Citations

Abstract Neuroimaging can assess different pathophysiological features of the brain in Alzheimer's disease (AD), including structure, function and protein deposition, and can be used to help identify patients for clinical treatment trials and monitor and assess potential treatment effects on disease progression. While large scale longitudinal studies are underway to validate potential neuroimaging biomarkers in amnestic AD, less efforts have focused on atypical clinical presentations, such as language, visual, dysexecutive, behavioral and motor presentations of AD. These patients would be ideal candidates for treatment trials given that they are relatively young, and the disease is less influenced by age‐associated co‐pathologies, providing a purer disease model of AD. Rates of brain atrophy provide excellent surrogate disease markers in amnestic AD and they will also likely have utility in atypical AD, although cortical, rather than medial temporal, measurements perform better. Optimum sample size estimates required to detect a 20% treatment effect in both the language and visual variants of AD were obtained with rates of inferior temporal atrophy in our sample (113 and 100 patients required per arm, respectively). However, heterogeneity in rates and patterns of atrophy are observed across phenotypes, and with age and disease stage, and these factors would need to be considered in clinical trials. Utilizing direct markers of tau deposition from PET would be optimal. Tau uptake increases over time in relatively consistent patterns across atypical AD phenotypes, although rates vary across patients and with age and baseline levels, with tau uptake plateauing at high levels potentially limiting utility as an outcome measure in patients with advanced disease. Metrics of connectivity also worsen over time in atypical AD patients, with evidence that declines in default mode network connectivity may be common across phenotypes and have some utility, although once again changes are highly variable and influenced by age and tau burden. In summary, we are gaining a better understanding of factors that influence rates of change in atypical AD that need to be considered in treatment trials, although more work is needed to identify optimum biomarkers to track disease progression across the AD clinical spectrum.

Abstract Background The quantification of Neurofilament Light chain (NfL) in blood and cerebrospinal fluid (CSF) has proven valuable for diagnosing and prognosing various neurological disorders, including Alzheimer's disease, frontotemporal dementia, amyotrophic lateral sclerosis, parkinsonism, multiple sclerosis, and ischemic damage. However, there is considerable variability in clinical and laboratory practices between centers, primarily due to different contexts of use (COU), analytical methods, and the application of cutoffs or scales. Additionally, for the same biochemical profile, the interpretation and reporting of results may vary from one center to another, raising concerns about the commutability of the tests. To date, no consensus has been reached among laboratories to define the most appropriate use of cutoffs or conclusions/comments based on NfL profiles. Method This international project involves 38 laboratories across 18 countries, all specialized in the biochemical diagnosis of neurological disorders. Using a questionnaire, we obtained descriptions from each center regarding their COU, pre‐analytical and analytical protocols (including the biological fluid and methods used to quantify NfL). Through a consensus approach, we developed a harmonized strategy for the use and reporting of NfL results across different centres according to their respective COU. Results Among the centers, 63% quantified NfL in the CSF, 87% in blood and 53% in both fluids. COU were as follow: frontotemporal dementia (71%), Alzheimer disease (71%), multiple sclerosis (61%), amyotrophic lateral sclerosis (61%), psychiatric syndrome (45%), Creutzfeldt‐Jakob disease (32%), Parkinson disease (39%), peripheral neuropathy (29%), traumatic brain injury (21%), Huntington's disease (13%), amyloid transthyretin related (11%) and cardiac arrest (8%). Most of the centers define pathological cutoffs based on publications (50%) and take age into account for this purpose (42%). Reporting is mostly transmitted through numeric results (95%). Conclusion Harmonizing cutoffs, reporting, and interpretation across various clinical contexts will facilitate the incorporation of this biomarker into routine clinical practice.

Abstract Background Extracellular vesicles (EVs) are key mediators in transferring pathological proteins associated with Alzheimer's disease (AD). The apolipoprotein E (APOE) gene, particularly the ε4 allele, is a major genetic risk factor for late‐onset AD. However, the influence of APOE genotype on the biological characteristics and cargo composition of brain‐derived extracellular vesicles (BDEVs) in AD remains poorly understood. Method In this study, BDEVs were isolated from human AD temporal cortex with APOE3/3 and APOE4/4 genotypes ( N = 20 / group). Nanoflow cytometry and super‐resolution microscopy were used to evaluate the tau load within these BDEVs. EV‐mediated tau transmission was assessed in aged human Tau KI and APP NL‐G‐F : Tau KI mice in vivo . BDEV uptake and tau seeding were evaluated in iPSC‐derived neurons through live imaging. Additionally, shotgun lipidomics and data independent acquisition proteomics were analyzed lipidome and proteome of BDEVs, respectively. Key molecules linked to APOE genotype were identified through integrated weighted gene co‐expression network and trait correlation analysis. Result APOE4/4 BDEVs exhibited a higher proportion of phosphorylated tau (pS396) at single EV levels. Upon injection of 300pg tau of BDEVs into aged Tau KI and APP NL‐G‐F : Tau KI mice, APOE4/4 BDEVs significantly increased AT8 + phosphorylated tau levels, and induced neuroinflammation in vivo . Moreover, APOE4/4 BDEVs demonstrated stronger neuronal uptake and tau transfer compared to APOE3/3 BDEVs, impairing the neuronal activity of iPSC‐derived neurons. Lipidomic revealed that APOE4/4 BDEVs were significantly more enriched in pro‐inflammatory long‐chain polyunsaturated fatty acids (PUFA) compared to APOE3/3 with Braak stage association. Furthermore, gene ontology pathway analysis using differentially expressed proteins in APOE4/4 BDEVs showed increased activity in oxidative phosphorylation and sterol‐binding. Multi‐omics integrated data revealed that upregulation of PUFA was significantly associated with elevated cell adhesion molecules and ATPase transporter activity with neural cell adhesion molecule as one of key molecule. Notably, neutralizing antibody against the molecule effectively reduced APOE4/4 BDEV internalization and mitigated EV‐mediated tau pathology in recipient cells. Conclusion Our comprehensive analysis suggested that AD APOE4/4 BDEV may enhance transmissibility of tau pathology and neuroinflammation via enriched pro‐inflammatory PUFA and cell adhesion molecules. Targeting these pathways presents a promising therapeutic strategy to mitigate disease progression, particularly in APOE4 individuals.

Abstract Background Dynamically predicting patients at risk of Alzheimer's disease (AD) is crucial for timely treatment and care in precision medicine. Method We present a novel architecture, a Kolmogorov‐Arnold networks (KANs)‐based joint prediction model of longitudinal and survival data (JM‐KAN). Real‐life data of patients with Mild Cognitive Impairment (MCI) with no prior AD diagnosis from National Alzheimer's Coordinating Center (NACC) dataset was utilized for individualized predictions. We built joint models with landmark times every 1 year up to 4 years after the MCI diagnosis, and predicted survival outcomes one year after each landmark (i.e. 1, 2, 3, and 4 years). Integrated AUC (iAUC), and integrated Brier score (iBS) were measured for existing prediction models and our novel architecture. Result Out of 2711 unique patients with MCI, 821 patients progressed to develop AD. Mean time‐to‐event in years was 3.19 (standard deviation (SD): 2.29). The number of visits prior to AD diagnosis was 4.03 (SD: 2.41). With iAUC of 0.789, iBS of 0.118, JM‐KAN has both the highest iAUC and lowest iBS among joint models, which indicates superiority in discrimination and overall performance. For individualized predictions, the model successfully predicted both longitudinal risk factors and survival probability, as new information came in every year. Conclusion Using neural network‐based Artificial Intelligence (AI) model, we demonstrated that this architecture improves upon prior methods in dynamically updating predictions of AD as new longitudinal data becomes available.

Purpose of review Increasing evidence suggests that epigenetic regulation plays a central role in chronic rhinosinusitis pathogenesis, heterogeneity, and treatment response. This review summarizes current knowledge of epigenetics in CRS pathogenesis, their role in endotype differentiation, and potential as diagnostic and therapeutic targets. Recent findings Distinct epigenetic signatures have been identified across CRS subtypes. Hypermethylation of TSLP and differential regulation of FZD5, IL8, and EMT-related genes distinguish eosinophilic CRSwNP from other phenotypes. Specific microRNAs (miR-941, miR-21, miR-125b, miR-155) correlate with disease severity, tissue eosinophilia, and corticosteroid responsiveness, highlighting their utility as noninvasive biomarkers. Experimental data suggest that targeting DNMTs or HDACs may reverse pathogenic remodeling. Emerging therapeutic approaches – such as biologics modulating epigenetically controlled cytokines (e.g. tezepelumab) and engineered extracellular vesicle-based miRNA delivery – illustrate translational promise. Summary Epigenetic mechanisms critically influence CRS pathogenesis and clinical variability. Their modulation offers novel opportunities for biomarker discovery, disease stratification, and personalized therapy. Future research should focus on standardizing epigenetic profiling methodologies, validating candidate biomarkers in diverse populations, and integrating multiomics and single-cell approaches to uncover cell-specific regulatory networks. These advances may enable precision medicine in CRS, bridging the gap between molecular mechanisms and targeted clinical management.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.

Abstract Background Chronic disease conditions lead to brain changes elevating dementia risk. We hypothesized that tissue microstructural changes caused by type 2 diabetes mellitus (T2DM) and hypertension could be effectively captured using an advanced diffusion MRI technique, Neurite Orientation Dispersion and Density Imaging (NODDI). Our goal was to evaluate the usefulness of composite NODDI scores, customized for increased hemoglobin (Hb) A1c and systolic blood pressure as predictors of cognition and compare their performance to other measures typically used to assess dementia risk. Method We collected multi‐shell diffusion MR on 1024 Mayo Clinic Study of Aging participants. NODDI measures (neurite density index (NDI), orientation dispersion index (ODI), and free water fraction (FWF)) in gray matter (GM) and white matter (WM) regions were estimated. To identify brain regions susceptible to both conditions, a multiple linear regression model was fitted for each NODDI metric, using hemoglobin (Hb) A1c (in N = 305 participants) or systolic blood pressure ( N = 906) as predictor, with age, sex, and body mass index (BMI) as covariates. Event‐based modeling was performed with the identified NODDI metrics to investigate the chronological pattern along the course of chronic condition development from this cross‐sectional dataset. Composite metrics were created as a weighted sum of susceptible NODDI metrics. We evaluated the impact of both conditions on cognition using gradient boosting models incorporating composite metrics, additional brain measurements, and demographics. Result Increased HbA1c impacts both GM and WM NODDI relatively evenly, whereas increased systolic blood pressure related NODDI changes were mainly seen in WM (data not shown). With increased HbA1c, the earliest changes were observed in GM ODI, followed by changes in the pons and cortical FWF (Figure 1A). With increased systolic blood pressure, the earliest changes occurred in association fibers and middle cerebellar peduncle, followed by FWF change in lobar and projection WM fibers (Figure 1B). Average feature importance from 100 gradient boosting models for cognition prediction suggests that diabetes‐induced GM changes play a moderately important role in cognitive decline (Figure 2). Conclusion Chronic medical conditions, especially T2DM, result in brain changes that are captured by NODDI and contribute moderately to cognitive performance.

Abstract Background Clinically available plasma p ‐tau217 assays may be utilized in the determination of eligibility for amyloid‐lowering therapies. Our institution recently introduced a plasma p ‐tau217 assay in clinical practice using a two‐cutpoint approach that optimizes the accuracy while minimizing false positive risk and categorizing ≤20% of results as intermediate. In this study, we prospectively evaluate the performance of a plasma p ‐tau217 assay for detection of amyloid pathology using these predefined cutpoints. Method Patients recruited were being considered for lecanemab therapy at a tertiary care subspecialty clinic. Plasma p ‐tau217 was measured using the clinically available Fujirebio Lumipulse p ‐tau217 assay at Mayo Clinic, and results (pg/mL) were classified as either negative (≤0.185), intermediate (0.186–0.324) or positive (≥0.325). Amyloid pathology was determined by amyloid‐PET and/or CSF p ‐tau181/Aβ42. Result Ninety‐three patients were recruited between November 2023 and December 2024. Of these, 49 underwent amyloid‐PET, 4 had CSF p ‐tau181/Aβ42 measurements, and 40 had both PET and CSF results (with 3/40 discordant cases), for use in the assessment of amyloid pathology. The three discordant cases were considered amyloid positive for purposes of plasma p ‐tau217 comparison. Overall amyloid positivity was 90%. Plasma p ‐tau217 results were positive in 83% (77/93), negative in 6% (6/93), and intermediate in 11% (10/93) of patients. An elevated p ‐tau217 predicted the presence of amyloid positivity with a diagnostic accuracy of 96% (95% CI 90% − 99%). In the intermediate range, 80% of patients were positive for amyloid pathology. In patients with a definitive p ‐tau217 result (positive or negative), the positive and negative predictive values were 97% and 83%. There were three patients with discordant results: one PET+/p‐tau217‐, one PET‐/p‐tau217+, and one CSF‐/p‐tau217+. Clinical presentation of these discordant cases was examined, and relevant findings will be discussed. Conclusion The Lumipulse plasma p ‐tau217 assay exhibited acceptable diagnostic accuracy for the determination of amyloid pathology when compared to either amyloid‐PET or CSF p ‐tau181/Aβ42. This prospective study confirms the utility a two‐cutpoint approach for interpreting plasma p ‐tau217 for the assessment of amyloid pathology eligibility for amyloid‐lowering therapies in a heterogenous clinical cohort.

Abstract Background The amygdala is closely connected to the olfactory bulb and involved in olfactory processing as well. It is a particularly susceptible region that accumulates a‐synuclein pathology in Parkinson's disease (PD). Our previous work revealed olfactory bulb gene alterations related to neuroinflammation, and neurotransmitter dysfunction to be associated with olfactory function. We aimed to extend this work and assess gene expression changes, affected pathways and co‐expression networks by transcriptomic profiling of the amygdala in subjects with and without clinicopathologically defined PD. Method Bulk RNA sequencing was performed on frozen human amygdala of 20 PD and 20 controls without dementia or any other neurodegenerative disorder, from the Arizona Study of Aging and Neurodegenerative Disorders. Result Differential expression analysis, corrected for age, sex and post‐mortem interval revealed 40 significantly differentially expressed genes (DEGs) in PD. Downregulated genes were mainly related to altered hemoglobin expression while upregulated genes included inflammatory markers and glutamine transporter. Significantly enriched pathways were involved in oxygen and carbon dioxide transport, cellular oxidant detoxification and regulation of glutamine secretion. Cell enrichment revealed these genes to be mainly expressed in neurons. Co‐expression network analysis using Weighted correlation network analysis (WGCNA ) subsequently identified four significant modules correlated with both PD and premortem olfactory dysfunction which were involved in pathways related to neuroinflammatory processes, mitochondrial, endoplasmic reticulum and lysosomal dysfunction. Conclusion These preliminary results suggest cellular alterations in the amygdala related to mitochondrial dysfunction, altered oxygen homeostasis, oxidative stress, inflammation as well as glutamatergic neurotransmitter dysfunction that may contribute to neurodegeneration in PD.

Abstract Background Recent technological advances have enhanced the precision of blood‐based multiplex proteomics and their application in Alzheimer's disease. However, these approaches remain underutilized in frontotemporal lobar degeneration (FTLD). Familial FTLD cohorts offer a unique opportunity to study presymptomatic periods of disease that cannot be studied in sporadic cases. We leveraged NUcleic acid Linked Immuno‐Sandwich Assay (NULISAseq) to identify blood‐detectable proteins that change prior to symptom onset in familial FTLD. Method 120 FTLD mutation carriers (40 C9orf72 , 40 GRN , 40 MAPT ) and 40 age and sex‐matched noncarrier controls from the ALLFTD Consortium completed blood draws and clinical assessment. Targeted plasma proteomics was performed via NUcleic acid Linked Immuno‐Sandwich Assay (NULISAseq; 132 proteins). Disease age estimates measuring predicted symptom onset were derived from a validated model incorporating FTLD gene‐specific clinical, neuroimaging, and biomarker data. Mass linear regression models identified disease age ‘thresholds’ where individual proteins significantly diverged between each mutation carrier group and controls. NULISAseq targets were validated against orthogonal assays, specifically Simoa (plasma) and SomaScan (cerebrospinal fluid [CSF]). Result Compared to controls, 14 plasma proteins significantly diverged ( p <.05) in C9orf72 , 24 in GRN , and 13 in MAPT . Beyond NEFL, 10 additional proteins significantly diverged in more than one mutation group, including markers of synaptic function (NPTX2, NPTXR) and vascular integrity (PGF) that deviated prior to estimated symptom onset (disease age range: ‐30 to 0). Proteins with gene‐specific signals included GFAP (increased in GRN at disease age ‐17), ENO2 (decreased in C9orf72 at disease age ‐7), and FLT1 (increased in MAPT at disease age ‐6). Plasma NULISA‐based NEFL and GFAP showed strong concordance when measured via plasma Simoa ( r s>0.88) and were among 38 targets with significant concordance when measured via CSF SomaScan ( r range: 0.16 to 0.75, p s<.05). Conclusion Alterations in plasma protein abundance are measurable in familial FTLD prior to predicted symptom onset. The validity and brain‐based relevance of blood‐detectable NULISA targets is bolstered by the observed convergence with plasma Simoa and CSF SomaScan. Replication of top hits in larger familial and sporadic FTLD sample sizes is warranted to determine generalizability and suitability for clinical trials.