Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

BackgroundMild behavioural impairment (MBI), characterized by later-life emergence of persistent neuropsychiatric symptoms (NPS), is an early clinical indicator of dementia risk. MBI as a global construct has been associated with Alzheimer disease (AD) pathology; studies have also explored MBI domains. Prior work has linked MBI-apathy to cerebrospinal fluid (CSF) biomarkers of AD, but whether similar associations are detectable using plasma-based biomarkers such as phosphorylated tau (p-tau) is unknown. Establishing such relationships is critical, as plasma biomarkers are more accessible than CSF. ObjectiveTo explore cross-sectional and longitudinal associations between MBI-apathy and plasma p-tau181 using Alzheimers Disease Neuroimaging Initiative data. MethodsOlder adults with normal cognition or mild cognitive impairment were categorized as MBI-apathy (n=69), non-MBI NPS (n=112), and no-NPS (n=215) based on Neuropsychiatric Inventory scores and symptom persistence over one year. Linear regression modelled cross-sectional associations between NPS group and plasma p-tau181 levels, adjusting for age, sex, education, apolipoprotein E4 status, and Mini-Mental State Examination score. Hierarchical linear mixed-effects modelling assessed associations over two and three years, including time-by-NPS group interactions. ResultsMBI-apathy was associated with significantly higher plasma p-tau181 levels at baseline (24.05% [6.06-45.08%]; adjusted p=0.014), and over two (26.46% [7.24-49.12%]; adjusted p=0.012) and three years (29.28% [10.17-51.72%]; adjusted p=0.004) compared to no-NPS. No significant associations were observed for non-MBI NPS. ConclusionsMBI-apathy is associated with elevated plasma p-tau181 cross-sectionally and longitudinally. These findings support MBI-apathy as a potential proxy marker of tau pathology for early AD detection.

Mild Behavioural Impairment (MBI) is defined by later-life onset of persistent behavioural changes and is recognized as a risk marker for cognitive decline and dementia. Apathy, a core MBI domain characterized by diminished interest, initiative, and emotional reactivity, can emerge before dementia and is hypothesized to be associated with structural brain changes. While previous studies have explored Alzheimer disease (AD)-related neuroanatomical substrates of apathy in the dementia clinical stage, few have investigated these associations in cognitively normal (CN) or mild cognitive impairment (MCI) individuals with persistent apathy consistent with MBI. Thus, this study explores structural brain differences between individuals with MBI-apathy and those without neuropsychiatric symptoms (no-NPS). Participants (n = 446; mean age = 69.6 years; 79.8% CN; 62.8% female) were drawn from the National Alzheimers Coordinating Center and categorized into MBI-apathy (n = 59) and no-NPS (n = 387) groups. Linear regressions were used to model associations between NPS group and regional brain measures, with adjustments for age, sex, years of education, apolipoprotein E4 carrier status, intracranial volume, and Mini-Mental State Examination score, with false discovery rate (FDR) correction for multiple comparisons. Primary outcomes included two predefined AD meta-regions-of-interest (ROIs): 1) thickness: a composite measure of mean cortical thickness across the entorhinal cortex, inferior temporal gyrus, middle temporal gyrus, inferior parietal lobule, fusiform gyrus, and precuneus; and 2) volume: a composite measure of mean cortical and subcortical grey matter volume across the hippocampus, entorhinal cortex, amygdala, middle temporal gyrus, inferior parietal lobule, and precuneus. Primary outcomes also included cortical thickness and grey matter volume among individual ROIs including the ventral striatum (VS), anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), ventrolateral prefrontal cortex (vlPFC), and dorsolateral prefrontal cortex (dlPFC). MBI-apathy status was associated with significantly lower AD-meta-ROI cortical thickness (Z-score difference [95% CI]; FDR-corrected p-value, -0.43 [-0.73 - [-0.12]]; 0.025) and lower AD meta-ROI grey matter volume (-0.50 [-0.71 - [-0.30]]; <0.001). MBI-apathy was also associated with significantly lower dlPFC thickness (-0.40, [-0.70 - [-0.09]]; 0.02) and volume (-0.28 [-0.50- [-0.06]]; 0.026) and lower OFC volume (-0.32, [-0.57 - [-0.07]]; 0.026) compared to the no-NPS group. Within a non-dementia sample, MBI-apathy was more strongly associated with established AD-vulnerable regions than with regions that have been traditionally implicated in apathy in dementia. Results suggests that during CN and MCI stages, MBI-apathy may reflect early AD-related neurodegeneration, with conventional apathy-related structural changes becoming more prominent as disease progresses.

ImportanceCognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offer an accessible approach for screening. ObjectiveTo assess whether audio recordings of patient-physician dialogue during routine primary care visits can be used to identify CI using acoustic speech features and machine learning (ML). DesignThis observational study conducted among older primary care patients involved audio recording primary care visits using a microphone and portable device. An external validation cohort was recruited in a separate city to assess reproducibility of findings. SettingThe study was conducted in primary care practices in New York City, with additional participants recruited from primary care practices in Chicago, Illinois, for validation. ParticipantsThe study included 787 English-speaking patients aged 55 years and older, without documented history of dementia or mild CI. Eligible patients were recruited from primary care practices during routine visits. For validation, 179 patients meeting the same eligibility criteria were recruited from primary care practices in Chicago. ExposuresMultiple thirty-second speech segments were extracted from recordings. Acoustic features were derived using foundation models (Whisper, HuBERT, Wav2Vec 2.0) and expert-defined methods (eGeMAPS, prosody). Main Outcomes and MeasuresCI was defined as Montreal Cognitive Assessment score [&ge;]1.0 standard deviations below age and education-adjusted norms. ML classifiers were trained to predict CI status from audio recordings. We calculated area under the receiver operating characteristic curve (AUC-ROC) and maximum F1 score (Fmax) for identifying CI participants. ResultsThe mean age was 66.8 years and 21% had CI. Models using Whisper-derived acoustic features performed best (AUC-ROC=0.733, 95% confidence interval [95%CI]=0.714-0.752; Fmax(CI)=0.504, 95%CI=0.474-0.534). Results generalized to the external site with similar performance (AUC-ROC=0.727, 95%CI=0.714-0.740; Fmax(CI)=0.459, 95%CI=0.442-0.476). Model interpretation identified pitch, timing, and variability features as key predictors. When used for screening, the algorithm achieved positive predictive value of 30.4% (95%CI=28.7%-32.1%), sensitivity of 68.2% (95%CI=61.8%-74.6%), and specificity of 63.6% (95%CI=59.8%-67.4%) on the holdout cohort. Conclusions and RelevanceML models trained on acoustic features from brief clinical conversations identified CI with high accuracy. These findings support the feasibility of passive, speech-based screening during routine primary care. Key Points QuestionCan acoustic features extracted from audio recordings of patient-physician conversations during routine primary care visits be used to screen for cognitive impairment? FindingsIn this study including 787 older adults without diagnosis of cognitive problems, machine learning models trained on acoustic features from audio segments of recordings of primary care visits achieved area under the receiver operating characteristic curve values of 0.72 for predicting cognitive impairment. The algorithm achieved a sensitivity of 83%, specificity of 44%, and positive predictive value of 28%, identifying a subset of primary care patients at higher risk for cognitive impairment. Models performed similarly on an external validation dataset of 179 participants. Interpretability analyses highlighted patient pause duration and energy-related features as salient indicators of cognition status. MeaningThese findings suggest that short segments of naturalistic clinical dialogue may contain useful acoustic signals for passively screening patients for cognitive impairment.

BackgroundPsycho-affective symptoms and repetitive negative thinking (RNT) may increase Alzheimers disease (AD) risk. As sleep plays a key role in emotional regulation, we investigated both cross-sectional and longitudinal associations between sleep disturbances and psycho-affective health, according to amyloid-beta (A{beta}) status. MethodsOne hundred and thirty-four cognitively unimpaired older adults from the Age-Well interventional study (mean age = 68.8 {+/-} 3.8 years; 82 women; 37 A{beta}+ individuals) completed psycho-affective (Geriatric Depression Scale, State-Trait Anxiety Inventory), RNT (Rumination Response Scale-brooding, Penn State Worry Questionnaire) and sleep questionnaires (Pittsburgh Sleep Quality Index, Insomnia Severity Index). Wrist actigraphy (n=131; mean recording duration = 7.7 {+/-} 0.5 nights) provided objective measures of sleep fragmentation (sleep fragmentation index, wake time after sleep onset) and their night-to-night variability. Cross-sectional multiple linear regressions and longitudinal linear mixed-effects models (mean follow-up = 3.98 {+/-} 1.15 years) examined associations between sleep and psycho-affective symptoms, adjusting for age, sex, continuous positive airway pressure use, and the non-pharmacological intervention group (for longitudinal analyses). ResultsIn the whole cohort and in A{beta}- individuals, higher self-reported sleep difficulties and insomnia symptoms were cross-sectionally associated with greater anxiety, depression and RNT (all pFDR-corr<0.05). In A{beta}+ individuals, objective sleep fragmentation and instability were cross-sectionally associated with higher anxiety, depression and RNT. Longitudinally, higher baseline insomnia severity, mean sleep fragmentation, and night-to-night sleep instability predicted brooding worsening in A{beta}+ individuals only, while poorer perceived sleep quality predicted depressive symptoms worsening (all pFDR-corr<0.05). ConclusionsIn cognitively unimpaired older adults, associations between sleep disturbances and psycho-affective symptoms differ according to A{beta} status. In A{beta}+ individuals specifically, objective sleep fragmentation and instability are linked to psycho-affective vulnerability and brooding worsening over time. Sleep disturbances could thus represent early modifiable targets to mitigate psycho-affective symptomatology in older adults.

ObjectivesA Script Generation Task (SGT), requiring participants to verbally describe the steps of everyday activities, was investigated as an efficient tool to detect mild functional difficulties and mild cognitive impairment (MCI). Methods83 participants (n=57 HC; n=26 MCI) completed a SGT, performance-based test of everyday function, cognitive tests, and questionnaires. SGT responses were transcribed and scored by human raters and automated text analysis. ResultsParticipants with MCI generated fewer SGT steps in a shorter amount of time and more pronouns relative to nouns, reflecting less specificity. Performance on the SGT was associated with cognitive tests of episodic memory, performance-based tests of everyday function, and questionnaires regarding everyday functioning. Conclusions The SGT holds promise as a highly efficient measure of mild cognitive and functional difficulties in older adults.

ObjectiveCognitive impairment is associated with language changes that may be elicited from verbal responses during neuropsychological assessments that are not captured in traditional scoring. The current study investigated the utility of a linguistic analysis of paragraph recall responses for differentiating participants with and without cognitive impairment. MethodsDigital voice recordings of Logical Memory (LM) were available from 598 participants from the Long Life Family Study with normal cognition and 112 with cognitive impairment. Linguistic polyfeature scores for immediate (PFS-IR) and delayed recall (PFS-DR) were created from a weighted sum of features associated with cognitive impairment. Logistic regression models assessed the predictive value of each PFS and demographics for classifying cognitive impairment. Repeated measures models with Generalized Estimating Equations assessed whether PFSs predict decline on a cognitive screener. ResultsBoth immediate and delayed PFSs were significantly associated with cognitive status (PFS-IR {beta} = 0.05, p<.001; PFS-DR {beta} = 0.07, p<.001). A classifier with PFS-DR and demographics closely approximated the accuracy of the traditional LM score and demographics (AUC-PR = 0.81 vs 0.84, respectively). A higher PFS-DR was also associated with greater cognitive decline over an average of 5 years of follow-up ({beta} = -0.08, p<.001). ConclusionQuantification of linguistic features from paragraph recall using a linguistic PFS provides sufficient information for detecting cognitive impairment and predicting incident cognitive decline. The linguistic PFS has the potential to be integrated into automated testing, recording, and scoring pipelines allowing for the implementation of sensitive neuropsychological assessments in broader clinical and research settings.

IntroductionThe eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks perpetuating health disparities due to the tests known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR) - a rapid, FDA-listed digital cognitive assessment - to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage. MethodsWe conducted a retrospective analysis using data from the multi-site Bio-Hermes-001 study (NCT04733989, N=945). Participants were clinically classified as cognitively unimpaired, mild cognitive Impairment, or probable Alzheimers dementia. We trained a Poisson elastic net regression model using age and multimodal digital features derived from the DCR (including drawing kinematics and voice acoustics) to predict MMSE scores. The model was tested for generalizability using an independent external validation cohort from the Apheleia study (NCT05364307, N=238). ResultsThe machine learning model predicted MMSE scores with a root mean squared error (RMSE) of 2.31 in the training cohort. This error margin falls within the established test-retest reliability range of the manual MMSE itself (2-4 points), suggesting the prediction is statistically non-inferior to human administration. External validation in the Apheleia cohort demonstrated robust generalizability (RMSE = 2.62). Crucially, the model exhibited demographic fairness, maintaining consistent accuracy across Race (White RMSE = 2.34; Non-White RMSE = 2.14) and Ethnicity (Hispanic RMSE = 2.26; Non-Hispanic RMSE = 2.31). DiscussionMachine learning can leverage multimodal features from the DCR to accurately and equitably crosswalk to MMSE scores in support of current guidelines, transforming a time-intensive manual test into a rapid, automated assessment. By deploying this "digital triage" engine, where traditional assessments are still used for DMT eligibility, healthcare systems can streamline the identification of DMT-eligible patients, reduce specialist referral bottlenecks, and ensure that access to life-altering therapies is determined by pathology rather than demography.

BackgroundLoneliness is an emerging public health concern linked to adverse mental and physical outcomes. It may play a key role in cognitive aging, yet its population-level association with subjective cognitive decline (SCD) across demographic groups is not well characterized. We evaluated how the frequency of loneliness relates to SCD in U.S. adults and whether associations differ by sex, age and race/ethnicity. MethodsWe performed a cross-sectional analysis of adults aged [&ge;]16 years using nationally representative 2016-2023 Behavioral Risk Factor Surveillance System data (BFRSS). Loneliness was categorized as never, rarely, sometimes, usually or always. The primary outcome was self-reported SCD in the past year. Survey-weighted logistic regression models adjusted for sociodemographic factors, health insurance, metropolitan status and survey year were used to estimate adjusted marginal probabilities of SCD across loneliness categories. Interaction terms and stratified margins evaluated effect modification by sex, age group (16-44, 45-64 and [&ge;]65 years) and race/ethnicity (non-Hispanic White, non-Hispanic Black and Hispanic). ResultsAmong 85,969 adults who reported loneliness, 13,879 (16.2%) experienced subjective cognitive decline (SCD), with a mean age of 65.7 {+/-} 10.6 years. Loneliness showed a strong dose-response relationship with SCD. Predicted probabilities of SCD increased from 9.9 % (95 % CI, 9.3-10.5 %) among respondents who never felt lonely to 15.0 % (14.1-15.9 %) for rarely, 24.9 % (23.6-26.1 %) for sometimes, 38.4 % (34.4-42.5 %) for usually and 45.7 % (41.0-50.4 %) for always lonely adults (p < 0.001). Women who were always lonely had an adjusted probability of SCD that was 10.7 percentage points higher than men; sex differences were negligible at lower loneliness levels. Age differences were minimal across most loneliness categories; however, among adults who were always lonely, those aged >64 years had significantly lower predicted cognitive function compared with adults aged 18-64 years (p < 0.001). Racial and ethnic differences were modest; the only significant contrast was a 1.7 percentage-point lower probability of SCD for non-Hispanic Black adults compared with Whites among those who never felt lonely. Other subgroup differences were not statistically significant. ConclusionsLoneliness is independently and strongly associated with higher likelihood of subjective cognitive decline among U.S. adults, and this relationship is most pronounced for chronic loneliness. While sex and age modified the effect of loneliness, racial/ethnic disparities were minimal. These findings identify loneliness as a modifiable social determinant of cognitive health, supporting the need for broad social connection initiatives and targeted efforts for women and mid-life adults with chronic loneliness.

INTRODUCTIONAgitation is a common and burdensome neuropsychiatric symptom in dementia that fluctuates from day to day, but objective tools for short-term risk stratification are limited. We examined whether nocturnal physiological signals from unobtrusive under-mattress sensors predict next-day daytime agitation and whether associations differ for agitation occurrence versus severity. METHODSWe extracted cardiorespiratory, movement, and sleep-proxy features from two long-term care cohorts (N=55; 333 nights) and one external home-monitoring cohort (N=18; 803 nights). A two-part mixed-effects framework was used to model next-day agitation episodes. RESULTSLower nocturnal respiratory rate and greater activity instability independently predicted higher odds of next-day agitation occurrence. Associations were stronger for motor than verbal agitation. Respiration-related predictors were validated externally. Conversely, no nocturnal features significantly predicted agitation severity. DISCUSSIONPassive sleep monitoring identified reproducible, physiologically interpretable markers of next-day agitation occurrence, supporting the potential of under-mattress sensing for short-term risk stratification and more proactive dementia care.

INTRODUCTIONSleep disturbances are prevalent in patients with Alzheimers disease (AD) and may emerge before overt clinical symptoms. We characterized sleep alterations in cognitively unimpaired older adults with cerebral amyloid deposition, and assessed their associations with regional amyloid deposition, cognitive and psychoaffective outcomes. METHODSSeventy-six older adults (69.1 {+/-} 3.4 years, 63.2% female) underwent a multi-night (4.5 {+/-} 0.8 nights) objective sleep assessment using the Somno-Art(R) wearable device, Florbetapir-PET scanning, and an extensive neuropsychological and psychoaffective evaluation. RESULTSAmyloid {beta} (A{beta})-positive individuals had a shorter total sleep time (TST) and greater night-to-night variability in rapid eye movement (REM) sleep duration than A{beta}-negative individuals. Across the whole sample, these sleep characteristics were associated with increased A{beta} deposition in widespread brain regions, but not with cognitive or psychoaffective measures. DISCUSSIONShorter sleep duration and greater REM sleep variability may index early AD-related brain changes, warranting longitudinal studies to establish their prognostic significance. Age-Well randomized clinical trial of the Medit-Ageing European Project. Trial registration number: EudraCT:2016-002,441-36; IDRCB:2016-A01767-44; ClinicalTrials.gov Identifier: NCT02977819

As cognitive decline progresses, older adults may self-regulate their driving. Avoidance of left turns across traffic is observable in naturalistic driving data but rarely self-reported. We studied 106 older adults using baseline and one-year follow-up neuropsychological assessments. In-vehicle sensors passively recorded driving behavior over 12 weeks. We identified 295,112 turns from vehicle heading changes. We used mixed-effects logistic regression to model the odds of turning left, with cognitive status category change from baseline to one-year follow-up as the predictor. Greater cognitive impairment, represented by movement to a more severe cognitive status category at one-year follow-up, was associated with reduced odds of turning left (odds ratio = 0.984, 95% confidence interval = 0.969-0.999; P value = .037). Left-turn avoidance may be a behavioral marker of early cognitive decline. Passive driving data could help detect functional changes, enabling intervention to preserve mobility and independence. Further research is needed to establish a clinical threshold of concern for decreasing trends in left turn frequency in older drivers.

Aging is the strongest known risk factor for Alzheimers disease (AD), and elevated plasma amyloid-{beta} (A{beta}) levels in healthy adults are associated with increased AD risk. Aging is also associated with autonomic imbalance, characterized by increased sympathetic and decreased parasympathetic activity. In our previous randomized clinical trial, we found that four weeks of daily slow-paced breathing designed to enhance parasympathetic activity reduced plasma A{beta}42 and A{beta}40 levels in younger and older adults and showed a trend toward increasing A{beta}42/A{beta}40 ratio only in older adults. The primary goal of the current study was to extend these findings in 62 adults aged 50 to 70 years using randomized assignment to 10 weeks of slow-paced breathing or a random-paced breathing control with three assessment time points. Secondary objectives included examining the effects of slow-paced breathing on brain structure (i.e., perivascular space and hippocampal volumes) and cognitive performance. Consistent with prior findings, the slow-paced breathing group showed greater decreases in plasma A{beta}42 than the control group. However, group differences were not significant for A{beta}40 or A{beta}42/A{beta}40 ratios, and no significant effects were observed for the secondary outcomes. The non-significant findings may be due to changes we made to both intervention and control condition methods relative to our previous trial. Further research is needed to explore the underlying mechanisms and potential effects of slow-paced breathing on A{beta} accumulation in the brain. HighlightsO_LIParticipants were randomly assigned to slow-placed breathing or a breathing control C_LIO_LIIndividualized protocols determined breathing paces C_LIO_LITen weeks of daily slow-paced breathing practice reduced plasma A{beta}42 levels C_LI

BackgroundCognitively unimpaired (CU) adults vary substantially in their risk of developing mild cognitive impairment (MCI), yet most subtyping approaches focus on downstream neurobiological or cognitive markers rather than upstream, modifiable risk factors. We aimed to identify clinically meaningful subgroups of CU adults defined by integrated comorbid, behavioral, and social risk profiles, and to evaluate heterogeneity in both incident MCI risk and cardiometabolic treatment effects. MethodsWe conducted a prospective cohort study of 121,322 CU adults aged [&ge;]50 years from the All of Us Research Program. Baseline comorbidities, lifestyle behaviors, and social determinants of health were jointly modeled using the Bayesian Mixed Integrative Data Subtyping framework, which integrates binary and continuous modalities via modality-specific likelihoods and shared latent constructs. Subtype-specific risk of incident MCI was assessed using multivariable Cox proportional hazards models adjusting for demographics and baseline medication use. A double/debiased machine learning interactive regression model with inverse probability of censoring weights to mitigate bias from informative censoring was implemented to estimate the average treatment effects of antihypertensive agents, Glucagon-Like Peptide (GLP) receptor agonists, and non-GLP antidiabetic medications on time to MCI. ResultsFour distinct subtypes were identified: I low-risk healthy aging, II behavioral/social vulnerability, III cardiometabolic-depressive multimorbidity, and IV mixed social-medical vulnerability profiles. Compared with Subtype I, Subtype III demonstrated the highest risk of incident MCI (HR: 3.69, 95% CI: 3.14-4.33), followed by Subtype IV and Subtype II. In treatment effect analyses, antihypertensive use was associated with a modest prolongation of MCI-free survival overall (time ratio:1.04, 95% CI: 1.03-1.06), with the largest benefit observed in Subtype III (time ratio: 1.14, 95% CI: 1.09-1.19). Non-GLP antidiabetic therapies were similarly associated with modest overall delay, with significant benefits in Subtypes I and III. GLP-class therapies were not associated with overall delay but showed a significant association in Subtype III. ConclusionsIntegrative subtyping based on comorbid, behavioral, and social risk factors reveals clinically meaningful heterogeneity in both cognitive risk and treatment response. Aligning dementia prevention strategies with dominant vulnerability pathways may enhance the effectiveness and equity of population-level precision prevention.

Structured AbstractO_ST_ABSBACKGROUNDC_ST_ABSPatient reports are the standard when examining subjective cognitive decline (SCD). Recent research suggests that informant and clinician reports may also be associated with cognition. This study examined differences between patient, informant, and clinician definitions of SCD and their relationship to cognition. METHODSData from 4290 older adults (n=1690 normal controls, NC; n=840 mild cognitive impairment, MCI; n=1760 Alzheimers disease, AD) were examined from the National Alzheimers Coordinating Center. Linear models examined the relationships between SCD status using the three definitions and cognition at baseline and over time. RESULTSIn NC, informant and clinician SCD were associated with worse cognition at baseline, with patient and clinician SCD associated with worse cognition over time. All definitions were associated with worse cognition at baseline and over time in MCI and AD. DISCUSSIONOur findings suggest the importance of examining different SCD definitions, especially the inclusion of clinician SCD.

Background and ObjectivesProgression from mild cognitive impairment (MCI) to Alzheimers Disease and Related Dementias (AD/ADRD) varies widely across individuals, yet the mechanisms underlying this heterogeneity remain unclear. Identifying clinical and social determinants influencing this transition could enable earlier intervention. While cardiovascular and social risk factors are established contributors to dementia incidence, their role in progression from MCI to dementia may differ. Few studies using real world clinical data have evaluated these potential determinants of MCI progression. MethodsUsing electronic health records (EHR) from patients with incident MCI at UCSF Health (2010-2024), we evaluated cardiovascular (blood pressure [BP], body mass index [BMI], and type II diabetes) and social (marital status, language preference, race/ethnicity, and neighborhood disadvantage) risk factors for rate of progression from MCI to AD/ADRD. Covariate-adjusted Cox proportional hazards models estimated hazard ratios for incident AD/ADRD, with evaluation of interactions by sex. ResultsAmong 6,529 patients, higher systolic BP was associated with AD/ADRD incidence (HR per 10 mmHg: 1.09, 95% CI: 1.05-1.14). BMI was inversely associated with incidence in both males (HR: 0.94; 95% CI: 0.92-0.97) and females (HR:0.98; 95% CI: 0.96-0.99). Compared to married individuals, widowed patients had a higher hazard of progression (HR: 1.15; 95% CI: 1.00-1.32). Spanish-speaking (HR: 1.38; 95% CI: 1.04-1.81), Chinese-speaking (HR: 1.19; 95% CI: 1.00-1.42), and "Other non-English" speaking patients (HR:1.24; 95% CI: 1.03-1.51) had a higher hazard of progression compared to English speakers. Latinx (HR:1.22; 95% CI: 1.01-1.48) and Asian patients (HR:1.14, 95% CI: 1.00-1.30; p=0.04) also had higher hazards of progression compared to White patients. Neighborhood disadvantage was not significantly associated with disease progression. DiscussionCardiovascular and social factors independently influence dementia progression, with some sex-specific patterns. Integrating clinical and social indicators highlights the potential of EHR data to identify high-risk patients earlier in the care continuum and support equitable dementia prevention.

ObjectiveDemographic corrections (e.g., sex, education, race, ethnicity) are often applied when assessing cognition in adults; however, these corrections have significant limitations (e.g., using years of education does not capture the quality of, or access to, education). It is therefore critical to develop novel assessment options that are less susceptible to demographic factors. This study compared demographic effects on a verbal memory test and a performance-based test of cognition and daily functioning in older adults. Based on prior work, we hypothesized the performance-based tests would be less susceptible to demographic factors than paired associates learning. MethodData from 1326 participants (mean{+/-}SD age=61.9{+/-}10.9 yrs; Female = 1066, 80%) were collected through the MindCrowd electronic cohort, with 79 (6%) non-White, 109 (8.2%) identifying as Hispanic/Latino ethnicity, and 327 (25%) reporting education as less than a college degree. Paired associates learning is a well-established measure of medial temporal lobe-dependent learning and memory through recall of word-pairs, scored as the number of correct word pairs entered out of 36 possible. The performance-based test involved functional upper-extremity movement, specifically transporting beans to target cups in a repeating sequence (a task also shown to be dependent on the medial temporal lobe), scored as the intraindividual variability (standard deviation) in trial time across four consecutive trials. ResultsAs hypothesized, linear regression analysis showed that PAL was significantly affected by sex, education, race (particularly Black/African American), and ethnicity, whereas the performance-based test was affected only by sex and with a much smaller effect size than that of PAL. ConclusionsPerformance-based assessments may be an equitable approach to evaluating cognition without requiring score corrections, particularly for diverse populations.

BACKGROUNDWhite matter hyperintensities (WMHs) are increasingly recognized as markers of cerebrovascular pathology in Alzheimers disease (AD), yet their temporal relationship with amyloid and tau accumulation remains unclear. While previous studies suggest bidirectional associations between WMHs and AD pathology, regional associations between WMHs and AD pathology have yet to be examined. This study investigated the temporal and regional associations between PET measures of amyloid (A{beta}) and tau pathology and WMH burden in older adults. METHODSData from the Alzheimers Disease Neuroimaging Initiative (ADNI) included 1,241 older adults with A{beta} and 636 with tau for cross-sectional analyses. Longitudinal analyses included 670 participants for A{beta} change and 1,079 for WMH change (A{beta} group), and 199 for tau change and 356 for WMH change (tau cohort). Linear models were used to i) assess associations between baseline regional WMH and A{beta} and tau pathology, and ii) examine whether baseline pathology in one measure was associated with change in the other measure over two years. RESULTSBaseline analyses revealed significant bidirectional associations between WMH burden and both A{beta} (t=2.09-4.16, p<.05) and tau pathology (t=2.44-2.87, p<.04), with stronger effects in posterior brain regions. Longitudinal analyses showed that baseline A{beta} levels were associated with future WMH progression in frontal and occipital regions (t=2.44-3.27, p<.03), while baseline tau was linked to WMH increases in frontal and parietal regions (t=2.48-3.51, p<.03). However, baseline WMH burden was not associated with future accumulation of either A{beta} or tau pathology in any region. CONCLUSIONSThese findings suggest that A{beta} and tau pathology drive future WMH progression rather than the reverse, with distinct regional patterns for each pathology type.

BackgroundSubjective cognitive decline (SCD) is common in older adults and may precede mild cognitive impairment (MCI). Whether longitudinal changes in self- or study partner (SP)-reported SCD improve early identification of individuals at risk for clinical progression, particularly along the Alzheimers disease (AD) biological continuum, remains unclear. MethodsWe pooled data from two longitudinal observational cohorts (DELCODE and ADNI). Cognitively unimpaired (CU) participants were recruited through public advertisement or memory clinics and included if baseline amyloid status, [&ge;]2 SCD assessments, and clinical follow-up were available. SCD was assessed using the Everyday Cognition questionnaire (self- and SP-report). Linear mixed-effects models examined longitudinal associations between SCD trajectories, baseline AD biomarkers, and progression to incident MCI. Multivariable Cox proportional hazards models tested whether one-year changes in SCD predicted subsequent progression. FindingsAmong 770 participants (median age 69{middle dot}9years [IQR 66{middle dot}0-74{middle dot}6]; 52{middle dot}6% women; median follow-up 5{middle dot}0years [4{middle dot}0-7{middle dot}0]), amyloid-positive individuals and those who progressed to MCI showed steeper longitudinal increases in both SCD reports. In amyloid-positive participants, only increases in SP-reported SCD differentiated progressors from non-progressors. One-year increases in SP-reported SCD predicted a higher risk of subsequent MCI compared with unchanged scores (hazard ratio 3{middle dot}24 [95%CI 1{middle dot}73-6{middle dot}07]), with effects confined to amyloid-positive participants. InterpretationLongitudinal increases in SP-reported cognitive difficulties, particularly over short intervals, are associated with near-term progression to MCI in amyloid-positive CU older adults. SP-based longitudinal monitoring may represent a low-burden approach to support earlier clinical surveillance in aging populations. FundingGerman Center for Neurodegenerative Diseases, US National Institutes of Health.

ObjectiveTo assess intra-individual cognitive variability (IICV) in relation to Alzheimers Disease (AD) biomarkers. MethodsThe sample included 879 adults from the National Alzheimers Coordinating Center, aged 50 and above with a complete neuropsychological evaluation and AD biomarker data available (64% cognitively intact; 36% cognitively impaired). We conducted a series of moderated regression models where AD biomarkers, neurocognitive status, and their interaction effects predicted IICV. IICV measures included demographically adjusted normed scores for the intraindividual standard deviation (iSD) and coefficient of variance (CoV). AD biomarkers included cerebrospinal fluid (CSF) measures of A{beta}1-42, phosphorylated tau 181 (p-Tau181), and total tau (t-Tau), as well as amyloid positron emission tomography (PET; with both continuous centiloid values and a dichotomous variable). ResultsIncreased AD biomarker burden was associated with increased IICV among cognitively impaired individuals (correlational strength ranging from .206 to .391 for iSD and from .149 to .460 for CoV) but not among the cognitively intact group (correlational strength ranging from .008 to .085 for iSD and from .016 to .085 for CoV). The pattern of results held even after controlling for demographic factors and was comparable in magnitude to the association between AD biomarkers and mean cognitive performance. ConclusionsIncreases in measures of amyloid, soluble tau, and neurodegeneration are associated with increased IICV among cognitively impaired older adults. The findings underscore the potential of IICV as a sensitive outcome measure in the AD clinical disease phase. Future studies should replicate findings longitudinally and in more diverse samples.

There is a predicted increase in older adults presenting with mild to severe cognitive impairment. Screening tools with high sensitivity are the first frontier in identifying a cognitive pathology; however, to ensure that they are measuring the intended concept or criterion, thorough psychometric procedures should be followed. In this study, convergent criterion validity of Riga Cognitive Screening Task was measured, using cortical thickness of regions of interest as the criterion. 106 older adults (Mage = 70.49, SD =8.08, 35.8% male) with varying levels of cognitive functioning were involved in the study. All participants underwent cognitive assessment with the screening task and a 3T MRI. Cortical thickness of selected temporal and parietal regions was used as a brain measure. Behavioural Partial Least Squares Correlation was conducted and one latent variable was extracted. The results confirmed that Riga Cognitive Screening Task shows good criterion validity, suggesting successful use for screening.