Citations

Introduction: Racial and ethnic groups are under-represented among research subjects who assent to brain donation in Alzheimer disease research studies. There has been little research on this important topic. Although there are some studies that have investigated the barriers to brain donation among African American study volunteers, there is no known research on the factors that influence whether or not Asians or Latinos are willing to donate their brains for research. Methods: African American, Caucasian, Asian, and Latino research volunteers were surveyed at 15 Alzheimer Disease Centers to identify predictors of willingness to assent to brain donation. Results: Positive predictors included older age, Latino ethnicity, understanding of how the brain is used by researchers, and understanding of what participants need to do to ensure that their brain will be donated. Negative predictors included African/African American race, belief that the body should remain whole at burial, and concern that researchers might not be respectful of the body during autopsy. Discussion: The predictive factors identified in this study may be useful for researchers seeking to increase participation of diverse ethnic groups in brain donation.

Abstract Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

The ability to aggregate clinical data across multiple diverse organizations and to use it for performance measurement, quality improvement, evaluation, and research is rapidly becoming a national necessity, but there are few examples of how to do that. This article uses lessons from a national effort to implement the collaborative care management model for patients with both depression and diabetes or heart disease across 8 partner organizations, 18 medical groups, and more than 170 clinics in 8 states to identify the challenges and provide experience-based recommendations for those tasks. The challenges are divided into those needed for (1) collecting similar data, (2) aggregating those data across care systems, and (3) using the data to both improve and evaluate care. Start with agreement on goals, methods, transparency, and a data system integrated into the electronic medical record while promptly addressing all the legal, regulatory, and human subject requirements.

AbstractIntroductionAfrican Americans' (AAs) late‐onset Alzheimer's disease (LOAD) genetic risk profile is incompletely understood. Including clinical covariates in genetic analyses using informed conditioning might improve study power.MethodsWe conducted a genome‐wide association study (GWAS) in AAs employing informed conditioning in 1825 LOAD cases and 3784 cognitively normal controls. We derived a posterior liability conditioned on age, sex, diabetes status, current smoking status, educational attainment, and affection status, with parameters informed by external prevalence information. We assessed association between the posterior liability and a genome‐wide set of single‐nucleotide polymorphisms (SNPs), controlling for APOE and ABCA7, identified previously in a LOAD GWAS of AAs.ResultsTwo SNPs at novel loci, rs112404845 (P = 3.8 × 10−8), upstream of COBL, and rs16961023 (P = 4.6 × 10−8), downstream of SLC10A2, obtained genome‐wide significant evidence of association with the posterior liability.DiscussionAn informed conditioning approach can detect LOAD genetic associations in AAs not identified by traditional GWAS.