Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling
IntroductionInfancy and early childhood are the key stage for the rapid development of brain structure and function, and brain development at this stage has a profound impact on the future intelligence, behavior and health of individuals. A growing body of research suggests that maternal inflammatio...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1530285/full |
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| author | Xianghui Huang Xianghui Huang Cuimin Su Ying Lin Ying Lin Tianyi Zhou Ruming Ye Ruming Ye Dan Li Dan Li Miaoshuang Liu Guanhong Wu Wanting Li Namei Xie Xiaofang Deng Nanxi Zhu Shaohong Lin Qin Li Kai Yan Deyi Zhuang Deyi Zhuang |
| author_facet | Xianghui Huang Xianghui Huang Cuimin Su Ying Lin Ying Lin Tianyi Zhou Ruming Ye Ruming Ye Dan Li Dan Li Miaoshuang Liu Guanhong Wu Wanting Li Namei Xie Xiaofang Deng Nanxi Zhu Shaohong Lin Qin Li Kai Yan Deyi Zhuang Deyi Zhuang |
| author_sort | Xianghui Huang |
| collection | DOAJ |
| description | IntroductionInfancy and early childhood are the key stage for the rapid development of brain structure and function, and brain development at this stage has a profound impact on the future intelligence, behavior and health of individuals. A growing body of research suggests that maternal inflammation, as a potential environmental factor, may affect brain development in infants and young children through a variety of mechanisms. Therefore, it is of great significance to evaluate the risk of maternal inflammation to early brain development in infants and young children based on multi-source data modeling to understand the mechanism of early development and prevent brain development disorders.Methods and analysisBetween December 2021 and May 2024, 360 pairs of pregnant women and their offspring were recruited into the Xiamen Children's Brain Development Cohort. Pregnant women's exposure during pregnancy was collected through standardized and structured questionnaires and medical records. All children were followed up to 3 years of age. We administered questionnaires, behavioral assessments, and performed neuroimaging. Environmental exposures during infancy and early childhood were collected. Children's cognitive, emotional, and linguistic development was evaluated, and blood samples were obtained for whole-exome sequencing and exposure-related biomarker analysis.ConclusionIn this study, we used deep learning artificial intelligence to construct an early risk assessment model for infant brain development based on the developmental trajectory and developmental results of early brain structure, function, and connections under the complex interaction of “gene-image-environment-behavior” multi-factors, which can improve the early identification and precise intervention of problems in this period, and improve infants cognitive learning and work performance in childhood, adolescence and even adulthood.Clinical trial registrationhttps://www.clinicaltrials.gov/; identifier [NCT05040542]. |
| format | Article |
| id | doaj-art-8977f80d45884aabab25d3e89a72edc8 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-8977f80d45884aabab25d3e89a72edc82025-08-20T03:49:56ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-07-011310.3389/fpubh.2025.15302851530285Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modelingXianghui Huang0Xianghui Huang1Cuimin Su2Ying Lin3Ying Lin4Tianyi Zhou5Ruming Ye6Ruming Ye7Dan Li8Dan Li9Miaoshuang Liu10Guanhong Wu11Wanting Li12Namei Xie13Xiaofang Deng14Nanxi Zhu15Shaohong Lin16Qin Li17Kai Yan18Deyi Zhuang19Deyi Zhuang20Fujian Key Laboratory of Neonatal Diseases, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaJinjiang Municipal Hospital, Jinjiang, ChinaFujian Key Laboratory of Neonatal Diseases, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaDepartment of Maternal and Child Health, School of Public Health, Peking University, Beijing, ChinaFujian Key Laboratory of Neonatal Diseases, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaFujian Key Laboratory of Neonatal Diseases, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaDepartment of Maternal and Child Health, School of Public Health, Peking University, Beijing, ChinaChildren's National Medical Center, Shanghai, ChinaFujian Key Laboratory of Neonatal Diseases, Xiamen, ChinaChildren's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, ChinaIntroductionInfancy and early childhood are the key stage for the rapid development of brain structure and function, and brain development at this stage has a profound impact on the future intelligence, behavior and health of individuals. A growing body of research suggests that maternal inflammation, as a potential environmental factor, may affect brain development in infants and young children through a variety of mechanisms. Therefore, it is of great significance to evaluate the risk of maternal inflammation to early brain development in infants and young children based on multi-source data modeling to understand the mechanism of early development and prevent brain development disorders.Methods and analysisBetween December 2021 and May 2024, 360 pairs of pregnant women and their offspring were recruited into the Xiamen Children's Brain Development Cohort. Pregnant women's exposure during pregnancy was collected through standardized and structured questionnaires and medical records. All children were followed up to 3 years of age. We administered questionnaires, behavioral assessments, and performed neuroimaging. Environmental exposures during infancy and early childhood were collected. Children's cognitive, emotional, and linguistic development was evaluated, and blood samples were obtained for whole-exome sequencing and exposure-related biomarker analysis.ConclusionIn this study, we used deep learning artificial intelligence to construct an early risk assessment model for infant brain development based on the developmental trajectory and developmental results of early brain structure, function, and connections under the complex interaction of “gene-image-environment-behavior” multi-factors, which can improve the early identification and precise intervention of problems in this period, and improve infants cognitive learning and work performance in childhood, adolescence and even adulthood.Clinical trial registrationhttps://www.clinicaltrials.gov/; identifier [NCT05040542].https://www.frontiersin.org/articles/10.3389/fpubh.2025.1530285/fullmaternal inflammationearly brain developmentmultimodal dataprospective cohortdeep learning |
| spellingShingle | Xianghui Huang Xianghui Huang Cuimin Su Ying Lin Ying Lin Tianyi Zhou Ruming Ye Ruming Ye Dan Li Dan Li Miaoshuang Liu Guanhong Wu Wanting Li Namei Xie Xiaofang Deng Nanxi Zhu Shaohong Lin Qin Li Kai Yan Deyi Zhuang Deyi Zhuang Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling Frontiers in Public Health maternal inflammation early brain development multimodal data prospective cohort deep learning |
| title | Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling |
| title_full | Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling |
| title_fullStr | Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling |
| title_full_unstemmed | Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling |
| title_short | Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling |
| title_sort | cohort protocol risk assessment of maternal inflammation and early brain development in infants and young children based on multi source data modeling |
| topic | maternal inflammation early brain development multimodal data prospective cohort deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1530285/full |
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