Biomarkers as Temporal Signals: A Decision-Linked Multi-Layer Framework for Exercise Recovery, Overload, and Adaptation

生物标志物作为时间信号:一种用于运动恢复、超负荷和适应的决策链接多层框架

阅读:2

Abstract

Exercise adaptation and training maladaptation arise from overlapping metabolic, redox, inflammatory, endocrine, and tissue-remodeling processes, so the translational question is not whether biomarkers change but when, where, and for which decision they become informative. This narrative review develops a decision-linked framework for minimally invasive biomarkers across the recovery–overload continuum and treats biomarker meaning as a molecule–matrix–time–decision relationship rather than as a stand-alone peak. The framework is organized around five coupled layers: stimulus architecture, signaling and release biology, sampling matrix and pre-analytics, bout-relative kinetics, and the monitoring decision to be supported. Current evidence indicates that no single biomarker reliably separates productive remodeling from delayed recovery, tissue strain, non-functional overreaching, or early maladaptation. Classical chemistry remains useful for bounded tasks, especially delayed tissue strain and stress reactivity; cfDNA appears promising for rapid load sensitivity; targeted metabolite panels are strongest for recovery phenotyping; and circulating RNAs and extracellular-vesicle cargo add mechanistic depth but remain constrained by pre-analytical fragility and incomplete standardization. The central practical implication is that overload is better interpreted as progressive loss of signal resolution than as threshold-crossing and that sparse temporally staggered panels are more likely to aid monitoring decisions than isolated markers or untimed high-dimensional profiles. Progress will depend on purpose-specific panels, transparent analytical standards, and prospective validation against symptoms, performance, and established measures across sex, hormonal, circadian, and training contexts.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。