Genetically predicted on-statin LDL response is associated with higher intracerebral haemorrhage risk

Drug classes affecting intracranial aneurysm risk: Genetic correlation and Mendelian randomization

INTRODUCTION

There is no non-invasive treatment to prevent aneurysmal subarachnoid hemorrhage (ASAH) caused by intracranial aneurysm (IA) rupture. We aimed to identify drug classes that may affect liability to IA using a genetic approach.

PATIENTS AND METHODS

Using genome-wide association summary statistics we calculated genetic correlation between unruptured IA (N = 2140 cases), ASAH (N = 5140) or the combined group, and liability to drug usage from 23 drug classes (N up to 320,000) independent of the risk factor high blood pressure. Next, we evaluated the causality and therapeutic potential of correlated drug classes using three different Mendelian randomization frameworks.

RESULTS

Correlations with IA were found for antidepressants, paracetamol, acetylsalicylic acid, opioids, beta-blockers, and peptic ulcer and gastro-esophageal reflux disease drugs. MR showed no evidence that genetically predicted usage of these drug classes caused IA. Genetically predicted high responders to antidepressant drugs were at higher risk of IA (odds ratio [OR] = 1.61, 95% confidence interval (CI) = 1.09-2.39, p = 0.018) and ASAH (OR = 1.68, 95% CI = 1.07-2.65, p = 0.024) if they used antidepressant drugs. This effect was absent in non-users. For beta-blockers, additional analyses showed that this effect was not independent of blood pressure after all. A complex and likely pleiotropic relationship was found between genetic liability to chronic multisite pain, pain medication usage (paracetamol, acetylsalicylic acid, and opioids), and IA.

CONCLUSIONS

We did not find drugs decreasing liability to IA and ASAH but found that antidepressant drugs may increase liability. We observed pleiotropic relationships between IA and other drug classes and indications. Our results improve understanding of pathogenic mechanisms underlying IA.

Genetic Determinants of Vascular Dementia

Vascular dementia (VaD) is a prevalent form of cognitive impairment with underlying vascular etiology. In this review, we examine recent genetic advancements in our understanding of VaD, encompassing a range of methodologies including genome-wide association studies, polygenic risk scores, heritability estimates, and family studies for monogenic disorders revealing the complex and heterogeneous nature of the disease. We report well known genetic associations and highlight potential pathways and mechanisms implicated in VaD and its pathological risk factors, including stroke, cerebral small vessel disease, and cerebral amyloid angiopathy. Moreover, we discuss important modifiable risk factors such as hypertension, diabetes, and dyslipidemia, emphasizing the importance of a multifactorial approach in prevention, treatment, and understanding the genetic basis of VaD. Last, we outline several areas of scientific advancements to improve clinical care, highlighting that large-scale collaborative efforts, together with an integromics approach can enhance the robustness of genetic discoveries. Indeed, understanding the genetics of VaD and its pathophysiological risk factors hold the potential to redefine VaD on the basis of molecular mechanisms and to generate novel diagnostic, prognostic, and therapeutic tools.

Genetic variation supports a causal role for valproate in prevention of ischemic stroke

BACKGROUND

Valproate is a candidate for ischemic stroke prevention due to its anti-atherosclerotic effects in vivo. Although valproate use is associated with decreased ischemic stroke risk in observational studies, confounding by indication precludes causal conclusions.

AIMS

We applied Mendelian randomization to determine whether genetic variants that influence seizure response among valproate users associate with ischemic stroke.

METHODS

We derived a genetic score for valproate response using genome-wide association data of seizure response after valproate intake from the Epilepsy Pharmacogenomics Consortium. We then tested this score among valproate users of the UK Biobank for association with incident and recurrent ischemic stroke using Cox proportional hazard models. As replication, we tested found associations in an independent cohort of valproate users of the Mass General Brigham Biobank.

RESULTS

Among 2150 valproate users (mean 56 years, 54% females), 82 ischemic strokes occurred over a mean 12 year follow-up. Higher valproate response genetic score was associated with higher serum valproate levels (+5.78 µg/ml per 1 standard deviation (SD), 95% confidence interval (CI) (3.45, 8.11)). After adjusting for age and sex, higher valproate response genetic score was associated with lower ischemic stroke risk (hazard ratio (HR) per 1 SD 0.73, 95% CI (0.58, 0.91)) with a halving of absolute risk in the highest compared to the lowest score tertile (4.8% vs 2.5%, p trend = 0.027). Among 194 valproate users with prevalent stroke at baseline, a higher valproate response genetic score was associated with lower recurrent ischemic stroke risk (HR per 1 SD 0.53, 95% CI (0.32, 0.86)) with reduced absolute risk in the highest compared to the lowest score tertile (3/51, 5.9% vs 13/71, 18.3%, p trend = 0.026). The valproate response genetic score was not associated with ischemic stroke among the 427,997 valproate non-users (p = 0.61), suggesting minimal pleiotropy. In 1241 valproate users of the Mass General Brigham Biobank with 99 ischemic stroke events over 6.5 years follow-up, we replicated our observed associations between the valproate response genetic score and ischemic stroke (HR per 1 SD 0.77, 95% CI (0.61, 0.97)).

CONCLUSION

These results demonstrate that a genetically predicted favorable seizure response to valproate is associated with higher serum valproate levels and reduced ischemic stroke risk among valproate users, providing causal support for valproate effectiveness in ischemic stroke prevention. The strongest effect was found for recurrent ischemic stroke, suggesting potential dual-use benefits of valproate for post-stroke epilepsy. Clinical trials will be required in order to identify populations that may benefit most from valproate for stroke prevention.

DATA ACCESS STATEMENT

UK Biobank participant data are available after approval of a research proposal. The weights of the used genetic scores are available in the Supplemental Tables.

Causal effects of circulating lipids and lipid-lowering drugs on the risk of urinary stones: a Mendelian randomization study

Background

Previous studies have yielded conflicting findings regarding the association between circulating lipids and lipid-lowering drugs with urinary stones, and the causal relationship between the two remains inconclusive.

Objective

This study aimed to assess the causal relationship between circulating lipids (Triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], apolipoprotein A [APOA], apolipoprotein B [APOB] and Pure hypercholesterolaemia), lipid-lowering drugs (HMGCR [HMG-CoA reductase] inhibitors and PCSK9[Proprotein Convertase Subtilisin/Kexin Type 9] inhibitors) and the risk of urinary stones, using genetic data.

Methods

Genetic instrumental variables (GIVs) for circulating lipids and lipid-lowering drugs were obtained from the UK Biobank and existing literature. Outcome data were extracted from a genetic association database with 3,625 urinary stone cases and 459,308 controls. Two-sample MR analysis, employing the TwoSampleMR software package in R 4.2.3, was conducted to assess the associations between multiple exposures. The primary outcome was determined using the inverse variance weighted (IVW) method for the causal relationship between exposure and outcome, while additional methods such as MR-Egger, weighted median, simple mode, and weighted mode were utilized as supplementary analyses. Robustness of the Mendelian Randomization (MR) analysis results was assessed through leave-one-out analysis and funnel plots.

Results

The MR analysis revealed a significant association between elevated TG levels per 1 standard deviation and the occurrence of urinary stones (odds ratio [OR]: 1.002, 95% confidence interval [CI]: 1.000-1.003, P = 0.010). However, no significant association was observed between factors other than TG exposure and the risk of urinary stone occurrence across all methods(LDL-C: [OR], 1.001; 95% [CI], 1.000-1.003, P=0.132;HDL-C: [OR], 0.999; 95% [CI], 0.998-1.000, P=0.151;APOA:[OR] being 1.000 (95% [CI], 0.999-1.001, P=0.721;APOB: [OR] of 1.001 (95% [CI], 1.000-1.002, P=0.058;Pure hypercholesterolaemia: [OR] of 1.015 (95% [CI], 0.976-1.055, P=0.455) and lipid-lowering drugs (HMGCR inhibitors: [OR], 0.997; 95% [CI], 0.990-1.003, P=0.301 and PCSK9 inhibitors:[OR], 1.002; 95% [CI], 1.000-1.005, P=0.099).

Conclusion

Our findings provide conclusive evidence supporting a causal relationship between an increased risk of urinary stones and elevated serum TG levels. However, we did not find a significant association between urinary stone occurrence and the levels of LDL-C, HDL-C, APOA, APOB, Pure hypercholesterolaemia and lipid-lowering drugs.

Mood swings are causally associated with intracranial aneurysm subarachnoid hemorrhage: A Mendelian randomization study

BACKGROUND

Mood swings have been observed in patients with intracranial aneurysm (IA), but it is still unknown whether mood swings can affect IA.

AIM

To explore the causal association between mood swings or experiencing mood swings and IA through a two-sample Mendelian randomization (MR) study.

METHODS

Summary-level statistics of mood swings, experiencing mood swings, IA, aneurysm-associated subarachnoid hemorrhage (aSAH), and non-ruptured IA (uIA) were collected from the genome-wide association study. Two-sample MR and various sensitivity analyses were employed to explore the causal association between mood swings or experiencing mood swings and IA, or aSAH, or uIA. The inverse-variance weighted method was used as the primary method.

RESULTS

Genetically determined mood swings (odds ratio [OR] = 5.23, 95% confidence interval (95%CI): 1.65-16.64, p = .005) and experiencing mood swings (OR = 2.50, 95%CI: 1.37-4.57, p = .003) were causally associated with an increased risk of IA. Mood swings (OR = 5.67, 95%CI: 1.40-23.04, p = .015) and experiencing mood swings were causally associated with the risk of aSAH (OR = 2.91, 95%CI: 1.47-5.75, p = .002). Neither mood swings (OR = 1.95, 95%CI: .31-12.29, p = .478) nor experiencing mood swings (OR = 1.20, 95%CI: .48-3.03, p = .693) were associated with uIA.

CONCLUSIONS

Mood swings and experiencing mood swings increased the risk of IA and aSAH incidence. These results suggest that alleviating mood swings may reduce IA rupture incidence and aSAH incidence.

The ABCD-GENE score influences vascular event rates in both users of clopidogrel and aspirin, as well as non-users of either drug in a population-based cohort study

Background and Objectives

Clopidogrel is an antiplatelet used in both primary and secondary prevention of cardiovascular diseases. It is a prodrug, requiring CYP2C19 for its metabolism to the active metabolite. The ABCD-GENE score, combining clinical attributes (age, body mass index, chronic kidney disease, diabetes mellitus), with genetic information (presence of 1 or 2 loss of function (LOF) alleles in the CYP2C19 gene) has been shown to identify patients with higher risk of recurrent cardiovascular events in high-risk populations undergoing percutaneous coronary intervention. We aimed to determine if the ABCD-GENE score or LOF alleles were associated with an increased risk of vascular events among clopidogrel users in a general population.

Methods

We conducted a population based cohort study with UK Biobank's primary care prescription records to identify clopidogrel users. ABCD-GENE scores were calculated with closest available data from the first date of clopidogrel prescription. The number of LOF alleles present, and the clinical component ABCD, were separate exposures. The outcome of interest was a composite endpoint of vascular events comprised of myocardial infarction, ischemic stroke, and death due to either of these. We performed Cox proportional hazards models with clopidogrel as a time varying exposure to predict hazards of these outcomes. In order to determine the drug specificity of these exposures, the analyses were repeated in aspirin users, and in non-users of either aspirin or clopidogrel.

Results

Among 11,248 clopidogrel users, 3,365 (30%) developed a vascular event over a mean follow-up of 5.95±3.94 years. ABCD-GENE score ≥10 was associated with an increased risk of vascular events (HR 1.13, 95% CI 1.03-1.23). In aspirin users, and in non-users of either aspirin or clopidogrel, the ABCD-GENE score was also associated with increased risk of vascular events. In clopidogrel users, aspirin users, and non-users of either drug, the ABCD score was associated with increased risk of vascular events. The presence of two CYP2C19 LOF alleles was associated with an increased risk of vascular events in aspirin and non-users but not in clopidogrel users.

Discussion

In this population-based cohort study, the ABCD-GENE score was associated with an increased risk of vascular events in clopidogrel users, aspirin users, and in non-users of either drug. The clinical component, ABCD was also associated with an increased risk of vascular events in all three groups. This suggests that the ABCD-GENE score is not specific to clopidogrel users in identifying persons at high risk of vascular events in a general sample with low baseline CYP2C19 LOF allele frequency.

Genetic variation supports a causal role for valproate in prevention of ischemic stroke

Valproate is a candidate for ischemic stroke prevention due to its anti-atherosclerotic effects in vivo. Although valproate use is associated with decreased ischemic stroke risk in observational studies, confounding by indication precludes causal conclusions. To overcome this limitation, we applied Mendelian randomization to determine whether genetic variants that influence seizure response among valproate users associate with ischemic stroke. We derived a genetic score for valproate response using genome-wide association data of seizure response after valproate intake from the Epilepsy Pharmacogenomics Consortium. We then tested this score among valproate users of the UK Biobank for association with incident and recurrent ischemic stroke using Cox proportional hazard models. Among 2,150 valproate users (mean 56 years, 54% females), 82 ischemic strokes occurred over a mean 12-year follow-up. Higher valproate response genetic score was associated with higher serum valproate levels (+5.78 μg/ml per one SD, 95% CI [3.45, 8.11]). After adjusting for age and sex, higher valproate response genetic score was associated with lower ischemic stroke risk (HR per one SD 0.73, [0.58, 0.91]) with a halving of absolute risk in the highest compared to the lowest score tertile (4.8% vs 2.5%, p-trend=0.027). Among 194 valproate users with prevalent stroke at baseline, a higher valproate response genetic score was associated with lower recurrent ischemic stroke risk (HR per one SD 0.53, [0.32, 0.86]) with reduced absolute risk in the highest compared to the lowest score tertile (3/51, 5.9% vs. 13/71, 18.3%, p-trend=0.026). The valproate response genetic score was not associated with ischemic stroke among the 427,997 valproate non-users (p=0.61), suggesting minimal pleiotropy. In an independent cohort of 1,241 valproate users of the Mass General Brigham Biobank with 99 ischemic stroke events over 6.5 years follow-up, we replicated our observed associations between the valproate response genetic score and ischemic stroke (HR per one SD 0.77, 95% CI: [0.61, 0.97]). These results demonstrate that a genetically predicted favorable seizure response to valproate is associated with higher serum valproate levels and reduced ischemic stroke risk among valproate users, providing causal support for valproate effectiveness in ischemic stroke prevention. The strongest effect was found for recurrent ischemic stroke, suggesting potential dual-use benefits of valproate for post-stroke epilepsy. Clinical trials will be required in order to identify populations that may benefit most from valproate for stroke prevention.

Mendelian Randomization Analysis Reveals Statins Potentially Increase Amyotrophic Lateral Sclerosis Risk Independent of Peripheral Cholesterol-Lowering Effects

BACKGROUND

Observational studies suggest that statins may affect amyotrophic lateral sclerosis (ALS). However, they are limited by confounding and reverse causality biases. Therefore, we aimed to investigate the potential causal associations between statins and ALS using a mendelian randomization (MR) approach.

METHODS

Two-sample MR and drug-target MR were performed. Exposure sources included GWAS summary statistics of statin use, low-density-lipoprotein cholesterol (LDL-C), HMGCR-mediated LDL-C and LDL-C response to statins.

RESULTS

Genetic predisposition to statin medication was associated with increased ALS risk (OR = 1.085, 95% CI = 1.025-1.148, p = 0.005). After removing SNPs significantly associated with statin use from the instrumental variables (IVs), LDL-C-related higher ALS risk was absent (before removing: OR = 1.075, 95% CI = 1.013-1.141, p = 0.017; after removing: OR = 1.036, 95% CI = 0.949-1.131, p = 0.432). HMGCR-mediated LDL-C (OR = 1.033, 95% CI = 0.823-1.296, p = 0.779) and blood LDL-C response to statins (OR = 0.998, 95% CI = 0.991-1.005, p = 0.538) had no association with ALS.

CONCLUSIONS

Here, we show that statins may be a risky exposure that increases ALS risk independent of the lowering effect of LDL-C in peripheral circulation. This provides insights into ALS development and prevention.

Genetically Proxied CRP (C-Reactive Protein) Levels and Lobar Intracerebral Hemorrhage Risk

BACKGROUND

Recent evidence suggests that higher CRP (C-reactive protein) levels are associated with lower risk of Alzheimer disease, speculating that CRP might be involved in Aβ clearance mechanisms. Testing this hypothesis, we explored whether genetically proxied CRP levels are also associated with lobar intracerebral hemorrhage (ICH), commonly caused by cerebral amyloid angiopathy.

METHODS

We used 4 genetic variants within the CRP gene that explain up to 64% of the variance of circulating CRP levels and explored in 2-sample Mendelian randomization analyses associations with risk of any, lobar, and deep ICH (1545 cases and 1481 controls).

RESULTS

Higher genetically proxied CRP levels were associated with lower odds of lobar ICH (odds ratio per SD increment in CRP, 0.45 [95% CI, 0.25-0.73]) but not deep ICH (odds ratio, 0.72 [95% CI, 0.45-1.14]). There was evidence of colocalization (posterior probability of association, 72.4%) in the signals for CRP and lobar ICH.

CONCLUSIONS

Our results provide supportive evidence that high CRP levels may have a protective role in amyloid-related pathology.

Multi-Omics Studies in Historically Excluded Populations: The Road to Equity

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Using genetic association data to guide drug discovery and development: Review of methods and applications

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.