Assessment of genetic associations between antidepressant drug targets and various stroke subtypes: A Mendelian randomization approach

To the Editor: Depression is a common psychiatric disorder, affecting over 260 million people of all ages globally.[1] Prior studies investigating the association between antidepressant use and stroke risk have yielded inconsistent results.[2,3] Consequently, it remains unclear which of the various antidepressant categories may affect stroke. Thus, the rational use of antidepressants is important for reducing stroke risk and recurrence, while offering candidate therapeutic targets. Drug-target Mendelian randomization (MR) analysis, which uses genetic variants located in or near the region of drug target genes as proxies for drug effects, is a promising tool for identifying causal links between drug targets and diseases. This study aimed to evaluate the causal associations between antidepressant target genes and stroke and its various subtypes (including any stroke [AS], ischemic stroke [IS], large artery atherosclerosis stroke [LAA], cardioembolic stroke [CES], and small vessel stroke [SVS]) using drug-target MR analysis. Various antidepressants were identified from the World Health Organization Collaborating Centre for Drug Statistics Methodology, and were classified by the Anatomical Therapeutic Chemical classification system. The DrugBank (https://go.drugbank.com/) and ChEMBL (https://www.ebi.ac.uk/chembl/) databases were used to determine the genes encoding the targets of antidepressants. To identify genetic variants as proxies for the effect of drug target genes, blood cis-expression quantitative trait loci (eQTL) data from the eQTLGen Consortium (n = 31,684) were used. The cis-eQTL located within 1 Mb downstream or upstream of the region of the drug target genes with a false discovery rate (FDR) <0.05 and F-statistic (calculated by the formula: F-statistic = beta2/se2) >10 were screened. Independent genetic variants without linkage disequilibrium (r2 <0.1) were used as the instrumental variables (IVs). Genome-wide association studies (GWAS) summary data for stroke and its subtypes were from GIGASTROKE consortium. Our study included only individuals of European ancestry, comprising AS (73,652 cases and 1,234,808 controls), IS (62,100 cases), LAA (6399 cases), CES (10,804 cases), and SVS (6811 cases). All participants enrolled in this study were of European ancestry, with no sample overlap with the exposure dataset in the main analysis. Detailed information of the different data sources is provided in Supplementary Table 1, https://links.lww.com/CM9/C261. All MR analyses were performed using TwoSampleMR R package in R software (v.4.0.3, R Development Core Team, Vienna, Austria), while the inverse variance weighted method was used to estimate the causal effects. The FDR method was applied for multiple testing, with an FDR <0.05 indicating statistical significance. Sensitivity analyses, including heterogeneity and pleiotropy tests, were performed using Cochrane's Q test, Rucker's Q test, MR-Egger intercept test, MR pleiotropy residual sum and outlier global test, and leave-one-out analysis. Colocalization analysis was conducted between the significant drug target genes identified in the primary MR analysis and stroke outcomes. A posterior probability of hypothesis 4 (PPH4) >0.8 was used to characterize significant evidence for colocalization. Further, we assessed the causal relationship between the candidate target genes and cerebrovascular risk factors. For drug target genes causally linked to both stroke and risk factors, a two-step mediation MR analysis was conducted to evaluate the effects of drug target genes (exposure) on stroke (outcomes) via the cerebrovascular risk factors (mediators). To determine whether the observed associations between antidepressant target gene expression and stroke risk are likely mediated by major depressive disorder (MDD) or independent of MDD, MR analysis was also conducted to evaluate the associations between MDD and stroke. Further details of this analysis are provided in the Supplementary Methods, https://links.lww.com/CM9/C261. A flow diagram of the study is presented in Supplementary Figure 1, https://links.lww.com/CM9/C261. A total of 111 drug targets encoding proteins have previously been experimentally shown to be modified by one or more antidepressants. After selecting the IVs for the antidepressant target genes, 29 of the 111 genes were identified in the outcome datasets [Supplementary Tables 2–4, https://links.lww.com/CM9/C261]. The associations between genetically predicted antidepressant target genes and stroke are presented in Figure 1, Supplementary Figures 2 and 3, and Supplementary Tables 5–11, https://links.lww.com/CM9/C261. Following FDR adjustment, we identified five drug target genes significantly associated with AS risk: KCNH2 (odds ratio [OR] = 1.057, 95% confidence interval [CI] 1.017–1.098, FDR = 0.027), MPO (OR = 1.071, 95% CI = 1.050–1.093, FDR = 7.81E−10), SIGMAR1 (OR = 0.952, 95% CI = 0.934–0.971, FDR = 1.12E−05), WARS (OR = 0.982, 95% CI = 0.973–0.992, FDR = 0.003), WARS2 (OR = 0.981, 95% CI = 0.970–0.993, FDR = 0.010). Moreover, four genetically predicted drug target genes were found to be significantly associated with IS risk: MPO (OR = 1.078, 95% CI = 1.052–1.105, FDR = 5.55E−08), SIGMAR1 (OR = 0.946, 95% CI = 0.927–0.965, FDR = 1.05E−06), SLC18A2 (OR = 0.942, 95% CI = 0.902–0.983, FDR = 0.043), WARS (OR = 0.980, 95% CI = 0.970–0.991, FDR = 0.002). Genetically predicted GRIN2D (LAA: OR = 0.465, 95% CI = 0.293–0.739, FDR = 0.034), KCNH2 (CES: OR = 1.222, 95% CI = 1.128–1.325, FDR = 2.95E−05), and WARS2 (SVS: OR = 0.938, 95% CI = 0.905–0.972, FDR = 0.011) levels were also found to be significantly associated with LAA, CES and SVS, respectively. Colocalization analysis indicated that MPO and IS, as well as GRIN2D and LAA, probably shared a causal single nucleotide polymorphism in the gene locus (MPO: PPH4 = 0.884; GRIN2D: PPH4 = 0.824; Supplementary Figure 4 and Supplementary Table 12, https://links.lww.com/CM9/C261).Figure 1: MR analysis of significant drug target genes with stroke risk. Five antidepressant targets (KCNH2, MPO, SIGMAR1, WARS, and WARS2) were significantly associated with AS risk after FDR adjustment. Additionally, four targets (MPO, SIGMAR1, SLC18A2, and WARS) showed significant associations with IS risk. Genetically predicted GRIN2D, KCNH2, and WARS2 were significantly linked to LAA, CES, and SVS, respectively. AS: Any stroke; CES: Cardioembolic stroke; CI: Confidence interval; FDR: False discovery rate; IS: Ischemic stroke; LAA: Large artery atherosclerosis stroke; MR: Mendelian randomization; OR: Odds ratio; SVS: Small-vessel stroke.The associations between MPO and GRIN2D with 14 cerebrovascular risk factors were also investigated [Supplementary Figure 5 and Supplementary Tables 13–15, https://links.lww.com/CM9/C261]. Genetically predicted MPO levels were significantly associated with atrial fibrillation (AF; OR = 1.043, 95% CI = 1.018–1.068, FDR = 0.003), heart failure (HF; OR = 1.048, 95% CI = 1.023–1.075, FDR = 0.002), and systolic blood pressure (SBP; OR = 1.256, 95% CI = 1.104–1.428, FDR = 0.003). MR analysis further revealed the causal effects of genetically predicted GRIN2D on AF (OR = 0.819, 95% CI = 0.732–0.917, FDR = 0.008) and triglyceride levels (OR = 0.829, 95% CI = 0.724–0.948, FDR = 0.045). A two-step mediation MR analysis was applied to evaluate the effects of MPO on stroke outcomes (AS and IS) via risk factors (AF, HF, and SBP). The proportions of the mediation effect of MPO on AS and IS via AF were 9.7% and 9.4%, respectively, while the corresponding values via SBP were 8.5% and 8.0%, respectively. The indirect effect of MPO on the risk of AS and IS via HF accounted for 29.7% and 30.2% of the total effect, respectively [Supplementary Figure 6 and Supplementary Table 16, https://links.lww.com/CM9/C261]. We found no evidence to support an association between genetically estimated MDD and AS, IS, LAA, CES, or SVS (all P values >0.05; stroke GWAS from GIGASTROKE or MEGASTROKE; Supplementary Figure 7 and Supplementary Tables 17 and 18, https://links.lww.com/CM9/C261). This indicates that the observed association of target genes with stroke is unlikely to be solely caused by MDD, and indicates that this association is likely independent of the association with MDD. The present study identified associations between antidepressant targets and stroke and its subtypes through drug-target MR analysis. In addition, we identified two candidate antidepressant target genes for IS and LAA (MPO and GRIN2D, respectively). Myeloperoxidase (MPO), a key inflammatory factor in the myeloid system, is highly expressed in activated human neutrophils, and plays an important role in inflammation and oxidative stress responses. Prior studies have shown that inhibition of MPO activity can reduce inflammation and enhance cellular protection against IS.[4]GRIN2D encodes the glutamate ionotropic receptor N-Methyl-D-Aspartate (NMDA) type subunit 2D (GluN2D), which is a subunit of the NMDA Receptor (NMDAR) and is involved in learning, memory, and synaptic functioning.[5] There is currently limited evidence linking GRIN2D with LAA or atherosclerosis, highlighting the need for further investigation. This study has several strengths. First, this MR study integrated the latest and largest GWAS and eQTL datasets to investigate causality and reduce confounding factors and reverse causation. Second, we systematically examined various antidepressant targets and stroke subtypes, and performed several sensitivity analyses to support our findings. Third, MR analysis of multiple cerebrovascular risk factors was performed to identify potential side effects and alternative indications crucial for future clinical applications. However, this study has several limitations, as follows. First, all participants included in the GWAS used in the present study were of European ancestry; therefore, our findings require validation in other races. Although our MR analysis indicated potential causal relationships, these associations should not be interpreted as direct evidence to indicate that antidepressants targeting these proteins would have causal effects on stroke risk. Inferring the actual pharmacological effects from genetic analyses is associated with complexities owing to variations in drug mechanisms, timing, magnitude, and duration of exposure. Although our colocalization analysis provided strong evidence to support the existence of shared causal variants in MPO and GRIN2D, the lack of colocalization evidence for other genes with MR evidence indicates that these relationships may require further investigation using larger datasets or complementary methods. Further studies are thus required to determine the effects of antidepressants on the risk of stroke. Our findings also require validation using independent datasets to ensure their robustness and broader applicability. Future research should thus explore downstream biomarkers to gain a more comprehensive understanding of the effects of antidepressant targets on stroke risk. As larger protein quantitative trait locus datasets become available, the investigation of drug-target relationships should be enhanced. Moreover, there is the potential for survivor bias because the GWAS primarily recruited survivors, possibly missing the genetic risk profiles of those who did not survive severe strokes. Finally, we identified a robust causal relationship between MPO and HF, with HF mediating the association between MPO and IS risk. Further research in non-HF patients is required to minimize potential pleiotropic effects. In conclusion, our drug target MR analysis provides insights into the associations between antidepressant targets and stroke, guiding the selection of antidepressants for individuals at risk of stroke, and identifying MPO and GRIN2D as promising stroke drug targets. However, further research is required to verify the long-term effects of antidepressants on stroke risk.