{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Su C"],"funding":["NIA NIH HHS","Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)","Foundation for the National Institutes of Health","Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)","NINDS NIH HHS","Alzheimer's Association","Alzheimer's Association","NIGMS NIH HHS","Michael J. Fox Foundation for Parkinson's Research","Alzheimer’s Disease Drug Discovery Foundation (ADDF); Ted and Maria Quirk Endowment; Joy Chambers-Grundy Endowment"],"pagination":["184"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11233682"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7(1)"],"pubmed_abstract":["Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We "],"journal":["NPJ digital medicine"],"pubmed_title":["Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data."],"pmcid":["PMC11233682"],"funding_grant_id":["RF1AG082211","R01 AG082118","R01 AG076234","R01 AG066707","U01 NS093334","RF1AG072449","R25 AG083721","R01 AG053798","MJFF-023081","3R01AG066707-02S1","3R01AG066707-01S1","R21AG083003","U01 AG073323","R01AG053798","U01NS093334","R01AG080991","P20GM109025","R01AG082118","R35 AG071476","R35AG71476","RF1NS133812","P30 AG072959","P20 GM109025","RF1 NS133812","R01AG066707","R01 AG076448","ALZDISCOVERY-1051936","AG083721-01","R01AG076448","R56 AG074001","RF1 AG072449","R56AG074001","R01AG076234","R21 AG083003","U01AG073323","RF1 AG082211","P30AG072959","R01 AG080991"],"pubmed_authors":["Zhou M","Li H","Su C","Cincotta MC","Cheng F","Zhu Y","Henchcliffe C","Zhang H","Maasch JRMA","Wang F","Bai Z","Bian J","Hou Y","Xu J","Shi X","Leverenz JB","Okun MS","Ke A","Brendel M","Xu Z","Cummings J"],"additional_accession":[]},"is_claimable":false,"name":"Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data.","description":"Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-07-15T05:15:39.216Z","creation":"2024-11-09T15:16:03.345Z"},"accession":"S-EPMC11233682","cross_references":{"pubmed":["38982243"],"doi":["10.1038/s41746-024-01175-9"]}}