to the People's Liberation Army General Hospital in China from January 2010 to June 2022. Random forest imputation was used for handling missing data. In the descriptive statistics section, patients were divided into two groups based on the occurrence of CTEs, and differences between the two groups were analyzed using chi-square tests for categorical variables and rank-sum tests for continuous variables. In the machine learning section, the patients were randomly divided into a training dataset (n = 4,225) and a validation dataset (n = 1,824) in a 7:3 ratio. Four machine learning models (logistic regression, decision tree, random forest, XGBoost) were trained on the training dataset and validated on the validation dataset.

RESULTS

The incidence of composite thromboembolic events was 19.53%. The Least Absolute Shrinkage and Selection Operator (LASSO) method, using 5-fold cross-validation, was applied to the training dataset and identified a total of 18 features that exhibited a significant association with the occurrence of CTEs. The random forest model outperformed other models in terms of area under the curve (ACC: 0.9144, SEN: 0.7725, SPE: 0.9489, AUC: 0.927, 95% CI: 0.9105-0.9443). The random forest model also showed good clinical validity based on the clinical decision curve. The Shapley Additive exPlanations (SHAP) showed that the top five features associated with the model were history of ischemic stroke, high triglyceride (TG), high total cholesterol (TC), high plasma D-dimer, age.

CONCLUSIONS

This study proposes an accurate model to stratify patients with a high risk of CTEs. The random forest model has good performance. History of ischemic stroke, age, high TG, high TC and high plasma D-Dimer may be correlated with CTEs.

Assessing ascertainment bias in atrial fibrillation across US minority groups

The aim of this study is to define atrial fibrillation (AF) prevalence and incidence rates across minority groups in the United States (US), to aid in diversity enrollment target setting for randomized controlled trials. In AF, US minority groups have lower clinically detected prevalence compared to the non-Hispanic or Latino White (NHW) population. We assess the impact of ascertainment bias on AF prevalence estimates. We analyzed data from adults in Optum's de-identified Clinformatics® Data Mart Database from 2017-2020 in a cohort study. Presence of AF at baseline was identified from inpatient and/or outpatient encounters claims using validated ICD-10-CM diagnosis algorithms. AF incidence and prevalence rates were determined both in the overall population, as well as in a population with a recent stroke event, where monitoring for AF is assumed. Differences in prevalence across cohorts were assessed to determine if ascertainment bias contributes to the variation in AF prevalence across US minority groups. The period prevalence was respectively 4.9%, 3.2%, 2.1% and 5.9% in the Black or African American, Asian, Hispanic or Latino, and NHW population. In patients with recent ischemic stroke, the proportion with AF was 32.2%, 24.3%, 25%, and 24.5%, respectively. The prevalence of AF among the stroke population was approximately 7 to 10 times higher than the prevalence among the overall population for the Asian and Hispanic or Latino population, compared to approximately 5 times higher for NHW patients. The relative AF prevalence difference of the Asian and Hispanic or Latino population with the NHW population narrowed from respectively, -46% and -65%, to -22% and -24%. The study findings align with previous observational studies, revealing lower incidence and prevalence rates of AF in US minority groups. Prevalence estimates of the adult population, when routine clinical practice is assumed, exhibit higher prevalence differences compared to settings in which monitoring for AF is assumed, particularly among Asian and Hispanic or Latino subgroups.

Discovery of TBX20 as a Novel Gene Underlying Atrial Fibrillation

Atrial fibrillation (AF), the most prevalent type of sustained cardiac dysrhythmia globally, confers strikingly enhanced risks for cognitive dysfunction, stroke, chronic cardiac failure, and sudden cardiovascular demise. Aggregating studies underscore the crucial roles of inherited determinants in the occurrence and perpetuation of AF. However, due to conspicuous genetic heterogeneity, the inherited defects accounting for AF remain largely indefinite. Here, via whole-genome genotyping with genetic markers and a linkage assay in a family suffering from AF, a new AF-causative locus was located at human chromosome 7p14.2-p14.3, a ~4.89 cM (~4.43-Mb) interval between the markers D7S526 and D7S2250. An exome-wide sequencing assay unveiled that, at the defined locus, the mutation in the TBX20 gene, NM_001077653.2: c.695A>G; p.(His232Arg), was solely co-segregated with AF in the family. Additionally, a Sanger sequencing assay of TBX20 in another family suffering from AF uncovered a novel mutation, NM_001077653.2: c.862G>C; p.(Asp288His). Neither of the two mutations were observed in 600 unrelated control individuals. Functional investigations demonstrated that the two mutations both significantly reduced the transactivation of the target gene KCNH2 (a well-established AF-causing gene) and the ability to bind the promoter of KCNH2, while they had no effect on the nuclear distribution of TBX20. Conclusively, these findings reveal a new AF-causative locus at human chromosome 7p14.2-p14.3 and strongly indicate TBX20 as a novel AF-predisposing gene, shedding light on the mechanism underlying AF and suggesting clinical significance for the allele-specific treatment of AF patients.

Racial, ethnic, and sex disparities in atrial fibrillation management: rate and rhythm control

BACKGROUND

Atrial fibrillation (AF) affects around 6 million Americans. AF management involves pharmacologic therapy and/or interventional procedures to control rate and rhythm, as well as anticoagulation for stroke prevention. Different populations may respond differently to distinct management strategies. This review will describe disparities in rate and rhythm control and their impact on outcomes among women and historically underrepresented racial and/or ethnic groups.

METHODS

This is a narrative review exploring the topic of sex and racial and/or ethnic disparities in rate and rhythm management of AF. We describe basic terminology, summarize AF epidemiology, discuss diversity in clinical research, and review landmark clinical trials.

RESULTS

Despite having higher rates of traditional AF risk factors, Black and Hispanic adults have lower risk of AF than non-Hispanic White (NHW) patients, although those with AF experience more severe symptoms and report lower quality-of-life scores than NHW patients with AF. NHW patients receive antiarrhythmic drugs, cardioversions, and invasive therapies more frequently than Black and Hispanic patients. Women have lower rates of AF than men, but experience more severe symptoms, heart failure, stroke, and death after AF diagnosis. Women and people from diverse racial and ethnic backgrounds are inadequately represented in AF trials; prevalence findings may be a result of underdetection.

CONCLUSION

Race, ethnicity, and gender are social determinants of health that may impact the prevalence, evolution, and management of AF. This impact reflects differences in biology as well as disparities in treatment and representation in clinical trials.