There is great interest in finding accessible biomarkers of neurodegeneration in Alzheimer's disease. Serum neurofilament light (NfL) is a marker of axonal damage that increases prior to symptom onset in familial Alzheimer's disease (FAD) and correlates with rates of whole brain atrophy (Weston et al., 2017); however, associations with downstream cognitive change have not been explored. We investigated whether baseline serum NfL concentration is associated with subsequent rate of cognitive decline. Forty-seven individuals from families with PSEN1 or APP mutations were recruited: 17 had symptomatic AD and 30 were asymptomatic but at 50% risk of carrying a mutation. Estimated years from symptom onset (EYO) was calculated by subtracting the age at which the participant's affected parent first displayed progressive cognitive symptoms from the participant's age at NfL sampling. Serum NfL concentrations were measured using an in house Single molecule array (Simoa) assay. Participants had at least one cognitive assessment (mean= 2.7) including MMSE, CDR, Trails A and B, Recognition Memory Test (RMT) for words and faces and verbal and performance IQ. Spearman coefficients tested for associations between baseline serum NfL and annualised rates of change in cognitive measures, which were calculated over a maximum interval of three years from initial assessment Nineteen of the asymptomatic participants were mutation carriers (mean EYO= −9.6 years); eleven were non-carriers (demographics in Table). Serum NfL concentration was higher in both symptomatic (mean = 46.2±21.4 pg/ml) and presymptomatic mutation carriers (mean = 16.7±7.7 pg/ml) than in non-carrier controls (mean = 12.7±7.2 pg/ml). There was evidence of correlations between baseline serum NfL and rates of change in RMT average (ρ =−0.46, p=0.03), Trails A (ρ=0.62, p=0.002), borderline for MMSE (ρ =−0.35, p=0.06), but not for CDR, change in IQ and Trails B (all p values >0.2) (Figure).
Abstract Background Blood biomarkers have the potential to advance clinical care and accelerate the development of disease‐modifying treatments. P‐tau181 is a promising blood biomarker, with levels increasing in Alzheimer’s disease (AD) dementia (doi:10.1016/j.jalz.2018.02.013). However, a better understanding of the timing and trajectory of plasma p‐tau181 changes is needed. Therefore, we conducted a longitudinal study in familial AD (FAD). Methods Using an in house Single molecule array method, P‐tau181 was measured in 153 plasma samples from 70 individuals from families with PSEN1 or APP mutations (mean ± SD = 2.2 ±1.3 samples/participant;median [IQR] duration of follow up =1.0 (0, 3.7) years). We compared plasma p‐tau181 between symptomatic mutation carriers, presymptomatic carriers, and noncarriers, adjusting for age and sex. We also examined the relationship between plasma p‐tau181 and estimated years to/from symptom onset (EYO), as well as years to/from actual symptom onset (AAO) in a symptomatic subgroup. In addition, we studied associations between plasma p‐tau181 and clinical severity, as well testing for differences in concentration between genetic subgroups (PSEN1 vs APP carriers, PSEN1 pre‐codon 200 vs PSEN1 post‐codon 200 carriers). Results 24 of the asymptomatic participants were mutation carriers (mean baseline EYO ‐9.6 years); 27 were noncarriers. Compared with noncarriers, plasma p‐tau181 concentration was higher in symptomatic (p<0.001) and presymptomatic mutation carriers (p<0.001) (Figure 1). Plasma p‐tau181 discriminated symptomatic (AUC 0·93[95% CI 0·84−0·98]) and presymptomatic (AUC 0.86 [95% CI 0·73−0·95]) carriers from noncarriers. Plasma p‐tau181 concentration increased in mutation carriers from 16 years prior to estimated symptom onset (p=0.049) (Figure 2). Plasma p‐tau181 in symptomatic mutation carriers, modelled using AAO, appeared eventually to plateau. Longitudinal p‐tau181 measures demonstrated significant inter‐ and intra‐individual variability, with some participants exhibiting large changes over relatively short time intervals. We did not find a difference in plasma p‐tau181 concentration between APP and PSEN1 carriers, but there was weak evidence (p=0.053) that symptomatic PSEN1 post‐codon 200 carriers had a 54% higher p‐tau181 concentration (95% CI:1% lower, 138% higher) than pre‐codon 200 carriers. Conclusion Our finding that plasma p‐tau181 concentration is increased in presymptomatic and symptomatic FAD suggests its potential utility as an easily accessible biomarker of AD pathology.
Introduction Alzheimer’s disease and other dementias affect >50 million individuals globally and are characterised by broad clinical and biological heterogeneity. Cohort and biobank studies have played a critical role in advancing the understanding of disease pathophysiology and in identifying novel diagnostic and treatment approaches. However, further discovery and validation cohorts are required to clarify the real-world utility of new biomarkers, facilitate research into the development of novel therapies and advance our understanding of the clinical heterogeneity and pathobiology of neurodegenerative diseases. Methods and analysis The Tallaght University Hospital Institute for Memory and Cognition Biobank for Research in Ageing and Neurodegeneration (TIMC-BRAiN) will recruit 1000 individuals over 5 years. Participants, who are undergoing diagnostic workup in the TIMC Memory Assessment and Support Service (TIMC-MASS), will opt to donate clinical data and biological samples to a biobank. All participants will complete a detailed clinical, neuropsychological and dementia severity assessment (including Addenbrooke’s Cognitive Assessment, Repeatable Battery for Assessment of Neuropsychological Status, Clinical Dementia Rating Scale). Participants undergoing venepuncture/lumbar puncture as part of the clinical workup will be offered the opportunity to donate additional blood (serum/plasma/whole blood) and cerebrospinal fluid samples for longitudinal storage in the TIMC-BRAiN biobank. Participants are followed at 18-month intervals for repeat clinical and cognitive assessments. Anonymised clinical data and biological samples will be stored securely in a central repository and used to facilitate future studies concerned with advancing the diagnosis and treatment of neurodegenerative diseases. Ethics and dissemination Ethical approval has been granted by the St. James’s Hospital/Tallaght University Hospital Joint Research Ethics Committee (Project ID: 2159), which operates in compliance with the European Communities (Clinical Trials on Medicinal Products for Human Use) Regulations 2004 and ICH Good Clinical Practice Guidelines. Findings using TIMC-BRAiN will be published in a timely and open-access fashion.
Background: Lewy Body Dementia is an increasingly prevalent condition, and is currently estimated to comprise a quarter of all dementias.Patients and carers commonly look to the world wide web as a source of health information, thus it is important that information provided is interpretable.Aim: The aim of this study is to assess the readability of information on the internet regarding Lewy Body Dementia.Methods: We searched Google, Yahoo and Bing for the term "Lewy Body Dementia".The first 50 consecutive websites from each search engine were potentially eligible for the study.The readability of information was examined using the Flesch Reading Ease Score (FRES).Results: Of the 150 potentially eligible websites, 91 were included in the study.The mean FRES score was 42.6 (standard deviation: 12.1).Conclusion: Internet information on Lewy Body Dementia is difficult to understand.The mean FRES score was 42.6; a score which is consistent with being readable only to those with a third level education.This study illustrates that improvements are required to provide patients and carers with clearer online information on Lewy body dementia.
Accessible biomarkers capable of tracking early neurodegeneration in Alzheimer's disease (AD) would be valuable. Serum neurofilament-light (NfL) is a promising marker of axonal degeneration that in a previous cross-sectional study was found to be increased in familial Alzheimer's disease (FAD) mutation carriers prior to symptom onset. Exactly how early serum NfL becomes abnormal in FAD is uncertain; intra-individual longitudinal changes have not previously been reported. Forty-eight individuals from families with PSEN1 or APP mutations were recruited. At baseline, 18 participants had progressive cognitive symptoms and 30 were asymptomatic but at 50% risk of carrying a mutation. Blood was taken at baseline; 26 participants also gave at least one follow-up sample (mean interval=2.5 years), with a total of 79 samples being collected. Serum NfL was measured using an ultrasensitive immunoassay on the Single molecule array (Simoa) platform. Blinded genetic testing was performed. Estimated years to onset was calculated based on parental age-at-onset. A longitudinal mixed effects statistical framework was used, incorporating all samples from all time points, to model change in NfL over time, after adjusting for age and gender, in both mutation carriers and non-carriers. At baseline, 19 of the asymptomatic participants were mutation carriers (mean EYO=−9.6 years); 11 were non-carriers (table). Longitudinal analysis showed serum NfL was increased (p<0.05) in mutation carriers compared with non-carriers approximately 11 years before estimated time of symptom onset (figure); mean rate of change in NfL becoming significantly different 12 years before expected onset. There was however high variability in intra-individual rates of change in NfL. Change in NfL with estimated years to/from symptom on set. A) displays observed NfL values, for mutation carriers (red) and non-carriers (black). Where more than one value applies to the same individual, the points are connected by a line. For the five individuals who provided three samples, only the first and third values are shown to prevent un-blinding of genetic status. Random jitter (of up to +/- 2 years) has been applied to the x-axis and two participants with outlier EYO values have been removed to prevent un-blinding. Longitudinal mixed effects modelling (mutation carriers in red, non-carriers in blue), incorporating all scans from all timepoints, estimates how B) NfL, and C) rate of change in NfL, change with time to/from onset (compared to normal individuals of the same age). Dotted lines represent 95% CIs.
Pharmacokinetic modelling on dynamic positron emission tomography (PET) data is a quantitative technique. However, the long acquisition time is prohibitive for routine clinical use. Instead, the semi-quantitative standardised uptake value ratio (SUVR) from a shorter static acquisition is used, despite its sensitivity to blood flow confounding longitudinal analysis. A method has been proposed to reduce the dynamic acquisition time for quantification by incorporating cerebral blood flow (CBF) information from arterial spin labelling (ASL) magnetic resonance imaging (MRI) into the pharmacokinetic modelling. In this work, we optimise and validate this framework for a study of ageing and preclinical Alzheimer's disease. This methodology adapts the simplified reference tissue model (SRTM) for a reduced acquisition time (RT-SRTM) and is applied to [ 18 F]-florbetapir PET data for amyloid-β quantification. Evaluation shows that the optimised RT-SRTM can achieve amyloid burden estimation from a 30-min PET/MR acquisition which is comparable with the gold standard SRTM applied to 60 min of PET data. Conversely, SUVR showed a significantly higher error and bias, and a statistically significant correlation with tracer delivery due to the influence of blood flow. The optimised RT-SRTM produced amyloid burden estimates which were uncorrelated with tracer delivery indicating its suitability for longitudinal studies.
Abstract Background The preclinical phase of Alzheimer’s disease (AD), where pathology slowly accumulates years before cognitive impairment becomes apparent, could offer a treatment window with the greatest potential to preserve cognitive function before downstream pathological processes gather momentum. Characterizing when biomarker trajectories deviate from normal ageing, and the heterogeneity therein, could facilitate targeted trial recruitment and improved biomarker‐based evidence of disease modification. However, reliably identifying early abnormal changes can be challenging due to various confounds, such as age and vascular factors, as well as disease heterogeneity. Method Data from the following cohorts were analysed: (1) individuals at risk for, or affected by, autosomal dominant AD (ADAD), including the Dominantly Inherited Alzheimer Network; (2) Insight 46, a neuroimaging substudy of the MRC National Survey of Heath and Development; (3) pre‐randomization study data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4); and (4) the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Linear regression assessed differences between at‐risk groups (biomarker‐ or mutation‐positive) and normal ageing. Longitudinal changepoint models estimated when atrophy rates deviate from normal ageing trajectories. Normative modelling, based on large reference datasets, identified outliers of regional atrophy assessed within‐group heterogeneity at the individual level. Data‐driven disease progression models (DPMs) were used to estimate quantitative signatures of AD, including imaging biomarkers. Result DPMs reveal that amyloid markers deviate first in ADAD, roughly ten years before neurodegenerative markers. Changepoint models indicate that atrophy rates in ADAD become abnormal 3‐8 years before expected symptom onset, with longitudinal tau PET data suggesting a similar trend. While cross‐sectional volumetric measures showed no evidence of association with amyloid markers, atrophy rates were independently related to both amyloid positivity and markers of cerebrovascular disease, indicating similar, additive effects. Both DPMs and normative modelling found evidence of heterogeneity within imaging biomarkers in ADNI and A4, which explained variability in cognitive decline observed in at‐risk participants. Conclusion Biomarker data from preclinical AD suggests a long temporal gap between amyloidosis and subsequent changes. This represents a treatment opportunity to remove amyloid while minimising irreversible damage. There is evidence of heterogeneity within the biomarker trajectories, which could impact the ability to detect disease modification.
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