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The goal of the study: to identify young patients at risk before any clinical symptoms develop.
A new genetic screen may predict the risk of osteoporosis and bone fracture, according to a researcher at Stanford University School of Medicine in California.
The study identified 899 regions in the human genome associated with low bone-mineral density-613 of which have never before been identified. People at high risk (about 2% of those tested) were about 17 times more likely than others to develop osteoporosis and about twice as likely to experience a bone fracture in their lifetimes.
“There are lots of ways to reduce the risk of a stress fracture, including vitamin D, calcium, and weight-bearing exercise,” said Stuart Kim, PhD, an emeritus professor of developmental biology at Stanford, who conducted the study. “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 is the sole author of the study, which was published online July 26 in PLOS ONE.
Low bone mineral density as predictor
Kim originally approached his investigation as a way to help elite athletes or members of the military learn whether they are at risk for bone injury during strenuous training. Once he had compiled the results, however, he saw a strong correlation between people predicted to have the highest risk of low bone mineral density and the development of osteoporosis and fracture.
Two previous studies have demonstrated a genetic component to osteoporosis. Recently, genetic studies on large groups of individuals have shown that hundreds of genes are likely involved.
Please click below for more details about the findings.
Developing an algorithm
Kim analyzed the genetic data and health information of nearly 400,000 people in the UK Biobank-a vast compendium of de-identified information freely available to public health researchers around the world. For each participant, Kim collected data on bone mineral density, age, height, weight, and sex, as well that participant’s genome sequence. He then developed a computer algorithm to identify naturally occurring genetic differences among people found with low bone mineral density.
Using the algorithm, Kim identified 1362 single-nucleotide polymorphisms that correlated with low bone mineral density readings. He then used a machine-learning method called LASSO, developed in 1996 by Stanford professor of biomedical data science and of statistics Robert Tibshirani, PhD, to further hone the data.
The resulting algorithm assigned a score to each of the nearly 400,000 participants to indicate their risk of low bone mineral density; subsequent analyses showed that those in the bottom 2.2% of these scores were 17 times more likely than their peers to have been diagnosed with osteoporosis and nearly twice as likely to have experienced a bone fracture.
“The analysis worked really well,” Kim said. “This is one of the largest genome-wide association studies ever completed for osteoporosis, and it clearly shows the genetic architecture that underlies this important public health problem.”
Source: Conger K. Osteoporosis, fracture risk predicted with genetic screen [press release]. Stanford, CA: Stanford Medicine; July 26, 2018.