Cardiac Risk Factors for Stroke: A Comprehensive Mendelian Randomization Study

Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve

PURPOSE OF REVIEW

Technology drives the field of cardiac electrophysiology. Recent computational advances will bring exciting changes. To stay ahead of the curve, we recommend electrophysiologists develop a robust appreciation for novel computational techniques, including deterministic, statistical, and hybrid models.

RECENT FINDINGS

In clinical applications, deterministic models use biophysically detailed simulations to offer patient-specific insights. Statistical techniques like machine learning and artificial intelligence recognize patterns in data. Emerging clinical tools are exploring avenues to combine all the above methodologies. We review three ways that computational medicine will aid electrophysiologists by: (1) improving personalized risk assessments, (2) weighing treatment options, and (3) guiding ablation procedures. Leveraging clinical data that are often readily available, computational models will offer valuable insights to improve arrhythmia patient care. As emerging tools promote personalized medicine, physicians must continue to critically evaluate technology-driven tools they consider using to ensure their appropriate implementation.

Shared genetic etiology of vessel diseases: A genome-wide multi-traits association analysis

BACKGROUND

The comorbidity among vascular diseases has been widely reported, however, the contribution of shared genetic components remains ambiguous.

METHODS

Based on genome-wide association study summary statistics, we employed statistical genetics methodologies to explore the shared genetic basis of eight vascular diseases: coronary artery disease, abdominal aortic aneurysm, ischemic stroke, peripheral artery disease, thoracic aortic aneurysm, phlebitis, varicose veins, and venous thromboembolism. We assessed global and local genetic correlations among these disorders by linkage disequilibrium score regression, high-definition likelihood, and local analysis of variant association. Cross-trait analyses conducted with CPASSOC identified pleiotropic variants and loci. Further, biological pathways at the multi-omics level were explored using multimarker analysis of genomic annotation, transcriptome-wide and proteome-wide association studies. Causal associations among the vascular diseases were evaluated by mendelian randomization and latent causal variable to assess vertical pleiotropic effects.

RESULTS

We found significant global genetic associations in 18 pairs of vascular diseases. Additionally, we discovered 317 unique genomic regions where at least one pair of traits demonstrated significant correlation. Multi-trait association analysis identified 19,361 significant potential pleiotropic variants in 274 independent pleiotropic loci. Multi-trait colocalization analysis revealed 56 colocalized loci in specific disease sets. Gene-based analysis identified 700 potential pleiotropic genes, which were subsequently validated at both transcriptome and protein levels. Gene-set enrichment analysis supports the role of biological pathways such as vessel wall structure, coagulation and lipid transport in vascular disease. Additionally, 7 pairs of vascular diseases have a causal relationship.

CONCLUSIONS

Our study indicates a shared genetic basis and the presence of common risk genes among vascular diseases. These findings offer novel insights into potential mechanisms underlying the association between vascular diseases, as well as provide guidance for interventions and treatments of multi-vascular conditions.

Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking

Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.

Emerging Strategies and Effective Prevention Measures for Investigating the Association Between Stroke and Sudden Cardiac Fatality

Stroke-related cardiac death is a significant concern for patients with stroke and their healthcare providers. It is a complex and multifaceted condition that requires careful management of both modifiable and non-modifiable risk factors. This review provides an overview of the pathophysiology, risk factors, and prevention strategies for stroke-related cardiac death. The review highlights the importance of identifying and managing modifiable risk factors such as hypertension, diabetes, and lifestyle factors, as well as non-modifiable risk factors such as age and genetics. Additionally, the review explores emerging strategies for prevention, including the use of wearable devices and genetic testing to identify patients at risk, stem cell therapy and gene therapy for cardiac dysfunction, and precision medicine for personalized treatment plans. Despite some limitations to this review, it provides valuable insights into the current understanding of stroke-related cardiac death and identifies important areas for future research. Ultimately, the implementation of evidence-based prevention strategies and personalized treatment plans has the potential to improve outcomes for patients with stroke and reduce the burden of stroke-related cardiac death in the population.

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.

Potential Causal Association between Elevated Gamma-Glutamyl Transferase Level and Stroke: A Two-Sample Mendelian Randomization Study

Researchers have suggested a potential relationship between gamma-glutamyl transferase (GGT) level and stroke. We investigated a potential causal relationship between GGT level as exposures and stroke and stroke subtypes (cardioembolic, small vessel, and large artery) in a European population. We performed a two-sample Mendelian randomization (MR) study using the genome-wide association study (GWAS) data from the UK Biobank as the exposure set. For the outcome set, we used stroke in the GWAS data from the GIGASTROKE Consortium. We considered alcohol consumption, atrial fibrillation, and body mass index as confounders. We used PhenoScanner searches for removal of SNPs and multivariable MR analysis for assessing confounders. We observed significant causal associations between GGT level and stroke (odds ratio [OR] = 1.23, 95% CI = [1.05-1.44], and p = 0.012 with IVW; OR = 1.19, 95% CI= [1.02-1.39], and p = 0.031 with MR-PRESSO). These results were consistent after removing SNPs related to confounding factors. Similarly, in multivariable MR, GGT was associated with stroke after adjusting for confounding factors (OR = 1.30, 95% CI 1.07-1.60), p = 0.010). Because GGT level has a causal relationship with stroke, researchers should test its significance as a potential risk factor for stroke. Additional research is required to validate these results.

Appraising the Causal Role of Risk Factors in Coronary Artery Disease and Stroke: A Systematic Review of Mendelian Randomization Studies

BACKGROUND Mendelian randomization (MR) offers a powerful approach to study potential causal associations between exposures and health outcomes by using genetic variants associated with an exposure as instrumental variables. In this systematic review, we aimed to summarize previous MR studies and to evaluate the evidence for causality for a broad range of exposures in relation to coronary artery disease and stroke. METHODS AND RESULTS MR studies investigating the association of any genetically predicted exposure with coronary artery disease or stroke were identified. Studies were classified into 4 categories built on the significance of the main MR analysis results and its concordance with sensitivity analyses, namely, robust, probable, suggestive, and insufficient. Studies reporting associations that did not perform any sensitivity analysis were classified as nonevaluable. We identified 2725 associations eligible for evaluation, examining 535 distinct exposures. Of them, 141 were classified as robust, 353 as probable, 110 as suggestive, and 926 had insufficient evidence. The most robust associations were observed for anthropometric traits, lipids, and lipoproteins and type 2 diabetes with coronary artery; disease and clinical measurements with coronary artery disease and stroke; and thrombotic factors with stroke. CONCLUSIONS Despite the large number of studies that have been conducted, only a limited number of associations were supported by robust evidence. Approximately half of the studies reporting associations presented an MR sensitivity analysis along with the main analysis that further supported the causality of associations. Future research should focus on more thorough assessments of sensitivity MR analyses and further assessments of mediation effects or nonlinearity of associations.

Risk factors for heart, cerebrovascular, and kidney diseases: evaluation of potential side effects of medications to control hypertension, hyperglycemia, and hypercholesterolemia

Background

Heart disease (HD), cerebrovascular disease (CBD), and kidney disease (KD) are serious diseases worldwide. These diseases constitute the leading causes of death worldwide and are costly to treat. An analysis of risk factors is necessary to prevent these diseases.

Data and Methods

Risk factors were analyzed using data from 2,837,334, 2,864,874, and 2,870,262 medical checkups obtained from the JMDC Claims Database. The side effects of medications used to control hypertension (antihypertensive medications), hyperglycemia (antihyperglycemic medications), and hypercholesterolemia (cholesterol medications), including their interactions, were also evaluated. Logit models were used to calculate the odds ratios and confidence intervals. The sample period was from January 2005 to September 2019.

Results

Age and history of diseases were found to be very important factors, and the risk of having diseases could be almost doubled. Urine protein levels and recent large weight changes were also important factors for all three diseases and made the risks 10%-30% higher, except for KD. For KD, the risk was more than double for individuals with high urine protein levels. Negative side effects were observed with antihypertensive, antihyperglycemic, and cholesterol medications. In particular, when antihypertensive medications were used, the risks were almost doubled for HD and CBD. The risk would be triple for KD when individuals were taking antihypertensive medications. If they did not take antihypertensive medications and took other medications, these values were lower (20%-40% for HD, 50%-70% for CBD, and 60%-90% for KD). The interactions between the different types of medications were not very large. When antihypertensive and cholesterol medications were used simultaneously, the risk increased significantly in cases of HD and KD.

Conclusion

It is very important for individuals with risk factors to improve their physical condition for the prevention of these diseases. Taking antihypertensive, antihyperglycemic, and cholesterol medications, especially antihypertensive medications, may be serious risk factors. Special care and additional studies are necessary to prescribe these medications, particularly antihypertensive medications.

Limitations

No experimental interventions were performed. As the dataset was comprised of the results of health checkups of workers in Japan, individuals aged 76 and above were not included. Since the dataset only contained information obtained in Japan and the Japanese are ethnically homogeneous, potential ethnic effects on the diseases were not evaluated.

Mendelian Randomization Study of Heart Failure and Stroke Subtypes

Background

Whether heart failure (HF) is an independent risk factor of ischemic stroke (IS) and hemorrhagic stroke remains controversial. We employed a multivariable Mendelian randomization (MR) to further investigate the causal effects of HF on the risk of stroke and stroke subtypes.

Methods

Genetically predicted HF was selected as an instrumental variable (IV) from published genome-wide association studies (GWAS) meta-analyses. Stroke data with different etiologies were extracted as outcome variables from another two GWAS meta-analyses. The random-effects inverse variance-weighted (IVW) model was applied as the main method, along with sensitivity analysis. Atrial fibrillation (AF), coronary heart disease (CHD), and systolic blood pressure (SBP) were controlled for mediating effects in multivariable MR.

Results

Genetically predicted HF was significantly associated with any IS [odds ratio (OR), 1.39; 95% CI, 1.12–1.74; p = 0.03], large artery stroke (LAS; OR, 1.84; 95% CI, 1.27–2.65; p = 0.001), and cardioembolic stroke (CES; OR, 1.73; 95% CI, 1.21–2.47; p = 0.003), but without small vessel stroke (SVS; OR, 1.1; 95% CI, 0.80–1.52; p = 0.56) and intracerebral hemorrhage (ICH; OR, 0.86; 95% CI, 0.41–1.83; p = 0.699) in univariable MR. However, these significant associations were attenuated to the null after adjusting for confounding factor in multivariable MR.

Conclusion

There was no direct causal association between HF and stroke in our study. The association between HF and IS can be driven by AF, CHD, and SBP.