Journal article
Investigating Predictors of Preserved Cognitive Function in Older Women Using Machine Learning: Women's Health Initiative Memory Study
Journal of Alzheimer's disease, Vol.84(3), pp.1267-1278
2021
DOI: 10.3233/JAD-210621
PMCID: PMC8934040
PMID: 34633318
Abstract
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.
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.
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.
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.
The predictors of preserved cognitive function include demographic, psychological, physical, metabolic, and vascular factors suggesting a complex mix of potential contributors.
Details
- Title: Subtitle
- Investigating Predictors of Preserved Cognitive Function in Older Women Using Machine Learning: Women's Health Initiative Memory Study
- Creators
- Ramon Casanova - Wake Forest UniversitySarah A Gaussoin - Wake Forest UniversityRobert Wallace - University of IowaLaura D Baker - Wake Forest UniversityJiu-Chiuan Chen - University of Southern CaliforniaJoAnn E Manson - Brigham and Women's HospitalVictor W Henderson - Stanford UniversityBonnie C Sachs - Wake Forest UniversityJamie N Justice - Wake Forest UniversityEric A Whitsel - University of North Carolina at Chapel HillKathleen M Hayden - Wake Forest UniversityStephen R Rapp - Wake Forest University
- Resource Type
- Journal article
- Publication Details
- Journal of Alzheimer's disease, Vol.84(3), pp.1267-1278
- DOI
- 10.3233/JAD-210621
- PMID
- 34633318
- PMCID
- PMC8934040
- NLM abbreviation
- J Alzheimers Dis
- ISSN
- 1387-2877
- eISSN
- 1875-8908
- Grant note
- HHSN268201100046C / NHLBI NIH HHS HHSN271201700002C / NIDA NIH HHS HHSN271201100004C / NIA NIH HHS HHSN268201100001C / WHI NIH HHS P30 AG049638 / NIA NIH HHS HHSN268201100002C / WHI NIH HHS P30 AG059307 / NIA NIH HHS P30 AG021332 / NIA NIH HHS HHSN268201100003C / WHI NIH HHS P30 AG066515 / NIA NIH HHS HHSN268201100004C / WHI NIH HHS
- Language
- English
- Date published
- 2021
- Academic Unit
- Epidemiology; Injury Prevention Research Center; Internal Medicine
- Record Identifier
- 9984364401702771
Metrics
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