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Alzheimer's disease (AD) and related dementias are rapidly increasing in prevalence, yet disease-modifying therapies remain largely focused on amyloid-β (Aβ) with limited efficacy against tau pathology and neuroinflammation-key drivers of neurodegeneration and clinical decline. Dual-specificity tyrosine-phosphorylation-regulated kinase 1a (Dyrk1a) phosphorylates tau and amyloid precursor protein and regulates inflammatory signaling, positioning it as a convergence point across pathogenic pathways. We show that brain Dyrk1a protein levels are consistently elevated across ADRDs, replicating findings in AD, confirming prior observations in Pick's disease, and demonstrating dysregulation in corticobasal degeneration and progressive supranuclear palsy. We developed DYR533, a selective, orally bioavailable, brain-penetrant Type 1 Dyrk1a kinase inhibitor that also inhibits autophosphorylation and reduces kinase abundance. Across three mouse models (3xTg-AD, PS19, Ts65Dn), DYR533 reduced pathological tau hyperphosphorylation, attenuated neuroinflammation, ameliorated amyloidosis, and improved anxiety-like behavior and spatial memory, collectively supporting Dyrk1a inhibition and DYR533 as a therapeutic strategy for ADRD.

ObjectiveOne of the most challenging aims of the scientific community in the last decade, is to find an easily accessible matrix in which neurodegeneration-related biomarkers can be measured and used to diagnose Alzheimer’s disease (AD) in vivo. Blood biomarkers have led the way in this regard, specifically, phosphorylated tau (p-tau) which demonstrates excellent diagnostic and prognostic properties. The recent success of the blood biomarkers for AD pathophysiology poses a new question – can p-tau be measured in other peripheral and even more accessible biofluids, and do they have relation to disease? Saliva contains biomarkers linked to neurodegeneration and it has been proposed as a potential sample type that would be minimally invasive to collect for this purpose.MethodsIn this study, we confirmed the presence of several p-tau species in saliva fluid and saliva gland tissue by Immunoprecipitation-Mass spectrometry (IP-MS) and immunohistochemistry, respectively. Furthermore, we measured saliva and plasma p-tau181 concentrations in 125 memory clinic participants, using ultrasensitive Single molecule array (Simoa) technology.ResultsDespite a weak correlation between saliva p-tau181 and CSF t-tau (rho = 0.13, p < 0.01), there were no significant differences in saliva p-tau181 concentration between the different clinical groups and the healthy controls.InterpretationFor this reason, we conclude that saliva p-tau181 is not acceptable as a biomarker for AD.

Blood-based biomarkers (BBMs) demonstrate high accuracy in detecting Alzheimer disease (AD) pathological changes in symptomatic individuals. In autosomal dominant AD and in individuals with Down syndrome, both populations with near-universal development of AD pathology, elevations in BBMs are detectable years before clinical onset, supporting their utility for identifying preclinical disease in these cases. Among BBMs, plasma phosphorylated tau 217 (p-tau217) exhibits strong concordance with established in vivo markers of AD pathology. However, its ability to identify older adults without cognitive impairment who are amyloid-positive remains variable across studies and settings. To assess the standardized effect size of mean differences and classification accuracy of p-tau217 for published studies that compared amyloid-positive and amyloid-negative older adults without cognitive impairment. PubMed, Embase, and EBSCOhost databases from inception to September 1, 2025. Observational studies or randomized clinical trials with baseline data on individuals without cognitive impairment who were classified as either amyloid positive or amyloid negative and reported numeric data for p-tau217 levels. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) reporting guideline was used for this study. Two authors independently carried out literature searches to identify studies with older adults without cognitive impairment who were classified as either amyloid positive or amyloid negative where p-tau217 was quantified. The standardized mean difference (Hedges g) was used to characterize differences in mean p-tau217 levels. A pooled area under the curve (AUC) value was used to summarize the diagnostic accuracy of p-tau217 in identifying amyloid-positive individuals. Between-study heterogeneity was investigated using subgroup and sensitivity analyses. Publication bias was assessed using Egger tests. Data for 7834 participants (2533 amyloid positive, 5301 amyloid negative) from 18 publications were analyzed. A large effect size was observed for p-tau217 (Hedges g = 1.50; 95% CI, 1.33-1.68). Values for p-tau217 also demonstrated high accuracy for identifying amyloid-positive individuals without cognitive impairment (AUC = 0.87; 95% CI, 0.85-0.90). These findings demonstrate that plasma p-tau217 can reliably detect AD pathology in the preclinical stage. These findings support the clinical utility of plasma p-tau217 as a scalable, minimally invasive tool for early identification of AD, particularly in settings where timely intervention with disease-modifying therapies may offer the greatest benefit in slowing or preventing disease progression.

BackgroundBlood based biomarkers of Alzheimer´s disease (AD) that exhibit a high degree of change over time and are associated with deterioration in cognitive performance and atrophy could be useful in clinical trials to monitor treatment responses. In this study we investigated the longitudinal changes in plasma p‐tau biomarkers, amyloid‐beta(Aβ)42/Aβ40, Glial fibrillary acidic protein (GFAP) and Neurofilament light (NfL) and assessed associations between changes in these biomarkers and cognitive decline.MethodWe included 718 participants (cognitively unimpaired (CU) or Mild Cognitive Impairment (MCI)) from the Swedish BioFINDER‐1 cohort who were followed upto 8 years. Plasma samples were analyzed for p‐tau217 (Lilly immunoassay), NfL, GFAP, p‐tau181 (Roche Prototype immunoassay), N‐terminal tau (NTA‐tau;Simoa immunoassay) and Aβ42/Aβ40 (mass spectrometry). We analyzed changes in individual plasma biomarker levels using linear mixed‐effects models, incorporating Aβ status*time interaction. Associations between biomarker slopes and cognitive decline (assessed with mini‐mental state examination [MMSE] and modified Preclinical Alzheimer´s Cognitive Composite[mPACC]) were assessed. An optimal model combining several biomarker slopes was also evaluated.ResultIn the whole cohort, p‐tau217, NfL, GFAP and p‐tau181 were significantly increased over time showing more accelerated increase in Aβ+ than Aβ‐ participants. The strongest effect was seen for p‐tau217 (β=0.200, 95%CI0.17‐0.23;p<0.001) followed by NfL(β=0.047, 95%CI0.015‐0.08;p=0.005), GFAP(β=0.068, 95%CI0.046‐0.09; p = <0.001) and p‐tau181(β=0.089, 95%CI0.051,0.13;p<0.001)(Table 1, Figure 1). In individual models, slopes of all biomarkers were associated with change in MMSE and mPACC in the whole cohort. In the Aβ+ group, all biomarkers except NTA‐tau demonstrated significant associations with longitudinal MMSE and mPACC. The strongest associations were seen for p‐tau217 in both the whole cohort [MMSE (β=‐0.353, 95%CI ‐0.40‐(‐0.31)]; for interaction between p‐tau217 slope and time to predict cognitive scores; mPACC(β=‐0.226 95%CI‐0.26‐(‐0.19);p<0.001) and in the Aβ+ group [MMSE(β=‐0.336, 95%CI‐0.40‐(‐0.27); mPACC(β=‐0.245, 95%CI‐0.31‐(‐0.18);p<0.001)] (Tables 2‐5). The best performing model for predicting longitudinal MMSE and mPACC included the interaction between p‐tau217 slope (but not other biomarker slopes) and time, both in the whole cohort [MMSE(β=‐0.340,95%CI ‐0.39‐(‐0.30)); mPACC(β=‐0.238, 95%CI‐0.27‐(‐0.20);p<0.001)] and in the Aβ+ group [MMSE(β=‐0.327, 95%CI ‐0.40‐(‐0.25)]; mPACC(β=‐0.263, 95%CI ‐0.32‐(‐0.20); p <0.001)].ConclusionThe longitudinal changes in plasma p‐tau217 might serve as a surrogate marker for monitoring some aspects of disease progression during treatment trials.

Recent advancements in biomarkers have transformed Alzheimer's disease (AD) diagnosis from being purely symptom-based to include biological criteria. With new treatments targeting AD's core biology, understanding the timeline of biological changes is crucial as the disease progresses over decades. Longitudinal data from amyloid-beta (Aβ) PET and cognitive tests (MMSE and ADAS-cog) from the Alzheimer's Disease Neuroimaging Initiative (n=1,448) and BioFINDER (n=2,088) were used to stage patients against an estimated continuous disease timeline (predicted time since Aβ-PET positivity). The estimated timeline was validated by comparing correlations with unseen biomarkers and cognitive measures against alternative staging approaches. Trajectories for plasma, CSF, MRI, and PET biomarkers, measuring Aβ, tau, and neurodegeneration, were mapped along this AD continuum. The proposed staging approach was found to produce stronger correlations with unseen cognitive measures and biomarkers compared to alternative staging methods, including amyloid and tau PET clocks (all pairwise p<0.05). Findings related to biomarker trajectories were highly consistent across cohorts. The period from Aβ-PET positivity to end-stage AD dementia (MMSE = 0) was estimated at 20-25 years, with a presymptomatic phase of 7-11 years. CSF Aβ42/40 became abnormal about a year before Aβ-PET positivity, CSF p-tau231, p-tau217, and plasma p/np-tau217 1-3 years after, and tau-PET about 8 years after. Neurodegenerative biomarkers, such as hippocampal volume, became clearly abnormal in early dementia stages, 14-16 years after Aβ-PET positivity. The progression from initial biomarker abnormality to severe AD spans two decades. Disease progression modeling elucidates the evolution of AD biomarkers and cognition, highlighting the relative timing of biomarker abnormalities. These models can determine disease stages, aiding prognosis and evaluation for disease-modifying treatments.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. Emerging evidence strongly suggests that protein glycosylation is strongly related to this disease. However, the extent and functional consequences of site-specific N-glycosylation alterations in AD remain to be further explored. Here, we employed a dendrimer boronic acid (DBA)-based enrichment strategy combined with multiplexed proteomics to systematically analyze protein N-glycosylation in post-mortem human brain tissues. We identified 3,105 N-glycosylation sites on 1,299 glycoproteins from nine AD cases and nine healthy controls, and performed a systematic and site-specific investigation of glycosylation alterations in AD. Glycoproteins involved in cholesterol efflux were upregulated, whereas those associated with chemical synaptic transmission and ion transport were significantly downregulated in AD compared to control brain samples. We observed widespread dysregulation of N-glycosylation across multiple protein domains, particularly in the ConA-like lectins/glucanases and Zn-dependent exopeptidases domains. Notably, we identified 161 N-glycosylation sites located within aggregation-prone regions (APRs), and reduced glycosylation at APRs on plaque-associated glycoproteins may be associated with protein aggregation and plaque formation. Additionally, downregulated N-glycosylation sites were enriched in synaptic membrane proteins, such as Ca2+ ion channels, GABA-gated chloride channels, and glutamate receptors, implicating glycosylation loss in synaptic dysfunction. Our findings suggest that the loss of N-glycosylation may contribute to the pathogenesis of AD through impairing synaptic transmission and promoting protein aggregation. This study provides novel insights into glycosylation-dependent mechanisms of neurodegeneration, highlighting N-glycosylation as a potential therapeutic target for AD treatment.

As plasma biomarkers like p-tau217 move towards clinical use in Alzheimer's disease (AD), it is important to understand how kidney function may influence their accuracy. Even mild chronic kidney disease (CKD) can alter biomarker levels, potentially impacting test performance. While accounting for renal function may improve specificity, it could reduce sensitivity without greatly changing overall diagnostic accuracy. Most studies focus on mild CKD, leaving gaps in understanding severe CKD—especially in real-world settings like primary care. Including renal indices such as eGFR in diagnostic models could help improve interpretation and minimize misclassification in older adults, where CKD is common.