ObjectivesThis longitudinal study compared emerging plasma biomarkers for neurodegenerative disease between controls, patients with Alzheimer’s disease (AD), Lewy body dementia (LBD), frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP).MethodsPlasma phosphorylated tau at threonine-181 (p-tau181), amyloid beta (Αβ)42, Aβ40, neurofilament light (NfL) and glial fibrillar acidic protein (GFAP) were measured using highly sensitive single molecule immunoassays (Simoa) in a multicentre cohort of 300 participants (controls=73, amyloid positive mild cognitive impairment (MCI+) and AD dementia=63, LBD=117, FTD=28, PSP=19). LBD participants had known positron emission tomography (PET)-Aβ status.ResultsP-tau181 was elevated in MCI+AD compared with all other groups. Aβ42/40 was lower in MCI+AD compared with controls and FTD. NfL was elevated in all dementias compared with controls while GFAP was elevated in MCI+AD and LBD. Plasma biomarkers could classify between MCI+AD and controls, FTD and PSP with high accuracy but showed limited ability in differentiating MCI+AD from LBD. No differences were detected in the levels of plasma biomarkers when comparing PET-Aβ positive and negative LBD. P-tau181, NfL and GFAP were associated with baseline and longitudinal cognitive decline in a disease specific pattern.ConclusionThis large study shows the role of plasma biomarkers in differentiating patients with different dementias, and at monitoring longitudinal change. We confirm that p-tau181 is elevated in MCI+AD, versus controls, FTD and PSP, but is less accurate in the classification between MCI+AD and LBD or detecting amyloid brain pathology in LBD. NfL was elevated in all dementia groups, while GFAP was elevated in MCI+AD and LBD.

BackgroundMarkers of cerebrovascular disease are common in dementia, and may be present before dementia onset. However, their clinical relevance in midlife adults at risk of future dementia remains unclear. We investigated whether the Cardiovascular Risk Factors, Ageing and Dementia (CAIDE) risk score was associated with markers of cerebral small vessel disease (SVD), and if it predicted future progression of SVD. We also determined its relationship to systemic inflammation, which has been additionally implicated in dementia and SVD.MethodsCognitively healthy midlife participants were assessed at baseline (n=185) and 2-year follow-up (n=158). To assess SVD, we quantified white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS), microbleeds and lacunes. We derived composite scores of SVD burden, and subtypes of hypertensive arteriopathy and cerebral amyloid angiopathy. Inflammation was quantified using serum C-reactive protein (CRP) and fibrinogen.ResultsAt baseline, higher CAIDE scores were associated with all markers of SVD and inflammation. Longitudinally, CAIDE scores predicted greater total (p<0.001), periventricular (p<0.001) and deep (p=0.012) WMH progression, and increased CRP (p=0.017). Assessment of individual CAIDE components suggested that markers were driven by different risk factors (WMH/EPVS: age/hypertension, lacunes/deep microbleeds: hypertension/obesity). Interaction analyses demonstrated that higher CAIDE scores amplified the effect of age on SVD, and the effect of WMH on poorer memory.ConclusionHigher CAIDE scores, indicating greater risk of dementia, predicts future progression of both WMH and systemic inflammation. Findings highlight the CAIDE score’s potential as both a prognostic and predictive marker in the context of cerebrovascular disease, identifying at-risk individuals who might benefit most from managing modifiable risk.

Objectives Dementia with Lewy bodies (DLB) is a major cause of degenerative dementia, yet the diagnosis is often missed or mistaken for Alzheimer's disease (AD). We assessed whether the revised Addenbrooke's Cognitive Examination (ACE‐R), a brief test for dementia, differentiates DLB from AD. Methods We first compared baseline ACE‐R performance in 76 individuals with DLB, 40 individuals with AD and 66 healthy controls. We then investigated the diagnostic accuracy of a simple standardised ‘memory/visuospatial’ ratio calculated from the ACE‐R subscores. Finally, as a comparison a logistic regression machine learning algorithm was trained to classify between DLB and AD. Results Individuals with AD had poorer memory (p = 0.001) and individuals with DLB had poorer visuospatial function (p = 0.005). Receiver operating characteristics curves confirmed that the ACE‐R total score could differentiate dementia from non‐dementia cases with 98% accuracy, but could not discriminate between dementia types (50%, or chance‐level accuracy). However, a ‘memory/visuospatial’ ratio ≥1.1 differentiated DLB from AD with 82% sensitivity, 68% specificity and 77% mean accuracy. The machine learning classifier did not improve the overall diagnostic accuracy (74%) of the simple ACE‐R subscores ratio. Conclusions The ACE‐R‐based ‘memory/visuospatial’ ratio, but not total score, demonstrates good clinical utility for the differential diagnosis of DLB from AD.

Background Considerable overlap exists between the risk factors of dementia and cerebral small vessel disease (SVD). However, studies remain limited to older cohorts wherein pathologies of both dementia (e.g. amyloid) and SVD (e.g. white matter hyperintensities) already co-exist. In younger asymptomatic adults, we investigated differential associations and interactions of modifiable and non-modifiable inherited risk factors of (future) late-life dementia to (present-day) mid-life SVD. Methods Cognitively healthy middle-aged adults (aged 40–59; mean 51.2 years) underwent 3T MRI (n = 630) as part of the PREVENT-Dementia study. To assess SVD, we quantified white matter hyperintensities, enlarged perivascular spaces, microbleeds, lacunes, and computed composite scores of SVD burden and subtypes of hypertensive arteriopathy and cerebral amyloid angiopathy (CAA). Non-modifiable (inherited) risk factors were APOE4 status and parental family history of dementia. Modifiable risk factors were derived from the 2020 Lancet Commission on dementia prevention (early/midlife: education, hypertension, obesity, alcohol, hearing impairment, head injuries). Confirmatory factor analysis (CFA) was used to evaluate the latent variables of SVD and risk factors. Structural equation modelling (SEM) of the full structural assessed associations of SVD with risk factors and APOE4*risk interaction. Results In SEM, the latent variable of global SVD related to the latent variable of modifiable midlife risk SVD (β = 0.80, p = .009) but not non-modifiable inherited risk factors of APOE4 or family history of dementia. Interaction analysis demonstrated that the effect of modifiable risk on SVD was amplified in APOE4 non-carriers (β = − 0.31, p = .009), rather than carriers. These associations and interaction effects were observed in relation to the SVD subtype of hypertensive arteriopathy, rather than CAA. Sensitivity analyses using separate general linear models validated SEM results. Conclusions Established modifiable risk factors of future (late-life) dementia related to present-day (mid-life) SVD, suggesting that early lifestyle modifications could potentially reduce rates of vascular cognitive impairment attributed to SVD, a major ‘silent’ contributor to global dementia cases. This association was amplified in APOE4 non-carriers, suggesting that lifestyle modifications could be effective even in those with genetic predisposition to dementia.

Background Lewy body dementia (LBD) includes dementia with Lewy bodies and Parkinson’s disease dementia, which shared a common pathological core feature of cognitive fluctuations. However, the clinical identification of fluctuations is still controversial. With Electroencephalogram (EEG) and Magnetoencephalography (MEG), it becomes possible to observe the neural correlates of fluctuations with a high temporal resolution in LBD. In this multimodel imaging and computational modelling of dementia with lewy bodies (MILOS) study, we combined EEG/MEG to explore the electro‐magnetic biomarkers of fluctuations in LBD. We hypothesized that LBD would show an increased variation in EEG/MEG compared with controls, indicating a potential underlying mechanism for fluctuations in LBD. Method Nine patients with LBD (mean age=72) and twelve healthy controls (mean age=67) were recruited and an animal/non‐animal object recognition task was conducted. 64‐channel EEG and 306‐channel MEG (including gradiometer (GRM) and magnetometer (MAG)) were simultaneously recorded during this task. Subsequently participants had MRI with T1‐weighted images for EEG/MEG source coregistration. Forward models for EEG/MEG were 3‐Shell Sphere and Single Shell respectively and minimum norm Least‐Squares as the inversion method. The variations of source post‐stimulus in object recognition was assessed. We used standardized clinical batteries to test cognitive functions and overall scalp sensors for EEG/MEG sensor‐level analyses. Result HC had shorter reaction time and higher accuracy rate than LBD in object recognition. The variations of reaction time across trials in object recognition was significantly higher in LBD than HC. LBD had weaker overall EEG amplitudes and stronger MEG variations across trials for post stimulus than HC. Phase lock value (PLV) indicated different EEG synchronization patterns between groups with higher theta and lower beta synchronization in LBD than HC. The MEG variations across trials in LBD were involved with theta, alpha and beta bands. The MEG variations were negatively correlated with MMSE, ACER, ACE‐III but positively correlated with UPDRS scores. Source variations in LBD involved a network of parietal areas, including postcentral gyrus and superial parietal areas. Conclusion Our study revealed the possible biomarkers of fluctuations in LBD by using EEG/MEG. It highlighted the importance of electrophysiological correlates of fluctuations in understanding the etiology of LBD.