Intergenerational transmission of comorbid internalizing and externalizing psychopathology at age 11: Evidence from an adoption design for general transmission of comorbidity rather than homotypic transmission / Kristine MARCEAU in Development and Psychopathology, 37-3 (August 2025)  

Public ISBD [article]  inDevelopment and Psychopathology > 37-3 (August 2025) . - p.1125-1138 Titre : Intergenerational transmission of comorbid internalizing and externalizing psychopathology at age 11: Evidence from an adoption design for general transmission of comorbidity rather than homotypic transmission Type de document : Texte imprimé et/ou numérique Auteurs : Kristine MARCEAU, Auteur ; Sohee LEE, Auteur ; Muskan DATTA, Auteur ; Olivia C. ROBERTSON, Auteur ; Daniel S. SHAW, Auteur ; Misaki N. NATSUAKI, Auteur ; Leslie D. LEVE, Auteur ; Jody M. GANIBAN, Auteur ; Jenae M. NEIDERHISER, Auteur Article en page(s) : p.1125-1138 Langues : Anglais (eng) Mots-clés : adoption design  comorbidity  intergenerational transmission  internalizing and externalizing  severity and directionality Index. décimale : PER Périodiques Résumé : Psychopathology is intergenerationally transmitted through both genetic and environmental mechanisms via heterotypic (cross-domain), homotypic (domain-specific), and general (e.g., "p-factor") pathways. The current study leveraged an adopted-at-birth design, the Early Growth and Development Study (57% male; 55.6% White, 19.3% Multiracial, 13% Black/African American, 10.9% Hispanic/Latine) to explore the relative influence of these pathways via associations between adoptive caregiver psychopathology (indexing potential environmental transmission) and birth parent psychopathology (indexing genetic transmission) with adolescent internalizing and externalizing symptoms. We included composite measures of adoptive and birth parent internalizing, externalizing, and substance use domains, and a general "p-factor." Age 11 adolescent internalizing and externalizing symptom scores were the average of adoptive parent reports on the Child Behavior Checklist (n = 407). Examining domains independently without addressing comorbidity can lead to incorrect interpretations of transmission mode. Therefore, we also examined symptom severity (like the "p-factor") and an orthogonal symptom directionality score to more cleanly disentangle transmission modes. The pattern of correlations was consistent with mostly general transmission in families with youth showing comorbid internalizing and externalizing symptoms, rather than homotypic transmission. Findings more strongly supported potential environmental or evocative mechanisms of intergenerational transmission than genetic transmission mechanisms (though see limitations). Parent-specific effects are discussed. En ligne : https://www.cambridge.org/core/product/E7250A64CD2FFA843076B81FAA9109BA Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=564 [article] Intergenerational transmission of comorbid internalizing and externalizing psychopathology at age 11: Evidence from an adoption design for general transmission of comorbidity rather than homotypic transmission [Texte imprimé et/ou numérique] / Kristine MARCEAU, Auteur ; Sohee LEE, Auteur ; Muskan DATTA, Auteur ; Olivia C. ROBERTSON, Auteur ; Daniel S. SHAW, Auteur ; Misaki N. NATSUAKI, Auteur ; Leslie D. LEVE, Auteur ; Jody M. GANIBAN, Auteur ; Jenae M. NEIDERHISER, Auteur . - p.1125-1138. Langues : Anglais (eng) in Development and Psychopathology > 37-3 (August 2025) . - p.1125-1138 Mots-clés : adoption design  comorbidity  intergenerational transmission  internalizing and externalizing  severity and directionality Index. décimale : PER Périodiques Résumé : Psychopathology is intergenerationally transmitted through both genetic and environmental mechanisms via heterotypic (cross-domain), homotypic (domain-specific), and general (e.g., "p-factor") pathways. The current study leveraged an adopted-at-birth design, the Early Growth and Development Study (57% male; 55.6% White, 19.3% Multiracial, 13% Black/African American, 10.9% Hispanic/Latine) to explore the relative influence of these pathways via associations between adoptive caregiver psychopathology (indexing potential environmental transmission) and birth parent psychopathology (indexing genetic transmission) with adolescent internalizing and externalizing symptoms. We included composite measures of adoptive and birth parent internalizing, externalizing, and substance use domains, and a general "p-factor." Age 11 adolescent internalizing and externalizing symptom scores were the average of adoptive parent reports on the Child Behavior Checklist (n = 407). Examining domains independently without addressing comorbidity can lead to incorrect interpretations of transmission mode. Therefore, we also examined symptom severity (like the "p-factor") and an orthogonal symptom directionality score to more cleanly disentangle transmission modes. The pattern of correlations was consistent with mostly general transmission in families with youth showing comorbid internalizing and externalizing symptoms, rather than homotypic transmission. Findings more strongly supported potential environmental or evocative mechanisms of intergenerational transmission than genetic transmission mechanisms (though see limitations). Parent-specific effects are discussed. En ligne : https://www.cambridge.org/core/product/E7250A64CD2FFA843076B81FAA9109BA Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=564

Sex-Based Differences in Prenatal and Perinatal Predictors of Autism Spectrum Disorder Using Machine Learning With National Health Data / Seung-Woo YANG ; Sohee LEE ; Kwang-Sig LEE ; Ki Hoon AHN in Autism Research, 18-7 (July 2025)  

Public ISBD [article]  inAutism Research > 18-7 (July 2025) . - p.1330-1341 Titre : Sex-Based Differences in Prenatal and Perinatal Predictors of Autism Spectrum Disorder Using Machine Learning With National Health Data Type de document : Texte imprimé et/ou numérique Auteurs : Seung-Woo YANG, Auteur ; Sohee LEE, Auteur ; Kwang-Sig LEE, Auteur ; Ki Hoon AHN, Auteur Article en page(s) : p.1330-1341 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  machine learning  risk factors  sex Index. décimale : PER Périodiques Résumé : ABSTRACT Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder influenced by genetic, epigenetic, and environmental factors. ASD is characterized by a higher prevalence in males compared to females, highlighting the potential role of sex-specific risk factors in its development. This study aimed to develop sex-specific prenatal and perinatal prediction models for ASD using machine learning and a national population database. A retrospective cohort design was employed, utilizing data from the Korea National Health Insurance Service claims database. The study included 75,105 children born as singletons in 2007 and their mothers, with follow-up data from 2007 to 2021. Twenty prenatal and perinatal risk factors from 2002 to 2007 were analyzed. Random forest models were used to predict ASD, with performance metrics including accuracy and area under the curve (AUC). Random forest variable importance and SHapley Additive exPlanation (SHAP) values were used to identify major predictors and analyze associations. The random forest models achieved high accuracy (0.996) and AUC (0.997) for the total population as well as for the male and female groups. Major predictors included pregestational body mass index (BMI) (0.3679), socioeconomic status (0.2164), maternal age at birth (0.1735), sex (0.0682), and delivery institution (0.0549). SHAP analysis showed that low maternal BMI increased ASD risk in both sexes, while high BMI was associated with greater risk in females. A U-shaped relationship between socioeconomic status and ASD risk was observed, with increased risk in males from lower socioeconomic backgrounds and females from higher ones. These findings highlight the importance of sex-specific risk factors, particularly pregestational BMI, and socioeconomic status, in predicting ASD risk. En ligne : https://doi.org/10.1002/aur.70054 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=565 [article] Sex-Based Differences in Prenatal and Perinatal Predictors of Autism Spectrum Disorder Using Machine Learning With National Health Data [Texte imprimé et/ou numérique] / Seung-Woo YANG, Auteur ; Sohee LEE, Auteur ; Kwang-Sig LEE, Auteur ; Ki Hoon AHN, Auteur . - p.1330-1341. Langues : Anglais (eng) in Autism Research > 18-7 (July 2025) . - p.1330-1341 Mots-clés : autism spectrum disorder  machine learning  risk factors  sex Index. décimale : PER Périodiques Résumé : ABSTRACT Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder influenced by genetic, epigenetic, and environmental factors. ASD is characterized by a higher prevalence in males compared to females, highlighting the potential role of sex-specific risk factors in its development. This study aimed to develop sex-specific prenatal and perinatal prediction models for ASD using machine learning and a national population database. A retrospective cohort design was employed, utilizing data from the Korea National Health Insurance Service claims database. The study included 75,105 children born as singletons in 2007 and their mothers, with follow-up data from 2007 to 2021. Twenty prenatal and perinatal risk factors from 2002 to 2007 were analyzed. Random forest models were used to predict ASD, with performance metrics including accuracy and area under the curve (AUC). Random forest variable importance and SHapley Additive exPlanation (SHAP) values were used to identify major predictors and analyze associations. The random forest models achieved high accuracy (0.996) and AUC (0.997) for the total population as well as for the male and female groups. Major predictors included pregestational body mass index (BMI) (0.3679), socioeconomic status (0.2164), maternal age at birth (0.1735), sex (0.0682), and delivery institution (0.0549). SHAP analysis showed that low maternal BMI increased ASD risk in both sexes, while high BMI was associated with greater risk in females. A U-shaped relationship between socioeconomic status and ASD risk was observed, with increased risk in males from lower socioeconomic backgrounds and females from higher ones. These findings highlight the importance of sex-specific risk factors, particularly pregestational BMI, and socioeconomic status, in predicting ASD risk. En ligne : https://doi.org/10.1002/aur.70054 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=565

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