Müller, Ralph

ABSTRACT The development of high-throughput genotyping technologies and large biobank collections, complemented with rapid methodological advances in statistical genetics, has enabled hypothesis-free genome-wide association studies (GWAS), which have identified hundreds of genetic variants across many loci associated with musculoskeletal conditions. Similarly, basic scientists have valuable molecular cellular and animal data based on musculoskeletal disease that would be enhanced by being able to determine the human translation of their findings. By integrating these large-scale human genomic musculoskeletal datasets with complementary evidence from model organisms, new and existing genetic loci can be statistically fine-mapped to plausibly causal variants, candidate genes, and biological pathways. Genes and pathways identified using this approach can be further prioritized as drug targets, including side-effect profiling and the potential for new indications. To bring together these big data, and to realize the vision of creating a knowledge portal, the International Federation of Musculoskeletal Research Societies (IFMRS) established a working group to collaborate with scientists from the Broad Institute to create the Musculoskeletal Knowledge Portal (MSK-KP)(http://mskkp.org/). The MSK consolidates omics datasets from humans, cellular experiments, and model organisms into a central repository that can be accessed by researchers. The vision of the MSK-KP is to enable better understanding of the biological mechanisms underlying musculoskeletal disease and apply this knowledge to identify and develop new disease interventions. © 2020 American Society for Bone and Mineral Research (ASBMR).

A bone loading estimation algorithm was previously developed that provides in vivo loading conditions required for in vivo bone remodelling simulations. The algorithm derives a bone's loading history from its microstructure as assessed by high-resolution (HR) computed tomography (CT). This reverse engineering approach showed accurate and realistic results based on micro-CT and HR-peripheral quantitative CT images. However, its voxel size dependency, reproducibility and sensitivity still need to be investigated, which is the purpose of this study. Voxel size dependency was tested on cadaveric distal radii with micro-CT images scanned at 25 µm and downscaled to 50, 61, 75, 82, 100, 125 and 150 µm. Reproducibility was calculated with repeated in vitro as well as in vivo HR-pQCT measurements at 82 µm. Sensitivity was defined using HR-pQCT images from women with fracture versus non-fracture, and low versus high bone volume fraction, expecting similar and different loading histories, respectively. Our results indicate that the algorithm is voxel size independent within an average (maximum) error of 8.2% (32.9%) at 61 µm, but that the dependency increases considerably at voxel sizes bigger than 82 µm. In vitro and in vivo reproducibility are up to 4.5% and 10.2%, respectively, which is comparable to other in vitro studies and slightly higher than in other in vivo studies. Subjects with different bone volume fraction were clearly distinguished but not subjects with and without fracture. This is in agreement with bone adapting to customary loading but not to fall loads. We conclude that the in vivo bone loading estimation algorithm provides reproducible, sensitive and fairly voxel size independent results at up to 82 µm, but that smaller voxel sizes would be advantageous.

ABSTRACT More accurate techniques to estimate fracture risk could help reduce the burden of fractures in postmenopausal women. Although micro-finite element (µFE) simulations allow a direct assessment of bone mechanical performance, in this first clinical study we investigated whether the additional information obtained using geometrically and materially nonlinear µFE simulations allows a better discrimination between fracture cases and controls. We used patient data and high-resolution peripheral quantitative computed tomography (HRpQCT) measurements from our previous clinical study on fracture risk, which compared 100 postmenopausal women with a distal forearm fracture to 105 controls. Analyzing these data with the nonlinear µFE simulations, the odds ratio (OR) for the factor-of-risk (yield load divided by the expected fall load) was marginally higher (1.99; 95% confidence interval [CI], 1.41–2.77) than for the factor-of-risk computed from linear µFE (1.89; 95% CI, 1.37–2.69). The yield load and the energy absorbed up to the yield point as computed from nonlinear µFE were highly correlated with the initial stiffness (R2 = 0.97 and 0.94, respectively) and could therefore be derived from linear simulations with little loss in precision. However, yield deformation was not related to any other measurement performed and was itself a good predictor of fracture risk (OR, 1.89; 95% CI, 1.39–2.63). Moreover, a combined risk score integrating information on relative bone strength (yield load-based factor-of-risk), bone ductility (yield deformation), and the structural integrity of the bone under critical loads (cortical plastic volume) improved the separation of cases and controls by one-third (OR, 2.66; 95% CI, 1.84–4.02). We therefore conclude that nonlinear µFE simulations provide important additional information on the risk of distal forearm fractures not accessible from linear µFE nor from other techniques assessing bone microstructure, density, or mass. © 2013 American Society for Bone and Mineral Research.

Abstract The incidence of distal forearm fractures peaks during the adolescent growth spurt, but the structural basis for this is unclear. Thus, we studied healthy 6- to 21-yr-old girls (n = 66) and boys (n = 61) using high-resolution pQCT (voxel size, 82 μm) at the distal radius. Subjects were classified into five groups by bone-age: group I (prepuberty, 6–8 yr), group II (early puberty, 9–11 yr), group III (midpuberty, 12–14 yr), group IV (late puberty, 15–17 yr), and group V (postpuberty, 18–21 yr). Compared with group I, trabecular parameters (bone volume fraction, trabecular number, and thickness) did not change in girls but increased in boys from late puberty onward. Cortical thickness and density decreased from pre- to midpuberty in girls but were unchanged in boys, before rising to higher levels at the end of puberty in both sexes. Total bone strength, assessed using microfinite element models, increased linearly across bone age groups in both sexes, with boys showing greater bone strength than girls after midpuberty. The proportion of load borne by cortical bone, and the ratio of cortical to trabecular bone volume, decreased transiently during mid- to late puberty in both sexes, with apparent cortical porosity peaking during this time. This mirrors the incidence of distal forearm fractures in prior studies. We conclude that regional deficits in cortical bone may underlie the adolescent peak in forearm fractures. Whether these deficits are more severe in children who sustain forearm fractures or persist into later life warrants further study.

Abstract Bone structure, strength, and load-strength ratios contribute to forearm fracture risk independently of areal BMD. Introduction: Technological and conceptual advances provide new opportunities for evaluating the contributions of bone density, structure, and strength to the pathogenesis of distal forearm fractures. Materials and Methods: From an age-stratified random sample of Rochester, MN, women, we compared 18 with a distal forearm fracture (cases) to 18 age-matched women with no osteoporotic fracture (controls). High-resolution pQCT was used to assess volumetric BMD (vBMD), geometry, and microstructure at the ultradistal radius, the site of Colles' fractures. Failure loads in the radius were estimated from microfinite element (μFE) models derived from pQCT. Differences between case and control women were assessed, and the risk of fracture associated with each variable was estimated by logistic regression analysis. Results: Given similar heights, estimated loading in a fall on the outstretched arm was the same in cases and controls. However, women with forearm fractures had inferior vBMD, geometry, microstructure, and estimated bone strength. Relative risks for the strongest determinant of fracture in each of the five main variable categories were as follows: BMD (total vBMD: OR per SD change, 4.2; 95% CI, 1.4–12), geometry (cortical thickness: OR, 4.0; 95% CI, 1.4–11), microstructure (trabecular number: OR, 2.3; 95% CI, 1.02–5.1), and strength (axial rigidity: OR, 3.8; 95% CI, 1.4–10); the factor-of-risk (fall load/μFE failure load) was 24% greater (worse) in cases (OR, 3.0; 95% CI, 1.2–7.5). Areas under ROC curves ranged from 0.72 to 0.82 for these parameters. Conclusions: Bone geometry, microstructure, and strength contribute to forearm fractures, as does BMD, and these additional determinants of risk promise greater insights into fracture pathogenesis.