Compartment‐specific effects of muscle strength on bone microarchitecture in women at high risk of osteoporosis

Abstract

Background It is well known that skeletal integrity is influenced by the musculature. Poor muscle strength (i.e. sarcopenia) is considered a major predictor of fragility fractures. While this observation appears particularly relevant for older women with increased risk of osteoporosis, there has been no comprehensive investigation to determine the influence of muscle performance on compartment-specific bone microarchitecture in multiple body regions. Methods We retrospectively analysed data from different muscle performance and bone microarchitecture assessments in 230 women (aged 21 to 87 years) at high risk of osteoporosis. Muscle performance tests included grip strength and chair rising test (CRT) combined with mechanography. Balance was determined by Romberg posturography. Areal bone mineral density (BMD) was measured by dual-energy X-ray absorptiometry (DXA) at the hip and lumbar spine. Compartment-specific volumetric BMD, microarchitecture, and geometry were assessed by second-generation highresolution peripheral quantitative computed tomography (HR-pQCT) at multiple skeletal sites (distal radius, tibia, and fibula). Regression models were applied to test for interactions between muscle and bone parameters. Subgroups were defined to compare women with osteoporosis and osteosarcopenia regarding BMD and microarchitecture. Results While osteoporosis was diagnosed in 115/230 (50.0%) women, sarcopenia was detected in 38/230 (16.5%). Positive associations of both grip strength and CRT maximum force with cortical geometric and microarchitectural parameters were detected at all measured sites, with the strongest effect applying to CRT maximum force and tibial parameters (e.g. tibial cortical area R 2 = 0.36, P < 0.0001, and tibial cortical thickness R 2 = 0.26, P < 0.0001). Balance parameters showed much weaker or no associations with HR-pQCT parameters. Major associations between muscle strength and trabecular parameters could not be confirmed. Age and body mass index were confirmed as negative and positive predictors for several microarchitectural parameters, respectively. An independent predictive value of grip strength on radial, tibial, and fibular (all P < 0.01) cortical area and of CRT maximum relative force on cortical thickness (all P < 0.05) was revealed. Women with osteosarcopenia showed significantly reduced cortical HR-pQCT parameters but no differences in DXA values compared with women with osteoporosis but no sarcopenia. Stratification by fracture and treatment status revealed that vertebral fractures and denosumab treatment altered the muscle-bone interaction. Conclusions A systemic interaction between muscle strength and bone microarchitecture was demonstrated, and this interaction appears to be primarily with the cortical bone compartment. The value of muscle assessments in fracture risk evaluation may be partly mediated by their effects on bone microarchitecture.

Interactions Between HR‐pQCT Bone Density and D3Cr Muscle Mass (or HR‐pQCT Bone Structure and HR‐pQCT Muscle Density) in Predicting Fractures: The Osteoporotic Fractures in Men Study

“…(27) Strong site-specific associations have also been observed been upper-and lower-limb muscle strength and bone structural parameters. (7,20) Thus, it has been suggested that both bone and muscle loss are biologically linked and the combined losses of these tissues in older adults may increase fracture risk. (19) However, our findings do not support a consistent interaction between bone and muscle on fracture risk.…”

“…(3)(4)(5) A recent review of preclinical and human work (6) suggests that bone and muscle loss during aging is underpinned by shared mechanisms relating to chemical and mechanical cross-talk. Considering mechanical interactions, a recent study (7) showed strong site-specific associations between grip strength and radial cortical bone and chair stand force and tibial cortical bone, measured by HR-pQCT. Indeed, it has been suggested that the joint loss of bone and muscle during aging, a concept known as osteosarcopenia (see reviews (8,9) ), may increase fracture risk in older adults.…”

“…(1) Yet the interactions between these bone and muscle variables (ie, volumetric BMD and D 3 -creatine dilution (D 3 Cr) muscle mass or cortical bone area and muscle density) in estimating the risk of fractures is currently unknown, as recently highlighted in an editorial. (19) Given that muscle size and strength is suggested to be a determinant of bone density and structure, (7,20) this question warrants investigation as it may influence future fracture risk assessments.…”

“…For instance, we examined whether the association between bone density or structure and fracture risk is the strongest in those with the lowest muscle mass, volume or density. Given the findings of previous studies, (7,20) we also examined the interactions between muscle strength (measured by grip strength or leg force) and bone density or structure (measured by HR-pQCT) in predicting fractures. Physical performance measures (such as gait speed or chair rise time) were not considered in the analyses as previous studies have shown no relationship (20) or weak relationships (7) with bone density/structure using pQCT or HR-pQCT.…”

Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography

“…In the past 2 decades, peripheral quantitative computed tomography (pQCT) and high resolution peripheral quantitative computed tomography (HR-pQCT) have emerged as essential technologies for segmentation and quantification of bone, muscle and adipose tissue properties at the diaphyseal regions of the limbs. Segmentation of hard and soft tissues in pQCT and HR-pQCT imaging has been used to assess the effects of type-2 diabetes mellitus (T2DM) (Starr et al, 2018), osteoporosis (Simon et al, 2022) and osteoarthritis (Chen et al, 2018), to establish measures for characterizing sex-, ethnic-, site-, and age-related outcomes (Gabel et al, 2018), to study the effect of exercise on the muscle and fat cross-sectional areas (Rowe et al, 2019), and in studies of aging and age-related diseases (Chow et al, 2022;Liu et al, 2022). A challenge in pQCT-based segmentation is subject movement and the associated motion artifacts.…”