Leonidas Chouliaras

Dementia with Lewy Bodies (DLB) is the most common neurodegenerative dementia after Alzheimer’s disease (AD), but it remains challenging to diagnose due to overlapping symptoms and mixed pathologies. This pilot study tested whether DLB has different metabolic blood proteomic profiles compared to AD and controls. Serum was analysed from people with DLB (n = 20), a group with AD (either with Alzheimer’s disease dementia or Mild Cognitive Impairment with a positive amyloid positron emission tomography scan (MCI+/AD) (n = 15), and similarly aged controls (n = 15) using the Olink Metabolism panel encompassing 92 proteins. Six proteins (PILRB, LRIG1, NECTIN2, TINAGL1, SSC4D, and FKBP4) were significantly different in DLB compared with the controls, and one protein (SERPINB8) was differentially expressed when compared with MCI+/AD. Receiver operating characteristic curves for biologically relevant proteins that have previously established roles in neurodegenerative and cognitive properties showed that RNASE3 levels could differentiate DLB from MCI+/AD (area under the curve (AUC) = 0.717, sensitivity = 0.850, and specificity = 0.600). A multimarker model incorporating RNASE3 with phosphorylated tau 217 (pTau217) and polygenic risk scores for LBD achieved improved accuracy in discriminating DLB from MCI+/AD (AUC = 0.963, sensitivity = 1.000, and specificity = 0.900). Pathway analyses revealed dysregulation in cortisol signalling, inflammation resolution, and ErbB4-mediated neuroplasticity, which point towards peripheral protein alterations related to the adaptation to stress, immune regulation, and synaptic integrity in DLB. The present exploratory study highlights potential pathophysiological mechanisms implicated in DLB, suggesting that a multimodal biomarker panel may perform better compared to single proteins. Considering the small sample sizes, the findings will need to be replicated in larger cohorts.

Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia, pathologically defined by the presence of Lewy bodies. Peripheral and central inflammation are increasingly recognised in DLB in clinical, post-mortem, and animal studies. Finding clinically relevant biomarkers of inflammation in DLB will support identification of novel pathways for disease modifying therapies or use in clinical trials of immunomodulatory agents. Whilst there are cross-sectional studies of inflammation markers in DLB, there is limited evidence on the association between these markers and cognitive decline over time. Twenty participants with DLB underwent blood sampling for serum inflammatory markers, paired with PET imaging of the translocator protein (TSPO) and up to four years of longitudinal cognitive testing. Thirty participants with Alzheimer’s disease—comprising both Alzheimer’s dementia and/or Mild Cognitive Impairment with biomarker evidence of amyloid pathology (AD/MCI+) and twenty-eight controls were also recruited for group comparisons. Data from forty-two baseline cytokine immunoassays and TSPO PET were used as predictors of longitudinal cognitive scores in linear mixed effects models. Partial least squares regression was used to test the association between peripheral and central inflammation. Using peripheral inflammatory markers as single predictors we identified fourteen associated with either slower or faster rate of cognitive decline in DLB, whilst no single marker was predictive of decline in AD/MCI+. As many inflammatory markers were highly correlated, we used principal components analysis to identify a cytokine component associated with reduced cognitive decline in both DLB and AD/MCI+, which overlapped with the single markers identified in the previous analysis. A separate component was associated with cognitive decline in AD/MCI+, or DLB with Alzheimer’s dementia co-pathology (ascertained by amyloid PET). Widespread TSPO binding was associated with reduced cognitive decline in DLB, whilst a fronto-temporal pattern was associated with more rapid cognitive decline in both DLB and AD/MCI+. There were associations between peripheral cytokines and TSPO PET in AD/MCI+, but these were not significant in DLB. Overall peripheral and central inflammation predicted cognitive decline in DLB. Specific patterns associated with both faster and slower rates of decline were identified. These profiles had both overlapping and contrasting associations when compared to AD/MCI+. Collectively, these data add to a body of evidence suggesting clinically relevant levels of inflammation in DLB. Future studies in larger, multi-site cohorts with multiple biomarker sampling points are required to understand the impact and dynamics of inflammation across all stages of disease.

Progressive supranuclear palsy (PSP) is a primary tauopathy characterized by atrophy and neuroinflammation of the brainstem, the basal ganglia and, to a lesser degree, the cortex. This study investigates the association of regional atrophy (structural MRI), neuroinflammation ([11C]-PK11195 PET), peripheral markers of neurodegeneration [plasma neurofilament light chain (NfL)] and clinical severity [PSP rating scale (PSPRS)] with survival in people with PSP. Fifty-nine people with PSP underwent longitudinal structural MRI, surviving on average 3.2 years from the first scan (MRI cohort). Sixteen participants (PET cohort) within this cohort underwent cross-sectional [11C]-PK11195 PET and blood sampling for plasma NfL. We applied modality-specific principal component analyses on imaging data and ran partial correlations, multivariate regressions and Bayesian models to evaluate the association between survival and imaging patterns, clinical severity and plasma NfL. In the PET cohort, higher levels of localized inflammation in subcortical regions [rho = −0.49, P = 0.02, Bayes factor (BF) = 8.07] and plasma NfL (rho = −0.57, P = 0.01, BF = 4.63) were associated with shorter survival, while PSPRS scores were not significant predictors of survival. Subcortical atrophy was associated with shorter survival in the larger cohort (r = −0.38, P = 0.001; β = −0.66, P = 0.001). Spearman’s correlations, multivariate regressions and Bayesian models converged to the same results. Regional subcortical atrophy is a robust biomarker associated with survival in people with PSP that can be utilized in large-scale clinical trials. Translocator protein (TSPO) PET and plasma NfL offer promising complementary markers for smaller-scale trials, where they may prove more sensitive than clinical scores or structural MRI alone. By linking neuroinflammation to survival, our results also highlight immunotherapy as a promising avenue for disease-modifying treatment in PSP.

Lewy body (LB) diseases are an umbrella term encompassing a range of neurodegenerative conditions all characterized by the hallmark of intra-neuronal α-synuclein associated with the development of motor and cognitive dysfunction. In this study, we have conducted a large meta-analysis of DNA methylation across multiple cortical brain regions, in relation to increasing burden of LB pathology. Utilizing a combined dataset of 1,217 samples across 844 unique donors, we identified a set of 24 false discovery rate (FDR) significant loci that are differentially methylated in association with LB pathology, the most significant of which were located in UBASH3B and PTAFR , as well as an intergenic locus. Ontological enrichment analysis of our meta-analysis results highlights several neurologically relevant traits, including synaptic alterations. We leverage our data to compare DNA methylation signatures between different neurodegenerative pathologies and highlight a shared epigenetic profile across LB diseases, Alzheimer’s disease and Huntington’s disease, although the top-ranked loci show disease specificity. Utilizing summary statistics from previous large-scale genome-wide association studies we identified significant enrichment of DNA methylation differences with respect to increasing LB pathology in the SNCA genomic region. We identified specific relationships between genetic risk variants for LB-Dementia and Parkinson’s disease to methylation quantitative trait loci in the promoter region and expression of SNCA transcript isoforms.

INTRODUCTION: Blood-based biomarkers have the potential to support early and accurate diagnosis of neurodegenerative diseases, which is sensitive to molecular pathology and predictive of outcome. We evaluated a novel multiplex proteomic method in people with diverse neurodegenerative diseases. METHODS: Serum from people with Alzheimer’s disease (N=36), Lewy body dementia (N=34), frontotemporal dementia (N=36) and progressive supranuclear palsy (N=36) and age-matched controls (N=30) was analysed with the NUcleic acid Linked Immuno-Sandwich Assay (NULISA) central nervous system panel (∼120 analytes) and inflammation panel (250 analytes). Biomarkers were compared across groups and included as predictors of survival. RESULTS: The NULISA panels demonstrated high sensitivity and reliability for detecting multiple biomarkers across neurodegenerative disorders. There were condition-specific proteomic biomarkers, while NfL, CRH, CD276 and S100A12 were significant transdiagnostic outcome predictors. DISCUSSION: The sensitive NULISA multiplex approach supports differential diagnosis and target identification, with prognostically informative dementia-related biomarkers.

The innate immune system plays an integral role in the progression of many neurodegenerative diseases. In addition to central innate immune cells (e.g., microglia), peripheral innate immune cells (e.g., blood monocytes, natural killer cells, and dendritic cells) may also differ in these conditions. However, the characterization of peripheral innate immune cell types across different neurodegenerative diseases remains incomplete. This study aimed to characterize peripheral innate immune profiles using flow cytometry for immunophenotyping of peripheral blood mononuclear cells in n = 148 people with Alzheimer’s disease (AD), frontotemporal dementia (FTD), corticobasal syndrome (CBS), progressive supranuclear palsy (PSP), Lewy body dementia (LBD) as compared to n = 37 healthy controls. To compare groups, we used multivariate dissimilarity analysis and principal component analysis across 19 innate immune cell types. We identified pro-inflammatory profiles that significantly differ between patients with all-cause dementia and healthy controls, with some significant differences between patient groups. Regression analysis confirmed that time to death following the blood test correlated with the individuals’ immune profile weighting, positively to TREM2+ and non-classical monocytes and negatively to classical monocytes. Taken together, these results describe transdiagnostic peripheral immune profiles and highlight the link between prognosis and the monocyte cellular subdivision and function (as measured by surface protein expression). The results suggest that blood-derived innate immune profiles can inform sub-populations of cells relevant for specific neurodegenerative diseases that are significantly linked to accelerated disease progression and worse survival outcomes across diagnoses. Blood-based innate immune profiles may contribute to enhanced precision medicine approaches in dementia, helping to identify and monitor therapeutic targets and stratify patients for candidate immunotherapies.

Neuroinflammation is an important pathogenic mechanism in many neurodegenerative diseases, including those caused by frontotemporal lobar degeneration. Post-mortem and in vivo imaging studies have shown brain inflammation early in these conditions, proportional to symptom severity and rate of progression. However, evidence for corresponding blood markers of inflammation and their relationships to central inflammation and clinical outcome are limited. There is a pressing need for such scalable, accessible and mechanistically relevant blood markers because these will reduce the time, risk and costs of experimental medicine trials. We therefore assessed inflammatory patterns of serum cytokines from 214 patients with clinical syndromes associated with frontotemporal lobar degeneration in comparison to healthy controls, including their correlation with brain regional microglial activation and disease progression. Serum assays used the MesoScale Discovery V-Plex-Human Cytokine 36 plex panel plus five additional cytokine assays. A subgroup of patients underwent ¹¹C-PK11195 mitochondrial translocator protein PET imaging, as an index of microglial activation. A principal component analysis was used to reduce the dimensionality of cytokine data, excluding cytokines that were undetectable in >50% of participants. Frequentist and Bayesian analyses were performed on the principal components to compare each patient cohort with controls and test for associations with central inflammation, neurodegeneration-related plasma markers and survival. The first component identified by the principal component analysis (explaining 21.5% variance) was strongly loaded by pro-inflammatory cytokines, including TNF-α, TNF-R1, M-CSF, IL-17A, IL-12, IP-10 and IL-6. Individual scores of the component showed significant differences between each patient cohort and controls. The degree to which a patient expressed this peripheral inflammatory profile at baseline was correlated negatively with survival (higher inflammation, shorter survival), even when correcting for baseline clinical severity. Higher pro-inflammatory profile scores were associated with higher microglial activation in frontal and brainstem regions, as quantified with ¹¹C-PK11195 mitochondrial translocator protein PET. A permutation-based canonical correlation analysis confirmed the association between the same cytokine-derived pattern and central inflammation across brain regions in a fully data-based manner. This data-driven approach identified a pro-inflammatory profile across the frontotemporal lobar degeneration clinical spectrum, which is associated with central neuroinflammation and worse clinical outcome. Blood-based markers of inflammation could increase the scalability and access to neuroinflammatory assessment of people with dementia, to facilitate clinical trials and experimental medicine studies.

Background: The epigenetic mechanism of DNA methylation has been implicated in the pathophysiology of neurodegenerative disorders such as Lewy body dementia (LBD). DNA methylation profiling may help to identify novel treatment targets and early disease markers. We hypothesized that DNA methylation alterations are detectable in blood due the influence of genetic and environmental factors as well as immune system changes associated with LBD.Method: Genome‐wide DNA methylation profiling was carried out using reduced representation bisulfite sequencing in blood samples from 89 LBD patients and 67 controls. Fifty five LBD patients had amyloid PET imaging. Data processing was performed using FastQC, TrimGalore, Bismark and MethylKit. DNA methylation alterations at specific CpG sites as well as at promoter, CpG island and CpG shore regions were compared between LBD cases and controls. A log odds logistic regression was used to identify differential DNA methylation after adjusting for age, sex and smoking status. The false discovery rate (FDR) was used for correction for multiple testing. We also tested the diagnostic classification performance of the differentially methylated regions using receiver operating characteristic (ROC) curve analysis and examined the potential of DNA methylation as a predictor of amyloid PET positivity in LBD.Result: Two single CpG sites at TNFRSF1B and EMC6 and three CpG shore regions at ABCG5, ETAA1 and TENM4 reached genome‐wide significance in the differential DNA methylation analysis comparing LBD cases and controls. The combined panel of the top three differentially methylated CpG shores reached an area under the curve (AUC) of 0.91 for the diagnosis of LBD compared to controls (sensitivity 0.89, specificity 0.85). DNA methylation at TENM4 was associated with amyloid PET positivity in LBD with an AUC of 0.84 (sensitivity 0.99, specificity 0.55).Conclusion: Our data suggest that DNA methylation alterations in blood are observed in a cohort of LBD patients compared to controls. Our findings need to be replicated in larger cohorts and tested for specificity in LBD in order to explore their future potential as diagnostic markers and/or targets for novel therapeutics.

Background: Dementia with Lewy bodies (DLB), Parkinson’s disease (PD) and Parkinson’s disease dementia (PDD) are termed Lewy body diseases (LBDs). These neurodegenerative diseases are classified by the accumulation of alpha‐synuclein in neurons, forming Lewy bodies (LB). Whilst pathologically similar, symptomatic staging is different. Previous genome wide association studies (GWAS) have identified classic Alzheimer’s disease (AD) and PD loci as risk loci in DLB. Therefore, we have used polygenic risk score (PRS) analysis to investigate the contribution of AD and PD variants in the LBDs.Method: DNA was extracted from bulk brain tissue from 481 individuals (162 controls, 95 DLB, 156 PDD and 68 PD) and genotyped on the Illumina global screening array. After a strict quality control and imputation pipeline, PRSs for multiple relevant traits using reference GWAS summary statistics were calculated using PRSice2.Result: Study groups have been sourced consisting of cases with PD, PDD and DLB based on LB deposition and clinical symptom staging. All groups, including controls are stratified for age and concomitant AD pathology. Assessing multiple PRSs for association with pathological development and clinical outcomes we have identified the contribution of genetic risk from related neurodegenerative and neuropsychiatric disorders in our cohort of PD, PDD and DLB samples.Conclusion: We have collated a well powered study cohort to interrogate the genetic basis of the Lewy body dementias and the relative contribution of AD and PD risk loci in the LBDs. We are currently generating methylation quantitative trait loci (mQTLs) for this dataset using previously generated matched DNA methylation data in the brain.

Background: The benefits of the early detection of the neurodegenerative and vascular pathologies which cause dementia are widely acknowledged. These include the opportunity to initiate risk‐reduction strategies, disease‐modifying therapies, and future care planning. However, current pathological biomarkers have several important drawbacks, as they are typically invasive, expensive, impractical, insensitive, or lack real‐world validation. Conversely, the targeted utilization of non‐invasive and inexpensive digital technologies which can be integrated into everyday life raises the possibility of capturing a ‘digital‐fingerprint’ of early disease.Method: The Early Detection of Neurodegenerative diseases (EDoN) initiative is a meta‐cohort study spearheaded by Alzheimer’s Research UK. EDoN is acquiring high‐dimensional data from a ‘digital toolkit’ comprising a smartwatch (Fitbit Charge 4/5), smartphone software (Mezurio and Longevity), and an electroencephalography headband (Dreem 3). In combination with conventional digital, cognitive and biological markers, these data will inform the development of powerful machine learning models to detect dementia‐causing diseases at the very earliest stages. The initial phase, incorporating four cohorts across three continents, is already underway, and will recruit nearly 900 participants. Cohorts are recruiting healthy individuals, as well as patients with subjective/mild cognitive impairment, or established dementia. Data from version‐1 of the toolkit is being collected over two‐week periods at 3‐monthly intervals for a minimum of 12 months.Result: We will present early insights from the implementation of the EDoN digital toolkit version‐1. These will include sample characteristics, feasibility and acceptability, and participants’ compliance. PPI data confirmed good acceptability but the potential for inequity due to technical problems and poor digital literacy. Pilot data indicate that six‐month adherence to the EDoN digital tools varies between 82‐89%. The forthcoming AAIC presentation will include important recruitment, toolkit acceptability and analytic updates.Conclusion: The initial implementation of the EDoN digital toolkit suggests that it is feasible and acceptable to collect high‐dimensional digital data over a six‐month period. These ‘digital‐fingerprints’ will form the basis of the initial development of machine learning models to identify dementia‐causing pathologies substantially earlier. The insights gained will also inform the development of version‐2 of the EDoN digital toolkit which will be incorporated into the next meta‐cohort prospective study of around 4,000 participants.