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http://dx.doi.org/10.1177/0003319719848209 | DOI Listing |
Genet Epidemiol
January 2025
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
Large-scale gene-environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C).
View Article and Find Full Text PDFAm J Med Open
December 2024
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR.
Background: Studies examining racial and ethnic disparities in-hospital mortality for patients hospitalized with COVID-19 had mixed results. Findings from patients within academic medical centers (AMCs) are lacking, but important given the role of AMCs in improving health equity.
Objective: The purpose of this study is to assess whether minority patients hospitalized with COVID-19 in National COVID Cohort Collaborative (N3C) institutions, which consist predominantly of AMCs, have higher mortality rates relative to White patients.
South Asians are at higher risk of dyslipidaemia-a modifiable risk factor for cardiovascular diseases (CVDs). We aimed to identify protein targets for dyslipidaemia and CVDs in this population. We used a two-sample Mendelian randomization (MR) approach, supplemented with MR-Egger, weighted median, colocalization, and generalized MR (GMR), to evaluate the effect of 2,800 plasma proteins on high/low/non-high-density lipoprotein cholesterol (HDL-C/LDL-C/nonHDL-C), total cholesterol, and triglycerides.
View Article and Find Full Text PDFAm J Epidemiol
January 2025
Department of Social Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Algorithmic estimations of dementia status are widely used in public health and epidemiological research, however, inadequate algorithm performance across racial/ethnic groups has been a barrier. We present improvements in the accuracy of group-specific "probable dementia" estimation using a transfer learning approach. Transfer learning involves combining models trained on a large "source" dataset with imprecise outcome assessments, alongside models trained on a smaller "target" dataset with high-quality outcome assessments.
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