Toward personalised prediction of fracture and bone health


Bone fracture is a serious public health problem worldwide because it is associated with reduced life expectancy. We have developed a predictive model called the Garvan Fracture Risk Calculator for personalised fracture risk assessment.


Bone fracture is a serious consequence of osteoporosis. Among women aged 50 years, almost half of them will sustain a fracture during their remaining life-time. The lifetime risk of a hip fracture for 50-year old women is about 15%, which is equivalent to that of invasive breast cancer. Almost 20% of patients with a hip fracture die within 12 months after the event. Men have a substantially greater risk of death following a fracture than women. However, it is not just hip fractures that impose a great mortality and morbidity burden; non-hip fractures are also associated with increased risk of mortality.


The challenge for both research and public health policy also lies in the identification of high-risk asymptomatic individuals. Over the past 30 years, studies from our group and others have provided a number of important clues to the pathogenesis of osteoporotic fractures. The most important factor is bone mineral density (BMD). People with low BMD, due largely to the age-related bone loss, are at greatest risk for sustaining a bone fracture. This is physiologically and mechanically plausible because BMD is the best measure of bone strength, and fracture occurs because bone is not strong enough to bear a force against it.


Apart from low BMD, other factors related to clinical history are also important risk factors for bone fracture. A personal history of fracture is among the strongest signals of a future fracture. Indeed, men and women with a prior fracture are associated with a two- to three-fold increased risk of subsequent fracture. In addition, a history of falls during the previous 12 months signifies a greater risk of a future fracture, especially hip fracture. Indeed, more than 90% of hip fracture cases are resulted from a fall.


Bone mineral density (BMD) is the best measure of bone strength, and people with low BMD (i.e., osteoporosis) are at significantly greater risk of fracture. Therefore, efforts to identify individuals with a high risk of fracture have focused largely on factors that are associated with BMD.


Doctors and scientists have long known that the between-individuals variation in BMD is due largely to genetic factors. Studies in twins and families have found that up to 80% of differences in BMD between us are attributable to heritable factors. The risk of hip fracture in women whose mothers have sustained a hip fracture is more than 2-fold higher than women whose mothers have not, because they have a deficit in BMD. Taken together, the evidence for genetic influences on osteoporosis and bone fracture are overwhelming.


Still, it remains a great challenge to find specific genes (in the pool of millions of genetic variants in our body) that are associated with BMD -- a task that is likened to finding needles in a haystack! Nevertheless, with advances in genetics and bioinformatics, after almost two decades of "gene hunting", we and others have identified more than 60 variants associated with BMD. A more recent study based on ~142,000 individuals from the UK Biobank found 307 genetic variants that are associated with another measure of bone strength called quantitative ultrasound measurement of the heel. While these findings represent a triumph of science and technology, a small twist is that these variants explained only 10-12% of differences in BMD between individuals.


With such a small proportion of variance explained, one may ask: how can GPs utilise genes for the identification of high-risk individuals in the general community? Individually, the variants identified have little clinical utility because they have tiny effects on fracture risk, but collectively, they can be of help. One way to pull the effects of genetic variants en masse is to generate a genetic signature for each individual, which can be used in the assessment of bone health. We have created such a signature -- termed as "osteogenomic profile" -- and found that it predicts the risk of fracture independently of age and clinical risk factors. We have recently found that the osteogenomic profile can also help assess bone loss in elderly people. Although we are excited about these findings, it is important to emphasize that the profile is not yet ready for use in the clinic. Nevertheless, the recent findings do, however, bring us a huge step closer to a more accurate personalised fracture risk assessment.

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© 2018 Tuan V Nguyen

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