| Literature DB >> 23381029 |
Tuan V Nguyen1, John A Eisman.
Abstract
Osteoporosis and its consequence of fragility fracture impose a considerable demand on health-care services because fracture is associated with a series of adverse events, including re-fracture and mortality. One of the major priorities in osteoporosis care is the development of predictive models to identify individuals at high risk of fracture for early intervention and management. Existing predictive models include clinical factors and anthropometric characteristics but have not considered genetic variants in the prediction. Genome-wide association studies conducted in the past decade have identified several genetic variants relevant to fracture risk. These genetic variants are common in frequency but have very modest effect sizes. A remaining challenge is to use these genetic data to individualize fracture risk assessment on the basis of an individual's genetic risk profile. Empirical and simulation studies have shown that the usefulness of a single genetic variant for fracture risk assessment is very limited, but a profile of 50 genetic variants, each with odds ratio ranging from 1.02 to 1.15, could improve the accuracy of fracture prediction beyond that obtained by use of existing clinical risk factors. Thus, genetic profiling when integrated with existing risk assessment models could inform a more accurate prediction of fracture risk in an individual.Entities:
Mesh:
Year: 2013 PMID: 23381029 DOI: 10.1038/nrendo.2013.3
Source DB: PubMed Journal: Nat Rev Endocrinol ISSN: 1759-5029 Impact factor: 43.330