Narrative review demonstrates how multiscale immunometabolic networks shape immune-cell function, suggesting targeted biopharmaceutical engineering can drive precision therapies.
INTRODUCTION: Immunometabolism integrates energy demands, biosynthesis, redox homeostasis, and immune-cell function, yet metabolic phenotypes remain context dependent. This review conceptualizes immunometabolic reprogramming as a multiscale regulatory network connecting microenvironmental pressures, nutrient sensing, metabolic flux, metabolite-mediated regulation, immune-cell states, systems-level biomarkers, and precision therapeutic engineering. METHODS: A structured narrative review synthesized literature published from January 2022 onward across five domains: microenvironmental metabolic control; metabolic signaling and pathway interactions; systems immunology and biomarker discovery; engineered therapeutic platforms; and translational evidence, safety, and patient stratification. Quantitative findings were retained only when their experimental context supported interpretation. RESULTS: Oxygen, nutrient, pH, cytokine, and metabolite availability can reshape AMPK, mTOR, HIF-1α, and related signaling, thereby altering glycolytic, mitochondrial, lipid, amino-acid, and redox metabolism. These changes interact with transcriptional and epigenetic mechanisms to regulate immune-cell states. Systems-level profiling enables biomarker development, while engineered biologics, targeted delivery systems, extracellular-vesicle platforms, and metabolically conditioned immune cells offer emerging therapeutic opportunities. However, translational maturity varies substantially across interventions. CONCLUSION: Immunometabolic reprogramming should be interpreted as a context-dependent network rather than a collection of isolated pathways. Clinical translation requires evidence-graded interventions, tissue- and dose-aware strategies, validated biomarkers, and patient stratification to distinguish established benefit from promising but unproven engineering approaches.
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Yousefi et al. (2026) studied this question.
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