| Application: | 17295, AUtomated Generation of Musculoskeletal phENotypes from the UK biobank exTended imaging study (AUGMENT Study) |
| Title: | Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice |
| Size: | 118517 B |
| Archived: | 4 Feb 2022 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
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Notes
Trabecular bone score (TBS) is a recently-developed analytical tool that performs novel grey-level texture measurements on lumbar spine dual X-ray absorptiometry (DXA) images, and thereby captures information relating to trabecular microarchitecture. In order for TBS to usefully add to bone mineral density (BMD) and clinical risk factors in osteoporosis risk stratification, it must be independently associated with fracture risk, readily obtainable, and ideally, present a risk which is amenable to osteoporosis treatment. This paper summarizes a review of the scientific literature performed by a Working Group of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis. Low TBS is consistently associated with an increase in both prevalent and incident fractures that is partly independent of both clinical risk factors and areal BMD (aBMD) at the lumbar spine and proximal femur. More recently, TBS has been shown to have predictive value for fracture independent of fracture probabilities using the FRAX algorithm. Although TBS changes with osteoporosis treatment, the magnitude is less than that of aBMD of the spine, and it is not clear how change in TBS relates to fracture risk reduction. TBS may also have a role in the assessment of fracture risk in some causes of secondary osteoporosis (e.g., diabetes, hyperparathyroidism and glucocorticoid-induced osteoporosis). In conclusion, there is a role for TBS in fracture risk assessment in combination with both aBMD and FRAX.Application 17295
AUtomated Generation of Musculoskeletal phENotypes from the UK biobank exTended imaging study (AUGMENT Study)
We aim to further develop and refine a suite of automated analyses generating secondary variables from DXA scans. These methods will then be used to derive a comprehensive set of musculoskeletal phenotypes from 100,000 participants in the extended imaging study, including hip and knee shape, vertebral fractures and scoliosis. The relationship between these DXA phenotypes and clinical outcomes related to osteoporosis and osteoarthritis, such as fractures and joint replacements, will subsequently be explored. We also plan to identify novel molecular pathways involved in the pathogenesis of musculoskeletal disease, based on genetic factors associated with DXA phenotypes and clinical outcomes. We aim to build a major musculoskeletal research resource for Biobank, by using state-of-the-art DXA methods, and deliver an augmented musculoskeletal phenotype for future Biobank researchers. We then aim to use these newly derived data to better understand the determinants of musculoskeletal disease, for example spinal fractures (as seen on DXA VFA scans), joint shape (hip and knee - which may predispose to osteoathritis), scoliosis (which can cause back pain). These musculoskeletal diseases are common and therefore our findings will have important impact for health throughout our society. Whole body scans: a. Regional bone density will be extracted using semi-automated methods to check for artefacts and regional alignment b. Scoliosis (curvature of the spine) will be derived based on automated methods in development Hip scans: a. Principal components of hip shape will be obtained using automated methods in development b. Hip structural analysis will be performed using available automated software to extract measures of hip strength Knee scans: Principal components of knee shape will be obtained using automated methods in development Whole spine scans: Vertebral fractures/ deformities will be detected using automated methods after further developmental refinement Phase 1: (n=5000) methods development Phase II: (n=100,000) methods application (i.e. the full cohort in whom DXA is available)
| Lead investigator: | Dr Celia Gregson |
| Lead institution: | University of Bristol |
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