Integrating Transcriptomics and Metabolomics for Clinical Disease Detection: A Critical Review of Emerging Diagnostic Methodologies.

Abstract

Transcriptomics and metabolomics occupy mechanistically complementary positions in the molecular hierarchy of disease: gene expression characterizes upstream regulatory dysregulation, while metabolomics captures the downstream biochemical consequence at the functional endpoint of that cascade. Applied alone, each yields only a partial view of complex, multifactorial disease. This critical review evaluates the workflows, platforms and computational strategies of both disciplines, appraising their diagnostic utility, translational limitations and clinical laboratory adaptability.We examine integration methodologies spanning correlation-based approaches, multiblock latent factor models, network-guided pathway fusion and machine-learning architectures, and set out the parameter sensitivity and reproducibility limitations that the applied literature systematically underreports. These methodological claims are then tested against published practice through two in-depth case studies, in sepsis and non-small cell lung cancer, selected as contrasting exemplars. Persistent barriers are identified and discussed: pre-analytical variability, the transcriptome-metabolome correlation gap (r ≈ 0.3-0.4), incomplete reference databases, the absent proteome layer, and the lack of standardized clinical validation frameworks. We further argue that the missing proteome layer is not only a mechanistic gap but a formal obstacle to causal inference from two-layer data.Emerging solutions, including deep-learning fusion, explainable artificial intelligence and foundation models, are assessed alongside their documented limitations. We report that no biomarker derived from integrated transcriptomic-metabolomic analysis has undergone analytical validation, entered regulatory qualification, or reached clinical implementation, and that where diagnostic performance is reported it belongs to a single-platform model rather than to the integrated signature. Harmonized workflows, standardized reporting and regulatory-grade validation remain prerequisites for clinical adoption.