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.

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.

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.

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.

A Metabolomics Analysis of a Novel Phenotype of Older Adults at Higher Risk of Dementia.

BACKGROUND: Older adults presenting with dual-decline in cognition and walking speed face a 6-fold higher risk for dementia compared with those showing no decline. We hypothesized that the metabolomics profile of dual-decliners would be unique even before they show signs of decline in cognition and gait speed. OBJECTIVE: The objective of this study was to determine if plasma metabolomics signatures can discriminate dual-decliners from no decliners, purely cognitive decliners, and purely motor decliners prior to decline. METHODS: A retrospective cross-sectional study using baseline plasma for untargeted metabolomics analyses to investigate early signals of later dual-decline status in study participants (n = 76) with convenient sampling. Dual-decline was operationalized as decline in gait speed (>10 cm/s) and cognition (>2 points decline in Montreal Cognitive Assessment score) on at least two consecutive 6-monthly assessments. The participants' decliner status was evaluated 3 years after the blood sample was collected. Pair-wise comparison of detected compounds was completed using principal components and hierarchical clustering analyses. RESULTS: Analyses did not detect any cluster separation in untargeted metabolomes across baseline groups. However, follow-up analyses of specific molecules detected 4 compounds (17-Hydroxy-12-(hydroxymethyl)-10-oxo-8 oxapentacyclomethyl hexopyranoside, Fleroxacin, Oleic acid, and 5xi-11,12-Dihydroxyabieta-8(14),9(11),12-trien-20-oic acid) were at significantly higher concentration among the dual-decliners compared to non-decliners. The pure cognitive decliner group had significantly lower concentration of six compounds (1,3-nonanediol acetate, 4-(2-carboxyethyl)-2-methoxyphenyl beta-D-glucopyranosiduronic acid, oleic acid, 2E-3-[4-(sulfo-oxy)phenyl] acrylic acid, palmitelaidic acid, and myristoleic acid) compared to the non-decliner group. CONCLUSIONS: The unique metabolomics profile of dual-decliners warrants follow-up metabolomics analysis. Results may point to modifiable pathways.

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.

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.

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.

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.

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 = 70.1; 66.8% female) covering a 44-year longitudinal band (53-97 years). With an established memory latent variable arrayed as individualized trajectories, we applied Mplus 8.5 to determine the best fitting longitudinal growth model. Main analyses were conducted in three sequential phases to investigate: 1) memory trajectory prediction by PP, 2) moderation by BIN1 genetic risk, and 3) stratification by sex. RESULTS: We first confirmed that good vascular health (lower PP) was associated with higher memory level and shallower decline and males were more severely affected by worsening PP in both memory performance and longitudinal decline. Second, the PP prediction of memory trajectories was significant for BIN1 C/C and C/T carriers but not for persons with the highest AD risk (T/T homozygotes). Third, when further stratified by sex, the BIN1 moderation of memory prediction by PP was selective for females. CONCLUSION: We observed a novel interaction whereby BIN1 (linked with tauopathy in AD) and sex sequentially moderated a benchmark PP prediction of differential memory decline in asymptomatic aging. This multi-modal biomarker interaction approach, disaggregated by sex, can be an effective method for enhancing precision of AD genetic risk assessment.

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. METHODS: We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class. RESULTS: For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aß1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aß1-40, higher depressive symptomology, and lower body mass index. CONCLUSION: Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.

Ageing Together: Interdependence in the Memory Compensation Strategies of Long-Married Older Couples.

People live and age together in social groups. Across a range of outcomes, research has identified interdependence in the cognitive and health trajectories of ageing couples. Various types of memory decline with age and people report using a range of internal and external, social, and material strategies to compensate for these declines. While memory compensation strategies have been widely studied, research so far has focused only on single individuals. We examined interdependence in the memory compensation strategies reported by spouses within 58 older couples. Couples completed the Memory Compensation Questionnaire, as well as an open-ended interview about their memory compensation practices. We found that internal, intra-individual memory compensation strategies were not associated within couples, but external, extra-individual strategies showed interdependence. Individuals' scores on material/technological compensation strategies were positively correlated with their partners'. Reported reliance on a spouse was higher for men and increased with age. Our open-ended interviews yielded rich insights into the complex and diverse resources that couples use to support memory in day-to-day life. Particularly evident was the extent of interaction and coordination between social and material compensation, such that couples jointly used external compensation resources. Our results suggest that individuals' reports of their compensation strategies do not tell the whole story. Rather, we propose that older couples show interdependence in their memory compensation strategies, and adopt complex systems of integrated material and social memory compensation in their day-to-day lives.

Development of a Novel Railway Positioning System Using RFID Technology.

Currently, a number of positioning systems are in use to locate trains on the railway network; but these generally have limited precision. Thus, this paper focuses on testing and validating the suitability of radio frequency identification (RFID) technology, for aligning vehicles to switch and crossing (S&C) positions on the railway network. This offers the possibility of accurately knowing the position of vehicles equipped with monitoring equipment, such as the network rail track recording vehicle (TRV), and aligning the data with reference to the locations of the S&C (and ideally to key elements within a particular S&C). The concept is to install two tags, one on the switch-toe sleeper and the second on the crossing-nose sleeper, with an RFID reader that will be installed underneath the vehicle. Thus, the key features of the S&C, the switch toe and crossing nose, will be considered as a definitive reference point for the inspection vehicle's position. As a monitoring vehicle passes over a piece of S&C, the proposed positioning system will provide information about this S&C's ID, which is stored inside the RFID tags and will indicate the S&C's GPS coordinates. As part of the research in this paper, more than 400 tests have been performed to investigate two different RFID technologies, passive and semi-passive, tested in a variety of conditions: including different passage speeds, different distances between the RFID reader and the tags, and varied strength signal transmitted between the reader and the tags. Based on lab testing and analysis of the recorded data, it is concluded that passive RFID technology is the most suitable of the two technologies. The conclusions find that the proposed RFID-based solution can offer a more precise positioning solution to be a reference point for the train location within the network.

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 robust proxy for vascular health), and sex. METHOD: The longitudinal design featured memory trajectories across a 40-year band (55-95 years) of nondemented aging (N = 580; Mage = 70.2 years; 65% female) from the Victoria Longitudinal Study. First, latent class growth analyses identified distinct classes of memory trajectories. Second, we used the three-step method (R3STEP) to predict membership in the declining memory classes using six measures: memory complaints, memory concerns, memory compensation, memory self-efficacy, PP, and sex. RESULTS: First, we identified four classes of memory aging trajectories: (a) stable memory aging (STABLE), (b) typical memory aging (TYPICAL), (c) slowly declining memory aging (SLOW), and (d) rapidly declining memory aging (RAPID). Second, more memory concerns predicted membership in the SLOW and RAPID classes. Higher PP predicted membership in the SLOW class. Male sex predicted membership in the declining (TYPICAL, SLOW, RAPID) classes. CONCLUSION: Among SMD facets, memory concerns represent the most severe degree of apprehension about subjectively experienced memory losses. The present integrative data-driven analysis indicated that such concerns predicted membership in declining memory trajectory classes in addition to worse vascular health (higher PP) and sex (male). In nondemented aging, concerns about increasing memory failures may be veridical indicators of memory loss, especially when coupled with vascular comorbidity and being male. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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). We then tested these associations as stratified by APOE (ɛ4-/ɛ4+), PP (low/high), and APOE+ PP (low/intermediate/high) risk. RESULTS: First, concurrently, higher ventricular size predicted lower EF/attention performance and, longitudinally, increasing ventricular size predicted steeper EF/attention decline. Second, concurrently, higher ventricular size predicted lower EF/attention performance selectively in APOEɛ4+ carriers, and longitudinally, increasing ventricular size predicted steeper EF/attention decline selectively in the low PP group. Third, ventricular size and EF/attention associations were absent in the high APOE+ PP risk group both concurrently and longitudinally. CONCLUSION: As AD progresses, a threshold effect may be present in which ventricular enlargement in the context of exacerbated APOE+ PP risk does not produce further cognitive decline.

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

Background: 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. Second, we test whether all three associations are moderated by the most penetrant AD genetic risk [Apolipoprotein E (APOE) ε4+ allele]. Third, we test whether a non-APOE AD genetic risk score further moderates these APOE × multimodal risk score associations. Methods: We assembled a longitudinal data set (spanning a 40-year band of aging, 53-95 years) with non-demented older adults (baseline n = 602; Mage = 70.63(8.70) years; 66% female) from the Victoria Longitudinal Study (VLS). The measures included for each modifiable risk score were: (1) functional-health [pulse pressure (PP), grip strength, and body mass index], (2) lifestyle-reserve (physical, social, cognitive-integrative, cognitive-novel activities, and education), and (3) the combination of functional-health and lifestyle-reserve risk scores. Two AD genetic risk markers included (1) APOE and (2) a combined AD-genetic risk score (AD-GRS) comprised of three single nucleotide polymorphisms (SNPs; Clusterin[rs11136000], Complement receptor 1[rs6656401], Phosphatidylinositol binding clathrin assembly protein[rs3851179]). The analytics included confirmatory factor analysis (CFA), longitudinal invariance testing, and latent growth curve modeling. Structural path analyses were deployed to test and compare prediction models for EF performance and change. Results: First, separate analyses showed that higher functional-health risk scores, lifestyle-reserve risk scores, and the combined score, predicted poorer EF performance and steeper decline. Second, APOE and AD-GRS moderated the association between functional-health risk score and the combined risk score, on EF performance and change. Specifically, only older adults in the APOEε4- group showed steeper EF decline with high risk scores on both functional-health and combined risk score. Both associations were further magnified for adults with high AD-GRS. Conclusion: The present multimodal AD risk network approach incorporated both modifiable and genetic risk scores to predict EF trajectories. The results add an additional degree of precision to risk profile calculations for asymptomatic aging populations.

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

Objective: 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. Using data from the Victoria Longitudinal Study we examined two goals: (a) detect different underlying subgroups (or classes) of EF performance and structure and (b) test multiple risk predictors for best discrimination of these detected subgroups. Method: We used a classification sample (n = 778; Mage = 71.42) for the first goal and a prediction subsample (n = 570; Mage = 70.10) for the second goal. Eight neuropsychological measures represented three EF dimensions (inhibition, updating, shifting). Fifteen predictors represented five domains (genetic, functional, lifestyle, mobility, demographic). Results: First, we observed two distinct classes: (a) lower EF performance and unidimensional structure (Class 1) and (b) higher EF performance and multidimensional structure (Class 2). Second, Class 2 was predicted by younger age, more novel cognitive activity, more education, lower body mass index, lower pulse pressure, female sex, faster balance, and more physical activity. Conclusions: Data-driven modeling approaches tested the possibility of an EF aging class that displayed both preserved EF performance levels and sustained multidimensional structure. The two observed classes differed in both performance level (lower, higher) and structure (unidimensional, multidimensional). Machine learning prediction analyses showed that the higher performing and multidimensional class was associated with multiple brain health-related protective factors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Association of sex differences in dementia risk factors with sex differences in memory decline in a population-based cohort spanning 20-76 years.

Sex differences in late-life memory decline may be explained by sex differences in dementia risk factors. Episodic memory and dementia risk factors were assessed in young, middle-aged and older adults over 12 years in a population-based sample (N = 7485). For men in midlife and old age, physical, cognitive and social activities were associated with less memory decline, and financial hardship was associated with more. APOE e4 and vascular risk factors were associated with memory decline for women in midlife. Depression, cognitive and physical activity were associated with memory change in older women. Incident midlife hypertension (ß = - 0.48, 95% CI - 0.87, - 0.09, p = 0.02) was associated with greater memory decline in women and incident late-life stroke accounted for greater memory decline in men (ß = - 0.56, 95% CI - 1.12, - 0.01), p = 0.05). Women have fewer modifiable risk factors than men. Stroke and hypertension explained sex differences in memory decline for men and women respectively.

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. Specifically, we tested whether (1) modalities of early frailty profiles could be empirically determined, (2) the extracted profiles were differentially related to longitudinal cognitive decline, and (3) the profile and prediction patterns were robust for males and females. METHODS: Participants (n = 649; M age = 70.61, range 53-95) were community-dwelling older adults from the Victoria Longitudinal Study who contributed data for baseline multi-morbidity assessment and longitudinal cognitive trajectory analyses. An exploratory factor analysis on 50 multi-morbidity items produced 7 separable health domains. The proportion of deficits in each domain was calculated and used as continuous indicators in a data-driven latent profile analysis (LPA). We subsequently examined how frailty profiles related to the level and rate of change in a latent neurocognitive speed variable. RESULTS: LPA results distinguished three profiles: not-clinically-frail (NCF; characterized by limited impairment across indicators; 84%), mobility-type frailty (MTF; characterized by impaired mobility function; 9%), and respiratory-type frailty (RTF; characterized by impaired respiratory function; 7%). These profiles showed differential neurocognitive slowing, such that MTF was associated with the steepest decline, followed by RTF, and then NCF. The baseline frailty index scores were the highest for MTF and RTF and increased over time. All observations were robust across sex. CONCLUSIONS: A data-driven approach to early frailty assessment detected differentiable profiles that may be characterized as morbidity-intensive portals into broader and chronic frailty. Early inventions targeting mobility or respiratory deficits may have positive downstream effects on frailty progression and cognitive decline.