Gender Selectively Mediates the Association Between Sex and Memory in Cognitively Normal Older Adults.

Background And Objectives: Sex and gender are important topics of increasing interest in aging and dementia research. Few studies have jointly examined sex (as a biological attribute) and gender (as a sociocultural and behavioral characteristic) within a single study. We explored a novel data mining approach to include both sex and gender as potentially related influences in memory aging research.

Proteomic analysis of APOEε4 carriers implicates lipid metabolism, complement and lymphocyte signaling in cognitive resilience.

Background: Apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late onset Alzheimer's disease (AD). This case-cohort study used targeted plasma biomarkers and large-scale proteomics to examine the biological mechanisms that allow some APOEε4 carriers to maintain normal cognitive functioning in older adulthood.

Methods: APOEε4 carriers and APOEε3 homozygotes enrolled in the Women's Health Initiative Memory Study (WHIMS) from 1996 to 1999 were classified as resilient if they remained cognitively unimpaired beyond age 80, and as non-resilient if they developed cognitive impairment before or at age 80.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study.

Background: Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz.

Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and Tree SHAP interpretation.

Background: Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.

Method: Participants were 48 well-characterized PD patients ( = 71.

Gender specific factors contributing to cognitive resilience in APOE ɛ4 positive older adults in a population-based sample.

Although APOE ɛ4 has been identified as the strongest genetic risk factor for Alzheimer's Disease, there are some APOE ɛ4 carriers who do not go on to develop Alzheimer's disease or cognitive impairment. This study aims to investigate factors contributing to this "resilience" separately by gender. Data were drawn from APOE ɛ4 positive participants who were aged 60 + at baseline in the Personality and Total Health Through Life (PATH) Study (N = 341, Women = 46.

Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes.

Background: Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses.

Objective: To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers.

Subjective memory concerns, poor vascular health, and male sex predict exacerbated memory decline trajectories: An integrative data-driven class and prediction analysis.

Objective: Subjective memory decline (SMD) has been identified as a potential early marker of nonnormal and accelerated cognitive decline. We performed data-driven analyses that integrated trajectory classification with prediction modeling to test declining trajectory class prediction by SMD facets, pulse pressure (PP; i.e.

In multiple facets of subjective memory decline sex moderates memory predictions.

Introduction: Two established subjective memory decline facets (SMD; complaints, concerns) are early indicators of memory decline and Alzheimer's disease. We report (1) a four-facet SMD inventory (memory complaints, concerns, compensation, self-efficacy) and (2) prediction of memory change and moderation by sex.

Methods: The longitudinal design featured 40 years (53 to 97) of non-demented aging (= 580) from the Victoria Longitudinal Study.