From forces to bone: General formula unravelling the role of mechanical cues in bone remodelling

A group led by Professor Haisheng Yang (Beijing University of Technology, China) have summarized findings from in vivo mechanical loading studies to elucidate the quantitative relationships between diverse mechanical signals and bone adaptive responses across various animal models. They proposed a general formula that models bone adaptive responses as a function of key loading parameters.

Bone dynamically adapts to its mechanical environment by altering its shape, size, and microarchitecture, a phenomenon first described over a century ago in The Law of Bone Remodeling. This process is further conceptualized by The Mechanostat, which establishes a quantitative relationship between bone tissue deformation (strain) and adaptive responses. However, experimental observations reveal that even under constant strain magnitude, bone adaptation varies significantly with changes in loading parameters such as frequency, rate, number of cycles, rest insertion, and waveform. Despite these insights, the precise relationship between mechanical signals and bone adaptation remains incompletely understood.

Prof. Haisheng Yang’s group has focused on orthopaedic biomechanics and mechanobiology of bone adaption and regeneration (https://english.bjut.edu.cn/info/1595/6706.htm). In this review, they have summarized the quantitative relationships between diverse mechanical signals and bone responses and also analyzed how these relationships are modulated by pathophysiological factors, including age, sex, and estrogen deficiency. Additionally, mechanistic studies that explore the role of cellular mechanical microenvironments in shaping these responses are discussed. Building on these insights, they propose a general formula that models bone adaptive responses as a function of key loading parameters. By integrating these findings, this review aims to deepen our understanding of bone adaptation and provide a foundation for developing targeted mechanotherapies to prevent bone loss in clinical settings.

Although bone is mechanical adaptive, not all forms of exercise are equally effective at eliciting an osteogenic response. This formula provides deeper insights into the relationship between mechanical signals and adaptive bone responses. This formula may offer theoretical guidance for optimizing exercise regimens, rehabilitation strategies, and the design of medical devices to promote bone health and treat osteoporosis. This is consistent with recent studies showing that high-frequency training is beneficial for increasing bone mineral density, and tailored whole-body vibration therapies have been proven effective in improving bone density. Additionally, Equation can be used within physiological ranges to identify the optimal mechanical stimuli for maximizing bone health, thereby guiding the design of exercise regimens and medical devices. Future research could incorporate human exercise studies to further validate and apply this formula and determine the effectiveness of various exercise modalities and training doses for more targeted interventions to enhance bone health, particularly in preventing osteoporosis.