<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Buthmann JL</submitter><funding>Christopher Hess Research Fund</funding><funding>Alfred E. Mann Foundation</funding><funding>NIMH NIH HHS</funding><funding>March of Dimes Foundation</funding><funding>Charles B. and Ann L. Johnson Endowed Fund</funding><funding>National Institute of Mental Health</funding><funding>National Institutes of Health</funding><funding>Bill and Melinda Gates Foundation</funding><funding>Tad Taube Maternal and Child Health Research Fund</funding><funding>NIGMS NIH HHS</funding><funding>NIH HHS</funding><funding>Stanford Maternal and Child Health Research Institute</funding><pagination>1098-1107</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11265978</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>65(8)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties.&lt;h4>Methods&lt;/h4>Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their</pubmed_abstract><journal>Journal of child psychology and psychiatry, and allied disciplines</journal><pubmed_title>Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.</pubmed_title><pmcid>PMC11265978</pmcid><funding_grant_id>R35GM138353</funding_grant_id><funding_grant_id>R37MH101495</funding_grant_id><funding_grant_id>R37 MH101495</funding_grant_id><funding_grant_id>R35 GM138353</funding_grant_id><pubmed_authors>Shaw GM</pubmed_authors><pubmed_authors>Wong RJ</pubmed_authors><pubmed_authors>Miller JG</pubmed_authors><pubmed_authors>Buthmann JL</pubmed_authors><pubmed_authors>Gotlib IH</pubmed_authors><pubmed_authors>Aghaeepour N</pubmed_authors><pubmed_authors>King LS</pubmed_authors><pubmed_authors>Stevenson DK</pubmed_authors></additional><is_claimable>false</is_claimable><name>Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.</name><description>&lt;h4>Background&lt;/h4>Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties.&lt;h4>Methods&lt;/h4>Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Aug</publication><modification>2026-03-17T15:29:55.63Z</modification><creation>2025-08-16T03:06:54.78Z</creation></dates><accession>S-EPMC11265978</accession><cross_references><pubmed>38287782</pubmed><doi>10.1111/jcpp.13948</doi></cross_references></HashMap>