BACKGROUND
There is increasing evidence for the role of central and peripheral inflammation across neurodegenerative disorders, with animal models and post-mortem studies identifying T-cell infiltration in the brain associated with pathology and neurodegeneration. Peripheral T-cell changes have been measured in Alzheimer's disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP). This study examines a unique cohort of blood-based T-cell profiles across a range of neurodegenerative dementias including AD, DLB, FTD, corticobasal syndrome (CBS), PSP, and aged-matched healthy controls. Then it also explores their associations with dementia-relevant plasma biomarkers and clinical outcomes.
METHODS
Freshly prepared peripheral blood mononuclear cells (PBMCs) from 174 participants (AD = 20, DLB = 24, FTD = 19, CBS = 18, PSP = 58, controls = 35) were studied using a flow-cytometry panel designed to analyse major T-cell subpopulations, including memory and T-helper subtypes. Neurodegeneration-relevant biomarkers (p-tau217, p-tau231, GFAP, NfL, and A-beta42/40) were measured in plasma samples. T-cell populations were compared between groups and in association with biomarkers, and principal components analysis (PCA) was used to identify T-cell profiles and their association with dementia-relevant biomarkers in diagnostic classification and survival prediction.
RESULTS
There was a significant reduction in the fraction of CD3+ cells in patients with DLB compared to other diagnostic groups, and an increase in relative Th1/17-like cell levels in patients with FTD compared to controls. This increase in Th1/17-like cells correlated with NfL and GFAP plasma levels in patients with FTD. PCA identified five components primarily representing CD4+ memory cell population subsets. After sex and age adjustments, component 4 marked by effector memory types including Th2-like, Th-like1 and Th1/17-like cells was a significant predictor of FTD, however was not as accurate as plasma NfL. Higher scores in specific T-cell components (1 and 3) were associated with reduced mortality across all diseases, with component 3 remaining a significant predictor even when controlling for traditional neurodegenerative biomarkers like NfL and p-tau217.
CONCLUSIONS
This study provides evidence that T-cell dysregulation is not unified in patients with neurodegenerative diseases. We observe different involvement across different dementia types establishing adaptive immunity as a key contributor to disease heterogeneity. However, although plasma biomarkers such as NfL and p-tau217 exhibit superior diagnostic accuracy for clinical classification, peripheral T-cell signature were associated with survival outcomes across diagnostic groups, highlighting their promise for prognostic applications and disease monitoring. The characterisation of T-cell populations across neurodegenerative conditions may inform target development and patient stratification for new interventional trials.
Frederika Malichova, P. Swann, S. Kigar et al.· Alzheimer's Research & Thera...· 0 citations
Importance
Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used.
Objective
To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads.
Data Sources
PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025).
Study Selection
Studies were included if they provided Centiloids, radiotracer, age, and sex.
Data Extraction and Synthesis
Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis.
Main Outcomes and Measures
Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available.
Results
This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I2 = 80%).
Conclusions and Relevance
In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.
Ganna Blazhenets, David N Soleimani-Meigooni, Konstantinos Chiotis et al.· Journal of the American Medi...· 2 citations
Alzheimers disease pathology and cognitive outcomes frequently diverge, yet current single-axis definitions cannot identify resilient (high pathology, preserved cognition) and resistant (high risk, low pathology) subgroups reliably at scale, obscuring the mechanisms that uncouple pathological burden from cognitive decline. Here, we developed a multivariate blood-based framework integrating 19 molecular assays and six risk instruments in the Bio-Hermes-001 cohort (n=1,009). Unsupervised clustering identified resilient (n=91) and resistant (n=81) subgroups, together comprising 17% of the cohort, with distinct amyloid, tau, and neurodegeneration profiles. Amyloid-PET yielded convergent but only partially overlapping classifications. Proteomic, cytokine, and polygenic profiling further distinguished resistance through an APOE-centred genomic signature and resilience through neuroinflammatory markers associated with progression toward clinical Alzheimers disease. A four-biomarker panel (A{beta}40, p-tau217, p-tau181, NfL) reproduced subgroup assignments with 83% accuracy. These findings support resilience and resistance as molecularly distinct subgroups and provide a scalable framework for pathology-informed stratification and mechanistic investigation.
K. Mavromati, C. Dalby, A. Dibble et al.· medRxiv· 0 citations