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.

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.

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.

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.

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. The present research examined (a) BMI prediction of neuropsychological performance and decline in executive function (EF), neurocognitive speed, and memory and (b) sex stratification of BMI effects. METHOD: Participants (n = 869; 573 females; M age = 71.75, range = 53-85 years) were older adults from the Victoria Longitudinal Study. Latent growth modeling was used to examine BMI as a predictor of level and change in three latent variables of cognition. The data were then stratified by sex to test whether BMI effects differed for females and males. We adjusted for selected medical, psychosocial, and demographic characteristics. RESULTS: Higher BMI predicted less decline in EF, neurocognitive speed, and memory. Interestingly, when the data were stratified by sex, higher BMI predicted less neuropsychological decline across domains for females only. BMI was unrelated to cognitive aging trajectories for males. CONCLUSIONS: We found that elevated BMI was a risk-reducing factor for cognitive decline only for females. Results may be used to enhance the precision with which intervention protocols may target specific subgroups of older adults. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Preference for emotionally meaningful activity in Alzheimer's disease.

Objectives: Socioemotional selectivity theory (SST) contends that future time perspective is the central determinant of healthy older adults' prioritization of emotional gratification. We have shown elsewhere that individuals with Alzheimer's disease (AD) are disoriented to future time perspective. This study examined whether these same participants would prioritize emotional gratification despite having distorted time perspective. Method: Performance of individuals with Alzheimer's disease (AD) was compared against young, young-old, and old-old adults on a social activity preference card-sort task. We examined whether activity preferences differentially related to subjective wellbeing. Results: Multidimensional scaling revealed common dimensions along which groups considered social activities. The importance of these dimensions varied across healthy participant groups in ways predicted by SST. Dimensions related to knowledge acquisition were more important in youth than older age; emotional dimensions were more important to the older age groups. Despite AD, these individuals also prioritzed emotional gratification, suggesting that cognitive impairment is not a barrier to socioemotional selectivity. Preference for emotionally meaningful activities was positively associated with subjective wellbeing. Conclusion: Persons with AD are motivated towards emotionally meaningful ends and retain high levels of wellbeing. These findings have implications in the caregiving context for shaping social programs to better match goals and preferences.

Time perspective and positivity effects in Alzheimer's disease.

This study tested whether time perspective, a central tenant of socioemotional selectivity theory (Carstensen, 2006), moderates positivity effects in emotional memory. To provide measures of time perspective, young (YA; M = 22.48 years), young-old (YO; M = 67.56 years), old-old adults (OO; M = 80.24 years), and participants with moderate severity Alzheimer's disease (PAD; M = 84.28 years) completed a line task and reported subjective age. As expected, YA, YO, and OO reported successively more constrained future time perspectives. PAD showed distortion in time perspective, envisioning a future comparable with the YO, although closer matched in chronological age to OO adults. To evince positivity effects, participants were oriented to pairs of emotional images and were then tested for memory (recall and recognition) of the images. Recall and recognition memory for the images indicated an age-related advantage for positive over negative material (positivity effects). Time perspective, however, did not moderate these age effects. In memory performance, PAD were more comparable with OO adults with whom they shared a similar chronological age, rather than YO adults, who had a corresponding time perspective. These results suggest that age correlates that are shared by PAD and OO, such as reduced processing resources, rather than time perspective, may drive the age associated positivity effects. (PsycINFO Database Record