biostudies-literatureMelendez JDoD Alzheimer's Disease Neuroimaging InitiativeDoD Alzheimer's Disease Neuroimaging InitiativeNIA NIH HHSAlzheimer's Association Zenith Fellows AwardMichael J. Fox Foundation for Parkinson's ResearchNational Institutes of HealthChan Zuckerberg InitiativeCLC NIH HHSClinical CenterWashington University Tracy Family SILQ CenterNIH HHSCharles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. LouisMichael J. Fox Foundation for Parkinson's Researche14230https://www.ebi.ac.uk/biostudies/studies/S-EPMC11488306biostudies-literatureUnknown23(9)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 yeaAging cellAn interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.PMC11488306U01AG058922W81XWH‐12‐2‐0012ZEN-22-848604RF1AG058501R01AG044546U19 AG024904RF1 AG074007RF1AG053303P01AG026276P01AG03991RF1 AG058501W81XWH-12-2-0012RF1 AG053303RF1AG074007R01 AG044546P30 AG066444P01AG003991U01 AG058922P30AG066444P01 AG026276P01 AG003991Yoo ACruchaga COrr MMelendez JBateman RSchindler SSung YJfalseAn interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.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 yea2024-01-01T00:00:00Z2024 Sep2025-04-22T19:45:21.307Z2025-04-06T02:52:06.099ZS-EPMC114883063892373010.1111/acel.14230