{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2025"],"submitter":["Jiang L"],"pubmed_abstract":["<h4>Background</h4>Metabolic syndrome (MetS) is one of the most common chronic disease complications and significantly increases the prevalence of chronic diseases. This study aims to identify different patterns of MetS development using longitudinal data and explore their influencing factors.<h4>Method</h4>Based on the physical examination cohort of Shanghai railway workers, longitudinal data spanning 5 years (from January 1, 2019, to December 31, 2023) were collected to analyze the development trajectories of 1954 participants with MetS. Latent growth mixture model (LGMM) was employed to classify the development trajectories of MetS into distinct groups. Additionally, mixed-effect models were utilized to explore the influencing factors, and machine learning models were constructed for tr"],"journal":["International journal of endocrinology"],"pagination":["9237368"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12638151"],"repository":["biostudies-literature"],"pubmed_title":["Identifying Four Developmental Trajectories of Metabolic Syndrome and Their Influencing Factors: A Longitudinal Cohort Study of Railway Employees' Physical Examinations."],"pmcid":["PMC12638151"],"pubmed_authors":["Jiang L","Xiao L","Yang M","Cong X","Chen Y","Wang H","Jiang T","Xiao B"],"additional_accession":[]},"is_claimable":false,"name":"Identifying Four Developmental Trajectories of Metabolic Syndrome and Their Influencing Factors: A Longitudinal Cohort Study of Railway Employees' Physical Examinations.","description":"<h4>Background</h4>Metabolic syndrome (MetS) is one of the most common chronic disease complications and significantly increases the prevalence of chronic diseases. This study aims to identify different patterns of MetS development using longitudinal data and explore their influencing factors.<h4>Method</h4>Based on the physical examination cohort of Shanghai railway workers, longitudinal data spanning 5 years (from January 1, 2019, to December 31, 2023) were collected to analyze the development trajectories of 1954 participants with MetS. Latent growth mixture model (LGMM) was employed to classify the development trajectories of MetS into distinct groups. Additionally, mixed-effect models were utilized to explore the influencing factors, and machine learning models were constructed for tr","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-05T17:51:27.176Z","creation":"2026-05-19T03:11:31.288Z"},"accession":"S-EPMC12638151","cross_references":{"pubmed":["41281501"],"doi":["10.1155/ije/9237368"]}}