<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Nguyen Ho PT</submitter><funding>NIA NIH HHS</funding><funding>ZonMw</funding><pagination>5506-5517</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7616996</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>19(12)</volume><pubmed_abstract>&lt;h4>Introduction&lt;/h4>Reliable models to predict amyloid beta (Aβ) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease.&lt;h4>Methods&lt;/h4>We developed Aβ prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500).&lt;h4>Results&lt;/h4>The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia</pubmed_abstract><journal>Alzheimer's &amp; dementia : the journal of the Alzheimer's Association</journal><pubmed_title>Predicting amyloid-beta pathology in the general population.</pubmed_title><pmcid>PMC7616996</pmcid><funding_grant_id>#10510032120003</funding_grant_id><funding_grant_id>#73305095007</funding_grant_id><funding_grant_id>10510032120003</funding_grant_id><funding_grant_id>P01 AG036694</funding_grant_id><funding_grant_id>73305095007</funding_grant_id><funding_grant_id>733050817</funding_grant_id><pubmed_authors>Vernooij MW</pubmed_authors><pubmed_authors>Nguyen Ho PT</pubmed_authors><pubmed_authors>van Rooij FJA</pubmed_authors><pubmed_authors>Roshchupkin GV</pubmed_authors><pubmed_authors>Ikram MA</pubmed_authors><pubmed_authors>van Arendonk J</pubmed_authors><pubmed_authors>Neitzel J</pubmed_authors><pubmed_authors>Steketee RME</pubmed_authors></additional><is_claimable>false</is_claimable><name>Predicting amyloid-beta pathology in the general population.</name><description>&lt;h4>Introduction&lt;/h4>Reliable models to predict amyloid beta (Aβ) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease.&lt;h4>Methods&lt;/h4>We developed Aβ prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500).&lt;h4>Results&lt;/h4>The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Dec</publication><modification>2026-06-01T17:45:43.325Z</modification><creation>2025-04-04T12:25:33.413Z</creation></dates><accession>S-EPMC7616996</accession><cross_references><pubmed>37303116</pubmed><doi>10.1002/alz.13161</doi></cross_references></HashMap>