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      Birthweight, Type 2 Diabetes Mellitus, and Cardiovascular Disease : Addressing the Barker Hypothesis With Mendelian Randomization

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          Abstract

          BACKGROUND:

          Low birthweight has been associated with a higher risk of hypertension, type 2 diabetes mellitus (T2D), and cardiovascular disease. The Barker hypothesis posits that intrauterine growth restriction resulting in lower birthweight is causal for these diseases, but causality is difficult to infer from observational studies.

          METHODS:

          We performed regression analyses to assess associations of birthweight with cardiovascular disease and T2D in 237 631 individuals from the UK Biobank. Further, we assessed the causal relationship of such associations using Mendelian randomization.

          RESULTS:

          In the observational analyses, birthweight showed inverse associations with systolic and diastolic blood pressure (β, −0.83 and −0.26; per raw unit in outcomes and SD change in birthweight; 95% confidence interval [CI], −0.90 to −0.75 and −0.31 to −0.22, respectively), T2D (odds ratio, 0.83; 95% CI, 0.79−0.87), lipid-lowering treatment (odds ratio, 0.84; 95% CI, 0.81−0.86), and coronary artery disease (hazard ratio, 0.85; 95% CI, 0.78−0.94), whereas the associations with adult body mass index and body fat (β, 0.04 and 0.02; per SD change in outcomes and birthweight; 95% CI, 0.03−0.04 and 0.01−0.02, respectively) were positive. The Mendelian randomization analyses indicated inverse causal associations of birthweight with low-density lipoprotein cholesterol, 2-hour glucose, coronary artery disease, and T2D and positive causal association with body mass index but no associations with blood pressure.

          CONCLUSIONS:

          Our study indicates that lower birthweight, used as a proxy for intrauterine growth retardation, is causally related with increased susceptibility to coronary artery disease and T2D. This causal relationship is not mediated by adult obesity or hypertension.

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          Most cited references29

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          Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

          Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.
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            Fetal and infant growth and impaired glucose tolerance at age 64.

            To discover whether reduced fetal and infant growth is associated with non-insulin dependent diabetes and impaired glucose tolerance in adult life. Follow up study of men born during 1920-30 whose birth weights and weights at 1 year were known. Hertfordshire, England. 468 men born in east Hertfordshire and still living there. Fasting plasma glucose, insulin, proinsulin, and 32-33 split pro-insulin concentrations and plasma glucose and insulin concentrations 30 and 120 minutes after a 75 g glucose drink. 93 men had impaired glucose tolerance or hitherto undiagnosed diabetes. They had had a lower mean birth weight and a lower weight at 1 year. The proportion of men with impaired glucose tolerance fell progressively from 26% (6/23) among those who had weighted 18 lb (8.16 kg) or less at 1 year to 13% (3/24) among those who had weighed 27 lb (12.25 kg) or more. Corresponding figures for diabetes were 17% (4/23) and nil (0/24). Plasma glucose concentrations at 30 and 120 minutes fell with increasing birth weight and weight at 1 year. Plasma 32-33 split proinsulin concentration fell with increasing weight at 1 year. All these trends were significant and independent of current body mass. Blood pressure was inversely related to birth weight and strongly related to plasma glucose and 32-33 split proinsulin concentrations. Reduced growth in early life is strongly linked with impaired glucose tolerance and non-insulin dependent diabetes. Reduced early growth is also related to a raised plasma concentration of 32-33 split proinsulin, which is interpreted as a sign of beta cell dysfunction. Reduced intrauterine growth is linked with high blood pressure, which may explain the association between hypertension and impaired glucose tolerance.
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              Birth weight and subsequent risk of type 2 diabetes: a meta-analysis.

              The "small baby syndrome hypothesis" suggests that an inverse linear relation exists between birth weight and risk of type 2 diabetes. The authors conducted a meta-analysis to examine this association. They included studies that reported odds ratios and 95% confidence intervals (or data with which to calculate them) for the association of type 2 diabetes with birth weight. Fourteen studies involving a total of 132,180 persons were identified. Low birth weight ( /=2,500 g, was associated with increased risk of type 2 diabetes (odds ratio (OR) = 1.32, 95% confidence interval (CI): 1.06, 1.64). High birth weight (>4,000 g), as compared with a birth weight of
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                Author and article information

                Contributors
                Journal
                101714113
                47211
                Circ Genom Precis Med
                Circ Genom Precis Med
                Circulation. Genomic and precision medicine
                2574-8300
                25 March 2019
                June 2018
                01 June 2019
                : 11
                : 6
                : e002054
                Affiliations
                Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA.
                Stanford Cardiovascular Institute, Stanford University School of Medicine, CA.
                Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA.
                Stanford Cardiovascular Institute, Stanford University School of Medicine, CA.
                Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Sweden
                Division of Cardiology, Department of Pediatrics, Stanford University School of Medicine, CA.
                MRC Biostatistics Unit and Department of Public Health and Primary Care, University of Cambridge, United Kingdom.
                Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA.
                Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Sweden
                Author notes
                Correspondence Erik Ingelsson, MD, PhD, Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, 300 Pasteur Dr, Mail Code: 5773, Stanford, CA 94305. eriking@ 123456stanford.edu
                Article
                NIHMS1010458
                10.1161/CIRCGEN.117.002054
                6447084
                29875125
                6947f209-2606-41c6-8bd9-ca71d3f5f388

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited ( http://creativecommons.org/licenses/by/4.0/).

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                cardiovascular disease,diabetes mellitus, type 2,genetics,hypertension,obesity

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