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Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.

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Title: Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.
Authors: Buthmann, Jessica L., Miller, Jonas G., Aghaeepour, Nima, King, Lucy S., Stevenson, David K., Shaw, Gary M., Wong, Ronald J., Gotlib, Ian H.
Source: Journal of Child Psychology & Psychiatry. Aug2024, Vol. 65 Issue 8, p1098-1107. 10p.
Subjects: Risk assessment, Temperament in children, Academic medical centers, Data analysis, Research funding, Cell physiology, Questionnaires, Descriptive statistics, Cellular signal transduction, Behavior disorders in children, Proteomics, Child development, Statistics, First trimester of pregnancy, Machine learning, Pathological psychology, Biomarkers, Pregnancy
Abstract: Background: 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. Methods: 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 3‐ to 5‐year‐old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist). Results: We found that machine learning models of prenatal proteomics predict 5%–10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes. Conclusions: Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell‐signaling pathways were enriched in relation to child temperament and psychopathology measures. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.
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  Data: <searchLink fieldCode="AR" term="%22Buthmann%2C+Jessica+L%2E%22">Buthmann, Jessica L.</searchLink><br /><searchLink fieldCode="AR" term="%22Miller%2C+Jonas+G%2E%22">Miller, Jonas G.</searchLink><br /><searchLink fieldCode="AR" term="%22Aghaeepour%2C+Nima%22">Aghaeepour, Nima</searchLink><br /><searchLink fieldCode="AR" term="%22King%2C+Lucy+S%2E%22">King, Lucy S.</searchLink><br /><searchLink fieldCode="AR" term="%22Stevenson%2C+David+K%2E%22">Stevenson, David K.</searchLink><br /><searchLink fieldCode="AR" term="%22Shaw%2C+Gary+M%2E%22">Shaw, Gary M.</searchLink><br /><searchLink fieldCode="AR" term="%22Wong%2C+Ronald+J%2E%22">Wong, Ronald J.</searchLink><br /><searchLink fieldCode="AR" term="%22Gotlib%2C+Ian+H%2E%22">Gotlib, Ian H.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Child+Psychology+%26+Psychiatry%22">Journal of Child Psychology & Psychiatry</searchLink>. Aug2024, Vol. 65 Issue 8, p1098-1107. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Temperament+in+children%22">Temperament in children</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+medical+centers%22">Academic medical centers</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+physiology%22">Cell physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Cellular+signal+transduction%22">Cellular signal transduction</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+disorders+in+children%22">Behavior disorders in children</searchLink><br /><searchLink fieldCode="DE" term="%22Proteomics%22">Proteomics</searchLink><br /><searchLink fieldCode="DE" term="%22Child+development%22">Child development</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22First+trimester+of+pregnancy%22">First trimester of pregnancy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Pathological+psychology%22">Pathological psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Pregnancy%22">Pregnancy</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: 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. Methods: 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 3‐ to 5‐year‐old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist). Results: We found that machine learning models of prenatal proteomics predict 5%–10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes. Conclusions: Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell‐signaling pathways were enriched in relation to child temperament and psychopathology measures. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jcpp.13948
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 1098
    Subjects:
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Temperament in children
        Type: general
      – SubjectFull: Academic medical centers
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Cell physiology
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Cellular signal transduction
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      – SubjectFull: Behavior disorders in children
        Type: general
      – SubjectFull: Proteomics
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      – SubjectFull: Child development
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: First trimester of pregnancy
        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Pathological psychology
        Type: general
      – SubjectFull: Biomarkers
        Type: general
      – SubjectFull: Pregnancy
        Type: general
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      – TitleFull: Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.
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              M: 08
              Text: Aug2024
              Type: published
              Y: 2024
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