{"doi":"10.64898/2026.01.10.26343864","title":"Associations of dementia polyexposure scores to Alzheimer’s disease endophenotypes in diverse populations","abstract":"<jats:title>ABSTRACT</jats:title>\n                <jats:sec>\n                  <jats:title>INTRODUCTION:</jats:title>\n                  <jats:p>Dementia clinical risk scores (CRS) provide accessible tools for identifying individuals at risk for Alzheimer’s disease (AD), yet their performance across diverse populations and relationships to AD endophenotypes remains unclear.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>METHODS:</jats:title>\n                  <jats:p>We evaluated four CRS, mCAIDE, WHICAP, LIBRA, and CogDRisk, in relation to cognitive impairment diagnoses and endophenotypes for AD, including plasma biomarkers, neuroimaging measures, and cognitive composite scores. Logistic and linear regression models stratified by self-reported race/ethnicity were used to assess the associations of CRS with diagnosis and their predictive performance, and associations with endophenotypes.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>RESULTS:</jats:title>\n                  <jats:p>Higher CRS were consistently associated with increased odds of dementia across all races/ethnicities. CogDRisk showed the strongest and most consistent performance across diagnostic and endophenotypic outcomes. The other three CRS performed similarly, with mCAIDE performing the worst and lacking associations with plasma biomarkers.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>CONCLUSIONS:</jats:title>\n                  <jats:p>CRS capture AD-related risk across diverse populations and modestly reflect underlying biological endophenotypes, supporting their utility in community-based risk assessment.</jats:p>\n                </jats:sec>","journal":null,"year":null,"id":639736,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1061486,"name":"Xiaqing Jiang","orcid":"0000-0003-3633-0688","position":1,"is_corresponding":false},{"id":55010,"name":"Kristine Yaffe","orcid":"0000-0003-0919-3825","position":2,"is_corresponding":false},{"id":250333,"name":"Jennifer S. Yokoyama","orcid":"0000-0001-7274-2634","position":3,"is_corresponding":false},{"id":233006,"name":"Shea J. Andrews","orcid":"0000-0002-1921-9470","position":4,"is_corresponding":false},{"id":1456479,"name":"Michael C Okorie","orcid":"0000-0002-0487-3031","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Associations of dementia polyexposure scores to Alzheimer’s disease endophenotypes in diverse populations","abstract":"<jats:title>ABSTRACT</jats:title>\n                <jats:sec>\n                  <jats:title>INTRODUCTION:</jats:title>\n                  <jats:p>Dementia clinical risk scores (CRS) provide accessible tools for identifying individuals at risk for Alzheimer’s disease (AD), yet their performance across diverse populations and relationships to AD endophenotypes remains unclear.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>METHODS:</jats:title>\n                  <jats:p>We evaluated four CRS, mCAIDE, WHICAP, LIBRA, and CogDRisk, in relation to cognitive impairment diagnoses and endophenotypes for AD, including plasma biomarkers, neuroimaging measures, and cognitive composite scores. Logistic and linear regression models stratified by self-reported race/ethnicity were used to assess the associations of CRS with diagnosis and their predictive performance, and associations with endophenotypes.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>RESULTS:</jats:title>\n                  <jats:p>Higher CRS were consistently associated with increased odds of dementia across all races/ethnicities. CogDRisk showed the strongest and most consistent performance across diagnostic and endophenotypic outcomes. The other three CRS performed similarly, with mCAIDE performing the worst and lacking associations with plasma biomarkers.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>CONCLUSIONS:</jats:title>\n                  <jats:p>CRS capture AD-related risk across diverse populations and modestly reflect underlying biological endophenotypes, supporting their utility in community-based risk assessment.</jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41646760","pmcid":null,"openalex_id":"https://openalex.org/W7123895602","authors":[],"funders":[{"funder_name":"NIBIB NIH HHS","grant_id":"P41 EB015922","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG062588","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"U54 NS123985","title":null},{"funder_name":"NIA NIH HHS","grant_id":"P30 AG062422","title":null},{"funder_name":"NIA NIH HHS","grant_id":"P01 AG019724","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG057234","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG070862","title":null},{"funder_name":"NIA NIH HHS","grant_id":"U19 AG078109","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG058533","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG054073","title":null},{"funder_name":"NIA NIH HHS","grant_id":"U19 AG079774","title":null}],"total_grants":11,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2026,"count":1}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2026/01/13/2026.01.10.26343864.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2026/01/13/2026.01.10.26343864.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.64898/2026.01.10.26343864","host_type":"publisher"},{"url":"https://doi.org/10.64898/2026.01.10.26343864","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41646760","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12870693","host_type":"repository"}],"fields_of_study":["Dementia and Cognitive Impairment Research","Alzheimer's disease research and treatments","Nutritional Studies and Diet"],"mesh_terms":[],"keywords":["Disease","Endophenotype","Dementia","Alzheimer's disease","Epidemiology","Population","Alzheimer’s disease","biomarkers","clinical risk score","health disparities","modifiable risk factors"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T01:26:15.028298Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}