Prediction of fracture risk in the elderly can be improved by adding genetic information to the current predictive models that are based on clinical risk factors alone, a study shows.
The research involving 1400 adults aged over 60 years taking part in the Dubbo Osteoporosis Epidemiology Study showed that a genetic risk score based on variants associated with bone mineral density could improve the accuracy of fracture prediction by 12%.
“This can be the difference between being eligible for preventive treatment or not. It can change clinical decisions, prevent fractures and prevent deaths, lead author Professor Tuan Nguyen from the Garvan Institute of Medical Research told the limbic.
According to Professor Nguyen there was an urgent need to better identify individuals at high risk of fractures and intervene appropriately. He noted that more than 50% of women and more than 70% of men who develop fractures do not have low bone mineral density.
“At the moment first line treatment is bisphosphonates which are pretty effective and can reduce the fracture risk by about 50% over three years. However about 80% of people are not being identified as high risk,” said the Professor who heads up the Genetic Epidemiology of Osteoporosis team at the Institute.
The study published in the Journal of Bone and Mineral Research followed the cohort between 1990 and 2015.
About 44% of women and 26% of men recorded a non-trauma fracture, mostly of the hip, vertebra or wrist.
The participants’ genetic profiles were determined from 62 bone mineral density -associated single nucleotide polymorphisms including the vitamin D receptor gene and collagen 1 alpha 1 gene.
“These genes are fairly common in the population and individually have a very low effect so a lot of doctors are suspicious about whether they make any difference,” Professor Nguyen said.
“Individually the genetic variants have very little effect but when combined in a genetic signature and added to clinical risk factors such as the Garvan Fracture Risk Calculator or the Fracture Risk Assessment Tool (FRAX), they can significantly improve the accuracy of prediction.”