To investigate whether serum neurofilament light (NfL) concentration is increased in familial Alzheimer disease (FAD), both pre and post symptom onset, and whether it is associated with markers of disease stage and severity.
Methods:
We recruited 48 individuals from families with PSEN1 or APP mutations to a cross-sectional study: 18 had symptomatic Alzheimer disease (AD) and 30 were asymptomatic but at 50% risk of carrying a mutation. Serum NfL was measured using an ultrasensitive immunoassay on the single molecule array (Simoa) platform. Cognitive testing and MRI were performed; 33 participants had serial MRI, allowing calculation of atrophy rates. Genetic testing established mutation status. A generalized least squares regression model was used to compare serum NfL among symptomatic mutation carriers, presymptomatic carriers, and noncarriers, adjusting for age and sex. Spearman coefficients assessed associations between serum NfL and (1) estimated years to/from symptom onset (EYO), (2) cognitive measures, and (3) MRI measures of atrophy.
Results:
Nineteen of the asymptomatic participants were mutation carriers (mean EYO −9.6); 11 were noncarriers. Compared with noncarriers, serum NfL concentration was higher in both symptomatic (p < 0.0001) and presymptomatic mutation carriers (p = 0.007). Across all mutation carriers, serum NfL correlated with EYO (ρ = 0.81, p < 0.0001) and multiple cognitive and imaging measures, including Mini-Mental State Examination (ρ = −0.62, p = 0.0001), Clinical Dementia Rating Scale sum of boxes (ρ = 0.79, p < 0.0001), baseline brain volume (ρ = −0.62, p = 0.0002), and whole-brain atrophy rate (ρ = 0.53, p = 0.01).
Conclusions:
Serum NfL concentration is increased in FAD prior to symptom onset and correlates with measures of disease stage and severity. Serum NfL may thus be a feasible biomarker of early AD-related neurodegeneration.
Alzheimer's disease (AD) is recognized to have a long presymptomatic period, during which there is progressive accumulation of molecular pathology, followed by inexorable neuronal damage. The ability to identify presymptomatic individuals with evidence of neurodegenerative change, to stage their disease, and to track progressive changes will be important for early diagnosis and for prevention trials. Despite recent advances, particularly in magnetic resonance imaging, our ability to identify early neurodegenerative changes reliably is limited. The development of diffusion-weighted magnetic resonance imaging, which is sensitive to microstructural changes not visible with conventional volumetric techniques, has led to a number of diffusion imaging studies in AD; these have largely focused on white matter changes. However, in AD cerebral grey matter is affected very early, with pathological studies suggesting that grey matter changes predate those in white matter. In this article we review the growing number of studies that assess grey matter diffusivity changes in AD. Although use of the technique is still at a relatively early stage, results so far have been promising. Initial studies identified changes in diffusion measures in the hippocampi of patients with mild cognitive impairment, which predated macroscopic volume loss, with positive predictive value for progression to AD dementia. More recent studies have identified abnormalities in multiple neocortical areas (particularly the posterior cingulate) at various stages of disease progression. Studies of patients who carry genetic mutations predisposing to autosomal dominant familial AD have shown cortical and subcortical grey matter diffusivity changes several years before the expected onset of the first clinical symptoms. The technique is not without potential methodological difficulties, especially relating to partial volume effects, although recent advances appear to be reducing such issues. Going forward, further utilization of grey matter diffusion measurements in AD may improve our understanding with regards to the timing and nature of the earliest presymptomatic neurodegenerative changes. This imaging technique may also be useful in comparing and contrasting subtle variations in different disease subgroups, and as a sensitive outcome measure for presymptomatic clinical trials in AD and other neurodegenerative diseases.
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 With an aging population, it is essential to identify subtle features of brain pathology – both neurodegenerative and vascular – at an early stage, which may predict risk of future decline. We used diffusion MRI (dMRI) to assess grey matter cortical microstructure and investigate associations with 1) Alzheimer’s disease (AD) pathology and 2) mid/late‐life vascular risk (as measured by blood pressure (BP)). Method 151 asymptomatic individuals from the British 1946 birth cohort underwent combined PET/MR with [18F]florbetapir Aβ‐PET at ∼73yrs, and [18F]MK‐6240 tau‐PET at ∼76yrs. Multi‐shell diffusion MRI was acquired; neurite orientation dispersion and density imaging (NODDI) quantified orientation dispersion index (ODI), a proxy measure of dendritic morphology/complexity, with DTI measuring mean diffusivity (MD), a less specific measure of degenerative change. Cortical volume was estimated using GIF. PET SUVRs were calculated. Amyloid load was assessed in a cortical composite region, with tau PET and MRI measures assessed in 1) a temporal meta‐ROI (analogous to early Braak stages), and 2) a larger neocortical meta‐ROI, analogous to later Braak stages. BP measurements were taken throughout mid and late life, at ages 43, 53, 63, and 73. Result Seventy participants were amyloid positive (A+), of whom 27 were tau positive (T+). Global cortical ODI was significantly lower in A+/T+ than A+T‐ (p=0.005) and in A+/T+ than A‐/T‐ (p=0.001), but with no significant difference between A+/T‐ and A‐/T‐. The same pattern was found for ODI in the temporal ROI. No consistent differences were found across A/T groupings for MD or volume. MD (both global and temporal) at aged 76 showed significant associations (p<0.05) with BP at ages 43, 53 and 63, but not at 73. These association remained after adjusting for A/T pathology and cortical volume. For ODI, associations with mid‐life BP were less consistent. Conclusion Mid‐life BP is associated with late life cortical microstructural breakdown, as measured by MD, in the absence of detectable volume changes and independent of Alzheimer’s disease (AD) pathology. ODI appears to be more sensitive and specific to dendritic tau‐related changes. Cortical dMRI offers promise in the presymptomatic identification, staging and risk stratification of both AD and vascular dementia.
Understanding and identifying the earliest pathological changes of Alzheimer's disease (AD) is key to realizing disease-modifying treatments, which are likely to be most efficacious when given early. However, identifying individuals in the earliest, presymptomatic stage of typical, sporadic, late onset Alzheimer's disease (LOAD) is challenging. There is therefore of considerable interest to investigate the rarer autosomal-dominantly inherited forms of AD (ADAD). ADAD provides the opportunity to identify asymptomatic "at risk" individuals prior to the onset of cognitive decline, and has a predictable (if imperfect) age of symptom onset within families. This allows recruitment of presymptomatic individuals for observational/clinical trials. An important question is whether presymptomatic changes in ADAD mirror those in LOAD. We therefore performed a model-based analysis of biomarker changes in ADAD and LOAD. We used data-driven models to analyze biomarker data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid, cognitive test scores) from two multi-center observational studies: the Dominantly Inherited Alzheimer Network (DIAN) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). From cross-sectional data we estimated pathological cascades using an event-based model1,2 (EBM). The EBM estimates a maximum-likelihood sequence of abnormality and its uncertainty (full posterior). To explore biomarker trajectories, we analyzed short-term longitudinal data using covariate-adjusted, nonparametric differential equation models3,4 (DEMs). Each trajectory enables estimation of a "transition time" between normal cognition and symptom onset, which combine to give a pathological cascade estimated from DEMs. We compared both estimated disease progression sequences across ADAD and LOAD. We fit DEMs to mutation carriers in DIAN and ApoE4-positive clinical progressors in ADNI. Both models estimate similar pathological cascades for ADAD and LOAD (Figure 1): accumulation of molecular pathology (amyloid, and tau where measured) followed by cognitive abnormalities, then brain hypometabolism/atrophy. Biomarker trajectories (e.g., Figure 2) accelerated from normal to abnormal levels for both ADAD and LOAD, with some evidence for plateauing/deceleration (consistent with sigmoidal behavior5) beyond symptom onset. AD progression sequences from data-driven models. (a)-(b) (upper): ADAD (DIAN-DF6). (c)-(d) (lower): LOAD (ADNI-1). Left: Event-based model (EBM). Biomarkers (imaging/molecular/cognitive) along the vertical axis are ordered by the maximum likelihood disease progression sequence (from top to bottom). The horizontal axis shows estimated variance in the posterior sequence, with positional likelihood per event given by grayscale intensity. Right: Differential equation models (DEMs). Biomarker trajectories estimated from differential data are anchored in time at the average point of symptom onset (right-hand edge). The disease progression sequence along the vertical axis (top to bottom) is determined by the average abnormality transition time from normal cognition to symptom onset (posterior median shown as red dots; median absolute deviation shown as red lines). The empirical probability of biomarker abnormality as a function of years prior to symptom onset is shown in grayscale intensity, increasing from left to right. Trajectories of adjusted bilateral hippocampus volume (in mm3) against years relative to symptom onset, estimated from data-driven differential equation models. (a) ADAD (left); (b) LOAD (right). Black dashed line is the average. Gray lines are sample trajectories from the posterior. Overlaid in blue (right-hand vertical axis on each plot) is the probability density for the abnormality transition time between normal level (shown as green lines with green box-plots to the left of each axis) and abnormal level (symptom onset; red lines and box-plots). a = asymptomatic; s = symptomatic; MC = mutation carrier (ADAD); N = normal (ADNI stable CN diagnosis); A = abnormal (symptom onset among ADNI clinical progressors). We found data-driven support for broadly similar biomarker progression of ADAD and LOAD (only minor differences), thus supporting research findings in ADAD being applicable to LOAD. Our data-driven model-based approaches increase understanding of pathological cascades with potential utility for patient staging and prognosis.
Abstract Background We investigated imaging biomarkers of Aß and neurodegeneration in relation to tau‐PET Braak stage in a preclinical birth cohort. Method Cognitively normal individuals enrolled in Insight 46, the neuroimaging sub‐study of the MRC National Survey of Health and Development (1946 British birth cohort), were scanned on combined PET/MR with [18F]florbetapir Aß‐PET at age ∼70 years and again at ∼73 years. A sub‐sample enriched for Aß (Aß+; Centiloid> = 13, hole cerebellum reference) is currently being assessed with [18F]MK‐6240 tau‐PET at age ∼76 years. For this interim analysis, tau‐PET images (90‐110 minutes post‐injection) were co‐registered with T1‐weighted MRI. Anatomical areas were parcellated on the T1 to form Braak regions. Standard uptake value ratios (SUVRs) were calculated using an inferior cerebellar grey reference without partial volume correction. Tau‐PET positivity (Tau+) was defined using Gaussian mixture modelling in each region. Participants were assigned to Tau‐, Braak I‐II, III‐IV or V‐VI groups based on the most advanced Tau+ region. Group differences in baseline Aß (Centiloids), Aß accumulation (Centiloids/year) and hippocampal atrophy rate (%/year) were investigated with Mann‐Whitney U tests. Result Analysis included 80 individuals with tau‐PET data (Table 1), 45% of the sample were Aß+ by age 73. Three individuals did not conform to the Braak stage hierarchy (orange triangles, Figure 1). No Aß‐ individuals were Tau+ beyond Braak I. Figure 2 shows baseline Aß, Aß accumulation and hippocampal atrophy rates for Braak stage groups for participants who had data at all timepoints (N = 78). Tau+ individuals (in any Braak region) had significantly higher baseline Aß than Tau‐ individuals. Rate of annual Aß accumulation was higher for Braak III‐IV and Braak V‐VI compared to Tau‐ individuals. Hippocampal atrophy rate was elevated for Braak V‐VI compared to Tau‐, and Braak III‐IV was borderline significant. Conclusion In this preliminary analysis, tau‐PET positivity beyond Braak I was restricted to individuals who were Aß+ three years prior. Participants with elevated tau aged 76 had increased Aß at age 70. Increased rates of hippocampal atrophy were occurring at least three years prior to tau scanning in individuals with advanced tau pathology. These findings will be updated as more data is acquired.
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.
Diffusion-weighted MRI is sensitive to microstructural changes; with studies to date largely focusing on white matter. However, in AD cortical grey matter is affected early. Assessment of cortical diffusivity changes may therefore allow detection of early neurodegeneration. We aimed to assess this in individuals at risk of familial AD (FAD). Seventy-seven participants were recruited: 38 with pathological mutations (in PS1 or APP) and 39 controls (Table). Of the mutation carriers, 16 had developed progressive cognitive symptoms and 22 were presymptomatic. Presymptomatic inidivduals were divided, depending on time to predicted symptom onset (median time to onset=8.1 years), in to early presymptomatic and late presymptomatic. T1-weighted and diffusion-weighted images were acquired. T1 images underwent cortical parcellation using Freesurfer (v5.3.0), prior to being registered to the diffusion images. Mean diffusivity (MD), which rises with increasing microstructural breakdown, was calculated for each cortical region. A region of interest approach was taken, using a 6-region "cortical signature", containing regions identified previously as undergoing most cortical thinning in symptomatic FAD (entorhinal cortex, inferior parietal cortex, precuneus, superior frontal gyrus, superior parietal lobule, supramarginal gyrus). Across all mutation carriers, correcting for age and gender, a significant positive correlation was found between cortical MD and disease progression (time to/from onset) in 5 of the 6 signature regions, and in a mean signature summary measure (p<0.0001). In late presymptomatic individuals, compared to controls, cortical MD was higher in all 6 regions, reaching statistical significance in the left inferior parietal cortex (p=0.04), and trend significance in the right inferior parietal cortex (p=0.06). A support vector machine approach found cortical MD of the 6 regions to have a sensitivity and specificity of 80% in separating late presymptomatic individuals from controls. In early presymptomatic individuals, contrary to what one may expect, 5 of the 6 regions had reduced MD compared to controls; possibly indicating an early inflammatory response to amyloid. Measurement of cortical MD has potential as a method for detecting microstructural cortical change in AD, and correlates with disease progression. Presymptomatic microstructural breakdown is detectable, providing reasonable sensitivity and specificity for differentiating early AD from normal inidividuals.
Abstract Background There is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity‐based tau spreading model in AD variants with distinct tau deposition patterns is crucial. Method We included 269 amyloid‐ß‐positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA‐AD; 83 logopenic variant primary progressive aphasia, lvPPA‐AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS‐AD) and 68 with typical AD from 12 international cohorts, who underwent tau‐PET (54% [18F]AV1451/[18F]flortaucipir/Tauvid, 27% [18F]MK6240, 19% [18F]PI2620). Using Gaussian mixture modeling including amyloid‐ß‐negative controls, cross‐sectional tau‐PET standardized uptake value ratios within Schaefer‐200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity‐based distance (using the 30% strongest positive region‐to‐region connections of a group‐average connectivity matrix from ADNI elderly controls) and tau‐PET covariance (group‐average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1‐4) and tau positivity probabilities were assessed accordingly. Result Tau positivity probabilities matched clinical variants, with a posterior pattern in PCA‐AD, left‐hemispheric dominant pattern in lvPPA‐AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS‐AD, and temporo‐parietal predominance in typical AD (Fig.1). In line with this, tau epicenters were highly heterogeneous across variants (Fig.1). In all variants, greater tau‐PET covariance was associated with shorter functional connectivity‐based distance (Fig.2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Fig.3). Conclusion This multi‐center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant‐specific endpoints in clinical trials.
There is great interest in finding accessible biomarkers of early neuronal loss in Alzheimer's disease. Serum neurofilament light (NfL) is a blood-based marker of axonal degeneration that may be useful in staging and tracking progression in early AD. The study of individuals at risk of familial AD (FAD) allows presymptomatic changes to be assessed. We assessed whether serum NfL concentration is increased in FAD both pre- and post-symptom onset, and its association with markers of disease stage and severity. Forty-eight individuals from families with PSEN1 or APP mutations that cause FAD were recruited: 18 had symptomatic AD and 30 were asymptomatic but at 50% risk of carrying a mutation. Serum NfL was measured using an ultrasensitive immunoassay on the Single molecule array (Simoa) platform. Cognitive testing and MRI were performed; 33 participants had serial MRI (mean interval=1.3 years), allowing calculation of atrophy rates. Blinded genetic testing established mutation status. A generalised least squares regression model was used to compare serum NfL between symptomatic mutation carriers, presymptomatic carriers and non-carriers, adjusting for age and gender. Spearman coefficients assessed associations between serum NfL and 1) estimated years to/from symptom onset (EYO), 2) cognitive measures, and 3) MRI measures of atrophy. Nineteen of the asymptomatic participants were mutation carriers (mean EYO=−9.6 years); eleven were non-carriers. Adjusting for age and gender, serum NfL concentration was significantly higher in both symptomatic mutation carriers (estimated difference in means 29.2pg/mL, 95% CI 19.3−39.1; p<0.0001) and presymptomatic mutation carriers (6.1pg/mL, 1.6−10.5, p=0.007) compared to non-carrier controls. Serum NfL correlated with EYO, across all mutation carriers (rho=0.81, p<0.0001) and in both the presymptomatic (rho=0.55, p=0.01) and symptomatic (rho=0.49, p=0.04) groups separately. Across all mutation carriers, serum NfL correlated with multiple cognitive and imaging measures, including MMSE (Spearman's rho=−0.62, p=0.0001), CDR SOB (rho=0.79, p<0.0001), baseline brain volume (rho=−0.62, p=0.0002), and subsequent rate of brain atrophy (rho=0.53, p=0.01).
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