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Introduction Early detection of cognitive decline in older adults is a public health priority. Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA), a multisite study, is validating cognition, emotion, motor, and sensory modules of the National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in the aging spectrum from cognitively normal to dementia of the Alzheimer's type (DAT). Methods Participants 65 to 85 years old, in demographic groups racially proportional to the general US population, are recruited in one of three groups to validate the NIHTB: cognitively normal, amnestic mild cognitive impairment (aMCI), or mild DAT. Additional special emphasis cohorts include (1) Blacks in the three clinical groups; (2) Spanish-speakers in the three clinical groups; (3) cognitively normal, population-proportional, over age 85. Discussion Longitudinal study will determine whether NIHTB can predict cognitive decline and is associated with Alzheimer's disease biomarkers. Here, we detail the methods for the ARMADA study.

Background The ability to capture people’s movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI). Methods One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts—the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory. Results Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3–13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1–9.1], p = .03). Conclusions The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.

Background: Current conventional cognitive assessments are limited in their efficiency and sensitivity, often relying on a single score such as the total correct items. Typically, multiple features of response go uncaptured. Objectives: We aim to explore a new set of automatically derived features from the Digit Span (DS) task that address some of the drawbacks in the conventional scoring and are also useful for distinguishing subjects with Mild Cognitive Impairment (MCI) from those with intact cognition. Methods: Audio-recordings of the DS tests administered to 85 subjects (22 MCI and 63 healthy controls, mean age 90.2 years) were transcribed using an Automatic Speech Recognition (ASR) system. Next, five correctness measures were generated from Levenshtein distance analysis of responses: number correct, incorrect, deleted, inserted, and substituted words compared to the test item. These per-item features were aggregated across all test items for both Forward Digit Span (FDS) and Backward Digit Span (BDS) tasks using summary statistical functions, constructing a global feature vector representing the detailed assessment of each subject’s response. A support vector machine classifier distinguished MCI from cognitively intact participants. Results: Conventional DS scores did not differentiate MCI participants from controls. The automated multi-feature DS-derived metric achieved 73% on AUC-ROC of the SVM classifier, independent of additional clinical features (77% when combined with demographic features of subjects); well above chance, 50%. Conclusion: Our analysis verifies the effectiveness of introduced measures, solely derived from the DS task, in the context of differentiating subjects with MCI from those with intact cognition.

In the past decade a large body of evidence has accumulated on risk factors for dementia, primarily from Europe and North America. Drawing on recent integrative reviews and a consensus workshop, the International Research Network on Dementia Prevention developed a consensus statement on priorities for future research. Significant gaps in geographical location, representativeness, diversity, duration, mechanisms, and research on combinations of risk factors were identified. Future research to inform dementia risk reduction should fill gaps in the evidence base, take a life-course, multi-domain approach, and inform population health approaches that improve the brain-health of whole communities.

Objectives To investigate potential birth cohort effects in depression symptoms in older adults. Design Population-based prospective cohort. Setting Small-town communities in Pennsylvania. Participants Three thousand two hundred and twenty seven older adults (average baseline age = 71.6) born between 1902 and 1941. Measurements Four decade-long birth cohorts were the primary predictors in this study: 1902–1911, 1912–1921, 1922–1931, and 1932–1941. The outcome was symptoms of depression assessed at baseline and follow-up study visits using a modified Center for Epidemiologic Studies Depression Scale (mCES-D). The depression outcome was operationalized as: 1). A binary outcome of having greater than equal to 5 depression symptoms on the total mCES-D at any study visit, and 2). A continuous outcome of four factor-analyzed component scores of the mCES-D including depressed mood, anergia/hopelessness, withdrawal, and poor self-esteem. All analyses were jointly modeled with attrition and adjusted for age, sex, education, Mini Mental State Examination score, antidepressant medications, and total prescription medications. Results Participants from more recently born cohorts were significantly less likely to have a study visit in which they reported greater than or equal to 5 depression symptoms, controlling for attrition. Specifically, in comparison to the 1902–1911 referent cohort, the 1912–1921 birth cohort was 43% less likely (odds ratio [OR] = 0.566, 95% confidence interval [CI]: 0.341–0.939), the 1922–1931 birth cohort was 63% less likely (OR = 0.0369, 95% CI: 0.215–0.632), and the 1932–1941 cohort was 79% less likely (OR = 0.205, 95% CI: 0.106–0.399). The cohort effect was most evident in the depressed mood and anergia/hopelessness symptom composites. Conclusion Reduced rates of depression symptoms observed in successive birth cohorts of older adults may reflect compression of morbidity or other secular trends.

There is an urgent need for interventions that can prevent or delay cognitive decline and dementia. Decades of epidemiological research have identified potential pharmacological strategies for risk factor modification to prevent these serious conditions, but clinical trials have failed to confirm the potential efficacy for such interventions. Our multidisciplinary international group reviewed seven high-potential intervention strategies in an attempt to identify potential reasons for the mismatch between the observational and trial results. In considering our findings, we offer constructive recommendations for the next steps. Overall, we observed some differences in the observational evidence base for the seven strategies, but several common methodological themes that emerged. These themes included the appropriateness of trial populations and intervention strategies, including the timing of interventions and other aspects of trials methodology. To inform the design of future clinical trials, we provide recommendations for the next steps in finding strategies for effective dementia risk reduction.

Background and Objectives The coronavirus disease 2019 (COVID-19) pandemic has limited older adults’ access to in-person medical care, including screenings for cognitive and functional decline. Remote, technology-based tools have shown recent promise in assessing changes in older adults’ daily activities and mood, which may serve as indicators of underlying health-related changes (e.g., cognitive decline). This study examined changes in older adults’ driving, computer use, mood, and travel events prior to and following the COVID-19 emergency declaration using unobtrusive monitoring technologies and remote online surveys. As an exploratory aim, the impact of mild cognitive impairment (MCI) on these changes was assessed. Research Design and Methods Participants were 59 older adults (41 cognitively intact and 18 MCI) enrolled in a longitudinal aging study. Participants had their driving and computer use behaviors recorded over a 5-month period (75 days pre- and 76 days post-COVID emergency declaration) using unobtrusive technologies. Measures of mood, overnight guests, and frequency of overnight travel were also collected weekly via remote online survey. Results After adjusting for age, gender, and education, participants showed a significant decrease in daily driving distance, number of driving trips, highway driving, and nighttime driving, post-COVID-19 as compared to pre-COVID-19 (p < .001) based on generalized estimating equation models. Further, participants spent more time on the computer per day post-COVID-19 (p = .03). Participants endorsed increases in blue mood (p < .01) and loneliness (p < .001) and decreases in travel away from home and overnight visitors (p < .001) from pre- to post-COVID-19. Cognitive status did not impact these relationships. Discussion and Implications From pre- to post-COVID-19 emergency declaration, participants drove and traveled less, used their computer more, had fewer overnight visitors, and reported greater psychological distress. These results highlight the behavioral and psychological effects of stay-at-home orders on older adults who are cognitively intact and those with MCI.

Vascular mild cognitive impairment (MCI) is a critical disease. Its prognosis includes not only onset of vascular dementia, but also death by cardiovascular disease. The vascular risk factors for vascular MCI are treatable, and appropriate treatment can prevent or delay the progression to dementia. Therefore, this group is an excellent candidate for secondary prevention. However, community-dwelling older adults with vascular MCI are often undetected and are not clinically identified until they develop frank dementia. Furthermore, older adults with undetected vascular MCI often have decreased ability to follow their medication regimens and this poor medication adherence worsens their vascular comorbidities. This vicious cycle needs to be prevented through community-based interventions. There is evidence that treatment of hypertension or diabetes mellitus could lead to a reduced incidence of vascular MCI and dementia. In this review article, we first explain the background and etiology of vascular MCI. We then summarize phenotype of subcortical vascular dementia which is often unrecognized or "hidden" in the community. Then we introduce the Osaki-Tajiri and Kurihara Projects which have been conducted in Northern Japan, as an example of prevention projects aimed to identify early-stage vascular MCI in the community, reduce the risk factors and facilitate their treatment. Early identification of vascular MCI in the community could lead to a large reduction in the dementia burden worldwide. The outreach efforts presented here could be useful in developing secondary prevention strategies targeted to vascular MCI.

Background Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall. Method In both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period. Results We observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s−1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01. Conclusions While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.

Author(s): Jicha, Greg A; Abner, Erin L; Arnold, Steven E; Carrillo, Maria C; Dodge, Hiroko H; Edland, Steven D; Fargo, Keith N; Feldman, Howard H; Goldstein, Larry B; Hendrix, James; Peters, Ruth; Robillard, Julie M; Schneider, Lon S; Titiner, Jodi R; Weber, Christopher J; Clinical Trial Advancement & Methodology Professional Interest Area of the International Society to Advance Alzheimer's Research and Treatment (ISTAART) | Abstract: BackgroundConsensus guidance for the development and identification of high-quality Alzheimer's disease clinical trials is needed for protocol development and conduct of clinical trials.MethodsAn ad hoc consensus committee was convened in conjunction with the Alzheimer's Association to develop consensus recommendations.ResultsConsensus was readily reached for the need to provide scientific justification, registration of trials, institutional review board oversight, conflict of interest disclosure, funding source disclosure, defined trial population, recruitment resources, definition of the intervention, specification of trial duration, appropriate payment for participant engagement, risk-benefit disclosure as part of the consent process, and the requirement to disseminate and/or publish trial results even if the study is negative.ConclusionsThis consensus guidance should prove useful for the protocol development and conduct of clinical trials, and may further provide a platform for the development of education materials that may help guide appropriate clinical trial participation decisions for potential trial participants and the general public.

In recent years, behavioral markers such as spoken language and lexical preferences have been studied in the early detection of mild cognitive impairment (MCI) using conversations. While the combination of linguistic and acoustic signals have been shown to be effective in detecting MCI, they have generally been restricted to structured conversations in which the interviewee responds to fixed prompts. In this study, we show that linguistic and acoustic features can be combined synergistically to identify MCI in semi-structured conversations. Using conversational data from an on-going clinical trial (Clinicaltrials.gov: NCT02871921), we find that the combination of linguistic and acoustic features on semi-structured conversations achieves a mean AUC of 82.7, significantly (p < 0.01) out-performing linguistic-only (74.9 mean AUC) or acoustic-only (65.0 mean AUC) detections on hold-out data. Additionally, features (linguistic, acoustic and combination) obtained from semi-structured conversations outperform their counterparts obtained from structured weekly conversations in identifying MCI. Some linguistic categories are significantly better at predicting MCI status (e.g., death, home) than others.

BackgroundSocial isolation is associated with an increased risk of adverse health outcomes, including functional decline, cognitive decline, and dementia. Intergenerational engagement, i.e. structured or semi structured interactions between non-familial older adults and younger generations is emerging as a tool to reduce social isolation in older adults and to benefit children and adults alike. This has great potential for our communities, however, the strength and breadth of the evidence for this is unclear. We undertook a systematic review to summarise the existing evidence for intergenerational interventions with community dwelling non-familial older adults and children, to identify the gaps and to make recommendations for the next steps.MethodsMedline, Embase and PsychInfo were searched from inception to the 28th Sept 2020. Articles were included if they reported research studies evaluating the use of non-familial intergenerational interaction in community dwelling older adults. PROSPERO registration number CRD42020175927 RESULTS: Twenty articles reporting on 16 studies were included. Although all studies reported positive effects in general, numerical outcomes were not recorded in some cases, and outcomes and assessment tools varied and were administered un-blinded. Caution is needed when making interpretations about the efficacy of intergenerational programmes for improving social, health and cognitive outcomes.DiscussionOverall, there is neither strong evidence for nor against community based intergenerational interventions. The increase in popularity of intergenerational programmes alongside the strong perception of potential benefit underscores the urgent need for evidence-based research.

© 2020 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's AssociationIntroduction: Non-pharmacological treatments (NPTs) have the potential to improve meaningful outcomes for older people at risk of, or living with dementia, but research often lacks methodological rigor and continues to produce mixed results. Methods: In the current position paper, experts in NPT research have specified treatment targets, aims, and ingredients using an umbrella framework, the Rehabilitation Treatment Specification System. Results: Experts provided a snapshot and an authoritative summary of the evidence for different NPTs based on the best synthesis efforts, identified main gaps in knowledge and relevant barriers, and provided directions for future research. Experts in trial methodology provide best practice principles and recommendations for those working in this area, underscoring the importance of prespecified protocols. Discussion: We conclude that the evidence strongly supports various NPTs in relation to their primary targets, and discuss opportunities and challenges associated with a unifying theoretical framework to guide future efforts in this area.

Dementia is a risk factor for epilepsy. While seizures have a well-established association with Alzheimer's disease (AD), their association with dementia with Lewy bodies (DLB) is not established. We utilized the National Alzheimer's Coordinating Centers' Uniform Data Set (NACC-UDS V1-3) to analyze occurrence of seizures in DLB and seizure occurrence associations with mortality. We excluded subjects with conventional seizure risk factors. Seizure occurrence was noted in 36 subjects (2.62%) out of 1376 subjects with DLB. Among 500 subjects with pathologically confirmed DLB, seizure occurrence was documented in 19 (3.8%) subjects. Half of the subjects had onset of seizures three years before or after DLB diagnosis. Two-year mortality for subjects with DLB with seizures was high at 52.8% but no increased risk was noted as compared with subjects with DLB without seizures. More prospective and long-term longitudinal studies are needed to clarify relationships between DLB, seizure occurrence, and risk of increased mortality.

Background The current clinical trial assessment methodology relies on a combination of self-report measures, cognitive and physical function tests, and biomarkers. This methodology is limited by recall bias and recency effects in self-reporting and by assessments that are brief, episodic, and clinic based. Continuous monitoring of ecologically valid measures of cognition and daily functioning in the community may provide a more sensitive method to detect subtle, progressive changes in patients with cognitive impairment and dementia. Objective This study aimed to present an alternative trial approach using a home-based sensing and computing system to detect changes related to common treatments employed in Alzheimer disease (AD). This paper introduces an ongoing study that aims to determine the feasibility of capturing sensor-based data at home and to compare the sensor-based outcomes with conventional outcomes. We describe the methodology used in the assessment protocol and present preliminary results of feasibility measures and examples of data related to medication-taking behavior, activity levels, and sleep. Methods The EVALUATE-AD (Ecologically Valid, Ambient, Longitudinal and Unbiased Assessment of Treatment Efficacy in Alzheimer’s Disease) trial is a longitudinal naturalistic observational cohort study recruiting 30 patients and 30 spouse coresident care partners. Participants are monitored continuously using a home-based sensing and computing system for up to 24 months. Outcome measures of the automated system are compared with conventional clinical outcome measures in AD. Acceptance of the home system and protocol are assessed by rates of dropout and protocol adherence. After completion of the study monitoring period, a composite model using multiple functional outcome measures will be created that represents a behavioral-activity signature of initiating or discontinuing AD-related medications, such as cholinesterase inhibitors, memantine, or antidepressants. Results The home-based sensing and computing system has been well accepted by individuals with cognitive impairment and their care partners. Participants showed good adherence to the completion of a weekly web-based health survey. Daily activity, medication adherence, and total time in bed could be derived from algorithms using data from the sensing and computing system. The mean monitoring time for current participants was 14.6 months. Medication adherence, as measured with an electronic pillbox, was 77% for participants taking AD-related medications. Conclusions Continuous, home-based assessment provides a novel approach to test the impact of new or existing dementia treatments generating objective, clinically meaningful measures related to cognition and everyday functioning. Combining this approach with the current clinical trial methodology may ultimately reduce trial durations, sample size needs, and reliance on a clinic-based assessment. International Registered Report Identifier (IRRID) DERR1-10.2196/17603

Introduction Multinutrient approaches may produce more robust effects on brain health through interactive qualities. We hypothesized that a blood-based nutritional risk index (NRI) including three biomarkers of diet quality can explain cognitive trajectories in the multidomain Alzheimer prevention trial (MAPT) over 3-years. Methods The NRI included erythrocyte n-3 polyunsaturated fatty acids (n-3 PUFA 22:6n-3 and 20:5n-3), serum 25-hydroxyvitamin D, and plasma homocysteine. The NRI scores reflect the number of nutritional risk factors (0–3). The primary outcome in MAPT was a cognitive composite Z score within each participant that was fit with linear mixed-effects models. Results Eighty percent had at lease one nutritional risk factor for cognitive decline (NRI ≥1: 573 of 712). Participants presenting without nutritional risk factors (NRI=0) exhibited cognitive enhancement (β = 0.03 standard units [SU]/y), whereas each NRI point increase corresponded to an incremental acceleration in rates of cognitive decline (NRI-1: β = −0.04 SU/y, P = .03; NRI-2: β = −0.08 SU/y, P, Highlights • Multi-nutrient approaches may produce more robust effects through interactive properties • Nutritional risk index can objectively quantify nutrition-related cognitive changes • Optimum nutritional status associated with cognitive enhancement over 3-years • Suboptimum nutritional status associated with cognitive decline over 3-years • Optimizing this nutritional risk index may promote cognitive health in older adults

Introduction Physical activities (PA) may lead to improved cognition in mild cognitive impairment (MCI), Alzheimer's disease (AD), and dementia. The mechanisms mediating potential PA effects are unknown. Assessment of PA effects on relevant biomarkers may provide insights into mechanisms underlying potential PA effects on cognition. Methods We systematically reviewed randomized controlled trials (RCTs) that studied PA effects on biomarkers in MCI, AD, and dementia populations. We examined whether biological mechanisms were hypothesized to explain associations among PA, biomarkers, and cognitive functions. We used the PubMed database and searched for RCTs with PA until October 31, 2019. Results Of 653 studies examining changes in biomarkers in PA trials, 18 studies met inclusion criteria for the present review. Some studies found favorable effects of PA on neurotrophic and inflammatory biomarkers. AD pathological markers were rarely investigated, with inconclusive results. Most studies were relatively small in sample size, of limited duration, and not all studies compared the changes in biomarkers between the control and experimental groups. Discussion There is only limited use of potentially informative biomarkers in PA trials for MCI, AD, and dementia. Most studies did not examine the role of biomarkers to study associations between PA and cognitive functions in their analyses. Several potential biomarkers remain uninvestigated. Careful use of biomarkers may clarify mechanisms underlying PA effects on cognition. Our review serves as a useful resource for developing future PA RCTs aimed at improving cognitive functions in MCI, AD, and dementias.

BACKGROUND Norms for the Uniform Data Set Version 3 Neuropsychological Battery are available for cognitively normal individuals based on age, education, and sex; however, these norms do not include race. We provide expanded norms for African Americans and whites. METHODS Data from 32 Alzheimer's Disease Centers (ADCs) and ADC affiliated cohorts with global Clinical Dementia Rating Scale (CDR) Dementia Staging Instrument scores of 0 were included. Descriptive statistics for each test were calculated by age, sex, race, and education. Multiple linear regressions were conducted to estimate the effect of each demographic variable; squared semipartial correlation coefficients measured the relative importance of variables. RESULTS There were 8313 participants (16% African American) with complete demographic information, ranging from 6600 to 7885 depending on the test. Lower scores were found for older and less educated groups, and African Americans versus whites. Education was the strongest predictor for most tests, followed in order by age, race, and sex. Quadratic terms were significant for age and education, indicating some nonlinearity, but did not substantially increase R. CONCLUSIONS Although race-based norms represent incomplete proxies for other sociocultural variables, the appropriate application of these norms is important given the potential to improve diagnostic accuracy and to reduce misclassification bias in cognitive disorders of aging such as Alzheimer disease.

BACKGROUND: Cerebrovascular disease is a common cause of dementia in older adults, and potentially preventable with early intervention. Oxylipins are produced from the oxidation of long-chain polyunsaturated fatty acids (PUFA) possessing potent vascular effects. Oxylipins generated from the cytochrome P450 pathway are enzymatically converted to diols by soluble epoxide hydrolase (sEH); sEH products have been associated with small vessel ischemic disease. Little is known about oxylipins’ impact on markers of dementia risk. OBJECTIVE: An exploratory examination of the association between omega-6 and omega-3 derived oxylipins, brain MRI, and cognition. METHODS: Thirty-seven non-demented participants with controlled hypertension (mean age 65.6 years) were enrolled in a dementia prevention study investigating fish oil and lipoic acid on preserving cognitive function. Baseline associations between plasma oxylipins, white matter hyperintensity (WMH), and Trails-B were examined using linear regression. P450-derived diol/epoxide ratio was an indirect measure of sEH activity. RESULTS: Omega-6 derived 9-HODE was associated with increased WMH (p = 0.017) and reduced grey matter volume (p = 0.02). Omega-6 P450-derived diol/epoxide ratio 9,10-DiHOME/9,10-EpOME was associated with increased WMH (p = 0.035) and poorer performance on Trails-B (p = 0.05); ratio14,15-DHET/14,15-EET was associated with increased WMH (p = 0.045). Omega-3 P450-derived diol/epoxide ratio 19,20-DiHDPE/19,20-EpDPE was associated with increased WMH (p = 0.04) and poorer performance on Trails-B (p = 0.04). Arachidonic acid was associated with better performance on Trails-B (p = 0.012); Omega-3 derived 16,17-EpDPE was associated with decreased WMH (p = 0.005). CONCLUSIONS: With the exception of arachidonic acid, it was specific oxylipin products, not their parent PUFAs, that were associated with unfavorable and favorable MRI and cognitive markers of dementia risk.

Introduction Agitation, experienced by patients with dementia, is difficult to manage and stressful for caregivers. Currently, agitation is primarily assessed by caregivers or clinicians based on self‐report or very brief periods of observation. This limits availability of comprehensive or sensitive enough reporting to detect early signs of agitation or identify its precipitants. The purpose of this article is to provide proof of concept for characterizing and predicting agitation using a system that continuously monitors patients’ activities and living environment within memory care facilities. Methods Continuous and unobtrusive monitoring of a participant is achieved using behavioral sensors, which include passive infrared motion sensors, door contact sensors, a wearable actigraphy device, and a bed pressure mat sensor installed in the living quarters of the participant. Environmental sensors are also used to continuously assess temperature, light, sound, and humidity. Episodes of agitation are reported by nursing staff. Data collected for 138 days were divided by 8‐hour nursing shifts. Features from agitated shifts were compared to those from non‐agitated shifts using t‐tests. Results A total of 37 episodes of agitation were reported for a male participant, aged 64 with Alzheimer's disease, living in a memory care unit. Participant activity metrics (eg, transitions within the living room, sleep scores from the bedmat, and total activity counts from the actigraph) significantly correlated with occurrences of agitation at night (P

BACKGROUND Understanding contributions of different brain pathologies to domain-specific cognitive trajectories in the oldest old is crucial to guide future intervention studies. METHODS Two-hundred-twenty Oregon Alzheimer's Disease Center research participants who were cognitively intact at entry were followed on average for 7.3 years with annual neuropsychological testing until death (mean age, 93.7 y) and autopsy. Mixed effects models examined the relationship between trajectories in memory, verbal fluency, and mini-mental state examination (MMSE) and pathology (neurofibrillary tangles, neuritic plaques, gross infarcts, hippocampal sclerosis, Lewy bodies, APOE genotype, age at death, and years of education). The association between the MMSE trajectory and pathologic variables were examined using a Poisson model with MMSE errors as outcomes given the nonlinear distribution of MMSE scores. RESULTS Memory trajectory was associated with the APOe4 allele (P=0.006). Verbal fluency trajectory was associated with gross infarcts (P=0.008). MMSE trajectory was associated with high Braak scores (P=0.03), gross infarcts (P

ObjectiveWe aimed to describe and compare baseline cerebral blood flow (CBF) and microstructural characteristics of normal-appearing white matter (NAWM) within the vulnerable periventricular white matter hyperintensity (PVWMH) penumbra region in predicting white matter hyperintensity (WMH) growth over time.MethodsFifty-two patients, aged 82.8 years, underwent serial brain MRI, including pulsed arterial spin labeling and diffusion tensor imaging (DTI). New WMH and persistent NAWM voxels in relation to WMH penumbra at follow-up were identified. Mean baseline CBF and DTI variables of the new WMH and persistent NAWM voxels were computed. Univariate analyses with paired t tests were performed. Generalized estimating equation analyses were used to compare the relationships of baseline CBF, and structural penumbras with WMH growth, controlling for confounders.ResultsLow baseline CBF and fractional anisotropy, and high mean diffusivity (MD), were independently associated with new PVWMH voxels, with MD being the best predictor of WMH growth. A separate model demonstrated that radial diffusivity had the strongest relationship with WMH growth compared with CBF and axial diffusivity.ConclusionCBF and DTI measures independently predict WMH growth over time. DTI is a more sensitive predictor of WMH growth than CBF, with WMH progression likely due to demyelinating injury secondary to low perfusion. Findings support the use of MD as a sensitive marker of NAWM vulnerability in future trials aimed at preserving WM integrity.

Background Driving is a key functional activity for many older adults, and changes in routine driving may be associated with emerging cognitive decline due to early neurodegenerative disease. Current methods for assessing driving such as self-report are inadequate for identifying and monitoring subtle changes in driving patterns that may be the earliest signals of functional change in developing mild cognitive impairment (MCI). Objective This proof of concept study aimed to establish the feasibility of continuous driving monitoring in a sample of cognitively normal and MCI older adults for an average of 206 days using an unobtrusive driving sensor and demonstrate that derived sensor-based driving metrics could effectively discriminate between MCI and cognitively intact groups. Methods Novel objective driving measures derived from 6 months of routine driving monitoring were examined in older adults with intact cognition (n = 21) and MCI (n = 7) who were enrolled in the Oregon Center for Aging and Technology (ORCATECH) longitudinal assessment program. Results Unobtrusive continuous monitoring of older adults' routine driving using a driving sensor was feasible and well accepted. MCI participants drove fewer miles and spent less time on the highway per day than cognitively intact participants. MCI drivers showed less day-to-day fluctuations in their driving habits than cognitively intact drivers. Conclusion Sensor-based driving measures are objective, unobtrusive, and can be assessed every time a person drives his or her vehicle to identify clinically meaningful changes in daily driving. This novel methodology has the potential to be useful for the early detection and monitoring of changes in daily functioning within individuals.

Objective To determine clinical and neuropathological outcomes following a clinical diagnosis of mild cognitive impairment (MCI). Methods Data were drawn from a large autopsy series (N = 1,337) of individuals followed longitudinally from normal or MCI status to death, derived from 4 Alzheimer Disease (AD) Centers in the United States. Results Mean follow-up was 7.9 years. Of the 874 individuals ever diagnosed with MCI, final clinical diagnoses were varied: 39.2% died with an MCI diagnosis, 46.8% with a dementia diagnosis, and 13.9% with a diagnosis of intact cognition. The latter group had pathological features resembling those with a final clinical diagnosis of MCI. In terms of non-AD pathologies, both primary age-related tauopathy (p < 0.05) and brain arteriolosclerosis pathology (p < 0.001) were more severe in MCI than cognitively intact controls. Among the group that remained MCI until death, mixed AD neuropathologic changes (ADNC; ≥1 comorbid pathology) were more frequent than “pure” ADNC pathology (55% vs 22%); suspected non-Alzheimer pathology comprised the remaining 22% of cases. A majority (74%) of subjects who died with MCI were without “high”-level ADNC, Lewy body disease, or hippocampal sclerosis pathologies; this group was enriched in cerebrovascular pathologies. Subjects who died with dementia and were without severe neurodegenerative pathologies tended to have cerebrovascular pathology and carry the MCI diagnosis for a longer interval. Interpretation MCI diagnosis usually was associated with comorbid neuropathologies; less than one-quarter of MCI cases showed “pure” AD at autopsy. Ann Neurol 2017;81:549–559

Introduction Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial. Methods Data came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI ( ClinicalTrials.gov : NCT01571427 ). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants. Results MCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%. Discussion Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.

Purpose of the study We evaluated the feasibility and reliability of commonly used clinical dementia assessments when administered via direct-to-home telemedicine videoconferencing. To date, few studies assessed the suitability of these measures when used in this setting. Design and methods Sixty-six participants (33 patients with Alzheimer's disease (AD) and their 33 caregivers) consented to assessment with a battery of tests in both the clinic setting and via telemedicine. We administered cognitive, behavior, and mood assessments to persons with mild, moderate, and severe AD both in the clinic setting and via direct-to-home telemedicine videoconferencing; test-retest reliability was assessed. We also explored how three caregiver measures performed when administered via telemedicine. Assessments were administered 2 weeks apart. Participant feedback about their experience was solicited. Results Twenty-eight dyads completed the assessments. Reliability was found to be good to excellent in all measures when used with direct-to-home telemedicine. For the most part, participants and clinicians found telemedicine to be a feasible option for assessing cognitive function and caregiver coping. Implications Findings indicate that these measures can be used to assess persons with AD, as well as their caregivers, across the telemedicine platform, directly to their homes. Use of this technology can expand access to care to the millions across the United States with AD and their caregivers.

Journal of Alzheimer's disease. 2020;73(3):887-896., 令和2年度

OBJECTIVES: Nutrients and their metabolites have interactive qualities that may be harnessed for prevention of cognitive decline. Simultaneous modulation of one-carbon, fatty acid and vitamin D metabolism (25-OH-D) may offer neuroprotection. We examined whether n-3 polyunsaturated fatty acids (n-3 PUFA), 25-OH-D, and homocysteine (HCy) formed into a Nutritional Risk Index (NRI) can explain cognitive performance of older non-demented adults. METHODS: The NBAS enrolled older participants from the NIA-Layton Oregon Alzheimer's Disease Center aging studies with serum samples available yielding 306 cognitively characterized older adults. Plasma fatty acids were quantified by GC-FID and 25-hydroxyvitamin D and homocysteine by LC-MS/MS. Nutritional risk defined as population nutrient biomarker tertiles with NRI calculated as the number of nutrient biomarkers meeting a sub-optimum criterion with scores ranging from 0 to 3. Global and domain specific cognitive z-scores were fit with multivariate linear regression models and NRI as the primary exposure of interest. RESULTS: Mean age was 85.8 (7.6) years, MMSE was 27.8 (2.8) and 70% were female. Sixty-five % met criteria for ‘nutritional risk’ (NRI ³ 1: 193/293). Participants with optimum nutritional status exhibited superior global cognitive performance (NRI-0: mean global z-score ± SE = 0.10 ± 0.097) while each addition NRI point score associated with an incremental decrease in cognitive performance (NRI-1: 0.02 ± 0.09; NRI-2: −0.23 ± 0.13; NRI-3: −0.53 ± 0.19, P for trend = 0.002). Significant and similar trends were seen in specific cognitive domains, including attention (NRI-0: mean z-score ± SE = 0.20 ± 0.11; NRI-1: 0.02 ± 0.10; NRI-2: −0.32 ± 0.13; NRI-3: −0.38 ± 0.19, P for trend

Vascular risk factors for age-related cognitive decline are significant, and their management may ultimately prove the most successful strategy for reducing risk and sustaining cognitive health. This randomized, double-blinded, placebo-controlled trial with parallel group allocation to either marine n-3 polyunsaturated fatty acids (n-3 PUFA) or soybean oil placebo assesses the effects on the total volume of accumulation in cerebral white matter hyperintensities (WMH), a potentially modifiable neurovascular component of age-related cognitive decline. Total WMH accumulation over 3 years is the primary endpoint. The safety and efficacy of n-3 PUFA is evaluated in older adults with significant WMH and suboptimum plasma n-3 PUFA as inclusion criteria. One hundred and two non-demented older adults were enrolled with a mean age of 81.1 (&plusmn, 4.4) years, WMH of 19.4 (&plusmn, 16.1) cm3, and a plasma n-3 PUFA of 86.64 (&plusmn, 29.21) &micro, g/mL. 61% were female, 28% were apolipoprotein E epsilon 4 carriers, and the mean mini-mental state exam (MMSE) was 27.9 (&plusmn, 1.7). This trial provides an initial evaluation of n-3 PUFA effects on WMH, a reproducible and valid risk biomarker for cognitive decline, as well as on inflammatory biomarkers thought to play a role in WMH accumulation. We present the baseline results and operational experience of enriching a study population on advanced age, blood n-3 PUFA, and magnetic resonance imaging (MRI) derived WMH with biomarker outcomes (WMH, inflammation markers) in a dementia prevention paradigm.

Altres ajuts: Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, "Una manera de hacer Europa", La "Marató TV3" grant (20141210 to J.F.) Alzheimer's disease and related dementias (ADRDs) are a global crisis facing the aging population and society as a whole. With the numbers of people with ADRDs predicted to rise dramatically across the world, the scientific community can no longer neglect the need for research focusing on ADRDs among underrepresented ethnoracial diverse groups. The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART; alz.org/ISTAART) comprises a number of professional interest areas (PIAs), each focusing on a major scientific area associated with ADRDs. We leverage the expertise of the existing international cadre of ISTAART scientists and experts to synthesize a cross-PIA white paper that provides both a concise "state-of-the-science" report of ethnoracial factors across PIA foci and updated recommendations to address immediate needs to advance ADRD science across ethnoracial populations.

1569 Background: Knowledge of changes in health that precede a cancer diagnosis is challenging because of a lack of longitudinal, objective measurement techniques. Current approaches rely on periodic assessment via self-report which may miss when and how health changes, particularly when changes may appear subtly over time. Remote-monitoring technologies provide a mechanism to continuously, passively, and unobtrusively monitor changes in health so that trajectories of change in relation to a life event (i.e., cancer diagnosis, treatment) can be detected and described. We examined the changes in digital indicators of health and life events 1 year before and after a self-reported cancer diagnosis in community-dwelling adults aged 65 and older. Methods: This is a secondary, retrospective data analysis of older adults who self-reported a new cancer diagnosis in the Oregon Center for Aging & Technology (ORCATECH) cohort. Ten older adults (age = 71.8±4.9 years, 30% women) were included with various cancer types (esophageal, prostate, uterine, pancreatic, b-cell follicular lymphoma, multiple myeloma, basal cell melanoma, basal and/or squamous cell carcinoma). Daily physical activity was measured using step counts derived from an actigraph watch. Weekly health and life events (pain severity, loneliness, hospitalization/emergency room (ER) visits, days away from home overnight) were self-reported from weekly online surveys. Results: A total of 3,624 days of actigraphy data (210 ± 88 days pre-cancer; 153 ± 81 days post-cancer) and 750 weeks of self-reported online survey data (36 ± 12 weeks pre-cancer; 39 ± 14 weeks post-cancer) were collected. Longitudinal linear mixed-effects models revealed that the trajectory of step counts was different pre- and post-cancer (β = -1.52, p

Introduction The presence of cerebrovascular pathology may increase the risk of clinical diagnosis of Alzheimer's disease (AD). Methods We examined excess risk of incident clinical diagnosis of AD (probable and possible AD) posed by the presence of lacunes and large infarcts beyond AD pathology using data from the Statistical Modeling of Aging and Risk of Transition study, a consortium of longitudinal cohort studies with more than 2000 autopsies. We created six mutually exclusive pathology patterns combining three levels of AD pathology (low, moderate, or high AD pathology) and two levels of vascular pathology (without lacunes and large infarcts or with lacunes and/or large infarcts). Results The coexistence of lacunes and large infarcts results in higher likelihood of clinical diagnosis of AD only when AD pathology burden is low. Discussion Our results reinforce the diagnostic importance of AD pathology in clinical AD. Further harmonization of assessment approaches for vascular pathologies is required.