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Background: Our goal was to examine the causal link between blood metabolites, their ratios, and the risk of developing proliferative diabetic retinopathy (PDR) from a genetic insight.Methods: Summary‐level data about 1400 blood metabolites and their ratios, as well as PDR, were sourced from prior genome‐wide association studies (GWAS). A two‐sample univariate and multivariate Mendelian randomization (MR) approach was utilized. Additionally, metabolic pathway analysis and sensitivity analysis were also conducted.Results: After adjusting for multiple tests, four blood metabolites significantly correlated with PDR risk. Two ceramides, including glycosyl‐N‐palmitoyl‐sphingosine (d18:1/16:0) (odds ratio [OR] = 1.12, 95% confidence interval (CI): 1.06–1.17, p < 0.001, false discovery rate (FDR) = 0.005) and glycosyl‐N‐behenoyl‐sphingadienine (d18:2/22:0) (OR = 1.11, 95% CI: 1.06–1.16, p < 0.001, FDR = 0.017), were linked to increased risk. Additionally, 3‐methylcytidine (OR = 1.05, 95% CI: 1.03–1.08, p < 0.001, FDR = 0.021) also posed a risk, whereas (N(1)+N(8))‐acetylspermidine (OR = 0.91, 95% CI: 0.87–0.94, p < 0.001, FDR = 0.002) appeared protective. Multivariable MR analysis further confirmed a direct, protective effect of (N(1)+N(8))‐acetylspermidine on PDR risk (OR = 0.94, 95% CI: 0.89–1.00, p = 0.040). The sensitivity analysis results indicated that evidence for heterogeneity and pleiotropy was absent.Conclusion: These metabolites have the potential to be used as biomarkers and are promising for future research into the mechanisms and drug targets for PDR.
Background: Our goal was to examine the causal link between blood metabolites, their ratios, and the risk of developing proliferative diabetic retinopathy (PDR) from a genetic insight.Methods: Summary‐level data about 1400 blood metabolites and their ratios, as well as PDR, were sourced from prior genome‐wide association studies (GWAS). A two‐sample univariate and multivariate Mendelian randomization (MR) approach was utilized. Additionally, metabolic pathway analysis and sensitivity analysis were also conducted.Results: After adjusting for multiple tests, four blood metabolites significantly correlated with PDR risk. Two ceramides, including glycosyl‐N‐palmitoyl‐sphingosine (d18:1/16:0) (odds ratio [OR] = 1.12, 95% confidence interval (CI): 1.06–1.17, p < 0.001, false discovery rate (FDR) = 0.005) and glycosyl‐N‐behenoyl‐sphingadienine (d18:2/22:0) (OR = 1.11, 95% CI: 1.06–1.16, p < 0.001, FDR = 0.017), were linked to increased risk. Additionally, 3‐methylcytidine (OR = 1.05, 95% CI: 1.03–1.08, p < 0.001, FDR = 0.021) also posed a risk, whereas (N(1)+N(8))‐acetylspermidine (OR = 0.91, 95% CI: 0.87–0.94, p < 0.001, FDR = 0.002) appeared protective. Multivariable MR analysis further confirmed a direct, protective effect of (N(1)+N(8))‐acetylspermidine on PDR risk (OR = 0.94, 95% CI: 0.89–1.00, p = 0.040). The sensitivity analysis results indicated that evidence for heterogeneity and pleiotropy was absent.Conclusion: These metabolites have the potential to be used as biomarkers and are promising for future research into the mechanisms and drug targets for PDR.
BackgroundPeripheral neuropathy (PN) is a common neurological disorder, and circulating plasma proteins with causal genetic evidence are a major source of therapeutic targets. This study identifies several potential plasma proteins that are causally related to PN risk, providing new insights into protein-mediated pathogenesis of PN and potential targets for novel therapies.MethodsTo identify potential therapeutic targets for PN, we employed two-sample Mendelian randomization (MR) to identify plasma proteins associated with six common PN. First, we screened for proteins related to PN using genome-wide association studies (GWAS), obtaining genetic data on plasma proteomes from 35,559 Icelanders. Summary data for six common PN, including Carpal Tunnel Syndrome (CTS), Trigeminal Neuralgia (TN), Alcoholic Neuropathy (AIP), Drug-induced Neuropathy (DIP), Diabetic Neuropathy (DP), and Guillain-Barré syndrome (GBS), were obtained from the FinnGen database. Two-sample MR and colocalization analyses were then conducted to further identify protein-PN pairs with presumed causal relationships. Enrichment analysis of positive proteins revealed potential biological processes and pathways. Based on drug-gene interaction analysis, we ultimately identified causal proteins associated with PN that could serve as potential drug targets for treating PN.ResultsThrough MR analysis, we identified eight proteins (UBC12, SEM4C, IL23R, Prothrombin, CBS, Microglobulin, MATN4, COLEC12) with causal relationships to PN. We found that UBC12 is a protective factor for DP and CTS, while the remaining proteins are risk factors. Further colocalization analysis showed a posterior probability of hypothesis 4 (PPH4) less than 0.75, indicating no positive colocalization results were found. From the pathway enrichment analysis, we discovered that the proteins were mainly concentrated in pathways related to defense response to bacterium, receptor signaling pathway via STAT, cell killing, negative regulation of cytokine production, and leukocyte mediated immunity. Finally, in Drug-Gene Interaction database (DGIdb), we identified three protein-coding genes (IL23R, F2, CBS) as potential drug targets for PN.ConclusionMendelian randomization studies confirm the causal relationship between genetically predicted PN-related risk and genetically predicted plasma protein abundance. Plasma proteins, as biomarkers associated with PN, can provide potential drug targets for etiological intervention research in PN.
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