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

Diagnostic value of plasma phosphorylated tau181 in Alzheimer's disease and frontotemporal lobar degeneration

With the potential development of new disease-modifying Alzheimer’s disease (AD) therapies, simple, widely available screening tests are needed to identify which individuals, who are experiencing symptoms of cognitive or behavioral decline, should be further evaluated for initiation of treatment. A blood-based test for AD would be a less invasive and less expensive screening tool than the currently approved cerebrospinal fluid or amyloid β positron emission tomography (PET) diagnostic tests. We examined whether plasma tau phosphorylated at residue 181 (pTau181) could differentiate between clinically diagnosed or autopsy-confirmed AD and frontotemporal lobar degeneration. Plasma pTau181 concentrations were increased by 3.5-fold in AD compared to controls and differentiated AD from both clinically diagnosed (receiver operating characteristic area under the curve of 0.894) and autopsy-confirmed frontotemporal lobar degeneration (area under the curve of 0.878). Plasma pTau181 identified individuals who were amyloid β-PET-positive regardless of clinical diagnosis and correlated with cortical tau protein deposition measured by 18F-flortaucipir PET. Plasma pTau181 may be useful to screen for tau pathology associated with AD

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e − 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e − 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e − 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis

A statistical framework for cross-tissue transcriptome-wide association analysis

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene–trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies