Citations

On March 25 and 26, 2015, the National Heart, Lung, and Blood Institute sponsored a meeting on the State of the Science in Transfusion Medicine on the National Institutes of Health (NIH) campus in Bethesda, Maryland, which was attended by a diverse group of 330 registrants. The meeting's goal was to identify important research questions that could be answered in the next 5 to 10 years and which would have the potential to transform the clinical practice of transfusion medicine. These questions could be addressed by basic, translational, and/or clinical research studies and were focused on four areas: the three “classical” transfusion products (i.e., red blood cells, platelets, and plasma) and blood donor issues. Before the meeting, four working groups, one for each area, prepared five major questions for discussion along with a list of five to 10 additional questions for consideration. At the meeting itself, all of these questions, and others, were discussed in keynote lectures, small‐group breakout sessions, and large‐group sessions with open discourse involving all meeting attendees. In addition to the final lists of questions, provided herein, the meeting attendees identified multiple overarching, cross‐cutting themes that addressed issues common to all four areas; the latter are also provided. It is anticipated that addressing these scientific priorities, with careful attention to the overarching themes, will inform funding priorities developed by the NIH and provide a solid research platform for transforming the future practice of transfusion medicine.

AbstractIntroductionAlzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders.MethodsWe review the contributions of the MRI core from present and past cycles of ADNI (ADNI‐1, ‐Grand Opportunity and ‐2). We also review plans for the future‐ADNI‐3.ResultsContributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials.DiscussionOver the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

Abstract Objective Clinicians’ ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). Materials and Methods The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. Results There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. Conclusion Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.

Background— Instruments to assess symptom burden and quality of life among patients with atrial fibrillation (AF) have not been well evaluated in community practice or associated with patient outcomes. Methods and Results— Using data from 10 087 AF patients in the Outcomes Registry for Better Informed Treatment of AF (ORBIT-AF), symptom severity was evaluated using the European Heart Rhythm Association (EHRA) classification system, and quality of life was assessed using the Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire. The association between AF-related symptoms, quality of life, and outcomes was assessed using Cox regression. The majority of AF patients (61.8%) were symptomatic (EHRA >2) and 16.5% had severe or disabling symptoms (EHRA 3–4). EHRA symptom class was well correlated with the AFEQT score (Spearman correlation coefficient −0.39). Over 1.8 years of follow-up, AF symptoms were associated with a higher risk of hospitalization (adjusted hazard ratio for EHRA ≥2 versus EHRA 1 1.23, 95% confidence interval, 1.15–1.31) and a borderline higher risk of major bleeding. Lower quality of life was associated with a higher risk of hospitalization (adjusted hazard ratio for lowest quartile of AFEQT versus highest 1.49, 95% confidence interval, 1.2–1.84), but not other major adverse events, including death. Conclusions— In a community-based study, most patients with AF were symptomatic and had impaired quality of life. Quality of life measured by the AFEQT correlated closely with symptom severity measured by the EHRA class. AF symptoms and lower quality of life were associated with higher risk of hospitalization but not mortality during follow-up.

Background Patient sex and age may influence rates of death after receiving an implantable cardioverter‐defibrillator for primary prevention. Differences in outcomes other than mortality and whether these differences vary by heart failure symptoms, etiology, and left ventricular ejection fraction are not well characterized. Methods and Results We studied 2954 patients with left ventricular ejection fraction ≤0.35 undergoing first‐time implantable cardioverter‐defibrillator for primary prevention within the Cardiovascular Research Network; 769 patients (26%) were women, and 2827 (62%) were aged >65 years. In a median follow‐up of 2.4 years, outcome rates per 1000 patient‐years were 109 for death, 438 for hospitalization, and 111 for heart failure hospitalizations. Procedure‐related complications occurred in 8.36%. In multivariable models, women had significantly lower risks of death (hazard ratio 0.67, 95% CI 0.56 to 0.80) and heart failure hospitalization (hazard ratio 0.82, 95% CI 0.68 to 0.98) and higher risks for complications (hazard ratio 1.38, 95% CI 1.01 to 1.90) than men; patients aged >65 years had higher risks of death (hazard ratio 1.55, 95% CI 1.30 to 1.86) and heart failure hospitalization (hazard ratio 1.25, 95% CI 1.05 to 1.49) than younger patients. Age and sex differences were generally consistent in strata according to symptoms, etiology, and severity of left ventricular systolic dysfunction, except the higher risk of complications in women, which differed by New York Heart Association classification ( P =0.03 for sex–New York Heart Association interaction), and the risk of heart failure hospitalization in older patients, which differed by etiology of heart failure ( P =0.05 for age–etiology interaction). Conclusions The burden of adverse outcomes after receipt of an implantable cardioverter‐defibrillator for primary prevention is substantial and varies according to patient age and sex. These differences in outcome generally do not vary according to baseline heart failure characteristics.

Abstract A genome-wide association study (GWAS) of prostate cancer in Kaiser Permanente health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) revealed a new independent risk indel rs4646284 at the previously identified locus 6q25.3 that replicated in PEGASUS (N = 7,539) and the Multiethnic Cohort (N = 4,679) with an overall P = 1.0 × 10−19 (OR, 1.18). Across the 6q25.3 locus, rs4646284 exhibited the strongest association with expression of SLC22A1 (P = 1.3 × 10−23) and SLC22A3 (P = 3.2 × 10−52). At the known 19q13.33 locus, rs2659124 (P = 1.3 × 10−13; OR, 1.18) nominally replicated in PEGASUS. A risk score of 105 known risk SNPs was strongly associated with prostate cancer (P < 1.0 × 10−8). Comparing the highest to lowest risk score deciles, the OR was 6.22 for non-Hispanic whites, 5.82 for Latinos, 3.77 for African-Americans, and 3.38 for East Asians. In non-Hispanic whites, the 105 risk SNPs explained approximately 7.6% of disease heritability. The entire GWAS array explained approximately 33.4% of heritability, with a 4.3-fold enrichment within DNaseI hypersensitivity sites (P = 0.004). Significance: Taken together, our findings of independent risk variants, ethnic variation in existing SNP replication, and remaining unexplained heritability have important implications for further clarifying the genetic risk of prostate cancer. Our findings also suggest that there may be much promise in evaluating understudied variation, such as indels and ethnically diverse populations. Cancer Discov; 5(8); 878–91. ©2015 AACR. This article is highlighted in the In This Issue feature, p. 783

ABSTRACTBioinformatics approaches to examine gene‐gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge‐driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty‐three SNP‐SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell‐to‐cell adhesion signaling, cell‐cell junction organization, and cell‐cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP‐SNP models, which included signal transduction and PI3K‐Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology‐driven method, applicable for any genome‐wide association study dataset.

Abstract Background: High body mass index (BMI) is consistently linked to increased risk of colorectal cancer for men, whereas the association is less clear for women. As risk estimates from observational studies may be biased and/or confounded, we conducted a Mendelian randomization study to estimate the causal association between BMI and colorectal cancer. Methods: We used data from 10,226 colorectal cancer cases and 10,286 controls of European ancestry. The Mendelian randomization analysis used a weighted genetic risk score, derived from 77 genome-wide association study–identified variants associated with higher BMI, as an instrumental variable (IV). We compared the IV odds ratio (IV-OR) with the OR obtained using a conventional covariate-adjusted analysis. Results: Individuals carrying greater numbers of BMI-increasing alleles had higher colorectal cancer risk [per weighted allele OR, 1.31; 95% confidence interval (CI), 1.10–1.57]. Our IV estimation results support the hypothesis that genetically influenced BMI is directly associated with risk for colorectal cancer (IV-OR per 5 kg/m2, 1.50; 95% CI, 1.13–2.01). In the sex-specific IV analyses higher BMI was associated with higher risk of colorectal cancer among women (IV-OR per 5 kg/m2, 1.82; 95% CI, 1.26–2.61). For men, genetically influenced BMI was not associated with colorectal cancer (IV-OR per 5 kg/m2, 1.18; 95% CI, 0.73–1.92). Conclusions: High BMI was associated with increased colorectal cancer risk for women. Whether abdominal obesity, rather than overall obesity, is a more important risk factor for men requires further investigation. Impact: Overall, conventional epidemiologic and Mendelian randomization studies suggest a strong association between obesity and the risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev; 24(7); 1024–31. ©2015 AACR.