Is multiple SNP testing in BRCA2 and BRCA1 female carriers ready for use in clinical practice? Results from a large Genetic Centre in the UK.

BRCA1 and BRCA2 are major breast cancer susceptibility genes. Nineteen single nucleotide polymorphisms (SNPs) at 18 loci have been associated with breast cancer. We aimed to determine whether these predict breast cancer incidence in women with BRCA1/BRCA2 mutations. BRCA1/2 mutation carriers identified through the Manchester genetics centre between 1996 and 2011 were included. Using published odds ratios (OR) and risk allele frequencies, we calculated an overall breast cancer risk SNP score (OBRS) for each woman. The relationship between OBRS and age at breast cancer onset was investigated using the Cox proportional hazards model, and predictive ability assessed using Harrell's C concordance statistic. In BRCA1 mutation carriers we found no association between OBRS and age at breast cancer onset: OR for the lowest risk quintile compared to the highest was 1.20 (95% CI 0.82-1.75, Harrell's C = 0.54), but in BRCA2 mutation carriers the association was significant (OR for the lowest risk quintile relative to the highest was 0.47 (95% CI 0.33-0.69, Harrell's C = 0.59). The 18 validated breast cancer SNPs differentiate breast cancer risks between women with BRCA2 mutations, but not BRCA1. It may now be appropriate to use these SNPs to help women with BRCA2 mutations make maximally informed decisions about management options.

Incident knee pain in the Nottingham community: a 12-year retrospective cohort study.

OBJECTIVES: To determine the community incidence of knee pain and associated risk factors over a 12-year period in people over the age of 40 years. METHOD: A cohort study of knee pain was undertaken in 2156 people from four general practices in North Nottinghamshire, UK. Knee pain was defined as 'pain around the knee for most days of at least a month'. Cumulative incidence over 12 years and person-year incidence rate of knee pain were estimated. Survival analysis was undertaken for time to the onset of knee pain. Hazard ratio (HR) and 95% confidence interval (CI) were estimated for relative risk between exposure and non-exposure. Cox regression model was used to adjust for confounding factors. RESULTS: The 12-year cumulative incidence of knee pain was 34.4% (32% for men and 35% for women), corresponding to an average incidence rate of 32 (31 for men and 34 for women)/1000 person-years. Incident knee pain was associated with female gender (HR 1.27, 95% CI 1.08, 1.49), obesity (1.80; 95% CI 1.37, 2.38), varus (1.68, 95% CI 1.15, 2.47) and valgus (1.83, 95% CI 1.05, 3.20) mal-alignment, and knee injury (2.37, 95% CI 2.98, 2.85). CONCLUSIONS: For people over age 40, one in three will develop knee pain within 12 years. On average, the risk of knee pain was 32/1000 person-years. This risk is associated with a variety of constitutional and environmental biomechanical insults to the knee. Some of these could be modified to possibly reduce the incidence of the condition.

Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.

BACKGROUND: Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. METHODS: A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. RESULTS: A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p < 0.01) and poor discriminative ability (ROC 0.54) in the OAI cohort. CONCLUSIONS: To our knowledge, this is the first risk prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population.