Krell-Roesch, Janina

Background The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults. Objective To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults. Methods The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.e., age, sex, education), NPS (i.e., Neuropsychiatric Inventory Questionnaire; Beck Depression and Anxiety Inventories), at least one AD biomarker (i.e., plasma-, neuroimaging- and/or cerebrospinal fluid [CSF]-derived), and at least 2 repeated neuropsychological assessments. We trained and tested ML models using a stepwise feature addition approach to predict decline versus non-decline in global and domain-specific (i.e., memory, language, visuospatial, and attention/executive function) cognitive scores. Results ML models had better performance when NPS were included along with a) neuroimaging biomarkers for predicting decline in global cognition, as well as language and visuospatial skills; b) plasma-derived biomarkers for predicting decline in visuospatial skills; and c) CSF-derived biomarkers for predicting decline in attention/executive function, language, and memory. Conclusions NPS, added to ML models including demographic and AD biomarker data, improves prediction of downward trajectories in global and domain-specific cognitive scores among community-dwelling older adults, albeit effect sizes are small. These preliminary findings need to be confirmed by future cohort studies.

Background: Studies that assess cognition prospectively and study in detail anxiety history in the participants’ medical records within the context of brain aging and Alzheimer’s disease are limited. Objective: To examine the associations of anxiety and unspecified emotional distress (UED) acquired throughout a person’s life with prospectively collected cognitive outcomes. Methods: Mayo Clinic Study of Aging participants who were cognitively unimpaired at baseline were included. Anxiety and UED data were abstracted from the medical record using the Rochester Epidemiology Project (REP) resources and were run separately as predictors in our models. The data were analyzed using Cox proportional hazards models for the outcomes of incident mild cognitive impairment (MCI) and dementia and using linear mixed effects models for the outcomes of global and domain specific cognitive z-scores and included key covariates. Results: The study sample ( n = 1,808) had a mean (standard deviation) age of 74.5 (7.3) years and 51.4% were male. Anxiety was associated with increased risk of MCI and dementia and was associated with lower baseline cognitive z-scores and accelerated decline over time in the global, memory, and attention domains. UED was associated with faster decline in all domains except visuospatial but did not show evidence of association with incident cognitive outcomes. These results varied by medication use and timing of anxiety. Conclusions: Anxiety and UED both showed inverse associations with cognition. Utilization of anxiety and UED data from across the life course, as available, from the REP system adds robustness to our results.

There is a growing interest in the application of machine learning (ML) in Alzheimer’s disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

Background: Considering the world’s rapidly increasing life expectancy, with people working and maintaining active lifestyles longer than ever before, addressing the effects of aging on cognition is of utmost importance. A greater understanding of cognitive aging may also be critical in distinguishing natural cognitive aging from pre-clinical stages of Alzheimer’s disease and related cognitive disorders. Objective: To systematically examine the association between aging and cognitive performance in a cognitively and otherwise healthy probability population-based sample using a computer-based method. Methods: This cross-sectional study enrolled 673 cognitively and otherwise healthy participants aged 25–89 years (mean age 52.3±14.2 years, 52.5% of whom were female) from the Kardiovize study cohort. Mild cognitive impairment and dementia cases were excluded, followed by measurement of cognitive performance with the computer-administered Cogstate Brief Battery. We used ANCOVA and Modified Signed-Likelihood Ratio tests to examine patterns of cognition across age groups. Results: We found a gradual decrease in cognitive performance across the lifespan, which required two decades to demonstrate significant changes. In contrast to attention and learning, psychomotor speed and working memory showed the most significant age-related decrease and variability in performance. The established pattern of cognitive aging was not altered by sex or education. Conclusion: These findings corroborate, validate, and extend the current understanding of natural cognitive aging and pinpoint specific cognitive domains with the most extensive age-related interindividual differences. This will contribute to the development of strategies to preserve cognition with aging and may also serve to improve early diagnostics of cognitive disorders using computer-based methods.

Abstract Introduction We investigated the longitudinal relationship between cortical amyloid deposition, anxiety, and depression and the risk of incident mild cognitive impairment (MCI). Methods We followed 1440 community‐dwelling, cognitively unimpaired individuals aged ≥ 50 years for a median of 5.5 years. Clinical anxiety and depression were assessed using Beck Anxiety and Depression Inventories (BAI, BDI‐II). Cortical amyloid beta (Aβ) was measured by Pittsburgh compound B positron emission tomography (PiB‐PET) and elevated deposition (PiB+) was defined as standardized uptake value ratio ≥ 1.48. We calculated Cox proportional hazards models with age as the time scale, adjusted for sex, education, and medical comorbidity. Results Cortical Aβ deposition (PiB+) independent of anxiety (BAI ≥ 10) or depression (BDI‐II ≥ 13) increased the risk of MCI. There was a significant additive interaction between PiB+ and anxiety (joint effect hazard ratio 6.77; 95% confidence interval 3.58–12.79; P = .031) that is, being PiB+ and having anxiety further amplified the risk of MCI. Discussion Anxiety modified the association between PiB+ and incident MCI.

Abstract Objective Neuropsychiatric symptoms (NPS) are associated with the risk of incident mild cognitive impairment (MCI) and dementia. We examined associations between NPS and the outcomes of global and domain‐specific cognitive trajectories. Methods In this longitudinal study conducted in the setting of the population‐based Mayo Clinic Study of Aging, 5081 community‐dwelling, nondemented individuals aged ≥50 years (51% males) underwent NPS assessment using Neuropsychiatric Inventory Questionnaire (NPI‐Q), and Beck Depression and Anxiety Inventories (BDI‐II, BAI). Global and domain‐specific (memory, language, attention, and visuospatial skills) cognitive performance was assessed through neuropsychological testing every 15 months. Associations between baseline NPS and trajectories for individual yearly change in cognitive z‐scores were calculated using linear mixed‐effect models. Results Cognition declined regardless of NPS status over the median follow‐up of 4.5 years. Presence of NPS was associated with increased cognitive decline. Differences in annualized change in global cognition z‐scores for participants with NPS compared to without NPS ranged from −0.018 (95% CI −0.032, −0.004; p = 0.011) for irritability to −0.159 (−0.254, −0.065; p = 0.001) for hallucinations. Associations between NPS and annual decline in global cognition were significant for most NPI‐Q‐assessed NPS and clinical depression (BDI‐II≥13). Participants with NPI‐Q‐assessed depression, apathy, nighttime behavior, and clinical depression had greater decline in all domain‐specific z‐scores; presence of delusions and anxiety was associated with more pronounced decline in language, attention and visuospatial skills. Conclusion NPS were associated with a more accelerated cognitive decline. Clinical assessment and potential treatment of NPS is warranted even in a community setting as NPS may impact cognitive decline in nondemented individuals.

Background: Little is known about the association between physical activity (PA) and cognitive trajectories in older adults. Objective: To examine the association between PA and change in memory, language, attention, visuospatial skills, and global cognition, and a potential impact of sex or Apolipoprotein E (APOE) ɛ4 status. Methods: Longitudinal study derived from the population-based Mayo Clinic Study of Aging, including 2,060 cognitively unimpaired males and females aged ≥70 years. Engagement in midlife (ages 50–65) and late-life (last year) PA was assessed using a questionnaire. Neuropsychological testing was done every 15 months (mean follow-up 5.8 years). We ran linear mixed-effect models to examine whether mid- or late-life PA at three intensities (mild, moderate, vigorous) was associated with cognitive z-scores. Results: Light intensity midlife PA was associated with less decline in memory function compared to the no-PA reference group (time x light PA; estimate [standard error] 0.047 [0.016], p = 0.004). Vigorous late-life PA was associated with less decline in language (0.033 [0.015], p = 0.030), attention (0.032 [0.017], p = 0.050), and global cognition (0.039 [0.016], p = 0.012). Females who were physically inactive in midlife experienced more pronounced cognitive decline than females physically active in midlife and males regardless of PA (p-values for time interaction terms with midlife PA levels and sex were all p < 0.05 for global cognition). APOE ɛ4 carriership did not moderate the association between PA and cognition. Conclusion: Engaging in PA, particularly of vigorous intensity in late-life, was associated with less pronounced decline in global and domain-specific cognition. This association may differ by sex.