Abstract
Bone turnover markers (BTMs) are commonly used in osteoporosis treatment as indicators of cell activities of bone-resorbing osteoclasts and bone-forming osteoblasts. The wide variability in their values due to multiple factors, such as aging and diseases, makes it difficult for physicians to utilize them for clinical decision-making. The progenitors of osteoclasts and osteoblasts are indispensable for a comprehensive interpretation of the variability in BTM values because these upstream progenitors strongly regulate the downstream cell activities of bone turnover. However, understanding the complex interactions among the multiple populations of bone cells is challenging. In this study, we aimed to gain a fundamental understanding of the mechanism by which the progenitor dynamics affect the variability in bone turnover through in silico experiments by exploring the cell dynamics with aging effects on osteoporosis. Negative feedback control driven by the consumptive loss of progenitors prevents rapid bone loss due to excessive bone turnover, and through feedback regulation, aging effects on osteoclast differentiation and osteoclast progenitor proliferation cause variability in the osteoclast and osteoblast activity balance and its temporal transition. By expressing the variability in the bone turnover status, our model describes the individualities of patients based on their clinical backgrounds. Therefore, our model could play a powerful role in assisting tailored treatment and has the potential to resolve the various health problems associated with osteoporosis worldwide.