Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study
Fuente:
Wiley Open Access
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Artículo Open Access |
| Publié: |
Wiley
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1867020078459912192 |
|---|---|
| author | Jessica L. Buthmann Jonas G. Miller Nima Aghaeepour Lucy S. King David K. Stevenson Gary M. Shaw Ronald J. Wong Ian H. Gotlib |
| author_facet | Jessica L. Buthmann Jonas G. Miller Nima Aghaeepour Lucy S. King David K. Stevenson Gary M. Shaw Ronald J. Wong Ian H. Gotlib Jessica L. Buthmann Jonas G. Miller Nima Aghaeepour Lucy S. King David K. Stevenson Gary M. Shaw Ronald J. Wong Ian H. Gotlib |
| collection | Wiley Open Access |
| contents | Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study Jessica L. Buthmann Jonas G. Miller Nima Aghaeepour Lucy S. King David K. Stevenson Gary M. Shaw Ronald J. Wong Ian H. Gotlib Journal of Child Psychology and Psychiatry 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. 10.1111/jcpp.13948 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/jcpp.13948 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_jcpp_13948 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study Jessica L. Buthmann Jonas G. Miller Nima Aghaeepour Lucy S. King David K. Stevenson Gary M. Shaw Ronald J. Wong Ian H. Gotlib Journal of Child Psychology and Psychiatry Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study Jessica L. Buthmann Jonas G. Miller Nima Aghaeepour Lucy S. King David K. Stevenson Gary M. Shaw Ronald J. Wong Ian H. Gotlib Journal of Child Psychology and Psychiatry 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. 10.1111/jcpp.13948 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Large‐scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study |
| topic | Journal of Child Psychology and Psychiatry |
| url | https://acamh.onlinelibrary.wiley.com/doi/10.1111/jcpp.13948 |