Integrated multi-omics approaches have fundamentally redefined investigative framework in systems biology and precision medicine. Despite substantial technological progress, precise molecular characterization of pre-disease states remains a pivotal challenge in the disease management and proactive healthcare. High dimensionality, heterogeneity, and technical noise inherent to multi-omics datasets complicate the robust detection of the subtle pre-pathological signals. For instance, elucidating the critical tipping point between reversible metabolic dysregulation and irreversible Type 2 Diabetes requires distinguishing coherent synergistic deviations across transcriptomic and metabolomic layers from stochastic physiological noise. Furthermore, prevailing analytical methods often struggle to establish the causal, dynamic relationships necessary to predict transition and distinguish pre-disease from health. To address these challenges, this article proposes a pre-disease state centered research methodology, focusing on the cross-scale regulatory architectures and multivariate synergistic dynamics inherent to complex living systems. It aims to advance methodological approaches for the investigation of spatiotemporally resolved dynamic processes and to establish a theoretically grounded foundation for proactive health. We highlight how emerging conceptual, computational, and technological breakthroughs can be leveraged to decode the molecular architecture of pre-disease state. It provides a novel systems-level window to overcome the limitations in current multi-omics studies. Ultimately, we advocate for this paradigm as a critical bridge between multi-omics insight and interceptive medicine, shifting clinical action into the pre-symptomatic phase.
Lu et al. (Sun,) studied this question.