Type 1 diabetes metabolomics and multi-omics: Current applications, limitations, and future directions - A narrative review.

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by immune-mediated destruction of pancreatic β-cells, resulting in absolute insulin deficiency and lifelong dependence on exogenous insulin. This disease, if not treated, leads to acute and chronic complications affecting the cardiovascular, renal, and retinal systems and introduces significant psychosocial burdens. But even with advances in insulin therapy, optimal glycemic control remains difficult to achieve. T1DM progression spans from autoantibody seroconversion to overt diabetes, accompanied by dynamic metabolic perturbations, contributing to β-cell dysfunction. These metabolic alterations correlate with glycemic markers and provide integrated signatures of gene-environment interactions and disease heterogeneity. Therefore, metabolomics has emerged as a promising approach for identifying metabolic dysregulation associated with T1DM, with some studies reporting metabolite alterations detectable before clinical onset or autoantibody positivity; targeted and untargeted approaches enable comprehensive profiling of disease-associated metabolic pathways, although these findings largely reflect associations from observational cohorts rather than validated early-detection tools. Here, the analysis of biofluids with advanced platforms such as UPLC-MS and NMR provides high-resolution insights into systemic metabolism, supporting the discovery of candidate biomarkers that may, pending further validation, contribute to earlier diagnosis and precision medicine strategies in T1DM. Even though current metabolomic studies have substantially improved the classification of metabolic alterations associated with T1DM, the field still necessitates clarification of causal mechanisms, standardization of methodologies, validation of findings across diverse populations, and integration of metabolomics with other omics platforms in robust longitudinal designs. These gaps highlight a clear necessity of moving beyond descriptive profiling toward systematically standardized and validated metabolomic markers embedded within multi-omics and longitudinal frameworks.