Plasma Tau and Neurofilament Light in Frontotemporal Lobar Degeneration and Alzheimer Disease

Objective: To test the hypothesis that plasma total tau (t-tau) and neurofilament light chain (NfL) concentrations may have a differential role in the study of frontotemporal lobar degeneration syndromes (FTLD-S) and clinically diagnosed Alzheimer disease syndromes (AD-S), we determined their diagnostic and prognostic value in FTLD-S and AD-S and their sensitivity to pathologic diagnoses. Methods: We measured plasma t-tau and NfL with the Simoa platform in 265 participants: 167 FTLD-S, 43 AD-S, and 55 healthy controls (HC), including 82 pathology-proven cases (50 FTLD-tau, 18 FTLD-TDP, 2 FTLD-FUS, and 12 AD) and 98 participants with amyloid PET. We compared cross-sectional and longitudinal biomarker concentrations between groups, their correlation with clinical measures of disease severity, progression, and survival, and cortical thickness. Results: Plasma NfL, but not plasma t-tau, discriminated FTLD-S from HC and AD-S from HC. Both plasma NfL and t-tau were poor discriminators between FLTD-S and AD-S. In pathology-confirmed cases, plasma NfL was higher in FTLD than AD and in FTLD-TDP compared to FTLD-tau, after accounting for age and disease severity. Plasma NfL, but not plasma t-tau, predicted clinical decline and survival and correlated with regional cortical thickness in both FTLD-S and AD-S. The combination of plasma NfL with plasma t-tau did not outperform plasma NfL alone. Conclusion: Plasma NfL is superior to plasma t-tau for the diagnosis and prediction of clinical progression of FTLD-S and AD-S. Classification of Evidence: This study provides Class III evidence that plasma NfL has superior diagnostic and prognostic performance vs plasma t-tau in FTLD and AD

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials

Disease progression models of familial frontotemporal lobar degeneration and the temporal ordering of biomarker changes in an international cohort

Background: Clinical trials are underway to treat familial frontotemporal lobar degeneration (f-FTLD). This is a rare disease, and a limited number of mutation carriers have been identified; thus, efficient trial design is critical. Multimodal, latent disease progression models (DPM) can estimate time to symptom onset and define the temporal ordering of biomarker changes. DPMs can also be leveraged to select endpoints and potentially supplement analyses by integrating historical data. Recent draft FDA guidance for gene therapy trials in neurological disease supports these novel approaches to clinical trials. Method: Participants included 1,049 members of families affected by f-FTLD, due to mutations in GRN, MAPT, or C9orf72 genes, who were enrolled in ALLFTD or GENFI. A Bayesian repeated measures model incorporated multimodal data to estimate disease progression, conditional on latent disease age (proximity to symptom onset), in 677 mutations carriers (GRN (n=233), MAPT (n=151) and C9orf72 (n=293)). Family members without pathogenic mutations were used as the reference group. Mean follow-up was 1.1 (SD=1.1) years. Jointly modeled longitudinal variables included neuropsychological scores, CDR®+NACC-FTLD Box Score, MRI volumes of brain regions affected by f-FTLD, and plasma levels of neurofilament light chain (NfL). Result: Disease progression curves were similar across ALLFTD and GENFI cohorts. Plasma NfL elevations occurred earliest, up to 10 years before symptom onset, and NfL was the most powerful endpoint in the asymptomatic stage. MRI abnormalities occurred next, closer to symptom onset. The earliest MRI changes relative to symptom onset were observed in C9orf72+. GRN mutation carriers showed the most rapid acceleration in all biomarkers, and this acceleration occurred in close proximity to symptom onset. Neuropsychological measures and CDR®+NACC-FTLD Box Score were among the most promising endpoints in the symptomatic stage. Trial simulations indicated that using latent disease age as an enrollment criterion would allow some asymptomatic mutation carriers to be enrolled without sacrificing power. Conclusion: Similarity in disease progression across ALLFTD and GENFI participants suggests these models will apply to international trials. Model-derived estimates of disease progression curves indicate that endpoint selection should be specific to disease stage and mutation, and DPMs would facilitate greater participant enrollment

Temporal order of clinical and biomarker changes in familial frontotemporal dementia

Data availability: The datasets analyzed for the current study reflect collaborative efforts of two research consortia: ALLFTD and GENFI. Each consortium provides clinical data access based on established policies for data use: processes for request are available for review at allftd.org/data for ALLFTD data and by emailing [email protected]. Certain data elements from both consortia (for example raw MRI images) may be restricted due to the potential for identifiability in the context of the sensitive nature of the genetic data. The deidentified combined dataset will be available for request through the FTD Prevention Initiative in 2023 (https://www.thefpi.org/).Code availability: Custom R code is available at https://doi.org/10.5281/zenodo.6687486.Copyright © The Author(s). Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.Data collection and dissemination of the data presented in this paper were supported by the ALLFTD Consortium (U19: AG063911, funded by the National Institute on Aging and the National Institute of Neurological Diseases and Stroke) and the former ARTFL and LEFFTDS Consortia (ARTFL: U54 NS092089, funded by the National Institute of Neurological Diseases and Stroke and National Center for Advancing Translational Sciences; LEFFTDS: U01 AG045390, funded by the National Institute on Aging and the National Institute of Neurological Diseases and Stroke). The manuscript was reviewed by the ALLFTD Executive Committee for scientific content. The authors acknowledge the invaluable contributions of the study participants and families as well as the assistance of the support staffs at each of the participating sites. This work is also supported by the Association for Frontotemporal Degeneration (including the FTD Biomarkers Initiative), the Bluefield Project to Cure FTD, Larry L. Hillblom Foundation (2018-A-025-FEL (A.M.S.)), the National Institutes of Health (AG038791 (A.L.B.), AG032306 (H.J.R.), AG016976 (W.K.), AG062677 (Ron C. Peterson), AG019724 (B.L.M.), AG058233 (Suzee E. Lee), AG072122 (Walter Kukull), P30 AG062422 (B.L.M.), K12 HD001459 (N.G.), K23AG061253 (A.M.S.), AG062422 (RCP), K24AG045333 (H.J.R.)) and the Rainwater Charitable Foundation. Samples from the National Centralized Repository for Alzheimer Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG021886 (T.F.)) awarded by the National Institute on Aging (NIA), were used in this study. This work was also supported by Medical Research Council UK GENFI grant MR/M023664/1 (J.D.R.), the Bluefield Project, the National Institute for Health Research including awards to Cambridge and UCL Biomedical Research Centres and a JPND GENFI-PROX grant (2019–02248). Several authors of this publication are members of the European Reference Network for Rare Neurologic Diseases, project 739510. J.D.R. and L.L.R. are also supported by the National Institute for Health and Care Research (NIHR) UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. J.D.R. is also supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). M.B. is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517). RC and C.G. are supported by a Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). J.B.R. is supported by NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care), the Wellcome Trust (220258), the Cambridge Centre for Parkinson-plus and the Medical Research Council (SUAG/092 G116768); I.L.B. is supported by ANR-PRTS PREV-DemAls, PHRC PREDICT-PGRN, and several authors of this publication are members of the European Reference Network for Rare Neurological Diseases (project 739510). J.L. is funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy – ID 390857198). R.S.-V. was funded at the Hospital Clinic de Barcelona by Instituto de Salud Carlos III, Spain (grant code PI20/00448 to RSV) and Fundació Marató TV3, Spain (grant code 20143810 to R.S.-V.). M.M. was, in part, funded by the UK Medical Research Council, the Italian Ministry of Health and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, by Canadian Institutes of Health Research operating grants (MOP- 371851 and PJT-175242) and by funding from the Weston Brain Institute. R.L. is supported by the Canadian Institutes of Health Research and the Chaire de Recherche sur les Aphasies Primaires Progressives Fondation Famille Lemaire. C.G. is supported by the Swedish Frontotemporal Dementia Initiative Schörling Foundation, Swedish Research Council, JPND Prefrontals, 2015–02926,2018–02754, Swedish Alzheimer Foundation, Swedish Brain Foundation, Karolinska Institutet Doctoral Funding, KI Strat-Neuro, Swedish Dementia Foundation, and Stockholm County Council ALF/Region Stockholm. J.L. is supported by Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (German Research Foundation, EXC 2145 Synergy 390857198). The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health Research UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK

Neuroimaging in Dementia.

Although the diagnosis of dementia still is primarily based on clinical criteria, neuroimaging is playing an increasingly important role. This is in large part due to advances in techniques that can assist with discriminating between different syndromes. Magnetic resonance imaging remains at the core of differential diagnosis, with specific patterns of cortical and subcortical changes having diagnostic significance. Recent developments in molecular PET imaging techniques have opened the door for not only antemortem but early, even preclinical, diagnosis of underlying pathology. This is vital, as treatment trials are underway for pharmacological agents with specific molecular targets, and numerous failed trials suggest that earlier treatment is needed. This article provides an overview of classic neuroimaging findings as well as new and cutting-edge research techniques that assist with clinical diagnosis of a range of dementia syndromes, with an emphasis on studies using pathologically proven cases

Tracking disease progression in familial and sporadic frontotemporal lobar degeneration: Recent findings from ARTFL and LEFFTDS.

IntroductionFamilial frontotemporal lobar degeneration (f-FTLD) due to autosomal dominant mutations is an important entity for developing treatments for FTLD. The Advancing Research and Treatment for Frontotemporal Lobar Degeneration (ARTFL) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS) longitudinal studies were designed to describe the natural history of f-FTLD.MethodsWe summarized recent publications from the ARTFL and LEFFTDS studies, along with other recent publications describing the natural history of f-FTLD.ResultsPublished and emerging studies are producing data on all phases of f-FTLD, including the asymptomatic and symptomatic phases of disease, as well as the transitional phase when symptoms are just beginning to develop. These data indicate that rates of change increase along with disease severity, which is consistent with commonly cited models of neurodegeneration, and that measurement of biomarkers may predict onset of symptoms.DiscussionData from large multisite studies are producing important data on the natural history of f-FTLD that will be critical for planning intervention trials

A comparison of biofluid cytokine markers across platform technologies: Correspondence or divergence?

BACKGROUND: Quantification of biofluid cytokines is a rapidly growing area of translational research. However, comparability across the expanding number of available assay platforms for detection of the same proteins remains to be determined. We aimed to directly compare a panel of commonly measured cytokines in plasma of typically aging adults across two high sensitivity quantification platforms, Meso Scale Discovery high performance electrochemiluminiscence (HPE) and single-molecule immunosorbent assays (Simoa) by Quanterix. METHODS: 57 community-dwelling older adults completed a blood draw, neuropsychological assessment, and brain MRI as part of a healthy brain aging study. Plasma samples from the same draw dates were analyzed for IL-10, IP-10, IL-6, TNFα, and IL-1β on HPE and Simoa, separately. Reliable detectability (coefficient of variance (CV) < 20% and outliers 3 interquartiles above the median removed), intra-assay precision, absolute concentrations, reproducibility across platforms, and concurrent associations with external variables of interest (e.g., demographics, peripheral markers of vascular health, and brain health) were examined. RESULTS: The proportion of cytokines reliably measured on HPE (87.7-93.0%) and Simoa (75.4-93.0%) did not differ (ps > 0.32), with the exception of IL-1β which was only reliably measured using Simoa (68.4%). On average, CVs were acceptable at <8% across both platforms. Absolute measured concentrations were higher using Simoa for IL-10, IL-6, and TNFα (ps < 0.05). HPE and Simoa shared only small-to-moderate proportions of variance with one another on the same cytokine proteins (range: r = 0.26 for IL-10 to r = 0.64 for IL-6), though platform agreement did not dependent on cytokine concentrations. Cytokine ratios within each platform demonstrated similar relative patterns of up- and down-regulation across HPE and Simoa, though still significantly differed (ps < 0.001). Supporting concurrent validity, all 95% confidence intervals of the correlations between cytokines and external variables overlapped between the two platforms. Moreover, most associations were in expected directions and consistently so across platforms (e.g., IL-6 and TNFα), though with several notable exceptions for IP-10 and IL-10. CONCLUSIONS: HPE and Simoa showed comparable detectability and intra-assay precision measuring a panel of commonly examined cytokine proteins, with the exception of IL-1β which was not reliably detected on HPE. However, Simoa demonstrated overall higher concentrations and the two platforms did not show agreement when directly compared against one another. Relative cytokine ratios and associations demonstrated similar patterns across platforms. Absolute cytokine concentrations may not be directly comparable across platforms, may be analyte dependent, and interpretation may be best limited to discussion of relative associations

Association of Blood and Cerebrospinal Fluid Tau Level and Other Biomarkers With Survival Time in Sporadic Creutzfeldt-Jakob Disease

Importance: Fluid biomarkers that can predict survival time in sporadic Creutzfeldt-Jakob disease (sCJD) will be critical for clinical care and for treatment trials. Objective: To assess whether plasma and cerebrospinal fluid (CSF) biomarkers are associated with survival time in patients with sCJD. Design, Setting, and Participants: In this longitudinal cohort study, data from 193 patients with probable or definite sCJD who had codon 129 genotyping referred to a tertiary national referral service in the United States were collected from March 2004 to January 2018. Participants were evaluated until death or censored at the time of statistical analysis (range, 0.03-38.3 months). We fitted Cox proportional hazard models with time to event as the outcome. Fluid biomarkers were log-transformed, and models were run with and without nonfluid biomarkers of survival. Five patients were excluded because life-extending measures were performed. Main Outcomes and Measures: Biomarkers of survival included sex, age, codon 129 genotype, Barthel Index, Medical Research Council Prion Disease Rating Scale, 8 CSF biomarkers (total tau [t-tau] level, phosphorylated tau [p-tau] level, t-tau:p-tau ratio, neurofilament light [NfL] level, β-amyloid 42 level, neuron-specific enolase level, 14-3-3 test result, and real-time quaking-induced conversion test), and 3 plasma biomarkers (t-tau level, NfL level, and glial fibrillary acidic protein level). Results: Of the 188 included participants, 103 (54.8%) were male, and the mean (SD) age was 63.8 (9.2) years. Plasma t-tau levels (hazard ratio, 5.8; 95% CI, 2.3-14.8; P < .001) and CSF t-tau levels (hazard ratio, 1.6; 95% CI, 1.2-2.1; P < .001) were significantly associated with survival after controlling for codon 129 genotype and Barthel Index, which are also associated with survival time. Plasma and CSF t-tau levels were correlated (r = 0.74; 95% CI, 0.50-0.90; P < .001). Other fluid biomarkers associated with survival included plasma NfL levels, CSF NfL levels, t-tau:p-tau ratio, 14-3-3 test result, and neuron-specific enolase levels. In a restricted subset of 23 patients with data for all significant biomarkers, the hazard ratio for plasma t-tau level was more than 40% larger than any other biomarkers (hazard ratio, 3.4; 95% CI, 1.8-6.4). Conclusions and Relevance: Cerebrospinal fluid and plasma tau levels, along with several other fluid biomarkers, were significantly associated with survival time in patients with sCJD. The correlation between CSF and plasma t-tau levels and the association of plasma t-tau level with survival time suggest that plasma t-tau level may be a minimally invasive fluid biomarker in sCJD that could improve clinical trial stratification and guide clinical care