CSF Measures Research Articles

A-086 Clinical Performance of a Plasma ATN Panel for Assessment of Alzheimer’s Disease

Abstract Background In recent years, the progression of Alzheimer’s disease has widely been characterized through the utilization of an ATN classification system consisting of biomarkers for three areas of neuropathologic change: development of Amyloid associated plaques, formation of neurofibrillary tangles associated with Tau proteins, and onset of Neurodegeneration. However, the assessment of biomarkers related to each of these stages has predominantly relied on neuroimaging or CSF measurements which are costly and invasive. With the advent of high sensitivity immunoassays, measurements of ATN biomarkers in venous plasma has become possible and would provide a more accessible and affordable tool to assist physicians with the diagnosis of AD. Methods Assessment of amyloid status was performed by determining the ratio of amyloid-beta 1-42 to amyloid-beta 1-40 (Aβ42/40) in plasma where measurements for individual analytes were performed in parallel on a SysmexTM HISCL-5000 platform. Assessments of tau and neurodegeneration were performed using a Roche cobas® e801 platform to measure phosphorylated-tau181 (pTau181) and neurofilament light chain (NfL), respectively. As these assays have not yet received FDA approval, each was validated internally using CLSI guidance. The clinical efficacy of each assay was investigated using 200 clinical specimens with amyloid status defined using positron emission tomography (PET) imaging. Assay results were analyzed using receiver operator characteristic (ROC) analysis with respect to amyloid PET results. In addition, results from the clinical cohort were also assessed with respect: amyloid PET centiloid results, clinically defined cognitive status, and mental state as determined using a Mini-Mental State Exam (MMSE). Results ROC analysis of clinical sample results from validated assays produced an area-under-the-curve (AUC) of 0.941 for Aβ42/40, 0.847 for pTau181, and 0.666 for NfL. These results are consistent with amyloid PET status as well as the progression of AD along the ATN continuum. With respect to Aβ+ subjects, Aβ42/40 results did not change due to worsening cognitive status, increasing amyloid PET results, or worsening MMSE scores. Levels of pTau181, however, were found to increase with worsening cognitive status (p < 0.01), increasing amyloid PET centiloid results (p < 0.05), as well as worsening MMSE scores (p < 0.05). In addition, NfL levels become elevated with worsening MMSE scores (p < 0.01). Conclusions The clinical utility of the plasma ATN panel was found to be appropriate for assisting with diagnosis of AD and is now available for physician use.

Glial reactivity is linked to synaptic dysfunction across the aging and Alzheimer's disease spectrum.

Previous studies have shown that glial and neuronal changes may trigger synaptic dysfunction in Alzheimer's disease(AD). However, the link between glial and neuronal markers and synaptic abnormalities in the living brain is poorly understood. Here, we investigated the association between biomarkers of astrocyte and microglial reactivity and synaptic dysfunction in 478 individuals across the aging and AD spectrum from two cohorts with available CSF measures of amyloid-β(Aβ), phosphorylated tau(pTau181), astrocyte reactivity(GFAP), microglial activation(sTREM2), and synaptic biomarkers(GAP43 and neurogranin). Elevated CSF GFAP levels were linked to presynaptic and postsynaptic dysfunction, regardless of cognitive status or Aβ presence. CSF sTREM2 levels were associated with presynaptic biomarkers in cognitively unimpaired and impaired Aβ + individuals and postsynaptic biomarkers in cognitively impaired Aβ + individuals. Notably, CSF pTau181 levels mediated all associations between GFAP or sTREM2 levels and synaptic dysfunction biomarkers. These results suggest that neuronal-related synaptic biomarkers could be used in clinical trials targeting glial reactivity in AD.

Benchmarking of a multi-biomarker low-volume panel for Alzheimer's Disease and related dementia research.

Alzheimer's Disease (AD) biomarker measurement is key to aid in the diagnosis and prognosis of the disease. In the research setting, participant recruitment and retention and optimization of sample use, is one of the main challenges that observational studies face. Thus, obtaining accurate established biomarker measurements for stratification and maximizing use of the precious samples is key. Accurate technologies are currently available for established biomarkers, mainly immunoassays and immunoprecipitation liquid chromatography-mass spectrometry (IP-MS), and some of them are already being used in clinical settings. Although some immunoassays- and IP-MS based platforms provide multiplexing for several different coding proteins there is not a current platform that can measure all the stablished and emerging biomarkers in one run. The NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) is a mid-throughput platform with antibody-based measurements with a sequencing output that requires 15μL of sample volume to measure more than 100 analytes, including those typically assayed for AD. Here we benchmarked and compared the AD-relevant biomarkers including in the NULISA against validated assays, in both CSF and plasma. Overall, we have found that CSF measures of Aß42/40, NfL, GFAP, and p-tau217 are highly correlated and have similar predictive performance when measured by immunoassay, mass-spectrometry or NULISA. In plasma, p-tau217 shows a performance similar to that reported with other technologies when predicting amyloidosis. Other established and exploratory biomarkers (total tau, p-tau181, NRGN, YKL40, sTREM2, VILIP1 among other) show a wide range of correlation values depending on the fluid and the platform. Our results indicate that the multiplexed immunoassay platform produces reliable results for established biomarkers in CSF that are useful in research settings, with the advantage of measuring additional novel biomarkers using minimal sample volume.

Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations in Memory Clinic Patients Without Dementia.

Patients' comorbidities can affect Alzheimer disease (AD) blood biomarker concentrations. Because a limited number of factors have been explored to date, our aim was to assess the proportion of the variance in fluid biomarker levels explained by the clinical features of AD and by a large number of non-AD-related factors. MEMENTO enrolled 2,323 individuals with cognitive complaints or mild cognitive impairment in 26 French memory clinics. Baseline evaluation included clinical and neuropsychological assessments, brain MRI, amyloid-PET, CSF (optional), and blood sampling. Blood biomarker levels were determined using the Simoa-HDX analyzer. We performed linear regression analysis of the clinical features of AD (cognition, AD genetic risk score, and brain atrophy) to model biomarker concentrations. Next, we added covariates among routine biological tests, inflammatory markers, demographic and behavioral determinants, treatments, comorbidities, and preanalytical sample handling in final models using both stepwise selection processes and least absolute shrinkage and selection operator (LASSO). In total, 2,257 participants were included in the analysis (median age 71.7, 61.8% women, 55.2% with high educational levels). For blood biomarkers, the proportion of variance explained by clinical features of AD was 13.7% for neurofilaments (NfL), 11.4% for p181-tau, 3.0% for Aβ-42/40, and 1.4% for total-tau. In final models accounting for non-AD-related factors, the variance was mainly explained by age, routine biological tests, inflammatory markers, and preanalytical sample handling. In CSF, the proportion of variance explained by clinical features of AD was 24.8% for NfL, 22.3% for Aβ-42/40, 19.8% for total-tau, and 17.2% for p181-tau. In contrast to blood biomarkers, the largest proportion of variance was explained by cognition after adjustment for covariates. The covariates that explained the largest proportion of variance were also the most frequently selected with LASSO. The performance of blood biomarkers for predicting A+ and T+ status (PET or CSF) remained unchanged after controlling for drivers of variance. This comprehensive analysis demonstrated that the variance in AD blood biomarker concentrations was mainly explained by age, with minor contributions from cognition, brain atrophy, and genetics, conversely to CSF measures. These results challenge the use of blood biomarkers as isolated stand-alone biomarkers for AD.

Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease.

Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.

Imaging Biomarker for Early-Stage Alzheimer Disease: Utility of Hippocampal Histogram Analysis of Diffusion Metrics.

Biomarkers have been required for diagnosing early Alzheimer disease. We assessed the utility of hippocampal diffusion parameters for diagnosing Alzheimer disease pathology in mild cognitive impairment. Sixty-nine patients with mild cognitive impairment underwent both CSF measurement and multi-shell diffusion imaging at 3T. Based on the CSF biomarker level, patients were classified according to the presence (Alzheimer disease group, n = 35) or absence (non-Alzheimer disease group, n = 34) of Alzheimer disease pathology. Neurite orientation dispersion and density imaging and diffusion tensor imaging parametric maps were generated. Two observers independently created the hippocampal region of interest for calculating histogram features. Interobserver correlations were calculated. The statistical significance of intergroup differences was tested by using the Mann-Whitney U test. Logistic regression analyses, using both the clinical scale and the image data, were used to predict intergroup differences, after which group discriminations were performed. Most intraclass correlation coefficient values were between 0.59 and 0.91. In the regions of interest of both observers, there were statistically significant intergroup differences for the left-side neurite orientation dispersion and density imaging-derived intracellular volume fraction, right-side diffusion tensor imaging-derived mean diffusivity, left-side diffusion tensor imaging-derived mean diffusivity, axial diffusivity, and radial diffusivity (P < .05). Logistic regression models revealed that diffusion parameters contributed the most to discriminating between the groups. The areas under the receiver operating characteristic curve for the regions of interest of observers A/B were 0.69/0.68, 0.69/0.68, 0.73/0.68, 0.71/0.68, and 0.68/0.68 for the left-side intracellular volume fraction (mean), right-side mean diffusivity (mean), left-side mean diffusivity (10th percentile), axial diffusivity (10th percentile), and radial diffusivity (mean). Hippocampal diffusion parameters might be useful for the early diagnosis of Alzheimer disease.

Plasma p‐tau212 is an early biomarker to identify Alzheimer’s Disease pathology in Down Syndrome

AbstractBackgroundThe life‐time risk of Alzheimer´s disease (AD) in people with Down Syndrome (DS) is above 95% by the seventh decade of life. Understanding the strong biological relation between those conditions is important to accelerate progress in diagnostics and treatment. Tau phosphorylation at threonine‐212 (p‐tau212) is very sensitive to Dual‐specificity tyrosine phosphorylation‐regulated kinase 1A (DYRK1A), which is encoded in chromosome 21. Published results showed that p‐tau212 was significantly increased in AD‐DS male brains. This suggests a possible involvement of p‐tau212 in early progression to AD in individuals with DS. Here, we assessed the potential of these biomarker in DS and sporadic AD.MethodUsing SIMOA technology, we tested in‐house developed p‐tau212 immunoassay (n = 245 for plasma, n = 329 for CSF and n = 115 paired samples) and p‐tau181 (n = 459 for plasma, n = 357 for CSF and n = 245 for p‐tau212 paired plasma samples). A subset of participants had amyloid‐PET data. We used AUC‐ROC to examine diagnostic potential and DeLong test for comparison. Spearman correlations were used to establish associations. Age‐related changes of p‐tau212 and p‐tau181 were examined in relation to amyloid positivity in CSF and Amyloid‐PET.ResultPlasma/CSF p‐tau212 and p‐tau181 showed excellent analytical performances. Plasma p‐tau212 started increasing around 20 years before people were diagnosed as prodromal AD‐DS (pDS) and CSF p‐tau212 approximately 27 years. Paired plasma and CSF p‐tau212 measurements correlated strongly (r = 0.71;p&lt;0.001). Both plasma biomarkers were successful in differentiating asymptomatic DS (aDS) from dementia‐DS (dDS) with an AUC = 91% for p‐tau212 and AUC = 88% for p‐tau181. Plasma p‐tau212 had greater than p‐tau181 magnitude of change in early stages of AD performing 3.4 vs 2‐fold change for pDS and 3 vs 2.1 for vs mild cognitive impairment (MCI‐AD). Pattern remained the same for dDS, AD and CSF measurements. Moreover, plasma p‐tau212 was 2.4‐fold increased in aDS people, when compared to CN. Aside of great discrimination in DS, plasma p‐tau212 had significantly better performance than p‐tau181 for differentiating CN from MCI‐AD (AUC = 84%‐91%;p = 0.026) and CN from MCI‐AD+AD (AUC = 86%‐93%;p = 0.026).ConclusionPlasma p‐tau212 is useful for early DS and AD differentiation. Blood‐based p‐tau212 will be a cost‐effective and simple‐to‐implement alternative to in vivo and autopsy‐based evaluations for AD changes in DS and sporadic cases.

Staging Neurodegeneration Across the AD Continuum

AbstractBackgroundWhile amyloid and tau staging are ubiquitous in Alzheimer’s disease (AD), there are no equivalent staging techniques for AD‐related neurodegeneration. Currently, neurodegeneration in AD is commonly assessed with structural MRI measures such as hippocampal volume and cortical thickness. However, recent work suggests that markers for microstructural neurodegeneration derived from diffusion MRI (DWI) may provide crucial additional information related to the onset and progression of AD. Neurite density index (NDI) and return to origin probability (RTOP) are two DWI metrics that have shown promise for characterizing these microstructural alterations.To investigate the utility of DWI for staging neurodegeneration in AD, we implemented logistic regression to compare the abilities of NDI, RTOP, and hippocampal volume to predict AD dementia in 481 late‐middle‐aged participants on the AD continuum.Methods481 participants (383 cognitively unimpaired [CU], 68 Mild Cognitive Impairment [MCI], 30 AD) from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center were imaged with T1‐weighted MRI and multi‐shell DWI. After delineating hippocampal volume, average NDI and RTOP values were extracted from the hippocampus and a whole brain white matter skeleton. All measures were fit to a logistic model with age, sex, and scanner as covariates.Corresponding ROC curves were used to derive cutoff values for NDI and RTOP by maximizing the Youden Index.ResultsBox plots and ROC curves are provided in Figures 1‐2 and cutoff values for each DWI metric are provided in Table 1. NDI and RTOP outperformed hippocampal volume in predicting AD across brain regions. The best performing marker was WM NDI (AUC = 0.905), followed closely by WM RTOP (AUC = .878). Meanwhile, hippocampal RTOP (AUC = 0.861) slightly outperformed hippocampal NDI (AUC = 0.844).ConclusionOur work adds to a growing body of literature which suggests that neuroimaging markers of brain microstructure may capture unique signatures of AD progression. Furthermore, the fact that both DWI measures performed best in whole brain WM reinforces WM degeneration as a core feature of AD pathophysiology. Future work will incorporate CSF and PET measures of amyloid and tau to better characterize AD‐specific neurodegeneration.

Plasma based assessment of extracellular vesicles biomarkers in Alzheimer’s Disease

AbstractBackgroundCurrently there are no widely accepted diagnostic tests for Alzheimer’s disease (AD). Although there is much excitement in the field of biomarker development, most proposed tests for AD are expensive, require highly specialized sample handling, invasive approaches or radiation exposure. The goal of this project was to develop non‐invasive AD biomarker assessment using nanoflow cytometry to examine proteins on circulating brain‐derived extracellular vesicles in plasma.MethodPlasma samples were collected from a population of individuals clinically diagnosed with MCI and mild, moderate or severe Alzheimer’s disease and cognitively intact control subjects. Plasma was incubated with fluorescently‐conjugated antibodies against candidate biomarkers including oligomeric amyloid, fibrillar amyloid, amyloidβ‐42, pTau‐T181, pTau‐T217, pTau‐S235, Ubiquitin, Neurofilament, α‐synuclein and synaptophysin. After incubation, labelled events were quantified using the Apogee Microplus nanoflow cytometer. Using Receiver‐Operator Curve (ROC) analysis, the ability of individual markers and various combinations to distinguish diagnostic groups was determined.ResultNanoflow cytometry quantification of pTau‐T181, pTau‐T217, pTau‐S235, pTau‐T231, Aβ‐42 (among others) successfully distinguished MCI and AD plasma from healthy controls. Using ROC analysis, our most sensitive antibodies (pTau‐ T231) separate controls from AD patients with an AUC of 0.96 (Sens: 0.95/Spec: 1.0). Other prominent antibodies include pTau 181 with an AUC of 0.93 (Sens: 0.92/Spec: 0.92). The most promising combination of antibodies combines beta‐amyloid42 and pTau‐181 to separate AD patients from controls with an AUC of 0.960 (Sens: 0.93, Spec: 0.92). Isolation of EVs from plasma and validation of marker labeling was performed with Western Blot, ELISA and transmission electron microscopy.ConclusionHere we demonstrate the use of nanoflow cytometry to directly quantitate proteins on circulating extracellular vesicles in plasma from MCI and AD patients. The assays are rapid, inexpensive, directly quantitative, and require mini sample volume and manipulation. Future studies are required involving a larger population of patients who have had ‘gold standard’ biomarkers (Amyloid and tau CSF measurements), longitudinal follow‐up and/or autopsy confirmed diagnosis of AD.

Sequencing cortical microstructural changes along the Alzheimer’s disease continuum

AbstractBackgroundMicrostructural abnormalities likely precede macrostructural changes in the Alzheimer’s disease (AD) cascade. Diffusion MRI (dMRI) is sensitive to microstructural properties of brain tissue but few studies have evaluated dMRI measures in cortical gray matter where many early AD histopathological changes occur. Event‐based modeling (EBM) is a data‐driven approach for probabilistically sequencing cross‐sectional biomarkers in the order that they likely become abnormal. Here, we used EBM to examine the sequence of changes in cortical dMRI measures relative to more widely used amyloid, tau, brain volume, and cognitive biomarkers in two independent AD studies.MethodT1w, dMRI, and amyloid‐PET (FBB/FBP) data were analyzed in 461 ADNI3 participants and 188 OASIS3 participants (Figure 1A). Some ADNI3 participants also had tau‐PET (AV‐1451) and CSF pTAU‐181 and Aß1‐42 data. In addition to DTI, novel single‐shell adaptations of multi‐shell models, NODDI‐DTI and MAP‐AMURA, were fit to the dMRI data. 10 mean cortical dMRI measures were extracted using FreeSurfer parcellated T1w‐images (Figure 1B). T1w cortical and hippocampal volumes were also extracted, and all MRI measures harmonized using ComBat. Mean cortical amyloid‐PET SUVRs were converted to centiloids. Associations between each of 17 biomarkers (Figure 1C) and cognitive impairment (CI –MCI+Dementia– vs CN) were tested in ADNI3. Significant biomarkers were included in an ADNI3 EBM to determine event ordering.The EBM was validated in OASIS3 and used to classify clinical diagnosis.ResultAll biomarkers except DTI FA showed significant differences between CN and CI ADNI3 participants (Figure 2A). ADNI3 EBM confirmed existing AD models: CSF amyloid abnormalities preceded PET, followed by CSF pTau, while cognitive and volumetric abnormalities occurred at later stages. Changes in all dMRI biomarkers preceded CSF or PET Tau, cognitive, and volumetric abnormalities (Figure 2B). Estimated disease stages in ADNI3 distinguish dementia from CN with an AUC = 0.96 (10‐fold cross‐validation). OASIS3 disease stages, estimated using ADNI3 biomarker distributions excluding CSF measures and tau‐PET, classified diagnosis with an AUC = 0.85 (Figure 3).ConclusionCortical microstructural measure abnormalities, including greater diffusivity, lower restriction and lower dispersion potentially due to neurite loss and lower dendritic arborization complexity, may precede those detectable with conventional T1w biomarkers.

Sequencing cortical microstructural changes along the Alzheimer’s disease continuum

AbstractBackgroundMicrostructural abnormalities likely precede macrostructural changes in the Alzheimer’s disease (AD) cascade. Diffusion MRI (dMRI) is sensitive to microstructural properties of brain tissue but few studies have evaluated dMRI measures in cortical gray matter where many early AD histopathological changes occur. Event‐based modeling (EBM) is a data‐driven approach for probabilistically sequencing cross‐sectional biomarkers in the order that they likely become abnormal. Here, we used EBM to examine the sequence of changes in cortical dMRI measures relative to more widely used amyloid, tau, brain volume, and cognitive biomarkers in two independent AD studies.MethodT1w, dMRI, and amyloid‐PET (FBB/FBP) data were analyzed in 461 ADNI3 participants and 188 OASIS3 participants (Figure 1A). Some ADNI3 participants also had tau‐PET (AV‐1451) and CSF pTAU‐181 and Aβ1‐42 data. In addition to DTI, novel single‐shell adaptations of multi‐shell models, NODDI‐DTI and MAP‐AMURA, were fit to the dMRI data. 10 mean cortical dMRI measures were extracted using FreeSurfer parcellated T1w‐images (Figure 1B). T1w cortical and hippocampal volumes were also extracted, and all MRI measures harmonized using ComBat. Mean cortical amyloid‐PET SUVRs were converted to centiloids. Associations between each of 17 biomarkers (Figure 1C) and cognitive impairment (CI –MCI+Dementia– vs CN) were tested in ADNI3. Significant biomarkers were included in an ADNI3 EBM to determine event ordering.The EBM was validated in OASIS3 and used to classify clinical diagnosis.ResultAll biomarkers except DTI FA showed significant differences between CN and CI ADNI3 participants (Figure 2A). ADNI3 EBM confirmed existing AD models: CSF amyloid abnormalities preceded PET, followed by CSF pTau, while cognitive and volumetric abnormalities occurred at later stages. Changes in all dMRI biomarkers preceded CSF or PET Tau, cognitive, and volumetric abnormalities (Figure 2B). Estimated disease stages in ADNI3 distinguish dementia from CN with an AUC = 0.96 (10‐fold cross‐validation). OASIS3 disease stages, estimated using ADNI3 biomarker distributions excluding CSF measures and tau‐PET, classified diagnosis with an AUC = 0.85 (Figure 3).ConclusionCortical microstructural measure abnormalities, including greater diffusivity, lower restriction and lower dispersion potentially due to neurite loss and lower dendritic arborization complexity, may precede those detectable with conventional T1w biomarkers.

Microtubule‐binding region tau fragment tau368 as a biomarker of Alzheimer’s disease

AbstractBackgroundRecent studies suggest that microtubule‐binding region (MTBR) tau fragments may be markers of tau tangles in Alzheimer’s disease (AD) when used in ratio with total tau (MTBR‐tau/T‐tau) to normalize for T‐tau secretion. One such candidate marker is MTBR‐tau containing residue 368 (MTBR‐tau368). In this study, it was cross‐sectionally evaluated in participants from a memory clinic cohort and an autopsy series.MethodsWe included Aβ‐negative cognitively unimpaired (CU) individuals, individuals in the AD continuum (Aβ‐positive CU, mild cognitive impairment (MCI) and AD dementia), and with non‐AD dementia from the BioFINDER‐1 study, who all underwent [18F]‐flortaucipir‐PET. Further, we included CU and CI participants from UCSD Alzheimer’s disease research center (UCSD‐ADRC) who underwent lumbar puncture (LP) antemortem and then donated their brain for autopsy, examining AD neuropathologic change (ADNC) and non‐AD pathologies. All participants had immunoassay‐based CSF measures of T‐tau and MTBR‐tau368 by commercial immunoassays (Euroimmun [BioFINDER‐1] and Lumipulse [UCSD‐ADRC] T‐tau) and an in‐house Single molecule array (Simoa) assay, respectively. Statistical analyses were performed using linear models (covarying for age and sex) and Pearson correlations.ResultsSixty‐seven individuals from UCSD‐ADRC (mean [SD] age at LP 74.3 [8.4] years, 4.3 [2.1] years before death) and 181 from BioFINDER‐1 (72.5 [7.4] years) were included for this study. There were no group‐level differences in MTBR‐tau368 levels alone in any of the cohorts. In the UCSD‐ADRC cohort, MTBR‐tau368/T‐tau was decreased in participants with high (estimated marginal mean [EM = 0.06) vs. low (EM = 0.09, P = 0.01) or no (EM = 0.1, P = 0.001) ADNC and with higher Braak stage (V‐VI; EM = 0.06 vs. III‐IV; EM = 0.08, P&lt;0.05, and I‐II; EM = 0.09, P&lt;0.001). In BioFINDER‐1, Aβ‐positive MCI and AD dementia had lower MTBR‐tau368/T‐tau (EM∼0.04) compared with all other groups (EM = 0.06‐0.07, P&lt;0.05‐&lt;0.001). Further, MTBR‐tau368/T‐tau inversely correlated with tau‐PET in Braak‐like regions III‐IV and V‐VI (r = ‐0.35 and ‐0.42, P&lt;0.001). MTBR‐tau368/T‐tau was unchanged in individuals in the frontotemporal dementia (FTD) spectrum (behavioral variant FTD/semantic dementia, and corticobasal syndrome/progressive supranuclear palsy).ConclusionConcordant with recent data, MTBR‐tau fragments alone do not change in AD or other neurodegenerative disorders. However, when normalizing MTBR fragments to T‐tau secretion, their capability of reflecting the extent of tau pathology provides opportunities of staging AD using CSF biomarkers.

Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer’s disease in the EMIF‐AD Multimodal Biomarker Discovery study

AbstractBackgroundThe aim of this study was to identify DNA methylation signatures that were associated with 15 CSF biomarker measures of Alzheimer’s disease (AD) or neurodegeneration.MethodWe profiled DNA methylation in 886 blood samples from the EMIF‐AD study using the Illumina EPIC array, identifying differentially methylated loci, and regions consisting of multiple adjacent differentially methylated sites.ResultsWe identified Bonferroni‐significant differences in DNA methylation with respect to CSF t‐tau Z‐score (five loci), Aβ42 levels (one loci), YKL‐40 levels (six loci) and NFL (seven loci). Significant differentially methylated regions were identified in 13 of the 15 analyses, with overlapping regions featuring in multiple CSF measures(MX2, RHOH, ANKMY1, ZFP57) analyses, which was unsurprising given the high correlation between measures. Interestingly, we identified several regions that overlapped between the tau and amyloid (ZBTB22), tau and NFL (S100A13), NFL and neurogranin (SORD), and neurogranin and amyloid (STRA6) regional analyses.ConclusionIn this blood‐based epigenome‐wide association study of AD‐relevant CSF biomarkers we identified several interesting genomic loci and regions.

Trajectories of CSF and plasma biomarkers across Alzheimer's disease continuum: Disease staging by NF-L, p-tau181, and GFAP

CSF-to-plasma transition will open new avenues for molecular phenotyping of Alzheimer's disease (AD). Here we evaluated a panel of AD biomarkers in matched CSF and plasma samples across the AD continuum, from preclinical AD to dementia. The aims were to: 1) compare diagnostic performance of the two biofluids, 2) evaluate trajectories of the biomarkers along AD progression. We analyzed CSF and plasma Aβ42/40, p-tau181, p-tau231, t-tau, NF-L, GFAP, UCHL-1 and CSF SNAP-25 in a cohort (n = 173) of preclinical AD, MCI-AD, AD dementia, frontotemporal dementia patients, and controls. We found a significant correlation between CSF and plasma levels of Aβ42/40, p-tau181, p-tau231, NF-L, and GFAP, while no CSF-plasma correlation was observed for t-tau and UCHL-1. Next to the core CSF biomarkers (Aβ42/40, p-tau181, t-tau), those providing the best discrimination between controls and preclinical AD were CSF p-tau231 and SNAP-25 and plasma Aβ42/40, p-tau231, and GFAP. Among plasma biomarkers, we found Aβ42/Aβ40, GFAP, and p-tau231 to show the largest rate of change at the CSF biomarker-defined cut-offs for amyloidosis and tauopathy. Finally, we identified GFAP, NF-L, and p-tau181 as the biomarkers most significantly associated with disease progression in both CSF and plasma. We suggest that a well-standardized and validated panel of selected plasma markers can facilitate early AD diagnosis, even at the asymptomatic disease stage. We propose that both CSF and plasma measurement of NF-L, p-tau181, and GFAP may play a significant role in disease staging and monitoring.

1 Neuromarker of Mind Wandering Predicts CSF-based Biomarkers of Amyloid and Tau Pathology

Objective:Mind-wandering—the spontaneous shift in attention away from the external task to internal thoughts (including daydreaming, fantasizing, rumination, and worrying)—is negatively associated with performance across a variety of tasks including the sustained attention to response task, the Stroop task, tasks of working memory, choice reaction time, visual search, as well as more ecologically related tasks like reading comprehension and mathematics. There has also been promising evidence suggesting a potential link between mind-wandering, functional connectivity of the canonical networks of the brain, and Alzheimer’s disease (AD). However, no study has directly examined the relationship between neural correlates of mind-wandering and AD pathogenesis. In prior work, our lab has identified a whole-brain, functional connectivity-based marker of mind-wandering—the mwCPM—which predicted response time variability in older adults. In this study, we sought to evaluate the ability of this mind wandering CPM, derived from response time variability, to predict CSF p-tau/Aß42 ratio in 289 older adults from the Alzheimer’s Disease Neuroimaging Initiative. We hypothesized that the combined mind-wandering model including functional connections that predict high mind-wandering and functional edges that predict stability in attention, would predict AD pathology.Participants and Methods:Resting-state functional MRI data from 289 older adults (147 healthy older adults, 111 individuals with mild cognitive impairment, and 31 older adults with AD) from the Alzheimer’s Disease NeuroImaging Initiative was analyzed for the current study. Participants were only included in the analyses if they had resting-state fMRI data, CSF measures of amyloid beta and tau pathology, and performance on cognitive composites of global cognition, episodic memory, and executive functioning. Using the well-established methodology of connectome-based predictive modeling, the mind-wandering model was applied to the resting-state fMRI data to predict CSF-based biomarker levels of p-tau and Aß42. Moreover, we also examined if this mind-wandering model predicted individual differences in composite measures of global cognition, episodic memory, and executive functioningResults:The high mwCPM model successfully predicted measured CSF p-tau/Aß ratios (high model: p = .137, p = .0196), controlling for mean framewise displacement. However, the combined network and the low MW network were not significant (combined model: p = .0731, p = .216; low model: p = -.0027, p = .960). We next examined the association between connectivity strengths of the high mwCPM and cognitive functioning in the domains of general cognition, episodic memory, and executive functioning. Connectivity strength in the high mwCPM—functional edges that were associated with high behavioral variability—were negatively associated with all three cognitive composites (global cognition: r = -.239, p &lt; .0001; episodic memory: r = -.208, p = &lt; .0001; executive functioning: r = -.178, p &lt; .0001).Conclusions:This study provides the first empirical support for a link between a neuromarker of mind-wandering and AD pathophysiology. Moreover, mind-wandering also has downstream consequential effects for key domains of cognitive functioning in older adults. Interventions targeted at reducing mind-wandering, particularly before the onset of AD pathogenesis, may make a significant contribution to the prevention of AD-related cognitive decline.

3 Olfactory Dysfunction as a Preclinical Biomarker of AD: Psychophysical Olfactory Performance Reflects Hippocampal Integrity in Non-Demented Older Adults

Objective:One of the greatest challenges of the Alzheimer’s disease (AD) epidemic is identifying the disease prior to substantial neurological compromise. The established biomarkers of AD, such as measures of cognitive impairment, hippocampal atrophy, and CSF measures of beta amyloid and tau, used in research and drug trials are less indicative of AD pathology in preclinical, non-demented, populations. Olfactory dysfunction, a well-established sensory impairment of AD found to correlate strongly with tau burden and hippocampal volume measures, has shown to be a promising preclinical biomarker for AD progression. Several studies have found either impaired odor identification or odor memory at baseline to predict 5-year follow-up cognitive decline and conversion from MCI to AD, but less is known about how olfactory performance reflects the integrity of associated brain regions such as the hippocampus. The present analysis aims to explore the value of psychophysical olfactory assessment as biomarker measure in preclinical AD studies and drug trials by investigating its relationships with structural measures of the hippocampus.Participants and Methods:A sample consisted of non-demented older adults (age &gt;75), recruited from the UCSD Alzheimer’s Disease Research Center as part of a ongoing olfactory biomarker study. Participants completed the AD Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13), San Diego Odor Identification Test (SDOIT), tests of odor recognition memory (ORMem) and odor associative memory (OAM), and MRI derived hippocampal volumes and average hippocampal occupancy (Avg HOC). Left and right hippocampal volumes were adjusted for each participant’s estimated intracranial volume. Bivariate correlations were calculated for ADAS-Cog-13 and SDOIT total scores, performance scores for odor recognition and odor associative memory tests, and the three hippocampal measures (bilateral volumes and average occupancy).Results:ADAS-Cog-13 score did not show significant correlations with either hippocampal measure at the .05 level. SDOIT scores were significantly correlated with the measure of Avg HOC (p&lt;.05). ORMem false positive responses were significantly correlated with Avg HOC (p&lt;.01) and right hippocampal volume (p&lt;.05). ORMem miss responses and OAM errors were both correlated with left (p&lt;.05) and right (p&lt;.01) hippocampal volumes.Conclusions:These results demonstrate that psychophysical assessments of odor identification and odor memory can better reflect the integrity of the hippocampus in nondemented older adults, compared to the neuropsychological ADAS-Cog-13. This is congruent with olfactory dysfunction preceding cognitive-memory decline in AD cases and provides support for the utility of psychophysical olfactory assessment along with other established AD biomarkers in research and drug trials in preclinical populations.Acknowledgements:Supported by NIH grant # R01AG062006-04 from the National Institute on Aging to CM. Special thank you to the staff and participants of the UCSD ADRC, especially Christina Gigliotti, and Aaron Jacobson at the UCSD Center for fMRI.

Greater cognitive reserve is related to lower cortical excitability in healthy cognitive aging, but not in early clinical Alzheimer's disease.

To investigate the relationship between cortico-motor excitability and cognitive reserve (CR) in cognitively unimpaired older adults (CU) and in older adults with mild cognitive impairment or mild dementia due to Alzheimer's disease (AD). Data were collected and analyzed from 15 CU and 24 amyloid-positive AD participants aged 50-90 years. A cognitive reserve questionnaire score (CRQ) assessed education, occupation, leisure activities, physical activities, and social engagement. Cortical excitability was quantified as the average amplitude of motor evoked potentials (MEP amplitude) elicited with single-pulse transcranial magnetic stimulation delivered to primary motor cortex. A linear model compared MEP amplitudes between groups. A linear model tested for an effect of CRQ on MEP amplitude across all participants. Finally, separate linear models tested for an effect of CRQ on MEP amplitude within each group. Exploratory analyses tested for effect modification of demographics, cognitive scores, atrophy measures, and CSF measures within each group using nested regression analysis. There was no between-group difference in MEP amplitude after accounting for covariates. The primary model showed a significant interaction term of group*CRQ (R2adj = 0.18, p = 0.013), but no main effect of CRQ. Within the CU group, higher CRQ was significantly associated with lower MEP amplitude (R2adj = 0.45, p = 0.004). There was no association in the AD group. Lower cortico-motor excitability is related to greater CRQ in CU, but not in AD. Lower MEP amplitudes may reflect greater neural efficiency in cognitively unimpaired older adults. The lack of association seen in AD participants may reflect disruption of the protective effects of CR. Future work is needed to better understand the neurophysiologic mechanisms leading to the protective effects of CR in older adults with and without neurodegenerative disorders.

Transcriptomic profiling of brain amyloidosis and Alzheimer’s disease phenotypes identifies novel gene targets from peripheral blood gene expression data

AbstractBackgroundIn an effort towards pre‐symptomatic risk assessment, precision medicine and biomarker development in AD, we evaluated the efficacy of characterizing and clustering peripheral blood transcriptomic data with respect to brain amyloidosis.Method356 ADNI participants with blood gene expression, Florbetapir SUVR, neurodegeneration measures and CSF measures (CN = 120, EMCI = 130, LMCI = 72 and AD = 34) were identified (Table‐1). Differential expressed genes from ∼50,000 transcripts (p&lt;0.001) were identified. Pairwise euclidean distance between genes was used to construct a gene‐gene distance matrix followed by K‐means clustering (Figure‐1). Gene enrichment analysis was performed on each cluster to identify biological processes associated with the cluster. “Eigengene”; a data vector representing the whole cluster was obtained for each cluster using Principal Component Analyses. The top driver genes were identified from each cluster. We used the tool MAGMA (Multi‐marker Analysis of GenoMic Annotation) to analyze variants in the driver genes. Voxel‐wise multiple regression in SPM12 with age and sex as covariates was used to visualize the pattern of association of driver gene expression with brain amyloidosis. The association of the transcripts identified through our analyses to amyloid SUVR, and disease diagnosis were validated in the external ImaGene(n = 160) dataset.ResultWe identified five clusters with distinct biological processes highly relevant in AD. All five clusters were significantly associated with Florbetapir SUVR and CSF Abeta measures (p&lt;0.05) (Figure‐2). 28 driver genes were identified. Six driver genes (BAIAP3, E2F2, PSMF1, SMOX, UBE20 and RNF11) had negative association with amyloidosis in the lateral temporal, lateral parietal and temporo‐ and parieto‐occipital areas, in addition to regions of the frontal lobe (Figure‐3). One of the driver genes, KANK2, and overall, 29 of the differentially expressed genes had SNPs significantly associated with AD and MCI phenotype (Table‐2). The top driver genes showed similar correlation to amyloid SUVR in the ImaGene dataset (Figure‐4B). A logistic regression model with 28 driver genes, and age and sex as covariates predicted amnestic MCI diagnosis in the external dataset with an AUC of 0.82(Figure‐4A).ConclusionUsing a data driven approach we identified novel gene targets from peripheral blood which directly correlate to amyloid‐related pathogenesis and AD phenotype

Association of Chronic Kidney Disease With Plasma NfL and Other Biomarkers of Neurodegeneration: The H70 Birth Cohort Study in Gothenburg.

Studies associate chronic kidney disease (CKD) with neurodegeneration. This study investigated the relationship between kidney function, blood, CSF, and structural brain MRI markers of neurodegeneration in a sample including individuals with and without CKD. Participants from the Gothenburg H70 Birth Cohort Study, with data on plasma neurofilament light (P-NfL), estimated glomerular filtration rate (eGFR), and structural brain MRI were included. Participants were invited to also have the CSF collected. The primary endpoint of this study was to determine any association between CKD and P-NfL. Secondary endpoints included cross-sectional associations between CKD, eGFR, and CSF-derived and MRI-derived markers of neurodegeneration and Alzheimer disease (AD) pathology (MRI: cortical thickness, hippocampal volume, lateral ventricle volume, and white matter lesion volume; CSF: β-amyloid (Aβ) 42, Aβ42/40, Aβ42/p-tau, t-tau, p-tau, and NfL). Participants with P-NfL and eGFR at baseline were re-examined on eGFR, 5.5 (5.3-6.1) years (median; IQR) after the first visit, and the predictive value of P-NfL levels on incident CKD was estimated longitudinally, using a Cox proportional hazards model. We included 744 participants, 668 without CKD (age 71 [70-71] years, 50% males) and 76 with CKD (age 71 [70-71] years, 39% males). Biomarkers from the CSF were analyzed in 313 participants. A total of 558 individuals returned for a re-examination of eGFR (75% response rate, age 76 [76; 77] years, 48% males, 76 new cases of CKD). Participants with CKD had higher P-NfL levels than those with normal kidney function (median; 18.8 vs 14.1 pg/mL, p < 0.001), while MRI and CSF markers were similar between the groups. P-NfL was independently associated with CKD after adjustment for confounding variables, including hypertension and diabetes (OR; 3.231, p < 0.001), in a logistic regression model. eGFR and CSF Aβ 42/40: R = 0.23, p = 0.004 correlated in participants with Aβ42 pathology. P-NfL levels in the highest quartile were associated with incident CKD at follow-up (HR; 2.39 [1.21: 4.72]). In a community-based cohort of 70-year olds, P-NfL was associated with both prevalent and incident CKD, while CSF and/or imaging measures did not differ by CKD status. Participants with CKD and dementia presented similar levels of P-NfL.

Fully automated measurement of intracranial CSF and brain parenchyma volumes in pediatric hydrocephalus by segmentation of clinical MRI studies.

Brain parenchyma (BP) and intracranial cerebrospinal fluid (iCSF) volumes measured by fully automated segmentation of clinical brain MRI studies may be useful for the diagnosis and follow-up of pediatric hydrocephalus. However, previously published segmentation techniques either rely on dedicated sequences, not routinely used in clinical practice, or on spatial normalization, which has limited accuracy when severe brain distortions, such as in hydrocephalic patients, are present. We developed a fully automated method to measure BP and iCSF volumes from clinical brain MRI studies of pediatric hydrocephalus patients, exploiting the complementary information contained in T2- and T1-weighted images commonly used in clinical practice. The proposed procedure, following skull-stripping of the combined volumes, performed using a multiparametric method to obtain a reliable definition of the inner skull profile, maximizes the CSF-to-parenchyma contrast by dividing the T2w- by the T1w- volume after full-scale dynamic rescaling, thus allowing separation of iCSF and BP through a simple thresholding routine. Validation against manual tracing on 23 studies (four controls and 19 hydrocephalic patients) showed excellent concordance (ICC>0.98) and spatial overlap (Dice coefficients ranging from 77.2% for iCSF to 96.8% for intracranial volume). Accuracy was comparable to the intra-operator reproducibility of manual segmentation, as measured in 14 studies processed twice by the same experienced neuroradiologist. Results of the application of the algorithm to a dataset of 63 controls and 57 hydrocephalic patients (19 with parenchymal damage), measuring volumes' changes with normal development and in hydrocephalic patients, are also reported for demonstration purposes. The proposed approach allows fully automated segmentation of BP and iCSF in clinical studies, also in severely distorted brains, enabling to assess age- and disease-related changes in intracranial tissue volume with an accuracy comparable to expert manual segmentation.