Acute myeloid leukemia (AML) is a lethal clonal hematopoietic malignancy. Several reports have shown that serum metabolite alterations have been implicated in AML, but the causal relationship and underlying biological mechanisms remain unclear. We performed bidirectional Mendelian randomization (MR) to evaluate the association between 486 serum metabolites and AML. The analytical approaches used to minimize research bias included the inverse variance weighting (IVW), MR-Egger and weighted median (WM) methods. Sensitivity analyses were performed using Cochran’s Q Test, MR-Egger, MR pleiotropy residual sum and outlier (MR-PRESSO), and Leave-one-out (LOO) analysis. Metabolic pathway analysis was conducted using the MetaboAnalyst 6.0 platform. We utilized RNA-seq data to explore the potential genes and mechanisms underlying the regulation of AML occurrence by serum metabolites. We identified 23 serum metabolites (13 known and 10 unknown) significantly associated with AML. Sensitivity analyses further validated the robustness of these associations. No evidence of reverse causality was detected by reverse MR analysis. The core pathways were histidine metabolism and fructose/mannose metabolism. Transcriptomic integration revealed 39 overlapping genes (differentially expressed genes vs. metabolite-associated genes) as key mediators, enriched in neuroactive ligand signaling, synaptic vesicle cycle, and GABAergic synapse (KEGG), plus synapse assembly and calmodulin binding and neuron-to-neuron synapse (GO). This study establishes causal links between specific serum metabolites and AML, revealing neuro-related mechanistic pathways. These findings provide novel biomarkers and therapeutic targets for AML precision medicine.

Chronic periodontitis (CP) is a multifactorial inflammatory disease. Growing evidence links dysregulated serum metabolites to CP pathogenesis, yet their causal roles in disease progression remain poorly defined. We performed bidirectional Mendelian randomization (MR) analysis to evaluate causal relationships between 486 serum metabolites and CP. Inverse-variance weighted, MR-Egger, and weighted median methods were employed to mitigate research bias. Sensitivity was evaluated via the Cochran Q test, MR-Egger, and leave-one-out analysis. Metabolic pathway analysis (based on metabolomics data) was carried out via the MetaboAnalyst 6.0. We analyzed RNA-seq data to identify genes and mechanisms involved in serum metabolite regulation of CP development. Bidirectional MR identified 22 metabolites associated with CP risk, with 6 metabolites exhibiting protective effects and 16 linked to risk-increasing associations. Sensitivity analyses confirmed robustness across multiple MR methods, and reverse MR excluded reverse causality. Caffeine metabolism and lysine degradation emerged as the core pathways. Nineteen overlapping genes were identified via transcriptomic integration as key mediators. Enrichment analysis highlighted FoxO signaling and p53-mediated signaling. This study deepens the understanding of metabolic crosstalk and its role in CP pathogenesis, revealing novel biomarkers and therapeutic targets that inform diagnostic precision and therapeutic innovation. However, limitations such as sample size constraints should be noted. Future research could validate these findings in larger populations and explore the underlying biological mechanisms linking these metabolites to CP.

Multiple myeloma (MM) is a clonal plasma cell neoplasm characterized by autonomous immunoglobulin overproduction. Despite associations between serum metabolites and MM, causal mechanisms remain unclear. Here, we employed bidirectional Mendelian randomization (MR) using 452 serum metabolites to elucidate causal associations with MM risk. The inverse variance-weighted (IVW) method was prioritized, complemented by MR-Egger and weighted median (WM) analyses to address horizontal pleiotropy. Sensitivity analyses—including Cochran’s Q test, MR-Egger intercept evaluation, and leave-one-out (LOO) robustness checks—confirmed result stability. Pathway enrichment was performed using MetaboAnalyst 6.0. RNA-seq data were integrated to identify transcriptional regulators and signaling pathways mediating serum metabolite-driven MM. Among 21 metabolites significantly associated with MM, 8 exhibited protective inverse correlations, while 13 showed risk-enhancing effects. Sensitivity analyses further confirmed the validity of the observed relationships, while bidirectional MR confirmed no reverse causality. Pathway enrichment highlighted valine/leucine/isoleucine biosynthesis and biotin metabolism as pivotal pathways. Integrating transcriptomic data revealed 11 overlapping genes enriched in metal ion transmembrane transporter activity and glycosaminoglycan biosynthesis—chondroitin sulfate/dermatan sulfate. This study established a causal relationship between specific serum metabolites and MM and revealed that key genes may affect the development of MM through metabolic-epigenetic crosstalk, providing preliminary insights into potential therapeutic targets.