{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["22(1)"],"submitter":["Yang Y"],"pubmed_abstract":["<h4>Background</h4>The identification of factors that specifically influence pathological and successful cognitive aging is a prerequisite for implementing disease prevention and promoting successful aging. However, multi-domain behavioral factors that characterize the difference between successful and pathological cognitive aging are not clear yet.<h4>Methods</h4>A group of community-dwelling older adults (N = 1347, aged 70-88 years) in Beijing was recruited in this cross-sectional study, and a sub-cohort was further divided into successful cognitive aging (SCA, N = 154), mild cognitive impairment (MCI, N = 256), and cognitively normal control (CNC, N = 173) groups. Analyses of variance, regression models with the Shapley value algorithm, and structural equation model (SEM) analyses were "],"journal":["BMC geriatrics"],"pagination":["831"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9628084"],"repository":["biostudies-literature"],"pubmed_title":["Contributions of early-life cognitive reserve and late-life leisure activity to successful and pathological cognitive aging."],"pmcid":["PMC9628084"],"pubmed_authors":["Li X","Yang Y","Chen K","Zhang Z","Chen Y","Yang C"],"additional_accession":[]},"is_claimable":false,"name":"Contributions of early-life cognitive reserve and late-life leisure activity to successful and pathological cognitive aging.","description":"<h4>Background</h4>The identification of factors that specifically influence pathological and successful cognitive aging is a prerequisite for implementing disease prevention and promoting successful aging. However, multi-domain behavioral factors that characterize the difference between successful and pathological cognitive aging are not clear yet.<h4>Methods</h4>A group of community-dwelling older adults (N = 1347, aged 70-88 years) in Beijing was recruited in this cross-sectional study, and a sub-cohort was further divided into successful cognitive aging (SCA, N = 154), mild cognitive impairment (MCI, N = 256), and cognitively normal control (CNC, N = 173) groups. Analyses of variance, regression models with the Shapley value algorithm, and structural equation model (SEM) analyses were ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T11:57:21.046Z","creation":"2025-02-19T00:31:50.289Z"},"accession":"S-EPMC9628084","cross_references":{"pubmed":["36319960"],"doi":["10.1186/s12877-022-03530-5"]}}