{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:24:46Z","timestamp":1760145886899,"version":"build-2065373602"},"reference-count":92,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia","award":["2020 BP 00261"],"award-info":[{"award-number":["2020 BP 00261"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7\u201318 years, with a CMS prevalence of 82.9%. We applied the XGBoost algorithm to analyze key noninvasive variables, including self-rated health, sunlight exposure, screen time, consanguinity, healthy and unhealthy dietary habits, discretionary salt and sugar consumption, birthweight, and birth order, father and mother education, oral hygiene behavior, and family history of dyslipidemia, obesity, hypertension, and diabetes using five-fold cross-validation. The model achieved high sensitivity (94.7% \u00b1 4.8) and specificity (78.8% \u00b1 13.7), with an area under the ROC curve (AUC) of 0.867 \u00b1 0.087, indicating strong predictive performance and significantly outperformed triponderal mass index (TMI) (adjusted paired t-test; p &lt; 0.05). The most critical selected modifiable factors were sunlight exposure, screen time, consanguinity, healthy and unhealthy diet, dietary fat type, and discretionary salt consumption. This study emphasizes the clinical importance of early identification of at-risk individuals to implement timely interventions. It offers a promising tool for CMS risk screening. These findings support using predictive analytics in clinical settings to address the rising CMS epidemic in children and adolescents.<\/jats:p>","DOI":"10.3390\/info15090564","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T08:45:53Z","timestamp":1726217153000},"page":"564","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prevention of Cardiometabolic Syndrome in Children and Adolescents Using Machine Learning and Noninvasive Factors: The CASPIAN-V Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4408-2397","authenticated-orcid":false,"given":"Hamid Reza","family":"Marateb","sequence":"first","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2620-3733","authenticated-orcid":false,"given":"Mahsa","family":"Mansourian","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0817-3844","authenticated-orcid":false,"given":"Amirhossein","family":"Koochekian","sequence":"additional","affiliation":[{"name":"Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0938-6451","authenticated-orcid":false,"given":"Mehdi","family":"Shirzadi","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"}]},{"given":"Shadi","family":"Zamani","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7217-0282","authenticated-orcid":false,"given":"Marjan","family":"Mansourian","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9836-6083","authenticated-orcid":false,"given":"Miquel Angel","family":"Ma\u00f1anas","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"},{"name":"Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7455-1495","authenticated-orcid":false,"given":"Roya","family":"Kelishadi","sequence":"additional","affiliation":[{"name":"Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Fan, S., Xue, Z., Yuan, J., Zhou, Z., Wang, T., Liu, J., Bawudun, A., Nurmamat, N., and Wang, Y. 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