A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States / Md Roungu AHMMAD in Research in Autism, 131 (March 2026)  

Public ISBD [article]  inResearch in Autism > 131 (March 2026) . - 202826 Titre : A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States Type de document : texte imprimé Auteurs : Md Roungu AHMMAD, Auteur ; Harry PANTAZOPOULOS, Auteur ; Sahil Hareshbhai KOTHIYA, Auteur Article en page(s) : 202826 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder  Propensity Score Matching  Machine Learning  Chronic Disease  Genetic Disorder Index. décimale : PER Périodiques Résumé : Purpose Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition often accompanied by diverse comorbidities. This study aimed to develop and evaluate a machine learning framework to identify key predictors of ASD and characterize disease–disease relationships. Methods We analyzed 2022–2023 National Survey of Children’s Health (NSCH) data. Propensity score matching (PSM) was applied to reduce confounding and approximate a randomized controlled design. Multiple machine learning algorithms were implemented to identify important predictors and assess disease–disease interactions. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and cross-validation. Results In matched cohorts, epilepsy showed the strongest association with ASD (20.0 % vs. 6.1 %; OR = 3.85, 95 % CI: 2.88–5.20). Down syndrome (DS) (14.5 % vs. 4.7 %; OR = 3.45, 95 % CI: 1.75–7.30) and congenital heart disease (CHD) (7.5 % vs. 5.4 %; OR = 1.42, 95 % CI: 1.14–1.77) were also significant, whereas diabetes, cystic fibrosis, and allergies demonstrated no consistent associations. Across machine learning analyses, epilepsy consistently emerged as the top predictor, with DS and CHD also ranked highly. Children with both epilepsy and CHD represented the highest-risk subgroup (>32 %), while those without epilepsy but with both allergies and CHD had moderate risk (>10 %). Model discrimination was good (AUC = 0.771) with satisfactory calibration. Conclusion Epilepsy is the strongest predictor of ASD, with epilepsy - CHD comorbidity conferring the highest-risk subgroup, whereas no epilepsy but allergies and CHD represented moderate-risk groups. This framework highlights the value of advanced analytics for early identification and targeted interventions in vulnerable populations. En ligne : https://doi.org/10.1016/j.reia.2026.202826 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=581 [article] A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States [texte imprimé] / Md Roungu AHMMAD, Auteur ; Harry PANTAZOPOULOS, Auteur ; Sahil Hareshbhai KOTHIYA, Auteur . - 202826. Langues : Anglais (eng) in Research in Autism > 131 (March 2026) . - 202826 Mots-clés : Autism Spectrum Disorder  Propensity Score Matching  Machine Learning  Chronic Disease  Genetic Disorder Index. décimale : PER Périodiques Résumé : Purpose Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition often accompanied by diverse comorbidities. This study aimed to develop and evaluate a machine learning framework to identify key predictors of ASD and characterize disease–disease relationships. Methods We analyzed 2022–2023 National Survey of Children’s Health (NSCH) data. Propensity score matching (PSM) was applied to reduce confounding and approximate a randomized controlled design. Multiple machine learning algorithms were implemented to identify important predictors and assess disease–disease interactions. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and cross-validation. Results In matched cohorts, epilepsy showed the strongest association with ASD (20.0 % vs. 6.1 %; OR = 3.85, 95 % CI: 2.88–5.20). Down syndrome (DS) (14.5 % vs. 4.7 %; OR = 3.45, 95 % CI: 1.75–7.30) and congenital heart disease (CHD) (7.5 % vs. 5.4 %; OR = 1.42, 95 % CI: 1.14–1.77) were also significant, whereas diabetes, cystic fibrosis, and allergies demonstrated no consistent associations. Across machine learning analyses, epilepsy consistently emerged as the top predictor, with DS and CHD also ranked highly. Children with both epilepsy and CHD represented the highest-risk subgroup (>32 %), while those without epilepsy but with both allergies and CHD had moderate risk (>10 %). Model discrimination was good (AUC = 0.771) with satisfactory calibration. Conclusion Epilepsy is the strongest predictor of ASD, with epilepsy - CHD comorbidity conferring the highest-risk subgroup, whereas no epilepsy but allergies and CHD represented moderate-risk groups. This framework highlights the value of advanced analytics for early identification and targeted interventions in vulnerable populations. En ligne : https://doi.org/10.1016/j.reia.2026.202826 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=581

Transcriptomic Profiling of the Amygdala of Children With Autism Spectrum Disorder / Jobin BABU in Autism Research, 19-9 (September 2026)  

Public ISBD [article]  inAutism Research > 19-9 (September 2026) . - p. e70333 Titre : Transcriptomic Profiling of the Amygdala of Children With Autism Spectrum Disorder Type de document : texte imprimé Auteurs : Jobin BABU, Auteur ; Ayushi LAL, Auteur ; Lavanya CHALLAGUNDLA, Auteur ; Obie ALLEN IV, Auteur ; Madeline GRIFFIN, Auteur ; Barbara GISABELLA, Auteur ; Harry PANTAZOPOULOS, Auteur Article en page(s) : p. e70333 Langues : Anglais (eng) Mots-clés : amygdala  autism spectrum disorders  development  extracellular matrix  immune response  synaptic signaling Index. décimale : PER Périodiques Résumé : ABSTRACT A growing number of studies point to a key role of the amygdala in Autism Spectrum Disorders (ASD). The amygdala is involved in several processes in ASD including emotional valence, facial recognition, regulation of social learning, empathy, and anxiety. Brain imaging and postmortem studies demonstrate altered amygdala development in children with ASD, associated with impairment in social behavior and anxiety. There is limited information regarding the molecular pathology of the amygdala in children with ASD. We conducted RNAseq profiling on postmortem amygdala samples from male children (4?14?years old) with ASD (n?=?8) and normotypic male children (n?=?6). Furthermore, we conducted drug repurposing analysis to identify compounds predicted to reverse the transcriptomic signatures identified in order to identify potential therapeutic targets for development of early intervention treatments. Full transcriptome gene expression profiling implicated molecular pathways involved in neuroimmune signaling, glycogen and carbohydrate metabolism, matrix metalloproteases, neurodevelopment, estrogen receptor signaling, and synaptic signaling. Targeted pathway analysis of the top 10% of genes ranked by log2 fold change implicated pathways involved in extracellular matrix organization (ECM), immune signaling, and synaptic signaling. Our drug repurposing analysis identified sleep modifying compounds and anti-inflammatory compounds including COX2 and GSK3 inhibitors amongst the top predicted therapeutic compounds. PDGF receptor tyrosine kinase inhibitors were identified as a top potential therapeutic mechanism of action. Collectively, we identified alterations in immune signaling, ECM, and synaptic signaling in the amygdala of children with ASD. Furthermore, our findings highlight a number of potential therapeutic drug targets for development of early intervention strategies. En ligne : https://doi.org/10.1002/aur.70333 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=592 [article] Transcriptomic Profiling of the Amygdala of Children With Autism Spectrum Disorder [texte imprimé] / Jobin BABU, Auteur ; Ayushi LAL, Auteur ; Lavanya CHALLAGUNDLA, Auteur ; Obie ALLEN IV, Auteur ; Madeline GRIFFIN, Auteur ; Barbara GISABELLA, Auteur ; Harry PANTAZOPOULOS, Auteur . - p. e70333. Langues : Anglais (eng) in Autism Research > 19-9 (September 2026) . - p. e70333 Mots-clés : amygdala  autism spectrum disorders  development  extracellular matrix  immune response  synaptic signaling Index. décimale : PER Périodiques Résumé : ABSTRACT A growing number of studies point to a key role of the amygdala in Autism Spectrum Disorders (ASD). The amygdala is involved in several processes in ASD including emotional valence, facial recognition, regulation of social learning, empathy, and anxiety. Brain imaging and postmortem studies demonstrate altered amygdala development in children with ASD, associated with impairment in social behavior and anxiety. There is limited information regarding the molecular pathology of the amygdala in children with ASD. We conducted RNAseq profiling on postmortem amygdala samples from male children (4?14?years old) with ASD (n?=?8) and normotypic male children (n?=?6). Furthermore, we conducted drug repurposing analysis to identify compounds predicted to reverse the transcriptomic signatures identified in order to identify potential therapeutic targets for development of early intervention treatments. Full transcriptome gene expression profiling implicated molecular pathways involved in neuroimmune signaling, glycogen and carbohydrate metabolism, matrix metalloproteases, neurodevelopment, estrogen receptor signaling, and synaptic signaling. Targeted pathway analysis of the top 10% of genes ranked by log2 fold change implicated pathways involved in extracellular matrix organization (ECM), immune signaling, and synaptic signaling. Our drug repurposing analysis identified sleep modifying compounds and anti-inflammatory compounds including COX2 and GSK3 inhibitors amongst the top predicted therapeutic compounds. PDGF receptor tyrosine kinase inhibitors were identified as a top potential therapeutic mechanism of action. Collectively, we identified alterations in immune signaling, ECM, and synaptic signaling in the amygdala of children with ASD. Furthermore, our findings highlight a number of potential therapeutic drug targets for development of early intervention strategies. En ligne : https://doi.org/10.1002/aur.70333 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=592

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