Citations

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.

New-onset diabetes after transplantation (NODAT) is common after liver transplant and associated with poorer outcomes. The aim of this study was to identify risk factors for NODAT in liver transplant recipients off corticosteroids. In 225 adult nondiabetic liver transplant recipients, the mean age was 51.7 years, the majority were men (71%), and half had HCV (49%). The mean calculated MELD score at transplantation was 18.7, and 19% underwent living-donor transplant (LDLT). One year after transplantation, 17% developed NODAT, and an additional 16% had impaired fasting glucose. The incidence of NODAT in patients with HCV was 26%. In multivariate analysis, HCV, pretransplant FPG, and LDLT were significant. Each 10 mg/dL increase in pretransplant FPG was associated with a twofold increase in future development of NODAT. The incidence of NODAT after liver transplant in patients off corticosteroids is 17%. Risk factors for developing NODAT include HCV and pretransplant FPG; LDLT is protective.

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.

Abstract A positive association has been reported between greater bone density and higher breast cancer risk, suggesting that these women could be at reduced risk of fracture. To estimate fracture risk among unselected community women with breast cancer and to systematically assess associations with various risk factors including breast cancer treatments, we conducted a population-based historical cohort study of 608 Olmsted County, MN, USA, women with invasive breast cancer first diagnosed in 1990 to 1999 (mean age 61.6 ± 14.8 years), who were followed for 5776 person-years. Altogether, 568 fractures were observed in 270 women (98 per 1000 person-years). Overall fracture risk was elevated 1.8-fold, but the absolute increase in risk was only 9%, and 56% of the women did not experience a fracture during follow-up. Excluding pathologic fractures (15%) and those found incidentally (24%), to allow for ascertainment bias, the standardized incidence ratio was 1.2 (95% confidence interval [CI] 0.99 to 1.3) for total fracture risk and 0.9 (95% CI 0.7 to 1.2) for osteoporotic fracture risk alone. Various breast cancer treatments were associated with an increased risk of fracture, but those associations were strongest for pathologic fractures, which were relatively more common among the women who were premenopausal when their breast cancer was diagnosed. Moreover, underlying clinical characteristics prompting different treatments may have been partially responsible for the associated fracture outcomes (indication bias). These data thus demonstrate that breast cancer patients in general are not at greatly increased risk of fracture but neither are they protected from fractures despite any determinants that breast cancer and high bone density may have in common. © 2012 American Society for Bone and Mineral Research.

Abstract Ischemic stroke (IS) is among the leading causes of death in Western countries. There is a significant genetic component to IS susceptibility, especially among young adults. To date, research to identify genetic loci predisposing to stroke has met only with limited success. We performed a genome-wide association (GWA) analysis of early-onset IS to identify potential stroke susceptibility loci. The GWA analysis was conducted by genotyping 1 million SNPs in a biracial population of 889 IS cases and 927 controls, ages 15–49 years. Genotypes were imputed using the HapMap3 reference panel to provide 1.4 million SNPs for analysis. Logistic regression models adjusting for age, recruitment stages, and population structure were used to determine the association of IS with individual SNPs. Although no single SNP reached genome-wide significance (P < 5 × 10−8), we identified two SNPs in chromosome 2q23.3, rs2304556 (in FMNL2; P = 1.2 × 10−7) and rs1986743 (in ARL6IP6; P = 2.7 × 10−7), strongly associated with early-onset stroke. These data suggest that a novel locus on human chromosome 2q23.3 may be associated with IS susceptibility among young adults.

AbstractOver the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large‐scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene‐gene and gene‐environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.Genet. Epidemiol. 36 : 22–35, 2012. © 2011 Wiley Periodicals, Inc.

OBJECTIVE Patients with diabetes may experience high burden of treatment (BOT), including treatment-related effects and self-care demands. We examined whether patients with type 2 diabetes and their clinicians discuss BOT, the characteristics of their discussions, and their attempts to address BOT during visits. RESEARCH DESIGN AND METHODS Two coders independently reviewed videos of 46 primary care visits obtained during a practice-based trial and identified utterances concerning BOT, classifying them by topic and by whether BOT was addressed (i.e., whether statements emerged aimed at alleviating BOT). RESULTS Of the 46 visits, 43 (93.5%) contained BOT discussions. Both coders identified 83 discussions: 12 involving monitoring, 28 treatment administration, 19 access, and 24 treatment effects. BOT was unambiguously addressed only 30% of the time. CONCLUSIONS BOT discussions usually arise during visits but rarely beget problem-solving efforts. These discussions represent missed opportunities for reducing treatment-related disruptions in the lives of patients with diabetes, which may affect adherence and well-being.