Citations

Background —The outcome of aortic valve replacement in patients with severe aortic stenosis, low transvalvular gradient, and severe left ventricular dysfunction is not well known. Methods and Results —Between 1985 and 1995, 52 patients with left ventricular ejection fraction (EF) ≤35% and aortic stenosis with transvalvular mean gradient <30 mm Hg underwent aortic valve replacement. The mean (±SD) preoperative characteristics included EF, 26±8%; aortic valve mean gradient, 23±4 mm Hg; aortic valve area, 0.7±0.2 cm 2 ; and cardiac output, 3.7±1.2 L/min. Simultaneous coronary artery bypass graft surgery was performed in 32 patients (62%). Perioperative (30-day) mortality was 21% (11 of 52 patients). Ten additional patients died during follow-up. Advanced age ( P =0.048) and small aortic prosthesis size ( P =0.03) were significant predictors of hospital mortality by univariate analysis. By multivariate analysis, the only predictor of surgical mortality was smaller prosthesis size. The only predictor of postoperative survival was improvement in postoperative functional class ( P =0.04). Postoperative functional improvement occurred in most patients. Postoperative EF was assessed in 93% of survivors; 74% demonstrated improvement. Positive change in EF was related to smaller preoperative aortic valve area and female sex. Conclusions —Despite severe left ventricular dysfunction, low transvalvular mean gradient, and increased operative mortality, aortic valve replacement was associated with improved functional status. Postoperative survival was related to younger patient age and larger aortic prosthesis size, and medium-term survival was related to improved postoperative functional class.

Abstract Purpose: To explore the effects of single-nucleotide polymorphisms (SNP) on pancreatic cancer risk and overall survival (OS). Experimental Design: The germ line DNA of 531 pancreatic cancer cases and 305 healthy controls from a hospital-based study was genotyped at SNPs previously reported to be associated with pancreatic cancer risk or clinical outcome. We analyzed putative risk SNPs for replication of their reported effects on risk and tested for novel effects on OS. Similarly, we analyzed putative survival-associated SNPs for replication of their reported effects on OS and tested for novel effects on risk. Finally, we conducted a genome-wide association study (GWAS) of OS using a subset of 252 cases, with two subsequent validation sets of 261 and 572 patients, respectively. Results: Among seven risk SNPs analyzed, two (rs505922 and rs9543325) were associated with risk (P &lt; 0.05). Among 24 survival-associated SNPs analyzed, one (rs9350) was associated with OS (P &lt; 0.05). No putative risk SNPs or putative survival-associated SNPs were found to be associated with OS or risk, respectively. Furthermore, our GWAS identified a novel SNP [rs1482426, combined stage I and II, P = 1.7 × 10−6, per-allele HR, 1.74; 95% confidence interval (CI), 1.38–2.18] to be putatively associated with OS. Conclusions: The effects of SNPs on pancreatic cancer risk and OS were replicated in our study, although further work is necessary to understand the functional mechanisms underlying these effects. More importantly, the putative association with OS identified by GWAS suggests that GWAS may be useful in identifying SNPs associated with clinical outcome in pancreatic cancer. Clin Cancer Res; 18(14); 3942–51. ©2012 AACR.

Abstract Skeletal muscle and bone form highly-integrated systems that undergo significant age-related changes, but the relationships between muscle mass and trabecular versus cortical bone or trabecular microarchitecture have not been systematically investigated. Thus, we examined the association between appendicular skeletal muscle mass (ASM) relative to height squared (relative ASM) and bone parameters at several sites assessed by conventional as well as high-resolution peripheral QCT in a cohort of 272 women and 317 men aged 20 to 97 years. In women, relative ASM was associated with cortical thickness (CtTh) at the femoral neck, lumbar spine, radius, and tibia (age-and physical activity adjusted r = 0.19–0.32; all p &lt; 0.01). Relative ASM was also associated with trabecular volumetric bone mineral density (vBMD) at the femoral neck and spine (all p &lt; 0.05), and trabecular bone volume to tissue volume (BV/TV), number (TbN), thickness (TbTh), and separation (TbSp) at the radius (all p ≤ 0.05). In all men, relative ASM was associated with CtTh at all sites (age- and physical activity–adjusted r = 0.17–0.28; all p &lt; 0.01). Associations between relative ASM and trabecular vBMD at the spine in men were lost after adjusting for age; however, relative ASM was associated with trabecular vBMD at the femoral neck and TbN and TbSp at the radius (all p &lt; 0.01). We also investigated circulating factors associated with bone health that may be indicative of relative ASM and found that serum insulin-like growth factor (IGF) binding protein-2 (IGFBP-2) levels were the most robust negative predictors of relative ASM in both sexes. Collectively, these data add to the growing body of evidence supporting the highly-integrated nature of skeletal muscle and bone, and provide new insights into potential biomarkers that reflect the health of the musculoskeletal system. © 2012 American Society for Bone and Mineral Research.

Abstract Advanced bone imaging with quantitative computed tomography (QCT) has had limited success in significantly improving fracture prediction beyond standard areal bone mineral density (aBMD) measurements. Thus, we examined whether a machine learning paradigm, gradient boosting machine (GBM) modeling, which can incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction. We studied two cohorts of postmenopausal women: 105 with and 99 without distal forearm fractures (Distal Forearm Cohort) and 40 with at least one grade 2 or 3 vertebral deformity and 78 with no vertebral fracture (Vertebral Cohort). Within each cohort, individual bone density, structure, or strength variables had areas under receiver operating characteristic curves (AUCs) ranging from 0.50 to 0.84 (median 0.61) for discriminating women with and without fracture. Using all possible variables in the GBM model, the AUCs were close to 1.0. Fracture predictions in the Vertebral Cohort using the GBM models built with the Distal Forearm Cohort had AUCs of 0.82–0.95, whereas predictions in the Distal Forearm Cohort using models built with the Vertebral Cohort had AUCs of 0.80–0.83. Attempts at capturing a comparable parametric model using the top variables from the Distal Forearm Cohort resulted in resulted in an AUC of 0.81. Relatively high AUCs for differing fracture types suggest that an underlying fracture propensity is being captured by this modeling approach. More complex modeling, such as with GBM, creates stronger fracture predictions and may allow deeper insights into information provided by advanced bone imaging techniques. © 2012 American Society for Bone and Mineral Research.

Abstract The 19p13.1 breast cancer susceptibility locus is a modifier of breast cancer risk in BRCA1 mutation carriers and is also associated with the risk of ovarian cancer. Here, we investigated 19p13.1 variation and risk of breast cancer subtypes, defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status, using 48,869 breast cancer cases and 49,787 controls from the Breast Cancer Association Consortium (BCAC). Variants from 19p13.1 were not associated with breast cancer overall or with ER-positive breast cancer but were significantly associated with ER-negative breast cancer risk [rs8170 OR, 1.10; 95% confidence interval (CI), 1.05–1.15; P = 3.49 × 10−5] and triple-negative (ER-, PR-, and HER2-negative) breast cancer (rs8170: OR, 1.22; 95% CI, 1.13–1.31; P = 2.22 × 10−7). However, rs8170 was no longer associated with ER-negative breast cancer risk when triple-negative cases were excluded (OR, 0.98; 95% CI, 0.89–1.07; P = 0.62). In addition, a combined analysis of triple-negative cases from BCAC and the Triple Negative Breast Cancer Consortium (TNBCC; N = 3,566) identified a genome-wide significant association between rs8170 and triple-negative breast cancer risk (OR, 1.25; 95% CI, 1.18–1.33; P = 3.31 × 10−13]. Thus, 19p13.1 is the first triple-negative–specific breast cancer risk locus and the first locus specific to a histologic subtype defined by ER, PR, and HER2 to be identified. These findings provide convincing evidence that genetic susceptibility to breast cancer varies by tumor subtype and that triple-negative tumors and other subtypes likely arise through distinct etiologic pathways. Cancer Res; 72(7); 1795–803. ©2012 AACR.

Abstract Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case–control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%–10% representing 5,222–50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08–1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants. Cancer Res; 72(6); 1478–84. ©2012 AACR.