A phenome-wide bidirectional Mendelian randomization analysis of atrial fibrillation

Innate immunity-mediated neuroinflammation promotes the onset and progression of post-stroke depression

Post-stroke depression (PSD) is a prevalent psychiatric disorder after stroke, with the incidence of approximately one-third among stroke survivors. It is classified as an organic mental disorder and has a well-documented association with stroke affecting various aspects of patients, such as the recovery of limb motor function, daily living self-care ability, and increasing the mortality of stroke survivors. However, the pathogenesis of PSD is not yet fully understood. Currently, immune inflammation is a research hotspot. This review focuses on the pathogenesis of PSD, particularly elucidating the role of inflammation in mediating neuroinflammation through innate immunity. Simultaneously, we highlight that peripheral inflammation following a stroke may trigger a detrimental cycle of neuroinflammation by activating innate immune pathways within the central nervous system, which could potentially contribute to the development of PSD. Lastly, we summarize potential treatments for PSD and propose targeting cytokines and innate immune pathways as novel therapeutic approaches.

Fibroblast growth factor 5 as a target for atrial fibrillation treatment: Evidence from mendelian randomization

BACKGROUND

Previous studies have found that inflammatory proteins are involved in the pathogenesis of atrial fibrillation (AF). We used mendelian randomization to explore the potential pathogenic inflammatory proteins of AF.

METHODS

This study adopts a Mendelian randomization design to primarily assess causal associations using the Wald ratio and the inverse variance weighting method. It leverages protein quantitative trait locus (pQTL) data encompassing 91 types of inflammatory proteins from 14,824 participants of European ancestry. The primary analysis phase utilizes AF GWAS data from 55,106 participants, with an additional 237,690 participants included in the validation stage. Sensitivity analyses, including reverse causality analysis, Bayesian colocalization analysis, and phenotype scanning, were conducted. Finally, the study explores potential targeted drugs.

RESULTS

The findings highlight a causal link between 7 inflammatory proteins and AF, with 2 showing positive correlations and 5 exhibiting negative correlations. Among these, fibroblast growth factor 5 (FGF5) emerges as particularly robust in sensitivity analysis. Colocalization analysis indicates a shared genetic variation between FGF5 and AF, supporting its potential as a targeted therapy for AF. Importantly, this causal relationship remains unaffected by reverse causality. Furthermore, significant pleiotropic effects were observed in phenotype scanning. Finally, the causal association between FGF5 and AF was successfully replicated during the validation phase.

CONCLUSION

FGF5 may become an intervention target for AF targeted therapy.

Causal inference in the field of arrhythmia: An introduction to mendelian randomization

Mendelian randomization (MR) uses genetic variants associated with an exposure (eg, high blood pressure) as instrumental variables to test causal effects on an outcome (eg, atrial fibrillation [AF]). By leveraging the random assortment of genetic variants during gamete formation, MR reduces biases like confounding and reverse causation. We screened 391 papers, examining 277 that applied MR to investigate arrhythmia and, in others, cardiovascular traits, lifestyle, behavioral traits, and body composition. Our analysis focused on MR studies of arrhythmia and cardiovascular traits. Key findings highlight high systolic blood pressure, low resting heart rate, elevated cardiac troponin I levels, coronary artery disease, and heart failure as risk factors for AF, whereas AF itself increases heart failure risk. As genetic data become more accessible, MR's relevance grows. Sensitivity analyses and integrating MR with other methodologies in a triangulation framework enhance the robustness of causal inferences by navigating different biases.

Systematic Mendelian Randomization Exploring Druggable Genes for Hemorrhagic Strokes

Patients with hemorrhagic stroke have high rates of morbidity and mortality, and drugs for prevention are very limited. Mendelian randomization (MR) analysis can increase the success rate of drug development by providing genetic evidence. Previous MR analyses only analyzed the role of individual drug target genes in hemorrhagic stroke; therefore, we used MR analysis to systematically explore the druggable genes for hemorrhagic stroke. We sequentially performed summary-data-based MR analysis and two-sample MR analysis to assess the associations of all genes within the database with intracranial aneurysm, intracerebral hemorrhage, and their subtypes. Validated genes were further analyzed by colocalization. Only genes that were positive in all three analyses and were druggable were considered desirable genes. We also explored the mediators of genes affecting hemorrhagic stroke incidence. Finally, the associations of druggable genes with other cardiovascular diseases were analyzed to assess potential side effects. We identified 56 genes that significantly affected hemorrhagic stroke incidence. Moreover, TNFSF12, SLC22A4, SPARC, KL, RELT, and ADORA3 were found to be druggable. The inhibition of TNFSF12, SLC22A4, and SPARC can reduce the risk of intracranial aneurysm, subarachnoid hemorrhage, and intracerebral hemorrhage. Gene-induced hypertension may be a potential mechanism by which these genes cause hemorrhagic stroke. We also found that blocking these genes may cause side effects, such as ischemic stroke and its subtypes. Our study revealed that six druggable genes were associated with hemorrhagic stroke, and the inhibition of TNFSF12, SLC22A4, and SPARC had preventive effects against hemorrhagic strokes.

Bidirectional Association Between Atrial Fibrillation and Myocardial Infarction, and Relation to Mortality in the Framingham Heart Study

BACKGROUND

Individuals with both atrial fibrillation (AF) and myocardial infarction (MI) have higher mortality compared with individuals with only 1 condition. Whether mortality differs according to the temporal order of AF and MI is unclear.

METHODS AND RESULTS

We included participants from the FHS (Framingham Heart Study) from 1960 and onwards. We assessed the hazard ratio (HR) of new-onset AF and MI, and mortality according to MI and AF status (prevalent and interim) using multivariable-adjusted Cox proportional hazards models. Interim diseases were modeled as time-varying variables. For the analysis of new-onset AF, 10 923 participants (55% women; mean±SD age, 54±8 years) were included. For new-onset MI, 10 804 participants (55% women; mean±SD age, 54±8 years) were included. Compared with no MI, the hazard of new-onset AF was higher in participants with prevalent (HR, 1.60 [95% CI, 1.32-1.94]) and interim MI (HR, 3.96 [95% CI, 3.18-4.91]). Both ST-segment-elevation MI and non-ST-segment-elevation MI were associated with new-onset AF. Interim AF, not prevalent AF, was associated with higher hazard rate of new-onset MI (HR, 2.21 [95% CI, 1.67-2.92]). Interim AF was associated with both ST-segment-elevation MI and non-ST-segment-elevation MI. Mortality was significantly greater among participants with AF and MI compared with participants with 1 of the 2, regardless of temporal order.

CONCLUSIONS

We report a bidirectional association between AF and MI, which was observed for both non-ST-segment-elevation MI and ST-segment-elevation MI. Participants with both AF and MI had considerably higher mortality compared with participants with only 1 of the 2 conditions, regardless of order.

Exploring the link between gut microbiota and alopecia areata: a two-sample Mendelian randomization analysis

BACKGROUND

While observational studies have suggested a link between gut microbiota diversity and alopecia areata (AA), the causal relationship remains unclear.

METHODS

We leveraged data from the MiBioGen and FinnGen consortiums' Genome-wide association studies (GWAS) encompassing gut microbiota (n = 13,266) and AA (n = 211,428) datasets. A comprehensive Mendelian randomization (MR) and reverse MR approach were employed, utilizing five statistical methods to evaluate causality. Sensitivity analyses were also conducted to corroborate the MR results.

RESULTS

Inverse variance weighted (IVW) analysis indicated a protective effect against AA from Butyricimonas (OR = 0.37, 95% CI: 0.18-0.77, P = 0.01), Enterorhabdus (OR = 0.40, 95% CI: 0.16-0.95, P = 0.04), Eubacterium (xylanophilum group) (OR = 0.36, 95% CI: 0.15-0.84, P = 0.02), and Phascolarctobacterium (OR = 0.37, 95% CI: 0.15-0.91, P = 0.03), while Ruminococcaceae UCG003 posed as a risk factor (OR = 2.79, 95% CI: 1.27-6.14, P = 0.01). Reverse MR showed no significant causal link between AA and gut microbiota, with no significant heterogeneity or horizontal pleiotropy.

CONCLUSIONS

Our analysis suggests probable causality between certain gut microbiota and AA, shedding light on its pathogenesis and potential intervention strategies.

Causal Effects of Basal Metabolic Rate on Cardiovascular Disease: A Bidirectional Mendelian Randomization Study

BACKGROUND

Despite the health effects of basal metabolic rate (BMR), the causal effect of BMR on cardiovascular diseases (CVDs) remains undetermined. To elucidate the causal nature, Mendelian randomization (MR) analyses were performed.

METHODS AND RESULTS

Summary genome-wide association statistics regarding BMR and 5 CVDs were obtained from European databases. A 2-sample bidirectional MR was performed to assess the causal association between BMR and CVDs. The causal effects were estimated using inverse variance weighting. Simultaneously, multiple sensitivity analyses were performed to validate the robustness and reliability of the results. Our results indicated that genetically predicted BMR was significantly positively associated with the risk of heart failure (odds ratio, 1.53 [95% CI, 1.39-1.67]; P<0.001), atrial fibrillation and flutter (odds ratio, 2.12 [95% CI, 1.87-2.40]; P<0.001), and aortic aneurysm (odds ratio, 1.64 [95% CI, 1.41-1.92]; P<0.001). Genetically predicted BMR may not be causally associated with coronary artery disease and ischemic stroke risk. Furthermore, a significant causal effect of CVDs on BMR was not found in the reverse MR analysis. Multivariable MR was applied to further assess the direct effect of BMR on CVDs. Multivariable MR indicated that a high level of BMR still increased the risk of heart failure and atrial fibrillation and flutter after adjustment independent of possible confounders. However, the P value of aortic aneurysm was not significant.

CONCLUSIONS

The present study provides robust evidence that genetically predicted BMR is independently causally associated with heart failure and atrial fibrillation and flutter but not vice versa. These findings have implications for the prevention and treatment of CVDs in clinical practice.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation.

METHODS

A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate.

STRUCTURE

Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Patients With Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation.

METHODS

A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate.

STRUCTURE

Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

The mediating role of atrial fibrillation in causal associations between risk factors and stroke: a Mendelian randomization study

OBJECTIVES

Atrial fibrillation (AF) contributes to stroke development and progression. We aimed to quantify the mediating role of AF in the causal associations between a wide range of risk factors and stroke via a Mendelian randomization (MR) framework.

METHODS

We assessed the associations of 108 traits with stroke and its subtypes in a 2-sample univariable MR approach, then conducted a bidirectional MR analysis between these 108 traits and AF to evaluate the presence and direction of their causal associations. Finally, to further investigate the extent to which AF mediated the effects of eligible traits on stroke, we applied multivariable and 2-step MR techniques in a mediation analysis where outcomes were restricted to stroke types causally affected by AF (any stroke [AS], any ischemic stroke [AIS], and cardioembolic stroke [CES]).

RESULTS

Among 108 traits, 42 were putatively causal for at least 1 stroke type; of these 42 traits, 20 that had no bidirectional relationship with AF were retained. Finally, 33 associations of 15 eligible traits were examined in the mediation analysis. The mediation analyses for AS, AIS, and CES each included 11 eligible traits. After AF adjustment, the direct effects of all traits on CES were attenuated to null (all p>0.05), while the associations with AS and AIS persisted for most traits (AF-mediated proportion: from 6.6% [95% confidence interval, 2.7 to 0.6] to 52.0% [95% confidence interval, 39.8 to 64.3]).

CONCLUSIONS

The causal associations between all eligible traits and CES were largely mediated through AF, while most traits affected AS and AIS independently of AF.

A proteomic analysis of atrial fibrillation in a prospective longitudinal cohort (AGES-Reykjavik study)

AIMS

Atrial fibrillation (AF) is associated with high risk of comorbidities and mortality. Our aim was to examine causal and predictive relationships between 4137 serum proteins and incident AF in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik (AGES-Reykjavik) study.

METHODS AND RESULTS

The study included 4765 participants, of whom 1172 developed AF. Cox proportional hazards regression models were fitted for 4137 baseline protein measurements adjusting for known risk factors. Protein associations were tested for replication in the Cardiovascular Health Study (CHS). Causal relationships were examined in a bidirectional, two-sample Mendelian randomization analysis. The time-dependent area under the receiver operating characteristic curve (AUC)-statistic was examined as protein levels and an AF-polygenic risk score (PRS) were added to clinical risk models. The proteomic signature of incident AF consisted of 76 proteins, of which 63 (83%) were novel and 29 (38%) were replicated in CHS. The signature included both N-terminal prohormone of brain natriuretic peptide (NT-proBNP)-dependent (e.g. CHST15, ATP1B1, and SVEP1) and independent components (e.g. ASPN, AKR1B, and LAMA1/LAMB1/LAMC1). Nine causal candidates were identified (TAGLN, WARS, CHST15, CHMP3, COL15A1, DUSP13, MANBA, QSOX2, and SRL). The reverse causal analysis suggested that most AF-associated proteins were affected by the genetic liability to AF. N-terminal prohormone of brain natriuretic peptide improved the prediction of incident AF events close to baseline with further improvements gained by the AF-PRS at all time points.

CONCLUSION

The AF proteomic signature includes biologically relevant proteins, some of which may be causal. It mainly reflects an NT-proBNP-dependent consequence of the genetic liability to AF. N-terminal prohormone of brain natriuretic peptide is a promising marker for incident AF in the short term, but risk assessment incorporating a PRS may improve long-term risk assessment.

A Discussion of the Contemporary Prediction Models for Atrial Fibrillation

Atrial Fibrillation is a complex disease state with many environmental and genetic risk factors. While there are environmental factors that have been shown to increase an individual's risk of atrial fibrillation, it has become clear that atrial fibrillation has a genetic component that influences why some patients are at a higher risk of developing atrial fibrillation compared to others. This review will first discuss the clinical diagnosis of atrial fibrillation and the corresponding rhythm atrial flutter. We will then discuss how a patients' risk of stroke can be assessed by using other clinical co-morbidities. We will then review the clinical risk factors that can be used to help predict an individual patient's risk of atrial fibrillation. Many of the clinical risk factors have been used to create several different risk scoring methods that will be reviewed. We will then discuss how genetics can be used to identify individuals who are at higher risk for developing atrial fibrillation. We will discuss genome-wide association studies and other sequencing high-throughput sequencing studies. Finally, we will touch on how genetic variants derived from a genome-wide association studies can be used to calculate an individual's polygenic risk score for atrial fibrillation. An atrial fibrillation polygenic risk score can be used to identify patients at higher risk of developing atrial fibrillation and may allow for a reduction in some of the complications associated with atrial fibrillation such as cerebrovascular accidents and the development of heart failure. Finally, there is a brief discussion of how artificial intelligence models can be used to predict which patients will develop atrial fibrillation.

The bidirectional association between atrial fibrillation and myocardial infarction

Atrial fibrillation (AF) is associated with an increased risk of myocardial infarction (MI) and vice versa. This bidirectional association relies on shared risk factors as well as on several direct and indirect mechanisms, including inflammation, atrial ischaemia, left ventricular remodelling, myocardial oxygen supply-demand mismatch and coronary artery embolism, through which one condition can predispose to the other. Patients with both AF and MI are at greater risk of stroke, heart failure and death than patients with only one of the conditions. In this Review, we describe the bidirectional association between AF and MI. We discuss the pathogenic basis of this bidirectional relationship, describe the risk of adverse outcomes when the two conditions coexist, and review current data and guidelines on the prevention and management of both conditions. We also identify important gaps in the literature and propose directions for future research on the bidirectional association between AF and MI. The Review also features a summary of methodological approaches for the study of bidirectional associations in population-based studies.

Proteome-Wide Mendelian Randomization Analysis Identified Potential Drug Targets for Atrial Fibrillation

Background Finding effective and safe therapeutic drugs for atrial fibrillation (AF) is an important concern for clinicians. Proteome-wide Mendelian randomization analysis provides new ideas for finding potential drug targets. Methods and Results Using a proteome-wide Mendelian randomization approach, we assessed the genetic predictive causality between thousands of proteins and AF risk and found that genetically predicted plasma levels of phosphomevalonate kinase, tumor necrosis factor ligand superfamily member 12, sulfhydryl oxidase 2, interleukin-6 receptor subunit alpha, and low-affinity immunoglobulin gamma Fc region receptor II-b might decrease AF risk, while genetically predicted plasma levels of beta-mannosidase, collagen alpha-1(XV) chain, ANXA4 (annexin A4), COF2 (cofilin-2), and RAB1A (Ras-related protein Rab-1A) might increase AF risk (P<3.4×10-5). By using different Mendelian randomization methods and instrumental variable selection thresholds, we performed sensitivity analyses in 30 scenarios to test the robustness of positive findings. Replication analyses were also performed in independent samples to further avoid false-positive findings. Drugs targeting tumor necrosis factor ligand superfamily member 12, interleukin-6 receptor subunit alpha, low-affinity immunoglobulin gamma Fc region receptor II-b, and annexin A4 are approved or in development. The results of the phenome-wide Mendelian randomization analysis showed that changing the plasma levels of phosphomevalonate kinase, cofilin-2, annexin A4, Ras-related protein Rab-1A, sulfhydryl oxidase 2, and collagen alpha-1(XV) chain did not increase the risk of other diseases while decreasing the risk of AF. Conclusions We found a significant causal association between genetically predicted levels of 10 plasma proteins and AF risk. Four of these proteins have drugs targeting them that are approved or in development, and our results suggest the potential for these drugs to treat AF or cause AF. Sulfhydryl oxidase 2, low-affinity immunoglobulin gamma Fc region receptor II-b, and beta-mannosidase have not been suggested by previous laboratory or epidemiological studies to be associated with AF and may reveal new pathophysiological pathways as well as therapeutic targets for AF.

Novel Drug Targets for Atrial Fibrillation Identified Through Mendelian Randomization Analysis of the Blood Proteome

PURPOSE

Novel, effective, and safe preventive therapy targets for AF are still needed. Circulating proteins with causal genetic evidence are promising candidates. We aimed to systematically screen circulating proteins for AF drug targets and determine their safety and efficacy using genetic methods.

METHODS

The protein quantitative trait loci (pQTL) of up to 1949 circulating proteins were retrieved from nine large genome-proteome-wide association studies. Two-sample Mendelian Randomization (MR) and colocalization analyses were used to estimate the causal effects of proteins on the risk of AF. Furthermore, phenome-wide MR was conducted to depict side effects and the drug-target databases were searched for drug validation and repurposing.

RESULTS

Systematic MR screen identified 30 proteins as promising AF drug targets. Genetically predicted 12 proteins increased AF risk (TES, CFL2, MTHFD1, RAB1A, DUSP13, SRL, ANXA4, NEO1, FKBP7, SPON1, LPA, MANBA); 18 proteins decreased AF risk (PMVK, UBE2F, SYT11, CHMP3, PFKM, FBP1, TNFSF12, CTSZ, QSOX2, ALAD, EFEMP1, FLRT2, LRIG1, OLA1, SH3BGRL3, IL6R, B3GNT8, FCGR2A). DUSP13 and TNFSF12 possess strong colocalization evidence. For the proteins that were identified, extended phe-MR analysis was conducted to assess their side-effect profiles, while drug-target databases provided information on their approved or investigated indications.

CONCLUSION

We identified 30 circulating proteins as potential preventive targets for AF.

Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.