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Background: Young onset dementia (YOD) is characterized by an atypical clinical manifestation, and it is unclear to what extent impairments in everyday functioning are part of this manifestation. This study aims to describe the prevalence and differences of difficulties in instrumental activities of daily living (IADL) in YOD. Methods: In this cross‐sectional study, 394 subjects with sporadic YOD (onset<65 years,mean(M)age 58.4±standard deviation(SD)4.4 years;49.2% female) were included from the Amsterdam Dementia Cohort (ADC). Diagnoses were established in multidisciplinary consensus meetings according to disease‐specific diagnostic criteria. Everyday functioning was assessed using the proxy‐based Amsterdam IADL Questionnaire (A‐IADL‐Q). Linear regression analyses were performed to assess differences between YOD subtypes in IADL with group (YOD subtypes with AD as reference group) and IADL as outcome, adjusted for age, sex, education and disease severity (Mini Mental State Examination). A bifactor analysis was performed to identify item clusters and explore differences between groups (using linear regression). Results: The majority of sporadic YOD were diagnosed with Alzheimer's Disease (AD,n = 291(74%)), followed by Dementia with Lewy Bodies (DLB,n = 25(6%)), behavioral variant Frontotemporal Dementia (bvFTD,n = 49(12%)), Primary Progressive Aphasia (PPA,n = 20(5%)) and semantic variant Primary Progressive Aphasia (svPPA,n = 9(2%)). Most IADL difficulties were observed in DLB ((M) A‐IADL‐Q 41.8±(SD)7.78, reflecting moderate problems), and the fewest in PPA ((M)59.9±(SD)6.62, reflecting no problems)(Figure 1). When compared to AD (reference group), multiple linear regression analysis confirmed lower IADL scores for DLB (β = ‐6.37, SE = 1.63,p≤0.001), and higher for PPA (β = 10.29, SE = 1.76, p≤0.001), and svPPA (β = 6.83, SE = 2.61,p = 0.009), after adjustment for all covariates and confirmed in post‐hoc testing (Table 1). The bifactor analysis showed good reliability of the A‐IADL‐Q (Omega (ω_total) = 0.833), suggesting the presence of an underlying unimodal construct. Additional item clusters (Table 1) showed differences across groups, e.g. bvFTD showed more impairments in household activities compared to AD (β[95%CI] = ‐3.12 [‐5.64 – ‐0.61]). Further activity‐level differences can be found in Figure 2. Conclusions: Our results suggest that YOD‐subtypes differ in everyday functioning difficulties, independent of disease stage. These findings contribute to the understanding of everyday functioning difficulties in YOD, ultimately contributing to earlier recognition of the disease and personalized care. [ABSTRACT FROM AUTHOR]

Background: Verbal fluency, especially semantic fluency, may hold promise to predict clinical progression in the preclinical Alzheimer's disease (AD) stage, where patients show no objective cognitive impairment. We examined verbal fluency trajectories in amyloid‐negative and amyloid‐positive individuals with subjective cognitive decline (SCD), as well as whether baseline fluency characteristics (total scores and item‐level) predicted progression to MCI or AD dementia. Method: We retrospectively selected data of 471 Dutch individuals with SCD, with at least 1 follow‐up fluency assessment, from the Amsterdam Dementia Cohort (Follow‐up years = 4.3±2.9, Age = 61±8, Female n = 199(42%), Amyloid‐positive n = 131(28%), clinical progression n = 66(14%), Table 1). We longitudinally measured total scores for semantic (animals) and phonemic fluency (alternate versions) and semantic‐phonemic‐discrepancy scores (subtracting phonemic from semantic fluency). For a subset (n = 82) we obtained baseline item‐level characteristics: lexical frequency and word length (both continuous), errors, repetitions and duplications between semantic and phonemic fluency (all dichotomized). We compared baseline fluency measures between amyloid groups and clinical progression groups. Fluency trajectories and their associations with amyloid‐status were examined with linear mixed models. We examined associations between baseline fluency and progression to MCI/AD dementia using Cox proportional hazard models. Cox models for total and discrepancy scores were performed in the total group including a fluency*amyloid interaction, whereas models for item‐level metrics only included amyloid‐positive individuals (n = 44). All models were adjusted for age, sex and education. Results: At baseline no significant differences in any of the fluency measures between amyloid groups were observed (p's > 0.05, Table 2). Longitudinally, amyloid‐positive individuals declined more steeply on semantic fluency (β = ‐0.37,95%CI = ‐0.53—0.20) and phonemic‐semantic‐discrepancy (β = ‐0.32,95%CI = ‐0.49—0.14) compared to amyloid‐negative individuals. Groups did not differ on phonemic fluency (β = ‐0.05,95%CI = ‐0.15–0.05, Figure 1). While baseline discrepancy scores were lower in patients who later progressed (Mean = 9.0±SD = 5.4) than in non‐progressors (Mean = 10.7±SD = 5.1,W = 14317,p = 0.023), none of the baseline fluency measures predicted clinical progression after adjusting for covariates (p's>0.05). Conclusion: Longitudinal decline in semantic fluency and discrepancy scores, but not phonemic fluency, was associated with amyloid positivity in individuals with SCD. Baseline fluency did not predict clinical progression. These findings suggest that longitudinal follow‐up is essential to capture semantic fluency decline that is associated with AD pathophysiology. [ABSTRACT FROM AUTHOR]

Background: In cognitively unimpaired (CU) older adults, the presence of a subjective cognitive decline (SCD) combined with evidence of abnormal b‐amyloid (Ab) is proposed as stage 2 of Alzheimer's disease (AD) by the NIA‐AA framework (Jack et al., 2018). However, the associations found between SCD and preclinical AD are inconsistent across studies, highlighting the importance of better understanding which specific SCD features are associated with either Ab or tau burden. Methods: The present study includes cross‐sectional data from 9 independent cohorts with a total of 7217 CU older adults (57% female), aged 69.34 (1.20) years, recruited from general and memory‐clinic populations. Ab and tau biomarkers were measured by positron emission tomography (PET) or cerebrospinal fluid (CSF). Using established cut‐offs, 28% of participants were Aβ+, and 12% were Aβ+T+ (approximately one‐third of the sample had available tau data). We examined four SCD‐plus criteria as well as the mean number of SCD criteria met (i.e., mean SCD‐severity) in relation to both biomarker status and levels in logistic/linear regressions adjusted for age and sex for each cohort. Summary statistics were extracted for meta‐analyses. Results: The overall frequency of stage 2 AD varied from 7‐16% [4‐26%] according to each SCD‐plus criterion endorsement. Only 1‐5% [0‐8%] of participants meeting the SCD‐plus criteria also had both high Aβ and tau burden (Fig.1). The presence of self‐reported memory decline (SMD), an associated concern/worry, and a higher mean SCD‐severity were each associated with high Ab (status and continuous). Only the latter was associated with high tau status (Fig.2). Onset of SCD within the last 5 years, and feeling of worse performance than same‐age peers, were not associated with AD biomarkers at a Bonferroni‐corrected threshold. Conclusions: Our results suggest that widespread endorsement of multiple SCD features is more powerful than a single criterion alone in identifying both Aβ and tau in CU older adults. We found that the isolated SCD criterion was particularly sensitive to elevated Aβ, even after adjustment for tau, supporting the power of SCD as a very early behavioral marker of preclinical AD. [ABSTRACT FROM AUTHOR]

Background: It remains unclear to what extent global cognition translates to everyday functioning, although this is essential to interpreting the clinical meaningfulness of cognitive deficits. Here, we investigate potential linking between the Mini‐Mental State Examination (MMSE) and the proxy‐based Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q). Methods: Cross‐sectional data from 1228 amyloid‐positive participants (age = 64±7yrs; 51.1%F), with Subjective Cognitive Decline(n = 156), Mild Cognitive Impairment(n = 228) or Probable Alzheimer's Disease(n = 844) were included from the Amsterdam Dementia Cohort. Amyloid positivity was based on amyloid PET or cerebrospinal fluid, according to local cut‐off values. All participants completed MMSE, and all proxies A‐IADL‐Q. Equipercentile linking, unidimensional Item response theory (IRT) calibration and multiple regression analyses were explored as linking methods. For each MMSE quartile, we explored the proportion of reported problems on the A‐IADL‐Q total score and each A‐IADL‐Q item. We described the most commonly affected activities. Results: Assumptions for direct linking (equipercentile/IRT) were not met (r = 0.53,95%CI[0.48,0.57]). We proceeded with multiple regression analyses, with MMSE as independent variable and A‐IADL‐Q as dependent variable (adjusted for age, sex and education). Higher MMSE scores were associated with higher everyday functioning in the total sample (B =.96;95%CI[0.87,1.04]), with a one‐point increase in MMSE corresponding to nearly one additional point in A‐IADL‐Q scores. In the highest MMSE quartile (27‐30), 35% of participants experienced 'no problems', while 47%,17%, and 1% experienced 'mild,' 'moderate,' and 'severe problems' respectively (Figure 1). In the lowest quartile, 5% reported 'no problems', with 22%,44%, and 29% experiencing 'mild,' 'moderate,' and 'severe problems'. Most commonly affected activities in the highest MMSE quartile were working (74% at least mild problems), followed by using a computer (52%) and managing the household budget (47%). The latter two activities were also most commonly affected in the lowest MMSE quartile, but to a much higher degree (93%,95%) alongside filling in forms (96%). Conclusion: Although direct linking between cognition and daily functioning was not possible, our findings support more functional dependencies with increasing cognitive problems. Functional difficulties in the highest MMSE group suggest limited sensitivity of cognitive screening. Our findings suggest a nuanced link between global cognition and everyday functioning, supporting complementary use of both instruments. [ABSTRACT FROM AUTHOR]

Background: Assessment of self‐perceived cognitive decline (SCD) is relevant in the context of Alzheimer's disease and related disorders, but heterogeneity in measurement limits its interpretability and usability. Selection of optimal content for a questionnaire measuring SCD requires statistical information and expert opinion to ensure reliability as well as acceptability and feasibility. This study aims to survey experts about their perspectives on essential features of SCD measurement. Method: The survey was distributed through the ISTAART Subjective Cognitive Decline PIA, International Neuropsychological Society's Dementia Special Interest Group, and authors' professional networks. Targeted experts included professionals working with older adults with SCD through research and/or clinical practice. Main topics were item content, item format, and questionnaire features. Topics were evaluated with multiple choice questions and open‐ended questions. Furthermore, experts were shown items from existing SCD questionnaires with relatively high information value as identified in a previous statistical harmonization effort using Item Response Theory analyses (see Rabin et al., 2021), and asked to select items they viewed as most relevant. Frequency analyses were performed. Result: Ninety‐one experts (66% female) across four continents completed the questionnaire (Table 1). Thirty‐nine experts (43%) indicated routinely using an instrument to assess SCD (17 different instruments were mentioned). Seventy‐nine experts (87%) perceived a need for a harmonized SCD instrument. Whereas most experts (72%) responded that a questionnaire should measure both current ability and change in ability, views on the most relevant timeframe varied substantially (top choices: past year (33%) and past six months (25%)). Figure 1a and 1b show that items with the highest statistical information value were not necessarily selected as most relevant by experts. Preferred practical features of a questionnaire were availability of an informant version and coverage of multiple cognitive domains (chosen by 13% and 14% respectively). Preferred validation approaches were distinguishing psychiatric illness from neurodegenerative disorders and the correlation with neuropsychological test performance (both chosen by 20%). Conclusion: Despite agreeing on the need for a harmonized SCD instrument, experts express varying views on optimal content, format, and validation approaches. The complementary information derived from statistical results and expert opinion can facilitate more comprehensive measurement of SCD. [ABSTRACT FROM AUTHOR]

Background: cCOG, a web‐based cognitive test tool, has been shown to detect mild cognitive impairment (MCI) and dementia accurately. However, the test now takes approximately 25 minutes to complete, which could be burdensome for patients, especially with cognitive impairment. We assumed that shortening certain subtasks leads to a decrease in total test duration and therefore increases the user‐friendliness of cCOG. In this study, we examined whether shortening these subtasks influences the discriminative value of cCOG. Method: From the PredictND and Amsterdam Dementia Cohort (ADC) cohort, we included cCOG results of 424 participants (n = 234 cognitively normal (CN), n = 84 MCI, n = 106 dementia) who were on average 70±9 years and 54% were female. cCOG consists of five subtasks: episodic memory learning, reaction time, modified trail‐making A and B and episodic memory recall. Scores for all subtasks except episodic memory recall were abbreviated into shorter versions, e.g. by cutting the number of items/trials. A global cognitive score (GCS) was computed using a linear regression model based on scores of all subtasks for both the original as the short version. To investigate the diagnostic accuracy of both versions, we calculated pairwise area under the curve (AUC) comparisons for differentiating MCI and dementia from CN. Result: Shortening of subtasks led to an average decrease of 4.7 minutes of the total test time (20.1%) for the PredictND cohort (n = 314), with even 5.7 minutes decrease in the dementia group (Figure 1). The accuracy of the GCS based on the short version for differentiating MCI from CN was lower than based on the original version though still considered excellent (AUC = 0.87 [0.83‐0.92] original; AUC = 0.86 [0.81‐0.90] short; Figure 2). For differentiating dementia from CN, the short version performed comparable to the original version (AUC = 0.93 [0.89‐0.96] original; AUC = 0.92 [0.88‐0.96] short; Figure 3). Conclusion: Shortening subtasks decreased the test duration of cCOG with approximately 5 minutes, while the diagnostic accuracy was preserved. An additional pilot study is needed to test the effect of shortening subtasks on episodic memory recall and global cognitive score. Further development of a user‐friendly and accurate web‐based cognitive test tool is crucial to keep dementia care accessible and affordable. [ABSTRACT FROM AUTHOR]

Background: Speech is a complex process, relying on multiple interacting cognitive functions. Fine‐grained speech analysis offers the potential for measuring subtle cognitive deficits in early‐stage Alzheimer's disease (AD). As such, digital recordings of natural speech are a promising digital biomarker, particularly when measured in bursts (repeated short measurements). This pilot study focused on the usability of remote speech bursts to assess natural speech in cognitively normal (CN) adults with and without AD pathologic change. Additionally, the association between amyloid‐beta (Aβ)‐pathology and speech characteristics was examined. Method: We included 49 CN older adults from AMYPAD‐PNHS(location VUmc), SCIENCe, and Amsterdam Dementia Cohort (Age mean(M) = 68±standard deviation(SD) = 6, n = 28(57%) female, n = 23(47%) Aβ‐positive, Table 1). Aβ‐status was based on local cut‐offs for Aβ1‐42‐concentrations in cerebrospinal‐fluid or visual inspection of amyloid positron emission tomography (PET)‐imaging. The remote burst assessment comprised seventeen tablet‐based a)picture description, b)journal‐prompt storytelling and c)verbal‐fluency subtasks, administered over five consecutive days in an unsupervised home‐setting. Daily administration time was approximately 10 minutes. To assess usability, participants completed the system usability scale (SUS). Various acoustic features, including silent pauses, pause‐to‐word‐ratio and mean intensity (i.e., perceived loudness), were extracted from the voice recordings, and mean burst scores were calculated over the five‐day period. The association between Aβ‐status and acoustic features was investigated using two‐sample t‐tests stratified by subtask. Results: The 49 participants rated usability with a mean SUS‐score of 86(SD = 10). For example, 46(94%) participants thought the assessment‐app was easy to use (Figure 1). Ratings were lower in Aβ‐positive (M = 83±10) than Aβ‐negative individuals (M = 88±9, p = 0.02), reflecting good and excellent usability respectively. Regarding the association between Aβ‐status and acoustic features, more silent pauses were observed for Aβ‐positive (M = 0.30±0.09) compared to Aβ‐negative individuals (M = 0.25±0.06, p = 0.025, Figure 2) in the storytelling subtask. No group differences for pause‐to‐word‐ratio or mean intensity were observed (p>0.05). Conclusion: Remote burst assessment of speech is feasible in CN Aβ‐positive and Aβ‐negative adults. Additionally, preliminary results demonstrated that Aβ‐pathology was related to more silent pauses, indicating its potential as a digital AD biomarker. These findings support remote burst speech assessments in monitoring cognitive changes, for example in observational studies and decentralized clinical trials of preclinical AD. [ABSTRACT FROM AUTHOR]

Background: The Amsterdam Instrumental ADL Questionnaire (A‐IADL‐Q) has shown promising performance in detecting subtle impairment in IADL independency. Importantly, it has not been validated in older people in a primary care setting, i.e., where the initial work‐up and triaging of cognitive impairment occurs. The main aim was to examine the accuracy of the A‐IADL‐Q for estimating the cognitive stage in a primary care sample, both in the whole population and within the Alzheimer's disease (AD) continuum. Secondary aims were to examine associations with the clinical dementia rating (CDR) scale and global cognition (MMSE), including the effects of non‐cognitive co‐morbidities and medications. Method: This cross‐sectional study includes informants of 362 participants from the BioFINDER‐Primary care study. They completed the A‐IADL‐Q (higher scores = "better") either on an iPad or at home on a computer. Cognitive stage was assessed by a dementia expert (blinded to A‐IADL‐Q scores). Amyloid status was determined using FDA‐approved CSF or PET measures. The main outcome was cognitive stage according to the NIA‐AA scale. Secondary outcomes were CDR‐sum of the boxes (CDR‐SOB) and MMSE. Receiver Operating Curves (ROC) analyses were used to calculate the area under the curves (AUCs). Result: The mean age of participants (44.9% female) was 76.1 (SD 7.2) years. Out of which, 83 (23%) had subjective cognitive decline (SCD), 169 (47%) mild cognitive impairment (MCI), and 110 (30%) dementia. The AUC (95% CI) for differentiating between SCD versus MCI/dementia was 0.89 (0.85‐0.92), and it was 0.89 (0.85‐0.93) for SCD/MCI versus dementia. In amyloid positive participants (n = 303), AUCs were similar. Better A‐IADL‐Q scores were associated with worse results on CDR‐SOB (r = ‐0.73, p<0.001) and better global cognition (MMSE, r = 0.361, p<0.001). The effects of different co‐morbidities on the A‐IADL score, adjusted for cognitive stage, will be presented at AAIC. Conclusion: Our findings suggest that the A‐IADL‐Q accurately estimates the cognitive stage among cognitively impaired older people (with high prevalence of comorbidities) in a primary care setting. This can aid primary care physicians in the diagnosis of cognitive impairment, clinical management/support, and as part of the work‐up for identifying eligible participants for anti‐amyloid treatment. [ABSTRACT FROM AUTHOR]

Background: Automated analysis of natural speech is emerging as a promising digital biomarker of Alzheimer's disease (AD). As speech is a complex process, relying on multiple interacting cognitive functions, fine‐grained analysis of speech may have the potential to capture subtle cognitive deficits in the very early stages of AD. Here, we examined the association between amyloid‐beta (Aβ) pathology and acoustic speech characteristics in a group of cognitively normal Dutch adults. Method: We included 50 cognitively normal (n = 23 Aβ‐positive, 46%) older adults from three clinical cohorts at the Alzheimer Center Amsterdam (Table 1). Aβ‐status was based on local cut‐offs for Aβ1‐42‐concentrations in cerebrospinal‐fluid or visual inspection of amyloid positron emission tomography (PET)‐imaging. In a five day remote burst assessment, seventeen tablet‐based tasks (picture description, journal‐prompt storytelling, verbal‐fluency) were administered, with a daily administration time of approximately 10 minutes. Various acoustic features, including silent pauses, pause‐to‐word‐ratio, and perceived loudness, were extracted from the voice recordings. Mean burst scores were calculated over the five‐day period. The association between Aβ‐pathology and acoustic features was investigated using linear regression models separately for each subtask. Results: Pause‐to‐word ratio was higher in Aβ‐positive individuals in the repetitive picture description subtask (β = ‐0.05, p = 0.04) and the journaling subtask (β = ‐0.07, p = 0.03) compared to the Aβ‐negative individuals. Although statistical significance between groups was not reached for other acoustic features, a pattern was observed that the Aβ‐positive group consistently displayed more medium pauses, longer audio duration, more local jitter, higher intensity variance, a higher fundamental frequency, and fewer long pauses in all multi‐day subtasks (Figure 1). Conclusion: Our results demonstrated that Aβ‐pathology was associated with more pauses during free speech in cognitively healthy adults. This supports the notion that remote multi‐day speech assessments have the potential to monitor cognitive changes, for example in observational studies and decentralized clinical trials in preclinical AD. Next steps include examining the association between Aβ‐pathology and linguistic features using the same sample. [ABSTRACT FROM AUTHOR]

Background: Evidence is emerging that subtle language decline is an early indicator of Alzheimer's disease (AD). This study assessed change in semantic and letter fluency in individuals with subjective cognitive decline (SCD), and examined whether trajectories differed between individuals with and without amyloid‐beta (Aβ)‐pathology. Method: Longitudinal data from 437 individuals of the memory clinic‐based Amsterdam Dementia Cohort were included (Age 61±8 years, 184(42%) Female, follow‐up time 5.3±3.7 years; Table 1). Participants were classified as SCD at baseline, had available baseline Aβ‐information and had at least 1 follow‐up on semantic or letter fluency. Baseline Aβ‐status was dichotomized as positive/negative based on local cut‐offs for Aβ1‐42 concentrations in cerebrospinal fluid, or visual inspection of amyloid positron emission tomography‐imaging (Florbetapir or PiB). Two linear mixed models (LMM) were used to investigate change on semantic and letter fluency. Models included time and time*Aβ‐status as predictors of interest, a random intercept for participant, and age, sex, education were adjusted for. LMMs were subsequently stratified for Aβ‐status. Result: At baseline, the groups did not differ in semantic (p = 0.313) or letter fluency (p = 0.294). Over time Aβ‐positive individuals changed more rapidly on semantic fluency compared to Aβ‐negative individuals (β = ‐0.32,CI = ‐0.49 – ‐0.14;p<.001). The Aβ‐positive group declined with 0.35 words per year (CI = ‐0.49 – ‐0.20;p<.001), while Aβ‐negative individuals did not show change (β = 0.03;CI = ‐0.12 – 0.06;p = 0.496) (Figure 1). On letter fluency Aβ‐positive individuals changed differently compared to Aβ‐negative individuals (β = ‐0.38;CI = ‐0.72 – ‐0.04;p = 0.028), to the extent that the Aβ‐positive group did not progress (β = 0.10;CI = ‐0.21 – 0.41;p = 0.533), while Aβ‐negative individuals improved with 0.47 words per year (CI = 0.29 – 0.65;p<.001) (Figure 2). Conclusion: Our results indicate that Aβ‐positive individuals decreased on semantic fluency, but remained stable on letter fluency. In contrast, Aβ‐negative individuals remained stable on semantic fluency, while they improved on letter fluency over time, which might suggest a learning effect. These findings suggest that the presence of Aβ‐pathology is characterized by semantic loss, highlighting the potential of language based performance as a biomarker for Aβ‐pathology in SCD. [ABSTRACT FROM AUTHOR]

Background: Finding eligible participants for dementia prevention trials is challenging, resulting in prolonged recruitment periods, underpowered studies, and excessive costs. The Dutch Brain Research Registry (DBRR) is an online platform to facilitate recruitment of research participants. We evaluated different recruitment strategies with the ambition to optimize recruitment for the multi‐domain lifestyle intervention trial FINGER‐NL. Method: DBRR registrants were prescreened for FINGER‐NL based on available profile information. Potentially eligible registrants received an automatic study invitation. Those interested were contacted for additional telephonic screening and, if eligible, included in the trial. Five recruitment strategies within 2022 were executed with the goal to increase the number of DBRR registrants: 1) a paid Facebook‐campaign, 2) referral via (patient)‐organizations, 3) newspaper articles, 4) live‐events, and 5) national television appearance (W.F&E.S.). Recruitment source was traced (online; UTM tags) and/or reported by the participant. For most recruitment strategies, we involved a nationally well‐known ambassador (E.S.) who advocates a healthy lifestyle to improve brain‐health (Figure 1). For each strategy, we described the number of individuals registered, eligible and included in FINGER‐NL. We calculated efficiency (%eligible/prescreened) as a reflection of the proportion of eligible participants reached, and effectiveness (%included/registered) as a reflection of the proportion of participants included in FINGER‐NL Result: In 2022, the DBRR recruited 11234 new registrants. The highest numbers of registrants and highest number of inclusions were reached with national television appearance and the Facebook‐campaign (Table 1). In particular, the Facebook advertisement with a lifestyle theme and well‐known ambassador outperformed all other advertisements in number of registrations (Figure 1). After initial prescreening, between 18‐35% of registrants were eligible. Most efficient were national television and the Facebook‐campaign. Between 4‐12% of new registrants were included in the trial. Most effective strategies were national television appearance and referrals via patient‐organizations Conclusion: Our results show that mass‐media, i.e. tailor‐made Facebook‐campaigns, national television appearance and events, are efficient ways to reach large numbers of eligible participants, especially when well‐known ambassador(s) were included. Recruitment strategies with detailed information about in‐ and exclusion of the trial are most effective. The DBRR provides an efficient and effective recruitment and prescreening process for the FINGER‐NL trial. [ABSTRACT FROM AUTHOR]

Background: (Physical) inactivity and disturbances in the rest‐activity rhythm (e.g. more daytime sleeping and more night‐time restlessness) are common in institutionalised persons living with dementia (iPwD). Both are associated with worse quality of life (QoL), which underlines the need for non‐pharmacological interventions. The purpose of this study was to examine if the implementation of personalised activity cards (PACs) could enhance the amount of time spent on goal‐directed daily (physical) activities and improve the rest‐activity rhythm in iPwD. Method: This prospective longitudinal quasi‐experimental multicentre study was conducted on 11 wards in 8 nursing homes in the Netherlands. The intervention consisted of PACs, a structured method for offering personalised activities based on input from (professional) caregivers. The PACs were implemented on 5 wards. The other 6 wards received care as usual (control group). Intervention compliance was checked every two weeks with an online survey. Outcome variables included minutes spent on goal‐directed daily (physical) activity as measured with an adjusted version of the Physical Activity Survey in Long‐Term Care (PAS‐LTC; observations by researchers), and rest‐activity rhythm was assessed using Actiwatch activity monitors. Outcomes were assessed at baseline (T0), and after 12‐15 weeks (T1). Result: We analysed the data of 84 iPwD; intervention group: n = 35, control group: n = 49 (Table 1). Intention‐to‐treat mixed model analyses showed no significant interaction effects of the intervention (group × time) on goal‐directed daily (physical) activity and rest‐activity rhythm variables (Table 2). One ward in the intervention group did not implement the PACs. The four wards that did implement the PACs reported diverging percentages of the execution of planned activities, even as low as 33.5% (range 33.5‐74.2%). The most reported reason for not executing activities on the PACs was that employees were too busy. Conclusion: Contrary to our expectations, implementing PACs did not show any effect on goal‐directed daily (physical) activity in iPwD. The lack of improvement in activity level might be an explanation for the lack of improvement in rest‐activity rhythm. These findings support previous research that it can be difficult to implement new methods in the nursing home environment, and underline the importance of more implementation research [ABSTRACT FROM AUTHOR]

Background: The Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q) measures impairments in everyday functioning in the context of dementia. To facilitate interpretation and clinical implementation of the A‐IADL‐Q, we aimed to generate demographically‐adjusted norm scores and a clinical cutoff value. Method: Cross‐sectional data were used from Dutch Brain Research Registry(N = 1,064; mean (M) age = 62±11year; 69.5% female) European Medial Information Framework‐Alzheimer's Disease(EMIF‐AD) 90+ (N = 63; Mage = 92±2year; 52.4% female), and European Prevention of Alzheimer's Dementia Longitudinal Cohort Study(EPAD‐LCS)(N = 300; Mage = 62±7year; 71.7% female)(Table 1). Norm scores were generated using a I) linear regression model approach; and II) Box‐Cox Power Exponential(BCPE) model approach (the latter falling within the generalized model for location shape and size(GAMLSS) framework). For both methods, different combinations of the variables age, sex and education were tested (including polynomial age and age*education interaction) and the best model was selected based on the lowest Aikake Information Criteria(AIC) value. Next, diagnostic contrast was generated using an independent memory clinic sample (the Amsterdam Dementia Cohort(ADC)(N = 2,123, Mage = 64±8year, 46.1% female)(Table 1), including people with dementia (both Alzheimer's Disease(AD) and non‐AD). The optimal cut‐off value for cognitively healthy versus dementia was determined, and corresponding diagnostic accuracy was calculated using area under the receiver operating curves(AUC‐ROC). This optimal cutoff was then applied to different diagnostic contrasts, including mild cognitive impairment(MCI) and subjective cognitive decline(SCD). Result: The best suitable normative model was the linear regression model, including cubic polynomial for age, education, and age*education interaction. This model was considered superior to the best BCPE model, which had infinitely large residuals due to bad model fit. The optimal cutoff to distinguish normal functioning from dementia, based on bootstrap average estimates of the Youden Index, was 1.82 standard deviation(SD) below the population mean, resulting in an AUC of 0.97[95%CI 0.98‐0.98]). Normative data are made publicly available in an online tool(Fig.1). Conclusion: We established demographically adjusted norm scores for the A‐IADL‐Q using a large representative sample, and provided an optimal cutoff value for cognitively healthy versus dementia, with outstanding accuracy. Our results, captured in the online tool, can help clinicians to interpret A‐IADL‐Q scores more easily by allowing comparison of functioning against a reference population. [ABSTRACT FROM AUTHOR]

Background: Lower cognitive performance in Alzheimer's Disease (AD) patients has been correlated with changes in blood‐based biomarkers Amyloid‐βeta‐40 (Aβ40), Amyloid‐βeta‐42 (Aβ42), phosphorylated Tau (pTau), neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). However, it is still unclear whether there is an association between cognitive performance and plasma biomarkers in the oldest old. Here we investigate I) The association of plasma biomarkers with age across a wide age‐continuum until extreme age (≥100 years); II) To what extent plasma biomarkers associate with cognitive performance ≥100 years. Method: The Dutch 100‐plus study cohort comprises of self‐reported cognitively healthy centenarians. Biomarkers were measured in 202 centenarians (137 females) on the Simoa platform. We also included centenarian siblings, offspring and their respective partners (N = 318), who were cognitively healthy. I) To investigate the relationship between age and biomarkers, we standardized all biomarkers and performed linear regression analyses. II) Cognitive performance in centenarians was determined using a comprehensive neuropsychological test battery assessing several cognitive domains. Biomarker concentrations were divided into tertiles. Cognitive performance was assessed adjusting for age, sex and education. Result: I) We observed an age‐related increase Aβ40 (rs = 0.70; range 46.5‐286.5 pg/mL), Aβ42 (rs = 0.53; range 1.4‐17.7 pg/mL), pTau‐181(rs = 0.68; range 0.5‐12.8 pg/mL), NfL (rs = 0.81; range 4.5‐378.7 pg/mL) and GFAP (rs = 0.76; range 24.4‐1085.1 pg/mL), or decrease in Aβ42/40 (rs = ‐0.43; range 0.01–0.10 pg/mL; P<0.001 for all biomarkers, Figure 1). II) Lower Aβ42/40 was associated with lower cognitive performance on global cognition (based on composite z‐score of all tests) β(95%CI) 0.26(0.00–0.51), P = 0.002) and domain executive functioning β(95%CI) 0.16(‐0.07–0.40), P = 0.039), but not with the Mini‐Mental State Examination (MMSE) or the other cognitive domains. Higher concentrations of NfL were associated with lower cognitive performance on MMSE β(95%CI) ‐0.22(‐1.48–1.04), P = 0.005), but not with the separate cognitive domains tested. Conclusion: Levels of plasma biomarkers rise with age, also in cognitively healthy individuals. However, correlation with cognitive performance varies across biomarkers and cognitive tests, and deserves further evaluation. [ABSTRACT FROM AUTHOR]

Background: Previous research showed that plasma NfL (neurofilament light chain) associated with survival. Here we perform exploratory analyses to associate a broader range of plasma biomarkers Amyloid‐βeta‐40 (Aβ40), Amyloid‐βeta‐42 (Aβ42), phosphorylated Tau‐181 (pTau‐181), NfL and glial fibrillary acidic protein (GFAP), with survival after reaching 100 years. Method: The longitudinal Dutch 100‐plus study cohort comprises of self‐reported cognitively healthy centenarians. Biomarkers were measured in 202 centenarians (137 females, ISCED education level median [IQR] 3 [2–4]) on the Simoa platform. Information on survival was obtained via family report until January 2023. Centenarians who were alive were censored at date of last visit or date last confirmed alive. We investigated the relationship between biomarkers and survival by cox regression analyses, adjusting for age at blood draw, sex and cognitive functioning. We performed maximally selected log‐rank statistics between all plasma biomarkers and survival rate to find an optimal cut‐off, and constructed Kaplan‐Meier survival curves. Result: Of 202 centenarians (median age [IQR] at blood draw 101.4 [100.4‐102.9]), 173 (85.6%) were deceased (median age [IQR] at death 103.2 [102.0‐104.3]). The median (IQR) survival time after blood draw was 14.4 (5.5‐25.1) months. All biomarkers Aβ40, Aβ42, pTau‐181, NfL and GFAP intercorrelated (rs = 0.17–0.79; all P‐values<0.05). Cox regression analyses showed that for each standard deviation increase in NfL, adjusted for age, sex, and MMSE score, the risk of mortality increased by 28% (HR 1.28; 95% CI, 1.11–1.47; P<0.001), Table 1. Maximally selected log‐rank statistics split up NfL into survival groups high and low (cut‐off 70.34 pg/mL, M = 3.803, P = 0.003, Figure 1). The median (IQR) shorter survival time of high versus low NfL was 7.8 (5.38‐9.27) months. No statistically significant cut‐off values were identified for the remaining biomarkers. Conclusion: Our results indicate that elevated levels of NfL, but not Aβ40, Aβ42, pTau‐181 and GFAP, associate with lower survival. Furthermore, we were able to find an optimal cut‐off for NfL at which survival in centenarians could be split twofold. [ABSTRACT FROM AUTHOR]

Background: Remote neuropsychological assessment through video teleconferencing (VTC) can improve accessibility to dementia care, even beyond the COVID‐19 pandemic. Evidence for the usability and reliability of VTC is still limited in a memory clinic population, particularly for tests assessing non‐memory and non‐verbal functions. This study aims to investigate stability of performances on neuropsychological tests across in‐person and VTC modality, as well as VTC user experiences, in memory clinic patients. Method: Patients were included at the Alzheimer Center Amsterdam between August 2020 and February 2021. Patients underwent in‐person neuropsychological assessment, and subsequently through VTC within 4 months. Stability of performances on all tests across assessments was investigated with intraclass correlations coefficients/percentage agreement and independent samples t‐tests/Wilcoxon signed‐rank tests. Rank order stability was investigated with Pearson or Spearman correlation/Chi‐square tests. For each test, we converted standardized (T‐)scores into seven performance classes (see Figure 1) and described the proportion of patients showing a clinically relevant difference in performance class. Furthermore, we described stability of patients' overall assessment performance (no impairment, single‐domain impairment, multi‐domain impairment). User friendliness and system usability were assessed with questionnaires by patients and neuropsychologists. Scores were set against previously defined acceptability cut‐offs, and compared across diagnostic groups (subjective cognitive decline [SCD] vs. MCI/dementia) with independent samples t‐tests. Result: Thirty‐one patients (62±6.7 years, 55% male, 58% SCD, 42% MCI/dementia) completed VTC assessment. Intraclass correlation coefficients ranged from moderate (0.5; TMT index) to excellent (>0.9; RAVLT, LDST), see Table 1.Significant differences in mean performances were observed for Digit span and Stroop test (SCD group only). For all tests, the majority of patients showed no clinically relevant difference in performance (Figure 1, 61%‐100% stable). Nineteen patients (61%) had the same overall assessment performance across modalities (Figure 2). User friendliness (mean total scores patients/psychologists:164/143) and usability (mean item scores patients/psychologists:3.0/3.0) reached acceptability and did not differ between diagnostic groups. Conclusion: VTC assessment may serve as an accessible and user friendly method to test cognitive functioning in the memory clinic, with largely similar reliability coefficients as in‐person assessments. Implications of VTC assessment for the evaluation of impairment severity, and possibly diagnosis, should be further investigated. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease is characterized by accumulation of amyloid and tau in the brain. While amyloid is hypothesized to accumulate earlier, tau has been shown to be more strongly related to clinical outcomes. Higher‐order cognitive functioning in daily life is a clinically meaningful outcome that has been shown to decline, even in early disease stages. We aimed to understand the cross‐sectional relationship between regional cerebral tau accumulation and everyday functioning in cognitively normal older adults. Method: We used data from the A4 and LEARN Studies and included participants who underwent tau positron emission tomography (PET) scans using 18F‐flortaucipir (n = 447, age 71.9±4.9 years, 57.5% female). Everyday functioning was reported by both participant and study partner using the Alzheimer's Disease Cooperative Study Activities of Daily Living‐Prevention Instrument (ADCS ADL‐PI). We dichotomized ADCS ADL‐PI total scores as ≤44 vs 45 (the maximum score). Additionally, based on previous analyses, we focused on four items and dichotomized scores as no difficulty vs. some/a lot of difficulty. We performed logistic regressions adjusting for age, sex and education. Result: Participants (median 45, range 33–45) and study partners (median 45, range 23–45) reported high levels of everyday functioning. Neither entorhinal nor inferior temporal tau was associated with overall self‐ or study partner‐reported everyday functioning (Table; Figure1). Greater entorhinal tau was associated with study partner‐reported difficulties remembering important dates (odds ratio (OR) = 1.24, 95% confidence interval (95%CI) = [1.06, 1.45], p = 0.008). Greater inferior temporal tau was associated with self‐reported difficulties managing to find personal belongings (OR = 1.23, 95%CI = [1.04, 1.46], p = 0.018). Greater entorhinal and inferior temporal tau were associated with study partner‐reported difficulties remembering the details of TV programs and movies (OR = 1.32, 95%CI = [1.08, 1.61], p = 0.007 and OR = 1.39, 95%CI = [1.11, 1.75], p = 0.005, respectively). Tau was not associated with difficulties remembering current events (Table; Figure2). Conclusion: Our results suggest a weak cross‐sectional relationship between everyday functioning and tau in cognitively normal older adults. These findings are likely driven by the restricted variability in scores on the ADCS ADL‐PI, which could improve with longitudinal data. Moreover, future work should incorporate a measurement of everyday functioning that has a wider range of performance in this population even at baseline. [ABSTRACT FROM AUTHOR]

Background: The Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q) is a proxy‐based instrument aimed at measuring difficulties in daily functioning due to cognitive decline in the context of dementia. To facilitate its interpretation and clinical implementation, normative data from the general population are necessary. Here, we aimed to compose normative scores for the A‐IADL‐Q and provide clinical cutoff values for mild cognitive impairment (MCI) and dementia. Method: Cross‐sectional data from three Dutch cohorts (i.e., Dutch Brain Research Registry, European Medial Information Framework‐Alzheimer's Disease (EMIF‐AD) 90+, and Amsterdam Dementia Cohort (ADC)) were used, including 1127 cognitively healthy individuals and 380 individuals with MCI and/or dementia. Regression‐based norms were constructed in the healthy sample, using linear models including age, sex, education(high/low) and their interactions, and the optimal model was selected using backward elimination. Individual differences between observed and expected IADL t‐scores were divided by the residual standard error of the model. Optimal normative score cutoffs to distinguish healthy from MCI and/or dementia were based on bootstrap average estimates(500 repeats) of the Youden Index. Diagnostic accuracy was evaluated using receiver operating curves (ROC), area under the curve (AUC), sensitivity and specificity. Result: Normative data (mean age = 64±13year, 68.6%female, 67.8% highly educated) and memory clinic data (mean age = 66±8year, 43.9%female, 38.9% highly educated) were used (Table1). The best normative model (i.e., with the lowest Akaike information criterion) included age‐squared, sex, education and the interaction between age and education. The optimal normative score cutoffs to distinguish healthy from dementia and MCI/dementia were, respectively, 1.78SD and 1.79SD below the mean, with high AUCs of 0.96 [95%CI 0.95‐0.98] and 0.94 [95%CI 0.92,0.95]. The optimal cutoff to distinguish healthy from MCI was higher; 1.01SD below the mean, also with a high AUC‐score of 0.86 [95%C 0.81‐0.9])(Table2, Figure1). Conclusion: We composed normative scores with clinical cutoff values that can aid in the distinction between cognitively healthy from MCI and/or dementia with high diagnostic accuracy. These normative scores are accessible via an online tool to further facilitate and implement clinical use of the A‐IADL‐Q. Future studies should include participants from different countries, cultures and different socio‐economic background to facilitate world‐wide use. [ABSTRACT FROM AUTHOR]

Background: As clinical trials increasingly focus on the earliest stages of Alzheimer's disease (AD), there is a need for reliable and valid functional measures in these stages. The National Institute of Aging and Alzheimer's Association (NIA‐AA) defined six clinical stages of AD, with stages 1 and 2 covering preclinical AD with normal cognitive and functional performance. Here, we explored the psychometric characteristics of the Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q), separately in stage1‐2 AD and amyloid negative controls. Method: Data from four European memory clinic and community‐based cohorts (SCIENCe, ALFA+, EPAD‐LCS and INSIGHT pre‐AD) were jointly analyzed (Fig.1;selection criteria). Amyloid positivity was determined by cerebrospinal fluid or positron‐emission tomography, using local cut‐offs. We explored three measurement properties:(1) internal consistency, using confirmatory factor analysis and Cronbach's alpha, (2) construct validity, by correlating A‐IADL‐Q performance with demographics and neuropsychological tests covering global cognition, executive functions, memory and attention, using Pearson's r or Kendall's tau, and (3) responsiveness, using linear mixed modelling with time as variable of interest, adjusted for age, sex and education. Result: A total of 396 controls (mean age = 62±7year, 71% female) and 242 stage1‐2 individuals (mean age = 65±9year, 60.7% female) were included (Table1). High internal consistency was supported by a single factor structure (comparative fit index = 0.997, Cronbach's alpha>0.905). In line with previous validation studies, low correlations were found between A‐IADL‐Q performance and demographic characteristics. Low correlations were found with neuropsychological test performance (Table2). Regarding responsiveness (Ncontrols = 219, mean follow‐up = 1.45±0.75 year, Nstage1‐2 = 127, mean follow‐up = 2.16±1.15 year), A‐IADL‐Q showed a decline in stage1‐2, although this did not reach statistical significance (β = ‐0.1, p = 0.45). Controls showed a small increase in performance, again non‐significant (β = 0.06, p = 0.41)(Fig.2). At baseline, stage1‐2 individuals had lower A‐IADL‐Q scores than controls (Welch two‐sample t‐test: t = ‐3.13, p<0.01). Conclusion: Our findings support the psychometric characteristics of the A‐IADL‐Q in stage1‐2 individuals and controls. Construct validity was partially supported, as expected, because cognitive impairments are not evident in this population. In line with this, findings regarding responsiveness may have been affected by the short follow‐up time of one year. Future validation in preclinical AD should include a longer follow‐up duration, to adequately capture potential decline. [ABSTRACT FROM AUTHOR]

Background: Accurate selection of outcome measures, sensitive to the earliest cognitive changes, is essential to demonstrate treatment efficacy in secondary prevention trials in preclinical Alzheimer's disease(AD). These outcome measures may differ according to biomarker inclusion criteria. We aimed to determine sensitivity to change over time, of commonly used cognitive measures. Method: Participants with longitudinal neuropsychological assessments with available baseline biomarkers were included, from the SCIENCe project. We created different (overlapping) target groups based on inclusion criteria in pre‐clinical AD trials; (1)amyloid PET or CSF (A+, n = 114), (2)CSF p‐tau (T+, n = 124), (3)AT(N)+ (n = 41), (4)APoE4 carriers(E4+, n = 195) and (5)APoE4 carriers with amyloid(E4+A+,n = 65). As a control population, we included 150 controls with normal biomarkers. Linear mixed models, adjusting for age, sex and education, were used to investigate the sensitivity to change of 14 individual (standardized) neuropsychological tests and two composites with time as the variable of interest. Composites were based on Preclinical Alzheimer Cognitive Composite; PACC(1) and the Cognitive‐Functional Composite; CFC(2). Result: A total of 556 participants where included(Age = 61.7±8.7, 44%F, follow‐up 3.1y ±2.7y)(Table 1). Linear mixed models in controls showed improvement over time in several tests, perhaps reflecting a learning effect: Stroop II/III, Wordlist Immediate Recall, Wordlist Delayed Recall and Letter Fluency. In contrast, all target groups showed significant decline in MMSE(Beta[‐0.09–0.26]), Digit‐Span‐Backwords (Beta[‐0.04‐ ‐0.06]), Trailmaking‐A (Beta[‐0.06–0.19]),‐B (Beta[‐0.15–0.29]), Stroop‐I (Beta[‐0.04–0.09]), Word‐List‐Recognition (Beta[‐0.09–0.16]) and Category‐Fluency (Beta[‐0.06–0.12])(Table 2). Linear mixed models showed a steeper decline on tests in AT(N)+ group. The composite measures showed significant decline in A+, AT(N)+ and E4+A+, (Beta[‐0.08–0.26]). With a sensitivity analyses we limited the follow up time to 2 years, leading to fewer neuropsychological tests showing decline and no decline in the composite measures. Conclusion: We demonstrated that in preclinical AD, commonly‐used neuropsychological tests capture cognitive decline dependent on biomarker profiles. Tests for memory, attention and executive functioning were sensitive to decline, as were composite measures. When follow‐up time was limited to 2 years, fewer tests were sensitive to change including the composite measures. Therefore, careful selecting cognitive outcome measures and an appropriate period of time is important when defining the target population for the clinical trial. [ABSTRACT FROM AUTHOR]

Background: Cognitively complex everyday tasks may be affected in early stages of Alzheimer's disease. Performance‐based digital assessments could facilitate more precise measurement of subtle changes in everyday functioning. We piloted a novel smartphone app 'Assessment of Smartphone Everyday Tasks' (ASSET), to investigate its utility for measuring everyday functioning in cognitively normal older adults. ASSET consists of Patient Portal and Calendar tasks for renewing prescriptions, making payments, and scheduling urgent and recurring appointments (Figure1). It tracks response time and accuracy. Method: ASSET was piloted in 34 participants (n = 20 cognitively normal young adults (YN): 21.3±2.6 years, 85%female, median 14.5 years of education; n = 14 cognitively normal older adults (ON), 75.1±4.8 years, 71%female, median 18 years of education). At baseline, participants completed the first version of ASSET and the Preclinical Alzheimer's Cognitive Composite (PACC5) in the research clinic. ON participants and their study partners completed the Alzheimer's Disease Cooperative Study Activities of Daily Living – Prevention Instrument (ADCS ADL‐PI). We calculated accuracy rates (correct responses/task duration) for both tasks and computed Pearson's r correlation coefficients between subtasks (internal consistency) and with PACC5 and ADCS ADL‐PI (construct validity). To investigate test‐retest reliability, participants subsequently completed the first and five alternate versions of ASSET remotely on a fixed, biweekly schedule. Result: At baseline, the Patient Portal and Calendar tasks were highly correlated (r = 0.78, 95% confidence interval (95%CI) = [0.60,0.89]). YN performed better on both tasks than ON, completing them faster and making fewer errors (Patient Portal: Cohen's d = ‐3.0, 95%CI = [‐4.2, ‐1.8]; Calendar: d = ‐1.6, 95%CI = [‐2.4, ‐0.7]). Performance was moderately correlated with PACC5 (Patient Portal: r = 0.59, 95%CI = [0.49,0.67]; Calendar: r = 0.48, 95%CI = [0.37,0.58]). Among ON, performance on ASSET was not significantly correlated with either self‐ or study partner‐reported ADCS ADL‐PI. Over repeated assessments, Patient Portal accuracy increased, while Calendar accuracy increased but dropped at the last assessment among all participants (see Figure2). Conclusion: Initial results show that ASSET may have good internal consistency and good ability to distinguish young from older cognitively normal adults. Future research should aim to corroborate current findings and extend with Alzheimer's disease biomarkers to better understand the value of ASSET in detecting early changes in everyday functioning. [ABSTRACT FROM AUTHOR]

Background: For the item analysis project of the Subjective Cognitive Decline Initiative (SCD‐I) Working Group, we have pooled item level data from North American, European, and Australian cognitive aging studies. As a step toward developing a questionnaire that can be used across settings and studies, we examined similarly worded groups of items and compared their psychometric quality. Method: Item‐level subjective cognitive data were combined from 21 studies (>18,000 participants). Unidimensional models consistent with item response theory captured a general subjective cognition factor using ADNI as the reference sample. Other studies were calibrated by using parameter constraints on common items. We characterized the measurement properties of 648 items from 35 measures utilized in the contributing studies. Next, we identified 20 groups of items (median items per group=6) with overlapping content from our previous study (Rabin et al., 2015), and plotted their item information curves, reflecting the items' ability to discriminate between trait scores at different levels of the subjective cognition factor score. We then considered items with the highest peak information across groupings and identified commonalities in key features such as terminology of item stems, temporal referents, and response options. Result: We present instrument characteristics (Table 1) and item information curves for 6 groupings (Figure 1). For items with the greatest information, we observed trends for the following features: clarity and directness of language of stem, utilization of current as opposed to other time referents, assessment of ability/level of difficulty rather than change over time or frequency of problem (Table 2). Conclusion: We identified key linguistic/structural features of subjective cognitive functioning items with the greatest contributions to measurement precision. We next plan to analyze all item groupings in our test bank to facilitate construction of a measure with high quality items from cognitive domains of interest (e.g., memory, language, executive functioning). Ultimately, we hope to derive a subjective cognitive screening tool that enables reliable identification of subtle cognitive changes in clinically normal individuals. [ABSTRACT FROM AUTHOR]

Background: Evidence on the effectiveness of multidomain lifestyle interventions to prevent cognitive decline in non‐demented older individuals is mixed. The multidomain FINGER study1 found small positive effects on cognition among older people at risk for dementia. As part of the World‐Wide FINGERS initiative to replicate and build on these results, FINGER‐NL aims to investigate the effectiveness of a 2‐year high versus low intensive multidomain lifestyle intervention on cognitive functioning in Dutch older at‐risk individuals. Method: FINGER‐NL is a Dutch multi‐center, randomized, controlled, multidomain lifestyle intervention trial aiming to include 1,206 adults at risk for cognitive decline. Adults aged 60‐80 years with at least 3 modifiable dementia risk factors (based on 'LIfestyle for BRAin Health' (LIBRA))2 and either a family history of dementia or self‐reported cognitive decline are eligible for inclusion. Participants are randomized (1:1 ratio) to either a high (HI‐group) or low‐intensity (LI‐group) intervention group. The multi‐domain intervention comprises a combination of 7 lifestyle components (physical exercise, cognitive training, cardiovascular risk management, nutritional counseling (with a specific focus on the MIND diet), sleep counseling, stress management, social activities) and 1 nutritional product (Souvenaid, 125ml). The HI‐group receives a personalized, supervised and hybrid intervention consisting of group meetings and individual sessions guided by a lifestyle coach. The LI‐group receives online lifestyle‐related health education. Primary outcome is 2‐year change from baseline on the cognitive composite score covering processing speed, executive function and memory. Secondary outcomes including changes in specific cognitive domains, instrumental activities of daily living, LIBRA score, and lifestyle component specific outcomes. For recruitment, we use the Dutch Brain Research Registry.3 Result: Participant recruitment started in January 2022. So far, 2405 people were interested, 1711 individuals have been screened for eligibility, 949 underwent baseline testing and 880 participants (73% of target sample) have been randomized and started with the intervention. The intervention is well‐accepted based on drop‐out rate (0.5%) and use of the online intervention portal. We expect to finalize randomization in Q2 of 2023 and complete the two‐year trial in 2025. Conclusion: Results so far show that FINGER‐NL is well on track, the intervention is well‐accepted and the trial will reach its inclusion target. [ABSTRACT FROM AUTHOR]

Background: A potential explanation for the limited success of past therapeutic trials for Alzheimer's disease (AD) lies in disease heterogeneity. We set out to pool and jointly analyze data from completed trials using a hypothesis‐free machine learning approach, aiming to identify participant characteristics related to increased responsiveness to treatment. Considering methodological difficulties, we also aim to describe the pooling and analytical process. Method: We requested data from fifteen completed phase I–III trials investigating several compounds in AD individuals and were granted access to six trial datasets using compounds LY450139 (semagacestat), GSK933776, and BI409306 (with donepezil) with 2,397 participants (73.3±7.8 years old, 55% female, Table 1), through the Vivli platform. Causal Forest analyses, a machine learning technique that extracts a minimal subset of patient characteristics that can explain heterogeneity in outcomes, were used with change on (1) ADAS‐Cog and (2) MMSE as outcome, accounting for treatment group (placebo vs. compound). Result: The included trial datasets showed considerable differences in available variables and data structure, constraining the amount of data that may be used in joint analysis. We were able to pool and analyze twenty‐seven characteristics shared across trial datasets in the model, covering demographics, vital signs, MRI volumetrics, baseline cognitive and functional performance, apolipoprotein ε4‐carriership and drug compound. Causal forest analyses did not reveal heterogeneity in pooled treatment effects on ADAS‐Cog (estimate for differential forest prediction: ‐2.25, p = 0.96) or MMSE (‐2.97, p = 0.98), indicating that these patient characteristics were not predictive for increased responsiveness to treatment. Stratifying by compound, similar results were found. Conclusion: We took an innovative approach and provided a methodological basis to examine heterogeneity in treatment effects by pooling existing trial datasets. Our results imply that responsiveness to treatment may not be well‐explained by the patient characteristics to our disposal, or that different compounds should be investigated. More research into other factors possibly related to treatment response is needed, e.g., fluid biomarkers and genetics. Based on our experience, we recommend that trial sponsors improve collaboration and data access, as we work towards personalized medicine by identifying which therapeutic approach works best for whom. [ABSTRACT FROM AUTHOR]

Background: Neuropsychiatric symptoms (NPS) are increasingly recognized as non‐cognitive manifestation of Alzheimer's disease (AD). However, the relationship between AD‐related pathology and NPS remains unclear. We aimed to investigate the cross‐sectional association between Alzheimer biomarkers and neuropsychiatric symptoms (NPS) in individuals with subjective cognitive decline (SCD) presenting at a memory clinic. In addition, we evaluated personality traits and education as potentially modifying factors of this association. Method: We included 346 individuals with SCD (age: 62.9±7.8) from the SCIENCe cohort, including 92 amyloid positive participants (age: 66.4±7.0, Female: 44.6%, MMSE: 28.5±1.3) and 254 amyloid negative participants (age: 61.6±7.7, Female: 40.6%, MMSE: 28.6±1.3). Amyloid status was determined by amyloid PET or CSF biomarkers according to visual read or established cut points. Neuropsychiatric symptoms were assessed using the Geriatric Depression Scale (GDS; n = 282), the Center for Epidemiological Studies‐Depression (CES‐D; n = 225), the Hospital Anxiety and Depression Scale – anxiety subscale (HADS‐A; n = 225) and the Neuropsychiatric Inventory Questionnaire (NPI‐Q; n = 198). We used linear regression analyses, adjusted for age and sex, to assess the association between amyloid status and NPS. In secondary analyses, we tested for interactions between amyloid status and (1) neuroticism (Dutch Personality Questionnaire), (2) somatization, (Four‐Dimensional Symptom Questionnaire), and (3) educational level (Dutch Verhage scale). Result: Amyloid status was not associated with GDS, CES‐D, HADS‐A and NPI‐Q scores in univariable analyses. When we evaluated effect modification, we found an interaction between amyloid status and neuroticism in the association with NPI‐Q (p<0.05), indicating a positive association between amyloid status and NPS in participants with a low level of neuroticism (β: 2.56±2.24), and a negative association in participants with a high level of neuroticism (β: ‐4.09±3.23). Conclusion: In this cross‐sectional analysis in individuals with SCD, amyloid status was hardly associated with neuropsychiatric symptoms. In future studies, we focus on amyloid status as determinant of change in NPS over time. [ABSTRACT FROM AUTHOR]

Background: Reduced learning is an early sign of memory impairment and relevant in the context of Alzheimer's disease. The Visual Association Test (VAT) is a brief visual associative memory task comprising two sets (A and B) of six interactive pictures (e.g., a monkey holding an umbrella). We hypothesized that the VAT may be able to elicit learning deficits on set B when administered directly following set A, particularly in patients with Alzheimer's disease‐related episodic memory deficits. We related learning on the VAT to medial temporal lobe atrophy (MTA) and amyloid‐β positivity. Method: We selected 3,599 patients (63.9±8.9 years, 41% female) who completed two VAT sets. Patients had subjective cognitive decline (n = 1,369, 38%), mild cognitive impairment (n = 756, 21%), Alzheimer's disease dementia (n = 856, 24%), or other types of dementia (n = 618, 17%). Average MTA scores of both hemispheres from magnetic resonance imaging were available for all; amyloid‐β status was obtained from cerebrospinal fluid or positron emission tomography for n = 2,769 (77%). We modeled learning curves on both VAT sets using linear mixed models, including an interaction with amyloid‐β status. Using logistic regressions, we analyzed the relationship between learning of associations on both sets (either all associations on both sets, or all associations on the first, but not the second set) and MTA, as well as amyloid‐β status. Result: Most patients learned all associations on both sets (n = 2,442, 68%). Patients who learned all associations on the first, but not the second set (n = 640, 18%) were more likely to have higher MTA scores and to be amyloid‐β positive than those who learned all associations (Table 1). Figure 1 shows learning curves on both sets of the VAT, stratified by amyloid‐β status. Less steep learning curves seem to suggest that amyloid‐β positive patients benefited less from completing a first set than amyloid‐β negative patients. Conclusion: We observed reduced learning on the second set, with those who did not learn all associations on both sets having more medial temporal lobe atrophy and more often being amyloid‐β positive. The VAT may be a useful, simple tool to assess early episodic memory deficits in the presence of Alzheimer's disease pathology. [ABSTRACT FROM AUTHOR]

Background: As cognitive assessments can be burdensome to patients, new approaches are emerging to assess symptomatic changes in Alzheimer's Disease (AD). Since typing on smartphones requires a variety of cognitive functions, passively and remotely collected typing behaviour has the potential to provide day‐to‐day insights into cognitive decline in AD. We investigated therefore the feasibility and reliability of a keyboard application for smartphones (Neurokeys) that passively collects keystroke dynamics (KD) to monitor cognition in people with preclinical and clinical AD. Method: Participants with preclinical AD (N = 8) and patients with a clinical AD diagnosis (N = 8, Table 1) were included from the Amsterdam Dementia Cohort. Participants used Neurokeys for 28 consecutive days in the context of the 'A Personalized Medicine Approach for Alzheimer's Disease' (ABOARD)‐project. The retention rate (active users on day 28) and the average number of daily keystrokes were calculated to determine feasibility. KD were aggregated into two commonly investigated features: time between keystrokes (flight‐time) and duration of a keystroke (hold‐time), which were standardized into z‐scores. Intra‐class correlations (ICC) between first and last 14 days of data collection for both features were determined to assess reliability. Finally, KD features were correlated to scores on the Mini‐Mental State Examination (MMSE) to explore the association between KD and global cognition. Results: The retention rate was 93.75% for all participants (Figure 1). Participants with preclinical AD used the keyboard for 26.25±2.12 days with 653.67±305.61 keystrokes each day, and clinical AD patients used the keyboard for 22.63±4.10 days with 409.44±288.73 daily keystrokes. For all participants, the ICC for flight‐time and hold‐time are 0.85 and 0.89 respectively. MMSE is negatively correlated with flight‐time (r = ‐0.68, p = 0.003), but not with hold‐time (r = ‐0.20, p = 0.45, Figure 2). Conclusion: The number of daily keystrokes is sufficient to obtain reliable KD features in preclinical and clinical AD. The results show that people with better global cognition type faster, indicating that passive logging of keystrokes can be a feasible measure for cognition in AD. To elaborate on these first findings, a larger validation study can further explore the utility of smartphone interactions and aid the development of digital biomarkers in AD. [ABSTRACT FROM AUTHOR]

Background: The prevalence of amyloid‐beta (Aβ) pathology increases at higher ages in non‐demented individuals1. With anti‐amyloid‐beta therapies on the horizon and the growing number of elderly in our population, a clear perception of the association between accumulated Aβ pathology, APOE genotype and cognitive performance in the oldest‐old is warranted. Methods: In 93 post‐mortem brains donated by participants of the Dutch 100‐plus Study, who self‐reported to be cognitively healthy at inclusion, we evaluated: 1) the spatiotemporal Aβ spread using Thal phases2; and 2) Aβ load in frontal, parietal, temporal and occipital cortices using a quantitative image analysis method. Spread and load of Aβ pathology were compared between different APOE groups (ɛ2/2 and ɛ2/3 (n = 20); ɛ3/3 (n = 65); ɛ2/4 and ɛ3/4 (n = 8)). Post‐mortem Aβ measurements were correlated to ante‐mortem test scores on a comprehensive neuropsychological test battery3 and a composite z‐score for global cognitive performance. Median age at death was 103.3 (interquartile range (IQR): 102.3‐104.9) and median time between neuropsychological assessment and brain donation amounted 0.61 years (IQR: 0.28‐0.93). Results: Notably, only 10% of the centenarians had not accumulated Aβ pathology. APOE ɛ2/2 and ɛ2/3 carriers had lower Aβ load (median frontal AB load = 0.30%, IQR: 0.02‐6.15), but not spread, compared to APOE ɛ2/4 and ɛ3/4 carriers (median frontal AB load = 5.08%, IQR: 2.13‐30.71) (p = 0.041), but not compared to ɛ3/3 carriers (median frontal AB load = 2.19%, IQR:0.23‐7.54) (p = 0.245). Higher Aβ load in all four analyzed regions associated with lower performance on the Digit Span Backward test (rfrontal = ‐0.29, rparietal = ‐0.29, rtemporal = ‐0.28, roccipital = ‐0.32, FDR corrected p<0.05) and the Clock Drawing test (rfrontal = ‐0.40, rparietal = ‐0.29, rtemporal = ‐0.29, roccipital = ‐0.33, FDR corrected p<0.05), which assess executive‐ and visuospatial‐functioning. Moreover, higher Aβ load in the frontal cortex correlated with lower performance on the Mini Mental State Examination (MMSE) (r = ‐0.24, p = 0.049) and a seperate composite score of global cognitive performance (r = ‐0.28, p = 0.019). Conclusion: Although Aβ pathology seems nearly inevitable at extreme ages, our results suggest that Aβ load, rather than Aβ spread, affects cognitive performance in the oldest‐old 1 https://doi.org/10.1002/alz.12899 2 https://doi.org/10.1212/wnl.58.12.1791 3 https://doi.org/10.1007/s10654-018-0451-3 [ABSTRACT FROM AUTHOR]

Introduction: 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. [ABSTRACT FROM AUTHOR]

Background: Assessment of daily functioning may be used to capture clinically meaningful decline in Alzheimer's disease (AD). In this multi‐center study, we aimed to investigate 'instrumental activities of daily living' (IADL) trajectories along the AD disease continuum from no symptoms to overt dementia, as defined in the 2018 National Institute on Aging – Alzheimer's Association research framework (Jack et al., 2018). Method: A total of 1,557 individuals with longitudinal IADL data (72.1±8 years; 41% female; mean follow‐up 2.4±2 years) were selected from memory clinics and community‐based cohorts (Harvard Aging Brain Study, ADNI, National Alzheimer's Coordinating Center, Amsterdam Dementia Cohort and EMIF‐AD PreclinAD and 90+). Amyloid positivity was determined at baseline using positron emission tomography or cerebrospinal fluid. Amyloid positive individuals (n=980) were classified into four NIA‐AA clinical stages based on cut‐offs on measures of subjective cognitive decline and cognitive functioning (Table 1, independent of IADL measures). Amyloid negative individuals without memory impairment or complaints (n=577) served as a control group. Everyday functioning was assessed using study‐partner‐based questionnaires as available in the different cohorts: the Everyday Cognition, Functional Activities Questionnaire and Amsterdam IADL Questionnaire. To allow comparison between the instruments, scores were standardized into Z‐scores using baseline means and standard deviations in the control group. Changes over time were analyzed using linear mixed models with random intercepts and slopes. Result: Baseline characteristics and distribution of individuals across stages can be found in Table 2. Baseline Z‐scores did not differ between amyloid negatives and stage 1; baseline Z‐scores were significantly lower in each following stage. A decline in function was found across the AD continuum, which accelerated along the clinical stages (Figure 1 and Table 3). From stage 2 onwards, IADL decline was faster compared to controls (β=‐0.085, 95% confidence interval = [‐0.116, ‐0.053], adjusted for age, sex, and education; Table 3). Conclusion: Our results suggest that AD‐related functional decline accelerates along the AD continuum, as shown by steeper rates of decline in each successive stage in people with amyloid pathology. These findings were robust across different cohorts, and can be used to inform participant selection and trial design for AD clinical trials. [ABSTRACT FROM AUTHOR]

Background: With the shift towards preclinical phases of Alzheimer's disease (AD) and other neurodegenerative diseases in research studies and clinical trials, the question arises if participants can reliably self‐report their level of everyday functioning. Here, we aim to investigate the factors related to concordance between participant‐ and study partner‐report versions of the Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q) in a community‐based volunteer population. Method: Community based volunteers (participants) and their study partners were recruited from a Dutch brain research registry. IADL concordance between participant and study partner was investigated using intraclass correlation coefficient (ICC). Dyads were subsequently categorized as (1) concordant (<2 points differences in A‐IADL‐Q score); (2) discordant, with study partner reporting more difficulties (study partner>participant) and (3) discordant, participant>study partner. Multinomial logistic regression models were used to investigate the association between subjective cognitive decline, depressive symptoms (Geriatric Depression Scale‐5), type of relationship, objective cognitive functioning (Cognitive Online Self‐Test Amsterdam), and concordance as dependent variable. Result: A total of 1213 participants (18‐93 years of age, 68.3% female) and 1213 study partners (18‐88 years, 45.8% female) completed the A‐IADL‐Q. The majority of participants (52.8%) and study partners (59.5%) reported no IADL difficulties. Figure 1 shows the percentage of IADL difficulty reported for individual activities by both participants and study partners. Of the dyads, 653 (53.8%) were in concordance, with an ICC of.55 (95%CI=[.51,.59], Figure 2). Depressive symptoms and memory complaints increased the odds of participants reporting more IADL difficulties compared to their study partner (odds ratio (OR)=1.31, p<.01 and OR=2.56, p<.01, Table 1). An inverse association was found between A‐IADL‐Q and concordance, to the extent that higher A‐IADL‐Q scores were associated with a lower odds of study partner>participant discordance (OR=0.68, p<.01). Conclusion: A slight majority of dyads showed concordance between participant‐ and study partner‐reported IADL scores. Our findings suggest that concordance is less likely in the presence of memory complaints, depressive symptoms and with lower levels of everyday functioning. These findings implicate that participant‐ and study partner report capture different aspects of functional decline, which might influence the study design of longitudinal aging studies. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease and related disorders (ADRD) are characterized by progressive cognitive and functional decline. To improve prognosis, it is important to predict the expected rate of functional decline. In the current study, we sought to uncover patterns of changes in everyday functioning among memory clinic patients, and to explore factors associated with said groups. Method: Memory clinic patients (N=573, 64.7±7.9 years old, 39% female) underwent extensive dementia screening at baseline, and were followed over time (median 1.0 years, range 0.3–6.2 years, N observations=1632). Diagnoses included subjective cognitive decline, mild cognitive impairment, and dementia with varying etiologies. We measured everyday functioning with the Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q). Longitudinal latent class analysis (LLCA) was used to identify groups of patients based on their rate of change in IADL, including non‐linear trajectories. We subsequently used random forests to explore the associations between the LLCA groups and demographics, neuropsychological test performance, and magnetic resonance imaging rating scales for medial temporal and global atrophy. Result: We identified six groups: two stable (high and average), three declining (slow, moderate, and fast), and one improving group (see Table 1, Figure 1). All groups differed in baseline A‐IADL‐Q scores, with the declining groups having lower scores. The fast declining group consisted mostly of dementia patients, whereas the high stable group consisted mostly of patients with subjective cognitive decline. The area under the curve of the random forest classification was.95, thus accurately assigning patients to the correct group based on baseline characteristics. The following variables contributed most to the classification of the groups: baseline A‐IADL‐Q score (mean decrease accuracy [MDA]=200.5), a visuospatial task (copy of Rey Complex Figure, MDA=36.6) and a processing speed task (Letter Digit Substitution Task, MDA=18.5). Atrophy rating scales contributed less. Conclusion: Different trajectories of functional decline were distinguished, and associated with baseline levels of cognitive and daily functioning. Our findings suggest that neuropsychological assessment may provide additional information on future changes in daily functioning. These findings may contribute to a more accurate prognosis of future functional decline, and by extension, future care needs. [ABSTRACT FROM AUTHOR]

Background: Increased public awareness of cognitive decline among older adults increases the need for reliable self‐testing of cognitive performance at home. In this study, we aimed to assess the usability of unsupervised, at‐home cognitive testing using the Cognitive Online Self‐Test Amsterdam (COST‐A). We established norm scores in a reference sample and validated them in an independent sample of cognitively normal individuals. In a clinical sample of individuals diagnosed with mild cognitive impairment (MCI) or dementia, we validated the capacity of COST‐A to distinguish normal from impaired cognitive functioning. Method: We recruited adults who self‐reported to be cognitively normal through the Dutch Brain Research Registry (reference sample), and invited them to complete and evaluate the COST‐A. We computed expected COST‐A scores based on age, sex and education level using multiple linear regression, to obtain demographically‐adjusted, standardized norm scores. We applied ‐1.5 SD units as a cut‐off for (ab)normal performance. Result: Out of 3529 participants who started, 3088 participants (60.6±12.1 years old, 70% women, 68% highly educated) successfully completed the COST‐A (Figure 1). These participants considered COST‐A to be of adequate difficulty and duration (±17 minutes), with clear instructions. Based on the norm scores (Figure 2), and corresponding with our cut‐off definition, 242 participants (7.8%) in the reference sample scored below ‐1.5. Results from the independent validation cohort (N=2444, 58.9±12.1 years old, 42% female, 54% highly educated) corroborated this finding: 186 (7.6%) scored below the cut‐off. In the clinical sample, 75% of MCI and 82% of dementia patients scored below ‐1.5. Conclusion: COST‐A is an easy‐to‐use online test of cognitive performance for individuals worried about their memory that can be used in the comfort of their own home. This is especially relevant in light of the COVID‐19 pandemic, where digital care is increasingly the norm. Moving forward, optimizing user experiences is key to overcome challenges regarding successful completion of the COST‐A at home, even in cognitively normal individuals. With a high accuracy for distinguishing normal from impaired cognition, COST‐A can serve as a screening tool to identify those individuals who require formal assessment by a healthcare professional and/or for research studies. [ABSTRACT FROM AUTHOR]

Background: Functional decline is a gradual process occurring along the Alzheimer's disease (AD) continuum, starting years before dementia onset. Different instruments, such as the ADCS‐ADL and the Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q) aim to capture early functional changes. In the current study, we compared measurement instruments and scoring techniques to find the optimal measurement technique for detecting functional impairment in early AD. Method: Study partners of participants from the international 'Capturing Changes in Cognition' study cohort completed both the A‐IADL‐Q and ADCS‐ADL over the course of one year (mean follow‐up 10.8±2.3 months). For both measures, we computed the following scores: (1) sum score, (2) weighted average, and (3) number of items with maximum score. For the A‐IADL‐Q, we also computed item response theory (IRT) scores, using predetermined parameters that take into account varying difficulty of included items (see Jutten et al., 2017). To compare the ability to detect changes over time for each scoring method, we performed linear mixed modeling, adjusted for sex and baseline age. Result: We included 159 individuals (72.2±8.4 years old, 45% female) with subjective cognitive decline (SCD, N=12), mild cognitive impairment (MCI, N=66) or mild dementia (N=81). Overall, a significant decline in functioning was detected with all scoring techniques (all p<.01), except the technique of the number of items with most impairment for the ADCS‐ADL (see Table 1). When comparing decline in the diagnostic groups (see Figure 1 and Table 1), linear mixed models showed functional decline in all scoring techniques of both instruments in mild dementia. In MCI, all A‐IADL‐Q scoring methods showed a significant decline over time (p<.05), whereas the ADCS‐ADL scores did not. No decline was detected in SCD. Conclusion: The A‐IADL‐Q showed greater sensitivity to functional decline over one year than the ADCS‐ADL in those with MCI. Our findings suggest that the ability to detect subtle functional changes appears to be mostly dependent on instrument, and to a lesser extent on the scoring technique used. These findings imply that the selection of appropriate instruments is pivotal for optimal longitudinal study design, and clinical trial design in patients with MCI. [ABSTRACT FROM AUTHOR]

Introduction: We estimated the age‐specific duration of the preclinical, prodromal, and dementia stages of Alzheimer's disease (AD) and the influence of sex, setting, apolipoprotein E (APOE) genotype, and cerebrospinal fluid tau on disease duration. Methods: We performed multistate modeling in a combined sample of 6 cohorts (n = 3268) with death as the end stage and estimated the preclinical, prodromal, and dementia stage duration. Results: The overall AD duration varied between 24 years (age 60) and 15 years (age 80). For individuals presenting with preclinical AD, age 70, the estimated preclinical AD duration was 10 years, prodromal AD 4 years, and dementia 6 years. Male sex, clinical setting, APOE ε4 allele carriership, and abnormal cerebrospinal fluid tau were associated with a shorter duration, and these effects depended on disease stage. Discussion: Estimates of AD disease duration become more accurate if age, sex, setting, APOE, and cerebrospinal fluid tau are taken into account. This will be relevant for clinical practice and trial design. [ABSTRACT FROM AUTHOR]

Introduction: In this multicenter study on subjective cognitive decline (SCD) in community‐based and memory clinic settings, we assessed the (1) incidence of Alzheimer's disease (AD) and non‐AD dementia and (2) determinants of progression to dementia. Methods: Eleven cohorts provided 2978 participants with SCD and 1391 controls. We estimated dementia incidence and identified risk factors using Cox proportional hazards models. Results: In SCD, incidence of dementia was 17.7 (95% Poisson confidence interval 15.2‐20.3)/1000 person‐years (AD: 11.5 [9.6‐13.7], non‐AD: 6.1 [4.7‐7.7]), compared with 14.2 (11.3‐17.6) in controls (AD: 10.1 [7.7‐13.0], non‐AD: 4.1 [2.6‐6.0]). The risk of dementia was strongly increased in SCD in a memory clinic setting but less so in a community‐based setting. In addition, higher age (hazard ratio 1.1 [95% confidence interval 1.1‐1.1]), lower Mini–Mental State Examination (0.7 [0.66‐0.8]), and apolipoprotein E ε4 (1.8 [1.3‐2.5]) increased the risk of dementia. Discussion: SCD can precede both AD and non‐AD dementia. Despite their younger age, individuals with SCD in a memory clinic setting have a higher risk of dementia than those in community‐based cohorts. [ABSTRACT FROM AUTHOR]