Mendelian randomization for studying the effects of perturbing drug targets

Evaluating lipid-lowering drug targets for full-course diabetic retinopathy

BACKGROUND

Implementing lipid control in patients with diabetes is regarded as a potential strategy for halting the advancement of diabetic retinopathy (DR). This study seeks to use Mendelian randomisation (MR) to assess the causal relationship between lipid traits and lipid-lowering drug targets and full-course DR (background DR, severe non-proliferative DR (NPDR) and proliferative DR (PDR)).

METHODS

A two-sample MR and drug target MR to decipher the causal effects of lipid traits and lipid-lowering drug targets on full-course DR, including background DR, severe NPDR and PDR, was conducted in the study. Genetic variants associated with lipid traits and genes encoding the protein targets of lipid-lowering drugs were extracted from the Global Lipids Genetics Consortium and UK Biobank. Summary-level data of full-course DR are obtained from FinnGen.

RESULTS

No significant causal relationship was found between lipid traits and full-course DR. However, in drug target MR analysis, peroxisome proliferator-activated receptor gamma (PPARG) enhancement was associated with lower risks of background DR (OR=0.12, p=0.005) and PDR (OR=0.25, p=0.006). Additionally, mediation MR analysis showed that lowering fasting insulin (p=0.015) and HbA1c (p=0.005) levels mediated most of the association between PPARG and full-course DR.

CONCLUSIONS

This study reveals PPARG may be a promising drug target for full-course DR. The activation of PPARG could reduce the risk of full-course DR, especially background DR and PDR. The mechanism of the PPARG agonists' protection of full-course DR may be dependent on the glucose-lowering effect.

HORNET: tools to find genes with causal evidence and their regulatory networks using eQTLs

Motivation

Nearly two decades of genome-wide association studies (GWAS) have identify thousands of disease-associated genetic variants, but very few genes with evidence of causality. Recent methodological advances demonstrate that Mendelian randomization (MR) using expression quantitative loci (eQTLs) as instrumental variables can detect potential causal genes. However, existing MR approaches are not well suited to handle the complexity of eQTL GWAS data structure and so they are subject to bias, inflation, and incorrect inference.

Results

We present a whole-genome regulatory network analysis tool (HORNET), which is a comprehensive set of statistical and computational tools to perform genome-wide searches for causal genes using summary level GWAS data, i.e. robust to biases from multiple sources. Applying HORNET to schizophrenia, eQTL effects in the cerebellum were spread throughout the genome, and in the cortex were more localized to select loci.

Availability and implementation

Freely available at https://github.com/noahlorinczcomi/HORNET or Mac, Windows, and Linux users.

Genetic association of circulating lipids and lipid-lowering drug targets with vascular calcification

BACKGROUND AND AIMS

Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. This study explores the genetic causal associations of different circulating lipids and lipid-lowering drug targets with coronary artery calcification (CAC) and abdominal aortic artery calcification (AAC).

METHODS

We obtained single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTLs) associated with seven circulating lipids and 13 lipid-lowering drug targets from publicly available genome-wide association studies and eQTL databases. Causal associations were investigated by univariable, multivariable, drug-target, and summary data-based Mendelian randomization (MR) analyses. Potential mediation effects of metabolic risk factors were evaluated.

RESULTS

MR analysis revealed that genetic proxies for low-density lipoprotein cholesterol (LDL-C), triglycerides (TC) and Lipoprotein (a) (Lp(a)) were causally associated with CAC severity, and apolipoprotein B (apoB) level was causally associated with AAC severity. A significant association was detected between hepatic Lipoprotein(A) (LPA) gene expression and CAC severity. Colocalisation analysis supported the hypothesis that the association between LPA expression and CAC quantity is driven by different causal variant sites within the ±1 Mb flanking region of LPA. Serum calcium and phosphorus had causal associations with CAC severity.

CONCLUSIONS

Inhibitors targeting LPA might represent CAC drug candidates. Moreover, T2DM, hypercalcemia, and hyperphosphatemia are positively causally associated with CAC severity, while chronic kidney disease and estimated glomerular filtration rate are not.

Leveraging drug-target Mendelian randomization for tailored lipoprotein-lipid lowering

PURPOSE OF REVIEW

The study of naturally occurring genetic variation in human populations has laid the foundation for proprotein converts subtilisin/kexin type 9 inhibitors, and more recently new classes of lipid-lowering drugs such as lipoprotein(a) inhibitors and lipoprotein lipase pathway activators. These emerging therapies lower plasma lipoprotein-lipid levels that are not adequately managed by traditional low-density lipoprotein (LDL) cholesterol-lowering medications. By targeting different risk factors, these therapies could help manage the important residual cardiovascular risk of LDL cholesterol medications.

RECENT FINDINGS

We review the latest insights into the pharmacological and genetic modulation of these new therapeutic targets. We highlight that the drugs remarkably recapitulate the lipid effects observed in genetic studies. In addition to lowering lipoprotein-lipid levels, robust genetic evidence support that these drugs may prevent cardiometabolic outcomes.

SUMMARY

Emerging lipid-lowering therapies could launch a new era for preventive medicine in which treatments are optimally tailored to patient's lipoprotein-lipid profiles.

PCSK9 Inhibitors and the Risk of Vitiligo: A Mendelian Randomization Study

Lipid-lowering agents have been suggested as a therapeutic option for vitiligo on the basis of the potential pathogenic role of lipid metabolism abnormalities. We aimed to explore the impact of genetically proxied lipid-lowering agents on the risk of vitiligo and potentially associated mediators. GWAS summary statistics for European ancestry were extracted from the largest available meta-analysis for vitiligo: the Global Lipids Genetics Consortium for 7 lipid profiles and 2 large biobanks, UK Biobank and deCODE, for 4719 proteins. After identifying lipid-lowering agents with genetically proxied protective effects against vitiligo using lipid-lowering and protein-inhibition Mendelian randomization (MR) analyses, multivariable and 2-step MR analyses were conducted to identify potential mediators between lipid-lowering agents and vitiligo. Lipid-lowering MR indicated a potential role of PCSK9 in reducing the vitiligo risk (OR [95% confidence interval] = 0.71 [0.52-0.95]), which was replicated in PCSK9-inhibition MR analyses across 2 separate biobanks (UK Biobank: OR [95% confidence interval] = 0.82 [0.71-0.96]; deCODE: OR [95% confidence interval] = 0.78 [0.67-0.91]). Multivariable MR suggested that well-known lipid profiles do not mediate the relationship between PCSK9 and vitiligo, whereas 2-step MR analyses identified 5 potential protein mediators (CCN5, CXCL12, FCRL1, legumain, and FGF2). Hence, PCSK9 inhibitor may attenuate the vitiligo risk; PCSK9 and the potential protein mediators can serve as promising novel therapeutic targets for its effective treatment.

Association of GLP1R locus with mental ill-health endophenotypes and cardiometabolic traits: A trans-ancestry study in UK Biobank

AIMS

Glucagon-like peptide 1 receptor agonists (GLP1RA), used to treat type 2 diabetes and obesity, have been associated with off-target behavioural effects. We systematically assessed genetic variation in the GLP1R locus for impact on mental ill-health (MIH) and cardiometabolic phenotypes across diverse populations within UK Biobank.

MATERIALS AND METHODS

All genetic variants with minor allele frequency >1% in the GLP1R locus were investigated for associations with MIH phenotypes and cardiometabolic phenotypes. Linear or Logistic regression analyses (adjusted for age, sex, population structure and genotyping chip) were conducted separately in unrelated individuals of self-reported white British (N = 408 774), white European (N = 50 314), South Asian (N = 7667), multiple-ancestry groups (N = 10 437) or African-Caribbean (N = 7641) subsets. All ancestries were subsequently combined in an inverse variance-weighted fixed effects meta-analysis. Bonferroni correction for multiple testing was applied (for number of independent genetic variants).

RESULTS

Associations were identified between GLP1R variants and body mass index (BMI), blood pressure and type 2 diabetes in all ancestries. All ancestries except South Asian had significant MIH associations (mood instability: rs111265626-G, odds ratio [OR] 0.851 [confidence interval, CI 0.79-0.92], risk-taking behaviour: rs75408972-T, OR 1.05 [CI 1.03-1.08] or chronic pain: rs9296280-C, OR 0.645 [CI 0.54-0.78]). The trans-ancestry meta-analysis showed mainly consistent effect sizes and directions for metabolic traits, but discordant directions MIH associations. Only signals for chronic pain, stroke and BMI influenced expression of GLP1R.

CONCLUSIONS

GLP1R variants have consistent cardiometabolic effects across ancestries, but effects on MIH phenotypes are more varied. Any observed behavioural changes with GLP1RA are likely not acting directly through GLP1R.

Association between beta-1-adrenoceptor blockade and risk of Raynaud's phenomenon: Mendelian randomisation study

Introduction/Objectives

Raynaud's phenomenon is a common vasospastic disorder associated with reduced health-related quality of life and, occasionally, ischaemic tissue damage depending on aetiology. The effect of beta-1-adrenoceptor blockers (e.g. bisoprolol, atenolol) on Raynaud's phenomenon remains unclear. We aimed to assess the association between genetically mimicked beta-1-adrenoceptor blockade and the risk of Raynaud's phenomenon.

Methods

We used two protein-coding single nucleotide polymorphisms in the ADRB1 gene, rs1801252 (A > G; Ser49Gly) and rs1801253 (G > C; Arg389Gly), to derive an unweighted allele count as the instrumental variable, using individual-level UK Biobank data. Raynaud's phenomenon was defined using International Classification of Diseases or Read codes. We used the ratio method and analysis was performed separately using systolic and diastolic blood pressure as the biomarker. To examine the validity of this approach and the Raynaud's phenomenon case definition, we also tested the known association between phosphodiesterase-5 inhibition and Raynaud's phenomenon risk.

Results

Analysis included 4743 individuals with Raynaud's phenomenon (mean age 58 years, 68% female) and 403,762 controls. There was no evidence of an effect of genetically mimicked beta-1-adrenoreceptor blockade on the risk of Raynaud's phenomenon, using systolic blood pressure (odds ratio = 0.93 per mmHg reduction; 95% confidence interval = [0.83, 1.04]; p = 0.19) or diastolic blood pressure (odds ratio = 0.91 per mmHg reduction; 95% confidence interval = [0.78, 1.05]; p = 0.19). The positive control exposure phosphodiesterase-5 inhibition was associated with reduced Raynaud's phenomenon risk.

Conclusions

We found no genetic evidence to support a causal association between beta-1-adrenoceptor blockade and Raynaud's phenomenon risk in either direction. Randomised controlled trials are required to confirm the safety of beta-1-adrenoceptor blockers in people with Raynaud's phenomenon.

Impact of Glucagon-like Peptide-1 Receptor Agonists on Mental Illness: Evidence from a Mendelian Randomization Study

Emerging evidence suggests that glucagon-like peptide-1 receptor (GLP1R) agonists may have potential benefits for mental illnesses. However, their exact effects remain unclear. This study investigated the causal relationship between glucagon-like peptide-1 receptor agonist (GLP1RA) and the risk of 10 common mental illnesses, including attention deficit and hyperactivity disorder, anorexia nervosa, anxiety disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, post-traumatic stress disorder, schizophrenia, cannabis use disorder, and alcohol use disorder. We selected GLP1RA as the exposure and conducted a Mendelian randomization (MR) analysis. The cis-eQTLs of the drug target gene GLP1R, provided by eQTLGen, were used to simulate the pharmacological effects of GLP1RA. Type 2 diabetes and BMI were included as positive controls. Using data from both the Psychiatric Genomic Consortium and FinnGen, we conducted separate MR analyses for the same disease across these two independent databases. Meta-analysis was used to pool the results. We found genetic evidence suggesting a causal relationship between GLP1RA and a reduced risk of schizophrenia [OR (95% CI) = 0.84 (0.71-0.98), I2 = 0.0%, common effects model]. Further mediation analysis indicated that this effect might be unrelated to improvements in glycemic control but rather mediated by BMI. However, the findings of this study provide insufficient evidence to support a causal relationship between GLP1RA and other mental illnesses. Sensitivity analyses did not reveal any potential bias due to horizontal pleiotropy or heterogeneity in the above results (p > 0.05). This study suggests that genetically proxied activation of glucagon-like peptide-1 receptor is associated with a lower risk of schizophrenia. GLP1R is implicated in schizophrenia pathogenesis, and its agonists may exert potential benefits through weight management. Our study provides useful information for understanding the neuropsychiatric effects of GLP1RA, which may contribute to refining future research designs and guiding clinical management. Moreover, our findings could have significant implications for overweight individuals at high risk of schizophrenia when selecting weight-loss medications. Future research should further investigate the potential mechanisms underlying the relationship between GLP1RA and schizophrenia.

Lipid-lowering therapies for aortic stenosis: a drug-target Mendelian randomization study

INTRODUCTION

Large observational and Mendelian randomization (MR) studies have demonstrated a strong association between both elevated LDL cholesterol (LDL-c) and triglycerides (TG) with risk of aortic stenosis (AS), although randomized trials showed no benefit of statins for AS. It consequently remains uncertain whether lipid-lowering therapies have a role to prevent or treat AS. We used a drug-target MR approach to investigate the genetically predicted effect of lipid-lowering therapies on risk of AS.

METHODS AND RESULTS

We collected summary statistics for LDL-c, TG, and AS from genome-wide association studies (GWAS) including 1 320 016, 1 253 277, and 412 181 European participants from the Global Lipids Genetics Consortium and FinnGen study, respectively. We identified genetic proxies for PCSK9 inhibitors, statins, bempedoic acid, and ezetimibe as single nucleotide polymorphisms in or within 200 kb of the target genes (PCSK9, HMGCR, ACLY, and NPC1L1, respectively), which were also significantly associated with LDL-c at P < 5 × 10-8. We used a similar approach to identify genetic proxies for the TG-lowering agents fenofibrates, APOC3 inhibitors, and ANGPTL3 inhibitors using the target genes PPARA, APOC3, and ANGPTL3, respectively. Inverse variance-weighted was the primary analysis method. Sensitivity analyses included weighted median, weighted mode, and MR-Egger, followed by the outlier-exclusion approaches MR-PRESSO and Cook's distance. We also performed multivariable analyses to evaluate whether the predicted effect of PCSK9 inhibition may be mediated by lipoprotein(a). We performed replication and negative control analyses using GWAS of AS and height including 653 867 and 408 112 participants, respectively. Genetically proxied PCSK9 inhibition was significantly associated with reduced AS risk (odds ratio [OR] 0.61, 95% confidence interval [CI] 0.52-0.72, P < 0.0001) on main, replication, and all sensitivity analyses. Genetically proxied ezetimibe (OR 0.49, 95% CI 0.31-0.78, P = 0.003), bempedoic acid (OR 0.0054, 95% CI 0.0002-0.12, P = 0.0009), and statins (OR 0.61, 95% CI 0.46-0.81, P = 0.0006) were similarly associated with reduced AS risk, although the latter were not significant on replication analyses. Amongst the TG-lowering agents, genetically proxied APOC3 inhibition was associated with reduced AS risk (OR 0.78, 95% CI 0.70-0.88, P < 0.0001), but fenofibrate (OR 0.64, 95% CI 0.09-4.53, P = 0.65) and ANGPTL3 inhibitors (OR 1.05, 95% CI 0.77-1.43, P = 0.74) were not.

CONCLUSIONS

Genetically proxied lipid-lowering therapies are significantly associated with reduced risk of AS. Early initiation and sustained administration of lipid-lowering therapies may prevent AS progression and warrants further research in the clinical trial setting.

Sirolimus as a repurposed drug for tendinopathy: A systems biology approach combining computational and experimental methods

BACKGROUND

Effective drugs for tendinopathy are lacking, resulting in significant morbidity and re-tearing rate after operation. Applying systems biology to identify new applications for current pharmaceuticals can decrease the duration, expenses, and likelihood of failure associated with the development of new drugs.

METHODS

We identify tendinopathy signature genes employing a transcriptomics database encompassing 154 clinical tendon samples. We then proposed a systems biology based drug prediction strategy that encompassed multiplex transcriptional drug prediction, systematic review assessment, deep learning based efficacy prediction and Mendelian randomization (MR). Finally, we evaluated the effects of drug target using gene knockout mice.

RESULTS

We demonstrate that sirolimus is a repurposable drug for tendinopathy, supported by: 1) Sirolimus achieves top ranking in drug-gene signature-based multiplex transcriptional drug efficacy prediction, 2) Consistent evidence from systematic review substantiates the efficacy of sirolimus in the management of tendinopathy, 3) Genetic prediction indicates that plasma proteins inhibited by mTOR (the target of sirolimus) are associated with increased tendinopathy risk. The effectiveness of sirolimus is further corroborated through in vivo testing utilizing tendon tissue-specific mTOR gene knockout mice. Integrative pathway enrichment analysis suggests that mTOR inhibition can regulate heterotopic ossification-related pathways to ameliorate clinical tendinopathy.

CONCLUSIONS

Our study assimilates knowledge of system-level responses to identify potential drugs for tendinopathy, and suggests sirolimus as a viable candidate. A systems biology approach could expedite the repurposing of drugs for human diseases that do not have well-defined targets.

Validation of L-type calcium channel blocker amlodipine as a novel ADHD treatment through cross-species analysis, drug-target Mendelian randomization, and clinical evidence from medical records

ADHD is a chronic neurodevelopmental disorder that significantly affects life outcomes, and current treatments often have adverse side effects, high abuse potential, and a 25% non-response rate, highlighting the need for new therapeutics. This study investigates amlodipine, an L-type calcium channel blocker, as a potential foundation for developing a novel ADHD treatment by integrating findings from animal models and human genetic data. Amlodipine reduced hyperactivity in SHR rats and decreased both hyperactivity and impulsivity in adgrl3.1-/- zebrafish. It also crosses the blood-brain barrier, reducing telencephalic activation. Crucially, Mendelian Randomization analysis linked ADHD to genetic variations in L-type calcium channel subunits (α1-C; CACNA1C, β1; CACNB1, α2δ3; CACNA2D3) targeted by amlodipine, while polygenic risk score analysis showed symptom mitigation in individuals with high ADHD genetic liability. With its well-tolerated profile and efficacy across species, supported by genetic evidence, amlodipine shows potential to be refined and developed into a novel treatment for ADHD.

Insights into drug adverse reactions prediction through Mendelian randomization: a review

Adverse drug reactions pose a significant threat to patient safety and public health and often become apparent only after widespread clinical use. Mendelian randomization (MR) analysis is a valuable tool that can be used to infer causality by using genetic variants as instrumental variables, which can predict the occurrence of adverse drug reactions before they occur. Compared with traditional observational studies, MR Analysis can reduce the potential bias of confounding factors. This article reviews the principles of MR Analysis and its application in the prediction of adverse drug reactions, the challenges and future directions, and summarizes how to harness the power of this innovative epidemiological method to put us at the forefront of improving drug safety assessment and personalized medicine.

Identification of Potential Therapeutic Targets for Sensorineural Hearing Loss and Evaluation of Drug Development Potential Using Mendelian Randomization Analysis

Background: Sensorineural hearing loss (SNHL) is a major contributor to hearing impairment, yet effective therapeutic options remain elusive. Mendelian randomization (MR) has proven valuable for drug repurposing and identifying new therapeutic targets. This study aims to pinpoint novel treatment targets for SNHL, exploring their pathophysiological roles and potential adverse effects. Methods: This research utilized the UKB-PPP database to access cis-protein quantitative trait locus (cis-pQTL) data, with SNHL data sourced from the FinnGen database as the endpoint for the MR causal analysis of drug targets. Colocalization analysis was employed to determine whether SNHL risk and protein expression share common SNPs. A phenotype-wide association analysis was conducted to assess the potential side effects of these targets. Drug prediction and molecular docking were subsequently used to evaluate the therapeutic potential of the identified targets. Results: Four drug target proteins significantly associated with sensorineural hearing loss (SNHL) were determined by Mendelian randomization (MR) analysis and co-localization analysis. These drug targets include LATS1, TEF, LMNB2, and OGFR and were shown to have fewer potential side effects when acting on these target proteins by phenotype-wide association analysis. Genes associated with sensorineural hearing loss are primarily implicated in the Hippo signaling pathway, cell-cell adhesion, and various binding regulatory activities and are involved in the regulation of cell proliferation and apoptosis. Next, drugs for the treatment of SNHL were screened by the DsigDB database and molecular docking, and the top 10 drugs were selected based on p-value. Among them, atrazine CTD 00005450 was identified as the most likely therapeutic target, followed by ampyrone HL60 DOWN and genistein CTD 00007324. In addition, LMNB2, LATS1, and OGFR could be intervened in by multiple drugs; however, fewer drugs intervened in TEF. Conclusion: This study has successfully identified four promising drug targets for SNHL, which are likely to be effective in clinical trials with minimal side effects. These findings could significantly streamline drug development for SNHL, potentially reducing the costs and time associated with pharmaceutical research and development.

The genomic architecture of circulating cytokine levels points to drug targets for immune-related diseases

Circulating cytokines orchestrate immune reactions and are promising drug targets for immune-mediated and inflammatory diseases. Exploring the genetic architecture of circulating cytokine levels could yield key insights into causal mediators of human disease. Here, we performed genome-wide association studies (GWAS) for 40 circulating cytokines in meta-analyses of 74,783 individuals. We detected 359 significant associations between cytokine levels and variants in 169 independent loci, including 150 trans- and 19 cis-acting loci. Integration with transcriptomic data point to key regulatory mechanisms, such as the buffering function of the Atypical Chemokine Receptor 1 (ACKR1) acting as scavenger for multiple chemokines and the role of tumor necrosis factor receptor-associated factor 1 (TRAFD1) in modulating the cytokine storm triggered by TNF signaling. Applying Mendelian randomization (MR), we detected a network of complex cytokine interconnections with TNF-b, VEGF, and IL-1ra exhibiting pleiotropic downstream effects on multiple cytokines. Drug target cis-MR using 2 independent proteomics datasets paired with colocalization revealed G-CSF/CSF-3 and CXCL9/MIG as potential causal mediators of asthma and Crohn's disease, respectively, but also a potentially protective role of TNF-b in multiple sclerosis. Our results provide an overview of the genetic architecture of circulating cytokines and could guide the development of targeted immunotherapies.

Lipid-Lowering Drugs and Pulmonary Vascular Disease: A Mendelian Randomization Study

The therapeutic value of lipid-lowering drugs in pulmonary vascular disease remains uncertain due to insufficient studies and evidence. This study aims to investigate the causal effects of lipid-lowering drugs (specifically, inhibitors of APOB, CETP, HMGCR, NPC1L1, and PCSK9) on pulmonary vascular diseases using a Mendelian randomization (MR) approach. We utilized summary-level statistics from genome-wide association studies (GWAS) to simulate the exposure to low-density lipoprotein cholesterol (LDL-C) and its outcomes on pulmonary arterial hypertension (PAH), pulmonary embolism (PE), and pulmonary heart disease (PHD). Single-nucleotide polymorphisms (SNPs) within or near drug target-associated LDL-C loci were selected as proxies for the lipid-lowering drugs. Data from the FinnGen cohort and UK Biobank (UKB) were incorporated to enhance the robustness and generalizability of the findings. The inverse variance weighted (IVW) and MR-Egger methods were employed to estimate MR effects. Our MR analysis indicated that LDL-C mediated by NPC1L1 (odds ratio [OR] = 104.76, 95% confidence interval [CI] = 2.01-5457.01, p = 0.021) and PCSK9 (OR = 10.20, 95% CI = 3.58-29.10, p < 0.001) was associated with an increased risk of PAH. In contrast, LDL-C mediated by APOB was associated with a decreased risk of PE (FinnGen: OR = 0.74, 95% CI = 0.60-0.91, p = 0.005; UKB: OR = 0.998, 95% CI = 0.996-1.000, p = 0.031) and PHD (FinnGen: OR = 0.73, 95% CI = 0.59-0.91, p = 0.004). However, LDL-C mediated by CETP and HMGCR did not show significant associations with the risks of PAH, PE, or PHD. This MR study revealed the causal effects of NPC1L1 and PCSK9 inhibitors on increased PAH risk, while APOB inhibitors appear to reduce the risks of PE and PHD. These findings enhance our understanding of the potential roles of lipid-lowering drugs in pulmonary vascular disease.

Introduction to Mendelian randomization

Mendelian randomization (MR), i.e. instrumental variable analysis using genetic instruments, is an approach that incorporates population genetics to improve causal inference. Given that genetics are randomly allocated at conception, this resembles the randomization process in randomized controlled trials and hence is more resistant to unobserved confounding compared to conventional observational studies (e.g. cohort studies). The seminar paper briefly described the origin of MR and its underlying assumptions (relevance, independence, and exclusion restriction). This was followed by introducing one sample MR designs (in which instrument-exposure and instrument-outcome associations are derived from the same sample) and one sample MR design (in which instrument-exposure and instrument-outcome associations are derived from different samples). The seminar paper then summarized key aspects of MR studies, such as instrument selection, data sources for conducting MR studies, and statistical analyses. Variations of MR design were also introduced, such as how this design can inform the effect of drug targets (drug target MR). The STROBE-MR checklist and relevant MR guidelines were introduced. The seminar paper concluded by discussing the credibility crisis of MR studies.

Decoding the Therapeutic Target SVEP1: Harnessing Molecular Trait GWASs to Unravel Mechanisms of Human Disease

Although human genetics has substantial potential to illuminate novel disease pathways and facilitate drug development, identifying causal variants and deciphering their mechanisms remain challenging. We believe these challenges can be addressed, in part, by creatively repurposing the results of molecular trait genome-wide association studies (GWASs). In this review, we introduce techniques related to molecular GWASs and unconventionally apply them to understanding SVEP1, a human coronary artery disease risk locus. Our analyses highlight SVEP1's causal link to cardiometabolic disease and glaucoma, as well as the surprising discovery of SVEP1 as the first known physiologic ligand for PEAR1, a critical receptor governing platelet reactivity. We further employ these techniques to dissect the interactions between SVEP1, PEAR1, and the Ang/Tie pathway, with therapeutic implications for a constellation of diseases. This review underscores the potential of molecular GWASs to guide drug discovery and unravel the complexities of human health and disease by demonstrating an integrative approach that grounds mechanistic research in human biology.

Genetic insights into the roles of fatty acids and gut microbiota in osteoarthritis: A Mendelian randomization study

Traditional observational studies have shown that fatty acids and gut microbiota are crucial in osteoarthritis (OA) progression, but their findings are often conflicting due to biases, confounding factors, and measurement errors. We conducted a two-sample Mendelian randomization analysis using genome-wide association study data on fatty acids from 136,016 individuals, the gut microbiota from 7738 individuals, and osteoarthritis from 314,870 individuals. Elevated levels of total (odds ratio [OR]: 0.92; 95% CI 0.84-1.00; P = .039), saturated fatty acids (OR: 0.91; 95% CI 0.84-0.99; P = .034), and linoleic acid (OR: 0.92; 95% CI 0.85-1.00; P = .040) were associated with reduced OA risk. In terms of gut microbiota, Bifidobacterium adolescentis (OR: 0.89; 95% CI 0.80-1.00; P = .048) and Escherichia (OR: 0.90; 95% CI 0.81-1.00; P = .042) demonstrated protective roles against OA. Conversely, Oscillibacter (OR: 1.16; 95% CI 1.00-1.34; P = .043), Bilophila (OR: 1.28; 95% CI 1.07-1.54; P = .007), Erysipelotrichaceae (OR: 1.08; 95% CI 1.00-1.16; P = .044), and Bilophila within the Desulfovibrionaceae family (OR: 1.19; 95% CI 1.04-1.36; P = .012) were associated with an increased risk of OA. The findings indicate that modulating dietary factors and gut microbiota can independently reduce the risk and progression of OA, potentially improving the quality of life and health management in aging populations.

Leveraging Mendelian randomization to inform drug discovery and development for ischemic stroke

Discovery and development of efficacious and safe pharmacological therapies is fraught with challenges. As proteins constitute the majority of drug targets and are encoded by genes, naturally occurring genetic variation within populations can provide valuable insights to inform drug discovery and development efforts. The drug target Mendelian randomization (MR) paradigm leverages these principles to investigate the causal effects of drug targets in humans. This review examines the application of drug target MR in informing the efficacy and development of therapeutics for ischemic stroke prevention and treatment. We consider applications of MR for existing and novel treatment strategies, including targeting blood pressure, lipid metabolism, coagulation, inflammation and glycemic control. Several of these genetically supported targets are under evaluation in late-stage clinical trials. Methodological limitations of drug target MR are addressed, followed by an outline of future research directions. We anticipate that careful application of drug target MR will enhance the efficiency of drug development for ischemic stroke, consequently accelerating the delivery of effective medications to patients.

Role of Genetic Testing in Kidney Stone Disease: A Narrative Review

PURPOSE OF REVIEW

Kidney stone disease (KSD) is a common and potentially life-threatening condition, and half of patients experience a repeat kidney stone episode within 5-10 years. Despite the ~50% estimate heritability of KSD, international guidelines have not kept up with the pace of discovery of genetic causes of KSD. The European Association of Urology guidelines lists 7 genetic causes of KSD as 'high risk'.

RECENT FINDINGS

There are currently 46 known monogenic (single gene) causes of kidney stone disease, with evidence of association in a further 23 genes. There is also evidence for polygenic risk of developing KSD. Evidence is lacking for recurrent disease, and only one genome wide association study has investigated this phenomenon, identifying two associated genes (SLC34A1 and TRPV5). However, in the absence of other evidence, patients with genetic predisposition to KSD should be treated as 'high risk'. Further studies are needed to characterize both monogenic and polygenic associations with recurrent disease, to allow for appropriate risk stratification. Durability of test result must be balanced against cost. This would enable retrospective analysis if no genetic cause was found initially. We recommend genetic testing using a gene panel for all children, adults < 25 years, and older patients who have factors associated with high risk disease within the context of a wider metabolic evaluation. Those with a genetic predisposition should be managed via a multi-disciplinary team approach including urologists, radiologists, nephrologists, clinical geneticists and chemical pathologists. This will enable appropriate follow-up, counselling and potentially prophylaxis.

Systematic review of Mendelian randomization studies on antihypertensive drugs

BACKGROUND

We systematically reviewed Mendelian randomization (MR) studies and summarized evidence on the potential effects of different antihypertensive drugs on health.

METHODS

We searched PubMed and Embase for MR studies evaluating the effects of antihypertensive drug classes on health outcomes until 22 May 2024. We extracted data on study characteristics and findings, assessed study quality, and compared the evidence with that from randomized controlled trials (RCTs).

RESULTS

We identified 2643 studies in the search, of which 37 studies were included. These studies explored a wide range of health outcomes including cardiovascular diseases and their risk factors, psychiatric and neurodegenerative diseases, cancer, immune function and infection, and other outcomes. There is strong evidence supporting the protective effects of genetically proxied antihypertensive drugs on cardiovascular diseases. We found strong protective effects of angiotensin-converting enzyme (ACE) inhibitors on diabetes whereas beta-blockers showed adverse effects. ACE inhibitors might increase the risk of psoriasis, schizophrenia, and Alzheimer's disease but did not affect COVID-19. There is strong evidence that ACE inhibitors and calcium channel blockers (CCBs) are beneficial for kidney and immune function, and CCBs showed a safe profile for disorders of pregnancy. Most studies have high quality. RCT evidence supports the beneficial effects of ACE inhibitors and CCBs on stroke, diabetes, and kidney function. However, there is a lack of reliable RCTs to confirm the associations with other diseases.

CONCLUSIONS

Evidence of the benefits and off-target effects of antihypertensive drugs contribute to clinical decision-making, pharmacovigilance, and the identification of drug repurposing opportunities.

A bidirectional Mendelian randomization study of spleen volume and Crohn disease

In observational studies, there has been an association found between spleen volume and Crohn disease. We conducted a two-way, two-sample Mendelian randomization analysis to determine whether these associations have a causal relationship. Single nucleotide polymorphisms (P < 5 × 10-8) were used as instrumental variables for spleen volume and Crohn disease. Estimates of the genetic associations between spleen volume and Crohn disease were obtained from the Integrative Epidemiology Unit, European Bioinformatics Institute, UK Biobank, and FinnGen databases. Analysis was performed using MR-Egger regression, weighted median estimator, inverse variance weighted, simple model, and weighted model. Genetically predicted spleen volume was found to be associated with Crohn disease. In the IEU database, the odds ratios (ORs) for Crohn disease caused by spleen volume were 1.237 (95% CI, 1.056-1.417, P = .021), and the ORs for spleen volume caused by Crohn disease were 1.015 (95% CI, 0.985-1.044; P = .049). In the EBI database, the ORs for Crohn disease caused by spleen volume were 1.292 (95% CI, 1.120-1.463, P = .003), and the ORs for spleen volume caused by Crohn disease were 1.026 (95% CI, 1.005-1.046; P = .013). Results from the UKB and FinnGen databases showed no causal relationship between the two. The summary results showed that Crohn disease caused an increase in spleen volume, with ORs of 1.009 (95% CI, 1.000-1.018; P = .047). This study provides evidence for a mutual causal relationship between spleen volume and an increased risk of Crohn disease.

The genetic landscape of neuro-related proteins in human plasma

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.

Plasma MERTK is causally associated with infection mortality

BACKGROUND

Infectious diseases are a major cause of mortality in spite of existing public health, anti-microbial and vaccine interventions. We aimed to define plasma proteomic associates of infection mortality and then apply Mendelian randomisation (MR) to yield biomarkers that may be causally associated.

METHODS

We used UK Biobank plasma proteomic data to associate 2923 plasma proteins with infection mortality before 31st December 2019 (240 events in 52,520 participants). Since many plasma proteins also predict non-infection mortality, we focussed on those associated with >1.5-fold risk of infection mortality in an analysis excluding survivors. Protein quantitative trait scores (pQTS) were then used to identify whether genetically predicted protein levels also associated with infection mortality. To conduct Two Sample MR, we performed a genome-wide association study (GWAS) of infection mortality using UK Biobank participants without plasma proteomic data (n = 363,953 including 984 infection deaths).

FINDINGS

After adjusting for clinical risk factors, 1142 plasma proteins were associated with risk of infection mortality (false discovery rate <0.05). 259 proteins were associated with >1.5-fold increased risk of infection versus non-infection mortality. Of these, we identified genetically predicted increasing MERTK concentration was associated with increased risk of infection mortality. MR supported a causal association between increasing plasma MERTK protein and infection mortality (odds ratio 1.46 per unit; 95% CI 1.15- 1.85; p = 0.002).

CONCLUSION

Plasma MERTK is causally associated with infection mortality and warrants exploration as a potential therapeutic target.

A Guide to Understanding Mendelian Randomization Studies

Epidemiology provides a powerful framework for characterizing exposure-disease relationships, but its utility for making causal inferences is limited because epidemiologic data are observational in nature and subject to biases stemming from undetected confounding variables and reverse causation. Mendelian randomization (MR) is an increasingly popular method used to circumvent these limitations. MR uses genetic variants, or instruments, as a natural experiment to proxy an exposure, thus allowing estimation of causal effects upon an outcome that are minimally affected by the usual biases present in epidemiologic studies. Notably, MR relies on three core assumptions related to the selection of the genetic instruments, and adherence to these assumptions must be carefully evaluated to assess the validity of the causal estimates. The goal of this review is to provide readers with a basic understanding of MR studies and how to read and evaluate them. Specifically, we outline the basics of how MR analysis is conducted, the assumptions underlying instrument selection, and how to assess the quality of MR studies.

Association of lipids and lipid-lowering drugs with peripheral arterial disease: A Mendelian randomization study

BACKGROUND

It remains unclear whether lipid profiles and lipid-lowering medications are causally related to peripheral arterial disease (PAD).

OBJECTIVE

Explain whether there is a causal relationship between lipid status and lipid-lowering drugs and PAD.

METHODS

In this two-sample Mendelian randomization (MR) analysis, we assessed the causal relationship between lipid traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TGs), total cholesterol (TC), and LDL-associated genetic variants (HMGCR, NPC1L1, PCSK9, APOB), and the risk of PAD using genetic variants associated with these lipid markers. The study analyzed data from 1,654,960 individuals derived from the Global Lipid Genetics Consortium and the UK Biobank, ensuring a robust and comprehensive genetic insight into the effects of lipid dysfunction on PAD.

RESULTS

We found genetically predicted associations between HDL-C (OR: 0.83, 95% CI: 0.83-0.77), LDL-C (OR: 1.29, 95% CI: 1.12-1.50), TC (OR: 1.14, 95% CI: 1.01- 1.29), TG (OR: 1.16, 95% CI: 1.04-1.24), APOB (OR: 1.31, 95% CI: 1.16-1.48), and APOA1 (OR: 0.84, 95% CI: 0.77-0.97), and the risk of PAD. In addition, inhibition of PCSK9 was associated with a reduced risk of PAD (OR: 0.68, 95% CI: 0.57-0.79, P<0.001), while no association between the other three gene proxies of LDL inhibition including HMGCR (OR: 1.21, 95% CI: 0.87-1.69, P=0.250), NPC1L1 (OR: 0.77, 95% CI: 0.44-1.33, P=0.344), and APOB (OR: 1.01, 95% CI: 0.87-1.26, P=0.890), and the risk of PAD were found.

CONCLUSIONS

Based on genetic evidence, dyslipidemia is an important risk factor for PAD. Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors may be an effective strategy for the treatment of PAD.

Genetically Proxied Interleukin-13 Inhibition Is Associated With Risk of Psoriatic Disease: A Mendelian Randomization Study

OBJECTIVE

Inhibitors of the interleukin 13 (IL-13) pathway, such as dupilumab, are licensed for atopic dermatitis and asthma. Adverse events resembling psoriatic disease after dupilumab initiation have been reported, but evidence is limited to case reports with uncertain causality. We aimed to investigate whether genetically mimicked IL-13 inhibition (IL-13i) is associated with risk of psoriatic arthritis (PsA) and psoriasis.

METHODS

We instrumented IL-13i using a protein-coding variant in the IL13 gene, rs20541, that is associated with circulating eosinophil count (biomarker of IL-13i) at genome-wide significance in a study of 563,946 individuals. Outcome genetic data were taken from studies of PsA, psoriasis, and related spondyloarthritis traits in up to 10,588 cases and 209,287 controls. Colocalization analysis was performed to examine genetic confounding. We additionally used circulating IgE as a biomarker to test whether associations were replicated, both in the test and in an independent genetic dataset. We also replicated analyses using individual-level data from the UK Biobank.

RESULTS

Genetically proxied IL-13i was associated with increased risk of PsA (odds ratio [OR] 37.39; 95% confidence interval [95% CI] 11.52-121.34; P = 1.64 × 10-9) and psoriasis (OR 20.08; 95% CI 4.38-92.01; P = 1.12 × 10-4). No consistent associations were found for Crohn disease, ulcerative colitis, ankylosing spondylitis, or iritis. Colocalization showed no strong evidence of genetic confounding for psoriatic disease. Results were replicated using circulating IgE for the exposure, using independent outcome data and using individual-level data.

CONCLUSION

We provide supportive genetic evidence that IL-13i is linked to increased risk of PsA and psoriasis. Physicians prescribing IL-13 inhibitors should be vigilant for these adverse events.

HORNET: Tools to find genes with causal evidence and their regulatory networks using eQTLs

Motivation

Nearly two decades of genome-wide association studies (GWAS) have identify thousands of disease-associated genetic variants, but very few genes with evidence of causality. Recent methodological advances demonstrate that Mendelian Randomization (MR) using expression quantitative loci (eQTLs) as instrumental variables can detect potential causal genes. However, existing MR approaches are not well suited to handle the complexity of eQTL GWAS data structure and so they are subject to bias, inflation, and incorrect inference.

Results

We present a whole-genome regulatory network analysis tool (HORNET), which is a comprehensive set of statistical and computational tools to perform genome-wide searches for causal genes using summary level GWAS data that is robust to biases from multiple sources. Applying HORNET to schizophrenia, we identified differential magnitudes of gene expression causality. Applying HORNET to schizophrenia, we identified differential magnitudes of gene expression causality across different brain tissues.

Availability and Implementation

Freely available at https://github.com/noahlorinczcomi/HORNETor Mac, Windows, and Linux users.

Contact

njl96@case.edu .

Causal inference in health and disease: a review of the principles and applications of Mendelian randomization

Mendelian randomization (MR) is a genetic epidemiological technique that uses genetic variation to infer causal relationships between modifiable exposures and outcome variables. Conventional observational epidemiological studies are subject to bias from a range of sources; MR analyses can offer an advantage in that they are less prone to bias as they use genetic variants inherited at conception as "instrumental variables", which are proxies of an exposure. However, as with all research tools, MR studies must be carefully designed to yield valuable insights into causal relationships between exposures and outcomes, and to avoid biased or misleading results that undermine the validity of the causal inferences drawn from the study. In this review, we outline Mendel's laws of inheritance, the assumptions and principles that underlie MR, MR study designs and methods, and how MR analyses can be applied and reported. Using the example of serum phosphate concentrations on liability to kidney stone disease we illustrate how MR estimates may be visualized and, finally, we contextualize MR in bone and mineral research including exemplifying how this technique could be employed to inform clinical studies and future guidelines concerning BMD and fracture risk. This review provides a framework to enhance understanding of how MR may be used to triangulate evidence and progress research in bone and mineral metabolism as we strive to infer causal effects in health and disease.

Association of inflammatory cytokines with lung function, chronic lung diseases, and COVID-19

We investigated the effects of 35 inflammatory cytokines on respiratory outcomes, including COVID-19, asthma (atopic and non-atopic), chronic obstructive pulmonary disease (COPD), and pulmonary function indices, using Mendelian randomization and colocalization analyses. The emerging associations were further explored using observational analyses in the UK Biobank. We found an inverse association between genetically predicted macrophage colony stimulating factor (MCSF), soluble intercellular adhesion molecule-1 (sICAM), and soluble vascular cell adhesion molecule-1 with risk of COVID-19 outcomes. sICAM was positively associated with atopic asthma risk, whereas tumor necrosis factor-alfa showed an inverse association. A positive association was shown between interleukin-18 and COPD risk (replicated in observational analysis), whereas an inverse association was shown for interleukin-1 receptor antagonist (IL-1ra). IL-1ra and monocyte chemotactic protein-3 were positively associated with lung function indices, whereas inverse associations were shown for MCSF and interleukin-18 (replicated in observational analysis). Our results point to these cytokines as potential pharmacological targets for respiratory traits.

Common pitfalls in drug target Mendelian randomization and how to avoid them

BACKGROUND

Drug target Mendelian randomization describes the use of genetic variants as instrumental variables for studying the effects of pharmacological agents. The paradigm can be used to inform on all aspects of drug development and has become increasingly popular over the last decade, particularly given the time- and cost-efficiency with which it can be performed even before commencing clinical studies.

MAIN BODY

In this review, we describe the recent emergence of drug target Mendelian randomization, its common pitfalls, how best to address them, as well as potential future directions. Throughout, we offer advice based on our experiences on how to approach these types of studies, which we hope will be useful for both practitioners and those translating the findings from such work.

CONCLUSIONS

Drug target Mendelian randomization is nuanced and requires a combination of biological, statistical, genetic, epidemiological, clinical, and pharmaceutical expertise to be utilized to its full potential. Unfortunately, these skillsets are relatively infrequently combined in any given study.

Disease coverage of human genome-wide association studies and pharmaceutical research and development

BACKGROUND

Despite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear.

METHODS

In this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts.

RESULTS

We show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development.

CONCLUSIONS

We produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication.

Addressing the credibility crisis in Mendelian randomization

BACKGROUND

Genome-wide association studies have enabled Mendelian randomization analyses to be performed at an industrial scale. Two-sample summary data Mendelian randomization analyses can be performed using publicly available data by anyone who has access to the internet. While this has led to many insightful papers, it has also fuelled an explosion of poor-quality Mendelian randomization publications, which threatens to undermine the credibility of the whole approach.

FINDINGS

We detail five pitfalls in conducting a reliable Mendelian randomization investigation: (1) inappropriate research question, (2) inappropriate choice of variants as instruments, (3) insufficient interrogation of findings, (4) inappropriate interpretation of findings, and (5) lack of engagement with previous work. We have provided a brief checklist of key points to consider when performing a Mendelian randomization investigation; this does not replace previous guidance, but highlights critical analysis choices. Journal editors should be able to identify many low-quality submissions and reject papers without requiring peer review. Peer reviewers should focus initially on key indicators of validity; if a paper does not satisfy these, then the paper may be meaningless even if it is technically flawless.

CONCLUSIONS

Performing an informative Mendelian randomization investigation requires critical thought and collaboration between different specialties and fields of research.

Proteome-Wide Multicenter Mendelian Randomization Analysis to Identify Novel Therapeutic Targets for Lung Cancer

INTRODUCTION

Lung cancer (LC) remains a leading cause of cancer mortality worldwide, underscoring the urgent need for novel therapeutic targets. The integration of Mendelian randomization (MR) with proteomic data presents a novel approach to identifying potential targets for LC treatment.

METHODS

This study utilized a proteome-wide MR analysis, leveraging publicly available data from genome-wide association studies (GWAS) and protein quantitative trait loci (pQTL) studies. We analyzed genetic association data for LC from the TRICL-ILCCO Consortium and proteomic data from the Decode cohort. The MR framework was employed to estimate the causal effects of specific proteins on LC risk, supplemented by external validation, co-localization analyses, and exploration of protein-protein interaction (PPI) networks.

RESULTS

Our analysis identified five proteins (TFPI, ICAM5, SFTPB, COL6A3, EPHB1) with significant associations to LC risk. External validation confirmed the potential therapeutic relevance of ICAM5 and SFTPB. Co-localization analyses and PPI network exploration provided further insights into the biological pathways involved and their potential mechanistic roles in LC pathogenesis.

CONCLUSION

The study highlights the power of integrating genomic and proteomic data through MR analysis to uncover novel therapeutic targets for lung cancer. The identified proteins, particularly ICAM5 and SFTPB, offer promising directions for future research and development of targeted therapies, demonstrating the potential to advance personalized medicine in lung cancer treatment.

Can NPC1L1 inhibitors reduce the risk of biliary tract cancer? Evidence from a mendelian randomization study

BACKGROUND & AIMS

Oxysterols have been implicated in biliary tract cancer (BTC), and Niemann-Pick C1-like 1 (NPC1L1) has been associated with oxysterol uptake in biliary and intestinal cells. Thus, our study aims to investigate the potential causal link between genetically proxied NPC1L1 inhibitors and the risk of BTC.

METHODS

In this study, we employed two genetic instruments as proxies for NPC1L1 inhibitors, which included LDL cholesterol-associated genetic variants located within or in close proximity to the NPC1L1 gene, as well as expression quantitative trait loci (eQTLs) of the NPC1L1 gene. Effect estimates were calculated using the Inverse-variance-weighted MR (IVW-MR) and summary-data-based MR (SMR) methods.

RESULTS

In MR analysis using the IVW method, both proxy instruments from the UK Biobank and the GLGC demonstrated a positive association between NPC1L1-mediated LDL cholesterol and BTC risk, with odds ratios (OR) of 10.30 (95% CI = 1.51-70.09; P = 0.017) and 5.61 (95% CI = 1.43-21.91; P = 0.013), respectively. Moreover, SMR analysis revealed a significant association between elevated NPC1L1 expression and increased BTC risk (OR = 1.19, 95% CI = 1.04-1.37; P = 0.014).

CONCLUSIONS

This MR study suggests a causal link between NPC1L1 inhibition and reduced BTC risk. NPC1L1 inhibitors, like ezetimibe, show potential for chemoprevention in precancerous BTC patients, requiring further clinical investigation.

Effects of ACLY Inhibition on Body Weight Distribution: A Drug Target Mendelian Randomization Study

Background: Adenosine triphosphate-citrate lyase (ACLY) inhibition has proven clinically efficacious for low-density lipoprotein cholesterol (LDL-c) lowering and cardiovascular disease (CVD) risk reduction. Clinical and genetic evidence suggests that some LDL-c lowering strategies, such as 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibition with statin therapy increase body weight and the risk of developing type 2 diabetes mellitus (T2DM). However, whether ACLY inhibition affects metabolic risk factors is currently unknown. We aimed to investigate the effects of ACLY inhibition on glycaemic and anthropometric traits using Mendelian randomization (MR). Methods: As genetic instruments for ACLY inhibition, we selected weakly correlated single-nucleotide polymorphisms at the ACLY gene associated with lower ACLY gene expression in the eQTLGen study (N = 31,684) and lower LDL-c levels in the Global Lipid Genetic Consortium study (N = 1.65 million). Two-sample Mendelian randomization was employed to investigate the effects of ACLY inhibition on T2DM risk, and glycaemic and anthropometric traits using summary data from large consortia, with sample sizes ranging from 151,013 to 806,834 individuals. Findings for genetically predicted ACLY inhibition were compared to those obtained for genetically predicted HMGCR inhibition using the same instrument selection strategy and outcome data. Results: Primary MR analyses showed that genetically predicted ACLY inhibition was associated with lower waist-to-hip ratio (β per 1 standard deviation lower LDL-c: -1.17; 95% confidence interval (CI): -1.61 to -0.73; p < 0.001) but not with risk of T2DM (odds ratio (OR) per standard deviation lower LDL-c: 0.74, 95% CI = 0.25 to 2.19, p = 0.59). In contrast, genetically predicted HMGCR inhibition was associated with higher waist-to-hip ratio (β = 0.15; 95%CI = 0.04 to 0.26; p = 0.008) and T2DM risk (OR = 1.73, 95% CI = 1.27 to 2.36, p < 0.001). The MR analyses considering secondary outcomes showed that genetically predicted ACLY inhibition was associated with a lower waist-to-hip ratio adjusted for body mass index (BMI) (β = -1.41; 95%CI = -1.81 to -1.02; p < 0.001). In contrast, genetically predicted HMGCR inhibition was associated with higher HbA1c (β = 0.19; 95%CI = 0.23 to 0.49; p < 0.001) and BMI (β = 0.36; 95%CI = 0.23 to 0.49; p < 0.001). Conclusions: Human genetic evidence supports the metabolically favourable effects of ACLY inhibition on body weight distribution, in contrast to HMGCR inhibition. These findings should be used to guide and prioritize ongoing clinical development efforts.

Translating Anti-Inflammatory Strategies for Atherosclerosis: Deep Phenotyping, Next-Generation Drug Targets, and Precision Medicine

Atherosclerosis is the main pathology underlying cardiovascular disease (CVD), including myocardial infarction and ischemic stroke [...].

Exploring the Genetic Roles of Diet and Other Modifiable Risk Factors in the Risk of Angina: A Causal Investigation Using Mendelian Randomization in UK Biobank and FinnGen Cohorts

BACKGROUND

Angina pectoris, a debilitating manifestation of coronary artery disease, has been associated with various modifiable risk factors. However, the causal underpinnings of these associations remain unclear. This study leveraged Mendelian randomization (MR) to investigate the causal roles of dietary patterns, smoking behaviors, body mass index (BMI), and physical activity in the development of angina.

METHODS

Two-sample MR analyses were performed using summary-level data from large-scale genome-wide association studies (GWASs) and biobank resources, including the UK Biobank (UKB) and FinnGen cohorts. Genetic variants associated with various types of exposure such as fruit and salad intake, smoking initiation and intensity, BMI, and physical activity were used as instrumental variables, and their causal effects on angina risk were assessed.

RESULTS

In the UKB cohort (336,683 individuals, 10,618 cases), genetically proxied fruit (OR = 0.95, 95% CI: 0.93-0.97) and cheese intake (OR = 0.98, 95% CI: 0.97-0.99) were associated with decreased angina risk, while smoking initiation (OR = 1.01, 95% CI: 1.002-1.012), maternal smoking (OR = 1.06, 95% CI: 1.03-1.09), and BMI (OR = 1.01, 95% CI: 1.01-1.02) were associated with increased risk. In the FinnGen cohort (206,008 individuals, 18,168 cases), fruit (OR = 0.30, 95% CI: 0.17-0.53) and salad intake (OR = 0.31, 95% CI: 0.12-0.55) were found to be protective, while smoking initiation (OR = 1.20, 95% CI: 1.04-1.37) and intensity (OR = 1.15, 95% CI: 1.04-1.26) and BMI (OR = 1.31, 95% CI: 1.18-1.47) increased angina risk.

CONCLUSIONS

This study provides robust evidence for the causal roles of various modifiable risk factors associated with angina development, highlighting the potential benefits of dietary interventions that promote increased fruit and vegetable consumption, smoking cessation, and weight management to mitigate angina risk. Further investigation is needed to generalize these findings to populations with diverse genetic backgrounds, lifestyles, and environmental exposures.

The Application of Mendelian Randomization in Cardiovascular Disease Risk Prediction: Current Status and Future Prospects

Cardiovascular disease (CVD), a leading cause of death and disability worldwide, and is associated with a wide range of risk factors, and genetically associated conditions. While many CVDs are preventable and early detection alongside treatment can significantly mitigate complication risks, current prediction models for CVDs need enhancements for better accuracy. Mendelian randomization (MR) offers a novel approach for estimating the causal relationship between exposure and outcome by using genetic variation in quasi-experimental data. This method minimizes the impact of confounding variables by leveraging the random allocation of genes during gamete formation, thereby facilitating the integration of new predictors into risk prediction models to refine the accuracy of prediction. In this review, we delve into the theory behind MR, as well as the strengths, applications, and limitations behind this emerging technology. A particular focus will be placed on MR application to CVD, and integration into CVD prediction frameworks. We conclude by discussing the inclusion of various populations and by offering insights into potential areas for future research and refinement.

Genetically proxied IL-6 signaling and risk of Alzheimer's disease and lobar intracerebral hemorrhage: A drug target Mendelian randomization study

INTRODUCTION

Evidence suggests that higher C-reactive protein (CRP) is associated with lower risk of Alzheimer's disease (AD) and lobar intracerebral hemorrhage (ICH). Whether interleukin (IL)-6 signaling, an active pharmacological target upstream of CRP, is associated with these amyloid-related pathologies remains unknown.

METHODS

We used 26 CRP-lowering variants near the IL-6 receptor gene to perform Mendelian randomization analyses for AD (111,326 cases, 677,663 controls) and ICH (1545 cases, 1481 controls). We explored the effect of genetically proxied IL-6 signaling on serum, cerebrospinal fluid (CSF), and brain proteome (971 individuals).

RESULTS

Genetically upregulated IL-6 receptor-mediated signaling was associated with lower risk of AD (OR per increment in serum logCRP levels: 0.87, 95% CI: 0.79-0.95) and lobar ICH (OR: 0.27, 95% CI: 0.09-0.89). We also found associations with 312, 77, and 79 brain, CSF, and plasma proteins, respectively, some of which were previously implicated in amyloid-clearing mechanisms.

DISCUSSION

Genetic data support that CRP-lowering through variation in the gene encoding IL-6 receptor may be associated with amyloid-related outcomes.

Highlights

Genetic variants proxying IL-6 inhibition are associated with AD and lobar ICH risk.The variants are also associated with amyloid clearing-related proteomic changes.Whether pharmacologic IL-6 inhibition is linked to AD or lobar ICH merits further study.

Effects of genetically proxied lipid-lowering drugs on acute myocardial infarction: a drug-target mendelian randomization study

OBJECTIVE

High low-density-lipoprotein (LDL) cholesterol has been associated with an increased risk of coronary artery diseases (CAD) including acute myocardial infarction (AMI). However, whether lipids lowering drug treatment is causally associated with decreased risk of AMI remains largely unknown. We used Mendelian randomization (MR) to evaluate the influence of genetic variation affecting the function of lipid-lowering drug targets on AMI.

METHODS

Single-nucleotide polymorphisms (SNPs) associated with lipids as instruments were extracted from the Global Lipids Genetics Consortium (GLGC). The genome-wide association study (GWAS) data for AMI were obtained from UK Biobank. Two sample MR analysis was used to study the associations between high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides (TG) with AMI (n = 3,927). Genetic variants associated with LDL cholesterol at or near drug target gene were used to mimic drug effects on the AMI events in drug target MR.

RESULTS

Genetically predicted higher LDL-C (per one SD increase in LDL-C of 38.67 mg/dL, OR 1.006, 95% CI 1.004-1.007) and TG (per one SD increase in TG of 90.72 mg/dL, 1.004, 1.002-1.006) was associated with increased risk of AMI, but decreased risk for higher HDL-C (per one SD increase in HDL-C of 15.51 mg/dL, 0.997, 0.995-0.999) in univariable MR. Association remained significant for LDL-C, but attenuated toward the null for HDL-C and TG in multivariable MR. Genetically proxied lower LDL-C with genetic variants at or near the PCSK9 region (drug target of evolocumab) and NPC1L1 (drug target of ezetimibe) were associated with decreased risk of AMI (0.997, 0.994-0.999 and 0.986, 0.975-0.998, respectively), whereas genetic variants at HMGCR region (drug target of statin) showed marginal association with AMI (0.995, 0.990-1.000). After excluding drug target-related SNPs, LDL-C related SNPs outside the drug target region remained a causal effect on AMI (0.994, 0.993-0.996).

CONCLUSIONS

The findings suggest that genetically predicted LDL-C may play a predominant role in the development of AMI. The drug MR results imply that ezetimibe and evolocumab may decrease the risk of AMI due to their LDL-C lowering effect, and there are other non-drug related lipid lowering pathways that may be causally linked to AMI.

Stroke genetics and how it Informs novel drug discovery

INTRODUCTION

Stroke is one of the main causes of death and disability worldwide. Nevertheless, despite the global burden of this disease, our understanding is limited and there is still a lack of highly efficient etiopathology-based treatment. It is partly due to the complexity and heterogenicity of the disease. It is estimated that around one-third of ischemic stroke is heritable, emphasizing the importance of genetic factors identification and targeting for therapeutic purposes.

AREAS COVERED

In this review, the authors provide an overview of the current knowledge of stroke genetics and its value in diagnostics, personalized treatment, and prognostication.

EXPERT OPINION

As the scale of genetic testing increases and the cost decreases, integration of genetic data into clinical practice is inevitable, enabling assessing individual risk, providing personalized prognostic models and identifying new therapeutic targets and biomarkers. Although expanding stroke genetics data provides different diagnostics and treatment perspectives, there are some limitations and challenges to face. One of them is the threat of health disparities as non-European populations are underrepresented in genetic datasets. Finally, a deeper understanding of underlying mechanisms of potential targets is still lacking, delaying the application of novel therapies into routine clinical practice.

Mendelian randomisation analysis for intestinal disease: achievement and future

Intestinal disease is a group of complex digestive system diseases imposing a significant burden globally. Identifying the risk factors and potential complications of intestinal disease is important for its prevention and treatment. However, traditional observational clinical studies are limited by confounding factors and reverse causation, making causal inference challenging. Mendelian randomisation (MR) method has been developed to effectively mitigate these constraints and assess the causal relationships. This review briefly introduces the MR method, summarises MR research on intestinal disease and delineates the prospective avenues for future research. Conventional risk factors, such as lifestyle behaviours (eg, physical activity, smoking and alcohol consumption), nutrients (eg, selenium), obesity markers (eg, body mass index and waist-to-hip ratio) and inflammatory biomarkers, have been validated in MR studies. Multiomics MR studies are becoming novel hotspots, which provide a theoretical foundation for the exploration of pathogenesis and the investigation of new drug targets. However, most of the recent studies are based on European individuals, and thus it is necessary to replicate the results in other ancestries. Moreover, triangulation integrating MR and other epidemiology methods is suggested as a validated paradigm for causal inference in future MR studies.

Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer

BACKGROUND

This article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation.

METHODS

Using a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer.

RESULTS

Genetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication.

IMPLICATIONS

Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value.

HIGHLIGHTS

The article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations.Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort.Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention.Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug's price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.

Genetically Determined Circulating Lactase/Phlorizin Hydrolase Concentrations and Risk of Colorectal Cancer: A Two-Sample Mendelian Randomization Study

Previous research has found that milk is associated with a decreased risk of colorectal cancer (CRC). However, it is unclear whether the milk digestion by the enzyme lactase-phlorizin hydrolase (LPH) plays a role in CRC susceptibility. Our study aims to investigate the direct causal relationship of CRC risk with LPH levels by applying a two-sample Mendelian Randomization (MR) strategy. Genetic instruments for LPH were derived from the Fenland Study, and CRC-associated summary statistics for these instruments were extracted from the FinnGen Study, PLCO Atlas Project, and Pan-UK Biobank. Primary MR analyses focused on a cis-variant (rs4988235) for LPH levels, with results integrated via meta-analysis. MR analyses using all variants were also undertaken. This analytical approach was further extended to assess CRC subtypes (colon and rectal). Meta-analysis across the three datasets illustrated an inverse association between genetically predicted LPH levels and CRC risk (OR: 0.92 [95% CI, 0.89-0.95]). Subtype analyses revealed associations of elevated LPH levels with reduced risks for both colon (OR: 0.92 [95% CI, 0.89-0.96]) and rectal cancer (OR: 0.92 [95% CI, 0.87, 0.98]). Consistency was observed across varied analytical methods and datasets. Further exploration is warranted to unveil the underlying mechanisms and validate LPH's potential role in CRC prevention.

Genetically proxied PCSK9 inhibition is associated with reduced psoriatic arthritis risk

BACKGROUND

Lipid pathways play a crucial role in psoriatic arthritis development, and some lipid-lowering drugs are believed to have therapeutic benefits due to their anti-inflammatory properties. Traditional observational studies face issues with confounding factors, complicating the interpretation of causality. This study seeks to determine the genetic link between these medications and the risk of psoriatic arthritis.

METHODS

This drug target study utilized the Mendelian randomization strategy. We harnessed high-quality data from population-level genome-wide association studies sourced from the UK Biobank and FinnGen databases. The inverse variance-weighted method, complemented by robust pleiotropy methods, was employed. We examined the causal relationships between three lipid-lowering agents and psoriatic arthritis to unveil the underlying mechanisms.

RESULTS

A significant association was observed between genetically represented proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition and a decreased risk of psoriatic arthritis (odds ratio [OR]: 0.51; 95% CI 0.14-0.88; P < 0.01). This association was further corroborated in an independent dataset (OR 0.60; 95% CI 0.25-0.94; P = 0.03). Sensitivity analyses affirmed the absence of statistical evidence for pleiotropic or genetic confounding biases. However, no substantial associations were identified for either 3-hydroxy-3-methylglutaryl-CoA reductase inhibitors or Niemann-Pick C1-like 1 inhibitors.

CONCLUSIONS

This Mendelian randomization analysis underscores the pivotal role of PCSK9 in the etiology of psoriatic arthritis. Inhibition of PCSK9 is associated with reduced psoriatic arthritis risk, highlighting the potential therapeutic benefits of existing PCSK9 inhibitors.

Can Lipid-Lowering Drugs Reduce the Risk of Cholelithiasis? A Mendelian Randomization Study

Background and Aims

Cholelithiasis etiology intricately involves lipid metabolism. We sought to investigate the plausible causal link between genetically proxied lipid-lowering medications-specifically HMGCR inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitors-and cholelithiasis risk.

Methods

Our study utilized two genetic instruments for exposure to lipid-lowering drugs. These instruments encompassed genetic variants linked to low-density lipoprotein (LDL) cholesterol within or in proximity to drug target genes, along with loci governing gene expression traits of these targets. Effect estimates were derived through Inverse-variance-weighted MR (IVW-MR) and summary-data-based MR (SMR) methods.

Results

Higher HMGCR-mediated LDL cholesterol levels (IVW-MR, OR = 2.15, 95% CI = 1.58-2.94; P = 0.000) and increased HMGCR expression (SMR, OR = 1.19, 95% CI = 1.04-1.37; P = 0.014) are linked to elevated cholelithiasis risk, suggesting potential benefits of HMGCR inhibition. In contrast, higher PCSK9-mediated LDL cholesterol levels (IVW-MR, OR = 0.72, 95% CI = 0.56-0.94; P = 0.015) and increased PCSK9 expression (SMR, OR = 0.90, 95% CI = 0.82-0.99; P = 0.035) both correlate with lower cholelithiasis risk, indicating that PCSK9 inhibition may elevate this risk. Nevertheless, no substantial link emerged between NPC1L1-mediated LDL cholesterol or NPC1L1 expression and cholelithiasis in both IVW-MR and SMR analyses.

Conclusion

This MR investigation affirms the causal link between the utilization of HMGCR inhibitors and a diminished risk of cholelithiasis. Additionally, it indicates a causal link between PCSK9 inhibitors use and increased cholelithiasis risk. However, no significant correlation was found between NPC1L1 inhibitors use and cholelithiasis risk.