Kiel, Douglas P

Abstract The aim of this international meta-analysis was to quantify the predictive value of BMI for incident fracture and relationship of this risk with age, sex, follow-up time, and BMD. A total of 1 667 922 men and women from 32 countries (63 cohorts), followed for a total of 16.0 million person-years were studied. 293 325 had FN BMD measured (2.2 million person-years follow-up). An extended Poisson model in each cohort was used to investigate relationships between WHO-defined BMI categories (Underweight: <18.5 kg/m2; Normal: 18.5-24.9 kg/m2; Overweight: 25.0-29.9 kg/m2; Obese I: 30.0-34.9 kg/m2; Obese II: ≥35.0 kg/m2) and risk of incident osteoporotic, major osteoporotic and hip fracture (HF). Inverse-variance weighted β-coefficients were used to merge the cohort-specific results. For the subset with BMD available, in models adjusted for age and follow-up time, the hazard ratio (95% CI) for HF comparing underweight with normal weight was 2.35 (2.10-2.60) in women and for men was 2.45 (1.90-3.17). Hip fracture risk was lower in overweight and obese categories compared to normal weight [obese II vs normal: women 0.66 (0.55-0.80); men 0.91 (0.66-1.26)]. Further adjustment for FN BMD T-score attenuated the increased risk associated with underweight [underweight vs normal: women 1.69 (1.47-1.96); men 1.46 (1.00-2.13)]. In these models, the protective effects of overweight and obesity were attenuated, and in both sexes, the direction of association reversed to higher fracture risk in Obese II category [Obese II vs Normal: women 1.24 (0.97-1.58); men 1.70 (1.06-2.75)]. Results were similar for other fracture outcomes. Underweight is a risk factor for fracture in both men and women regardless of adjustment for BMD. However, while overweight/obesity appeared protective in base models, they became risk factors after additional adjustment for FN BMD, particularly in the Obese II category. This effect in the highest BMI categories was of greater magnitude in men than women. These results will inform the second iteration of FRAX®.

Abstract Identifying individuals at risk for short-term fracture is essential to offer prompt beneficial treatment, especially since many fractures occur in those without osteoporosis by DXA-aBMD. We evaluated whether deficits in bone microarchitecture and density predict short-term fracture risk independent of the clinical predictors, DXA-BMD and FRAX. We combined data from eight cohorts to conduct a prospective study of bone microarchitecture at the distal radius and tibia (by HR-pQCT) and 2-year incidence of fracture (non-traumatic and traumatic) in 7327 individuals (4824 women, 2503 men, mean 69 ± 9 years). We estimated sex-specific hazard ratios (HR) for associations between bone measures and 2-year fracture incidence, adjusted for age, cohort, height, and weight, and then additionally adjusted for FN aBMD or FRAX for major osteoporotic fracture. Only 7% of study participants had FN T-score ≤ −2.5, whereas 53% had T-scores between −1.0 and −2.5 and 37% had T-scores ≥−1.0. Two-year cumulative fracture incidence was 4% (296/7327). Each SD decrease in radius cortical bone measures increased fracture risk by 38%-76% for women and men. After additional adjustment for FN-aBMD, risks remained increased by 28%-61%. Radius trabecular measures were also associated with 2-year fracture risk independently of FN-aBMD in women (HRs range: 1.21 per SD for trabecular separation to 1.55 for total vBMD). Decreased failure load (FL) was associated with increased fracture risk in both women and men (FN-aBMD ranges of adjusted HR = 1.47-2.42). Tibia measurement results were similar to radius results. Findings were also similar when models were adjusted for FRAX. In older adults, FL and HR-pQCT measures of cortical and trabecular bone microarchitecture and density with strong associations to short-term fractures improved fracture prediction beyond aBMD and FRAX. Thus, HR-pQCT may be a useful adjunct to traditional assessment of short-term fracture risk in older adults, including those with T-scores above the osteoporosis range.

ABSTRACT Most fracture risk assessment tools use clinical risk factors combined with bone mineral density (BMD) to improve assessment of osteoporosis; however, stratifying fracture risk remains challenging. This study developed a fracture risk assessment tool that uses information about volumetric bone density and three‐dimensional structure, obtained using high‐resolution peripheral quantitative compute tomography (HR‐pQCT), to provide an alternative approach for patient‐specific assessment of fracture risk. Using an international prospective cohort of older adults ( n = 6802) we developed a tool to predict osteoporotic fracture risk, called μFRAC. The model was constructed using random survival forests, and input predictors included HR‐pQCT parameters summarizing BMD and microarchitecture alongside clinical risk factors (sex, age, height, weight, and prior adulthood fracture) and femoral neck areal BMD (FN aBMD). The performance of μFRAC was compared to the Fracture Risk Assessment Tool (FRAX) and a reference model built using FN aBMD and clinical covariates. μFRAC was predictive of osteoporotic fracture (c‐index = 0.673, p < 0.001), modestly outperforming FRAX and FN aBMD models (c‐index = 0.617 and 0.636, respectively). Removal of FN aBMD and all clinical risk factors, except age, from μFRAC did not significantly impact its performance when estimating 5‐year and 10‐year fracture risk. The performance of μFRAC improved when only major osteoporotic fractures were considered (c‐index = 0.733, p < 0.001). We developed a personalized fracture risk assessment tool based on HR‐pQCT that may provide an alternative approach to current clinical methods by leveraging direct measures of bone density and structure. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

ABSTRACT Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p &lt; 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).

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).