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BackgroundSocially isolated individuals tend to have less access to cognitively stimulating activities, which could adversely impact their cognitive health. The Internet-Based Conversational Engagement Clinical Trial (I-CONECT) intervention was designed to deliver online conversation sessions to socially isolated older old adults to prevent cognitive decline. The current study examined the intervention efficacy on participants’ weekly time spent out-of-home and their social interaction with family and friends.MethodsThe intervention group engaged in frequent conversations with trained interviewers via the Internet. Both intervention and control group participants received 10-min weekly phone check-in calls over 48 weeks, during which they were asked to self-report their time (in hours) spent out of home and whether they had contacted family or friends during this week (yes/no). Linear mixed-effect models for repeated measures were run for time spent out-of-home, and mixed-effect models with a logistic link for contact with family and friends. The intervention effect was modeled by including an interaction term of time (measured in weeks) and group assignments (intervention vs. control). We ran subgroup analyses for participants with normal cognition (NC) and mild cognitive impairment (MCI). All models controlled for age, sex, race, education, and the historical event of COVID-19.Results5,495 weekly records were included in the analysis. The main effect of time was statistically significant (p

Abstract INTRODUCTION Sodium phenylbutyrate and taurursodiol (PB and TURSO) is hypothesized to mitigate endoplasmic reticulum stress and mitochondrial dysfunction, two of many mechanisms implicated in Alzheimer's disease (AD) pathophysiology. METHODS The first‐in‐indication phase 2a PEGASUS trial was designed to gain insight into PB and TURSO effects on mechanistic targets of engagement and disease biology in AD. The primary clinical efficacy outcome was a global statistical test combining three endpoints relevant to disease trajectory (cognition [Mild/Moderate Alzheimer's Disease Composite Score], function [Functional Activities Questionnaire], and total hippocampal volume on magnetic resonance imaging). Secondary clinical outcomes included various cognitive, functional, and neuropsychiatric assessments. Cerebrospinal fluid (CSF) biomarkers spanning multiple pathophysiological pathways in AD were evaluated in participants with both baseline and Week 24 samples (exploratory outcome). RESULTS PEGASUS enrolled 95 participants (intent‐to‐treat [ITT] cohort); cognitive assessments indicated significantly greater baseline cognitive impairment in the PB and TURSO (n = 51) versus placebo (n = 44) group. Clinical efficacy outcomes did not significantly differ between treatment groups in the ITT cohort. CSF interleukin‐15 increased from baseline to Week 24 within the placebo group (n = 34). In the PB and TURSO group (n = 33), reductions were observed in core AD biomarkers phosphorylated tau‐181 (p‐tau181) and total tau; synaptic and neuronal degeneration biomarkers neurogranin and fatty acid binding protein‐3 (FABP3); and gliosis biomarker chitinase 3‐like protein 1 (YKL‐40), while the oxidative stress marker 8‐hydroxy‐2‐deoxyguanosine (8‐OHdG) increased. Between‐group differences were observed for the Aβ42/40 ratio, p‐tau181, total tau, neurogranin, FABP3, YKL‐40, interleukin‐15, and 8‐OHdG. Additional neurodegeneration, inflammation, and metabolic biomarkers showed no differences between groups. DISCUSSION While between‐group differences in clinical outcomes were not observed, most likely due to the small sample size and relatively short treatment duration, exploratory biomarker analyses suggested that PB and TURSO engages multiple pathophysiologic pathways in AD. Highlights Proteostasis and mitochondrial stress play key roles in Alzheimer's disease (AD). Sodium phenylbutyrate and taurursodiol (PB and TURSO) targets these mechanisms. The PEGASUS trial was designed to assess PB and TURSO effects on biologic AD targets. PB and TURSO reduced exploratory biomarkers of AD and neurodegeneration. Supports further clinical development of PB and TURSO in neurodegenerative diseases.

Abstract INTRODUCTION While Alzheimer's disease (AD) is defined by amyloid‐β plaques and tau tangles in the brain, it is evident that many other pathophysiological processes such as inflammation, neurovascular dysfunction, oxidative stress, and metabolic derangements also contribute to the disease process and that varying contributions of these pathways may reflect the heterogeneity of AD. Here, we used a previously validated panel of cerebrospinal fluid (CSF) biomarkers to explore the degree to which different pathophysiological domains are dysregulated in AD and how they relate to each other. METHODS Twenty‐five CSF biomarkers were analyzed in individuals with a clinical diagnosis of AD verified by positive CSF AD biomarkers (AD, n = 54) and cognitively unimpaired controls negative for CSF AD biomarkers (CU‐N, n = 26) using commercial single‐ and multi‐plex immunoassays. RESULTS We noted that while AD was associated with increased levels of only three biomarkers (MMP‐10, FABP3, and 8OHdG) on a group level, half of all AD participants had increased levels of biomarkers belonging to at least two pathophysiological domains reflecting the diversity in AD. LASSO modeling showed that a panel of FABP3, 24OHC, MMP‐10, MMP‐2, and 8OHdG constituted the most relevant and minimally correlated set of variables differentiating AD from CU‐N. Interestingly, factor analysis showed that two markers of metabolism and oxidative stress (24OHC and 8OHdG) contributed independent information separate from MMP‐10 and FABP3 suggestive of two independent pathophysiological pathways in AD, one reflecting neurodegeneration and vascular pathology, and the other associated with metabolism and oxidative stress. DISCUSSION Better understanding of the heterogeneity among individuals with AD and the different contributions of pathophysiological processes besides amyloid‐β and tau will be crucial for optimizing personalized treatment strategies. Highlights A panel of 25 highly validated biomarker assays were measured in CSF. MMP10, FABP3, and 8OHdG were increased in AD in univariate analysis. Many individuals with AD had increased levels of more than one biomarker. Markers of metabolism and oxidative stress contributed to an AD multianalyte profile. Assessing multiple biomarker domains is important to understand disease heterogeneity.

BackgroundFrequent digital monitoring of cognition is a promising approach for assessing endpoints in prevention and treatment trials of Alzheimer’s disease and related dementias (ADRD). This study evaluated the feasibility of the MIND GamePack© for recurrent semi-passive assessment of cognition across a longitudinal interval.MethodsThe MIND GamePack consists of four iPad-based games selected to be both familiar and enjoyable: Word Scramble, Block Drop, FreeCell, and Memory Match. Participants were asked to play 20 min/day for 5 days (100 min) for 4 months. Feasibility of use by older adults was assessed by measuring gameplay time and game performance. We also evaluated compliance through semi-structured surveys. A linear generalized estimating equation (GEE) model was used to analyze changes in gameplay time, and a regression tree model was employed to estimate the days it took for game performance to plateau. Subjective and environmental factors associated with gameplay time and performance were examined, including daily self-reported questions of memory and thinking ability, mood, sleep, energy, current location, and distractions prior to gameplay.ResultsTwenty-six cognitively-unimpaired older adults participated (mean age ± SD = 71.9 ± 8.6; 73% female). Gameplay time remained stable throughout the 4-months, with an average compliance rate of 91% ± 11% (1946 days of data across all participants) and weekly average playtime of 210 ± 132 min per participant. We observed an initial learning curve of improving game performance which on average, plateaued after 22–39 days, depending on the game. Higher levels of self-reported memory and thinking ability were associated with more gameplay time and sessions.ConclusionMIND GamePack is a feasible and well-designed semi-passive cognitive assessment platform which may provide complementary data to traditional neuropsychological testing in research on aging and dementia.

Background Outcome measures available for use in Alzheimer’s disease (AD) clinical trials are limited in ability to detect gradual changes. Measures of everyday function and cognition assessed unobtrusively at home using embedded sensing and computing generated “digital biomarkers” (DBs) have been shown to be ecologically valid and to improve efficiency of clinical trials. However, DBs have not been assessed for their relationship to AD neuropathology. Objectives The goal of the current study is to perform an exploratory examination of possible associations between DBs and AD neuropathology in an initially cognitively intact community-based cohort. Methods Participants included in this study were ≥65 years of age, living independently, of average health for age, and followed until death. Algorithms, run on the continuously-collected passive sensor data, generated daily metrics for each DB: cognitive function, mobility, socialization, and sleep. Fixed postmortem brains were evaluated for neurofibrillary tangles (NFTs) and neuritic plaque (NP) pathology and staged by Braak and CERAD systems in the context of the “ABC” assessment of AD-associated changes. Results The analysis included a total of 41 participants (M±SD age at death = 92.2±5.1 years). The four DBs showed consistent patterns relative to both Braak stage and NP score severity. Greater NP severity was correlated with the DB composite and reduced walking speed. Braak stage was associated with reduced computer use time and increased total time in bed. Discussion This study provides the first data showing correlations between DBs and neuropathological markers in an aging cohort. The findings suggest continuous, home-based DBs may hold potential to serve as behavioral proxies that index neurodegenerative processes.

ObjectiveThe objective of this study was to examine associations between level of depressive symptoms in older adult spouse/partner couples and their physical health and social factors (social activity and number of close friends).MethodsUsing data from 116 community-dwelling couples (age 76.2 ± 8.5), we simultaneously analyzed associations between depressive symptoms (Geriatric Depression Scale, range 0–11) and dyadic physical health, engagement in social activities, and connectedness with close friends.ResultsGreater engagement in social activities was associated with fewer depressive symptoms in men, whereas more close friendships were associated with fewer depressive symptoms in women, controlling for partner effects, age, education, and cognitive function, with good model fit. Additionally, more disparate physical health within the couple (latent incongruence score) was associated with greater depressive symptoms in men.DiscussionLess social activity and fewer close friends were associated with depressive symptoms in older adult couples, but may be distinctly influential depending on gender and in the context of the older adult couple's physical health.

Introduction: Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities. Methods: CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants’ homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing. Results: The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (n = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160–169 min), physical mobility (e.g., mean daily transitions between rooms = 96–155), sleep (e.g., mean nightly sleep duration = 6.3–7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15–45%) were collected across cohorts. Conclusion: The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.

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.

Seasonal variation in rest-activity patterns has been observed in healthy adult populations. This study examined seasonal variation in total time spent overnight in the bedroom by cognitively intact older adults and older adults with mild cognitive impairment (MCI). We hypothesize that seasonal variation in rest-activity patterns is observed in the cognitively intact group and that this variation is disturbed in those with MCI. Study participants were 128 older adults; mean age 85.2 years. Ninety-eight were cognitively intact, and 30 had been diagnosed with MCI. All were enrolled in an ongoing longitudinal study using in-home passive monitoring technology. Infrared presence sensors were placed throughout each participant's home to monitor movement and presence in each room of the home. Activity data was collected from the sensors over a period of up to 527 days. Overnight time in bedroom was found to vary seasonally for the cognitively intact group, with longer times spent overnight in the bedroom during the winter months. This seasonal variation was not observed for those with non-amnestic MCI. MCI is associated with an attenuation of seasonal variation in total time spent in the bedroom at night. Detection of changes in infradian sleep patterns may be an early marker of cognitive decline. Which key determinants are driving these disturbed rhythms, such as features intrinsic to changes in the brain or to environmental factors or external cues, remains an important question for ongoing and future studies.

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status.Materials and Methods: Community-dwelling older adults living alone (n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed).Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88–0.94]; 0.59[0.48–0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = −0.04, p = 0.03).Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Background: Increasing social interactions through communication technologies could offer a cost-effective prevention approach that slows cognitive decline and delays the onset of Alzheimer's disease. This paper describes the protocol of an active project named “Internet-based conversational engagement clinical trial (I-CONECT)” (ClinicalTrials.gov: NCT02871921). The COVID-19 pandemic related protocol modifications are also addressed in the current paper.Methods: I-CONECT is a multi-site, assessor-blind, randomized controlled behavioral intervention trial (RCT). We aim to randomize 320 socially isolated adults 75+ years old [160 Caucasian and 160 African American participants, 50:50 split between those with normal cognition and mild cognitive impairment (MCI)] recruited from the community to either the video chat intervention group or the control group (1:1 allocation). Those in the video chat group receive a computer and Internet service for the duration of the study, which they use to video chat with study staff for 30 min/day 4×/week for 6 months (high dose), and then 2×/week for an additional 6 months (maintenance dose). Both video chat and control groups have a brief (about 10 min) telephone check-in with study staff once per week. The primary outcome is the change in global cognitive function measured by Montreal Cognitive Assessment (MoCA) from baseline to 6 months. Secondary outcomes include changes in cognition in memory and executive function domains, emotional well-being measured by NIH Toolbox emotional battery, and daily functional abilities assessed with the Revised Observed Tasks of Daily Living (OTDL-R). Eligible participants have MRIs at baseline and 6 months. Participants contribute saliva for genetic testing (optional consent), and all video chats, weekly check-in calls and neuropsychological assessment sessions are recorded for speech and language analysis. The pandemic halted research activities and resulted in protocol modifications, including replacing in-person assessment with remote assessment, remote deployment of study equipment, and revised targeted sample size.Discussion: This trial provides user-friendly hardware for the conversational-based intervention that can be easily provided at participants' homes. The trial aspires to use age and culture-specific conversational materials and a related platform developed in this trial for enhancing cognitive reserve and improving cognitive function.

Abstract Identifying the leading health and lifestyle factors for the risk of incident dementia and Alzheimer's disease has yet to translate to risk reduction. To understand why, we examined the discrepancies between observational and clinical trial evidence for seven modifiable risk factors: type 2 diabetes, dyslipidemia, hypertension, estrogens, inflammation, omega‐3 fatty acids, and hyperhomocysteinemia. Sample heterogeneity and paucity of intervention details (dose, timing, formulation) were common themes. Epidemiological evidence is more mature for some interventions (eg, non‐steroidal anti‐inflammatory drugs [NSAIDs]) than others. Trial data are promising for anti‐hypertensives and B vitamin supplementation. Taken together, these risk factors highlight a future need for more targeted sample selection in clinical trials, a better understanding of interventions, and deeper analysis of existing data.

Abstract Introduction Reproducibility and replicability of results are rarely achieved for digital biomarkers analyses. We reproduced and replicated previously reported sample size estimates based on digital biomarker and neuropsychological test outcomes in a hypothetical 4‐year early‐phase Alzheimer's disease trial. Methods Original data and newly collected data (using a different motion sensor) came from the Oregon Center for Aging & Technology (ORCATECH). Given trajectories of those with incident mild cognitive impairment and normal cognition would represent trajectories of the control and experimental groups in a hypothetical trial, sample sizes to provide 80% power to detect effect sizes ranging from 20% to 50% were calculated. Results For the reproducibility, identical P‐values and slope estimates were found with both digital biomarkers and neuropsychological test measures between the previous and current studies. As for the replicability, a greater correlation was found between original and replicated sample size estimates for digital biomarkers (r = 0.87, P

Abstract 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

Abstract 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.

Abstract Introduction Federally funded Alzheimer's Disease Centers in the United States have been using a standardized neuropsychological test battery as part of the National Alzheimer's Coordinating Center Uniform Data Set (UDS) since 2005. Version 3 (V3) of the UDS replaced the previous version (V2) in 2015. We compared V2 and V3 neuropsychological tests with respect to their ability to distinguish among the Clinical Dementia Rating (CDR) global scores of 0, 0.5, and 1. Methods First, we matched participants receiving V2 tests (V2 cohort) and V3 tests (V3 cohort) in their cognitive functions using tests common to both versions. Then, we compared receiver‐operating characteristic (ROC) area under the curve in differentiating CDRs for the remaining tests. Results Some V3 tests performed better than V2 tests in differentiating between CDR 0.5 and 0, but the improvement was limited to Caucasian participants. Discussion Further efforts to improve the ability for early identification of cognitive decline among diverse racial groups are required.

Abstract Introduction The use of digital biomarker data in dementia research provides the opportunity for frequent cognitive and functional assessments that was not previously available using conventional approaches. Assessing high‐frequency digital biomarker data can potentially increase the opportunities for early detection of cognitive and functional decline because of improved precision of person‐specific trajectories. However, we often face a decision to condense time‐stamped data into a coarser time granularity, defined as the frequency at which measurements are observed or summarized, for statistical analyses. It is important to find a balance between ease of analysis by condensing data and the integrity of the data, which is reflected in a chosen time granularity. Methods In this paper, we discuss factors that need to be considered when faced with a time granularity decision. These factors include follow‐up time, variables of interest, pattern detection, and signal‐to‐noise ratio. Results We applied our procedure to real‐world data which include longitudinal in‐home monitored walking speed. The example shed lights on typical problems that data present and how we could use the above factors in exploratory analysis to choose an appropriate time granularity. Discussion Further work is required to explore issues with missing data and computational efficiency.

Loneliness is a common condition in older adults and is associated with increased morbidity and mortality, decreased sleep quality, and increased risk of cognitive decline. Assessing loneliness in older adults is challenging due to the negative desirability biases associated with being lonely. Thus, it is necessary to develop more objective techniques to assess loneliness in older adults. In this paper, we describe a system to measure loneliness by assessing in-home behavior using wireless motion and contact sensors, phone monitors, and computer software as well as algorithms developed to assess key behaviors of interest. We then present results showing the accuracy of the system in detecting loneliness in a longitudinal study of 16 older adults who agreed to have the sensor platform installed in their own homes for up to 8 months. We show that loneliness is significantly associated with both time out-of-home (β = -0.88 andp 2 for the model was 0.35. We also show the model's ability to predict out-of-sample loneliness, demonstrating that the correlation between true loneliness and predicted out-of-sample loneliness is 0.48. When compared with the University of California at Los Angeles loneliness score, the normalized mean absolute error of the predicted loneliness scores was 0.81 and the normalized root mean squared error was 0.91. These results represent first steps toward an unobtrusive, objective method for the prediction of loneliness among older adults, and mark the first time multiple objective behavioral measures that have been related to this key health outcome.

There is a paucity of data regarding trends in dementia and its subtype prevalence in Japan. Our aims in the current paper are to: (1) summarize epidemiological studies of dementia in Japan including relevant details of study protocol and diagnostic criteria, (2) compare the age-specific prevalence of all-cause dementia among studies, and (3) assess the trends in Alzheimer's disease (AD) versus vascular dementia (VaD) over time. We reviewed diagnostic criteria, all-cause dementia prevalence, and the AD/VaD ratio from 8 large population studies of dementia in Japan. Compared with the Okinawa 1992 study, studies conducted in 1994, 1998, 2005, and 2008 had a higher prevalence of all-cause dementia using Poisson regression models, after controlling for age and sex. In contrast to the US and some European countries, all-cause dementia prevalence is increasing in Japan. The prevalence of AD as opposed to VaD seems to be increasing over time, but large variability in diagnostic criteria, possible regional variability, and differences in prevalence of subtypes of dementia between men and women make it difficult to draw a conclusion about this trend at the national level. Further studies, for example, comparing the population attributable risk of vascular diseases to the prevalence and incidence of dementia could help to clarify the regional variations in etiological subtypes.