Bone Mechanoregulation Allows Subject-Specific Load Estimation Based on Time-Lapsed Micro-CT and HR-pQCT in Vivo

Abstract

Patients at high risk of fracture due to metabolic diseases frequently undergo long-term antiresorptive therapy. However, in some patients, treatment is unsuccessful in preventing fractures or causes severe adverse health outcomes. Understanding load-driven bone remodelling, i.e., mechanoregulation, is critical to understand which patients are at risk for progressive bone degeneration and may enable better patient selection or adaptive therapeutic intervention strategies. Bone microarchitecture assessment using high-resolution peripheral quantitative computed tomography (HR-pQCT) combined with computed mechanical loads has successfully been used to investigate bone mechanoregulation at the trabecular level. To obtain the required mechanical loads that induce local variances in mechanical strain and cause bone remodelling, estimation of physiological loading is essential. Current models homogenise strain patterns throughout the bone to estimate load distribution in vivo, assuming that the bone structure is in biomechanical homoeostasis. Yet, this assumption may be flawed for investigating alterations in bone mechanoregulation. By further utilising available spatiotemporal information of time-lapsed bone imaging studies, we developed a mechanoregulation-based load estimation (MR) algorithm. MR calculates organ-scale loads by scaling and superimposing a set of predefined independent unit loads to optimise measured bone formation in high-, quiescence in medium-, and resorption in low-strain regions. We benchmarked our algorithm against a previously published load history (LH) algorithm using synthetic data, micro-CT images of murine vertebrae under defined experimental in vivo loadings, and HR-pQCT images from seven patients. Our algorithm consistently outperformed LH in all three datasets. In silico-generated time evolutions of distal radius geometries (n = 5) indicated significantly higher sensitivity, specificity, and accuracy for MR than LH (p < 0.01). This increased performance led to substantially better discrimination between physiological and extra-physiological loading in mice (n = 8). Moreover, a significantly (p < 0.01) higher association between remodelling events and computed local mechanical signals was found using MR [correct classification rate (CCR) = 0.42] than LH (CCR = 0.38) to estimate human distal radius loading. Future applications of MR may enable clinicians to link subtle changes in bone strength to changes in day-to-day loading, identifying weak spots in the bone microstructure for local intervention and personalised treatment approaches.

Meta-analysis of Diabetes Mellitus-Associated Differences in Bone Structure Assessed by High-Resolution Peripheral Quantitative Computed Tomography

“…Additionally, earlier research has discovered that T1DM might impair muscle function, as evidenced by decreased grip strength [ 54 ] and jumping mechanography measures [ 54 ]. Note that grip strength may be used as a surrogate for radius loading at the tissue level, which may influence bone structure changes [ 55 , 56 ]. Beyond the mechanics, T1DM [ 57 ] and T2DM [ 58 , 59 ] impair osteocyte function, causing an increase in expression of sclerostin, an endogenous inhibitor of bone formation, and may be directly related to hyperglycaemia reducing bone cell activity and, subsequently, levels of circulating bone turnover markers [ 60 ].…”

Clinical observation of diminished bone quality and quantity through longitudinal HR-pQCT-derived remodeling and mechanoregulation

“…Results from the FE analyses were spatially correlated with formation, resorption and quiescent bone volumes to assess local mechanoregulation. Here, conditional probability (CP) curves were generated for the remodeling events identified on the bone surface 55,56 , connecting the local mechanical environment (ε Eff ) with the observed formation, resorption or quiescence events. The effective strain distribution for each FE analysis was normalized using the average 99th percentile of the whole cohort and binned at 1% steps for each remodeling event.…”

Mechanostat parameters estimated from time-lapsedin vivomicro-computed tomography data of mechanically driven bone adaptation are logarithmically dependent on loading frequency

“…This dependency has been considered in silico (Levchuk et al 2014, Goda et al 2016, Louna et al 2019) by defining the surface velocity in response to the perceived mechanical signals guiding remodeling events, analogous to the mechanostat theory proposal. Besides, previous work has shown a dose-dependent effect of this mechanical stimulus (Mosley et al 1998, Sugiyama et al 2012, Scheuren et al 2020, Walle et al 2021, but an association of surface velocity with mechanical signal has not been investigated at the tissue level in vivo. On the one hand, it hinders more detailed comparisons of degenerative conditions that may conserve the mechanosensation ability of bone cells, associated with mechanical signal thresholds that regulate remodeling events, but influence the magnitude of the response to the mechanical stimuli and the overall bone turnover.…”