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• W3207387741 abstract "Background: Identification of factors that may help to preserve cognitive function in late life could elucidate mechanisms and facilitate interventions to improve the lives of millions of people. However, the large number of potential factors associated with cognitive function poses an analytical challenge. Objective: We used data from the longitudinal Women’s Health Initiative Memory Study (WHIMS) and machine learning to investigate 50 demographic, biomedical, behavioral, social, and psychological predictors of preserved cognitive function in later life. Methods: Participants in WHIMS and two consecutive follow up studies who were at least 80 years old and had at least one cognitive assessment following their 80th birthday were classified as cognitively preserved. Preserved cognitive function was defined as having a score ≥39 on the most recent administration of the modified Telephone Interview for Cognitive Status (TICSm) and a mean score across all assessments ≥39. Cognitively impaired participants were those adjudicated by experts to have probable dementia or at least two adjudications of mild cognitive impairment within the 14 years of follow-up and a last TICSm score < 31. Random Forests was used to rank the predictors of preserved cognitive function. Results: Discrimination between groups based on area under the curve was 0.80 (95%-CI-0.76–0.85). Women with preserved cognitive function were younger, better educated, and less forgetful, less depressed, and more optimistic at study enrollment. They also reported better physical function and less sleep disturbance, and had lower systolic blood pressure, hemoglobin, and blood glucose levels. Conclusion: The predictors of preserved cognitive function include demographic, psychological, physical, metabolic, and vascular factors suggesting a complex mix of potential contributors." @default.
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• W3207387741 date "2021-11-23" @default.
• W3207387741 modified "2023-09-28" @default.
• W3207387741 title "Investigating Predictors of Preserved Cognitive Function in Older Women Using Machine Learning: Women’s Health Initiative Memory Study" @default.
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• W3207387741 doi "https://doi.org/10.3233/jad-210621" @default.
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