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.

Associated risk and resilience factors of Alzheimer's disease in women with early bilateral oophorectomy: Data from the UK Biobank.

Background: Bilateral oophorectomy (BO) confers immediate estradiol loss. We examined prevalence and predictors of Alzheimer's disease (AD) in women with early BO comparing their odds ratios of AD to those of women with spontaneous menopause (SM).

Methods: A cohort from UK Biobank (n = 34,603) included women aged 60 + at baseline with and without AD who had early BO or SM.

Cognitive Speed in Neurodegenerative Disease: Comparing Mean Rate and Inconsistency Within and Across the Alzheimer's and Lewy Body Spectra in the COMPASS-ND Study.

Background: Alzheimer's disease (AD) and Lewy body disease (LBD) are characterized by early and gradual worsening perturbations in speeded cognitive responses.

Objective: Using simple and choice reaction time tasks, we compared two indicators of cognitive speed within and across the AD and LBD spectra: mean rate (average reaction time across trials) and inconsistency (within person variability).

Methods: The AD spectrum cohorts included subjective cognitive impairment (SCI, n = 28), mild cognitive impairment (MCI, n = 121), and AD (n = 45) participants.

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.

Dementia Risk Prediction in a Longitudinal Geriatric Parkinson's Disease Cohort: Evaluation and Application of the Montreal Parkinson Risk of Dementia Scale.

Background: Parkinson's disease (PD) increases risk for dementia and cascading adverse outcomes. The eight-item Montreal Parkinson Risk of Dementia Scale (MoPaRDS) is a rapid, in-office dementia screening tool. We examine predictive validity and other characteristics of the MoPaRDS in a geriatric PD cohort by testing a series of alternative versions and modelling risk score change trajectories.

Bridging Integrator 1 (BIN1, rs6733839) and Sex Are Moderators of Vascular Health Predictions of Memory Aging Trajectories.

Background: A promising risk loci for sporadic Alzheimer's disease (AD), Bridging Integrator 1 (BIN1), is thought to operate through the tau pathology pathway.

Objective: We examine BIN1 risk for a moderating role with vascular health (pulse pressure; PP) and sex in predictions of episodic memory trajectories in asymptomatic aging adults.

Methods: The sample included 623 participants (Baseline Mean age = 70.

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.

Differential Cognitive Decline in Alzheimer's Disease Is Predicted by Changes in Ventricular Size but Moderated by Apolipoprotein E and Pulse Pressure.

Background: Differential cognitive trajectories in Alzheimer's disease (AD) may be predicted by biomarkers from multiple domains.

Objective: In a longitudinal sample of AD and AD-related dementias patients (n = 312), we tested whether 1) change in brain morphometry (ventricular enlargement) predicts differential cognitive trajectories, 2) further risk is contributed by genetic (Apolipoprotein E [APOE] ɛ4+) and vascular (pulse pressure [PP]) factors separately, and 3) the genetic + vascular risk moderates this pattern.

Methods: We applied a dynamic computational approach (parallel process models) to test both concurrent and change-related associations between predictor (ventricular size) and cognition (executive function [EF]/attention).

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.

A Multimodal Risk Network Predicts Executive Function Trajectories in Non-demented Aging.

Multiple modalities of Alzheimer's disease (AD) risk factors may operate through interacting networks to predict differential cognitive trajectories in asymptomatic aging. We test such a network in a series of three analytic steps. First, we test independent associations between three risk scores (functional-health, lifestyle-reserve, and a combined multimodal risk score) and cognitive [executive function (EF)] trajectories.

Data-driven approaches to executive function performance and structure in aging: Integrating person-centered analyses and machine learning risk prediction.

Executive function (EF) performance and structure in nondemented aging are frequently examined with variable-centered approaches. Person-centered analytics can contribute unique information about classes of persons by simultaneously considering EF performance and structure. The risk predictors of these classes can then be determined by machine learning technology.

Portals to frailty? Data-driven analyses detect early frailty profiles.

Background: Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. Two prominent approaches for measuring frailty are the frailty phenotype and the frailty index. We explored a complementary data-driven approach for frailty assessment that could detect early frailty profiles (or subtypes) in relatively healthy older adults.

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.

Integrating Three Characteristics of Executive Function in Non-Demented Aging: Trajectories, Classification, and Biomarker Predictors.

Objective: With longitudinal executive function (EF) data from the Victoria Longitudinal Study, we investigated three research goals pertaining to key characteristics of EF in non-demented aging: (a) examining variability in EF longitudinal trajectories, (b) establishing trajectory classes, and (c) identifying biomarker predictors discriminating these classes.

Method: We used a trajectory analyses sample (n = 781; M age = 71.42) for the first and second goals and a prediction analyses sample (n = 570; M age = 70.

Body mass index predicts cognitive aging trajectories selectively for females: Evidence from the Victoria Longitudinal Study.

Objective: Elevated body weight in midlife is an established risk factor for accelerated cognitive decline, impairment, and dementia. Research examining the impact of later-life body mass index (BMI) on normal cognitive aging has produced mixed results. There is a need for longitudinal designs, replication across multiple cognitive domains, and consideration of BMI effects in the context of important moderators.

A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap.

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures.

Investigation of antihypertensive class, dementia, and cognitive decline: A meta-analysis.

Objective: High blood pressure is one of the main modifiable risk factors for dementia. However, there is conflicting evidence regarding the best antihypertensive class for optimizing cognition. Our objective was to determine whether any particular antihypertensive class was associated with a reduced risk of cognitive decline or dementia using comprehensive meta-analysis including reanalysis of original participant data.

Age, cohort, and period effects on metamemory beliefs.

Questionnaires like the Metamemory in Adulthood Questionnaire (MIA; Dixon, Hultsch, & Hertzog, 1988) have been used to examine longitudinal changes and cross-sectional age differences in multiple metamemory facets (e.g., memory self-efficacy).

Mitonuclear interactions influence Alzheimer's disease risk.

We examined the associations between mitochondrial DNA haplogroups (MT-hgs; mitochondrial haplotype groups defined by a specific combination of single nucleotide polymorphisms labeled as letters running from A to Z) and their interactions with a polygenic risk score composed of nuclear-encoded mitochondrial genes (nMT-PRS) with risk of dementia and age of onset (AOO) of dementia. MT-hg K (Odds ratio [OR]: 2.03 [95% CI: 1.

Frailty effects on non-demented cognitive trajectories are moderated by sex and Alzheimer's genetic risk.

Background: Age-related frailty reflects cumulative multisystem physiological and health decline. Frailty increases the risk of adverse brain and cognitive outcomes, including differential decline and dementia. In a longitudinal sample of non-demented older adults, we examine whether (a) the level and/or change in frailty predicts trajectories across three cognitive domains (memory, speed, and executive function (EF)) and (b) prediction patterns are modified by sex or Alzheimer's genetic risk (Apolipoprotein E (APOE)).

Association of Alzheimer's Disease Genetic Risk Loci with Cognitive Performance and Decline: A Systematic Review.

The association of Apolipoprotein E (APOE) with late-onset Alzheimer's disease (LOAD) and cognitive endophenotypes of aging has been widely investigated. There is increasing interest in evaluating the association of other LOAD risk loci with cognitive performance and decline. The results of these studies have been inconsistent and inconclusive.

Physical Activity and Mobility Differentially Predict Nondemented Executive Function Trajectories: Do Sex and APOE Moderate These Associations?

Background: In nondemented aging, higher levels of everyday physical activity (EPA) and mobility performance are associated with better executive function (EF) trajectories. However, these associations may be moderated by both sex and Alzheimer's disease (AD) genetic risk.

Objectives: In a longitudinal study, we investigate sex differences in (a) EPA and mobility effects on EF performance (level) and change (slope) and (b) AD genetic risk moderation of these associations.

Nuances in Alzheimer's Genetic Risk Reveal Differential Predictions of Non-demented Memory Aging Trajectories: Selective Patterns by APOE Genotype and Sex.

Background: Apolipoprotein E (APOE) is a prominent genetic risk factor for Alzheimer's disease (AD) and a frequent target for associations with non-demented and cognitively impaired aging. APOE offers a unique opportunity to evaluate two dichotomous comparisons and selected gradations of APOE risk. Some evidence suggests that APOE effects may differ by sex and emerge especially in interaction with other AD-related biomarkers (e.

Modifiable Risk Factors Discriminate Memory Trajectories in Non-Demented Aging: Precision Factors and Targets for Promoting Healthier Brain Aging and Preventing Dementia.

Background: Non-demented cognitive aging trajectories are characterized by vast level and slope differences and a spectrum of outcomes, including dementia.

Objective: The goal of AD risk management (and its corollary, promoting healthy brain aging) is aided by two converging objectives: 1) classifying dynamic distributions of non-demented cognitive trajectories, and 2) identifying modifiable risk-elevating and risk-reducing factors that discriminate stable or normal trajectory patterns from declining or pre-impairment patterns.

Method: Using latent class growth analysis we classified three episodic memory aging trajectories for n = 882 older adults (baseline Mage=71.