Search

INTRODUCTION: The LIfestyle for BRAin Health (LIBRA) index yields a dementia risk score based on modifiable lifestyle factors and is validated in Western samples. We investigated whether the association between LIBRA scores and incident dementia is moderated by geographical location or sociodemographic characteristics. METHODS: We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort‐specific Cox proportional hazard regression analyses in a two‐step individual participant data meta‐analysis. RESULTS: A one‐standard‐deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow‐up. No interactions with sex, education, or socioeconomic position were observed. DISCUSSION: Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups. Highlights: A two‐step individual participant data meta‐analysis was conducted.This was done at a global scale using data from 21 ethno‐regionally diverse cohorts.The association between a modifiable dementia risk score and dementia was examined.The association was modified by geographical region and age at baseline.Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Reproductive health history may contribute to cognitive aging and risk for Alzheimer's disease, but this is understudied among Hispanic/Latina women. METHODS: Participants included 2126 Hispanic/Latina postmenopausal women (44 to 75 years) from the Study of Latinos‐Investigation of Neurocognitive Aging. Survey linear regressions separately modeled the associations between reproductive health measures (age at menarche, history of oral contraceptive use, number of pregnancies, number of live births, age at menopause, female hormone use at Visit 1, and reproductive span) with cognitive outcomes at Visit 2 (performance, 7‐year change, and mild cognitive impairment [MCI] prevalence). RESULTS: Younger age at menarche, oral contraceptive use, lower pregnancies, lower live births, and older age at menopause were associated with better cognitive performance. Older age at menarche was protective against cognitive change. Hormone use was linked to lower MCI prevalence. DISCUSSION: Several aspects of reproductive health appear to impact cognitive aging among Hispanic/Latina women. [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: Few studies have examined the associations of psychosocial factors with cognitive change in Hispanics/Latinos. METHODS: Data from the Hispanic Community Health Study/Study of Latinos‐Investigation of Neurocognitive Aging (HCHS/SOL INCA) and Sociocultural studies were used (n = 2,155; ages ≥45 years). Psychosocial exposures included intrapersonal (ethnic identity, optimism, purpose in life), interpersonal (family cohesion, familism, social networks, social support), and social factors (ethnic discrimination, loneliness, subjective social status). Survey‐linear regression models examined associations between psychosocial exposures and 7‐year cognitive change (global cognition [GC], verbal learning, memory, word fluency [WF], and digit symbol substitution [DSS]). RESULTS: Familism predicted decline in GC, verbal learning, and memory; family cohesion predicted DSS decline; and loneliness predicted memory decline. Ethnic identity was protective against decline in GC and memory, optimism and social support were protective against decline in memory, and purpose in life was protective against WF decline. DISCUSSION: Psychosocial factors are differentially related to cognitive changes. Culturally relevant factors should be explored in Hispanic/Latino cognitive aging research. Highlights: Psychosocial factors are differentially related to cognitive changes in Latinos.Role of culturally relevant factors on cognition should be further explored.Familism predicted decline in global cognition, verbal learning, and memory.Ethnic identity predicted increase in global cognition and memory. [ABSTRACT FROM AUTHOR]

Background: Greater within‐person variability in cognitive performance on mobile devices is emerging as a sensitive marker of mild cognitive impairment (MCI). It is unclear if sociodemographic differences are associated with this variability, and if so, whether these differences moderate associations between MCI status and within‐person variability. We use a digital health approach with ecological momentary assessments (EMA) to examine sociodemographic differences in within‐day variability among individuals with MCI and those who were cognitively unimpaired (CU). Method: A sample of 308 community‐dwelling older adults from the Einstein Aging Study (mean age = 77.49 years, SD = 4.86, range = 70‐90; 67% female; 45% non‐Hispanic White, 41% non‐Hispanic Black) completed neuropsychological testing and self‐reported questionnaires. Ninety‐eight individuals met Jak/Bondi criteria for MCI. All participants performed mobile tests of processing speed and memory binding on a smartphone up to six times daily over a two‐week period. We employed heterogeneous variance multilevel models to assess sociodemographic and MCI status associations with within‐day variability across assessments. We further tested whether differences in age, gender, race, and education moderated associations between MCI status and within‐day variability. Result: For processing speed, greater variability was apparent in individuals with MCI, non‐Hispanic Black participants, and individuals with less education (ps<.001). A MCI status*gender interaction occurred; the association between MCI status and variability remained significant for both genders but was smaller among women (p<.01). For memory binding, greater variability was apparent in individuals with MCI, older participants, non‐Hispanic Black participants, and individuals with less education (ps<.001). Numerous interactions occurred; in separate analyses, associations between MCI status and variability remained significant, but were smaller among women, non‐Hispanic Black participants, and individuals with less education (ps<.05). Conclusion: Individuals with MCI exhibited greater within‐day variability in mobile cognitive performance than those who were CU, but the strength of these associations varied by sociodemographic factors. These indices of variability in everyday life may serve as sensitive markers of early cognitive impairment. Future research should examine daily mechanisms and environmental perturbations (e.g., stress, sleep, air pollution) that may explain why associations between MCI status and cognitive variability appeared smaller among women, non‐Hispanic Black participants, and individuals with less education. [ABSTRACT FROM AUTHOR]

Introduction: Sex differences in dementia risk, and risk factor (RF) associations with dementia, remain uncertain across diverse ethno‐regional groups. Methods: A total of 29,850 participants (58% women) from 21 cohorts across six continents were included in an individual participant data meta‐analysis. Sex‐specific hazard ratios (HRs), and women‐to‐men ratio of hazard ratios (RHRs) for associations between RFs and all‐cause dementia were derived from mixed‐effect Cox models. Results: Incident dementia occurred in 2089 (66% women) participants over 4.6 years (median). Women had higher dementia risk (HR, 1.12 [1.02, 1.23]) than men, particularly in low‐ and lower‐middle‐income economies. Associations between longer education and former alcohol use with dementia risk (RHR, 1.01 [1.00, 1.03] per year, and 0.55 [0.38, 0.79], respectively) were stronger for men than women; otherwise, there were no discernible sex differences in other RFs. Discussion: Dementia risk was higher in women than men, with possible variations by country‐level income settings, but most RFs appear to work similarly in women and men. [ABSTRACT FROM AUTHOR]

Background: A growing body of evidence suggests that neuroinflammation contributes actively to pathophysiology Alzheimer's disease (AD) and promotes AD progression. The predictive value of neuroinflammatory biomarkers for disease‐staging or estimating disease progression is not well understood. In this study, we investigate the diagnostic (i.e., differentiating clinical stages of disease) and prognostic (estimating probability of cognitive decline) ability of inflammatory biomarkers in combination with conventional AD biomarkers. Methods: We included 242 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had CSF biomarkers of Aβ, tau, and inflammation. Outcome of interest was clinically meaningful cognitive decline (CMCD) as defined by an increase of ≥4 on the Alzheimer's Disease Assessment Scale Cognitive Subscore 11 (ADAS‐11, scores 0‐70, higher scores indicate worse cognition). Predictor variables were categorized as demographics (D; age, sex, and education), genetic information such as APOE4 status (G), inflammatory biomarkers (I; see Table 1 for details), and cerebrospinal fluid (CSF) biomarkers of β‐amyloid (A), p‐Tau (P), and total Tau (T). Logistic regression was performed to investigate if grouped inclusion of eleven CSF inflammatory biomarkers as covariates in the models improved classification of baseline clinical diagnosis (CN, cognitively normal; MCI, mild cognitive impairment; Dementia) as well as classification of individuals with and without CMCD one year after baseline. Results: At 1‐year follow up, 25.6% experienced CMCD. Inclusion of inflammatory biomarkers improved classification of MCI vs Dementia for models base on DG (p = 0.039) and DGAPT (p = 0.016) feature‐sets. Addition of inflammatory biomarkers to model with DGAPT features, but not the DG model, improved predictive performance for CMCD in CN (p = 0.021) and MCI (p < 0.001) participants. Conclusions: Addition of CSF Inflammatory biomarkers to AD biomarkers can improve diagnostic accuracy of clinical disease stage. Furthermore, inflammatory biomarkers add incremental value to AD biomarkers for prediction of clinical course and cognitive decline. [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: β‐amyloid (Aβ), a pathologic hallmark of Alzheimer's disease (AD) is the target of recently FDA‐approved drugs. In trials and in practice, identifying individuals likely to be β‐amyloid positive (Aβ+) with accessible and affordable tests will be increasingly important. Blood‐based biomarkers (BBMs) have shown early promise in determining amyloid status. We aimed to develop machine learning‐based integer risk scores using BBMs and other accessible measures to predict Aβ positivity by PET in older, non‐demented (cognitively unimpaired or mild cognitive impairment) adults. Methods: We used data from 722 individuals (72.1 ± 7.0 years old) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to develop integer risk scores for predicting Aβ positivity. The sample was divided into training (75%) and test (25%) sets. Variables were categorized as demographics (D: age, sex, and education), genetic information (G: APOE4 status), cognitive measures (C: ADAS‐Cog‐13 and MMSE), and plasma biomarkers of p‐Tau181(T) and neurofilament light (N: NfL). Feature‐sets were formed as hierarchically constructed combinations of these categorized variables (see Figure 1). Using the AutoScore algorithm, variables were ordered according to their Random Forest Gini Index and were iteratively included in the construction of logistic regression‐derived risk score models. Variables were included based on increases in Area Under the ROC Curves (AUCs). Results: Four models were developed based on four feature‐sets. All models utilized age and APOE4 status and, when considered, all models retained ADAS‐Cog‐13, p‐Tau, and NfL. The best performing model corresponded to the DGCTN feature‐set with performance on the dedicated test set of AUC = 0.810 (C.I.: 0.745‐0.876). Using the DGCTN model on the test set, 97% of individuals with scores in the top quartile of possible risk score points were correctly identified as Aβ+. Conclusions: We developed risk score models utilizing accessible and affordable measures to predict Aβ positivity. Plasma biomarkers improve performance of risk scores indicating their value. Applications include screening in clinical settings for therapeutic care and enrichment of clinical trials. These data can be used to optimize the efficient use of PET in determining eligibility for clinical trials and amyloid targeted therapies in practice. [ABSTRACT FROM AUTHOR]

Background: The Einstein Aging Study uses smartphones in an intensive burst design to digitally assess cognition up to 6 times daily for 2 weeks once a year. These burst assessments facilitate the estimation of novel cognitive markers including learning effects and asymptotic performance within bursts and over annual assessments. Here, we examine the influence of two blood‐based biomarkers, pTau‐181 and Neurofilament Light protein (NfL), on cognitive performance measured by digital cognitive markers at baseline and across 6 annual burst assessments. Method: Participants were 293 older adults (Age = 77.4±4.9) free of dementia at baseline. Negative exponential models were used to examine two primary features of response time from the Symbol Search Test of processing speed: (1) learning rate across days within a burst and its change in rate of learning over annual follow‐up; and (2) asymptotic performance (i.e., fastest response time) at baseline and asymptotic change (the rate in which asymptotic performance changes across years). Asymptotic performance is thought to reflect best possible performance and to be relatively free of practice effects. The associations of pTau‐181 and NfL with these features at baseline and change over annual follow‐up were evaluated, adjusting for baseline age, sex, years of education and race/ethnicity. Result: Primary model results are summarized in Table 1. The expected trajectory of symbol search response time across 16 study days within each of the six annual waves, for baseline levels of pTau‐181 and NfL is shown in Figures 1 and 2. Higher levels of both Tau‐181 and of NfL were associated with slower rates of learning in the baseline burst. Higher levels of these BBB at baseline were also associated with slower learning rates from burst to burst (over years) and with greater slowing in asymptotic performance over annual follow‐ups. Conclusion: Higher levels of BBB of Tau and of neurodegeneration are associated with slower learning and with slower response times. These novel parameters derived from ambulatory cognitive testing show expected relationships with biomarkers supporting their validity. Future applications of ambulatory cognition could include the identification of subtle cognitive impairment and in measuring within person change in cognition in clinical trials or clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: Studies suggest bilingualism may delay behavioral manifestations of adverse cognitive aging including Alzheimer's dementia. Methods: Three thousand nine hundred sixty‐three participants (unweighted mean population age ≈56 years) at Hispanic Community Health Study/Study of Latinos baseline (2008–2011) self‐reported their and their parents' birth outside the United States, Spanish as their first language, and used Spanish for baseline and comparable cognitive testing 7 years later (2015–2018). Spanish/English language proficiency and patterns of use were self‐rated from 1 = only Spanish to 4 = English > Spanish. Cognitive testing included test‐specific and global composite score(s) of verbal learning, memory, word fluency, and Digit Symbol Substitution (DSS). Survey linear regression models examined associations between baseline bilingualism scores and cognition. Results: Higher second‐language (English) proficiency and use were associated with higher global cognition, fluency, and DSS at follow‐up and better than predicted change in fluency. Discussion: The bilingual experience was more consistently related to 7‐year level versus change in cognition for Hispanics/Latinos. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to investigate whether coexistent self-reported neck pain influences cephalic and extracephalic pain sensitivity in individuals with migraine and tension-type headache (TTH) in relation to diagnosis and headache frequency. Methods: A population of 496 individuals completed a headache interview based on ICHD criteria, providing data collected by self-administered questionnaires, assessments of pericranial total tenderness score (TTS) and pressure pain thresholds (PPT). Stimulus-response (SR) functions for pressure vs. pain were recorded. Presence of neck pain in the past year was assessed by the self-administered questionnaire. We categorized participants by primary headache type. We also categorized participants into 3 groups by headache frequency: chronic (≥15) or episodic (<15 headache days/month) headache and controls. TTS, PPTs and the area under the SR curve were compared between subgroups using Generalized Linear Models with pairwise comparisons controlling for age and sex. Results: Individuals with chronic followed by episodic headache had higher TTS than controls (overall p≤0.001). The difference between chronic and episodic headache subgroups was significant in the group with neck pain (p≤0.001) but not in the group without neck pain. In individuals with neck pain, mean TTS was higher in coexistent headache (migraine and TTH), 23.2 ± 10.7, and pure TTH, 17.8 ± 10.3, compared to pure migraine, 15.9 ± 10.9 and no headache 11.0 ± 8.3 (overall p<0.001). Temporal and finger PPTs did not statistically differ among the chronic headache, the episodic headache and controls in individuals with and without neck pain. Temporalis and trapezius SR-functions showed that tenderness was increased in individuals with chronic headache to higher degree than in those with episodic headache, and more so in those with neck pain. Conclusions: Coexistent neck pain is associated with greater pericranial tenderness in individuals with chronic headache and to a lesser degree in those with episodic headache. Sensitization may be a substrate or consequence of neck pain and primary headache, but a longitudinal study would be needed for further clarification. [ABSTRACT FROM AUTHOR]

Background: Excessive alcohol intake and no alcohol intake are associated with an increased risk of dementia relative to low to moderate alcohol consumption. We aimed to investigate the cross‐sectional association between different levels of alcohol consumption and cognition in the Anti‐Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4 Study). We examined whether this association would differ between sexes or based on individuals' β‐amyloid (Aβ) status (Aβ+ vs Aβ‐; Aβ+: PET Composite SUVR > 1.15, Aβ‐: Others). Method: A total of 4387 cognitively unimpaired older adults who had both objective and subjective cognitive assessments and amyloid Positron Emission Tomography (PET) imaging. Participants were classified into 4 categories based on their average daily alcohol use: none, one, two, and three or more drinks/day. Multivariable linear regression was used to test the main effects as well as interactions with sex and Aβ levels. Result: Participants were on average 71.28 (SD = 4.67) years old and 59% female. In the whole population, individuals who reported no alcohol consumption had lower scores on the Preclinical Alzheimer Cognitive Composite (PACC) compared to those who reported consuming one (β[SE] = 0.19[0.08], p = 0.017) or two (β[SE] = 0.22[0.11], p = 0.05) drinks/day (Table 1, Figure 1). In sex‐stratified analysis, the association between alcohol consumption and cognition was more prominent in females. Female participants who consumed two drinks/day had better performance on PACC (β[SE] = 0.32[0.16], p = 0.043) and Cognitive Function Index (CFI; β[SE] = ‐0.27[0.12], p = 0.026) compared to those who reported no alcohol consumption (Figure 2). In an Aβ status‐stratified sample, the association between alcohol consumption and cognition was present only in the Aβ‐ subgroup. Among Aβ‐ individuals, those who consumed one drink/day had better performance on PACC (β[SE] = 0.28[0.09], p = 0.003) and CFI (β[SE] = ‐0.18[0.07], p = 0.009) compared to those who reported no alcohol consumption (Figure 3). The interaction between Aβ status and alcohol consumption on cognition was not significant. Conclusion: Low or moderate consumption of alcohol is associated with better cognitive performance in cognitively unimpaired individuals. The association is more prominent in Aβ‐ individuals, which might indicate that the pathophysiologic mechanism underlying the effect of alcohol on cognition is independent of AD‐pathology. The interaction with Aβ status requires further investigation with larger samples consuming 3 or more drinks/day. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a phenotypically and pathologically heterogenous disorder. We used MRI in amyloid positive patients enrolled in the placebo arm of the Phase 3 Trial of solanezumab for mild dementia due to AD (EXPEDITION3) to identify more homogeneous subgroups based on variation in volumetrics. Methods: We implemented the self‐organizing map algorithm on baseline volumetric MRI measures from nine brain regions of interest (ROIs) to cluster 1041 individuals enrolled in the placebo arm of the EXPEDITION3 trial. Volumetric MRI differences were compared among clusters. Demographics as well as baseline and longitudinal cognitive performance metrics were used to evaluate cluster characteristics. Results: Three distinct clusters, with an overall silhouette coefficient of 0.383, were identified based on MRI volumetrics. Cluster 1 (N=400) had the largest baseline volumetric measures across all ROIs and the best cognitive performance at baseline (Table 1). Cluster 2 (N=269) had larger hippocampal and medial temporal lobe volumes, but smaller parietal lobe volumes in comparison with the third cluster (N=372) (Figure 1). Spider plots show the normalized test performance at baseline and follow‐up with larger values corresponding to worse test performance; Cluster 1 shows relatively little cognitive change (Figure 2). Spider plots of regional MRI volumes show smaller volumes for Clusters 2 and 3 relative to Cluster 1 and asymmetries that reflect regional differences in atrophy (Figure 2). The mean change from baseline to week 80 in the ADAS‐cog14 score for Clusters 1, 2, and 3 were 4.28 (SE=0.35), 10.14 (SE=0.51), and 7.90 (SE=0.43), respectively. Significant between‐group mean differences were observed between Clusters 1 and 2 (difference, 2.38; 95% CI, 1.85 to 2.91; P<0.001), Clusters 1 and 3 (difference, 1.93; 95% CI, 1.41 to 2.44; P<0.001), but not between Clusters 2 and 3 (difference, 0.45; 95% CI, ‐0.11 to 1.02; P=0.146) in ADAS‐cog14 (Figure 3). Conclusions: Volumetric MRI can be used to identify homogenous clusters of amyloid positive individuals with mild dementia. The groups identified differ in baseline and longitudinal characteristics. Cluster 1 shows little ADAS‐cog14 change over the first 40 weeks of study on placebo treatment and may be unsuitable for identifying early benefits of treatment. [ABSTRACT FROM AUTHOR]

Objectives: To determine the impact of 12-month heading exposure on short-term learning.Design: A total of 105 active amateur soccer players, 45 women and 60 men, were administered an EMA-based test of working memory, a version of the two-back, once daily for 14 days.Methods: Heading exposure of the participants was assessed using "HeadCount", a validated structured questionnaire at the baseline visits. The short-term rate of learning of each individual is quantified by first fitting a quadratic model to the daily performance on the two-back test over a two-week period, then taking the instantaneous rate of the quadratic function at the 7th test. A linear regression model was used to test the association of heading exposure with rates of learning, including age, sex, years of education and history of concussion as covariates, as well as variables describing soccer play and heading within the two-week period. Sensitivity analyses were performed using different methods for quantifying the learning effects and different transformations on 12-month heading exposure.Results: Greater 12-month heading was associated with lower rates of learning among women (p = 0.008) but not among men (p = 0.74).Conclusions: We have identified evidence for an adverse, albeit subclinical, effect of soccer heading on brain function among young adult players, which selectively affects women in our sample. [ABSTRACT FROM AUTHOR]

Background: The Telephone‐Montreal Cognitive Assessment (T‐MoCA) is a widely‐used, remotely administered cognitive screen. The T‐MoCA demonstrates adequate psychometric properties and may be used when standard, in‐person administration of the MoCA is not feasible (Katz et al., 2020). There is a critical need to investigate the agreement of the T‐MoCA across administration modalities (in‐person versus telehealth) among ethnically and racially diverse populations. Methods: The Einstein Aging Study (EAS) includes a community‐based of racially/ethnically diverse community‐dwelling individuals, ≥ age 70 from the Bronx, NY. Participants (N = 424) were free of dementia at enrollment and completed the T‐MoCA along with in‐person neuropsychological tests including the MoCA‐22, which excludes items unsuitable for telephone administration paralleling the T‐MoCA (Table 1). Intraclass correlation coefficients (ICCs) between T‐MoCA and MoCA‐22 were used to measure agreement. Linear mixed effects models were applied to compare mean scores and ICCs among ethnic/racial groups, with and without adjusting for age, gender, education and depressive symptoms. Results: Participants were on average 78.2 years, 65.8% female, 47.6% non‐Hispanic White, 38.0% non‐Hispanic Black, and 14.4% Hispanic. There was no significant difference between mean T‐MoCA and MoCA‐22 scores and no differential associations with age, gender, education, and depressive symptoms. For both modalities, NH Black (difference ‐1.54, p<.0001) and Hispanic (difference ‐2.04, p<.0001) groups demonstrated significantly lower scores compared to non‐Hispanic Whites (Table 2). As shown in Table 2, ICCs among non‐Hispanic White, non‐Hispanic Black, and Hispanic groups were 0.64, 0.50 and 0.42, respectively, and comparisons of ICCs to non‐Hispanic White were significantly lower in non‐Hispanic Black (p = 0.0495) and borderline significant in Hispanic participants (p = 0.0552). Findings were similar after adjusting for covariates. Conclusion: The agreement between telephone and in‐person modalities of the MoCA was weaker in non‐Hispanic Black and Hispanic participants (compared to non‐Hispanic White). Further investigation of factors and/or items that contribute to these lower correlations is warranted. Although the T‐MoCA and other remotely administered cognitive screens have the potential to broaden accessibility to marginalized groups, researchers must use caution when developing or selecting appropriate demographically adjusted norms and interpreting scores of diverse older adults. [ABSTRACT FROM AUTHOR]

Background: Automatic classification techniques provide tools to analyze complex data and predict disease progression.Methods: A total of 305 cognitively normal; 475 patients with amnestic mild cognitive impairment (aMCI); and 162 patients with dementia were included in this study. We compared the performance of 3 different methods in predicting progression from aMCI to dementia: (1) index-based model; (2) logistic regression (LR); and (3) ensemble linear discriminant (ELD) machine learning models. LR and ELD models were trained using data from cognitively normal and dementia subgroups, and subsequently were applied to aMCI subgroup to predict their disease progression.Results: Performance of ELD models were better than LR models in prediction of conversion from aMCI to Alzheimer dementia at all time frames. ELD models performed better when a larger number of features were used for prediction.Conclusion: Machine learning models have substantial potential to improve the predictive ability for cognitive outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Despite increased risk of cognitive decline in Hispanics/Latinos, research on early risk markers of Alzheimer’s disease in this group is lacking. Subjective cognitive decline (SCD) may be an early risk marker of pathological aging. We investigated associations of SCD with objective cognition among a diverse sample of Hispanics/Latinos living in the United States. Methods: SCD was measured with the Everyday Cognition Short Form (ECog-12) and cognitive performance with a standardized battery in 6125 adults aged ≥ 50 years without mild cognitive impairment or dementia (x̄ age = 63.2 years, 54.5% women). Regression models interrogated associations of SCD with objective global, memory, and executive function scores. Results: Higher SCD was associated with lower objective global (B = −0.16, SE = 0.01), memory (B = −0.13, SE = 0.02), and executive (B = −0.13, SE = 0.02, p’s < .001) function composite scores in fully adjusted models. Discussion: Self-reported SCD, using the ECog-12, may be an indicator of concurrent objective cognition in diverse middle-aged and older community-dwelling Hispanics/Latinos. [ABSTRACT FROM AUTHOR]

Background: The asymptomatic period for persons with preclinical Alzheimer's disease (AD) lasts for several years despite the presence of AD biomarkers. These biomarkers are critical for disease detection and monitoring, but collection is burdensome and costly. Sensitive cognitive measures such as Stages of Objective Memory Impairment (SOMI) may be a low‐cost alternative or adjunctive marker of disease‐progression risk. Here, we used longitudinal data from the Knight Alzheimer's Disease Research Center to investigate the odds of disease progression associated with baseline SOMI stage and cerebrospinal fluid (CSF) biomarkers of Aβ42/Aβ40 ratio, p‐tau181, and t‐tau. Methods: We used data from 617 cognitively unimpaired participants with baseline Clinical Dementia Rating (CDR) of 0, CSF measures, and longitudinal Free and Cued Selective Reminding Test (FCSRT) scores used to classify participants into different SOMI stages (Table 1). We examined the association between SOMI stage, CSF biomarkers, and incident cognitive impairment based on time to conversion from CDR of 0 to CDR>0 (incident cognitive impairment) using Cox Models. Results: Participants, at enrollment (Table 2), were on average 67.2 (SD = 9.4) years old, 56.6% were female, and had average 7.5 years of follow‐up (range 1‐18). At baseline, 325 (52.7%) were SOMI‐0, 206 (33.4%) were SOMI‐1, 64 (10.4%) were SOMI‐2, and 22 (3.6%) were SOMI‐3 or ‐4 (merged groups). A total of 127 (20.6%) individuals converted to CDR>0. The Cox proportional hazards regression models indicated that in comparison with individuals in SOMI‐0 stage, those in SOMI‐3/4 stage were more than twice as likely to show disease progression (HR=2.43 (95% CI, 1.21‐4.89, p=0.013)) (Table 3). Adding individual CSF biomarkers to the models did not affect the association of SOMI‐3/4 with incident cognitive impairment (p<0.05 for all). In models that included SOMI stages and all CSF biomarkers, SOMI‐3/4 (HR=2.13, 95% CI 1.07‐4.12, p=0.033) Aβ42/Aβ40 (HR=9.76e‐6, 95% CI=2.70e‐10 ‐ 0.35, p=0.031) and t‐tau (HR=1.001, 95% CI=1.000‐1.002, p=0.018), but not p‐tau (p=0.645), showed significant association with incident cognitive impairment. Conclusions: SOMI‐3/4 predicts incident cognitive impairment (change in CDR) independently from CSF AD biomarkers. These results support the utility of SOMI stage as an early marker of incident cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: With recent technological advancements, neurodegenerative biomarkers (NDBMs) in blood plasma now produce results comparable to those obtained in cerebrospinal fluid, supporting the possibility of less invasive and costly determination of risk status for Alzheimer's disease (AD). However, most research with these novel biomarkers has been in predominately non‐Hispanic (NH) White samples. Hence, little is known about distributions of these biomarkers among NH Blacks despite their elevated risk for AD. Method: Blood plasma samples were obtained from 254 older participants in the EAS who were free of dementia (mean age [SD]=77.6[4.9] years; range 70‐93 years; 66.1% female; 46.5% NH Black, 53.5% NH White; 27.2% mild cognitive impairment (MCI) as defined by Jak/Bondi criteria). AD biomarkers [β‐amyloid (Aβ40, Aβ42), pTau181], along with biomarkers of generalized neuronal damage and astrocyte activation [neurofilament light (NfL) and glial fibrillary acidic protein (GFAP), respectively] were quantified in blood using Single molecule array (Simoa) assays on HD‐X Analyzers (Quanterix, Billerica, MA). Comparisons of NDBMs between groups were conducted using Wilcoxon rank sum test in unadjusted analyses, and linear regression models adjusting for covariates including age, gender, education, diabetes history, myocardial infarction, and hypertension. Result: We observed lower overall levels of NDBMs in NH Blacks vs. NH Whites regardless of MCI status (Aβ40, Aβ42, and NfL (ps<0.05) see Table 1). Among NH Whites, those with MCI had higher Aβ40, Aβ42, and NfL (ps<0.05) compared to cognitively normal individuals; no significant differences by MCI status were observed among NH Blacks (not shown). The above race differences in Aβ40, Aβ42, and NfL remained evident across the full sample in linear regression analyses (adjusted for MCI status and covariates). When stratified by MCI status, race differences remained evident for NfL (regardless of MCI status); for Aβ40, race differences were evident among individuals with MCI only (see Table 2). Although individual analytes differed, the Aβ42/Aβ40 ratio did not differ by race. Conclusion: NH Blacks exhibited lower overall levels of NDBMs compared to NH Whites, and associations between NDBMs and MCI were in the expected directions only among NH Whites. These race differences suggest that future work is needed to develop generalizable norms for blood‐based NDBMs. [ABSTRACT FROM AUTHOR]

Background: We developed a statistical region of interest (sROI) of gray matter (GM) for which cortical thickness (CT) significantly differed in a "training set" of 100 cognitively normal and 100 individuals with incident dementia in the Framingham Heart Study. sROI significantly predicts incident cognitive impairment and dementia in both FHS and the highly diverse UC Davis Alzheimer's disease longitudinal cohort. Here we describe a similar analysis of participants of Study of Latinos‐Investigation of Neurocognitive Aging‐MRI (SOL‐INCA‐MRI). Methods: Hispanics/Latinos (n=6,377 ages 50‐86) were enrolled in the Study of Latinos ‐ Investigation of Neurocognitive Aging (SOL‐INCA) of which 1,143 also participated in SOL‐INCA‐MRI ancillary study. Cognitive impairment (CI) was defined as any cognitive domain score falling below (<‐1.0 SD) the SOL‐INCA robust internal normative mean and significant cognitive decline (‐0.055 SD/yr), self‐reported cognitive decline and no or minimal functional impairment over 7 years. Participants with dementia were excluded. MRI CT was computed by the DiReCT method. sROI was derived from statistically significant (via 1,000 non‐parametric permutations) clusters of GM voxels having CT group difference t > 2 over the training set (Fig. 1). Extents of sROI overlap with standard atlas GM regions appear in Table 3. Group differences in variables were assessed by t‐test. Logistic regression assessed the impact on cognitive status of MRI factors: total brain, hippocampal, white matter hyperintensities (WMH), sROI volumes and infarction occurrence, controlling for age, sex, education, and background. Results: The cohort was 64.4 + 7 years of age, 69% female. Nearly 60% with high school or less education, and 21% CI. Table 1 summarizes demographic and MRI measures for CI and cognitively normal (CN). CI individuals were older, had less education, smaller brains, and greater amounts of WMH. Table 2 summarizes results of logistic regression. Education and sROI mean CT were the only significant predictors of cognitive status. Conclusion: Cortical thickness within the sROI was strongly associated with cognitive status, even when other commonly used MRI measures were not. sROI may therefore be a sensitive indicator of incipient CI within SOL‐INCA‐MRI where more traditional measures of Alzheimer's pathology and vascular disease appear insensitive. [ABSTRACT FROM AUTHOR]

Background: Normative data for the Uniform Data Set, Version 3 Neuropsychological Battery (UDSNB3.0), from the database of the University of Washington's National Alzheimer's Coordinating Center (NACC), are limited by an overrepresentation of white, female, and highly‐educated older adults. Original norms, based on >3000 cognitively normal participants from US Alzheimer's Disease Centers (ADCs), did not divide the sample by race/ethnicity as the sample is 83% White (Weintraub et al., 2018). In the current study, we compare the originally reported UDS norms with those derived from a systematically‐recruited, community‐residing, racially/ethnically diverse cohort of older adults and provide expanded norms for Black‐Americans and Whites. Methods: The Einstein Aging Study (EAS) is a longitudinal cohort of racially/ethnically diverse community‐dwelling individuals, ≥ age 70, who reside in the Bronx, NY. Analyses include data from 225 cognitively normal EAS participants and 1759 cognitively normal participants ≥ age 70 from the NACC database. Descriptive statistics are in Table 1. Linear regression models were used to examine the effects of demographic factors—including sex, age, education, and further including race—on UDSNB3.0 test performance in the two samples and to develop demographically adjusted z‐scores for the EAS cohort. Results: In both EAS and NACC samples, higher scores were observed for those with more years of education. Both samples demonstrated lower scores for non‐Hispanic Blacks and other racial/ethnic groups compared to non‐Hispanic Whites (Table 2). After accounting for sex, age, education, and race, mean performance was lower in the EAS sample compared to that of NACC across neurocognitive domains, including memory, visuospatial skills, attention, language, processing speed, and executive function (Tables 3‐4). Conclusion: Racial differences in UDSNB3.0 tests were observed in both EAS and NACC samples. These differences between the UDSNB 3.0 normative data and the EAS sample, after adjusting for age, sex, education, and race, suggest that normative data from samples of optimally healthy, well‐educated individuals are not generalizable to those in diverse, urban communities. Furthermore, results suggest a need for local norms for community‐dwelling, racially/ethnically diverse cohorts to improve diagnostic accuracy and the ability to distinguish cognitive impairment from healthy cognitive aging. [ABSTRACT FROM AUTHOR]

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge‐based cut‐offs for each component of ATN and compared it to two data‐driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data‐driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia‐like and normal groups. Both data‐driven classification methods performed better than the empirical cut‐offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data‐driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials. [ABSTRACT FROM AUTHOR]

Tension-type headache (TTH) is the most prevalent primary headache disorder. We assessed the cross-sectional impact of TTH on health related quality of life (HRQoL) in a general population. We also examined the association of HRQoL scores with headache frequency, disability, medication overuse, poor self-rated health, psychiatric comorbidity, and pain sensitivity in individuals with TTH. A sample of 547 subjects completed a headache diagnostic interview, the SF-12 to calculate physical (PCS) and mental (MCS) health component scores, depression (major depression inventory [MDI]) and neuroticism (Eysenck Personality Questionnaire) measures. We defined the following headache diagnosis categories: pure TTH, pure migraine, and coexistent headache (TTH + migraine). Cases were further classified into chronic (≥15) or episodic (<15 headache days/month). Using generalized linear models (GLM) adjusted for age, sex and education, both PCS-12 and MCS-12 scores varied in groups distinguished by migraine and TTH status; scores were lower for individuals with coexistent headache (TTH + migraine; n=83), followed by pure TTH (n=97) and pure migraine (n=43) compared to the no headache group (n=324) (p≤0.001). In analyses considering chronicity, PCS-12 scores were lower in chronic coexistent headache followed by pure chronic TTH (CTTH), episodic migraine +/− episodic TTH (ETTH) and pure ETTH than in the no headache group (p≤0.001). MCS-12 scores were lower in pure CTTH, followed by chronic coexistent headache, episodic migraine +/− ETTH and pure ETTH compared to the no headache group (p≤0.001). Multiple regression models showed that in TTH, lower PCS-12 scores were associated with age (p=0.04), female sex (p=0.02), and poor self-rated health (p≤0.001). Lower MCS-12 scores in TTH were associated with depression (p≤0.001). In a population sample, TTH, and to higher degree CTTH, are associated with decreased HRQoL. [ABSTRACT FROM AUTHOR]

Background: Inflammation is a risk factor for cognitive decline, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Past research suggests that inflammation relates to cognitive performance and MCI status in laboratory settings, but it remains unclear how inflammation relates to everyday cognitive performance. The present work addressed this gap by examining inflammatory biomarkers in association with working memory measured via ecological momentary assessments (EMA) among those with and without MCI. Gender differences in these links were also examined. Method: Older adults (n = 270, Mage = 76.97, 68% female) enrolled in the Einstein Aging Study were classified as MCI (n = 78) or no MCI using Jak Bondi criteria. Participants completed a two‐week EMA protocol which included daily periodic assessments of spatial working memory and provided two blood samples (one before and one following EMA), from which circulating (basal) and lipopolysaccharide (LPS)‐stimulated cytokines were quantified. Analyses examined correlations between inflammatory markers and working memory performance (i.e., accuracy – mean error distance) stratified by MCI status and by gender. Results: Among those with MCI, higher levels of circulating cytokines (interleukin [IL]‐1b, IL‐6, IL‐10) correlated with worse working memory performance. In the MCI group, higher levels of stimulated cytokines (IL‐4, IL‐6, IL‐8, IL‐10, tumor necrosis factor‐alpha [TNF‐ α]) were associated with better performance. When examined by gender, it was men with MCI who appeared to drive these effects, as higher levels of circulating IL‐1b correlated with worse performance and higher levels of stimulated cytokines (IL‐1b, IL‐6, IL‐8, IL‐10, TNF‐α) correlated with better performance. No significant associations were observed among men without MCI, or among women regardless of MCI status. Findings held when controlling for BMI, age, and education. Conclusion: Keeping with past work, the present study suggests that higher levels of circulating inflammation relate to worse working memory in everyday settings. Further, exhibiting a more robust response to immunological challenge (i.e., higher stimulated cytokines) may relate to better everyday working memory. However, these associations were observed only in men with MCI. Future longitudinal research is needed to better understand the links between circulating inflammation, inflammatory responses, and MCI; ultimately, this might inform early risk detection for MCI and AD. [ABSTRACT FROM AUTHOR]

Background: Prior studies have reported associations between neighborhood characteristics and late‐life cognition. These studies assessed neighborhood using either objective (e.g., area‐level data from public database) or subjective (individual‐level perception) measures. However, little research has been done to examine effects of objective and subjective measures simultaneously. The present study aims to investigate the independent associations of objective and subjective neighborhood characteristics with cognitive change in older adults. Method: Participants include 316 individuals in the Einstein Aging Study (mean age = 77; 67% females; 45% non‐Hispanic Whites, 40% non‐Hispanic Blacks). Subjective neighborhood measures included perceived neighborhood safety, aesthetic quality, social cohesion, and availability of healthy foods (range = 1 poor to 5 excellent). Objective neighborhood measures included violent crime, neighborhood deprivation, volunteering rates, percentage of healthy food retailers, and low access to healthy food. Processing speed was assessed using the brief, smartphone‐administered Symbol Search Test, and was completed 6x/day over two weeks; this was repeated annually from 2017 to 2022. To account for non‐linear retest/practice effects, double negative exponential models were used to estimate the relation of neighborhood measures to asymptotic performance (i.e., fastest response time) at baseline and to change in asymptotic performance over time. Result: Most subjective and objective neighborhood measures were significantly correlated with each other (Table 1). In models containing both objective and subjective neighborhood measures, better perceived safety was associated with greater annual improvement in asymptotic performance (40 milliseconds/year for one point increase in safety) while the objective measure of violent crime was not. Perceived availability of healthy foods was independently associated with a faster baseline asymptotic processing speed (estimate = 160 milliseconds) and with greater annual improvement rate (estimate = 54 milliseconds/year) while the objective measures of food environment were not (Table 2). Conclusion: Subjective rather than objective neighborhood characteristics were strongly associated with annual rates of change in processing speed over five years in community‐dwelling older adults. Perception of one's neighborhood may be a more proximal predictor of cognitive health as it may reflect an individual's lived experiences and determine one's psychological states and health‐related behaviors. Alternatively, more fine‐grained objective neighborhood measures (e.g., individual‐level) would be necessary to better characterize objective neighborhood environments. [ABSTRACT FROM AUTHOR]

Background: Conventional cognitive assessment typically provides a single measure of performance at a single point in time in a laboratory setting. Smartphone‐based ecological momentary assessments (EMA) facilitate short‐term intensive assessment of cognitive performance in people's natural environments. Though heterogeneous variance in digital cognitive assessments has been observed between older adults with mild cognitive impairment (MCI) and cognitively unimpaired (CU), the independent contribution of intra‐individual variability in performance to detection of MCI is poorly characterized in diverse populations of older adults. We examine the ability of digital cognitive assessments of processing speed in discriminating MCI from CU in older adults from a racially/ethnically diverse community sample. Method: Einstein Aging Study (EAS), smartphone‐based digital assessments of cognition, included the response time‐based symbol match test of processing speed. This was assessed 6 times per day over two weeks and was repeated annually. MCI was defined based on in‐house neuropsychological evaluations using Jak‐Bondi criteria. Statistical methods for modeling MCI using digital cognitive assessments include pattern‐mixture discriminant analysis based on heterogeneous linear mixed effects model for digital cognitive measures given MCI, joint modeling using shared random effects with random location scale for digital cognitive measures, and quadratic logistic regression models. All methods take into account the unbalanced adherence rate among individuals and control for covariates including age, sex, years of education, and race/ethnicity. Stratified analyses by race/ethnicity among non‐Hispanic (NH) Whites and Blacks were performed. Result: Among 311 systematically recruited community‐dwelling non‐demented older adults from the EAS (Mean age = 77.5 years, SD = 4.8; 67.5% Female; 45.3% non‐Hispanic (NH) White, 40.8% NH‐Black), 97 (31.2%) individuals had MCI. Adherence to the EMA protocol was slightly better in individuals who were CU compared to those with MCI. Results from the quadratic logistic model (the easiest to apply) showed evidence of contribution from the quadratic terms that reflect heterogeneous intra‐individual variability in all samples and particularly among NH‐Blacks (p = 0.008). Conclusion: We observe evidence that intra‐individual variability in processing speed improves the ability to discriminate cognitive impairment, particularly among NH‐Black older adults. Leveraging digital technology and performance variability across frequent assessments in the smartphone‐based EMA offers novel opportunities to identify cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Although cognitive lapses are a daily occurrence for many older adults, widely used self‐reports of cognitive function ask respondents to recollect lapses occurring over months or years. We used daily diaries to capture memory and other cognitive difficulties close in time to their occurrence and determined whether daily reports predicted concurrent and/or new cases of mild cognitive impairment (MCI) in the next year (i.e., at annual follow‐up). Method: In the Einstein Aging Study, daily digital diary indicators of cognitive lapses were assessed each evening over a two‐week period (82% of diaries completed). Conventional recall‐based indicators were the Cognitive Change Index total score and a single item capturing worsening memory (with/without worry), both assessed at each annual visit. Using time‐dependent lagged models with generalized estimating equations (GEE) to correct for within person correlation, we evaluated the association of current cognitive lapses with the probability of being MCI at the current or next annual visit among those diagnosed with MCI after baseline (n = 31 new cases). Result: In 307 community‐dwelling participants (Mage = 77.05, SD = 4.95; 67% female, 47% White) who were free of MCI at baseline, higher numbers of daily problems significantly predicted the likelihood of being diagnosed with MCI at the next annual visit (average number of visits = 2.72, range 1‐6 years). For every additional daily cognitive lapse, there was a 5% greater odds of being classified at the next visit (OR = 1.05, 95% CI: 1.01‐1.09). By contrast, conventional indicators did not significantly predict transition to MCI at the next visit (ps =.48 and.62, respectively). However, the single item regarding worsening memory was related to concurrent MCI diagnostic status (p<.01). Sensitivity analyses examining the first 3 days or first 7 days to determine the number of daily reports required to identify new MCI cases indicated that both 3 and 7 days significantly predicted future MCI (OR = 1.34, 95% CI: 1.12‐1.61; OR = 1.07, 95% CI: 1.00‐1.14, respectively). Conclusion: A brief daily diary assessment of cognitive lapses predicted new cases of MCI. The combination of daily and conventional methods may represent complementary approaches that offer the optimal approach for capturing self‐perceived cognitive deficits associated with current and future MCI. [ABSTRACT FROM AUTHOR]

Background: Digital cognitive assessments repeated over multiple days improve reliability compared to "single‐shot" assessments due to within person variability in function. Further, digital assessments are possible when clinic visits are not. We show the utility of digital approaches using data from the Einstein Aging Study (EAS) diverse cohort of community‐dwelling older adults. Method: Participants were administered conventional and ambulatory digital assessments annually. The later consisted of a 2‐week burst of digital cognitive and behavioral/exposure measures obtained 6 times/day. Digital cognitive assessments included three tests (processing speed (Symbol Match), visual short‐term memory binding (Color Shape), and spatial working memory (Grid Memory)). Exposure assessment included daily stress, pain, and subjective sleep quality. Feasibility was measured as compliance defined as proportion of completed assessments. Frequency of annual assessments completed was evaluated throughout the COVID‐19 pandemic. For digital measures, reliability was calculated using intraclass correlations (ICCs) across days within a burst and across annual visits. ICCs for conventional measures across annual visits were also computed. To assess predictive validity, we used Cox proportional hazard models to examine whether mean and variability of performance on the 3 digital cognitive tasks predicted incident MCI. Result: Compliance rates overall were 88% and were ≥ 85% in strata based on MCI status, age, race/ethnicity, education or prior experience with smartphones (Table 1). Data collection was maintained throughout the pandemic (Figure 1). ICCs for the digital cognitive measures was consistently high (>0.93). The same was true for exposure measures of stress, subjective sleep and pain (>.88). ICCs across annual follow‐up for digital measures ranged from 0.87 to 0.96 compared to 0.51 to 0.87 for conventional cognitive measures. Regarding predictive validity, worse mean performance in all three digital cognitive measures and higher within‐person variability (SD) in symbol search and color shape were associated with higher risk of incident MCI. Conclusion: Digital assessments are feasible to administer, have high reliability and show predictive validity for MCI. They may provide useful tools in observational studies and clinical trials due to improved classification of cognitive status and feasibility in situations where in person assessments are not possible. [ABSTRACT FROM AUTHOR]