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      Genetic and Metabolic Determinants of Plasma Levels of ANGPTL8


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          ANGPTL8 (A8) plays a key role in determining the tissue fate of circulating triglycerides (TGs). Plasma A8 levels are associated with several parameters of glucose and TG metabolism, but the causality of these relationships and the contribution of genetic variants to differences in A8 levels have not been explored.


          To characterize the frequency distribution of plasma A8 levels in a diverse population using a newly-developed enzyme-linked immunosorbent assay (ELISA) and to identify genetic factors contributing to differences in plasma A8 levels.


          We studied a population-based sample of Dallas County, comprising individuals in the Dallas Heart Study (DHS-1, n = 3538; DHS-2, n = 3283), including 2131 individuals with repeated measurements 7 to 9 years apart (age 18-85 years; >55% female; 52% Black; 29% White; 17% Hispanic; and 2% other). The main outcome measures were associations of A8 levels with body mass index (BMI), plasma levels of glucose, insulin, lipids, and hepatic TGs, as well as DNA variants identified by exome-wide sequencing.


          A8 levels varied over a 150-fold range (2.1-318 ng/mL; median, 13.3 ng/mL) and differed between racial/ethnic groups (Blacks > Hispanics > Whites). A8 levels correlated with BMI, fasting glucose, insulin, and TG levels. A variant in A8, R59W, accounted for 17% of the interindividual variation in A8 levels but was not associated with the metabolic parameters correlated with plasma A8 concentrations.


          A8 levels were strongly associated with indices of glucose and TG metabolism, but the lack of association of genetic variants at the A8 locus that impact A8 levels with these parameters indicates that differences in A8 levels are not causally related to the associated metabolic phenotypes.

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          Second-generation PLINK: rising to the challenge of larger and richer datasets

          PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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            Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

            The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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              Intraclass correlations: uses in assessing rater reliability.

              Reliability coefficients often take the form of intraclass correlation coefficients. In this article, guidelines are given for choosing among six different forms of the intraclass correlation for reliability studies in which n target are rated by k judges. Relevant to the choice of the coefficient are the appropriate statistical model for the reliability and the application to be made of the reliability results. Confidence intervals for each of the forms are reviewed.

                Author and article information

                J Clin Endocrinol Metab
                J Clin Endocrinol Metab
                The Journal of Clinical Endocrinology and Metabolism
                Oxford University Press (US )
                June 2021
                23 February 2021
                23 February 2021
                : 106
                : 6
                : 1649-1667
                [1 ] Department of Molecular Genetics, University of Texas Southwestern Medical Center , Dallas, TX, USA
                [2 ] The Eugene McDermott Center of Human Growth and Development, University of Texas Southwestern Medical Center , Dallas, TX, USA
                [3 ] Regeneron Pharmaceuticals , Tarrytown, NY, USA
                [4 ] The Center for Human Nutrition, University of Texas Southwestern Medical Center , Dallas, TX, USA
                [5 ] Howard Hughes Medical Institute, University of Texas Southwestern Medical Center , Dallas, TX, USA
                Author notes
                Correspondence: Helen H. Hobbs, Department of Molecular Genetics L5.134, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390–9046, USA. Email: helen.hobbs@ 123456utsouthwestern.edu .

                These authors contributed equally to the work.

                Author information
                © The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                : 21 December 2020
                : 18 February 2021
                : 22 April 2021
                Page count
                Pages: 19
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: 5 PO1 HL20948
                Clinical Research Articles

                Endocrinology & Diabetes
                Endocrinology & Diabetes
                angptl8, angptl3, triglycerides, glucose, insulin, obesity


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