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      Cord blood lipid correlation network profiles are associated with subsequent attention-deficit/hyperactivity disorder and autism spectrum disorder symptoms at 2 years: a prospective birth cohort study

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          Summary

          Background

          Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions with early life origins. Alterations in blood lipids have been linked to ADHD and ASD; however, prospective early life data are limited. This study examined (i) associations between the cord blood lipidome and ADHD/ASD symptoms at 2 years of age, (ii) associations between prenatal and perinatal predictors of ADHD/ASD symptoms and cord blood lipidome, and (iii) mediation by the cord blood lipidome.

          Methods

          From the Barwon Infant Study cohort (1074 mother-child pairs, 52.3% male children), child circulating lipid levels at birth were analysed using ultra-high-performance liquid chromatography-tandem mass spectrometry. These were clustered into lipid network modules via Weighted Gene Correlation Network Analysis. Associations between lipid modules and ADHD/ASD symptoms at 2 years, assessed with the Child Behavior Checklist, were explored via linear regression analyses. Mediation analysis identified indirect effects of prenatal and perinatal risk factors on ADHD/ASD symptoms through lipid modules.

          Findings

          The acylcarnitine lipid module is associated with both ADHD and ASD symptoms at 2 years of age. Risk factors of these outcomes such as low income, Apgar score, and maternal inflammation were partly mediated by higher birth acylcarnitine levels. Other cord blood lipid profiles were also associated with ADHD and ASD symptoms.

          Interpretation

          This study highlights that elevated cord blood birth acylcarnitine levels, either directly or as a possible marker of disrupted cell energy metabolism, are on the causal pathway of prenatal and perinatal risk factors for ADHD and ASD symptoms in early life.

          Funding

          The foundational work and infrastructure for the BIS was sponsored by the doi 10.13039/100014555, Murdoch Children's Research Institute; , doi 10.13039/501100001778, Deakin University; , and doi 10.13039/501100019573, Barwon Health; . Subsequent funding was secured from the doi 10.13039/501100016056, Minderoo Foundation; , the doi 10.13039/501100007601, European Union's Horizon 2020; research and innovation programme (ENDpoiNTs: No 825759), doi 10.13039/501100000925, National Health and Medical Research Council of Australia; (NHMRC) and doi 10.13039/501100001348, Agency for Science, Technology and Research Singapore; [APP1149047], The William and Vera Ellen Houston Memorial Trust Fund (via HOMER Hack), doi 10.13039/501100018898, The Shepherd Foundation; , doi 10.13039/100012698, The Jack Brockhoff Foundation; , the Scobie & Claire McKinnon Trust, the Shane O'Brien Memorial Asthma Foundation, the Our Women Our Children's Fund Raising Committee Barwon Health, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, doi 10.13039/501100020380, Geelong Medical and Hospital Benefits Association; , Vanguard Investments Australia Ltd, the Percy Baxter Charitable Trust, and Perpetual Trustees.

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          Most cited references86

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            A global measure of perceived stress.

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              mediation:RPackage for Causal Mediation Analysis

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                Author and article information

                Contributors
                Journal
                eBioMedicine
                EBioMedicine
                eBioMedicine
                Elsevier
                2352-3964
                09 January 2024
                February 2024
                09 January 2024
                : 100
                : 104949
                Affiliations
                [a ]Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville 3010, Australia
                [b ]Melbourne School of Population and Global Health, University of Melbourne, Parkville 3010, Australia
                [c ]Melbourne School of Mathematics and Statistics, University of Melbourne, Parkville 3010, Australia
                [d ]Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville 3010, Australia
                [e ]Department of Paediatrics, University of Melbourne, Parkville 3010, Australia
                [f ]Department of Paediatrics, Monash University, Clayton 3168, Australia
                [g ]Child Health Research Unit, Barwon Health, Geelong 3220, Australia
                [h ]School of Medicine, Deakin University, Geelong 3220, Australia
                [i ]Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne 3004, Australia
                [j ]Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Bundoora, VIC 3086, Australia
                Author notes
                []Corresponding author. Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC 3052, Australia. annelouise.ponsonby@ 123456florey.edu.au
                Article
                S2352-3964(23)00515-7 104949
                10.1016/j.ebiom.2023.104949
                10825361
                38199043
                b007c06a-1add-4994-8d47-d090dc03bd2f
                © 2024 The Authors. Published by Elsevier B.V.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 5 July 2023
                : 18 December 2023
                : 18 December 2023
                Categories
                Articles

                lipidomics,cord blood,fatty acids,acylcarnitine,autism spectrum disorder symptoms,attention-deficit/hyperactivity disorder symptoms

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