{"doi":"10.1002/alz.13793","title":"Data‐driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset","abstract":"INTRODUCTION: Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods. METHODS: Cluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the \"normal cognition\" subsample (n = 16,005). Survival analyses examined MCI or dementia progression. RESULTS: Five clusters were identified: \"Optimal\" cognitively normal (oCN; 13.2%), \"Typical\" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the \"normal cognition\" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI). DISCUSSION: Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC \"normal cognition\" group.","journal":"Alzheimer s & Dementia","year":2024,"id":424122,"datarank":0.7185552660735348,"base_score":3.1354942159291497,"endowment":3.1354942159291497,"self_citation_contribution":0.47032413238937254,"citation_network_contribution":0.24823113368416225,"self_endowment_contribution":0.47032413238937254,"citer_contribution":0.24823113368416225,"corpus_percentile":72.07395374023362,"corpus_rank":3611,"citation_count":22,"citer_count":18,"citers_with_citation_signal":9,"citers_with_endowment":9,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9162,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":316753,"name":"Kelsey R. Thomas","orcid":"0000-0003-4277-8876","position":1,"is_corresponding":false},{"id":383272,"name":"Steven Z. Rapcsak","orcid":"0000-0003-1089-0855","position":2,"is_corresponding":false},{"id":1221044,"name":"Shannon L. Lindemer","orcid":null,"position":3,"is_corresponding":false},{"id":316756,"name":"Lisa Delano‐Wood","orcid":"0000-0001-5529-8703","position":4,"is_corresponding":false},{"id":108608,"name":"David P. Salmon","orcid":"0000-0003-4533-4090","position":5,"is_corresponding":false},{"id":316758,"name":"Mark W. Bondi","orcid":"0000-0002-1742-3451","position":6,"is_corresponding":false},{"id":336798,"name":"Emily C. Edmonds","orcid":"0000-0002-5130-0500","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T01:58:07.191194Z","pmid":"38574399","pmcid":"PMC11095435","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":[]}