25 March, 2013:

A few decades ago, this might have been considered science fiction. But the ability to screen the entire human genome has become an important aid in the individualization of management of severe diseases. So far, gene profiling is mainly used in the hemato-oncology field and, to a lesser extent, in the area of chronic inflammatory diseases. A new review has shed light on both the research and clinical implications of genetic profiling as a means to identify individuals with high fracture risk [1]. To note, the tools already available in the market focus on people already afflicted with some serious disease, in which case gene profiling helps to decide on the best therapeutic approach. However, in regard to osteoporotic fractures, the idea is to detect future risk early and to implement primary prevention strategies accordingly. At the moment, bone mineral density (BMD) is the best predictor of fracture risk, and the initiation of anti-fracture therapies is based primarily on BMD results. However, several additional parameters (clinical, laboratory, personal and family history) should be part of decision-making as well. The FRAX score, adapted for each country, represents an approach better to identify individuals at higher risk for fractures. Early intervention according to predictive models has proved to have a beneficial effect in this respect [2]: in a fairly large study using the FRAX model, women lying at the 75th percentile of fracture probability (10-year probability, 24% at baseline) demonstrated a 27% reduction in fracture risk as a result of bisphosphonate therapy, whereas, in those with a fracture probability of 30% (90th percentile), the fracture risk reduction was 38%.

Nevertheless, the existing predictive models 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. All genetic variants identified so far when considered in a multivariate model explain < 5% of variance in BMD and fracture susceptibility. It is thus assumed that additional genes should be sought, and perhaps some of these will demonstrate a higher impact on risk prediction. 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 an 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.

At the moment, the number needed to treat (NNT) in order to save one hip fracture in postmenopausal women with osteoporosis ranges from 91 to 250 [3]. Selecting patients with a high risk of fracture would reduce the NNT. It is thus hoped that sometime soon we will have at hand an improved assessment tool that will allow optimization of treatment by selecting individuals on the basis of their unique clinical and genetic risk profile. Combining the conventional clinical risk factors with genetic profiling may create a major development in building future strategies to tackle osteoporosis and its consequent fragility fractures.

Amos Pines
Department of Medicine 'T', Ichilov Hospital, Tel-Aviv, Israel

References

1. Nguyen TV, Eisman JA. Genetic profiling and individualized assessment of fracture risk. Nat Rev Endocrinol 2013 Feb 5. Epub ahead of print

http://www.ncbi.nlm.nih.gov/pubmed/23381029 

2. McCloskey EV, Johansson H, Oden A, et al. Ten-year fracture probability identifies women who will benefit from clodronate therapy – additional results from a double-blind, placebo-controlled randomised study. Osteoporos Int 2009;20:811-17.

http://www.ncbi.nlm.nih.gov/pubmed/19002369 

3. Delmas PD, Rizzoli R, Cooper C, Reginster JY. Treatment of patients with postmenopausal osteoporosis is worthwhile. The position of the International Osteoporosis Foundation. Osteoporos Int 2005;16:1-5.

http://www.ncbi.nlm.nih.gov/pubmed/15565349 

Content updated 25 March 2013