Testing of a Model with Latino Patients That Explains the Links Among Patient-Perceived Provider Cultural Sensitivity, Language Preference, and Patient Treatment Adherence

Introduction Disparities in treatment adherence based on race and ethnicity are well documented but poorly understood. Specifically, the causes of treatment nonadherence among Latino patients living in the USA are complex and include cultural and language barriers. Purpose The purpose of this study was to examine whether patients’ perceptions in patient-provider interactions (i.e., trust in provider, patient satisfaction, and patient sense of interpersonal control in patient-provider interactions) mediate any found association between patient-perceived provider cultural sensitivity (PCS) and treatment adherence among English-preferred Latino (EPL) and Spanish-preferred Latino (SPL) patients. Methods Data from 194 EPL patients and 361 SPL patients were obtained using questionnaires. A series of language-specific structural equation models were conducted to test the relationship between patient-perceived PCS and patient treatment adherence and the examined mediators of this relationship among the Latino patients. Results No significant direct effects of patient-perceived PCS on general treatment adherence were found. However, as hypothesized, several significant indirect effects emerged. Preferred language appeared to have moderating effects on the relationships between patient-perceived PCS and general treatment adherence. Conclusion These results suggest that interventions to promote treatment adherence among Latino patients should likely include provider training to foster patient-defined PCS, trust in provider, and patient satisfaction with care. Furthermore, this training needs to be customized to be suitable for providing care to Latino patients who prefer speaking Spanish and Latino patients who prefer speaking English

Search for Gravitational Waves from Intermediate Mass Binary Black Holes

We present the results of a weakly modeled burst search for gravitational waves from mergers of non-spinning intermediate mass black holes (IMBH) in the total mass range 100--450 solar masses and with the component mass ratios between 1:1 and 4:1. The search was conducted on data collected by the LIGO and Virgo detectors between November of 2005 and October of 2007. No plausible signals were observed by the search which constrains the astrophysical rates of the IMBH mergers as a function of the component masses. In the most efficiently detected bin centered on 88+88 solar masses, for non-spinning sources, the rate density upper limit is 0.13 per Mpc^3 per Myr at the 90% confidence level.Comment: 13 pages, 4 figures: data for plots and archived public version at https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=62326, see also the public announcement at http://www.ligo.org/science/Publication-S5IMBH

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy.CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Germline selection shapes human mitochondrial DNA diversity.

Approximately 2.4% of the human mitochondrial DNA (mtDNA) genome exhibits common homoplasmic genetic variation. We analyzed 12,975 whole-genome sequences to show that 45.1% of individuals from 1526 mother-offspring pairs harbor a mixed population of mtDNA (heteroplasmy), but the propensity for maternal transmission differs across the mitochondrial genome. Over one generation, we observed selection both for and against variants in specific genomic regions; known variants were more likely to be transmitted than previously unknown variants. However, new heteroplasmies were more likely to match the nuclear genetic ancestry as opposed to the ancestry of the mitochondrial genome on which the mutations occurred, validating our findings in 40,325 individuals. Thus, human mtDNA at the population level is shaped by selective forces within the female germ line under nuclear genetic control, which ensures consistency between the two independent genetic lineages.NIHR, Wellcome Trust, MRC, Genomics Englan

Adherence to cardiovascular disease medications: Does patient-provider race/ethnicity and language concordance matter?

BACKGROUND: Patient-physician race/ethnicity and language concordance may improve medication adherence and reduce disparities in cardiovascular disease (CVD) by fostering trust and improved patient-physician communication. OBJECTIVE: To examine the association of patient race/ethnicity and language and patient-physician race/ethnicity and language concordance on medication adherence rates for a large cohort of diabetes patients in an integrated delivery system. DESIGN: We studied 131,277 adult diabetes patients in Kaiser Permanente Northern California in 2005. Probit models assessed the effect of patient and physician race/ethnicity and language on adherence to CVD medications, after controlling for patient and physician characteristics. RESULTS: Ten percent of African American, 11 % of Hispanic, 63% of Asian, and 47% of white patients had same race/ethnicity physicians.24% of Spanish-speaking patients were linguistically concordant with their physicians. African American (46%), Hispanic (49%) and Asian (52%) patients were significantly less likely than white patients (58%) to be in good adherence to all of their CVD medications (p&lt;0.001). Spanish-speaking patients were less likely than English speaking patients to be in good adherence (51%versus 57%, p&lt;0.001). Race concordance for African American patients was associated with adherence to all their CVD medications (53% vs. 50%, p&lt;0.05). Language concordance was associated with medication adherence for Spanish-speaking patients (51% vs. 45%, p&lt;0.05). CONCLUSION: Increasing opportunities for patient- physician race/ethnicity and language concordance may improve medication adherence for African American and Spanish-speaking patients, though a similar effect was not observed for Asian patients or Englishproficient Hispanic patients. © Society of General Internal Medicine 2010

Do Cerebral Small Vessel Disease and Multiple Sclerosis Share Common Mechanisms of White Matter Injury?

Background and Purpose- The role of inflammation in ischemic white matter disease is increasingly recognized, and further understanding of the pathophysiology might inform future treatment strategies. Multiple sclerosis (MS) is a chronic autoimmune condition in which inflammation plays a central role that also affects the white matter. We hypothesized that white matter injury might share common mechanisms and used statistical genetics techniques to assess whether having genetically elevated white matter hyperintensity (WMH) volume was associated with increased MS risk. Methods- We investigated the genetic association in 2 cohorts with magnetic resonance imaging-quantified ischemic white matter lesion volume (WMH in stroke; n=2797 and UK Biobank; n=8353) and 14 802 cases of MS and 26 703 controls from the International Multiple Sclerosis Genetics Consortium. We further performed individual-level polygenic risk score calculations for MS and measures of structural white matter disease in UK Biobank. Finally, we looked for evidence of overlapping risk across the whole genome. Results- There was no association of genetic variants influencing MS with WMH volume using summary statistics in the WMH in stroke cohort (relative risk score =1.014; 95% CI, 0.936-1.110) or in the UK Biobank cohort (relative risk score =1.030; 95% CI, 0.932-1.117). Conversely, assessing the contribution of single nucleotide polymorphisms significantly associated with WMH on the risk of MS there was no significant association (relative risk score =0.930; 95% CI, 0.736-1.191). There were no significant associations between polygenic risk scores calculations; these results were robust to the selection of single nucleotide polymorphisms at a range of significance thresholds. Whole genome analysis did not reveal any overlap of risk between the traits. Conclusions- Our results do not provide evidence to suggest a shared mechanism of white matter damage in ischemia and MS. We propose that inflammation acts in distinct pathways because of the differing nature of the primary insult.This study was supported by a programme grant from the British Heart Foundation (RG/16/4/32218). Hugh Markus is funded by a National Institute for Health Research (NIHR) Senior Investigator Award, and his work is supported by the Cambridge University NHS Trust Biomedical Research Centre. Stephen Burgess is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z)