Prediction model for knee osteoarthritis based on genetic and clinical information

We genotyped risk alleles of the three susceptibility genes, asporin (ASPN), growth differentiation factor 5 (GDF5), and double von Willebrand factor A domains (DVWA) for a total of 2,158 Japanese subjects (933 OA and 1,225 controls) and statistically analyzed their effects. After that, we constructed prediction models by using the logistic regression analysis.When the effects of each allele were assumed to be the same and multiplicative, each additional risk allele increased the odds ratio (OR) by a factor of 1.23 (95% confidence interval (CI), 1.12 to 1.34). Individuals with five or six risk alleles showed significantly higher susceptibility when compared with those with zero or one, with an OR of 2.67 (95% CI, 1.46 to 4.87; P = 0.0020). Statistical evaluation of the prediction power of models showed that a model using only genotyping data had poor predictability. We obtained a model with good predictability by incorporating clinical data, which was further improved by rigorous age adjustment.Our results showed that consideration of adjusted clinical information, as well as increases in the number of risk alleles to be integrated, is critical for OA prediction by using data from case-control studies. To the authors' knowledge, this is the first report of the OA-prediction model combining both genetic and clinical information.Osteoarthritis (OA) is the most common bone and joint disease and is characterized by progressive cartilage degeneration. OA is a polygenic disease caused by genetic and environmental factors [1]. Epidemiologic studies have suggested that genetic factors strongly affect the onset and development of OA [2]. Genetic association studies are now uncovering the genetic factors responsible for of OA, that is, its susceptibility genes. Candidate-gene approaches have identified several genes associated with OA, and genome-wide association studies have recently found several promising OA-susceptibility genes [1,3].Identification of OA-susceptibility ge