Metabolomics and lipidomics enable comprehensive profiling of metabolic states across diverse diseases and have generated a vast number of candidate biomarkers. Despite this progress, only a small fraction of metabolite-based biomarkers have achieved durable clinical translation. While this gap is often attributed to biological complexity or limited cohort size, increasing evidence suggests that failure more commonly reflects systematic misalignment between analytical measurement, biological interpretation, and clinical decision-making requirements. In this review, we argue that metabolites are not intrinsically unreliable biomarkers but are frequently overinterpreted as disease-specific indicators despite being highly context-dependent reporters of physiological state. We synthesize recurrent failure modes across the translational pipeline—including pre-analytical instability, ionization bias and semi-quantitative measurement, structural and annotation ambiguity, statistical overfitting, loss of disease specificity under systemic stress, and cohort-dependent performance collapse. Building on these insights, we propose a structured roadmap for the clinical translation of metabolite and lipid biomarkers. Rather than emphasizing further discovery, this framework prioritizes decision-oriented eligibility criteria encompassing pre-analytical robustness, analytical validity, molecular definition, biological interpretability, validation under real-world heterogeneity, and alignment with clinical utility and regulatory expectations. By reframing metabolic biomarkers as context-sensitive measurements embedded within clinical decision systems, this review provides practical guidance for investigators, clinicians, and regulators seeking to translate metabolomics and lipidomics into reliable tools for clinical practice.
Kim et al. (2026) studied this question.