<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Oh HS</submitter><funding>NCATS NIH HHS</funding><funding>European Research Council</funding><funding>NIA NIH HHS</funding><funding>NHLBI NIH HHS</funding><funding>U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)</funding><pagination>1592-1603</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12092275</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>31(5)</volume><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&lt;sub>181&lt;/sub>:Aβ&lt;sub>42&lt;/sub>, 11% beyond tau positron emission tomography, and 28% beyond CSF neurofilament, growth</pubmed_abstract><journal>Nature medicine</journal><pubmed_title>A cerebrospinal fluid synaptic protein biomarker for prediction of cognitive resilience versus decline in Alzheimer's disease.</pubmed_title><pmcid>PMC12092275</pmcid><funding_grant_id>75N92022D00003</funding_grant_id><funding_grant_id>R01 AG015819</funding_grant_id><funding_grant_id>U01 AG061356</funding_grant_id><funding_grant_id>AG072255</funding_grant_id><funding_grant_id>75N92022D00004</funding_grant_id><funding_grant_id>75N92022D00001</funding_grant_id><funding_grant_id>75N92022D00002</funding_grant_id><funding_grant_id>UL1 TR003142</funding_grant_id><funding_grant_id>R01 AG072255</funding_grant_id><funding_grant_id>P30 AG066515</funding_grant_id><funding_grant_id>U01 HL096917</funding_grant_id><funding_grant_id>P50 AG047366</funding_grant_id><funding_grant_id>75N92022D00005</funding_grant_id><funding_grant_id>U24 AG021886</funding_grant_id><funding_grant_id>U01 HL096902</funding_grant_id><funding_grant_id>101096455</funding_grant_id><funding_grant_id>P30 AG072975</funding_grant_id><funding_grant_id>R01 AG044546</funding_grant_id><funding_grant_id>U01 AG046152</funding_grant_id><funding_grant_id>P30AG066515</funding_grant_id><funding_grant_id>P30 AG066444</funding_grant_id><funding_grant_id>U01 AG058922</funding_grant_id><funding_grant_id>R21 AG058859</funding_grant_id><funding_grant_id>R01 AG048076</funding_grant_id><funding_grant_id>P30 AG010161</funding_grant_id><funding_grant_id>RF1 AG074007</funding_grant_id><funding_grant_id>RF1 AG058501</funding_grant_id><funding_grant_id>RF1 AG053303</funding_grant_id><funding_grant_id>U01 HL096814</funding_grant_id><funding_grant_id>U01 HL096899</funding_grant_id><funding_grant_id>U01 HL096812</funding_grant_id><funding_grant_id>P01 AG026276</funding_grant_id><funding_grant_id>R01 AG017917</funding_grant_id><funding_grant_id>K99 AG088304</funding_grant_id><funding_grant_id>P01 AG003991</funding_grant_id><funding_grant_id>F32 AG079666</funding_grant_id><pubmed_authors>Guldner IH</pubmed_authors><pubmed_authors>Leinonen V</pubmed_authors><pubmed_authors>Duggan MR</pubmed_authors><pubmed_authors>Lipponen A</pubmed_authors><pubmed_authors>Karlsson L</pubmed_authors><pubmed_authors>Zhu Z</pubmed_authors><pubmed_authors>Franzmeier N</pubmed_authors><pubmed_authors>Mormino E</pubmed_authors><pubmed_authors>Zetterberg H</pubmed_authors><pubmed_authors>Wyss-Coray T</pubmed_authors><pubmed_authors>Farinas A</pubmed_authors><pubmed_authors>Oh HS</pubmed_authors><pubmed_authors>Shen Y</pubmed_authors><pubmed_authors>Le Guen Y</pubmed_authors><pubmed_authors>Urey DY</pubmed_authors><pubmed_authors>Timsina J</pubmed_authors><pubmed_authors>Chen J</pubmed_authors><pubmed_authors>Hansson O</pubmed_authors><pubmed_authors>Ehrenberg AJ</pubmed_authors><pubmed_authors>Wagner AD</pubmed_authors><pubmed_authors>Luikku AJ</pubmed_authors><pubmed_authors>Yang C</pubmed_authors><pubmed_authors>Western D</pubmed_authors><pubmed_authors>Ali M</pubmed_authors><pubmed_authors>Walker KA</pubmed_authors><pubmed_authors>Poston KL</pubmed_authors><pubmed_authors>Cruchaga C</pubmed_authors><pubmed_authors>Coresh J</pubmed_authors><pubmed_authors>Channappa D</pubmed_authors><pubmed_authors>Gottesman RF</pubmed_authors><pubmed_authors>Bennett DA</pubmed_authors><pubmed_authors>Morshed N</pubmed_authors><pubmed_authors>Stevens B</pubmed_authors><pubmed_authors>Hiltunen M</pubmed_authors><pubmed_authors>Trelle A</pubmed_authors><pubmed_authors>Herukka SK</pubmed_authors><pubmed_authors>Rauramaa T</pubmed_authors><pubmed_authors>Wilson EN</pubmed_authors></additional><is_claimable>false</is_claimable><name>A cerebrospinal fluid synaptic protein biomarker for prediction of cognitive resilience versus decline in Alzheimer's disease.</name><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&lt;sub>181&lt;/sub>:Aβ&lt;sub>42&lt;/sub>, 11% beyond tau positron emission tomography, and 28% beyond CSF neurofilament, growth</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 May</publication><modification>2026-06-02T17:56:29.365Z</modification><creation>2026-04-18T03:11:55.752Z</creation></dates><accession>S-EPMC12092275</accession><cross_references><pubmed>40164724</pubmed><doi>10.1038/s41591-025-03565-2</doi></cross_references></HashMap>