Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study.
Journal: Psychiatry research
Volume: 334
Issue:
Year of Publication: 2024
Affiliated Institutions:
Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China.
Department of Children's Healthcare, Children's Hospital of Chongqing Medical University, China.
Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China; Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, No. . Zhongshan Er Rd, Yuzhong District, Chongqing , China.
Xi'an Children's Hospital, Xi'an, China.
Department of Children Rehabilitation, Hainan Women and Children's Medical Center, Haikou, China.
Department of Developmental and Behavioral Pediatric, The First Hospital of Jilin University, Changchun, China.
Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China.
Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Child Mental Health Research Center of Nanjing Brain Hospital, Nanjing, China.
Department of Child Health Care, The Affiliated Hospital of Qingdao University, Qingdao, China.
Maternal and Child Health Hospital of Baoan, Shenzhen, China.
Department of Child Healthcare, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
NHC Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China.
Deyang Maternity & Child Healthcare Hospital, Deyang, China.
Department of Children Health Care, Capital Institute of Pediatrics, Beijing, China.
Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, No. . Zhongshan Er Rd, Yuzhong District, Chongqing , China. Electronic address: ximing@hospital.cqmu.edu.cn.
Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China. Electronic address: tyli@vip.sina.com.
Abstract summary
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.
Authors & Co-authors:
Wei
Xiao
Yang
Chen
Chen
Wang
Zhang
Li
Jia
Wu
Hao
Ke
Yi
Hong
Chen
Fang
Wang
Wang
Jin
Xu
Li
Study Outcome
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