Unravelling disease ionomics: An ionome perspective on cancer and metabolic health

Aim This review delineates the mechanistic contributions of micronutrients and heavy metals to cancer and metabolic disorders by integrating ionomics with multi-elemental analytics and pharmacoinformatics for biomarker discovery, while critically synthesizing current evidence and establishing a framework for future clinical validation. Summary Micronutrients and heavy metals are indispensable for physiological homeostasis, and their dysregulation is increasingly implicated in cancer and metabolic disorders. Ionomics has emerged as a powerful system-level approach for investigating elemental distribution, homeostasis, and interactions under physiological and pathological conditions. This review summarizes recent advances in ionomics, highlighting multi-matrix elemental profiling across whole blood, serum, plasma, urine, hair, nails, and tumor tissues. Distinct ionomic signatures consistently show elevated copper (Cu), chromium (Cr), arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb), together with reduced selenium (Se), zinc (Zn), and manganese (Mn), indicating strong associations between elemental dyshomeostasis and disease progression. Disease-specific ionomic alterations across biological matrices further identify candidate elemental signatures with potential utility for diagnosis, prognosis, and therapeutic monitoring. However, the available evidence remains largely descriptive and associative, with substantial methodological heterogeneity that limits immediate clinical implementation. Conclusion Overall, ionomics provides valuable insights into disease-associated elemental dysregulation and highlights promising candidate biomarkers for cancer and metabolic disorders. Rather than demonstrating clinical potential biomarker readiness, this review provides a comprehensive evidence synthesis that maps recurring ionomic signatures, identifies current methodological and biological knowledge gaps, and establishes a foundation for future prospective validation, standardized analytical workflows, and precision medicine applications.