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      Lower birth weight is linked to poorer cardiovascular health in middle-aged population-based adults

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          Abstract

          Objective

          To examine associations of birth weight with clinical and imaging indicators of cardiovascular health and evaluate mechanistic pathways in the UK Biobank.

          Methods

          Competing risk regression was used to estimate associations of birth weight with incident myocardial infarction (MI) and mortality (all-cause, cardiovascular disease, ischaemic heart disease, MI), over 7–12 years of longitudinal follow-up, adjusting for age, sex, deprivation, maternal smoking/hypertension and maternal/paternal diabetes. Mediation analysis was used to evaluate the role of childhood growth, adulthood obesity, cardiometabolic diseases and blood biomarkers in mediating the birth weight–MI relationship. Linear regression was used to estimate associations of birth weight with left ventricular (LV) mass-to-volume ratio, LV stroke volume, global longitudinal strain, LV global function index and left atrial ejection fraction.

          Results

          258 787 participants from white ethnicities (61% women, median age 56 (49, 62) years) were studied. Birth weight had a non-linear relationship with incident MI, with a significant inverse association below an optimal threshold of 3.2 kg (subdistribution HR: 1.15 (1.08 to 1.22), p=6.0×10 –5) and attenuation to the null above this threshold. The birth weight–MI effect was mediated through hypertension (8.4%), glycated haemoglobin (7.0%), C reactive protein (6.4%), high-density lipoprotein (5.2%) and high cholesterol (4.1%). Birth weight–mortality associations were statistically non-significant after Bonferroni correction. In participants with cardiovascular magnetic resonance (n=19 314), lower birth weight was associated with adverse LV remodelling (greater concentricity, poorer function).

          Conclusions

          Lower birth weight was associated with greater risk of incident MI and unhealthy LV phenotypes; effects were partially mediated through cardiometabolic disease and systemic inflammation. These findings support consideration of birth weight in risk prediction and highlight actionable areas for disease prevention.

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          Most cited references29

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          WEIGHT IN INFANCY AND DEATH FROM ISCHAEMIC HEART DISEASE

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            Mediation Analysis with Multiple Mediators.

            Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators.
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              Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

              Background Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Methods Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). Results By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. Conclusions We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures. Electronic supplementary material The online version of this article (10.1186/s12968-018-0471-x) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Heart
                Heart
                heartjnl
                heart
                Heart
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1355-6037
                1468-201X
                April 2023
                16 November 2022
                : 109
                : 7
                : 535-541
                Affiliations
                [1 ] departmentBarts Heart Centre , Saint Bartholomew's Hospital, Barts Health NHS Trust , London, UK
                [2 ] departmentWilliam Harvey Research Institute, NIHR Barts Biomedical Research Centre , Queen Mary University of London , London, UK
                [3 ] Imperial College Healthcare NHS Trust , London, UK
                [4 ] departmentDivision of Cardiovascular Medicine, Radcliffe Department of Medicine , University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust , Oxford, UK
                [5 ] MRC Lifecourse Epidemiology Centre , Southampton, UK
                [6 ] NIHR Southampton Biomedical Research Centre , Southampton, UK
                [7 ] Health Data Research UK , London, UK
                [8 ] Alan Turing Institute , London, UK
                Author notes
                [Correspondence to ] Dr Zahra Raisi-Estabragh, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, EC1M 6BQ, UK; zahraraisi@ 123456doctors.org.uk

                NCH and SEP are joint senior authors.

                Author information
                http://orcid.org/0000-0002-7757-5465
                http://orcid.org/0000-0003-4622-5160
                Article
                heartjnl-2022-321733
                10.1136/heartjnl-2022-321733
                10086465
                36384749
                7e93b354-f7b6-4040-8355-7a2f39830167
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

                History
                : 04 August 2022
                : 03 November 2022
                Funding
                Funded by: Barts NIHR Biomedical Research Centre;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: Clinical Research Training Fellowship
                Award ID: FS/17/81/33318
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/P001009/1
                Funded by: European Union;
                Award ID: 825903
                Funded by: Oxford NIHR Biomedical Research Centre;
                Award ID: IS-BRC-1215-20008
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_12011/1
                Award ID: MR/L016311/1
                Categories
                Cardiac Risk Factors and Prevention
                1506
                Original research
                Custom metadata
                unlocked

                Cardiovascular Medicine
                epidemiology,risk factors,magnetic resonance imaging
                Cardiovascular Medicine
                epidemiology, risk factors, magnetic resonance imaging

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