Exploring potential therapeutic targets for cardiomyopathy: A proteome-wide Mendelian randomization analysis

Abstract

Cardiomyopathy often results in heart failure and mortality, significantly impairing patients’ quality of life. Advancements in genomics and proteomics now enable the identification of proteins associated with cardiomyopathy, offering valuable insights for its diagnosis and treatment. However, numerous potential pathogenic proteins remain unidentified, underscoring the need for further exploration of novel drug targets for cardiomyopathy. This study aims to employ Mendelian randomization (MR) to explore genetic associations between plasma proteins and cardiomyopathies, with the objective of identifying potential drug targets. Two-sample MR was employed to investigate causal relationships between cardiomyopathies and plasma proteins, using summary data from genome-wide association studies of different cardiomyopathy subtypes, such as dilated cardiomyopathy, hypertrophic cardiomyopathy (HCM), and restrictive cardiomyopathy (RCM). Cis-protein quantitative trait loci retrieved from the deCODE database served as genetic instruments. Steiger filtering was applied to assess and validate reverse causality. Enrichment analysis was conducted to elucidate potential biological effects, while protein–protein interaction networks were examined to explore interactions among proteins. Molecular docking was employed to evaluate the binding affinity between drugs and their targets. The MR analysis identified 70 significant proteins linked to cardiomyopathy, 12 to dilated cardiomyopathy, 60 to HCM, and 103 to RCM. Intersection analysis revealed 24 significant proteins. Following multiple hypothesis testing, 2 significant proteins (CCL17, SERPINA4) were identified for HCM, and 16 significant proteins (APOL3, C1QL1, CNDP1, CRLF1, CSF2RB, CTSH, GABARAPL2, GP1BA, ICAM5, NPPB, NTM, PDCD5, PTPRS, RNASET2, RTN4R, TCN2) were identified for RCM. Reverse causality testing provided no evidence of reverse causality for any positive genes. Enrichment analysis of protein–protein interaction networks indicated a potential biological role for the positive proteins. Moreover, potential drug targets for treating cardiomyopathy were identified. The genetic associations between plasma proteins and cardiomyopathy were analyzed, leading to the identification of specific proteins as potential biomarkers. Additionally, novel drug targets were identified, providing valuable insights for the diagnosis and treatment of cardiomyopathy.

1. Introduction

Cardiomyopathy is a disorder of the myocardium defined by abnormal structure and function in the absence of hypertension, congenital heart disease, coronary artery disease, or valvular heart disease, all of which can lead to myocardial abnormalities.^[1] Cardiomyopathy can be classified as primary or secondary. Primary cardiomyopathy is subdivided into dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RCM), arrhythmogenic right ventricular cardiomyopathy, and non-dilated left ventricular cardiomyopathy, with unclear etiologies.^[2] Secondary cardiomyopathy is often linked to infections, metabolic disorders, endocrine abnormalities, and other conditions. These conditions frequently lead to heart failure, arrhythmias, and mortality, significantly impacting patients’ quality of life and survival. Currently, treatment options for cardiomyopathy are limited, and surgical interventions pose significant risks. Advances in genomics and proteomics have deepened our understanding of the genetic basis of cardiomyopathy. Research has shown associations between specific genes and cardiomyopathy. For instance, variations in 12 genes, such as TTN, DSP, and MYH7, have been strongly linked to dilated cardiomyopathy,^[3] findings that have been validated through genetic testing.^[4] Genetic testing allows early screening of patients’ relatives for pathogenic genes, enabling timely intervention. Many potential pathogenic genes remain unidentified. Discovering new pathogenic genes, elucidating cardiomyopathy mechanisms, developing new therapies, and exploring drug repurposing are essential for advancing cardiomyopathy treatment.

Mendelian randomization (MR) uses single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) as genetic instruments to examine causal relationships between exposures and disease phenotypes. Since genetic variations are randomly assigned at fertilization and are unaffected by environmental factors, MR can partially address confounding bias and reverse causality typically found in traditional observational studies.^[5] Conducting randomized controlled trials in cardiovascular research may pose ethical concerns, making the MR method widely used to identify genetic variants associated with cardiovascular diseases.^[6–9] Plasma proteins, primarily synthesized by the liver, are the main solid components of plasma. They play essential roles in immune function, coagulation, anticoagulation, and nutrient storage. Plasma proteins are thus used to identify biomarkers, valuable targets for drug prediction and disease screening. Potential drug targets can be identified through GWAS and protein quantitative trait loci (pQTL) in MR analysis, facilitating early disease intervention.

This study employed the MR method to investigate genetic associations between plasma proteins and cardiomyopathy, aiming to identify novel pathogenic proteins and potential drug targets. This approach aims to enhance the understanding of cardiomyopathy pathogenesis, provide a foundation for developing new therapeutic agents, and offer new perspectives on treatment. We initially extracted relevant data from publicly available GWAS and protein databases to use as instrumental variables in the MR analysis. MR analysis was then applied to assess causal relationships between proteins and various cardiomyopathy phenotypes, identifying significantly associated proteins. Lastly, we conducted molecular docking to evaluate the binding energy and binding affinity between proteins and small molecules.

2. Materials and methods

This MR study on drug targets is based on summary data from GWAS and pQTL studies. Figure 1 illustrates the study design flow, as created using Figdraw.

Figure 1.

Study design flowchart.

2.1. Protein quantitative trait loci data

pQTLs are genomic regions that regulate protein expression levels, where protein expression corresponds to quantitative traits. pQTLs are categorized as cis-pQTLs and trans-pQTLs. Cis-pQTLs are located near the coding region of a gene, whereas trans-pQTLs are farther from the coding region and can reside on different chromosomes. pQTL data were obtained from a study by Ferkingstad et al, which measured 4907 plasma proteins in 35,559 Icelandic individuals.^[10] The pQTL data were processed by screening for genome-wide significant associations and removing linkage disequilibrium (P < 5 × 10⁻⁸, R² < 0.001, KB = 10,000, F > 10). This study used only cis-pQTLs as instrumental variables. Detailed information is available in Supplementary Materials S1 to S15, Supplemental Digital Content, https://links.lww.com/MD/P107.

2.2. Genome-wide association studies data

This study focuses on cardiomyopathy as the primary endpoint, encompassing DCM, HCM, and RCM. GWAS summary data were sourced from the FinnGen R10 database. The dataset comprises: cardiomyopathy – 318,492 individuals of European ancestry (nCase = 6338, nControl = 312,154); DCM – 178,162 individuals (nCase = 417, nControl = 177,745); HCM – 412,181 individuals (nCase = 1125, nControl = 411,056); RCM – 405,942 individuals (nCase = 99, nControl = 405,843). Detailed information on the data sources used in this study is provided in Table 1.

Table 1.

Data sources for the Mendelian randomization analysis in the study.

Phenotype	Sample size	Ancestry	Sources
Plasma protein
deCODE	4907	Icelander	Ferkingstad et al
Outcomes
Cardiomyopathy	6338/312,154	European	FinngenR10
DCM	417/177,745	European	FinngenR10
HCM	1125/411,056	European	FinngenR10
RCM	99/405,843	European	FinngenR10

Sample size shown as a total number for quantitative traits and cases/controls for binary traits.

DCM = dilated cardiomyopathy, HCM = hypertrophic cardiomyopathy, RCM = restrictive cardiomyopathy.

2.3. Mendelian randomization analysis

In this study, plasma proteins were considered the exposure and cardiomyopathy the outcome to evaluate the relevance, independence, and exclusivity of genetic variables using MR analysis. The causal relationship between plasma proteins and cardiomyopathy was examined using various MR methods, including inverse variance weighted (IVW), Mendelian randomization-Egger (MR-Egger), weighted median, simple mode, and weighted mode, with IVW as the primary approach. The screening criterion for the IVW method was defined as P < .05. MR estimates for individual SNPs were calculated using the Wald ratio method. Sensitivity analyses were performed to evaluate the robustness of the results. Cochran Q test was used to evaluate the heterogeneity of genetic variants, where a P-value >.05 indicated no heterogeneity.^[11] Horizontal pleiotropy was evaluated using the MR-Egger intercept, where a P-value >.05 suggested no horizontal pleiotropy, supporting a potential causal relationship.^[12] A leave-one-out analysis was performed, excluding individual SNPs to assess their impact on the results. An odds ratio below 1 indicates a protective effect, whereas an odds ratio above 1 suggests a risk factor. The significance levels of multiple tests were adjusted using false discovery rate correction, with a threshold of P < .2. Steiger filtering was also conducted to verify the direction of association between plasma proteins and cardiomyopathy. A P-value >.05 indicated possible reverse causality, necessitating the exclusion of that protein. The analysis was performed using the “TwoSampleMR” package in RStudio.

2.4. Functional enrichment analysis and PPI network construction

To further clarify the potential biological functions of the candidate targets, enrichment analysis was conducted using the Metascape platform. Positive proteins from the 4 phenotypes were input into the platform to identify statistically significant enriched terms, which were then grouped by cumulative hypergeometric P-values, enrichment factors, and protein Kappa scores. A Kappa score threshold of 0.3 was set to delineate distinct term clusters. The resulting pathways and enrichment analyses included terms from KEGG pathways, GO biological processes, Reactome gene sets, and canonical pathways. The top 20 most representative terms were visualized using bar charts, and a network graph was generated in Cytoscape to illustrate their interconnections. Additional interaction analysis for proteins with positive MR results was conducted using GeneMANIA. This analysis was used to construct a protein–protein interaction (PPI) network to illustrate the interactions among these proteins.

2.5. Drug target

To predict small-molecule drugs associated with the target proteins, an analysis was performed using the Drug Signatures Database on the Enrichr platform. The predicted drugs were ranked by P-value in descending order and visualized accordingly. To evaluate the druggability of the candidate targets, DrugBank Online was queried to assess the status of these targets in drug development.

2.6. Molecular docking

To evaluate the binding energy and interaction patterns between drugs and their targets, AutoDock Vina, a protein-ligand docking software, was employed. Initially, the 3D structures of key target proteins were obtained from the PDB database. The selection criteria included human-derived proteins, high resolution (≤2.5 Å), the presence of original ligands, and a crystallization pH as close to the normal physiological range as possible. The structures were subsequently downloaded in PDB format. Concurrently, the molecular structures of small-molecule drugs were obtained from the PubChem database in SDF format. These structures were converted to PDB format using OpenBabelGUI. The protein and ligand files were processed by removing water molecules, adding hydrogens, and converting them to PDBQT format. AutoDock Vina was used to dock the protein receptors with the small-molecule ligands. The binding energy calculations were performed. PyMOL was used to visualize the molecular docking results, providing a clear view of the interactions between the proteins and ligands.

3. Results

3.1. Protein quantitative trait loci data

By screening the obtained cis-pQTLs for strong correlation (P < 5 × 10⁻⁸, R² < 0.001, KB = 10,000, F > 10), we identified 4144 cis-pQTLs, which were used as SNPs.

3.2. Mendelian randomization analysis results

In RStudio, cardiomyopathy GWAS data were imported, and SNPs corresponding to the instrumental variables were extracted. Using the harmonize function, the SNPs were aligned with the exposure and outcome data, and MR analysis was subsequently conducted. A causal relationship between 70 proteins and cardiomyopathy (P < .05) was identified, with 37 proteins classified as risk factors (OR > 1) and 33 as protective factors (OR < 1). For DCM, 12 proteins showed a causal relationship (P < .05), with 8 proteins as risk factors (OR > 1) and 4 as protective factors (OR < 1). For HCM, 60 proteins demonstrated a causal relationship (P < .05), with 26 proteins as risk factors (OR > 1) and 34 as protective factors (OR < 1). For RCM, 103 proteins demonstrated a causal relationship (P < .05), with 52 proteins as risk factors (OR > 1) and 51 as protective factors (OR < 1). Following multiple hypothesis testing, 2 proteins were identified as associated with HCM: CCL17 (OR = 2.05, 95% CI = 1.41–2.98) and SERPINA4 (OR = 1.47, 95% CI = 1.19–1.80), both of which increased the risk of the disease. Sixteen proteins were identified as associated with RCM: APOL3, C1QL1, CNDP1, CRLF1, CSF2RB, CTSH, GABARAPL2, GP1BA, ICAM5, NPPB, NTM, PDCD5, PTPRS, RNASET2, RTN4R, and TCN2. Elevated levels of the following proteins were associated with an increased risk of RCM: APOL3 (OR = 2.60, 95% CI = 1.55–4.38), C1QL1 (OR = 4.62, 95% CI = 1.81–11.78), CNDP1 (OR = 2.75, 95% CI = 1.56–4.83), CSF2RB (OR = 2.48, 95% CI = 1.42–4.32), GABARAPL2 (OR = 46.42, 95% CI = 6.46–333.43), GP1BA (OR = 8.37, 95% CI = 2.30–30.40), ICAM5 (OR = 1.82, 95% CI = 1.31–2.52), NPPB (OR = 4.75, 95% CI = 1.94–11.65), RNASET2 (OR = 3.00, 95% CI = 1.54–5.89), RTN4R (OR = 3.42, 95% CI = 1.81–6.46). Reduced levels of the following proteins were linked to an increased risk of RCM: CRLF1 (OR = 0.24, 95% CI = 0.19–0.58), CTSH (OR = 0.64, 95% CI = 0.48–0.86), NTM (OR = 0.42, 95% CI = 0.28–0.63), PDCD5 (OR = 0.35, 95% CI = 0.20–0.63), PTPRS (OR = 0.03, 95% CI = 0.00–0.20), TCN2 (OR = 0.62, 95% CI = 0.47–0.81). Reverse causality tests showed that none of the positive genes exhibited reverse causality. Sensitivity analysis confirmed the robustness of the MR findings. Heterogeneity tests indicated some degree of heterogeneity, but this did not affect the IVW findings. Horizontal pleiotropy tests indicated that a few genes exhibited horizontal pleiotropy, but none of the positive genes showed significant horizontal pleiotropy. Detailed results are available in Supplementary Materials S1 to S6, Supplemental Digital Content, https://links.lww.com/MD/P107, and Figure 2.

Figure 2.

(A) Volcanic map of cardiomyopathy for plasma proteins. (B) Volcanic map of DCM for plasma proteins. (C) Volcanic map of HCM for plasma proteins. (D) Volcanic map of RCM for plasma proteins. DCM = dilated cardiomyopathy, HCM = hypertrophic cardiomyopathy, ln = natural logarithm, P = −log10 (P-value), PVE = proportion of variance explained, RCM = restrictive cardiomyopathy.

By intersecting the positive findings for DCM, HCM, and RCM, LMAN2L was identified as a common factor for both DCM and HCM. The proteins ANTXR1, ANXA2, CSF2RB, PLA2G2A, USP15, and FCRL6 were common to both HCM and RCM. Furthermore, FCRL6 was also associated with the cardiomyopathy phenotype. DCM, HCM, and RCM were each intersected with cardiomyopathy, showing that RCM intersected with cardiomyopathy through 5 proteins (CRLF1, HHIP, SULT1A3, TLR1, FCRL6), while HCM intersected through 14 proteins (ADH1B, ALDH2, APOBEC3G, CA3, CFH, CFHR3, HDHD2, HSP90B1, PGP, SERPING1, SRL, TIGIT, WFDC5, FCRL6). Detailed results are available in Supplementary Material S7, Supplemental Digital Content, https://links.lww.com/MD/P107 and Figure 3, which include Venn diagrams illustrating the intersection of the 4 phenotypes.

Figure 3.

Venn plots of 4 phenotypic intersections.

3.3. Enrichment analysis and PPI results

To gain deeper insight into the potential biological roles of the proteins, enrichment analysis was conducted, and the top 20 most significant enrichments were presented, ranked by P-value (Fig. 4). Enrichment analysis for cardiomyopathy revealed that most proteins were associated with immune response, complement activation, metabolism, and biosynthesis processes, primarily within the GO Biological Processes category, with a notable emphasis on immune response. The most significant pathway was the complement cascade (LogP = −10), involving 7 proteins (SERPING1, C1S, GZMM, CFH, PROS1, CFHR3, COLEC11), detailed in Supplementary Material S8, Supplemental Digital Content, https://links.lww.com/MD/P107. For DCM, functional enrichment analysis identified 4 pathways within the Reactome Gene Sets, with the most significant being “Transport to the Golgi and subsequent modification” (LogP = −4.3), involving 3 proteins (F5, MAN1A2, LMAN2L). However, current research has not identified an association between these 3 proteins and cardiomyopathy. Detailed results are available in Supplementary Material S9, Supplemental Digital Content, https://links.lww.com/MD/P107. Enrichment analysis for HCM identified 114 pathways, with associated proteins mainly involved in NABA, cell regulation, the complement cascade, immune response, and inflammation. The most significant pathway was “NABA Matrisome Associated” (LogP = −8.7), involving 13 proteins (ANXA2, F10|KNG1, SERPINA4, PTN, CCL17, SFRP4, TIMP2, VEGFA, GPC6, SEMA3G, ITIH5). Detailed results are available in Supplementary Material S10, Supplemental Digital Content, https://links.lww.com/MD/P107. Enrichment analysis for RCM identified 211 pathways, with associated proteins mainly involved in wound response, inflammation, metabolism, the complement cascade, and cell regulation. The most significant pathway was “negative regulation of the response to wounding” (LogP = −7.4), involving 7 proteins (ANXA2, GP1BA, MDK, PLG, PTPRS, VTN, RTN4R). Detailed results are available in Supplementary Material S11, Supplemental Digital Content, https://links.lww.com/MD/P107.

Figure 4.

(A) Cardiomyopathy enrichment analysis. (B) DCM enrichment analysis. (C) HCM enrichment analysis. (D) RCM enrichment analysis. DCM = dilated cardiomyopathy, HCM = hypertrophic cardiomyopathy, RCM = restrictive cardiomyopathy.

Protein–protein interaction analysis showed strong interactions among the proteins (Fig. 5). The PPI network of candidate cardiomyopathy proteins showed strong co-expression (61.21%), physical interactions (22.32%), and co-localization (6.96%). The PPI network of DCM candidate proteins showed strong genetic interactions (60.48%) and shared protein domains (39.52%). The PPI network of HCM candidate proteins showed strong co-expression (73.16%), co-localization (12.53%), and physical interactions (9.48%). The PPI network of RCM candidate proteins showed strong co-expression (69.69%), predicted interactions (15.53%), and co-localization (11.16%).

Figure 5.

(A) PPI network between candidate proteins for cardiomyopathy. (B) PPI network between candidate proteins for DCM. (C) PPI network between candidate proteins for HCM. (D) PPI network between candidate proteins for RCM. Each solid gray circle forming a circle represents a candidate protein, the outer solid gray circles represent proteins associated with candidate proteins. DCM = dilated cardiomyopathy, HCM = hypertrophic cardiomyopathy, PPI = protein–protein interaction, RCM = restrictive cardiomyopathy.

3.4. Potential drug targets

The predicted drugs for the 4 phenotypes are detailed in Supplementary Material S12 to S15, Supplemental Digital Content, https://links.lww.com/MD/P107 and Figure 6. In summary, among the top 10 drugs with the smallest p-values, those predicted by positive proteins for cardiomyopathy included treatments for myelodysplastic syndromes, anticonvulsants, contraceptives, dermatological agents, antioxidants, and unspecified drugs. The drugs predicted by positive proteins for DCM included antibiotics, β-2 adrenergic agonists, vitamin K antagonists, schistosomicides, preservatives, and other unspecified drugs. The drugs predicted by positive proteins for HCM included antibiotics, treatments for myelodysplastic syndromes, preservatives, immunosuppressants, peptide hormones, dermatological agents, vitamins, Ca²⁺-ATPase inhibitors, and other unspecified drugs. The drugs predicted by positive proteins for RCM included antibiotics, estrogenic steroids, antineoplastic agents, retinoids, insecticides, and other unspecified drugs. Certain drugs, such as treatments for myelodysplastic syndromes, antibiotics, and dermatological agents, appeared frequently, suggesting a significant association with various types of cardiomyopathy. The predicted drugs in this study may represent potential causes of cardiomyopathy, implying that adverse effects of drugs used for other conditions could contribute to the onset of cardiomyopathy. Moreover, these predicted drugs may represent potential new treatments for cardiomyopathy, warranting further investigation into the underlying biological mechanisms.

Figure 6.

(A) Cardiomyopathy predictive drugs. (B) DCM predictive drugs. (C) HCM predictive drugs. (D) RCM predictive drugs. DCM = dilated cardiomyopathy, HCM = hypertrophic cardiomyopathy, RCM = restrictive cardiomyopathy.

3.5. Molecular docking

Decitabine and Retinoic acid are both implicated in the prediction of cardiomyopathy and HCM, and thus are utilized as small-molecule drugs in molecular docking studies. In the prediction models for both phenotypes involving Decitabine, ALDH2 is consistently present and identified as a protective factor. Similarly, in the prediction models for both phenotypes involving Retinoic acid, HSP90B1 is consistently present and is the protein with the smallest P-value among the protective factors for HCM. Therefore, ALDH2 was selected for molecular docking with Decitabine, and HSP90B1 was chosen for molecular docking with Retinoic acid. The molecular structures of Decitabine and Retinoic Acid were sourced from the PubChem compound database, and the 3D coordinates of the ALDH2 and HSP90B1 proteins were obtained from the Protein Data Bank (PDB). Semiflexible molecular docking was conducted using AutoDock software, with binding energy used to assess the stability of the ligand-receptor interaction. Lower binding energy indicates higher stability of the interaction. The molecular docking results showed that the small molecules could bind to their targets, with binding energies below −5 kcal/mol, suggesting strong binding affinity between the active compounds and their targets. For ALDH2, the amino acids ILE-146, ILE-144, TYR-139, and THR-143 formed hydrogen bonds with the small-molecule drug, yielding a binding energy of −8.0 kcal/mol, indicating stable formation of the active pocket. For HSP90B1, the amino acid GLN-79 formed a hydrogen bond with the small molecule drug, yielding a binding energy of −7.4 kcal/mol, suggesting stable formation of the active pocket. The findings are shown in Figure 7.

Figure 7.

(A) Molecular docking results of decitabine with ALDH2. (B) Molecular docking results of retinoic acid with HSP90B1.

4. Discussion

Cardiomyopathy has garnered significant attention as a primary cause of heart failure and sudden cardiac death. Among adults, the prevalence of DCM is approximately 1/250, HCM is 1/500, while RCM is rare. These figures may be conservative, as they are often derived from screenings in generally healthy, young individuals.^[13] Cardiomyopathy is often hereditary, affecting multiple family members, and may present with minimal symptoms in its early stages. Advances in the treatment of cardiomyopathy remain relatively limited. However, screening for relevant genetic variants may facilitate early detection and timely intervention.

In this study, MR analysis identified CCL17 and SERPINA4 as risk factors for HCM. Although no prior research has specifically linked these proteins to HCM, they are known to play significant roles in cardiovascular diseases. CCL17 (C-C motif chemokine ligand 17) is a pro-inflammatory mediator expressed in macrophages and dendritic cells, which can inhibit Treg recruitment and exacerbate myocardial injury. Inhibiting CCL17 may reduce myocardial inflammation and improve left ventricular function.^[14] In viral myocarditis, CCL17 contributes to inhibiting myocardial inflammation, suggesting that CCL17-targeted therapy should be avoided in patients with acute viral myocarditis.^[15] CCL17 exhibits both protective and risk-enhancing roles in cardiac health; initial immune responses facilitate pathogen clearance, while prolonged inflammation can lead to myocardial damage.^[16] SERPINA4, part of the serine protease inhibitor family, encodes Kallistatin (KS), a protein with protective effects in cardiovascular diseases. KS inhibits apoptosis and inflammation, thereby protecting the myocardium from ischemia-reperfusion injury^[17] and reducing cardiac fibrosis following myocardial infarction.^[18]

The study found that decreased levels of LMAN2L are associated with an increased risk of DCM and HCM. Currently, no research has associated LMAN2L with other cardiovascular diseases. LMAN2L belongs to the glycoprotein secretion chaperone family. Abnormal glycoprotein secretion is a known cause of epilepsy, and mislocalization of LMAN2L can lead to brain development abnormalities that may result in intellectual disability and epilepsy.^[19] Early studies have suggested a potential association between LMAN2L and bipolar disorder.^[20] In plasma proteins, decreased levels of ANTXR1 and FCRL6, and increased levels of PLA2G2A and USP15, were linked to higher risks of RCM and HCM. Lower levels of ANXA2 and CSF2RB are linked to an increased risk of HCM, whereas higher levels are associated with an increased risk of RCM. Research indicates that anthrax toxin receptor 1 (ANTXR1) plays a crucial role in ischemia and angiogenesis-dependent vascular diseases.^[21] Mutations in ANTXR1 may impact cardiovascular diseases by causing extracellular matrix regulation abnormalities, leading to GAPO syndrome, which is characterized by growth retardation, alopecia, and pseudoanodontia.^[22] The PLA2G2A gene encodes secretory phospholipase A2 group IIA (sPLA2-IIA), responsible for hydrolyzing phospholipids. Early studies suggest that sPLA2 inhibitors may effectively mitigate atherosclerosis formation.^[23] Research by Liang et al indicates that USP15 (ubiquitin-specific peptidase 15) is a potential target of Mir26a and may mitigate myocardial cell damage induced by ischemic stress.^[24] Similarly, overexpression of colony-stimulating factor 2 receptor beta subunit (CSF2RB) exerts a protective effect on ischemic myocardial cell damage.^[25] Fc receptor-like 6 (FCRL6), part of the FCRL family, is often used as a target in tumor immunotherapy,^[26] though no reports have linked it to heart diseases. Annexin A2 (ANXA2) is considered a potential target that binds to integrin α5, mediating its translocation to lipid rafts, and may play a crucial role in atherosclerosis treatment.^[27]

Two pathways frequently implicated in cardiomyopathy, HCM, and RCM are the complement cascade and the regulation of receptor-mediated endocytosis. Within the complement cascade, crosstalk between the complement and coagulation systems has been reported to influence arrhythmogenic right ventricular cardiomyopathy and may contribute to myocardial injury.^[28] Members of the CFHR family participate in the complement cascade across all 3 phenotypes. CFHR has been associated with hepatocellular carcinoma^[29] and kidney diseases.^[30] SERPING1 is involved in the complement cascades of both HCM and cardiomyopathy. As part of the serine protease inhibitor (SERPIN) family, SERPING1 acts as a C1 inhibitor, mitigating myocardial injury in patients with acute myocardial infarction, improving cardiac function, and inhibiting complement activity.^[31] Research has shown that SERPING1 is elevated in ischemic cardiomyopathy and contributes to regulation via complement inhibition.^[32] The regulation of receptor-mediated endocytosis involves the internalization of extracellular macromolecules, with the plasma membrane enveloping the molecules to form vesicles that enter the cell. In atherosclerosis, this pathway regulates the endocytosis of low-density lipoproteins.^[33] Studies indicate that endothelial cell receptors contribute to cardiomyopathy by mediating lipoprotein transport.^[34]

Decitabine and retinoic acid were identified as potential drugs for both cardiomyopathy and HCM. Decitabine, a chemotherapeutic pyrimidine nucleoside analog, exerts its effects through DNA hypomethylation and subsequent changes in gene expression. It is typically administered intravenously to treat myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML). However, evidence suggests that treatment with decitabine may lead to myocarditis.^[35] Due to poor bioavailability, the use of oral decitabine has been limited. Research by Sheel et al indicated that co-administration of decitabine with cedazuridine improved its bioavailability, although cases of cardiomyopathy and heart failure were reported and resolved upon discontinuation of the medication.^[36] Retinoic acid, a vitamin A derivative, is used to treat conditions like acne vulgaris and certain types of promyelocytic leukemia. It functions by activating retinoic acid receptors and retinoid X receptors (RXR).^[37] In a study involving mice with gestational diabetes mellitus, RA treatment reduced cardiac damage, suggesting a potential protective role against gestational diabetes mellitus-induced cardiomyopathy.^[38] Isotretinoin, another retinoid among predicted drugs for RCM, is used to treat severe recalcitrant acne. However, a case report described a male patient developing DCM and renal infarction after isotretinoin treatment for severe acne.^[39] Therefore, while retinoic acid shows potential cardioprotective effects, its impact on the heart requires further investigation.

This study investigated the associations between genetic variants of plasma proteins and cardiomyopathy and its 3 phenotypes using MR. Several plasma proteins were identified as genetically associated with cardiomyopathy and could serve as biomarkers for early detection and intervention. Additionally, novel drug targets were uncovered; however, the underlying mechanisms for certain targets remain unclear and require further investigation.

This research advances current knowledge on cardiomyopathy targets. The use of MR helps mitigate confounding bias and reverse causality and circumvents ethical concerns. However, this study has certain limitations. The reliance on plasma proteins implies that their expression may differ from cellular or tissue contexts. The study population was of European descent, potentially limiting the generalizability of findings to non-European populations. Patient responses to pharmacological treatments can vary, and complex diseases are typically influenced by multiple genetic factors. Proteins may display variability among individuals due to environmental factors, which do not necessarily result in pathogenic outcomes.

Further research should explore the underlying mechanisms and roles of candidate proteins to enhance their application in the diagnosis and treatment of cardiomyopathy.

5. Conclusions

This study analyzed the genetic associations between plasma proteins and cardiomyopathy, including its 3 subtypes. Using MR analysis, 70 proteins associated with cardiomyopathy, 12 proteins linked to DCM, 60 proteins linked to HCM, and 103 proteins associated with RCM were identified. The intersection of these phenotypes revealed 24 significant proteins. After multiple hypothesis corrections, CCL17 and SERPINA4 were found to be strongly associated with HCM, while APOL3, C1QL1, CNDP1, CRLF1, CSF2RB, CTSH, GABARAPL2, GP1BA, ICAM5, NPPB, NTM, PDCD5, PTPRS, RNASET2, RTN4R, and TCN2 were associated with RCM. These candidate proteins showed strong co-expression, particularly in complement cascades, immune responses, and cell regulation, indicating their potential as biomarkers. Additionally, novel drug targets were identified, but further research is necessary to explore their mechanisms and assess their value in the diagnosis and treatment of cardiomyopathy.

Acknowledgments

We extend our gratitude to the participants and investigators of the GWAS Summary Database and the deCODE database.

Author contributions

Data curation: Chen Chen.

Funding acquisition: Wulin Gao.

Methodology: Maoxia Fan.

Supervision: Guohua Dai, Ruixia Liu, Xueyan Dong, Wulin Gao.

Writing – original draft: Yandi Ji.

Writing – review & editing: Yandi Ji, Guohua Dai, Maoxia Fan, Chen Chen, Ruixia Liu, Xueyan Dong, Wulin Gao.