{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Oh HS"],"funding":["NCATS NIH HHS","European Research Council","NIA NIH HHS","NHLBI NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)"],"pagination":["1592-1603"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12092275"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["31(5)"],"pubmed_abstract":["Rates of cognitive decline in Alzheimer's disease (AD) are extremely heterogeneous. Although biomarkers for amyloid-beta (Aβ) and tau proteins, the hallmark AD pathologies, have improved pathology-based diagnosis, they explain only 20-40% of the variance in AD-related cognitive impairment (CI). To discover novel biomarkers of CI in AD, we performed cerebrospinal fluid (CSF) proteomics on 3,397 individuals from six major prospective AD case-control cohorts. Synapse proteins emerged as the strongest correlates of CI, independent of Aβ and tau. Using machine learning, we derived the CSF YWHAG:NPTX2 synapse protein ratio, which explained 27% of the variance in CI beyond CSF pTau<sub>181</sub>:Aβ<sub>42</sub>, 11% beyond tau positron emission tomography, and 28% beyond CSF neurofilament, growth"],"journal":["Nature medicine"],"pubmed_title":["A cerebrospinal fluid synaptic protein biomarker for prediction of cognitive resilience versus decline in Alzheimer's disease."],"pmcid":["PMC12092275"],"funding_grant_id":["75N92022D00003","R01 AG015819","U01 AG061356","AG072255","75N92022D00004","75N92022D00001","75N92022D00002","UL1 TR003142","R01 AG072255","P30 AG066515","U01 HL096917","P50 AG047366","75N92022D00005","U24 AG021886","U01 HL096902","101096455","P30 AG072975","R01 AG044546","U01 AG046152","P30AG066515","P30 AG066444","U01 AG058922","R21 AG058859","R01 AG048076","P30 AG010161","RF1 AG074007","RF1 AG058501","RF1 AG053303","U01 HL096814","U01 HL096899","U01 HL096812","P01 AG026276","R01 AG017917","K99 AG088304","P01 AG003991","F32 AG079666"],"pubmed_authors":["Guldner IH","Leinonen V","Duggan MR","Lipponen A","Karlsson L","Zhu Z","Franzmeier N","Mormino E","Zetterberg H","Wyss-Coray T","Farinas A","Oh HS","Shen Y","Le Guen Y","Urey DY","Timsina J","Chen J","Hansson O","Ehrenberg AJ","Wagner AD","Luikku AJ","Yang C","Western D","Ali M","Walker KA","Poston KL","Cruchaga C","Coresh J","Channappa D","Gottesman RF","Bennett DA","Morshed N","Stevens B","Hiltunen M","Trelle A","Herukka SK","Rauramaa T","Wilson EN"],"additional_accession":[]},"is_claimable":false,"name":"A cerebrospinal fluid synaptic protein biomarker for prediction of cognitive resilience versus decline in Alzheimer's disease.","description":"Rates of cognitive decline in Alzheimer's disease (AD) are extremely heterogeneous. Although biomarkers for amyloid-beta (Aβ) and tau proteins, the hallmark AD pathologies, have improved pathology-based diagnosis, they explain only 20-40% of the variance in AD-related cognitive impairment (CI). To discover novel biomarkers of CI in AD, we performed cerebrospinal fluid (CSF) proteomics on 3,397 individuals from six major prospective AD case-control cohorts. Synapse proteins emerged as the strongest correlates of CI, independent of Aβ and tau. Using machine learning, we derived the CSF YWHAG:NPTX2 synapse protein ratio, which explained 27% of the variance in CI beyond CSF pTau<sub>181</sub>:Aβ<sub>42</sub>, 11% beyond tau positron emission tomography, and 28% beyond CSF neurofilament, growth","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 May","modification":"2026-06-02T17:56:29.365Z","creation":"2026-04-18T03:11:55.752Z"},"accession":"S-EPMC12092275","cross_references":{"pubmed":["40164724"],"doi":["10.1038/s41591-025-03565-2"]}}