Osteoporosis, or porous bone, causes a reduction in bone mass, especially in older people, and elevates the risk of fractures. It accounts for $19 billion in annual health care costs, according to the National Osteoporosis Foundation. People who have it are more likely to break a bone if they fall, or even during routine daily activities.
The disease is diagnosed through a bone mineral density test that measures the amount of minerals, such as calcium, in a person’s hip, spine or heel.
But testing is usually done only for people who have a family history of the disease or who have already fractured a bone — a factor that Stuart Kim, PhD, an emeritus professor of developmental biology at Stanford Medicine, contended hampers the ability to predict disease risk before fractures occur in the first place.
That’s why he believes genetic screening might provide a better opportunity for predicting the disease early and mitigating the effects as people age and bone mass decreases.
“There are lots of ways to reduce the risk of a stress fracture, including vitamin D, calcium and weight-bearing exercise,” said Kim, whose study was published July 26 in PLOS ONE. “But currently there is no protocol to predict in one’s 20s or 30s who is likely to be at higher risk, and who should pursue these interventions before any sign of bone weakening. A test like this could be an important clinical tool.”
Kim first approached his study of bone mineral density to help elite athletes and military personnel determine their risk of bone injury during strenuous training.
For his study, he used health and genetic data from 400,000 people in the UK Biobank, a vast collection of information that is available to health researchers around the world. Kim gathered data on bone mineral density, age, height, weight, gender and genome sequence for each participant.
Then he performed a genomewide association study to pinpoint genetic differences among people with low bone mineral density, leading him to 899 regions in the human genome associated with bone mineral density, 613 of which had never been identified.
He then used a machine-learning method called LASSO, developed by Stanford professor of biomedical data science and of statistics Robert Tibshirani, PhD, to hone the data and develop a predictive algorithm to assign a score to indicate each person’s risk of low bone mineral density; further analyses showed that those in the bottom 2.2 percent of the scores were 17 times more likely to have been diagnosed with osteoporosis and nearly twice as likely to have had a bone fracture.
The knowledge makes it possible to use genetics to pinpoint who is at risk and provide early intervention against the disease, Kim said.
“This is one of the largest genomewide association studies ever completed for osteoporosis, and it clearly shows the genetic architecture that underlies this important public health problem,” Kim said.