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Background: "SuperAgers" are older adults (ages 80+) whose cognitive performance resembles that of adults in their 50s to mid‐60s. Factors underlying their exemplary aging are underexplored in large, racially diverse cohorts. Using eight cohorts, we investigated the frequency of APOE genotypes in SuperAgers compared to middle‐aged and older adults. Method: Harmonized, longitudinal memory, executive function, and language scores in Non‐Hispanic White (NHW) and Non‐Hispanic Black (NHB) participants were obtained from the ADSP Phenotype Harmonization Consortium. Scores were age‐ and sex‐adjusted. SuperAgers (NHW = 1,625; NHB = 106) included individuals 80+ years of age with a memory score equal to or exceeding individuals aged 50‐64 and language and executive function domain scores within normal limits who remain cognitively normal across visits. SuperAgers were compared to Alzheimer's disease (AD) cases (NHW = 8,400; NHB = 925) and cognitively normal controls (NHW = 7,355; NHB = 1,305), as well as age‐defined subgroups (Young = ages 50‐64, Older = ages 65‐79, Oldest‐Old = age 80+). We performed binary logistic regression analyses comparing APOE‐ε2 and APOE‐ε4 alleles (0 = none, 1 = 1+ alleles present) among SuperAgers and their counterparts, covarying for sex and education. We corrected for multiple comparisons using the Benjamini‐Hochberg procedure. Results: Across racial groups, SuperAgers had significantly higher proportions with APOE‐ε2 alleles and lower proportions with APOE‐ε4 alleles compared to cases (Table 1, Figure 1). Similar differences were observed between SuperAgers and Young and Old Controls, although differences were restricted to APOE‐ε4 in NHB comparisons. NHW SuperAgers had lower proportions with APOE‐ε4 alleles compared to Oldest‐Old Controls; APOE‐ε2 proportions did not differ. Conclusion: Within our large, harmonized cohort, larger proportions of SuperAgers had APOE‐ε2 alleles and smaller proportions had APOE‐ε4 alleles than AD cases across both NHW and NHB participants. Crucially, higher proportions of NHW SuperAgers had APOE‐ε2 alleles than younger controls (ages<80) and lower proportions had APOE‐ε4 alleles than all controls including age‐matched controls (ages 80+). This work provides the strongest evidence to date that APOE is associated with SuperAging. APOE‐ε2 did not differentiate NHB SuperAgers from controls nor APOE‐ε4 from other oldest‐old adults in present analyses. Future work will extend to whole genome analysis to identify novel genomic drivers of SuperAging. [ABSTRACT FROM AUTHOR]

Background: In cognitively unimpaired (CU) individuals, the PACC is widely used as a cognitive outcome measure and endpoint in observational studies and clinical trials. However, it has drawn criticism for being heavily weighted towards memory. Increasing evidence indicates a decline spanning multiple cognitive domains in CU individuals. Therefore, using principal component analysis (PCA), we derived data‐driven domain‐specific cognitive composites. And subsequently, compared them against their summed z‐score counterparts in predicting progression to mild cognitive impairment (MCI). Method: Baseline cognitive, demographic, and genotype data of 2,853 CU older adults (aged 41.6 to 98.3) was obtained from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) Consortium. Using varimax‐rotated PCA, tests significantly loading (≥ 0.5) onto each principal component were extracted to derive domain‐specific cognitive composites. The resulting domain scores were normalised to a mean of 0 and SD of 1, with a higher score indicating better cognition. Cox regression was used to assess the association between progression to MCI and baseline demographics, cognition, and APOE ε4 carriage. Akaike information criteria (AIC) was used to compare the fitness of PCA‐derived composites against the zPACC and z‐score domain‐specific composites. Result: Baseline cohort characteristics are described in Table 1. PCA explained 68% of the variance and resulted in four independent cognitive composites (Figure 1): memory; executive function; attention and processing speed; and global cognition. At 15 years from baseline, 309 participants progressed to MCI, while 2,544 remained CU. Cox regression showed that the four cognitive composites, age and APOE genotype significantly predicted progression to MCI (Concordance = 77%, p < 0.001, AIC = 4105, Table 1). Additionally, the PCA‐derived composites performed comparably, if not better than the summed z‐score counterparts, the PACC (Concordance = 74%, p < 0.001, AIC = 4163) and domain‐specific composites (Concordance = 75%, p < 0.001, AIC = 4142). Baseline older age, APOE ε4 carriage in a dose‐dependent manner (Figure 2) and poorer cognition for each PCA‐composite were independently associated with progression to MCI. Conclusion: Together with APOE ε4 carriage, our PCA‐derived domain‐specific composites performed better than their summed z‐score counterparts at predicting progression to MCI 15 years before symptom onset. [ABSTRACT FROM AUTHOR]

Background: Few normative data for computerized measures administered in unsupervised remote environments are available. We aimed to determine what variables to include in normative models for remote self‐administered assessments, develop normative data for measures administered through Mayo Test Drive (MTD, a multi‐device remote cognitive assessment platform) and evaluate application of norms. Method: 1240 adults ages 33‐100 (96% White) from the Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center met normative sample inclusion criteria that included a concordant Cognitively Unimpaired (CU) diagnosis (3 independent raters all diagnosed CU) and CDR = 0 (see Table 1 for sample characteristics). Raw test scores were converted to normalized scaled scores. We derived regression‐based normative data that adjusted for age, age2, sex and education (base model); alternative models are also provided (age+age2+sex; age+ age2). We evaluated whether additional model terms beyond the base model were needed using an a priori cut‐off of 1% variance improvement in adjusted R2 over and above the base model. We examined rates of low test performance defined as performance below ‐1 SD in independent validation samples (n = 167 concordant CU, n = 64 mild cognitive impairment (MCI), n = 14 dementia). Rates are considered significantly different than expected when 95% confidence intervals (CIs) do not include the expected 14.7% base rate frequency. Result: No additional potential model terms met our a priori cut‐off for inclusion beyond the base model (see Table 2), including other quadratic and cubic terms or interactions, device type, response input type, potential session interference, and national area deprivation index (ADI available in subset; N = 776). Application of norms showed expected rates of low test performance among CU participants (95% CI includes expected base rate) and significantly greater than expected rates of low test performance for all variables in MCI (e.g., 62.5% for composite) and dementia (e.g., 71.4% for composite; see Table 3). Conclusion: Typical normative models appear appropriate for remote self‐administered measures and are sensitive to cognitive impairment. Device type and response input did not explain enough variance to warrant inclusion in normative models but are important for individual level interpretation. Future work will improve representation of individuals from under‐represented groups. [ABSTRACT FROM AUTHOR]

Background: Cognitive dysfunction is central to clinicopathological models of Alzheimer's disease (AD). While AD prospective studies assess similar cognitive domains, the neuropsychological tests used vary between studies, limiting potential for aggregation. We examined a machine learning (ML) data harmonisation method1 for neuropsychological test data to develop a harmonised PACC score for the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) consortium. Method: ADOPIC included longitudinal clinicopathological data from AIBL (N = 1765), ADNI (N = 1779) and OASIS (N = 440) cohorts (Table 1). Harmonization involved three stages. First, cognitive domains of interest and the neuropsychological tests assessing each were defined. Second, a standardized scoring and naming convention was established for demographic and neuropsychological outcomes. Third, the ML harmonisation approach1 was applied. Test scores present in one cohort, but not another, were treated as missing and imputed using ML before calculating a PACC1. Imputation utilized data from neuropsychological tests, age, sex, years of education, and APOE‐ɛ4 status. We validated the harmonized PACC (H‐PACC) by randomly simulating missing cognitive scores and analysing percentage error of imputed scores versus actual data1. Validity of harmonized scores was determined by their sensitivity to decline associated with amyloid positivity and clinical disease stage. Linear mixed models (LMMs) modelled trajectory of change on H‐PACC and AIBL PACC scores (including identical tests), with time, CDR‐global score, and amyloid status (Aβ‐/Aβ+) interactions as fixed effects. Sex and age at baseline were covariates. Variation in baseline and decline in PACC scores was modelled using random intercepts and slopes. Effect sizes for LMMs were computed with pseudo‐R‐squared. Result: Percentage errors for imputed neuropsychological test scores showed high accuracy with low standard deviations (Figure 1). Sensitivity to clinicopathological disease stage was qualitatively similar for the H‐PACC and AIBL PACC (Figure 2), both discriminating (p<0.001) annual decline rates of Aβ+ CDR 0.5 and CDR≥1 groups from Aβ‐ CDR 0 group. Effect sizes were substantial, with H‐PACC and AIBL PACC data explaining 95% and 93% of variance, respectively. Conclusion: The H‐PACC, developed via ML harmonization, was precise and has utility for aggregating neuropsychological test data to be used in prospective AD studies. References: 1https://doi.org/10.1002/alz.044302 2https://doi.org/10.1093/bioinformatics/btr597 [ABSTRACT FROM AUTHOR]

INTRODUCTION: We investigated longitudinal associations between self‐reported exercise and Alzheimer's disease (AD)‐related biomarkers in individuals with autosomal dominant AD (ADAD) mutations. METHODS: Participants were 308 ADAD mutation carriers aged 39.7 ± 10.8 years from the Dominantly Inherited Alzheimer's Network. Weekly exercise volume was measured via questionnaire and associations with brain volume (magnetic resonance imaging), cerebrospinal fluid biomarkers, and brain amyloid beta (Aβ) measured by positron emission tomography were investigated. RESULTS: Greater volume of weekly exercise at baseline was associated with slower accumulation of brain Aβ at preclinical disease stages β = –0.16 [–0.23 to –0.08], and a slower decline in multiple brain regions including hippocampal volume β = 0.06 [0.03 to 0.08]. DISCUSSION: Exercise is associated with more favorable profiles of AD‐related biomarkers in individuals with ADAD mutations. Exercise may have therapeutic potential for delaying the onset of AD; however, randomized controlled trials are vital to determine a causal relationship before a clinical recommendation of exercise is implemented. Highlights: Greater self‐reported weekly exercise predicts slower declines in brain volume in autosomal dominant Alzheimer's disease (ADAD).Greater self‐reported weekly exercise predicts slower accumulation of brain amyloid beta in ADAD.Associations varied depending on closeness to estimated symptom onset. [ABSTRACT FROM AUTHOR]

Prior authorization criteria for Federal Drug Administration (FDA) approved immunotherapeutics, among the class of anti‐amyloid monoclonal antibodies (mAbs), established by state drug formulary committees, are tailored for adults with late‐onset Alzheimer's disease. This overlooks adults with Down syndrome (DS), who often experience dementia at a younger age and with different diagnostic assessment outcomes. This exclusion may deny DS adults access to potential disease‐modifying treatments. To address this issue, an international expert panel convened to establish adaptations of prescribing criteria suitable for DS patients and parameters for access to Centers for Medicare & Medicaid Services (CMS) registries. The panel proposed mitigating disparities by modifying CMS and payer criteria to account for younger onset age, using alternative language and assessment instruments validated for cognitive decline in the DS population. The panel also recommended enhancing prescribing clinicians' diagnostic capabilities for DS and initiated awareness‐raising activities within healthcare organizations. These efforts facilitated discussions with federal officials, aimed at achieving equity in access to anti‐amyloid immunotherapeutics, with implications for national authorities worldwide evaluating these and other new disease‐modifying therapeutics for Alzheimer's disease. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Increasing evidence suggests that amyloid reduction could serve as a plausible surrogate endpoint for clinical and cognitive efficacy. The double‐blind phase 3 DIAN‐TU‐001 trial tested clinical and cognitive declines with increasing doses of solanezumab or gantenerumab. METHODS: We used latent class (LC) analysis on data from the Dominantly Inherited Alzheimer Network Trials Unit 001 trial to test amyloid positron emission tomography (PET) reduction as a potential surrogate biomarker. RESULTS: LC analysis categorized participants into three classes: amyloid no change, amyloid reduction, and amyloid growth, based on longitudinal amyloid Pittsburgh compound B PET standardized uptake value ratio data. The amyloid‐no‐change class was at an earlier disease stage for amyloid amounts and dementia. Despite similar baseline characteristics, the amyloid‐reduction class exhibited reductions in the annual decline rates compared to the amyloid‐growth class across multiple biomarker, clinical, and cognitive outcomes. DISCUSSION: LC analysis indicates that amyloid reduction is associated with improved clinical outcomes and supports its use as a surrogate biomarker in clinical trials. Highlights: We used latent class (LC) analysis to test amyloid reduction as a surrogate biomarker.Despite similar baseline characteristics, the amyloid‐reduction class exhibited remarkably better outcomes compared to the amyloid‐growth class across multiple measures.LC analysis proves valuable in testing amyloid reduction as a surrogate biomarker in clinical trials lacking significant treatment effects. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Reversion, or change in cognitive status from impaired to normal, is common in aging and dementia studies, but it remains unclear what factors predict reversion. METHODS: We investigated whether reverters, defined as those who revert from a Clinical Dementia Rating® (CDR®) scale score of 0.5 to CDR 0) differed on cognition and biomarkers from unimpaired participants (always CDR 0) and impaired participants (converted to CDR > 0 and had no reversion events). Models evaluated relationships between biomarker status, apolipoprotein E (APOE) ε4 status, and cognition. Additional models described predictors of reversion and predictors of eventual progression to CDR > 0. RESULTS: CDR reversion was associated with younger age, better cognition, and negative amyloid biomarker status. Reverters that eventually progressed to CDR > 0 had more visits, were older, and were more likely to have an APOE ε4 allele. DISCUSSION: CDR reversion occupies a transitional phase in disease progression between cognitive normality and overt dementia. Reverters may be ideal candidates for secondary prevention Alzheimer's disease (AD) trials. Highlights: Reverters had more longitudinal cognitive decline than those who remained cognitively normal.Predictors of reversion: younger age, better cognition, and negative amyloid biomarker status.Reverting from CDR 0.5 to 0 is a risk factor for future conversion to CDR > 0.CDR reversion may be a transitional phase in Alzheimer's Disease progression.CDR reverters may be ideal for Alzheimer's disease secondary prevention trials. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The extent to which the Big Five personality traits and subjective well‐being (SWB) are discriminatory predictors of clinical manifestation of dementia versus dementia‐related neuropathology is unclear. METHODS: Using data from eight independent studies (Ntotal= 44,531; Ndementia= 1703; baseline Mage= 49 to 81 years, 26 to 61% female; Mfollow‐up range = 3.53 to 21.00 years), Bayesian multilevel models tested whether personality traits and SWB differentially predicted neuropsychological and neuropathological characteristics of dementia. RESULTS: Synthesized and individual study results indicate that high neuroticism and negative affect and low conscientiousness, extraversion, and positive affect were associated with increased risk of long‐term dementia diagnosis. There were no consistent associations with neuropathology. DISCUSSION: This multistudy project provides robust, conceptually replicated and extended evidence that psychosocial factors are strong predictors of dementia diagnosis but not consistently associated with neuropathology at autopsy. Highlights: N(+), C(−), E(−), PA(−), and NA(+) were associated with incident diagnosis.Results were consistent despite self‐report versus clinical diagnosis of dementia.Psychological factors were not associated with neuropathology at autopsy.Individuals with higher conscientiousness and no diagnosis had less neuropathology.High C individuals may withstand neuropathology for longer before death. [ABSTRACT FROM AUTHOR]

INTRODUCTION: This study aimed to investigate the influence of the overall Alzheimer's disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers. METHODS: AD polygenic risk scores (PRS) were tested for association with DS‐related traits. RESULTS: The AD risk PRS was associated with disease status in several cohorts of sporadic late‐ and early‐onset and familial late‐onset AD, but not in familial early‐onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRSAPOE, p = 2.84 × 10−4; PRS excluding APOE, PRSnonAPOE, p = 1.60 × 10−2). PRSAPOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS. DISCUSSION: These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits. Highlights: Examination of the polygenic risk of AD in DS presented here is the first of its kind.AD PRS influences memory aspects in DS individuals, independently of APOE genotype.These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population.APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Studies suggest distinct differences in the development, presentation, progression, and response to treatment of Alzheimer's disease (AD) between females and males. We investigated sex differences in cognition, neuroimaging, and fluid biomarkers in dominantly inherited AD (DIAD). METHODS: Three hundred twenty‐five mutation carriers (55% female) and one hundred eighty‐six non‐carriers (58% female) of the Dominantly Inherited Alzheimer Network Observational Study were analyzed. Linear mixed models and Spearman's correlation explored cross‐sectional sex differences in cognition, cerebrospinal fluid (CSF) biomarkers, Pittsburgh compound B positron emission tomography (11C‐PiB PET) and structural magnetic resonance imaging (MRI). RESULTS: Female carriers performed better than males on delayed recall and processing speed despite similar hippocampal volumes. As the disease progressed, symptomatic females revealed higher increases in MRI markers of neurodegeneration and memory impairment. PiB PET and established CSF AD markers revealed no sex differences. DISCUSSION: Our findings suggest an initial cognitive reserve in female carriers followed by a pronounced increase in neurodegeneration coupled with worse performance on delayed recall at later stages of DIAD. [ABSTRACT FROM AUTHOR]

Background: Balancing the need to standardize cognitive measures with the need to evolve alongside advances in scientific knowledge is an inherent challenge for longitudinal studies of Alzheimer disease (AD). The Uniform Data Set (UDS) offers a solution for this in cognitive batteries and is implemented across Alzheimer Disease Research Centers (ADRCs). Using equipercentile equating, we combined data spanning two versions of the UDS and derived domain‐specific and general cognitive composites. We compared the relationships of these measures with common biomarkers of AD, as well as their statistical power to detect cognitive change in clinical trials. Method: Using longitudinal data from the Knight ADRC, domain‐specific cognitive composites were created using an exploratory factor analysis. The resulting four factors clustered episodic memory, semantic memory, working memory, and attention/processing speed tasks (Figure 1). We also computed a commonly‐used preclinical Alzheimer cognitive composite (PACC), an alternative Knight‐ADRC‐PACC, which did not include MMSE, and a global composite. Relationships between each cognitive composite and neuroimaging metrics of amyloid, tau, atrophy, and clinical symptoms, were compared cross‐sectionally and longitudinally (Figure 2). We also performed power analyses of all composites to calculate necessary sample sizes to observe a medium‐sized effect in a four‐year clinical trial. Result: Overall, general metrics of cognition slightly outperformed domain‐specific composites, although the working memory composite consistently reflected a poor relationship with AD biomarkers (Figure 2). Power analyses revealed that the global, Knight‐ADRC‐PACC, and attention/processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the ADCS‐PACC, required two to three times as many individuals (Figure 3). Conclusion: We found that data‐driven, domain‐specific, composites of cognitive performance can complement already‐established general cognitive composites, with the possible exception of the present working memory composite. In line with prior work, we highlight that attention and processing speed may be sensitive metrics of cognitive change in AD. The inclusion of the MMSE, such as in the ADCS‐PACC, may greatly reduce the sensitivity and statistical power of summary cognitive composites. Our results have important implications for the development of cognitive outcome metrics for future clinical trials. [ABSTRACT FROM AUTHOR]

Background: Estimates of "brain‐predicted age" quantify apparent brain age compared to normative trajectories of neuroimaging features. Brain‐predicted age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD). We used a previously‐trained model (DeepBrainNet [DBN], Bashyam et al., 2020), which requires minimal MRI pre‐processing, to predict brain age in the Dominantly Inherited Alzheimer Network (DIAN) and evaluated brain age as a marker of ADAD progression. Method: We used DBN to predict brain age from minimally‐processed, whole‐brain T1 structural MRI scans in 265 ADAD mutation‐carriers (MCs) and 183 non‐carrier controls (NCs). We then tested whether the brain age gap (BAG; difference between predicted and true age) differed as a function of mutation and cognitive status, or estimated years until symptom onset (EYO), and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid‐β‐42/40), phosphorylated tau (CSF and plasma pTau‐181), neurodegeneration (CSF and plasma neurofilament light [NFL]), and cognition (global neuropsychological composite and CDR‐sum of boxes). Finally, we compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of mutation variants (APP vs. PSEN1 before or after codon 200), APOE E4 carrier status, sex, and education. Result: DBN accurately predicted age in DIAN (Figure 1A). BAG was elevated in symptomatic and asymptomatic MCs, compared to NCs, beginning around ‐10 EYO (Figure 1B,C). BAG, hippocampal volume, and a cortical thickness summary followed similar trajectories across EYO (Figure 1D). BAG was positively associated with PiB PET, CSF and plasma pTau, and CSF and plasma NFL, and was associated with worse cognitive scores, especially in symptomatic MCs (Figure 2). BAG was most elevated in MCs with a PSEN1 mutation before codon 200, but did not differ as a function of APOE, sex, or education (Figure 3). Conclusion: We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG tracks well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, DBN‐based BAG offers unique benefits in simplicity of data processing and interpretation. [ABSTRACT FROM AUTHOR]

Background: Clinical trials of early Alzheimer's disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC. Methods: A dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML‐based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4‐fold cross‐validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML‐based composites was challenged by modelling mean (SD) change over time in the non‐demented clinical groups, using linear mixed model (LMM) analysis. Signal‐to‐noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group. Results: Each ML‐based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P<0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P<0.01). Conclusion: ML‐based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs. [ABSTRACT FROM AUTHOR]

Background: The ability to detect cognitive impairment from Alzheimer Disease (AD) in its earliest possible symptomatic stage is a highly desirable characteristic for neuropsychological measures. Because early cognitive changes are often subtle, measures with high sensitivity are of great importance. Variability in attention, often assessed using reaction time (RT) tasks, have been shown to discriminate between cognitively normal older individuals with and without positive AD biomarkers and is correlated with biological markers of neurodegeneration. However, what is less clear is how well measures of RT variability predict later development of cognitive symptoms of AD and whether common AD biomarkers predict declines in attention. Method: Cognitively normal older adults (N = 478, baseline age = 66.8 ± 8.9 years) completed a go/no‐go task, the Sustained Attention to Response Task (SART). Participants were instructed to respond to all numbers except for the digit "3". Variability in RT was measured using the Coefficient of Variation (CoV) to correct "go" trials. Cognitive status was assessed using the Clinical Dementia Rating® (CDR®). Logistic regression was used to test for the sensitivity of RT CoV in predicting eventual conversion to CDR > 0, adjusted for baseline age, education, and gender. A subset (N = 197) of participants had CSF Aβ42/Aβ40 within 3 years of the first SART assessment and 2+ visits allowing for testing longitudinal changes. Mixed effect models were used to track longitudinal change in RT CoV based on amyloid status. Result: Greater RT CoV (and thus poorer attention) predicted later conversion to CDR > 0 (p = 0.008), after accounting for baseline levels of education, age, and gender. There was a significant interaction between amyloid status and time (p = 0.048). Increases in RT CoV over time occurred for individuals designated as being amyloid positive (Aβ42/Aβ40<0.0673), but not for those initially identified as amyloid negative (see Figure 1). Conclusion: Variability in RT is a significant predictor of later development of cognitive symptoms associated with AD. Longitudinal changes in RT CoV are also sensitive to baseline amyloid status. These findings highlight the sensitivity of within‐person fluctuations of attention as a cognitive biomarker for AD. [ABSTRACT FROM AUTHOR]

Background: Practice effects (PEs) on cognitive tests are improvements in performance from repeated testing. We and others have shown that reductions in PEs on standard tests administered annually are associated with neurodegenerative disease. Using mobile technology, cognition can be assessed at much higher frequencies than standard in‐clinic assessments. These high‐frequency assessments offer a unique opportunity to measure PEs at multiple timescales, including retest intervals across months, weeks, days, within‐day, and even trial‐to‐trial. It is unclear if PEs observed across these different retest intervals are equally sensitive to clinical status and genetic risk of Alzheimer's disease (AD). Method: We examined timescales of PEs using a very brief, smartphone‐based processing speed test in 399 well‐characterized older adults (Table 1). Cognition was assessed four times per day over a week every six months for up to 2.5 years. PEs from the task were modeled at multiple timescales, including biannually, weekly, daily, and at the trial level. Additional mixed effects models compared PEs across clinical status and genetic risk for AD. Result: Across all 399 participants, there were significant practice effects at all timescales, with an average improvement in speed of 0.10s over a 6‐month retest interval, 0.02s within each day of testing, and 0.005s each individual testing session. Performance improved throughout each week, and the most performance gains occurred on the first day of testing. Comparing clinical status using the Clinical Dementia Rating (CDR®) revealed that CDR>0s had significantly slower overall processing speed (Cohen's d = 0.9) than CDR0s, however, CDR>0s displayed much more performance gains than CDR0s on their first three days of testing (Figure 1; d = 1.4). Across biannual visits, CDR>0s lost more performance gains than CDR 0s, indicating less retention over time. Comparing APOE e4 carriers and e4 noncarriers revealed similar patterns. Carriers of the e4 allele were slower than noncarriers (d = 0.3) and lost more performance gains across biannual visits. Conclusion: Magnitudes of PEs across multiple retest intervals are sensitive indicators of clinical status and genetic risk for AD. PEs observed at the daily level appear to be the most sensitive to clinical status and genetic risk for AD. [ABSTRACT FROM AUTHOR]

Background: Cognitive reserve (CR) has emerged as a critical factor in understanding clinical‐cognitive heterogeneity in Alzheimer's disease (AD). However, there is limited evidence of the effect of CR during asymptomatic phases of the disease, age at symptom onset (AAO) and longitudinal decline. In this study, we elucidate the impact of CR on AAO and decline rate using Dominantly Inherited Alzheimer's Disease (DIAD) as a disease progression model. Methods: DIAD participants were selected from the Dominantly Inherited Alzheimer Network (DIAN) study1. Using structural equation models, we utilized a residual approach to quantify CR. Variance in DIAN cognitive composite2 was decomposed into three latent components (Figure 1): demographic component (CogD), biomarker component (CogB), and reserve component (CogR). CogD refers to variance in the cognitive composite explained by demographic factors. CogB refers to the variance explained by AD biomarkers and markers of neurodegeneration. CogR represents the residual variance in the cognitive composite not explained by CogD or CogB. We examined the relationship between these components and clinical outcomes, including AAO, Clinical Dementia Rating®‐Sum of Boxes (CDR®‐SB), and transition from asymptomatic (CDR = 0) to symptomatic (CDR>0). Results: 319 participants (asymptomatic mutation carriers [aMC] n = 204, symptomatic MC [sMC] n = 115) were included in the analysis. Individuals with higher baseline CogR had greater odds ratio (OR) of remaining asymptomatic. For the aMC, after adjusting by mutation mean AAO3, one SD increase in the CogR component would multiply the OR of being CDR‐SB = 0 by 4.06 (95%CI 1.8‐8.9), while one SD increase in CogD and CogB component will multiply the OR by 2.6 (95%CI 1.1‐6.2) and 5.16 (95% CI 2.0‐13.3), respectively. For sMC, only the CogR and the CogB components were significant. One SD increase in CogR and CogB corresponds to 0.81 (95%CI 0.72‐0.92) and 0.60 (95%CI 0.50‐0.71) fold decrease in baseline CDR‐SB, respectively. Longitudinally, higher CogR values significantly predicted protection against conversion to CDR >0 and slower progression on CDR‐SB. Conclusions: CR significantly modifies both AAO and rate of clinical decline in DIAD. These findings highlight CR as a factor in AD progression and suggest potential avenues for intervention by enhancing CR to delay onset. [ABSTRACT FROM AUTHOR]

The widespread use of remote digital assessment technology has made it possible to obtain high‐frequency measurements of cognitive function in naturalistic settings (i.e., outside of the clinic). Measurement burst designs feature multiple waves of testing (e.g., multiple tests per day for several weeks) which are repeated at regular intervals (e.g., annually) and have been shown to be feasible, well‐tolerated by participants, and provide substantially more reliable estimates of mean performance than standard assessments. Measurement burst designs also afford an examination of cognitive variability, or the degree to which cognitive scores vary across repeated assessments. Variability can be measured at different time scales including momentary variability across trials within a task, or over different days. Measures of "contextual factors" such as perceived stress, sleep quality, mood, and personality traits are easy to add as well to test potential mechanisms for differences in cognitive variability. An integration of cognitive and contextual variability across multiple time scales provides a more complete picture of change in early‐stage disease and is particularly applicable to preclinical Alzheimer disease (AD) where cognitive declines are extremely subtle and heterogeneous. Using data from two high‐frequency assessment studies in which cognitively healthy and very mildly impaired older adults completed multiple daily cognitive assessments of episodic memory, working memory, attention, and processing speed for 1 to 3 weeks, we will examine how variability at both short (from trial to trial within a task) and longer (from day to day) timescales change as a function of preclinical AD. We examine potential mechanisms for cognitive variability including increased mind wandering, daily motivation, and variations in daily personality traits. Finally, we will discuss the utility of advanced statistical modeling techniques (e.g., mixed effects location scale models) to answer questions about cognitive variability, and we will examine how our understanding might be further enhanced by employing cognitive process models (such as the diffusion model) that incorporate both accuracy and response time to provide a more complete picture of daily cognitive fluctuations in AD. We end with practical considerations regarding administration of measurement bursts and pose additional questions that will stimulate additional research. [ABSTRACT FROM AUTHOR]

Background: Comparisons of late‐onset Alzheimer's disease (LOAD) and autosomal dominant AD (ADAD) are confounded by age. Methods: We compared biomarkers from cerebrospinal fluid (CSF), magnetic resonance imaging, and amyloid imaging with Pittsburgh Compound‐B (PiB) across four groups of 387 cognitively normal participants, 42 to 65 years of age, in the Dominantly Inherited Alzheimer Network (DIAN) and the Adult Children Study (ACS) of LOAD: DIAN mutation carriers (MCs) and non‐carriers (NON‐MCs), and ACS participants with a positive (FH+) and negative (FH–) family history of LOAD. Results: At baseline, MCs had the lowest age‐adjusted level of CSF Aβ42 and the highest levels of total and phosphorylated tau‐181, and PiB uptake. Longitudinally, MC had similar increase in PiB uptake to FH+, but drastically faster decline in hippocampal volume than others, and was the only group showing cognitive decline. Discussion: Preclinical ADAD and LOAD share many biomarker signatures, but cross‐sectional and longitudinal differences may exist. [ABSTRACT FROM AUTHOR]

Background: Prior findings indicate that both genetic factors and indices of cognitive reserve (CR) influence risk of cognitive decline, with APOE4 genetic status increasing risk, and APOE2 genetic status and higher CR indices decreasing risk. However, it remains unclear whether these genetic and lifestyle variables interact to influence long‐term cognitive trajectories. This study examined whether these variables modify baseline cognitive scores and longitudinal cognitive trajectories in N = 1819 participants who were cognitively normal at baseline and included in the Preclinical AD Consortium (PAC). Method: PAC datasets include harmonized data from five longitudinal cohort studies: ACS, AIBL, BIOCARD, BLSA, and WRAP. Longitudinal cognitive performance was measured by harmonized factor scores for global cognition, memory, and executive function (M follow‐up = 9.8y; Table 1). APOE genotypes were coded with separate indicator variables for APOE2 (n = 233) and APOE4 (n = 581), with APOE3/3 (n = 1005) as the reference group. A CR index was calculated by combining years of education and literacy scores. Result: In longitudinal mixed effect models, using time since baseline as the timescale (Table 2), higher CR index scores were associated with better baseline cognitive performance for all cognitive factor scores, but not with rate of change in cognition over time. In contrast, APOE4 genotype was associated with more negative rates of cognitive change. For the global and memory scores, there were also 3‐way CR index x APOE4 x time interactions, indicating the negative effect of APOE4 genotype on global and memory score change was attenuated among individuals with higher CR index scores (Figure 1). When the models were re‐run with the term for APOE4 genetic status reflecting the number of APOE4 alleles (i.e., 0, 1, 2; Table 3), the patterns of results were similar, except the 3‐way CR index x APOE4 x time interaction for global cognition was only marginal (p = 0.06). Conclusion: Higher CR proxy scores are associated with higher levels of cognitive performance, whereas APOE4 genetic status is associated with greater rates of cognitive decline. Higher levels of CR may attenuate APOE4‐related declines in global cognition and memory, but level of CR and APOE4 have largely independent effects on executive function. [ABSTRACT FROM AUTHOR]

Background: The balance between production, clearance, and toxicity of Ab peptides is central to AD pathobiology. Though highly variable in terms of age of symptom onset (AAO), hundreds of pathogenic variants in Presenilin‐1 (PSEN1) cause autosomal dominant forms of AD (ADAD). PSEN1 forms the catalytic core of the γ‐secretase complex and thereby directly mediates the production of longer, aggregation‐prone Aβ peptides relative to shorter, non‐aggregating peptides. We hypothesized that the broad AAO and biomarker heterogeneity seen across ADAD would be predictable based on mutation‐specific differences in γ‐secretase function, as measured by a ratio of production of shorter vs. longer Aβ species. Methods: Aβ‐37, 38, 40, 42, and 43 production was quantified from 162 unique PSEN1 variants expressed in HEK293 cells engineered to lack endogenous wild‐type PSEN1/2 (Figure 1). Prediction of AAO was carried out in 107 PSEN1 variants (characterized by Liu et al, 2022) and then replicated with a set 55 unique PSEN1 variants represented in the Dominantly Inherited Alzheimer Network (DIAN; n = 190 corresponding variant carriers with detailed cognitive and biomarker data; Figure 2). Results: Mutation‐level variations in Aβ production, including a novel composite representing γ‐secretase function (lower score = less g‐processivity), from the cellular model were highly predictive of actual AAO across the 162 mutants examined (Non‐DIAN variants [N = 107]: r = 0.71, p <2.2e‐16; DIAN variants [N = 55]: r = 0.61, p = 6.10e‐07). Our cell‐based γ‐secretase function composite was strongly associated with cerebral PiB‐PET β‐amyloid burden (B[SE] = ‐0.03[0.01], p = 4.06e‐07), MRI gray matter volume (B[SE] = 44.22[6.4], p = 5.15e‐01), cerebrospinal fluid Aβ42/40 (B[SE] = 6.3e‐04[1.5e‐04], p = 4.46e‐05) and phosphorylated tau‐217 (B[SE] = ‐0.01[0.002], p = 1.98e‐05), Clinical Dementia Rating®‐ Sum of Boxes (B[SE] = ‐0.07[0.02], p = 4.86e‐05), and Mini‐Mental State Exam (B[SE] = 0.11[.03], p = 2.17e‐04). Conclusions: Biochemical variations in γ‐secretase function across PSEN1 pathogenic variants broadly predicted the cross‐sectional clinical, cognitive, and biomarker course of ADAD, including AAO. These findings elucidate the critical link between γ‐secretase function, Aβ production, and severity of AD. The novel approach designed here also represents a tool to account for heterogeneity in ADAD clinical trials and to assess the pathogenicity of PSEN1 variants of unknown significance or with limited family history. [ABSTRACT FROM AUTHOR]

Background: At the Knight Alzheimer Disease Research Center (ADRC), clinicians are blinded to cognitive data and prior assessments to ensure that ratings are free of circularity and examiner bias. It is common for cognitive status to fluctuate between normal and abnormal in longitudinal studies of aging and dementia, even when using the gold standard Clinical Dementia Rating (CDRâ) scale. CDR fluctuation is common when clinical symptoms are mild. It was hypothesized reverters, defined as individuals who change from CDR 0.5 to CDR 0, would have worse cognition, more abnormal biomarkers, and more likely to progress to CDR>0 compared to participants who remain unimpaired ("CN"). Method: Cognitively normal participants with at least three annual visits were included (n = 924, Table 1). Participants were classified as CN, reverters, and converters to stable dementia ("converters"). The Knight PACC was used to compare cognition between groups. Participants (n = 187) with cerebrospinal fluid or positron imaging tomography biomarkers were classified as amyloid "positive" or "negative". The visit of first CDR>0 was defined as the "index visit" and the visit prior was defined as the "pre‐index visit." Linear models evaluated relationships between biomarker status, APOE ε4 status, and cognition at the pre‐index and index visit. Logistic regression evaluated predictors of converting to a dementia status ("conversion") and reverting from CDR 0.5 back to CDR 0 ("reversion"). Result: Of 924 participants, 127 (14%) were reverters and 142 (15%) were converters. Converters had the lowest PACC scores of the three groups at the pre‐index and index visit, with reverters having slightly better PACC scores than converters but significantly worse scores than CN (Figure 1). Predictors of conversion and reversion included older age, male gender, APOE ε4 carriage, and worse PACC scores. Amyloid positivity did not predict conversion or reversion. Reverters exhibited more longitudinal cognitive decline compared to CN but the magnitude of decline was less than converters (Figure 2). Conclusion: CDR reversion is a risk factor for future conversion to CDR>0 and is associated with older age, APOE ε4 carriage, and poor cognitive performance. CDR reversion appears to occupy a transitional phase in disease progression between cognitive stability and consistent progression to stable dementia. [ABSTRACT FROM AUTHOR]

Background: Easily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer's disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures. Method: 556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer's disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result: Lower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p's<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p's<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p =.01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p's≥.06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p's≥.29). A multivariable model with MTD explained significantly less variance (p =.04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p's>.05). Conclusion: MTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers. [ABSTRACT FROM AUTHOR]

Background: Brief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer's disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers. Method: Participants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer's disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aβ)‐42 and Aβ‐40 to create an Aβ42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result: Lower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p's≤.02), NfL (p's≤.003), and p‐tau181 (p's =.04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p's≤.01) and NfL (p's≤.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p's≤.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p's≤.02), and worse Digit Symbol Coding was associated with lower Aβ42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p =.04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p's>.05). Conclusion: Remote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD's criterion validity. [ABSTRACT FROM AUTHOR]

Background: Studies suggest distinct differences in the development, presentation and longitudinal progression of Alzheimer's Disease (AD) between women and men. However, most of these sex‐specific differences have been explored in symptomatic stages of sporadic AD. We investigated cross‐sectional sex differences in clinical‐cognitive assessments as well as fluid and imaging biomarkers in the spectrum of dominantly inherited AD (DIAD). Method: 319 DIAD mutation carriers of the Dominantly Inherited Alzheimer Network‐Observational study (DIAN‐OBS) were included in the analysis and, according to Clinical Dementia Rating® (CDR®) scores, classified as presymptomatic (CDR = 0, 66,1%) or symptomatic (CDR>0). All participants underwent clinical and neuropsychological assessment; lumbar puncture, structural MRI and 11C‐PiB‐PET imaging for amyloid‐β detection were conducted. Mann‐Whitney‐U‐test, Fisher's Exact and linear mixed models with fixed effects for sex, estimated age of onset (EYO) and their interaction, a random effect for family, and years of education and CDR Sum of Boxes as covariates (where appropriate) were used to explore cross‐sectional sex differences. Result: Demographics showed no significant sex differences in presymptomatic (pMC, 56.4% women, p>0.05) or symptomatic mutation carriers (sMC, 52,7% women, p>0.05). In pMC, men showed a worse performance on the Digit Symbol test (p<0.01, Fig.1) than women, and, in interaction with EYO, a better performance in immediate (p<0.05) and delayed (p<0.05) prose recall. In contrast, sMC men performed better on the Animal Naming test (p<0.05), but, in interaction with EYO, worse on the Digit Span forward (p<0.01). CSF analysis did not indicate sex‐related differences in Aβ42/Aβ40‐Ratio, phosphorylated Tau‐181 or total Tau levels (all p>0.05) in either group. In pMC men, PET imaging revealed significantly less tracer binding in the medial orbitofrontal cortex, the frontal pole and the rostral & caudal anterior cingulate in interaction with EYO (all p<0.05, Fig. 2). Furthermore, MRI volume analysis showed a significant reduction in the amygdala in women compared to men (p<0.01) in sMC. Conclusion: We report preliminary results suggesting cross‐sectional sex differences in presymptomatic and symptomatic DIAD mutation carriers regarding episodic, verbal and working memory; processing speed; as well as differential patterns of PiB‐PET burden within cerebral areas related to emotion, decision making and cognitive flexibility. [ABSTRACT FROM AUTHOR]

Background: We aimed to developed integer‐based risk scores for predicting incident cognitive impairment up to 10 years in initially cognitively normal older adults. Methods: Participants were 479 older adults (aged 60 to 90) from longitudinal studies of aging and Alzheimer's disease from the Knight ADRC at Washington University in St. Louis. The primary outcome was incident cognitive impairment, defined as conversion from Clinical Dementia Rating (CDR®) 0 to >0. The sample was divided into training (60%) and test (40%) sets. Feature‐sets were formed from combinations of four categories of variables: demographics (D), genetics (G), cognitive measures (C), and biomarkers (B) (Details in Figure 1 legend). Utilizing a modified version of the AutoScore‐Survival algorithm, variables were ranked based on variable importance using reduction in predictive accuracy due to replacement with a random permutation value in combination with Random Survival Forests. Variables were iteratively included in constructing risk score models based on coefficients of Cox regression models. Results: Five risk score models were developed using five feature‐sets. All models retained age, APOE4 status, free recall from the Free and Cued Selective Reminding Test, hippocampal atrophy, and Tau positivity when included as potential variables in the feature‐sets. The risk score derived from the DGCB model had a Harrell's concordance index (C‐index) of 0.754 and integrated Area Under the ROC Curve (iAUC) of 0.792. The best performing model without biomarkers used the DGC feature‐set and had a C‐index of 0.694 and iAUC of 0.699 while the risk score using the DG feature‐set only had a C‐index of 0.632 and iAUC of 0.647. Participants who had risk scores in the top quartile of the DGCB model risk score had an increased rate of conversion to CDR > 0. Conclusions: We developed five risk score models for predicting incident cognitive impairment. Risk scores consisted of different sets of measures which differed in ease‐of‐access and costs. Using such simple risk scores that do not include biomakers as clinical decision support tools is practical in the clinical setting. Including biomarkers in risk scores provides slight improvement to classification accuracy, emphasizing their importance in research settings. [ABSTRACT FROM AUTHOR]

Background: A recently recognized subset of older individuals are an anomaly of cognitive decline; the "SuperAgers", unsurprisingly named, achieve cognitive scores equivalent to much younger cognitively normal (CN) middle‐aged adults. Using longitudinal cognitive data harmonized across eight cohorts of aging and Alzheimer's Dementia (AD), we investigated the genetic drivers of SuperAging. Method: Harmonized memory, executive function, and language scores were estimated leveraging latent variable modeling and made available through the ADSP Phenotype Harmonization Consortium. SuperAgers (N = 1,095) were defined as individuals over 80 years with a mean sex‐adjusted memory score equal or exceeding CN individuals aged 50‐60, score within one age and sex‐adjusted standard deviation in the other two cognitive domains, and remain CN for all longitudinal visits. Young Cases (N = 1,906) were defined as individuals aged 50‐75 with a clinical diagnosis of AD. Old Controls (N = 3,247) were defined as CN individuals over 80, scoring within one age‐ and sex‐adjusted standard deviation in all three domains. We performed a GWAS on non‐Hispanic Whites using logistic regression comparing SuperAgers and their counterparts (Young Cases and Old Controls) with covaried adjustment for age, sex, education, and principal components for population substructure. Result: Comparing SuperAgers with Young Cases (Figure 1), only variants in the well‐established APOE region were associated with genome‐wide significance (GWAS; P<510−8). Additionally, we observed a locus on chromosome 13 approach GWS (rs138699163, P = 6.5610−8). The locus centered on a relatively uncharacterized ncRNA, MIR4500. Analyses comparing SuperAgers to Old Controls did not find any GWS associations, with the strongest association observed at rs116535931 on chromosome 5 (P = 1.5210−6). Conclusion: Our extreme‐phenotype GWAS comparing SuperAgers to Young Cases identified established and novel loci for AD. However, larger sample sizes may allow better characterization of the genetic architecture of SuperAging. Future analyses will extend to Case comparison groups to include Old Cases (age>80 years) and All Cases (age>50 years) and Control comparison groups to include Young Controls (age between 50‐60) and Agnostic Controls (age>50 years) with similar criteria. [ABSTRACT FROM AUTHOR]

Introduction: As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. Methods: We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five‐year risk of AD dementia was modeled using survival analysis. Results: A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5‐year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aβ)42/Aβ40 (AUC = 0.82). Discussion: An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults. [ABSTRACT FROM AUTHOR]

A crucial aspect of any clinical trial is using the right outcome measure to assess treatment efficacy. Compared to the rapidly evolved understanding and measurement of pathophysiology in preclinical and early symptomatic stages of Alzheimer's disease (AD), relatively less progress has been made in the evolution of clinical outcome assessments (COAs) for those stages. The current paper aims to provide a benchmark for the design and evaluation of COAs for use in early AD trials. We discuss lessons learned on capturing cognitive changes in predementia stages of AD, including challenges when validating novel COAs for those early stages and necessary evidence for their implementation in clinical trials. Moving forward, we propose a multi‐step framework to advance the use of more effective COAs to assess clinically meaningful changes in early AD, which will hopefully contribute to the much‐needed consensus around more appropriate outcome measures to assess clinical efficacy of putative treatments. Highlights: We discuss lessons learned on capturing cognitive changes in predementia stages of AD.We propose a framework for the design and evaluation of performance based cognitive tests for use in early AD trials.We provide recommendations to facilitate the implementation of more effective cognitive outcome measures in AD trials. [ABSTRACT FROM AUTHOR]

Introduction: As knowledge about neurological examination findings in autosomal dominant Alzheimer disease (ADAD) is incomplete, we aimed to determine the frequency and significance of neurological examination findings in ADAD. Methods: Frequencies of neurological examination findings were compared between symptomatic mutation carriers and non mutation carriers from the Dominantly Inherited Alzheimer Network (DIAN) to define AD neurological examination findings. AD neurological examination findings were analyzed regarding frequency, association with and predictive value regarding cognitive decline, and association with brain atrophy in symptomatic mutation carriers. Results: AD neurological examination findings included abnormal deep tendon reflexes, gait disturbance, pathological cranial nerve examination findings, tremor, abnormal finger to nose and heel to shin testing, and compromised motor strength. The frequency of AD neurological examination findings was 65.1%. Cross‐sectionally, mutation carriers with AD neurological examination findings showed a more than two‐fold faster cognitive decline and had greater parieto‐temporal atrophy, including hippocampal atrophy. Longitudinally, AD neurological examination findings predicted a significantly greater decline over time. Discussion: ADAD features a distinct pattern of neurological examination findings that is useful to estimate prognosis and may inform clinical care and therapeutic trial designs. [ABSTRACT FROM AUTHOR]

Background: Traditional pen‐and‐paper neuropsychological assessments fail to capture subtle cognitive changes in the early stages of Alzheimer's disease (AD). Remote and unsupervised digital assessments available on smartphones, tablets, and personal computers may offer a solution to this by increasing the amount and types of data available to researchers and clinicians, while simultaneously improving ecological validity and alleviating patient burden. As these remote and unsupervised digital cognitive assessment tools become more widely available, it is important that they are validated in a systematic way. In this review, we evaluate the validity of available remote tools, focusing on active assessment tools with which the patient interacts with (i.e., cognitive tests, active speech) that have been used to investigate subtle cognitive differences in preclinical AD, defined as clinically unimpaired individuals with biomarkers indicating increased risk of dementia due to AD. Methods: We performed a systematic literature review on PubMed, Web of Science, and PsycInfo using terms such as "digital," "remote," "unsupervised," "cognition," "aging," and "Alzheimer's," which resulted in a total of 1,453 unique peer‐reviewed articles and pre‐prints. After filtering for tools that were remotely self‐administered in humans, and excluding papers detailing findings in populations with other diseases than AD (e.g., multiple sclerosis, Parkinson's disease) or papers reporting findings of intervention studies, 14 papers reporting the use or planned use of 14 tools to detect AD pathology in preclinical AD were selected (as of Jan. 2024). Results: We evaluate each tool with regards to its use‐case and usability, as well as various types of validity, and suggest a framework with which future tools may also be evaluated. Additionally, we discuss current directions in the field of remote and unsupervised digital cognitive assessment in early AD. Conclusion: With this review, we hope to show that the systematic and validated use of such tools will increase our understanding of subtle changes in cognition due to AD pathology, as well as make screening for clinical trials and treatment more accessible. [ABSTRACT FROM AUTHOR]

Background: Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform optimized for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). Study aims were to determine 1) convergent validity of MTD through correlations with in‐person neuropsychological measures and 2) criterion validity of MTD by qualitatively comparing the ability of Mayo Test Drive and in‐person administered neuropsychological measures to differentiate biomarker‐defined groups, independent of clinical diagnosis. Methods: Mayo Clinic Study of Aging participants (N = 282) completed a brief remote cognitive assessment (mean age = 74, SD = 12; 262 CU and 20 MCI per consensus conference diagnosis; see Table 1). A subset of participants had brain amyloid and/or brain tau PET scans available within 3 years; overlapping groups were formed for 1) those on the Alzheimer's disease (AD) continuum (A+, n = 31) or not (A‐, n = 57) and for 2) those with biological AD (A+T+, n = 19) or without any AD biomarkers (A‐T‐, n = 47). Primary outcome variables were SLS sum of trials, AVLT sum of trials, SYM Correct Trials response time, Digit Symbol Coding, MTD composite and Mayo‐PACC (AVLT sum of trials, Trails B, animals). Bivariate correlations were performed across all participants. Linear model ANOVAs were used to investigate biomarker subgroup differences; Hedge's g effect sizes are reported. Results: Convergent validity was supported through significant correlation (p's <.001) of SLS and AVLT (r =.63), SYM and Coding (r = ‐.64), and MTD composite with Mayo‐PACC (r =.67); see Table 2. MTD subtests showed expected relationships with age and education (p's <.05; Table 3). All MTD performances were significantly lower for A+ and A+T+ participants relative to A‐ or A‐T‐ (p's<.001; Table 4). All MTD outcome measures showed a similar or greater group difference effect size relative to traditional in‐person administered neuropsychological measures for separating A+ vs. A‐ and A+T+ vs. A‐T‐ groups. See Figure 1 and Table 4. Conclusions: MTD subtests show evidence of convergent validity through strong correlations with traditional in‐person cognitive measures. Preliminary analyses suggest MTD has promise for differentiating PET biomarker groups. [ABSTRACT FROM AUTHOR]

Background: Remote assessment tools offer significant promise for aiding early detection of cognitive impairment. Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). We examined the diagnostic accuracy of the SLS and a MTD composite (SLS max span, SLS trials 1–5 total correct, SLS delay correct, [Symbols correct item response time*‐1]) for amnestic mild cognitive impairment (aMCI). We also explored diagnostic accuracy for a broader group that included individuals with possible MCI (pMCI; see Figure 1). Methods: Participants were recruited from the Mayo Clinic Study of Aging for this ancillary remote study. 226 were cognitively unimpaired (CU; concordant CU diagnosis by 3 independent raters). Fifty‐six participants had possible MCI (at least 1 of 3 raters indicated MCI) and 16 had a consensus diagnosis of aMCI. Primary outcome variables were SLS sum of trials, AVLT sum of trials, Symbols correct items response time, and MTD composite. Mean difference analyses used linear model ANOVAs (alpha =.05). Receiver operating characteristic (ROC) curves were applied; we derived optimal cutoff scores based on the Youden index method. Results: Both aMCI and possible MCI groups showed significantly lower performance than the CU group on SLS (Hedge's g aMCI = ‐1.72, pMCI = ‐1.04), Symbols (Hedge's g aMCI = 1.38, pMCI = 0.64), and MTD composite (Hedge's g aMCI = ‐1.89, pMCI = ‐1.09); see Tables 1 and 2 (all p's<.01 even when additionally covarying age, education, sex). Total area under the curve was high for differentiating of CU vs. aMCI (MTD Composite = 0.91, SLS = 0.91) and acceptable for CU vs. pMCI (MTD Composite = 0.77, SLS = 0.77); see Table 2. Consistent with our flexible platform, a variety of device types were used to complete remote testing (32% smartphone, 12% tablet, 56% PC). Conclusions: MTD and the SLS show high diagnostic accuracy for aMCI. MTD is a flexible, brief, and easy‐to‐use remote cognitive assessment tool with great potential for scalable use in future studies seeking to maximize inclusion of individuals with aMCI in clinical trials or for cognitive screening in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Variants in the apolipoprotein E (APOE) gene represent the greatest genetic risk factor for Alzheimer disease (AD). Carriers of the ε4 allele have an increased risk of developing AD pathology and a lower age of symptom onset. While prior work has focused on the sporadic form of AD, little is known about how the ε4 allele impacts AD‐specific pathology in autosomal dominant AD (ADAD). Method: 430 participants from the Charles and Joanne F. Knight Alzheimer Disease Research Center and 276 ADAD mutation‐carriers from the Dominantly Inherited Alzheimer Network, were included in these analyses. Participants were classified as ε4‐positive or ‐negative, and further grouped using Clinical Dementia Rating® (CDR®) scores as cognitively impaired or –unimpaired (Table 1). All participants completed magnetic resonance imaging (MRI), beta‐amyloid positron emission tomography (Ab‐PET), and cognitive testing within a 12‐month period. Result: Using regression models, we replicated prior work demonstrating that the ε4 allele is associated with an increase in AD pathology measured by Ab‐PET and MRI in a sporadic AD cohort. Furthermore, the ε4 allele was also associated with a decline in cognitive performance. Follow‐up pairwise comparisons revealed that within the sporadic‐AD cohort, the influence of the ε4 allele was most pronounced in individuals who were cognitively impaired. In contrast, in a cohort of individuals possessing autosomal dominant mutations, the ε4 allele was not associated with increases in pathology measured by Ab‐PET and MRI, nor was it associated with a decrease in cognition (Table 1; Figure 1). Conclusion: We present quantitative evidence that the APOE‐ ε4 allele differentially impacts the pathology and cognitive outcomes of sporadic‐ and autosomal dominant AD. In those with sporadic AD, possession of at least one copy of the ε4 allele is associated with increased amyloid deposition and cortical thinning, as well as decreased cognition, particularly in individuals who are cognitively impaired. However, in ADAD mutation‐carriers, these same patterns are not evident. We propose that this is due to the overwhelming influence of the autosomal dominant genetic mutations that drive this unique and rare form of AD. [ABSTRACT FROM AUTHOR]

Background: White matter (WM) injury visible on MRI is a common finding in Alzheimer's disease (AD) and is often attributed to small vessel ischemic changes secondary to increased systemic vascular risk. Increased WM injury has been associated with the progression of Autosomal Dominant AD (ADAD), though ADAD pathogenic variant carriers are relatively young and may not have elevated vascular risk factors. We hypothesized that WM injury in ADAD may reflect worsening of cerebral amyloid angiopathy (CAA) and neurodegeneration. Here we examine this hypothesis using cross‐sectional and longitudinal data from the Dominantly Inherited Alzheimer Network observational study (DIAN). Method: MRI data from ADAD pathogenic variant carriers (n=223) and non‐carriers (n=136) were used in the present study (Table 1). We extracted FreeSurfer‐based WM lesion (WML) volume from T1‐weighted images (hypointensities). Cortical microbleed (CMB) burden was assessed visually on susceptibility weighted/T2*‐weighted gradient echo images by experienced radiologists (blineded to the mutation status) at the Mayo Clinic in Rochester. Linear regression models compared WML volume at baseline in people with and without CMB. Linear mixed effect models assessed the relationships between longitudinal WML and both CMBs and FreeSurfer‐based total gray matter (GM) volume. Models were corrected for age and estimated years to symptom onset (EYO). Result: Greater baseline WML volume was seen in ADAD carriers vs. non‐carriers, particularly close to the age of estimated symptom onset. Baseline WML volume was greater in carriers with CMBs compared to those without (t=2.9, p=0.003, Figure 1). Longitudinal increase in WML amongst ADAD pathogenic variant carriers with CMBs was estimated to be 214 mm3/year greater than that amongst carriers without CMBs (t=4.1, p<0.001, Figure 2). Independent of this CMB effect, decreasing GM volume was strongly associated with increasing longitudinal WML volume (t=‐6.2, p<0.001, Figure 3). Similar analyses in the non‐carrier group yielded no significant findings. Conclusion: Consistent with prior reports, WML volume was increased in ADAD pathogenic variant carriers. However, the results here suggest WML in ADAD may not solely be due to small vessel ischemic changes, but rather may be a result of worsening CAA and more rapid neurodegeneration. [ABSTRACT FROM AUTHOR]

Introduction: Longitudinal changes in Alzheimer's disease (AD) biomarkers, including cerebrospinal fluid (CSF) analytes, amyloid uptakes from positron emission tomography (PET), structural outcomes from magnetic resonance imaging (MRI), and cognition, have not been compared between Blacks and Whites. Methods: A total of 179 Blacks and 1180 Whites who were cognitively normal at baseline and had longitudinal data from at least one biomarker modality were analyzed for the annual rates of change. Results: CSF amyloid beta (Aβ)42/Aβ40 declined more slowly (P =.0390), and amyloid (PET) accumulated more slowly (P =.0157), in Blacks than Whites. CSF Aβ42 changed in opposite directions over time between Blacks and Whites (P =.0039). The annual increase in CSF total tau and phosphorylated tau181 for Blacks was about half of that for Whites. Discussion: Longitudinal racial differences in amyloid biomarkers are observed. It will be important to comprehensively and prospectively examine the effects of apolipoprotein E genotype and sociocultural factors on these differences. [ABSTRACT FROM AUTHOR]

As prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and "sporadic" late‐onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy‐confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy‐confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences. [ABSTRACT FROM AUTHOR]

Background: Smartphone‐based ecological momentary assessments (EMA) have the potential to capture subtle changes in cognition associated with preclinical Alzheimer disease (AD) in older adults. The Ambulatory Research in Cognition (ARC) smartphone application is based on EMA principles and administers brief tests of associative memory, processing speed, and working memory ∼4 times per day over 7 consecutive days. Participants perform these 7‐day cycles every ∼6 months, longitudinally. ARC was designed to be performed unsupervised, on participants' personal devices, and in their everyday environments in order to increase accessibility, testing, and reliability. Method: We evaluated the reliability, validity, and feasibility of ARC in a large, well‐characterized sample of cognitively normal older adults (ages 65‐97) and individuals with very mild dementia (ages 61‐88). Participants completed at least one 7‐day cycle of ARC testing along with conventional cognitive assessments. Most participants also had cerebrospinal fluid, amyloid and tau PET, and structural MRI data available. The reliability, validity, and feasibility of ARC as an unsupervised, high‐frequency cognitive assessment tool using participants' personal smartphones was tested by examining (1) between‐subjects and test‐retest reliability, (2) correlations with age, conventional cognitive measures, and AD‐related biomarkers, and (3) adherence and attrition rates of older adults with varying technology familiarity. Result: First, ARC tasks demonstrated good reliability such that between‐person reliability across the 7‐day cycle and test‐retest reliabilities at 6‐month and 1‐year follow‐ups all exceeded intraclass correlations of 0.85. Second, ARC demonstrated construct validity as evidenced by correlations with conventional cognitive measures. Third, ARC measures correlated with AD biomarker burden at baseline to a similar degree as conventional cognitive measures. Finally, high adherence rates and low attrition rates indicated that ARC was feasible and, to some extent, enjoyable, for older adult participants. Conclusion: Ultimately, these results suggest that ARC is a reliable and valid measure of cognition in older adults at risk for AD and is a feasible tool for assessing subtle cognitive changes associated with the earliest stages of AD. [ABSTRACT FROM AUTHOR]

Background: Neuroinflammation, an intrinsic immune response of the nervous system, is important in an early phase of Alzheimer Disease (AD) pathogenesis. However, it is largely unclear how neuroinflammation promotes the clinical progression of AD. Extracellular water edema (ECW) from Neuro‐inflammation imaging (NII), a diffusion‐based MRI approach for neuroinflammation, has been utilized to asses white matter (WM) neuroinflammation in AD. This study aims to investigate the relationship between WM neuroinflammation and longitudinal cognitive changes in AD. Method: This study included 286 participants who were enrolled in ongoing research at the Knight ADRC at Washington University in Saint Louis. The CSF levels of Aβ42/Aβ40 and ptau181 measured with the automated Lumipulse platform were used to categorize participants as being positive or negative amyloid (A) and tau (T), respectively (Table 1). As measures of cognitive performance, the Knight ADRC Preclinical Alzheimer Cognitive Composite (Knight ADRC‐PACC) and a Global composite score were employed for each individual. The rates of change of cognitive measures were calculated for each individual. The NII was acquired with a multi‐b value scheme (bmax =1400s/mm2 and 25 directions). The total WM neuroinflammation (NII‐ECW) was obtained using TBSS (http://www.fmrib.ox.ac.uk/fsl) based WM skeleton. Randomise in FSL was utilized to examine voxel‐wise relationships between NII‐ECW and the rate of change of cognitive measures. Non‐parametric Kruskal‐Wallis and Chi‐square tests were employed for comparing continuous and categorical variables of the participants' demographics, respectively. The group differences of NII‐ECW were tested using one‐way ANOVA. All statistical tests were conducted in R (R Core Team, 2020). Age, sex, and APOE ε4 carrier status were adjusted as covariates. Result: Table 1 summarizes demographic data. When compared to the A‐T‐ group, the A+T+CDR0 and A+T+CDR0.5 groups had higher NII‐ECW (Figure 1). When the A+T+CDR1 group was compared to the other groups, there was no group difference in NII‐ECW. There were substantial negative associations between the NII‐ECW and the rates of change of cognitive measures in the widespread WM regions (Figure 2). Conclusion: Our data suggest that neuroinflammation increases early in preclinical AD and decreases later in the disease. Furthermore, baseline neuroinflammation as measured by NII‐ECW predicts cognitive decline in AD. [ABSTRACT FROM AUTHOR]

Background: Estimates of "brain‐predicted age" quantify apparent brain age compared to normative trajectories of neuroimaging features. Brain‐predicted age is elevated in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior studies have typically modeled brain‐predicted age with volumetric magnetic resonance imaging (MRI), but other modalities, including resting‐state functional connectivity (FC) and multimodal MRI, have more recently been explored. In the present study, we tested the sensitivity of brain‐age predictions derived from volumetric, FC, and multimodal MRI to group differences in AD status and individual differences in cognition. Method: We trained three Gaussian process regression models to predict age from (1) FC, (2) regional brain volumes, and (3) multimodal MRI (FC+volumetric) in 493 control participants (cognitively normal, amyloid‐negative [A‐], 18‐89 years old). We applied the trained models to independent samples staged by the presence or absence of amyloid (A+/A‐) and tau (T+/T‐) using PET (Centiloid) and CSF (amyloid β‐42/40 ratio, phosphorylated tau 181): 122 A‐T‐, 62 A+T‐, 62 A+T+, as well as 154 symptomatic AD participants. Global cognition was assessed using an established composite of cognitive tests across multiple domains. Result: All models successfully predicted age in the control training set, with the multimodal model (mean absolute error [MAE]=5.3 years) outperforming the unimodal volumetric (MAE=6.0 years) and FC models (MAE=8.3 years) (Figure 1). In all three models, the brain age gap (BAG) was significantly elevated in symptomatic AD compared to cognitively normal controls. In the FC and multimodal models, the BAG was significantly reduced in A+T+ participants compared to T‐ controls (Figure 2). In symptomatic AD, lower global cognitive composite scores were associated with elevated volumetric and multimodal BAGs (Figure 3). Conclusion: Both FC and volumetric estimates of brain age are sensitive to symptomatic AD, consistent with previous reports. Additionally, FC‐based brain age may exhibit a biphasic response to preclinical AD pathology while volumetric brain age may be sensitive to cognitive decline in the symptomatic stage. Multimodal estimates of brain age capture each of these modality‐specific relationships, and further, improve sensitivity to healthy age differences. Hence, multimodal neuroimaging models may be useful in staging early symptomatic and preclinical AD. [ABSTRACT FROM AUTHOR]

Background: In Alzheimer's disease (AD), patients and caregivers often report a pattern of increasing cognitive and behavioral difficulties in evening hours, known colloquially as "sundowning." Here, we describe the first known attempt to capture circadian fluctuations in cognition in humans at risk for AD using a novel smartphone assessment that samples cognition rapidly and repeatedly over a 7‐day period. We hypothesized that cognitive sundowning would manifest among individuals with abnormal AD biomarkers, even in preclinical stages of AD. Methods: Older adults (N=168, Ages 61‐94 years) were asked to complete 4 brief smartphone‐based testing sessions per day for 7 consecutive days at quasi random intervals throughout the day, from morning until evening. Tests included associate memory, processing speed, and visual working memory tasks (Figure 1). Cerebrospinal fluid biomarkers for beta amyloid (Ab42) and phosphorylated tau181 (ptau) were collected within ∼3 years of smartphone tests. Scores from morning hours (range ∼5am – 12pm) were averaged for comparison with averaged scores from evening hours (after 12pm). Mixed effect models evaluated time of day effects by CSF biomarker status and clinical status. Results: Participant characteristics are provided in Table 1. Models with terms for age, gender, education, APOE e4 status, and Clinical Dementia Rating™ Sum of Boxes tested the interaction between time of day and CSF ptau/Ab42 ratio in predicting cognition (Figure 2). Compared to those with normal biomarkers, participants with abnormal CSF ptau/Aβ42 ratio had worse associate memory (p=0.03) and processing speed performance (p=0.026) in evening hours. On processing speed, participants with abnormal ptau/Aβ42 ratios were slower by an average of 700ms in the evening compared to those with normal ptau/Aβ42 ratios tested in morning hours. When models were run in cognitively normal participants only, a similar pattern was observed, confirming the hypothesis that increasing loads of AD pathology result in worse cognitive performance in evening hours prior to symptom onset. Conclusion: Our results provide the first known evidence of cognitive sundowning in preclinical and early symptomatic AD and suggest that time of day is an important consideration for studies of cognition in AD populations. [ABSTRACT FROM AUTHOR]

Background: Cognitive decline in Alzheimer Disease (AD) is strongly correlated to the spatiotemporal pattern of tau‐protein neurofibrillary tangles (NFTs). Evidence suggests tau seeding spreads transneuronally in a prion‐like manner, a biological process distinct from NFT aggregation in previously‐affected regions. We previously proposed a method for quantifying global tau spatial spread (TSS) using tau positron emission tomography (PET) which found TSS is particularly informative in preclinical AD. The spread of tau pathology into specific regions may therefore be key to the onset of cognitive decline. Methods: 445 older adult and 21 younger control participants were recruited. Cognition was assessed with the Knight ADRC‐PACC and Tau‐PET images were parcellated using the Schaeffer400 atlas. For each region, TSS was calculated as the proportion of voxels with significant levels of tau. Abnormal voxels were identified by z‐scoring tau‐PET voxel‐wise relative to corresponding voxels in younger controls and then thresholded at z>1.96 for significance. This study predicted participant PACC from regional TSS using random forest regression (trained with 100 repeated 10‐folds). Feature importance (FI) was calculated with Mean Decrease in Impurity to identify regions in which tau spread is most significant to cognitive impairment. Results: Regional TSS demonstrated highly saturated regions in the limbic and temporal lobes with progressively lower levels in peripheral regions, largely sparing the frontal lobe (Figure 1). However, the importance of regional spread in predicting cognition did not reflect the same spatial pattern (Figure 2). Four regions emerged with the highest FI in the limbic and default networks, indicating the spread of tau within these brain regions is key to predicting cognitive impairment (Table 1). Conclusion: Our results suggest that tau pathology has spread extensively within medial and temporal regions early in AD. Regional TSS can be used to identify peripheral regions in which tau pathology is currently spreading into. As expected, limbic regions in the temporal lobe were critical to predicting participant cognitive impairment. However, regions in the temporal and parietal lobes belonging to the default network were additionally informative. Spread of tau pathology into new peripheral brain regions should therefore be evaluated when predicting future AD‐related cognitive decline. [ABSTRACT FROM AUTHOR]

Background: There has been a rise in remote cognitive testing for neurodegenerative diseases. The ALLFTD Mobile App is designed to capture clinical features of frontotemporal dementia (FTD) through repeated remote assessment on smartphones. This is a departure from traditional neuropsychological practice in which a single examiner‐led assessment occurs in a controlled setting. When participants complete tasks on their own, those with greater disease severity may be more variable in their performance for a variety of reasons (e.g. greater distractibility, less engagement). Here we study whether intraindividual cognitive variability (IIV) is associated with older age and increasing disease severity. Method: Participants were 279 (mean age = 54.9) healthy controls and participants with sporadic FTD or from familial FTD kindreds with a range of disease severities based on FTLD Clinical Dementia Rating scale (CDR®+NACC‐FTLD Global) scores: 0 (n = 132), 0.5 (n = 54), and ≥1 (n = 47). Participants completed three cognitive tasks at least two times across twelve days. Measures included an adaptive memory task and gamified versions of Flanker and Go/no‐go tasks. IIV was operationalized as the standard deviation in performance on each task. Regression analyses investigated the relationship of IIV with CDR®+NACC‐FTLD group, and with age in those without symptoms. Education and gender were included as covariates in all analyses. Result: Greater disease severity (CDR®+NACC‐FTLD) was associated with greater variability on Flanker (p<.001) and Go/No‐go (p =.02) tasks; a positive but non‐significant association was seen with memory testing (p =.16). Post‐hoc comparisons found that those with CDR®+NACC‐FTLD of 0.5 had significantly more IIV on the Flanker task (훃 =.39, p =.004) than those with CDR®+NACC‐FTLD's of 0. Moreover, those with a CDR®+NACC‐FTLD ≥1 had significantly greater IIV on the Flanker (훃 =.85, p<.001) and Go/No‐go tasks (훃 =.51, p =.007) than those with a CDR®+NACC‐FTLD of 0. In those without symptoms (CDR®+NACC‐FTLD = 0), age was associated with increased IIV on the Flanker (훃 =.27, p<.001) and Go/no‐go (훃 =.23, p<.001) tasks. Conclusion: Increased IIV, particularly on processing speed/executive functioning tasks, was associated with greater age and greater functional impairment, even in the mildest stages. Future investigations exploring predictors of IIV and the prognostic value of IIV are warranted. [ABSTRACT FROM AUTHOR]

Background: Breakdowns in attentional control may underscore cognitive changes seen in the earliest stages of Alzheimer's disease (AD). One way to measure these changes is to assess variability in cognitive performance. Variability in cognition may be most evident when repeatedly sampled over hours or days and in different environmental contexts or at different times of day. We analyzed data from an ongoing smartphone‐based study of cognition in AD which utilizes extremely brief cognitive tests taken remotely over several days in natural environments. Method: Older adults (N = 319, aged 75 +/‐ 5 years) completed repeated cognitive testing on the Ambulatory Research in Cognition (ARC) platform and had CSF or PET amyloid biomarkers and APOE genotype available. RTSD was assessed in a brief processing speed task by taking the SD of correct RTs. All models adjusted for baseline age, education, and gender. Result: Averaging across sessions, symptomatic participants (indicated by Clinical Dementia Rating® (CDR®) > 0) showed more variability than asymptomatic (CDR 0) participants (p = 0.009). APOE ε4 carriers showed more variability than non‐carriers, although this was not significant (p = 0.067). More fine‐grained models considered RTSD within each session (e.g. from trial‐to‐trial) using mixed effects models. Symptomatic participants exhibited greater trial‐to‐trial RTSD compared to asymptomatic participants (p = 0.001). APOE ε4 carriers also exhibited greater trial‐to‐trial RTSD compared to non‐carriers (p = 0.004). When limiting the sample to asymptomatic participants to determine if there were changes in the preclinical stage of AD, there remained a strong APOE ε4 effect, with carriers exhibiting greater trial‐to‐trial RTSD than non‐carriers (p = 0.005). Interestingly, amyloid was not significant in any models. Conclusion: Older adults at risk for AD show increased RTSD even in brief assessments. These subtle fluctuations in attention were most evident in APOE e4 carriers at very short time intervals, and less so when averaged across trials and sessions. Overall, these findings suggest that fluctuations in attention in preclinical AD are most evident from moment to moment, highlighting the need for fine‐grained and repeated assessment of attentional control. [ABSTRACT FROM AUTHOR]

Background: Alzheimer disease (AD) biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis, and identifying target treatments. Although the number of biomarkers continues to grow, the utility and uniqueness of each is poorly understood. Method: Data were obtained from 527 community dwelling volunteers enrolled in memory and aging studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St. Louis, MO. Hierarchical clustering was used to group 27 cross‐sectional imaging, cerebrospinal fluid (CSF), and plasma measures of Aβ, tau, neuronal injury, and inflammation drawn from magnetic resonance imaging, positron emission tomography (PET), mass‐spectrometry assays, and immunoassays. Neuropsychological and genetic measures were also included. Feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET positive individuals. Result: Four main clusters emerged reflecting core AD pathology (amyloid and tau), neurodegeneration, AT8 antibody‐associated phosphorylated (p‐tau), and neuronal dysfunction and inflammation. Relative to other measures, markers from the core AD pathology cluster were the best predictors of amyloid PET status with CSF pT217/T217 (%) being the most prominent followed by CSF pT111/T111 (%) and CSF ptau181/Aβ40. In the entire cohort, Aβ and tau markers (CSF pT217/T217 (%), total‐tau/Aβ40, ptau181/Aβ40) were also the best predictors of dementia. When examining only those with positive amyloid PET, CSF total‐tau/Aβ40 was the strongest predictor of cognitive impairment followed by tau PET, CSF ptau181/Aβ40, and pT217/T217 (%). Plasma Aβ42/40 alone did not perform as well as other PET and CSF measures. CSF measures of neuronal dysfunction and inflammation were relatively poor predictors of amyloid and cognitive status. Conclusion: Although many AD biomarkers are interrelated, relatively few have the ability to strongly predict clinical status. Relative to other markers, CSF pT217/T217 (%) is the best predictor of amyloid and impairment status across all individuals, while CSF total‐tau/Aβ40 is the best predictor of impairment in individuals already on the AD trajectory. Most non‐specific markers perform poorly in the context of AD. The current research provides insight into which biomarkers may be most useful in AD clinical practice and trials. [ABSTRACT FROM AUTHOR]