Sex-specific neuropsychological correlates of apathy and depression across neurodegenerative disorders.

BACKGROUND: Apathy and depression are common neuropsychiatric symptoms across neurodegenerative disorders and are associated with impairment in several cognitive domains, yet little is known about the influence of sex on these relationships. OBJECTIVES: We examined the relationship between these symptoms with neuropsychological performance across a combined cohort with mild or major neurodegenerative disorders, then evaluated the impact of sex. DESIGN, SETTING AND PARTICIPANTS: We conducted a cohort analysis of participants in the COMPASS-ND study with mild cognitive impairment (MCI), vascular MCI, Alzheimer's disease, mixed dementia, Parkinson's disease, frontotemporal dementia, and cognitively unimpaired (CU) controls. MEASUREMENTS: Participants with neurodegenerative disease and CU controls were stratified by the presence (severity ≥1 on Neuropsychiatric Inventory Questionnaire) of either depressive symptoms alone, apathy symptoms alone, both symptoms, or neither. A neuropsychological battery evaluated executive function, verbal fluency, verbal learning, working memory, and visuospatial reasoning. Analysis of covariance was used to assess group differences with age, sex, and education as covariates. RESULTS: Groups included depressive symptoms only (n = 70), apathy symptoms only (n = 52), both (n = 68), or neither (n = 262). The apathy and depression + apathy groups performed worse than the neither group on tests of working memory (t(312)  = -2.4, p = 0.02 and t(328)  = -3.8, p = 0.001, respectively) and visuospatial reasoning (t(301)  = -2.3, p = 0.02 and t(321)  = -2.6, p = 0.01, respectively). The depression, apathy, and depression + apathy groups demonstrated a similar degree of impairment on tests of executive function, processing speed, verbal fluency, and verbal learning when compared to participants without apathy or depression. Sex-stratified analyses revealed that compared to the male neither group, the male apathy and depression + apathy groups were impaired broadly across all cognitive domains except for working memory. Females with depression alone showed deficits on tests of executive function (t(166)  = 2.4, p = 0.01) and verbal learning (t(167)  = -4.3, p = 0.001) compared to the female neither group. CONCLUSIONS: This study demonstrated that in neurodegenerative diseases, apathy with or without depression in males was associated with broad cognitive impairments. In females, depression was associated with deficits in executive function and verbal learning. These findings highlight the importance of effectively treating apathy and depression across the spectrum of neurodegenerative disorders with the goal of optimizing neuropsychological outcomes.

Association of plasma biomarkers with cognition, cognitive decline, and daily function across and within neurodegenerative diseases: Results from the Ontario Neurodegenerative Disease Research Initiative.

INTRODUCTION: We investigated whether novel plasma biomarkers are associated with cognition, cognitive decline, and functional independence in activities of daily living across and within neurodegenerative diseases. METHODS: Glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), phosphorylated tau (p-tau)181 and amyloid beta (Aß)42/40 were measured using ultra-sensitive Simoa immunoassays in 44 healthy controls and 480 participants diagnosed with Alzheimer's disease/mild cognitive impairment (AD/MCI), Parkinson's disease (PD), frontotemporal dementia (FTD) spectrum disorders, or cerebrovascular disease (CVD). RESULTS: GFAP, NfL, and/or p-tau181 were elevated among all diseases compared to controls, and were broadly associated with worse baseline cognitive performance, greater cognitive decline, and/or lower functional independence. While GFAP, NfL, and p-tau181 were highly predictive across diseases, p-tau181 was more specific to the AD/MCI cohort. Sparse associations were found in the FTD and CVD cohorts and for Aß42/40 . DISCUSSION: GFAP, NfL, and p-tau181 are valuable predictors of cognition and function across common neurodegenerative diseases, and may be useful in specialized clinics and clinical trials.

Associations between Differential Aging and Lifestyle, Environment, Current, and Future Health Conditions: Findings from Canadian Longitudinal Study on Aging.

INTRODUCTION: An aging population will bring a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors, and chronic disease conditions. METHODS: We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA). The BioAge gap, which is the difference between BioAge index and chronological age, was used to quantify the differential aging, i.e., the difference between biological and chronological age, of the CLSA participants. We further investigated the associations between the BioAge gap and lifestyle, environmental factors, and current and future health conditions. RESULTS: BioAge gap had strong associations with existing adverse health conditions (e.g., cancers, cardiovascular diseases, diabetes, and kidney diseases) and future disease onset (e.g., Parkinson's disease, diabetes, and kidney diseases). We identified that frequent consumption of processed meat, pork, beef, and chicken, poor outcomes in nutritional risk screening, cigarette smoking, exposure to passive smoking are associated with positive BioAge gap ("older" BioAge than expected). We also identified several modifiable factors, including eating fruits, legumes, vegetables, related to negative BioAge gap ("younger" BioAge than expected). CONCLUSIONS: Our study shows that a BioAge index based on easily accessible blood tests has the potential to quantify the differential biological aging process that can be associated with current and future adverse health events. The identified risk and protective factors for differential aging indicated by BioAge gap are informative for future research and guidelines to promote healthy longevity.

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., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults. METHODS: The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects. RESULTS: We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast. CONCLUSIONS: Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Comprehensive Serum Lipidomics for Detecting Incipient Dementia in Parkinson's Disease.

While a number of methods are available for analyzing lipids, unbiased untargeted lipidomics with high coverage remains a challenge. In this work, we report a study of isotope-standard-assisted liquid chromatography mass spectrometry lipidomics of serum for biomarker discovery. We focus on Parkinson's disease (PD), a neurodegenerative disorder that often progresses to dementia. Currently, the diagnosis of PD is purely clinical and there is limited ability to predict which PD patients will transition to dementia, hampering early interventions. We studied serum samples from healthy controls and PD patients with no clinical signs of dementia. A follow-up 3 years later revealed that a subset of PD patients had transitioned to dementia. Using the baseline samples, we constructed two biomarker panels to differentiate (1) PD patients from healthy controls and (2) PD patients that remained cognitively stable from PD patients with incipient dementia (diagnosed 3 years after sample collection). The proposed biomarker panels displayed excellent performance and may be useful for detecting prodromal PD dementia, allowing early interventions and prevention efforts. The biochemistry of significantly changed lipids is also discussed within the current knowledge of neurological pathologies. Our results are promising and future work using a larger cohort of samples is warranted.

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 = 70.10) for the third goal. Eight neuropsychological EF measures were used as indicators of three EF dimensions: inhibition, updating, and shifting. Data-driven classification analyses were applied to the full trajectory distribution. Machine learning prediction analyses tested 15 predictors from genetic, functional, lifestyle, mobility, and demographic risk domains. RESULTS: First, we observed: (a) significant variability in EF trajectories over a 40-year band of aging and (b) significantly variable patterns of EF decline. Second, a four-class EF trajectory model was observed, characterized with classes differentiated by an algorithm of level and slope information. Third, the highest group class was discriminated from lowest by several prediction factors: more education, more novel cognitive activity, lower pulse pressure, younger age, faster gait, lower body mass index, and better balance. CONCLUSION: First, with longitudinal variability in EF aging, the data-driven approach showed that long-term trajectories can be differentiated into separable classes. Second, prediction analyses discriminated class membership by a combination of multiple biomarkers from demographic, lifestyle, functional, and mobility domains of risk for brain and cognitive aging decline.

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. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.

Alzheimer's Environmental and Genetic Risk Scores are Differentially Associated With General Cognitive Ability and Dementia Severity.

PURPOSE: We investigated the association of the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) and an Alzheimer disease (AD) genetic risk score (GRS) with cognitive performance. METHODS: The ANU-ADRI (composed of 12 risk factors for AD) and GRS (composed of 25 AD risk loci) were computed in 1061 community-dwelling older adults. Participants were assessed on 11 cognitive tests and activities of daily living. Structural equation modeling was used to evaluate the association of the ANU-ADRI and GRS with: (1) general cognitive ability (g), (2) dementia-related variance in cognitive performance (δ), and (3) verbal ability (VA), episodic memory (EM), executive function (EF), and processing speed (PS). RESULTS: A worse ANU-ADRI score was associated with poorer performance in "g" [ß (SE)=-0.40 (0.02), P<0.001], δ [-0.40 (0.04), P<0.001], and each cognitive domain [VA=-0.29 (0.04), P<0.001; EM=-0.34 (0.03), P<0.001; EF=-0.38 (0.03), P<0.001; and PS=-0.40 (0.03), P<0.001]. A worse GRS was associated with poorer performance in δ [-0.08 (0.03), P=0.041] and EM [-0.10 (0.03), P=0.035]. CONCLUSIONS: The ANU-ADRI was broadly associated with worse cognitive performance, including general ability and dementia severity, validating its further use in early dementia risk assessment.

The Comprehensive Assessment of Neurodegeneration and Dementia: Canadian Cohort Study.

BACKGROUND: The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) cohort study of the Canadian Consortium on Neurodegeneration in Aging (CCNA) is a national initiative to catalyze research on dementia, set up to support the research agendas of CCNA teams. This cross-country longitudinal cohort of 2310 deeply phenotyped subjects with various forms of dementia and mild memory loss or concerns, along with cognitively intact elderly subjects, will test hypotheses generated by these teams. METHODS: The COMPASS-ND protocol, initial grant proposal for funding, fifth semi-annual CCNA Progress Report submitted to the Canadian Institutes of Health Research December 2017, and other documents supplemented by modifications made and lessons learned after implementation were used by the authors to create the description of the study provided here. RESULTS: The CCNA COMPASS-ND cohort includes participants from across Canada with various cognitive conditions associated with or at risk of neurodegenerative diseases. They will undergo a wide range of experimental, clinical, imaging, and genetic investigation to specifically address the causes, diagnosis, treatment, and prevention of these conditions in the aging population. Data derived from clinical and cognitive assessments, biospecimens, brain imaging, genetics, and brain donations will be used to test hypotheses generated by CCNA research teams and other Canadian researchers. The study is the most comprehensive and ambitious Canadian study of dementia. Initial data posting occurred in 2018, with the full cohort to be accrued by 2020. CONCLUSION: Availability of data from the COMPASS-ND study will provide a major stimulus for dementia research in Canada in the coming years.

Évaluation complète d'une étude de cohorte canadienne portant sur la démence et la neuro-dégénérescence. Contexte : L'évaluation globale de la neuro-dégénérescence et de la démence (COMPASS-ND), étude de cohorte du Consortium canadien en neuro-dégénérescence associée au vieillissement (CCNV), représente une initiative nationale visant à promouvoir la recherche portant sur la démence et à soutenir les programmes de recherche des équipes du CCNV. Totalisant 2310 sujets recrutés partout au pays, cette cohorte longitudinale regroupe des individus fortement « phénotypés ¼ qui présentent diverses formes de démence et de pertes de mémoire légères. En plus de sujets âgés dont les fonctions cognitives sont intactes, ces 2310 sujets ont permis de valider les hypothèses formulées par les équipes du CCNV. Méthodes : Nous avons utilisé de nombreux documents pour décrire cette étude : le protocole de la COMPASS-ND ; la demande initiale de subvention ; le cinquième rapport d'étape semi-annuel du CCNV soumis aux Instituts de recherche en santé du Canada (IRSC) en décembre 2017 ; ainsi que d'autres documents produits à la suite de modifications consécutives à la mise en œuvre de ce projet. Résultats: L'étude de cohorte COMPASS-ND du CCNV inclut des participants de partout au Canada dont les divers états cognitifs sont associés à des maladies neurodégénératives ou au risque d'en souffrir. Ils feront l'objet d'un large éventail d'examens expérimentaux, cliniques, génétiques et d'imagerie afin d'aborder de manière spécifique les causes, le diagnostic, le traitement et la prévention de ces états cognitifs chez les personnes âgées. Les données obtenues à la suite d'évaluations cliniques et cognitives, ainsi que celles issues d'échantillons biologiques, d'imagerie cérébrale, de tests génétiques et de dons de cerveaux, seront utilisées pour tester les hypothèses générées par les équipes de recherche du CCNV et d'autres chercheurs canadiens. Cette étude constitue donc à ce jour l'étude canadienne la plus complète et la plus ambitieuse au sujet de la démence. La présentation des données initiales ayant eu lieu en 2018, la cohorte devrait atteindre sa taille maximale d'ici à 2020.Conclusion : La disponibilité des données de l'étude COMPASS-ND stimulera considérablement la recherche sur la démence au Canada au cours des prochaines années.

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. METHODS: The longitudinal design included nondemented adults (n = 532, mean age = 70.4 years, range 53-95) from the Victoria Longitudinal Study. Using structural equation analyses on an EF latent variable, we tested (a) sex moderation and (b) interactive effects of sex and APOE on observed EPA-EF and mobility-EF performance and change relationships. RESULTS: First, we observed independent sex effects for the EPA-EF and mobility-EF predictions. Whereas EPA had a significant effect on EF performance and change only for females, mobility had a significant effect for both sexes. Notably, males with lower mobility levels experienced steeper EF decline than females with lower mobility levels. Second, we observed significant sex × APOE interaction effects. The combination of lower genetic risk and higher EPA benefitted females but not males. In contrast, lower genetic risk and higher mobility benefited both sexes, although male APOE no-risk carriers with lower mobility levels had EF decline patterns that were similar to APOE risk carriers. CONCLUSIONS: Longitudinal analyses across a broad band of aging show that sex moderates the effects of both EPA and mobility on EF performance and change. Notably, this moderation occurs differentially across the AD genetic risk status. These results point to a precision health approach to observational and interventional research in which effects of physical activity and mobility on EF trajectories and dementia are examined in the personalized and interactive context of sex and AD risk.

Profiling novel metabolic biomarkers for Parkinson's disease using in-depth metabolomic analysis.

OBJECTIVE: To profile the amine/phenol submetabolome to determine potential metabolite biomarkers associated with Parkinson's disease (PD) and PD with incipient dementia. METHODS: At baseline of a 3-wave (18-month intervals) longitudinal study, serum samples were collected from 42 healthy controls and 43 PD patients. By wave 3 (year 3), 16 PD patients were diagnosed with dementia and were classified as PD with incipient dementia at baseline. Metabolomic profiling using dansylation isotope labeling liquid chromatography mass spectrometry was conducted to compare controls with the full PD, PD with no dementia, and PD with incipient dementia groups. RESULTS: Metabolomic analyses detected 719 common metabolites in 80% of the samples. Some were significantly altered in pairwise comparison of different groups (fold change of >1.2 or <0.83 with q < 0.05). We discriminated PD and controls by using a 5-metabolite panel, vanillic acid, 3-hydroxykynurenine, isoleucyl-alanine, 5-acetylamino-6-amino-3-methyluracil, and theophylline. The receiver operating characteristic curve produced an area-under-the-curve value of 0.955 with 87.5% sensitivity and 93.0% specificity. In comparing PD with no dementia with PD with incipient dementia, we used an 8-metabolite panel, His-Asn-Asp-Ser, 3,4-dihydroxyphenylacetone, desaminotyrosine, hydroxy-isoleucine, alanyl-alanine, putrescine [-2H], purine [+O] and its riboside. This produced an area-under-the-curve value of 0.862 with 80.0% sensitivity and 77.0% specificity. CONCLUSIONS: The significantly altered metabolites can be used to differentiate (1) PD patients from healthy controls with high accuracy and (2) the stable PD with no dementia group from those with incipient dementia. Following further validation in larger cohorts, these metabolites could be used for both discrimination and establishing prognosis in PD. © 2017 International Parkinson and Movement Disorder Society.

"Going episodic": collaborative inhibition and facilitation when long-married couples remember together.

Two complementary approaches to the study of collaborative remembering have produced contrasting results. In the experimental "collaborative recall" approach within cognitive psychology, collaborative remembering typically results in "collaborative inhibition": laboratory groups recall fewer items than their estimated potential. In the cognitive ageing approach, collaborative remembering with a partner or spouse may provide cueing and support to benefit older adults' performance on everyday memory tasks. To combine the value of experimental and cognitive ageing approaches, we tested the effects of collaborative remembering in older, long-married couples who recalled a non-personal word list and a personal semantic list of shared trips. We scored amount recalled as well as the kinds of details remembered. We found evidence for collaborative inhibition across both tasks when scored strictly as number of list items recalled. However, we found collaborative facilitation of specific episodic details on the personal semantic list, details which were not strictly required for the completion of the task. In fact, there was a trade-off between recall of specific episodic details and number of trips recalled during collaboration. We discuss these results in terms of the functions of shared remembering and what constitutes memory success, particularly for intimate groups and for older adults.

Apolipoprotein E and Clusterin can magnify effects of personality vulnerability on declarative memory performance in non-demented older adults.

OBJECTIVES: Recent research has linked psychological (personality) factors and specific genetic risk polymorphisms to performance on neurocognitive phenotypes. We examined whether episodic or semantic memory performance is associated with (a) three personality traits (i.e. neuroticism, extraversion, and openness to experience), (b) two neurodegenerative-related polymorphisms (i.e. Apolipoprotein E (APOE; rs7412; rs429358), Clusterin (CLU; rs11136000)), and (c) cross-domain risk interactions (magnification effects). METHODS: Linear growth models were examined to test independent associations between personality traits and declarative memory performance, and potential interaction effects with APOE and CLU genetic risk. Normal older adults (n = 282) with personality and genetic data from the Victoria Longitudinal Study were included at baseline and for up to 14 years of follow-up. RESULTS: First, we observed that higher openness to experience levels were associated with better episodic and semantic memory. Second, three significant gene × personality interactions were associated with poorer memory performance at baseline. These synergistic effects are: (a) APOE allelic risk (ε4+) carriers with lower openness to experience levels, (b) CLU (no risk: T/T) homozygotes with higher extraversion levels, and (c) CLU (no risk: T/T) homozygotes with lower neuroticism levels. CONCLUSIONS: Specific neurodegenerative-related genetic polymorphisms (i.e. APOE and CLU) moderate and magnify the risk contributed by selected personality trait levels (i.e. openness to experience, extraversion) on declarative memory performance in non-demented aging. Future research could target interactions of other personality traits and genetic polymorphisms in different clinical populations to predict other neurocognitive deficits or transitions to cognitive impairment and dementia.

Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging.

Rhythmic brain activity at low frequencies (<12Hz) during rest are thought to increase in neurodegenerative disease, but findings in healthy neurocognitive aging are mixed. Here we address two reasons conventional spectral analyses may have led to inconsistent results. First, spectral-power measures are compared to a baseline condition; when resting activity is the signal of interest, it is unclear what the baseline should be. Second, conventional methods do not clearly differentiate power due to rhythmic versus non-rhythmic activity. The Better OSCillation detection method (BOSC; Caplan et al., 2001; Whitten et al., 2011) avoids these problems by using the signal's own spectral characteristics as a reference to detect elevations in power lasting a few cycles. We recorded electroencephalographic (EEG) signal during rest, alternating eyes open and closed, in healthy younger (18-25 years) and older (60-74 years) participants. Topographic plots suggested the conventional and BOSC analyses measured different sources of activity, particularly at frequencies, like delta (1-4Hz), at which rhythms are sporadic; topographies were more similar in the 8-12Hz alpha band. There was little theta-band activity meeting the BOSC method's criteria, suggesting prior findings of theta power in healthy aging may reflect non-rhythmic signal. In contrast, delta oscillations were present at higher levels than theta in both age groups. In summary, applying strict and standardized criteria for rhythmicity, slow rhythms appear present in the resting brain at delta and alpha, but not theta frequencies, and appear unchanged in healthy aging.

APOE moderates the association between lifestyle activities and cognitive performance: evidence of genetic plasticity in aging.

The current study examined independent and interactive effects between Apolipoprotein E (APOE) genotype and two types of cognitively-stimulating lifestyle activities (CSLA)-integrated information processing (CSLA-II) and novel information processing (CSLA-NI)-on concurrent and longitudinal changes in cognition. Three-wave data across 6 years of follow-up from the Victoria Longitudinal Study (n=278; ages 55-94) and linear mixed model analyses were used to characterize the effects of APOE genotype and participation in CSLA-II and CSLA-NI in four cognitive domains. Significant CSLA effects on cognition were observed. More frequent participation in challenging activities (i.e., CSLA-NI) was associated with higher baseline scores on word recall, fact recall, vocabulary and verbal fluency. Conversely, higher participation in less cognitively-challenging activities (i.e., CSLA-II) was associated with lower scores on fact recall and verbal fluency. No longitudinal CSLA-cognition effects were found. Two significant genetic effects were observed. First, APOE moderated CSLA-II and CSLA-NI associations with baseline verbal fluency and fact recall scores. Second, APOE non-ɛ4 carriers' baseline performance were more likely to be moderated by CSLA participation, compared to APOE-ɛ4 carriers. Our findings suggest APOE may be a "plasticity" gene that makes individuals more or less amenable to the influence of protective factors such as CSLA.

The Return of Biomarker Results in Research: Balancing Complexity, Precision, and Ethical Responsibility.

Recent research aimed at the discovery, integration, and communication of health outcome measures (or "biomarkers") in Alzheimer's disease has raised challenging questions related to whether, how and when results from these investigations should be disclosed to research participants. Reflecting the apparent heterogeneity of many neurodegenerative diseases, biomarker or other risk factor results are often probabilistic, interactive, multi-modal, and selective. Such characteristics make it very complex to summarize and communicate to clinicians, researchers, and research participants. Whereas the format and content of academic literature is well-managed by the peer-review process, reporting individualized results to participants involves complex, sensitive, and ethical considerations. This paper describes three key factors to consider in decisions about the return of results to research participants: complexity, precision, and responsibility. The paper also presents six practical recommendations for implementing meaningful and ethical communication with research participants.

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. The LBD spectrum included Parkinson's disease (PD, n = 32), mild cognitive impairment in PD (PD-MCI, n = 21), and LBD (n = 18) participants. A cognitively unimpaired (CU, n = 39) cohort served as common benchmark. We conducted multivariate analyses of variance and discrimination analyses. Results: Within the AD spectrum, the AD cohort was slower and more inconsistent than the CU, SCI, and MCI cohorts. The MCI cohort was slower than the CU cohort. Within the LBD spectrum, the LBD cohort was slower and more inconsistent than the CU, PD, and PD-MCI cohorts. The PD-MCI cohort was slower than the CU and PD cohorts. In cross-spectra (corresponding cohort) comparisons, the LBD cohort was slower and more inconsistent than the AD cohort. The PD-MCI cohort was slower than the MCI cohort. Discrimination analyses clarified the group difference patterns. Conclusions: For both speed tasks, mean rate and inconsistency demonstrated similar sensitivity to spectra-related comparisons. Both dementia cohorts were slower and more inconsistent than each of their respective non-dementia cohorts.

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. Methods: Participants were 48 initially non-demented PD patients (Mage = 71.6 years, range = 65-84) from a three-year, three-wave prospective Canadian cohort study. A dementia diagnosis at Wave 3 was used to stratify two baseline groups: PD with Incipient Dementia (PDID) and PD with No Dementia (PDND). We aimed to predict dementia three years prior to diagnosis using baseline data for eight indicators that harmonized with the original report, plus education. Results: Three MoPaRDS items (age, orthostatic hypotension, mild cognitive impairment [MCI]) discriminated the groups both independently and as a composite three-item scale (area under the curve [AUC] = 0.88). The eight-item MoPaRDS reliably discriminated PDID from PDND (AUC = 0.81). Education did not improve predictive validity (AUC = 0.77). Performance of the eight-item MoPaRDS varied across sex (AUCfemales = 0.91; AUCmales = 0.74), whereas the three-item configuration did not (AUCfemales = 0.88; AUCmales = 0.91). Risk scores of both configurations increased over time. Conclusions: We report new data on the application of the MoPaRDS as a dementia prediction tool for a geriatric PD cohort. Results support the viability of the full MoPaRDS, and indicate that an empirically determined brief version is a promising complement.

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 = 46.3%). Participants were categorised into "resilient" and "non-resilient" groups using Latent Class Analysis based on their cognitive impairment status and cognitive trajectory across 12 years. Logistic regression was used to identify the risk and protective factors that contributed to resilience stratified by gender. For APOE ɛ4 carriers who have not had a stroke, predictors of resilience were increased frequency of mild physical activity and being employed at baseline for men, and increased number of mental activities engaged in at baseline for women. The results provide insights into a novel way of classifying resilience among APOE ɛ4 carriers and risk and protective factors contributing to resilience separately for men and women.

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 (Mbaseline age = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation. Results: An excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains. Conclusion: Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.