From Plasma Metabolites to Therapeutic Targets: Translational Prioritization of Metabolic Pathways in Intervertebral Disc Degeneration

Background: Intervertebral disc degeneration (IVDD) is increasingly recognized as a metabolic disease involving profound alterations in cellular metabolism, yet translating metabolomic discoveries into clinically actionable targets remains a major challenge. We sought to identify circulating metabolic pathways associated with IVDD and establish a translational framework linking plasma metabolites to candidate therapeutic targets.

Methods: Eighty genetically predicted plasma metabolites and metabolite ratios were systematically evaluated for associations with cervical IVDD, thoracic/thoracolumbar/lumbosacral IVDD (TTL_IVDD), and low back pain using a two-sample Mendelian randomization approach. Prioritized metabolic signals were subsequently integrated with effector-gene annotation and DrugBank to construct metabolite–gene–drug relationships with potential clinical relevance.

Results: Two plasma metabolites remained significant after multiple-testing correction, both for TTL_IVDD. Higher genetically predicted dimethylarginine (SDMA + ADMA), linked to DDAH1, was associated with reduced disease risk, whereas higher alpha-hydroxyisovalerate, linked to LDHA, was associated with increased risk. Additional suggestive associations implicated glycolysis, mitochondrial metabolism, one-carbon metabolism, and amino acid metabolism in IVDD pathogenesis. Importantly, integrating effector-gene annotation with DrugBank connected these metabolite-associated pathways to pharmacologically tractable targets, identifying multiple annotated compounds interacting with DDAH1 and LDHA. This integrative strategy extended metabolite discovery beyond association analysis by providing biologically interpretable and therapeutically relevant molecular targets.

Conclusions: Our study identifies phenotype-specific metabolic pathways underlying IVDD and establishes a translational metabolomics workflow that connects circulating metabolites to effector genes and pharmacologically tractable targets. This metabolite–gene–drug framework facilitates biological interpretation of metabolomic findings, prioritizes candidate therapeutic targets, and provides a scalable strategy for translating metabolomics into precision medicine.