Five blood pressure loci identified by an updated genome-wide linkage scan: meta-analysis of the Family Blood Pressure Program.

BACKGROUND: A preliminary genome-wide linkage analysis of blood pressure in the Family Blood Pressure Program (FBPP) was reported previously. We harnessed the power and ethnic diversity of the final pooled FBPP dataset to identify novel loci for blood pressure thereby enhancing localization of genes containing less common variants with large effects on blood pressure levels and hypertension. METHODS: We performed one overall and 4 race-specific meta-analyses of genome-wide blood pressure linkage scans using data on 4,226 African-American, 2,154 Asian, 4,229 Caucasian, and 2,435 Mexican-American participants (total N = 13,044). Variance components models were fit to measured (raw) blood pressure levels and two types of antihypertensive medication adjusted blood pressure phenotypes within each of 10 subgroups defined by race and network. A modified Fisher's method was used to combine the P values for each linkage marker across the 10 subgroups. RESULTS: Five quantitative trait loci (QTLs) were detected on chromosomes 6p22.3, 8q23.1, 20q13.12, 21q21.1, and 21q21.3 based on significant linkage evidence (defined by logarithm of odds (lod) score ≥3) in at least one meta-analysis and lod scores ≥1 in at least 2 subgroups defined by network and race. The chromosome 8q23.1 locus was supported by Asian-, Caucasian-, and Mexican-American-specific meta-analyses. CONCLUSIONS: The new QTLs reported justify new candidate gene studies. They may help support results from genome-wide association studies (GWAS) that fall in these QTL regions but fail to achieve the genome-wide significance

Five Blood Pressure Loci Identified by an Updated Genome-Wide Linkage Scan: Meta-Analysis of the Family Blood Pressure Program

Background A preliminary genome-wide linkage analysis of blood pressure in the Family Blood Pressure Program (FBPP) was reported previously. We harnessed the power and ethnic diversity of the final pooled FBPP dataset to identify novel loci for blood pressure thereby enhancing localization of genes containing less common variants with large effects on blood pressure levels and hypertension. Methods We performed one overall and 4 race-specific meta-analyses of genome-wide blood pressure linkage scans using data on 4,226African-American, 2,154 Asian, 4,229 Caucasian, and 2,435 Mexican- American participants (total N = 13,044). Variance components models were fit to measured (raw) blood pressure levels and two types of antihypertensive medication adjusted blood pressure phenotypes within each of 10 subgroups defined by race and network. A modified Fisher's method was used to combine the P values for each linkage marker across the 10 subgroups. Results Five quantitative trait loci (QTLs) were detected on chromosomes 6p22.3, 8q23.1, 20q13.12, 21q21.1, and 21q21.3 based on significant linkage evidence (defined by logarithm of odds (lod) score ≥3) in at least one meta-analysis and lod scores ≥1 in at least 2 subgroups defined by network and race. The chromosome 8q23.1 locus was supported by Asian-, Caucasian-, and Mexican-American-specific meta-analyses. Conclusions The new QTLs reported justify new candidate gene studies. They may help support results from genome-wide association studies (GWAS) that fall in these QTL regions but fail to achieve the genome-wide significance. American Journal of Hypertension advance online publication 9 December 2010;doi:10.1038/ajh.2010.23

The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial

Breast Cancer Incidence Among American Indian and Alaska Native Women: US, 1999–2004

BACKGROUND. Breast cancer is a leading cause of cancer morbidity and mortality among American Indian and Alaska Native (AI/AN) women. Although published studies have suggested that breast cancer rates among AI/AN women are lower than those among other racial and ethnic populations, accurate determinations of the breast cancer burden have been hampered by misclassification of AI/AN race. METHODS. Cancer incidence data from the National Program of Cancer Registries and the Surveillance, Epidemiology, and End Results Program were combined to estimate age-adjusted rates for the diagnosis years 1999 through 2004. Several steps were taken to reduce the misclassification of AI/AN race: linking cases to Indian Health Service (IHS) patient services database, restricting analyses to Contract Health Service Delivery Area counties, and stratifying results by IHS region. RESULTS. Breast cancer incidence rates among AI/AN women varied nearly 3- fold across IHS regions. The highest rates were in Alaska (134.8) and the Plains (Northern, 115.9; Southern, 115.7), and the lowest rates were in the Southwest (50.8). The rate in Alaska was similar to the rate among non-Hispanic white (NHW) women in Alaska. Overall, AI/AN women had lower rates of breast cancer than NHW women, but AI/AN women were more likely to be diagnosed with late-stage disease. CONCLUSIONS. To the authors’ knowledge, this report provides the most comprehensive breast cancer incidence data for AI/AN women to date. The wide regional variation indicates an important need for etiologic and health services research, and the large percentage of AI/AN women with late-stage disease demands innovative approaches for increasing access to screening

Different regression equations relate age to the incidence of Lauren types 1 and 2 stomach cancer in the SEER database: these equations are unaffected by sex or race

BACKGROUND: Although impacts upon gastric cancer incidence of race, age, sex, and Lauren type have been individually explored, neither their importance when evaluated together nor the presence or absence of interactions among them have not been fully described. METHODS: This study, derived from SEER (Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute) data, analyzed the incidences of gastric cancer between the years 1992–2001. There were 7882 patients who had developed gastric cancer. The total denominator population was 145,155, 669 persons (68,395,787 for 1992–1996, 78,759,882 for 1997–2001). Patients with multiple tumors were evaluated as per the default of the SEER*Stat program. 160 age-, five year period (1992–1996 vs 1997–2001)-, sex-, race (Asian vs non-Asian)-, Lauren type- specific incidences were derived to form the stratified sample evaluated by linear regression. (160 groups = 2 five year periods × 2 race groups × 2 sexes × 2 Lauren types × 10 age groups.) Linear regression was used to analyze the importance of each of these explanatory variables and to see if there were interactions among the explanatory variables. RESULTS: Race, sex, age group, and Lauren type were found to be important explanatory variables, as were interactions between Lauren type and each of the other important explanatory variables. In the final model, the contribution of each explanatory variable was highly statistically significant (t > 5, d.f. 151, P < 0.00001). The regression equation for Lauren type 1 had different coefficients for the explanatory variables Race, Sex, and Age, than did the regression equation for Lauren type 2. CONCLUSION: The change of the incidence of stomach cancer with respect to age for Lauren type 1 stomach cancer differs from that for Lauren type 2 stomach cancers. The relationships between age and Lauren type do not differ across gender or race. The results support the notion that Lauren type 1 and Lauren type 2 gastric cancers have different etiologies and different patterns of progression from pre-cancer to cancer. The results should be validated by evaluation of other databases

Point-of-Care Technologies for Precision Cardiovascular Care and Clinical Research

Point-of-care technologies (POC or POCT) are enabling innovative cardiovascular diagnostics that promise to improve patient care across diverse clinical settings. The National Heart, Lung, and Blood Institute convened a working group to discuss POCT in cardiovascular medicine. The multidisciplinary working group, which included clinicians, scientists, engineers, device manufacturers, regulatory officials, and program staff, reviewed the state of the POCT field; discussed opportunities for POCT to improve cardiovascular care, realize the promise of precision medicine, and advance the clinical research enterprise; and identified barriers facing translation and integration of POCT with existing clinical systems. A POCT development roadmap emerged to guide multidisciplinary teams of biomarker scientists, technologists, health care providers, and clinical trialists as they: 1) formulate needs assessments; 2) define device design specifications; 3) develop component technologies and integrated systems; 4) perform iterative pilot testing; and 5) conduct rigorous prospective clinical testing to ensure that POCT solutions have substantial effects on cardiovascular care

Genome-Wide Association Studies of the PR Interval in African Americans

The PR interval on the electrocardiogram reflects atrial and atrioventricular nodal conduction time. The PR interval is heritable, provides important information about arrhythmia risk, and has been suggested to differ among human races. Genome-wide association (GWA) studies have identified common genetic determinants of the PR interval in individuals of European and Asian ancestry, but there is a general paucity of GWA studies in individuals of African ancestry. We performed GWA studies in African American individuals from four cohorts (n = 6,247) to identify genetic variants associated with PR interval duration. Genotyping was performed using the Affymetrix 6.0 microarray. Imputation was performed for 2.8 million single nucleotide polymorphisms (SNPs) using combined YRI and CEU HapMap phase II panels. We observed a strong signal (rs3922844) within the gene encoding the cardiac sodium channel (SCN5A) with genome-wide significant association (p<2.5×10−8) in two of the four cohorts and in the meta-analysis. The signal explained 2% of PR interval variability in African Americans (beta  = 5.1 msec per minor allele, 95% CI  = 4.1–6.1, p = 3×10−23). This SNP was also associated with PR interval (beta = 2.4 msec per minor allele, 95% CI = 1.8–3.0, p = 3×10−16) in individuals of European ancestry (n = 14,042), but with a smaller effect size (p for heterogeneity <0.001) and variability explained (0.5%). Further meta-analysis of the four cohorts identified genome-wide significant associations with SNPs in SCN10A (rs6798015), MEIS1 (rs10865355), and TBX5 (rs7312625) that were highly correlated with SNPs identified in European and Asian GWA studies. African ancestry was associated with increased PR duration (13.3 msec, p = 0.009) in one but not the other three cohorts. Our findings demonstrate the relevance of common variants to African Americans at four loci previously associated with PR interval in European and Asian samples and identify an association signal at one of these loci that is more strongly associated with PR interval in African Americans than in Europeans