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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Behavioral Genetics and Its Designs01:23

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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Human Genetics01:28

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Sex-linked Disorders01:43

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Like autosomes, sex chromosomes contain a variety of genes necessary for normal body function. When a mutation in one of these genes results in biological deficits, the disorder is considered sex-linked.
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Related Experiment Video

Updated: May 20, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Sex-Based Differences in Prenatal and Perinatal Predictors of Autism Spectrum Disorder Using Machine Learning With

Ju Sun Heo1,2, Seung-Woo Yang3,4, Sohee Lee5,6,7

  • 1Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea.

Autism Research : Official Journal of the International Society for Autism Research
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

Sex-specific factors like pregestational body mass index (BMI) and socioeconomic status significantly predict autism spectrum disorder (ASD) risk. Machine learning models accurately identified these predictors in a large population study.

Keywords:
autism spectrum disordermachine learningrisk factorssex

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Area of Science:

  • Neurodevelopmental disorders
  • Genetics and epigenetics
  • Environmental health

Background:

  • Autism spectrum disorder (ASD) affects males more than females, suggesting sex-specific risk factors.
  • Understanding these factors is crucial for early identification and intervention.
  • Previous research indicates a complex interplay of genetic, epigenetic, and environmental influences.

Purpose of the Study:

  • To develop sex-specific machine learning models for predicting ASD risk.
  • To identify key prenatal and perinatal predictors of ASD.
  • To analyze sex-specific associations between risk factors and ASD.

Main Methods:

  • Retrospective cohort study using the Korea National Health Insurance Service claims database.
  • Inclusion of 75,105 singleton births and their mothers, with follow-up data.
  • Application of random forest models with SHapley Additive exPlanation (SHAP) for predictor analysis.

Main Results:

  • Random forest models demonstrated high accuracy (0.996) and AUC (0.997) for ASD prediction.
  • Key predictors included pregestational BMI, socioeconomic status, maternal age, sex, and delivery institution.
  • SHAP analysis revealed sex-specific patterns: low BMI increased risk for both sexes, high BMI for females, and U-shaped socioeconomic status risk.

Conclusions:

  • Sex-specific prediction models for ASD are feasible and accurate.
  • Pregestational BMI and socioeconomic status are critical, sex-dependent predictors of ASD risk.
  • These findings underscore the need for tailored risk assessment strategies in prenatal and perinatal care.