[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127779-en":3,"doc-seo-127779-105":30,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127779,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning-based health environmental-clinical risk scores in European children - Risk score study","Early-life environmental stressors can shape later chronic disease risk, but existing environmental risk score approaches often fail to represent the full complexity of exposures, especially for early determinants. This study used machine learning on 1622 mother-child pairs from the HELIX birth cohorts, integrating environmental, child peripheral, and maternal clinical markers to build environmental-clinical risk scores for behavioral difficulties, metabolic syndrome, and lung function. XGBoost-based models using Shapley values identified key psycho-social, prenatal pollutant, and omics features.","[https://doi.org/10.1038/s43856-024-00513-y](https://doi.org/10.1038/s43856-024-00513-y)  \nMachine learning-based health environmental-clinical risk scores in European children  \n Check for updates  \nA list of authors and their affiliations appears at the end of the paper  \n\n| Abstract |  | Plain language summary |\n| --- | --- | --- |\n| Background Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data.\u003Cbr>Methods In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 motherchild clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest andXGBoost. XGBoost ECRS were selectedto extract localfeature contributions using Shapley values and derive feature importance and interactions. Results ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health.\u003Cbr>Conclusions Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms. |  | Growing up in different environments can greatly affect children’s health later in life. This research looked at how living in cities, being exposed to chemicals, and other experiences before birth and during childhood, work together to influence children’s mental, cardiovascular and respiratory health. We used advanced computer programs to help us understand these effects and estimate health risk scores. These scores are simple numerical measures that help us quantify the likelihood of children developing health issues based on their environmental exposures. Using those scores, the study identified key factors impacting children’s health, in particular psycho-social, perceived environmental and prenatal pollutant exposures for mental health. It also revealed complex patterns and interactions between environmental factors. The results highlighted the potential of such risk scores to support the identification of actionable factors in high-risk children, informing tailored prevention measures in healthcare. |\n| The environment (i.e., non-genetic factors), is a key driver of physical and mental health, studied for a long time in the literature in both adults and children1,2. It has been shown that early exposure to adverse environmental agents during sensitive periods such as pregnancy, birth, and childhood can have along-term impact on both animal3 and human health4,5. For instance, while most mental disorders begin during adolescence and early adulthood (e.g., schizophrenia, psychosis), their onsets are preceded by a prodromal phase with deviance from the typical neurodevelopmental trajectory taking place before clinical psychiatric symptoms. Not only important early life | events such as obstet","cbCaihqo7JFggkrZ","https://ap.wps.com/l/cbCaihqo7JFggkrZ","pdf",3131194,1,14,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Exposome and environmental impacts","[{\"question\":\"What is the main goal of this research?\",\"answer\":\"To develop machine learning-based environmental-clinical risk scores for early life exposures and assess their links with children’s mental, cardiometabolic, and respiratory health outcomes.\"},{\"question\":\"Which data types were used to compute the risk scores?\",\"answer\":\"Environmental measures, child peripheral markers, and maternal clinical markers, totaling hundreds of environmental inputs, about 100 child peripheral markers, and 18 mother-child clinical markers.\"},{\"question\":\"How were the risk scores modeled and interpreted?\",\"answer\":\"Environmental-clinical risk scores were computed using LASSO, Random Forest, and XGBoost; XGBoost was then used with Shapley values to extract local feature contributions and derive importance and interactions.\"},{\"question\":\"What kinds of factors were identified as most predictive?\",\"answer\":\"Maternal stress, noise, and lifestyle exposures for mental health; proteome and metabolome features (including IL1B) for cardiometabolic health; and child BMI and urine metabolites for respiratory health.\"}]","Machine learning-based health environmental-clinical risk scores in European children - Risk score study | PDF",1785941589,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":91,"head_meta":93,"extra_data":95,"updated_unix":28},"machine-learning-based-health-environmental-clinical-risk-scores-in-european-children-risk-score-study","",{"@graph":36,"@context":90},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-health-environmental-clinical-risk-scores-in-european-children-risk-score-study/127779/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this research?","Question",{"text":76,"@type":77},"To develop machine learning-based environmental-clinical risk scores for early life exposures and assess their links with children’s mental, cardiometabolic, and respiratory health outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data types were used to compute the risk scores?",{"text":81,"@type":77},"Environmental measures, child peripheral markers, and maternal clinical markers, totaling hundreds of environmental inputs, about 100 child peripheral markers, and 18 mother-child clinical markers.",{"name":83,"@type":74,"acceptedAnswer":84},"How were the risk scores modeled and interpreted?",{"text":85,"@type":77},"Environmental-clinical risk scores were computed using LASSO, Random Forest, and XGBoost; XGBoost was then used with Shapley values to extract local feature contributions and derive importance and interactions.",{"name":87,"@type":74,"acceptedAnswer":88},"What kinds of factors were identified as most predictive?",{"text":89,"@type":77},"Maternal stress, noise, and lifestyle exposures for mental health; proteome and metabolome features (including IL1B) for cardiometabolic health; and child BMI and urine metabolites for respiratory health.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,140],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":46,"category_name":142,"show_sort_weight":111,"slug":143},19,"General","general"]