{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Melendez J"],"funding":["DoD Alzheimer's Disease Neuroimaging Initiative","DoD Alzheimer's Disease Neuroimaging Initiative","NIA NIH HHS","Alzheimer's Association Zenith Fellows Award","Michael J. Fox Foundation for Parkinson's Research","National Institutes of Health","Chan Zuckerberg Initiative","CLC NIH HHS","Clinical Center","Washington University Tracy Family SILQ Center","NIH HHS","Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. Louis","Michael J. Fox Foundation for Parkinson's Research"],"pagination":["e14230"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11488306"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(9)"],"pubmed_abstract":["Machine learning can be used to create \"biologic clocks\" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 yea"],"journal":["Aging cell"],"pubmed_title":["An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging."],"pmcid":["PMC11488306"],"funding_grant_id":["U01AG058922","W81XWH‐12‐2‐0012","ZEN-22-848604","RF1AG058501","R01AG044546","U19 AG024904","RF1 AG074007","RF1AG053303","P01AG026276","P01AG03991","RF1 AG058501","W81XWH-12-2-0012","RF1 AG053303","RF1AG074007","R01 AG044546","P30 AG066444","P01AG003991","U01 AG058922","P30AG066444","P01 AG026276","P01 AG003991"],"pubmed_authors":["Yoo A","Cruchaga C","Orr M","Melendez J","Bateman R","Schindler S","Sung YJ"],"additional_accession":[]},"is_claimable":false,"name":"An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.","description":"Machine learning can be used to create \"biologic clocks\" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 yea","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2025-04-22T19:45:21.307Z","creation":"2025-04-06T02:52:06.099Z"},"accession":"S-EPMC11488306","cross_references":{"pubmed":["38923730"],"doi":["10.1111/acel.14230"]}}