Alzheimer's Research & Therapy

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) frequently co-occurs with Alzheimer's disease (AD) pathology and has been studied mainly in older populations. We examined its frequency and expression in 196 individuals with autopsy-confirmed intermediate-to-high AD neuropathologic change, enriched for younger-onset cases, integrating neuropathology with antemortem clinical, cognitive and neuroimaging data. LATE-NC occurred in 31% overall and 19% of individuals with symptom onset [≤]65 years. Its frequency increased with older age at symptom onset, and its presence was associated with greater episodic memory impairment and smaller amygdala and hippocampal volumes. Among individuals with LATE NC, older age at symptom onset was associated with memory-first presentation and greater medial temporal involvement, including higher inferior-to-medial temporal metabolism ratios on FDG PET. Despite its lower frequency, LATE-NC co-pathology should still be considered in early-onset AD, where selected clinical features and neuroimaging markers described in older populations may be less applicable.

Open-source deep learning models promise scalable quantification of MRI-visible perivascular spaces (PVS), an emerging marker of glymphatic dysfunction in Alzheimer's disease (AD). Whether these models remain valid when applied to heterogeneous AD cohorts and preserve ground-truth derived biological associations has not been established. We benchmarked five open-source nnU-Net-based PVS segmentation models (nnU-Net T1, nnU-Net T1+FLAIR, mcPVS-Net, MedNet-PVS, ADNI-PVS-Net) against manual labels in 60 multi-site ADNI-3 participants balanced across cognitively unimpaired, mild cognitive impairment, and AD dementia, and in 12 publicly available scans from the VAscular Lesions object-level and segmentatiOn (VALDO) challenge. We evaluated overall, voxel, object, and lesion-wise Dice; stratified performance by region, diagnosis, scanner, PVS severity, and white matter hyperintensity (WMH) burden; and tested whether model-derived PVS burden reproduced ground-truth derived associations with AD-related biomarkers. Despite high internal performance, all models showed a specialist-generalist trade-off: the AD-trained ADNI-PVS-Net achieved highest internal performance (overall Dice = 0.58 {+/-} 0.14) but degraded most externally (Dice [≤] 0.11), while MedNet-PVS and mcPVS-Net generalized externally (Dice 0.27-0.30) but systematically misestimated PVS burden. Models were relatively robust across scanner and diagnosis but inconsistent across PVS severity, and some misclassified up to 32% of WMH as PVS. Notably, only AD-trained models reproduced the expected PVS-amyloid biomarker relationship; other models returned a null result, despite comparable or superior Dice scores. These findings show that internal segmentation accuracy and external generalizability alone do not guarantee downstream inferential validity. Careful model selection and cohort-specific validation against manual labels should precede clinical inference in AD.

Identifying progressive dementia during its prodromal stage is a challenging yet critical task for healthcare providers. Traditional clinical assessments are often not sensitive enough to detect warning signs of cognitive decline. Machine learning (ML) methods have shown promise in modeling clinical trajectories associated with neurodegenerative disorders such as Alzheimer's disease (AD). Validation of these models' forecasting abilities, however, is widely lacking in the literature. To address this gap, we established a longitudinal cohort from the National Alzheimer's Coordinating Center (n = 15,727) comprising individuals who were cognitively normal (CN) or had mild cognitive impairment (MCI) at baseline. Cognitive status and clinical observations collected over up to 10 visits were used to train several ML classifiers. We employed the gradient-boosted tree ensemble method XGBoost with a custom feature-engineering pipeline. On non-imputed data, our models achieved an AUC-ROC of 0.9447 [0.9417-0.9476] and PR-AUC of 0.8554 [0.8497-0.8611] for the CN to MCI task, and an AUC-ROC of 0.9059 [0.8983-0.9134] and PR-AUC of 0.9214 [0.9156-0.9272] for the MCI to AD task. In a lead-time forecasting analysis spanning mean follow-up periods of 56.1 months (range, 9.1-214.1) for CN to MCI and 42.7 months (range, 9.1-126.1) for MCI to AD, the non-imputed models detected future progression before diagnosis in 29.58% [28.29%-30.87%] of CN to MCI progressors and 22.41% [21.36%-23.46%] of MCI to AD progressors. Among these individuals, the mean lead time was 33.04 months [32.52-33.56] and 17.95 months [17.71-18.19], respectively. False alarm rates among non-progressors were 12.6% [11.5%-13.7%] and 20.0% [18.4%-21.6%], respectively. These findings suggest that longitudinal machine learning models can identify future cognitive decline before diagnosis, indicating clinical utility for early dementia risk stratification.

INTRODUCTION: Voice data combined with large language models may detect cognitive impairment, yet an interpretable framework has not been formally established. We developed and validated a scalable and interpretable framework, the Neurocognitive Speech Taxonomy (NST). METHODS: NST maps 314 voice features to 7 neurocognitive domains derived from neuropsychology, speech pathology, neurology and related fields. The domains capture features including articulatory precision, cognitive-linguistic, executive fluency & planning, phonation & laryngeal control, prosodic modulation, lexical-semantic, and morphosyntactic complexity. We evaluated NST structural stability, clinical validity and racial-educational equity in three cohorts (total N = 1,479). Cohort data included demographic and neuropsychological evaluations and AD biomarker status. We tested whether NST scores distinguish cognitive status (unimpaired vs impaired) and AD-biomarker status, track cognitive change, and correlate with AD neuroimaging measures. RESULTS: NST showed stable cross-cohort structure (Mantel r = 0.71-0.77) and differentiated MCI (d = -0.39) and AD biomarker status (d = -0.23). NST tracked longitudinal cognitive change, correlated with hippocampal volume (r up to 0.28, P < .001), and showed smaller racial and educational disparities relative to standard cognitive testing (NST d = 0.03-0.33, MoCA d = 0.37-0.72). DISCUSSION: NST provides a validated neurocognitive construct that facilitates interpretation of AI-driven voice biomarkers of AD. This study was not registered in a public trials registry.

INTRODUCTION: The locus coeruleus (LC) is one of the earliest sites of Alzheimers disease (AD) pathophysiological changes. LC magnetic resonance imaging (MRI) intensity may reflect effects of AD pathophysiology on the LC, providing a biomarker of early-stage AD. This study examined relationships among MRI-derived LC intensity, plasma phosphorylated tau 217 (pTau217), and memory in older adults without dementia. METHODS: Independently living adults aged 59-89 years (N = 163) underwent neuropsychological assessment and T1-weighted fast spin-echo MRI. We quantified a bilateral LC contrast ratio relative to a pontine reference region, averaged across axial slices. Plasma pTau217 was measured using the Quanterix ALZpath p-Tau217 Advantage PLUS assay. For a subset (n = 127), a memory composite score was calculated. RESULTS: After adjustment for age, sex, and APOE4 carrier status, lower LC intensity was associated with higher plasma pTau217 concentrations (B = 0.17, P = .02). Each 1-SD decrease in LC intensity was associated with 53% higher odds of pTau217 positivity (P = .03). In models additionally adjusted for education, a significant LC x pTau217 interaction for memory (B = 0.18, P = .009) indicated that lower LC intensity was associated with worse memory at low (P = .01), but not high (P = .64) pTau217 levels. DISCUSSION: These findings support MRI-derived LC intensity as a marker of early AD-related processes and suggest that LC intensity may provide complementary information not captured by plasma pTau217 alone. Longitudinal studies are warranted to determine how these relationships change across the early stages of AD.

MicroRNAs (miRNAs) are implicated in Alzheimer's disease (AD), but research has focused on individual miRNAs and direct targets. Existing approaches to miRNA regulation in AD identify associations rather than causal effects, and few methods estimate multi-stage chains from miRNAs through target genes to target transcription factor (TF) cascades. We developed the LSD pipeline (LASSO-SEM-DoWhy), integrating LASSO feature selection, multi-stage structural equation modeling, and DoWhy causal inference to identify and validate miRNA causal pathways in AD. Applying LSD to six blood miRNA and brain mRNA datasets, we identified four LSD-validated miRNAs (miR-30d-5p, miR-92a-3p, miR-296-5p, miR-193a-5p) as AD biomarkers, achieving >86% ROC accuracy in an independent validation cohort. Several miRNAs with no significant direct association with AD showed significant effects when estimated through their target networks, while others significant in direct analysis were not supported at the network level, underscoring the value of network-level analysis. Extending to the TF layer revealed complete miRNA [-&gt;] targets [-&gt;] TF cascades [-&gt;] AD causal chains, with HMGA1, NKX2-3, and PRRX2 as key intermediaries. Confirmed classic pathways converge primarily on tau pathology and synaptic dysfunction. miRNA effects were largely age-independent, suggesting miRNAs act as early initiators of AD pathogenesis. Beyond AD, the LSD pipeline provides a generalizable framework for uncovering causal regulatory mechanisms in other diseases.

Cellular mechanisms in early Alzheimers disease (AD) remain poorly understood despite generation of extensive observational multiomic data. Weighted integration across diverse datasets can prioritise convergent mechanisms and targets. We conducted a systematic review to identify datasets profiling cell-type-specific changes in early AD. We integrated multiomic findings using Meta-Analysis by Information Content (MAIC), a computational method which combines genomic data from diverse experimental sources. Electron transport chain dysfunction in mitochondria emerged as a shared pathway in neurons, microglia, and astrocytes, and Wnt signalling was a prominent astrocyte-specific signature. Pathological neuro-glial signalling mechanisms included APP-CD74, APOE-SORL1, and WNT-FZD/LRP6. Integration with pharmacological data identified therapeutics already under investigation in AD, such as guanfacine, and candidates not previously prioritised, such as eltrombopag and encorafenib. We present a systematic analysis of cell-type-specific changes in early AD to prioritise pathways for investigation and therapeutics for repurposing.

INTRODUCTION: The utility of blood-based Alzheimer's disease (AD) biomarkers in predicting clinical trajectories remains uncertain. METHODS: We assessed the diagnostic and prognostic performance of p-tau181, p-tau217, GFAP, and NfL in a large memory-clinic cohort (n=3,645) with comprehensive baseline clinical evaluation and annual follow-up for up to three decades. RESULTS: P-tau217, p-tau181 and GFAP were associated with amyloid-beta (A{beta}) pathology, while all biomarkers were elevated in AD and all-cause dementia. P-tau217 significantly outperformed others in detecting A{beta} pathology and clinical AD but performed similarly to NfL and GFAP for all-cause dementia. Prognostically, p-tau217 demonstrated the strongest association with cognitive decline, followed by GFAP and NfL, and best predicted progression status at 5- and 10-year intervals. All biomarkers provided added prognostic value beyond demographic and genetic covariates. DISCUSSION: These findings support plasma biomarkers for disease identification and risk stratification while underscoring the need for population-specific approaches.

Scalable and accessible biomarkers of neurofibrillary tangle pathology are needed for Alzheimer's disease prognosis, diagnosis and monitoring. Here, we show that a blood-based immunoassay for an endogenously-cleaved eMTBR-tau fragment of tau protein is strongly associated with autopsy-diagnosed Alzheimer neuropathologic change (ADNC) and predominantly reflects the burden and anatomical progression of tangle pathology but not amyloid plaque load despite being most elevated in amyloid-positive symptomatic individuals. Plasma eMTBR-tau was significantly more accurate than plasma p-tau217 in classifying Braak staging and ADNC statuses at autopsy. Importantly, neuropathologic classification by plasma eMTBR-tau remained high (AUC=0.91-0.95) even when increasing intervals between blood collection and death up to a decade, while maintaining superior sensitivity and specificity over p-tau217. Higher baseline plasma eMTBR-tau was associated with worse cognition cross-sectionally and longitudinally predicted faster worsening. These results were replicated in a living cohort with in vivo tangle and plaque neuroimaging. Plasma eMTBR-tau concordance with tau neuroimaging was up to 94%, suggesting surrogacy. Together, plasma eMTBR-tau captures biological signatures of Braak staging and ADNC over a prolonged pre-mortem window, highlighting potential for early disease detection, biological staging, prognosis and therapeutic development.

Therapeutic strategies targeting amyloid-{beta} (A{beta}) in Alzheimer disease (AD) include antibodies directed against fibrillar amyloid plaques or soluble amyloid-{beta} (A{beta}) species, yet how these approaches reshape brain pathology remains poorly understood. Postmortem neuropathological assessment is crucial for evaluating the degree of A{beta} removal, providing information and context that fluid biomarkers and positron emission tomography (PET) alone cannot, and guiding development of more effective disease modifying therapies for AD. In this study, using high resolution mass spectrometry we quantitatively measured A{beta} and tau proteoforms in postmortem brain tissue homogenates to evaluate the impact of long-term treatment with anti-A{beta} monoclonal antibodies gantenerumab or solanezumab in a clinical trial of dominantly inherited Alzheimer disease (DIAD). Symptomatic DIAD participants who had been treated for ~ 4 years with anti-A{beta} monoclonal antibodies (gantenerumab, n=4 or solanezumab, n=4) were compared with untreated, symptomatic DIAD mutation carriers (n=40) and with age-relevant non-DIAD controls with modest or no plaque pathology (n=18). Consistent with antemortem A{beta}-PET findings in the clinical trial, gantenerumab-treated participant samples showed significantly lower levels of specific A{beta} proteoforms (A{beta} 42, A{beta}40) in the insoluble plaque associated pool (range: 60-80 % reduction); they also showed lower levels of post-translationally modified A{beta} proteoform A{beta}3pGlu in both the insoluble and soluble pool, compared to untreated DIAD participants, but without reaching the levels found in non-DIAD controls. Samples from gantenerumab-treated participants also showed regionally lower amounts of insoluble phosphorylated tau species (pT181, pT205, pT208) and MTBR tau species (354-369, 306-317, 299-317) without apparent differences in insoluble total tau levels. In contrast, brain tissue samples from solanezumab-treated participants did not show significant differences overall in soluble or insoluble levels of A{beta} and tau proteoforms in comparison to those from non-treated DIAD individuals. These findings support that amyloid targeting treatment (ATT) with gantenerumab, an antibody that targets fibrillar A{beta}, can remove insoluble A{beta} from the DIAD brain and also mitigate downstream biochemical and pathophysiological changes associated with AD.

Background: Traditional neuropathological assessment of Alzheimer's disease (AD) relies on manual, semi-quantitative scoring of amyloid-beta (A{beta}) burden. This approach is limited by high inter-rater variability and often qualitative, failing to capture subtle, continuous pathology changes. While 11C-PiB PET neuroimaging provides non-invasive longitudinal monitoring, it has a limited dynamic range and lacks the sub-millimeter spatial resolution necessary to track localized microvascular and parenchymal A{beta} trajectories. Methods: We introduce ViTamyloid, a generalizable modular computational pathology pipeline for high-throughput A{beta} segmentation and classification in postmortem tissue. In Stage 1, a pathologist-supervised hybrid framework combining an EfficientNet-B4 encoder with a Residual U-Net decoder, complement with a localization-aware Faster R-CNN detector, to generate binary segmentation masks and isolated lesion candidates. In Stage 2, classification model powered by a frozen, high-resolution UNI2-h computational pathology foundation backbone and a ViT-Adapter, classifies candidates into a fine-grained four-class taxonomy: classic cored plaques (CCP), coarse-grained plaques (CGP), capillary cerebral amyloid angiopathy (CAA Type 1), and parenchymal/leptomeningeal CAA (CAA Type 2). We bench-marked out-of-distribution (OOD) robustness on external datasets from different brain banks, and validated clinical scalability across 1,646 whole-slide images from three brain banks. Results: The ViTamyloid classifier achieved strong feature-separation metrics, yielding area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) values of 0.994/0.958 for CCP, 0.989/0.945 for CGP, 0.992/0.977 for CAA Type 1, and 0.997/0.978 for CAA Type 2. More importantly, ViTamyloid consistently outperforms other pipelines' models and frozen features in all external datasets with different antibodies, and scanner settings, and image quality. Conclusion: By replacing empirical, from-scratch architectures with a generalizable foundation model adapter framework, ViTamyloid provides a highly sensitive scalable digital pathomics toolkit. This pipeline expose continuous microscopic neuropathological dynamics beyond the spatial and sensitivity plateaus of in vivo neuroimaging, offering objective, quantitative endpoints for Alzheimer's disease clinical trials and disease subtyping.

Objectives Alzheimer's disease (AD) progresses from a cognitively unimpaired stage through mild cognitive impairment (MCI) to dementia. Because language impairment often emerges before dementia, it is a candidate marker of disease stage. Yet the standard batteries of large AD research cohorts rely on a narrow set of tests that mainly evaluate the lexical-semantic domain (i.e., picture naming and verbal fluency), leaving other language domains, such as semantic memory, phonology, morphosyntax, and single-word reading, rarely assessed. We evaluated the diagnostic utility of standard and specialized language tests for distinguishing amyloid-positive MCI and dementia of the Alzheimer's type (DAT) from healthy controls (HC) and from each other. Methods We included 286 participants from the National Alzheimer's Coordinating Center (125 HC, 53 MCI, 108 DAT) assessed with three standard tests from UDSv3 (semantic fluency, letter fluency, and Multilingual Naming Test [MINT]) and nine specialized tests from the FTLD Module (regular and irregular word reading, semantic word-picture matching, semantic associates, Northwestern Anagram Test - Short Form [NAT-S], Northwestern Naming Battery noun and verb naming, sentence repetition, and sentence reading). For each test, the area under the receiver operating characteristic curve (AUC) was computed for DAT vs. HC, MCI vs. HC, and DAT vs. MCI, and AUCs were compared between tests with DeLong's test. Results Eight of 12 measures reached acceptable discrimination (AUC [&ge;] .700) for DAT vs. HC, five for MCI vs. HC, but only semantic fluency and NAT-S for DAT vs. MCI. Semantic fluency showed the highest discrimination in each contrast, though it did not significantly outperform the NAT-S for MCI vs. HC or DAT vs. MCI. MINT, letter fluency, and irregular word reading reached clinically meaningful accuracy in distinguishing MCI and DAT from HC, but not DAT from MCI. The remaining specialized tests reached it only for DAT vs. HC, or not at all. In a post hoc regression, adjusting for cognitive flexibility and working memory attenuated group differences; the DAT difference was no longer significant, whereas the MCI difference remained significant. Discussion Semantic fluency was the strongest single language marker across the AD continuum. Among specialized tests, NAT-S was the most informative, challenging the view that syntax is spared in early AD. Its performance was partly explained by executive function, mainly in DAT, whereas a residual morphosyntactic component remained in MCI, and several specialized tests were limited by ceiling effects. Clinically, the NAT-S may complement semantic fluency for staging, and specialized tests may help characterize heterogeneous language profiles in MCI and DAT.

Background: Studies indicate that the apolipoprotein E (APOE) {varepsilon}4 allele is the strongest genetic risk factor for Alzheimer's disease (AD), and individuals with this allele are at increased risk of developing AD, there remains debate regarding whether APOE {varepsilon}4 carriers experience more rapid cognitive decline over time compared to non-carriers. This study assessed this question using linear mixed-effects models applied to the placebo arm of EXPEDITION 1, a phase 3 clinical trial evaluating solanezumab in patients with mild-to-moderate Alzheimers disease. Methods: In this study we utilized data from 370 participants in the placebo group of the EXPEDITION 1 trial. After excluding 27 participants with undefined APOE genotype, all models and analyses were applied to the remaining 343 participants (686 ADAS-Cog14 observations) to determine whether APOE {varepsilon}4 carriers exhibit a faster rate of decline over the 80-week follow-up period. Five nested linear mixed-effects models with a participant-specific random intercept were fitted, and also a complementary change-score analysis was done. Results: Over 80 weeks, ADAS-Cog14 worsened by an average of 7.01 points. Carriers did not decline faster than non-carriers (additional decline 1.31 points; 95% CI -1.22 to 3.83; p = 0.310), and the change-score analysis showed the same pattern. Carriers had worse ADAS-Cog14 scores overall, but this difference was no longer significant after adjustment for baseline CDR-SB. Conclusion: In this cohort, APOE {varepsilon}4 carrier status was related to disease severity at baseline rather than to the rate of cognitive decline over 80 weeks.

Introduction: Early Alzheimer's disease (AD) staging remains challenging because clinical categories and global amyloid PET measures provide only coarse characterization of disease progression. We developed a continuous amyloid staging score from regional amyloid PET to complement global amyloid burden. Methods: Using longitudinal florbetapir PET from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we trained an end-to-end deep learning model to directly estimate a continuous amyloid staging score. We evaluated longitudinal consistency, its relationship with global amyloid burden, regional contributors to early-stage variation, and whether the staging score stratified subsequent memory decline among participants matched for baseline global amyloid burden. Cognitive analyses were replicated in A4+LEARN. Results: The staging score better preserved longitudinal visit order than existing amyloid staging approaches. During early disease stages, it captured regional amyloid variation despite relatively stable global amyloid burden. Among participants with similar baseline global amyloid burden, higher staging scores were associated with faster subsequent memory decline in both ADNI and A4+LEARN. Discussion: A continuous amyloid staging score complements global amyloid burden for early AD staging and cognitive risk stratification. Keywords: Alzheimer's disease; amyloid PET; disease staging; cognitive decline

Background: Cross-cohort machine-learning studies of tau positivity are increasingly used to assess generalizability of Alzheimer's disease biomarker models, but apparent differences between cohorts may reflect clinical severity structure and cohort stage composition rather than genuine biological divergence. Methods: We evaluated this problem across subject-level cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS3), and the National Alzheimer's Coordinating Center (NACC) using harmonized tabular predictors and a feature-block ablation framework. For ADNI and OASIS3, we performed matched-feature analyses restricted to shared harmonized variables (age, APOE e4, education, sex, the CDR(R) Dementia Staging Instrument sum-of-boxes score (CDR-SB), and the Functional Activities Questionnaire (FAQ)). Models were trained with five-fold stratified cross-validation using balanced logistic regression as the primary classifier. Random forests and extra-trees classifiers were evaluated as robustness checks and are reported in supplementary analyses. Results: In the primary matched-feature comparison, reference areas under the receiver operating characteristic curve (AUCs) were 0.851 for ADNI tau90, 0.860 for ADNI tau180, and 0.933 for OASIS3 tau180. Severity-only models retained 86-99% of reference performance, whereas demographic/genetic-only models retained 85-87%; bootstrap confidence intervals showed that the reference-minus-demo contrast remained positive across all primary cohorts. Before feature matching, OASIS3 showed substantially weaker demographic-only prediction (AUC approx. 0.54), attributable to missing APOE and age variables. After harmonization with a matched feature set including age, the pre-harmonization demographic gap was no longer attributable to feature availability; the residual difference was most consistent with cohort disease-stage composition asymmetry, with CDR-SB = 0 prevalence of 84.5-88.7% in OASIS3 versus 49.3-51.7% in ADNI. Conclusions: The original large ADNI-OASIS3 performance gap was largely a feature-availability artifact; after strict harmonization including age-field repair, the absolute gap narrowed and the signed ADNI-minus-OASIS3 difference reversed, with the residual difference most consistent with cohort disease-stage composition asymmetry. Clinical severity remains an important predictor across all cohorts, but the unique contributions of severity and demographic/genetic information depend strongly on stage composition. Cross-cohort biomarker modeling should account for both feature-set completeness and disease-stage composition heterogeneity, not only headline performance metrics.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Background: Plasma biomarkers offer a minimally invasive alternative for detecting Alzheimer disease (AD) pathology, yet the genetic determinants influencing their levels remain largely underexplored. Objective: To identify genetic variants associated with AD-related plasma biomarker levels in a Midwestern Amish cohort. Methods: We analyzed plasma levels of amyloid beta (A{beta}42/A{beta}40), phosphorylated tau 181 (p-tau181), and the p-tau181/A{beta}42 ratio in the Midwestern Amish. Genome-wide association studies (GWAS) were used to examine associations with common genetic variants, while genetic linkage analysis was used to assess the potential impact of rare variants. Results: GWAS found associations between plasma biomarkers and the APOE gene. Additionally, novel candidate genes were implicated, including MME for A{beta}42/A{beta}40 ratio, and VEGFC and LRRTM4 for p-tau181. Linkage analysis identified significant signals for p-tau181 on chromosome 1, potentially involving the CSF1 and AHCYL1genes. Conclusions: The novel genetic variants associated with AD pathology implicate metabolic, synaptic, neuronal, and immune pathways. The use of GWAS and linkage analysis sheds light on the genetic architecture of AD-related plasma biomarkers.

According to the AT(N) framework, Alzheimers disease (AD) can be biologically characterised by amyloid deposition, tau pathology, and neurodegeneration, yet how these processes relate to whole-brain dynamics remains unclear. Here, we investigated these relationships and the link between whole-brain dynamics and cognitive dysfunction using multimodal neuroimaging from 145 ADNI3 participants. We combined multiscale measures of turbulent dynamics with responses to in silico perturbations obtained from whole-brain models. AD progression was characterised by a scale-dependent reorganisation of turbulent dynamics where information flow showed the strongest contribution to the dynamics-related variance in cognitive performance. Large-scale turbulence was associated with all three AT(N) components. Model-based information capability and susceptibility measures progressively decreased across AD stages and were associated with amyloid burden and regional tau deposition. Model-free features discriminated comparisons involving AD dementia, whereas adding model-based measures produced modest, metric-dependent changes in classification performance. Exploratory analyses showed spatial alignment between large-scale turbulence alterations and AD-risk gene-expression gradients, with APOE among the strongest associations in both disease-stage contrasts examined. Lastly, we found a progressive decoupling between turbulent dynamics and the E:I balance across AD stages. Together, these findings link model-based and model-free measures of whole-brain dynamics to proteinopathy, neurodegeneration, and cognitive impairment, supporting their complementary value for characterising AD progression.

INTRODUCTIONInflammation plays a central role in Alzheimers Disease (AD), with oxylipins, endocannabinoids, and polyunsaturated fatty acids (PUFAs) acting as potent modulators. Soluble epoxide hydrolase (sEH), an enzyme with proinflammatory products, is upregulated in the AD brain. Emerging evidence suggests that AD-related inflammation may reflect systemic metabolic changes, however, peripheral organs remain understudied. Because the liver has a pivotal role in lipid metabolism and bioactive lipid mediator homeostasis, we hypothesized that inflammatory lipid mediator changes in the AD brain may be reflected in, and potentially shaped by, hepatic lipid metabolism. METHODSPaired postmortem brain and liver tissues from 16 cognitively normal and 63 AD donors with median postmortem intervals (PMI) of 3.08 hours were analyzed for free and esterified forms of oxylipins and endocannabinoids. ANCOVA models evaluated effects of AD diagnosis, sex, and AD-by-sex interactions, adjusting for analytical batch, age, body mass index, and PMI. RESULTSAD-associated differences in lipid mediator profiles were observed in both brain and liver, with a broader pattern of differences in liver. Differences involved CYP/sEH-related metabolites and fatty acid ethanolamides, with several metabolites also showing AD-by-sex interactions. Sex-associated differences were also more pronounced in the liver than the brain. DISCUSSIONThese results suggest AD-associated lipid dysregulation across both central and peripheral tissues. The prominent hepatic alterations highlight the liver as an important site of peripheral lipid mediator dysregulation in AD and suggest that hepatic metabolic changes may contribute to or reflect inflammatory lipid remodeling in the brain.

Depression in Alzheimer's disease (AD) is clinically challenging to characterise because affective and cognitive manifestations frequently overlap with the underlying neurodegenerative condition. This study investigates depression-related speech patterns in AD using an explainable multimodal framework that integrates linguistic and acoustic features from elicited picture-description speech. To address the absence of directly observed depression labels in the target AD cohort, we employ a source-informed cross-dataset transfer strategy. Unsupervised clustering identified two distinct speech sub-phenotypes within the AD cohort. By comparing these unsupervised representations to a depression-labelled source corpus, one cluster demonstrated mathematical proximity (measured via cosine similarity in a shared latent space) to the source depression centroid. We explicitly acknowledge that this proximity reflects geometric similarity in a shared latent space, which may encapsulate overlapping domain characteristics alongside affective variance. To validate the structural integrity and separability of these discovered clusters, a proxy supervised classification task was employed. The retained multimodal artificial neural network (ANN) integrated information from both modalities and achieved 92.73\% cluster separability accuracy. This demonstrates that the representation-derived two-cluster partition was highly learnable on the independent internal hold-out test set; it does not establish clinical validity, longitudinal stability, or diagnostic validity of the identified sub-phenotypes. For interpretability, Shapley Additive exPlanations (SHAP) analysis was utilised to extract feature attributions and organise influential acoustic and linguistic features into a multimodal taxonomy. Furthermore, a Multimodal SHAP Attribution Magnitude (MSAM) score was derived as an exploratory model-based attribution score rather than a clinically validated severity measure. Ultimately, this study provides an interpretable computational baseline for characterising depression-related speech variance in AD, establishing a structural foundation for future prospective validation using independently labelled clinical cohorts.