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  • Ovid Technologies (Wolters Kluwer Health)  (3)
  • 1
    In: Medicine, Ovid Technologies (Wolters Kluwer Health), Vol. 95, No. 17 ( 2016-04), p. e3379-
    Type of Medium: Online Resource
    ISSN: 0025-7974
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2016
    detail.hit.zdb_id: 2049818-4
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  • 2
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 146, No. Suppl_1 ( 2022-11-08)
    Abstract: Introduction: Cardiovascular disease (CVD) is the leading cause of death for people with type 2 diabetes (T2D). The predictive performance of existing CVD risk scores in T2D populations is suboptimal. Metabolomics is a promising method of identifying novel biomarkers which might improve risk prediction. We aimed to identify a group of metabolites associated with incident CVD in people with T2D and assess its predictive performance over-and-above a current CVD risk score (QRISK3). Methods: In 1,066 individuals with T2D (Edinburgh Type 2 Diabetes Study), a panel of 228 serum metabolites was measured at baseline and incident CVD events were identified over the subsequent 10 years. We applied 100 repeats of Cox LASSO (least absolute shrinkage and selection operator) to select metabolites with frequency 〉 90% as candidate components for a metabolites-based risk score (MRS). The MRS was calculated using the linear predictor in an unpenalized Cox regression model where only candidate metabolites were included. The predictive performance of the MRS was assessed in relation to a reference score which refitted components of QRISK3 plus prevalent CVD and statin use at baseline. Predictive metrics were internally validated using 500-repeat bootstrapping. Results: In 1,021 available individuals (mean age 67.9 years, 51.7% male), 255 people developed CVD (25.0%) during a median of 10.6 years of follow-up. Twelve metabolites relating to fluid balance, ketone bodies, amino acids, fatty acids, glycolysis and lipoproteins were selected to construct the MRS. C-statistics were 0.673 (95%CI 0.642, 0.704) for the MRS alone and 0.718 (95%CI 0.689, 0.747) for the reference score, increasing slightly to 0.736 (95%CI 0.707, 0.764) for the combination of the two. The improved prediction by combining the MRS with the reference score was internally validated in bootstrapping samples, where the C-statistics for the reference score and the combination were 0.679 (95%CI 0.650, 0.708) vs. 0.699 (95%CI 0.671, 0.728) separately. Conclusions: Metabolomics data might improve predictive performance of current CVD risk scores based on traditional risk factors in people with T2D. External validation is warranted to assess the generalizability of improved CVD risk prediction using the MRS.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
    detail.hit.zdb_id: 1466401-X
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  • 3
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 145, No. 18 ( 2022-05-03), p. 1398-1411
    Abstract: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. Methods: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in 〉 28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics–based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. Results: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis–protein quantitative trait loci–based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10–2.42]; P =0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05–2.21]; P =0.03), and infection (odds ratio, 1.60 [95% CI, 1.08–2.37]; P =0.02). Tissue- and cell type–specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. Conclusions: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
    detail.hit.zdb_id: 1466401-X
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