Next-generation platforms for NADH monitoring

Reduced nicotinamide adenine dinucleotide (NADH) serves as a central redox coenzyme and a critical indicator of cellular metabolic status. Its dynamic fluctuations are intimately linked to the pathogenesis of a wide range of diseases, making its precise, real-time monitoring imperative for advancing diagnostics, therapeutic monitoring, and fundamental metabolic research. While conventional analytical techniques are constrained by their operational complexity and lack of portability, the emergence of portable biosensors offers a paradigm-shifting solution. This review comprehensively synthesizes recent progress in three primary categories of portable NADH biosensors: optical (including fluorescence, colorimetric, and surface-enhanced Raman scattering), electrochemical, and synthetic biology-based platforms. We provide a critical analysis of their operational mechanisms, performance benchmarks in terms of sensitivity, selectivity, and response time, and their application in both ex vivo and in vivo settings. Despite significant advancements, the clinical translation of these technologies faces persistent challenges, such as interference in complex biological matrices, the need for long-term stability, and seamless integration into user-friendly, multiplexed devices. Looking forward, we posit that the convergence of advanced nanomaterials, innovative biorecognition elements, and machine learning analytics will be pivotal in overcoming these hurdles. The ultimate maturation of these portable platforms is poised to usher in an era of real-time metabolomic medicine, transforming NADH from a biochemical analyte into a dynamic, actionable biomarker for precision health. • First systematic integration of optical, electrochemical, and synthetic biology platforms for portable NADH sensing. • Establishes a multidimensional performance matrix, revealing critical application-specific trade-offs. • Identifies key bottlenecks in clinical translation and proposes targeted engineering solutions. • Highlights emerging AI-driven and multi-modal theranostic systems as future frontiers. • Provides a forward-looking framework to guide the next generation of metabolic monitoring devices.