Investigating Predictors of Preserved Cognitive Function in Older Women Using Machine Learning: Women’s Health Initiative Memory Study

Article type: Research Article

Authors: Casanova, Ramon^{a; *} | Gaussoin, Sarah A.^a | Wallace, Robert^{b; c} | Baker, Laura D.^d | Chen, Jiu-Chiuan^e | Manson, JoAnn E.^f | Henderson, Victor W.^g | Sachs, Bonnie C.^{h; i} | Justice, Jamie N.^d | Whitsel, Eric A.^j | Hayden, Kathleen M.^h | Rapp, Stephen R.^{h; k}

Affiliations: [a] Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA | [b] College of Public Health, University of Iowa, Iowa City, IA, USA | [c] Epidemiology and Internal Medicine, University of Iowa, Iowa City, IA, USA | [d] Department of Gerontology and Geriatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA | [e] Department of Preventive Medicine and Neurology, University of Southern California, Los Angeles, CA, USA | [f] Department of Medicine, Brigham and Women’s Hospital, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA | [g] Department of Epidemiology and Population Health and of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA | [h] Department of Social Sciences & Health Policy, Wake Forest School of Medicine, Winston-Salem, NC, USA | [i] Department of Neurology, Wake Forest School of Medicine, Winston-Salem, NC, USA | [j] Department of Epidemiology, Gillings School of Global Public Health and Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA | [k] Department of Psychiatry and Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA

Correspondence: [*] Correspondence to: Ramon Casanova, PhD, Associate Professor, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA. Tel.: +1 336 716 8309; E-mail: casanova@wakehealth.edu.

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.

Keywords: Cognitive preservation, machine learning, random forests, WHIMS, women

DOI: 10.3233/JAD-210621

Journal: Journal of Alzheimer's Disease, vol. 84, no. 3, pp. 1267-1278, 2021