Genome-Based Prediction of Breast Cancer Risk in the General Population: A Modeling Study Based on Meta-Analyses of Genetic Associations

Background: Genome-wide association studies identified novel breast cancer susceptibility variants that could be used to predict breast cancer in asymptomatic women. This review and modeling study aimed to investigate the current and potential predictive performance of genetic risk models. Methods: Genotypes and disease status were simulated for a population of 10,000 women. Genetic risk models were constructed from polymorphisms from meta-analysis including, in separate scenarios, all polymorphisms or statistically significant polymorphisms only. We additionally investigated the magnitude of the odds ratios (OR) for 1 to 100 hypothetical polymorphisms that would be needed to achieve similar discriminative accuracy as available prediction models [modeled range of area under the receiver operating characteristic curve (AUC) 0.70-0.80]. Results: Of the 96 polymorphisms that had been investigated in meta-analyses, 41 showed significant associations. AUC was 0.68 for the genetic risk model based on all 96 polymorphisms and 0.67 for the 41 significant polymorphisms. Addition of 50 additional variants, each with risk allele frequencies of 0.30, requires per-allele ORs of 1.2 to increase this AUC to 0.70, 1.3 to increase AUC to 0.75, and 1.5 to increase AUC to 0.80. To achieve AUC of 0.80, even 100 additional variants would need per-allele ORs of 1.3 to 1.7, depending on risk allele frequencies. Conclusion: The predictive ability of genetic risk models in breast cancer has the potential to become comparable to that of current breast cancer risk models. Impact: Risk prediction based on low susceptibility variants becomes a realistic tool in prevention of nonfamilial breast cancer. Cancer Epidemiol Biomarkers Prev; 20(1); 9-22. (C) 2011 AACR

One year health status benefits following treatment for new onset or exacerbation of peripheral arterial disease symptoms:The importance of patients' baseline health status

Objective/BackgroundLimited information is available on expected health status gains following invasive treatment in peripheral arterial disease (PAD). One year health status outcomes following invasive treatment for PAD were compared, and whether pre-procedural health status was indicative of 1 year health status gains was evaluated.MethodsPre-procedural and 1 year health status (Short Form-12, Physical Component Score [PCS]) was prospectively assessed in a cohort of 474 patients, enrolled from 2 Dutch vascular clinics (March 2006–August 2011), with new or exacerbation of PAD symptoms. One year treatment strategy (invasive vs. non-invasive) and clinical information was abstracted. Quartiles of baseline health status scores and mean 1 year health status change scores were compared by invasive treatment for PAD. The numbers needed to treat (NNT) to obtain clinically relevant changes in 1 year health status were calculated. A propensity weight adjusted linear regression analysis was constructed to predict 1 year PCS scores.ResultsInvasive treatment was performed in 39% of patients. Patients with baseline health status scores in the lowest quartile undergoing invasive treatment had the greatest improvement (mean invasive 11.3 ± 10.3 vs. mean non-invasive 5.3 ± 8.5 [p = .001, NNT = 3]), whereas those in the highest quartile improved less (.8 ± 6.3 vs. –3.0 ± 8.2 [p = .025, NNT = 90]). Undergoing invasive treatment (p < .0001) and lower baseline health status scores (p < .0001) were independently associated with greater 1 year health status gains.ConclusionSubstantial improvements were found in patients presenting with lower pre-procedural health status scores, whereas patients with higher starting health status levels had less to gain by an invasive strategy

Long-term prognostic risk in lower extremity peripheral arterial disease as a function of the number of peripheral arterial lesions

Background: Although patients with peripheral artery disease (PAD) are known to have an increased risk of adverse prognosis, simple techniques to further risk-stratify PAD patients would be clinically useful. A plausible but unexplored factor to predict such risk would be greater disease burden, manifested as multiple lower extremity lesions. The aim of this study was to examine the association between having multiple versus isolated lower extremity PAD lesions and long-term prognosis. Methods and Results: A prospective cohort of 756 newly diagnosed PAD patients underwent duplex ultrasound testing to determine the number of lower extremity lesions. Cox regression models examined the independent association of lesion number (>= 3 and 2 versus 1) and adverse prognosis (defined as a composite end point comprising first occurrence of either lower extremity amputation, admission for heart failure, nonfatal stroke, myocardial infarction, or unstable angina or mortality), adjusting for demographic and clinical risk factors. Analyses were replicated using an advanced Cox-based model for multiple events. A total of 173 patients (23%) had >= 3 lesions, 197 (26%) had 2 lesions, and 386 (51%) had 1 lesion. After a median follow-up of 3.2 years, patients with >= 3 lesions had an increased risk of experiencing a first adverse event (adjusted hazard ratio 1.60, 95% CI 1.08-2.38, P=0.020) and an increased risk of having multiple events (adjusted hazard ratio 1.53, 95% CI 1.08-2.18, P=0.018). Patients with 2 lesions had a prognosis similar to those with 1 lesion. Conclusions: Among PAD patients, a greater number of lesions is associated with an increased risk of an adverse prognosis over 3 years of follow-up. Assessing the number of lower extremity lesions might serve as a simple risk-stratification tool at initial PAD diagnosis

Improvement of Risk Prediction by Genomic Profiling: Reclassification Measures Versus the Area Under the Receiver Operating Characteristic Curve

Reclassification is observed even when there is no or minimal improvement in the area under the receiver operating characteristic curve (AUC), and it is unclear whether it indicates improved clinical utility. The authors investigated total reclassification, net reclassification improvement, and integrated discrimination improvement for different delta AUC using empirical and simulated data. Empirical analyses compared prediction of type 2 diabetes risk based on age, sex, and body mass index with prediction updated with 18 established genetic risk factors. Simulated data were used to investigate measures of reclassification against delta AUCs of 0.005, 0.05, and 0.10. Total reclassification and net reclassification improvement were calculated for all possible cutoff values. The AUC of type 2 diabetes risk prediction improved from 0.63 to 0.66 when 18 polymorphisms were added, whereas total reclassification ranged from 0% to 22.5% depending on the cutoff value chosen. In the simulation study, total reclassification, net reclassification improvement, and integrated discrimination improvement increased with higher delta AUC. When delta AUC was low (0.005), net reclassification improvement values were close to zero, integrated discrimination improvement was 0.08% (P > 0.05), but total reclassification ranged from 0 to 6.7%. Reclassification increases with increasing AUC but predominantly varies with the cutoff values chosen. Reclassification observed in the absence of AUC increase is unlikely to improve clinical utility

Determinants of invasive treatment in lower extremity peripheral arterial disease

Objective Since it is unknown what factors are weighed in a clinician's decision to refer patients with symptomatic lower extremity peripheral arterial disease (PAD) for invasive treatment, we examined the relationship between health status, lesion location, and site variations and invasive treatment referral ≤1 year following diagnosis in patients with PAD. Methods This was a prospective observational cohort study on ambulatory patients that presented themselves at two vascular surgery outpatient clinics. A total of 970 patients with new symptoms of PAD or with an exacerbation of existing PAD symptoms that required clinical evaluation and treatment (Rutherford Grade I) were eligible, 884 consented and were included between March 2006 and November 2010. We report on 505 patients in the current study. Prior to patients' initial PAD evaluation, the Short Form-12, Physical Component Scale (PCS) was administered to measure health status. Anatomical lesion location (proximal vs distal) was derived from duplex ultrasounds. PCS scores, lesion location, and site were evaluated as determinants of receiving invasive (endovascular, surgery) vs noninvasive treatment ≤1 year following diagnosis in Poisson regression analyses, adjusting for demographics, ankle-brachial index, and risk factors. Results Invasive treatment as a first-choice was offered to 167 (33%) patients. While an association between poorer health status and invasive therapy was found in unadjusted analyses (relative risk [RR], 0.98; 95% confidence interval [CI], 0.97-1.00; P = .011), proximal lesion location (RR, 3.66; 95% CI, 2.70-4.96; P < .0001) and site (RR, 1.69; 95% CI, 1.11-2.58; P = .014) were independent predictors of invasive treatment referral in the final model. Conclusions One-third of patients were treated invasively following PAD diagnosis. Patients' health status was considered in providers' decision to refer patients for invasive treatment, but having a proximal lesion was the strongest predictor. This study also found some important first indications of site variations in offering invasive treatment among patients with PAD. Future work is needed to further document these variations in care

Improvement of risk prediction by genomic profiling: Reclassification measures versus the area under the receiver operating characteristic curve

Reclassification is observed even when there is no or minimal improvement in the area under the receiver operating characteristic curve (AUC), and it is unclear whether it indicates improved clinical utility. The authors investigated total reclassification, net reclassification improvement, and integrated discrimination improvement for different ΔAUC using empirical and simulated data. Empirical analyses compared prediction of type 2 diabetes risk based on age, sex, and body mass index with prediction updated with 18 established genetic risk factors. Simulated data were used to investigate measures of reclassification against ΔAUCs of 0.005, 0.05, and 0.10. Total reclassification and net reclassification improvement were calculated for all possible cutoff values. The AUC of type 2 diabetes risk prediction improved from 0.63 to 0.66 when 18 polymorphisms were added, whereas total reclassification ranged from 0% to 22.5% depending on the cutoff value chosen. In the simulation study, total reclassification, net reclassification improvement, and integrated discrimination improvement increased with higher ΔAUC. When ΔAUC was low (0.005), net reclassification improvement values were close to zero, integrated discrimination improvement was 0.08% (P > 0.05), but total reclassification ranged from 0 to 6.7%. Reclassification increases with increasing AUC bu