Publications by authors named "J Forsyth Meigs"
Aims/hypothesis: Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European.
Methods: Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs.
Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP).
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- * We found 17 genetic loci associated with sleep duration impacting lipid levels, with 10 of them being newly identified and linked to sleep-related disturbances in lipid metabolism.
- * The research points to potential drug targets that could lead to new treatments for lipid-related issues in individuals with sleep problems, highlighting the connection between sleep patterns and cardiovascular health.
Article Synopsis
- Genome-wide association studies have found numerous genetic loci linked to glycemic traits, but connecting these loci to specific genes and biological pathways remains a challenge.
- Researchers conducted meta-analyses of exome-array studies across four glycemic traits, analyzing data from over 144,000 participants, which led to the identification of coding variant associations in more than 60 genes.
- The study revealed significant pathways related to insulin secretion, zinc transport, and fatty acid metabolism, enhancing understanding of glycemic regulation and making data available for further research.
Article Synopsis
- The study aimed to create and validate algorithms that can effectively identify cases of diabetic retinopathy (DR) from electronic health records (EHRs) across three different healthcare systems.
- The algorithms were assessed based on specific criteria for identifying DR cases and diabetes controls, yielding high positive and negative predictive values (PPV and NPV) across the different systems tested.
- Results showed that while the algorithms performed well overall, there were some variances in their effectiveness, especially when comparing different healthcare systems, highlighting the need for further validation to enhance their reliability.