Alzheimer's & Dementia: Translational Research & Clinical Interventions

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.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSINTRODUCTIONC_ST_ABSTreatment response in Alzheimers disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heterogeneous treatment effects (HTE) from observational data while controlling for confounding bias. METHODSWe developed a causal machine learning framework integrating expert-guided causal graphs, complementary HTE estimators, sensitivity analyses, and policy learning. We applied it to cholinesterase inhibitors (ChEIs) in MCI due to AD to patients from the NACC and ADNI cohorts. RESULTSAnalysing 4,049 patients with 12-month and 2,223 with 36-month follow-up, all estimators indicated null or negative long-term ChEI effects on cognitive and functional outcomes, notably on functional measures. ChEIs showed slightly more deleterious effects among men than women. DISCUSSIONThis framework provides a methodology for estimating HTE from observational data. It revealed no beneficial responder subgroups, highlighting the challenge of detecting treatment heterogeneity in moderately sized cohorts. This approach can inform treatment selection for other AD therapies including memantine, anti-amyloid agents, and emerging treatments.

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.

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

BackgroundGabapentin prescriptions have increased 123% since 2010, reaching 15.5 million Americans annually. Recent studies suggest gabapentin-dementia associations, but whether concomitant medications modify this risk is unknown. Both gabapentin and calcium channel blockers (CCBs) affect neuronal calcium signaling through distinct mechanisms, raising the possibility of pharmacodynamic interaction. MethodsActive comparator new-user cohort study using Rutgers Clinical Research Data Warehouse (2015-2024). Adults [&ge;]40 years with hypertension initiating gabapentin (n=28,058) or pregabalin (n=5,733) were followed for incident dementia. Inverse probability of treatment weighted (IPTW) Cox models estimated hazard ratios stratified by baseline CCB exposure. Validation analyses tested CCB subtype specificity (dihydropyridine [DHP] vs verapamil), dementia subtypes (F03/G30/F01), frailty stratification (CKD, stroke), lag periods, falsification outcomes, and non-2{delta} anticonvulsant comparisons. ResultsAmong 33,791 patients (502 dementia events; median follow-up 1.22 years), we identified a novel drug-drug interaction: gabapentin was associated with substantially elevated dementia risk among CCB users (HR=2.22, 95% CI 1.42-3.47, p=0.0005) compared to non-users (HR=1.15, 95% CI 0.99-1.33; interaction p=0.004). A time-varying analysis confirmed this finding: among gabapentin users who initiated CCB during follow-up, CCB-exposed person-time showed 65% higher dementia incidence (Rate Ratio=1.65, 95% CI 1.19-2.29). This interaction showed striking CCB subtype specificity: DHP CCBs drove the signal (HR=3.20) while verapamil showed no interaction (insufficient events for analysis). The signal concentrated in F03 unspecified dementia (HR=1.68, p=0.004) with short latency (median 240 days), consistent with drug-induced cognitive impairment rather than neurodegeneration. Pre-index symptom balance analysis showed 6/6 symptom families balanced between groups, arguing against protopathic bias. The interaction was paradoxically weaker in frail patients (CKD ratio=0.25, stroke ratio=0.14), arguing against confounding by illness severity. Lag analyses showed strengthening over time (HR 2.22[-&gt;]3.72), falsification outcomes were largely null (4/7), and non-2{delta} anticonvulsants showed no CCB interaction. ConclusionsWe identified a novel drug-drug interaction whereby DHP CCB co-medication amplifies gabapentin-associated dementia risk, confirmed by time-varying analysis (Rate Ratio=1.65). The DHP-specific signal is biologically plausible given independent evidence that DHP CCBs may adversely affect cognition (DREAM consortium), while the absence of interaction with verapamil aligns with its potential neuroprotective properties identified in drug repurposing studies. The F03-specific pattern suggests drug-induced cognitive impairment that may be reversible. These hypothesis-generating findings identify gabapentin-DHP CCB combinations as a target for cognitive safety monitoring and warrant confirmation with concurrent exposure measurement.

This study aimed to develop an artificial intelligence (AI) algorithm capable of distinguishing Alzheimers disease (AD) from healthy patients using gut microbiome metagenomics data. To do so, 16S rRNA gene and Whole Genome Shotgun (WGS) datasets available in the literature were utilised. Data was pre-processed and filtered. Then, an initial analysis was done to study classical parameters of microbiome data, such as alpha and beta diversity, as well as to unveil taxonomical differences between the groups that might be influencing disease development, using a unified workflow to enable integration of 16S rRNA gene and WGS data. Neural Network algorithms were subsequently applied to develop the classifier. First a classical multi-layer perceptron (MLPNN) architecture was tested, which showed limited classification performance, particularly in detecting AD cases. These results were improved using a convolutional neural network (CNN) architecture, due to its better comprehension of hierarchic data like taxa relative abundances sorted following phylogenetic tree structure. In addition, these AI methods were compared with a random forest (RF) classifier, a traditional machine learning algorithm. All models struggled to accurately identify AD cases due to the low number of samples used for algorithm training. Although the RF showed a better performance under such circumstances, observing the evaluation metrics the application of AI to this task reveals promising upon higher amounts of training data. The use of SMOTE, a data augmentation approach, confirmed this assumption improving performance across all models but specifically in the CNN. These results support the utility of microbiome-based diagnostics in AD, but highlight the need for larger, diverse datasets and multi-omics approaches to improve model reliability and uncover disease mechanisms.

INTRODUCTIONIt is unknown whether biomarkers of neurodegeneration collected from dried blood spots (DBS) in large-scale population settings are useful in predicting cognitive decline many years later. METHODSIn 2015, we collected DBS of 13,679 individuals aged 58 and older. All DBS were assayed for ApoE4 protein, and a smaller subsample for pTau217, GFAP, and NfL. In 2022, we obtained detailed cognition measures for 6,523 respondents. Regression analyses tested the likelihood of cognitive impairment as a function of biomarker levels. RESULTSRespondents with ApoE4 detection have worse cognitive performance seven years later as measured by six different cognitive performance measures (p<0.001). The combination of all four biomarkers is a statistically significant predictor for five cognitive performance measures, with pTau217 having the most systematic association. DISCUSSIONDBS-based biomarkers of neurodegeneration provide a cost-efficient and scalable early warning signal enabling preventive measures against AD/ADRD before the onset of serious symptoms.

BackgroundTransthyretin amyloidosis cardiomyopathy (ATTR-CM) and Alzheimers disease (AD) are age-related disorders characterized by pathological protein aggregation. Despite shared risk factors and mechanisms, the relationship between ATTR-CM and AD remains poorly understood. MethodsWe performed a population-based case-control study using the French National Health Data System from 2019 to 2023. Individuals aged 65 years or older diagnosed with ATTR-CM were matched with controls without ATTR-CM by age, sex, hypertension status, and area of residence. The main exposure was a diagnosis of AD within five years preceding the index date (ATTR-CM diagnosis or equivalent for controls). Conditional logistic regression estimated adjusted odds ratios (ORs) for the association between ATTR-CM and AD, accounting for major dementia risk factors including cardiovascular, metabolic, psychiatric, and lifestyle variables. ResultsAmong 96,200 participants (19,240 ATTR-CM cases and 76,960 controls), 28,990 (30.6%) were women, and the mean (SD) age was 82.3 (6.6) years. The adjusted OR for AD among ATTR-CM patients was 0.65 (99% CI, 0.56-0.75), indicating a lower likelihood of AD compared with controls. ConclusionsThis large nationwide study suggests that ATTR-CM is associated with a reduced risk of AD, warranting further investigation into underlying biological mechanisms or possible diagnostic bias.

BackgroundGamma entrainment shows promise for Alzheimers disease (AD) treatment in preclinical models, but human trials have yielded heterogeneous results. We hypothesized that the clinical efficacy of gamma entrainment depends on individual neurophysiological receptivity, specifically the capacity for neural circuit plasticity. MethodsIn this open-label pilot study, we screened 37 individuals and enrolled 16 participants with early AD (CDR 0.5-1.0, amyloid-positive) who completed 12 weeks of home-based flickering light stimulation at individually optimized gamma frequencies (32-40 Hz). Pre- and post-intervention assessments included 64-channel EEG recordings and MMSE. ResultsParticipants demonstrated dichotomous neurophysiological responses: 43.8% showed CF increase (ICF+) while 56.3% showed no change/decrease (ICF-). CF restoration was significantly associated with cognitive preservation (r=0.52, p=0.039). Notably, future responders exhibited distinct baseline signatures of "neural reserve," characterized by higher temporal gamma power (Cohens d=0.70-0.92) and stronger frontotemporal connectivity (Cohens d=1.11-1.47). Almost 30% of screened candidates failed to show baseline entrainment, highlighting a distinct "non-responsive" biological subtype. DiscussionCF restoration following personalized gamma entrainment identifies a neurophysiological subtype capable of meaningful plasticity. Rather than a universal remedy, gamma entrainment appears to act on specific neural substrates preserved in a subset of patients. These findings suggest that baseline electrophysiological profiling could unlock gamma entrainments therapeutic potential by stratifying likely responders for precision neuromodulation.

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.

INTRODUCTIONPlasma biomarkers for Alzheimers disease (AD) pathology promise scalable diagnostic access, yet their performance in diverse, population-representative cohorts remains uncharacterized. We evaluated equity and transportability of plasma amyloid-tau-neurodegeneration (ATN) biomarkers in a nationally representative U.S. aging cohort. METHODSCross-sectional analysis of 4,427 adults aged [&ge;]50 years from the 2016 Health and Retirement Study Venous Blood Study. Plasma biomarkers (A{beta}42/40, pTau181, NfL, GFAP) were classified using established ATN criteria. Survey weights produced population-representative estimates. Outcomes included biomarker-cognition associations, fairness metrics (sensitivity, specificity, predictive values) stratified by race/ethnicity and sex, and education-stratified analyses. RESULTSAmong 4,427 participants representing 36.6 million U.S. adults (weighted: 68 years, 55% female, 79% White), survey-weighted analysis revealed tau as the only biomarker maintaining robust cognitive associations ({beta}=-0.74, p<0.001), while amyloid ({beta}=0.11, p=0.43) and neurodegeneration ({beta}=-0.27, p=0.08) lost significance. White participants demonstrated 12-percentage-point higher sensitivity than Black participants (23.4% vs. 11.4%), with Black women showing lowest sensitivity (8.8%). Educational attainment modified biomarker effects: low-education groups showed paradoxical positive amyloid associations ({beta}=0.74, p=0.01) and amplified neurodegeneration effects ({beta}=-1.02, p=0.006). Race-specific optimal cutpoints differed by 40%. Vascular comorbidity burden was higher in Black (82%) and Hispanic (73%) versus White (65%) participants, yet associations persisted after vascular adjustment. DISCUSSIONPlasma ATN biomarkers demonstrate significant equity gaps and differential transportability across demographic subgroups. The 12-percentage-point sensitivity disparity and education-dependent effect modification highlight barriers to equitable implementation. Population-based validation with fairness metrics should be prerequisite for clinical deployment.

BackgroundWhile blood-based biomarkers for Alzheimers Disease (AD) such as p-Tau and NfL characterize established pathology, the systemic biological cascade triggering these events remains incompletely mapped. We hypothesized that proteins exhibiting a rising trajectory in the prodromal phase might reveal novel mechanisms of disease progression. MethodsUsing data from the UK Biobank Pharma Proteomics Project (N = 4,519 incident AD cases), we performed a blind trajectory scan of [~]3,000 plasma proteins. We utilized an elimination strategy, systematically excluding known AD markers (e.g., APOE, NEFL) and verified biological responses (e.g., MMP3, GLRX) to isolate novel signals. ResultsAfter excluding established markers, VSIG10L--a V-set and immunoglobulin domain-containing protein--emerged as the most significant novel marker (beta = - 0.037, P = 0.0019), exhibiting a progressive rise as patients approached diagnosis. Crucially, VSIG10L was accompanied by a cluster of co-regulated proteins involved in embryonic development and cell cycle regulation, including NACC1 (stem cell pluripotency), VASN (vasculogenesis), and ZBTB17 (cell cycle checkpoint). ConclusionThe emergence of VSIG10L and its associated developmental cohort suggests that prodromal AD is characterized by a retrogenesis phenomenon, the unsilencing of developmental programs in a failed attempt at neural repair. These proteins offer a new window into the brains response to neurodegeneration and represent potential therapeutic targets.

Structured AbstractO_ST_ABSBACKGROUNDC_ST_ABSPositron emission tomography (PET), cerebrospinal fluid (CSF), and plasma assessments are used to measure amyloid abnormality in Alzheimers disease (AD). However, it remains unclear if these three measures are similarly associated with brain structure and cognitive measures. METHODSLinear regressions examined the relationship between amyloid levels measured by PET, CSF, and plasma and brain volumes, white matter hyperintensities (WMHs), and cognitive measures. RESULTSModerate correlations were found between PET and CSF amyloid measurements and PET and plasma measurements, while weak correlations were found between CSF and plasma. PET, CSF, and plasma amyloid measurements differed in their associations with brain volume, WMHs, and cognition. DISCUSSIONUsing different measurement methods, amyloid was not consistently associated with volumetric or cognitive measures. Our findings also suggest that plasma markers may not be associated with cognitive and brain changes in the same manner as CSF and PET.

INTRODUCTIONThe pathophysiology and risk factors for Alzheimers disease (AD) and dementia are insufficiently known. We studied the connections between gut microbiome, overall dementia and AD in a prospective, population-based cohort. METHODSWe followed a population based random sample of 4,055 individuals (FINRISK 2022) for 16 years, with 330 cases of incident dementia and 280 AD cases. Gut microbiome community diversity and composition were assessed against future dementia and AD risk. Competing mortality risks were accounted for using Fine-Gray models. RESULTSCommunity diversity was not associated with dementia or AD. However, a supervised ordination with dbRDA suggested a possible compositional link between gut microbiome and dementia. One putative bacterial genus, Dorea, was associated with a decreased dementia risk. APOE {varepsilon}4 genotype associated with several taxa; of these, phylum Verrucomicrobiota and species Nocardia carnea were associated with incident dementia. DISCUSSIONThe gut-brain axis has a modest association on future dementia or AD risk. Microbial composition, rather diversities, may contribute to dementia risk.

ObjectivesTo characterize contemporary pharmacologic treatment patterns for Alzheimers disease and related dementias (ADRD) among U.S. long-term care residents and to examine facility- and resident-level factors associated with treatment. DesignRetrospective, observational study. Setting and ParticipantsElectronic health record data from 1,675,873 long-term care residents in the PointClickCare Life Sciences database included 359,801 with a documented ADRD diagnosis in skilled nursing facilities in the U.S. (January-April 2025). MethodsResidents were classified as treated/untreated based on receipt of guideline-directed ADRD therapy, consistent with Alzheimers Association guidelines. Analyses incorporated demographics, comorbidities, medication burden, and facility characteristics. Multivariate logistic regression estimated odds of receiving guideline-concordant therapy. ResultsOverall, 72.5% of residents with ADRD received [&ge;]1 pharmacologic treatment recommended for ADRD. Treatment was most common among residents with Lewy body dementia (83.9%) and early-onset Alzheimers disease (82.3%) and least frequent among residents aged [&ge;]90 years (65.1%), Black/African American residents (66.8%), and those with cerebral degeneration (66.8%). Treated residents exhibited higher medication burden (mean 4.4 vs 3.3). Diagnoses for other chronic conditions as well as specific ADRD subtypes strongly impacted probability of treatment; diabetes and hyperlipidemia were associated with lower odds of treatment, whereas ADRD subtypes strongly predicted treatment. Conclusions and ImplicationsMore than one-quarter of residents with ADRD remain untreated with guideline-recommended pharmacotherapy, and treatment varied significantly by non-clinical predictors. These findings underscore the need to investigate and understand possible treatment disparities, optimize polypharmacy management, and discover new ADRD treatments, as current options are often ineffective with many side effects. Brief SummaryThis study used real-world data from electronic health records (EHR) to understand treatment patterns of those with Alzheimers disease and related dementias (ADRD) in U.S. long-term care facilities. International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10) codes were used to identify ADRD diagnoses and medication orders were used to identify treatment. From January to April 2025, there were 359,801 with a documented ADRD diagnosis in skilled nursing facilities. Over 25% of those with ADRD did not have a medication order for a guideline-recommended pharmacological treatment. Comorbidities of diabetes and hyperlipidemia were associated with lower odds of receiving ADRD treatment, suggesting concerns related to adverse drug reactions and competing clinical priorities. The use of cognitive and disease-modifying therapies was low compared to behavioral/psychiatric medications; this finding suggests a need for more effective and safe drugs that target the root causes of ADRD opposed to the behavioral and psychiatric complications. Taken together, the results of this study call for targeted interventions to address disparities in treatment, enhanced clinical decision-making support regarding polypharmacy, and improved pharmacological options for those with ADRD.

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.

INTRODUCTIONAccurate Alzheimers disease (AD) detection remains challenging and often requires invasive or costly procedures. Blood-based metabolomic signatures offer a promising non-invasive approach. This study aimed to identify a serum metabolite panel and evaluate its performance alone and in combination with apolipoprotein E (APOE) {varepsilon}4 genotype status for distinguishing AD from cognitively normal (CN) individuals. METHODSBaseline data from 594 participants in the Alzheimers Disease Neuroimaging Initiative (237 AD, 357 CN) were analyzed. High-resolution serum metabolomics (Biocrates MxP(R) Quant 500) and APOE genotype data were used for LASSO-based feature selection, followed by machine learning model training and evaluation on a held-out test set. RESULTSA panel of 151 metabolites distinguished AD from CN with high accuracy (test-set AUC=0.90). Adding APOE to the panel further improved model performance (AUC=0.91 versus AUC=0.75 for APOE alone; p<0.001), achieving strong sensitivity (0.92), specificity (0.84), and negative predictive value (0.94). Key predictive metabolites included bile acids, ether-linked phosphatidylcholines, and acylcarnitines, which are associated with pathways related to lipid metabolism, mitochondrial function, and the gut-liver-brain axis. CONCLUSIONIntegrating serum metabolomics with APOE enables accurate, non-invasive AD detection and offers a scalable screening approach with strong potential to rule out AD in primary care. ClinicalTrials.gov IdentifierNCT00106899 and related ADNI phases.

Background and ObjectivesPhysical activity and sleep are potential modifiable risk factors for the development of Alzheimers disease and related disorders (ADRD), but few studies have objectively measured both domains in participants across the cognitive continuum. Research Design and MethodsStandard clinical assessment, accelerometry, and at-home EEG sleep data were obtained from older controls (n=9) and adults who met consensus diagnostic criteria for mild cognitive impairment (MCI; n=7), Alzheimers disease (AD; n=10), and dementia with Lewy bodies (DLB; n=11). Given these sample sizes, descriptive statistics are presented rather than formal statistical testing. ResultsThe MCI group had the most surprising findings--although they were cognitively similar to the control group, they were less physically active than the AD group and had the worst sleep efficiency. The DLB group had the most severe motor and neuropsychiatric symptoms, were the least physically active, spent the least amount of time in rapid eye movement (REM) sleep, and spent the highest amount of time in non-REM sleep with hypotonia (NRH). The AD group had physical activity counts that fell between the DLB and control groups; REM sleep and NRH levels that were similar to the control and MCI groups; and autonomic activation index (AAI) and sleep spindle durations that were higher than the MCI and DLB groups. Discussion and ImplicationsThese findings highlight interesting physical activity and sleep patterns between groups, but larger samples are needed to investigate how objectively measured physical activity and sleep might serve as disease-specific digital biomarkers of neurodegenerative disorders. Translational SignificanceThis study uses wearable technologies to measure physical activity and sleep in adults with and without cognitive impairment. The study found that adults with mild cognitive impairment had physical activity and sleep patterns that resembled people with dementia despite having cognitive scores that were closer to cognitively healthy controls. Sleep and activity patterns were distinct when comparing participants with Alzheimers and dementia with Lewy bodies. Larger studies are needed to validate these findings, but mobile health devices may be an accessible way to detect early cognitive decline and help differentiate dementia subtypes, resulting in earlier, targeted clinical care.

INTRODUCTIONCognitive decline in Alzheimers disease (AD) may arise not only from neuronal loss but also from disrupted temporal coordination across distributed networks. Prior works in healthy individuals has shown memory-related modulation of hippocampal-cortical phase-amplitude coupling (PAC). We hypothesized alteration of resting-state hippocampo-cortical PAC in AD which relates to cognitive impairment. METHODSResting-state magnetoencephalography (MEG) data were obtained from 78 AD patients and 70 healthy controls. Source-reconstructed hippocampal and cortical signals were analysed using antisymmetrized bispectral methods to quantify non-linear across-site PAC. RESULTSAD patients showed significant disruptions of hippocampo-cortical PAC, particularly involving frontal, parahippocampal, and posterior-cingulate cortices. PAC measures were significantly associated with Mini-Mental State Examination scores within the AD group, with higher PAC corresponding to greater cognitive impairment in regions showing increased coupling relative to controls. DISCUSSIONThese findings demonstrate altered hippocampo-cortical temporal coordination in AD and suggest PAC as a potential electrophysiological marker of disease-related network dysfunction.

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.