Frontotemporal lobar degeneration

ObjectiveThe prognostic value of cerebrospinal fluid neurofilament light chain, total tau, phosphorylated tau181, and amyloid beta1‐42 was examined in frontotemporal dementia subtypes.MethodsWe compared baseline biomarkers between 49 controls, 40 patients with behavioral variant frontotemporal dementia, 24 with semantic variant primary progressive aphasia, and 26 with nonfluent variant primary progressive aphasia. Linear mixed effect models were used to assess the value of baseline biomarkers in predicting clinical and radiographic change in patient cohorts over multiple yearly follow up visits.ResultsNeurofilament light chain concentrations were lowest in controls. Elevated baseline neurofilament light chain predicted faster worsening in clinical severity, frontotemporal volume and frontotemporal fractional anisotropy in patients with behavioral variant frontotemporal dementia and nonfluent variant primary progressive aphasia. High total tau similarly predicted faster progression in nonfluent variant primary progressive aphasia. In behavioral variant frontotemporal dementia, higher phosphorylated tau181 predicted faster clinical progression whereas lower amyloid beta1‐42 predicted faster volumetric and fractional anisotropy reduction. Neurofilament light chain and phosphorylated tau181 were of greater predictive value in patients with tau pathology as compared to TDP‐43 pathology. Baseline neurofilament light chain correlated with baseline clinical severity and frontotemporal volume in behavioral variant frontotemporal dementia. Baseline total tau correlated with baseline clinical severity in semantic variant primary progressive aphasia.InterpretationHigh cerebrospinal fluid neurofilament light chain predicts more aggressive disease in behavioral variant frontotemporal dementia and nonfluent variant primary progressive aphasia. Total tau, phosphorylated tau181, and amyloid beta1‐42 also predict some measures of disease aggressiveness in frontotemporal dementia.

Section: Methodsmentioning

confidence: 99%

Cerebrospinal fluid biomarkers predict frontotemporal dementia trajectory

Ljubenkov

Staffaroni

Rojas

et al. 2018

“…All participants underwent standard neurological and cognitive assessments and diagnosis was assigned according to consensus criteria 18, 19, 20, 21, 22, 23, 24. The control group of the validation cohort were labeled during a multidisciplinary consensus meeting as SCD when they presented with subjective cognitive complaints, but objective cognitive and laboratory investigations (including AD CSF biomarkers) were normal and thus comparable to controls (CON).…”

Section: Methodsmentioning

confidence: 99%

Novel CSF biomarkers to discriminate FTLD and its pathological subtypes

Campo

Galimberti

Elias

et al. 2018

ObjectiveFrontotemporal lobar degeneration (FTLD) is the second most prevalent dementia in young patients and is characterized by the presence of two main protein aggregates in the brain, tau (FTLD‐Tau) or TDP43 (FTLD‐TDP), which likely require distinct pharmacological therapy. However, specific diagnosis of FTLD and its subtypes remains challenging due to largely overlapping clinical phenotypes. Here, we aimed to assess the clinical performance of novel cerebrospinal fluid (CSF) biomarkers for discrimination of FTLD and its pathological subtypes.Methods YKL40, FABP4, MFG‐E8, and the activities of catalase and specific lysosomal enzymes were analyzed in patients with FTLD‐TDP (n = 30), FTLD‐Tau (n = 20), AD (n = 30), DLB (n = 29), and nondemented controls (n = 29) obtained from two different centers. Models were validated in an independent CSF cohort (n = 188).Results YKL40 and catalase activity were increased in FTLD‐TDP cases compared to controls. YKL40 levels were also higher in FTLD‐TDP compared to FTLD‐Tau. We identified biomarker models able to discriminate FTLD from nondemented controls (MFG‐E8, tTau, and Aβ 42; 78% sensitivity and 83% specificity) and non‐FTLD dementia (YKL40, pTau, p/tTau ratio, and age; 90% sensitivity, 78% specificity), which were validated in an independent cohort. In addition, we identified a biomarker model differentiating FTLD‐TDP from FTLD‐Tau (YKL40, MFGE‐8, βHexA together with βHexA/tHex and p/tTau ratios and age) with 80% sensitivity and 82% specificity.InterpretationThis study identifies CSF protein signatures distinguishing FTLD and the two main pathological subtypes with optimal accuracy (specificity/sensitivity > 80%). Validation of these models may allow appropriate selection of cases for clinical trials targeting the accumulation of Tau or TDP43, thereby increasing their efficiency and facilitating the development of successful therapies.

“…Participants were consecutively recruited from large studies of FTLD and related disorders (P01‐AG019724, P50‐AG023501, R01‐AG032306, R01‐AG038791). Patients were diagnosed using published criteria28, 29 after a comprehensive evaluation at the UCSF Memory and Aging Center including neurological history and examination, nursing assessment, laboratory evaluation, and a previously described neuropsychological assessment of memory, executive function, language, and mood 30. The majority of bvFTD patients were diagnosed using the consensus criteria published in 1998, which include imaging as a potentially supportive feature but do not require imaging findings for diagnosis.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic utility of ASL‐MRI and FDG‐PET in the behavioral variant of FTD and AD

Tosun

Schuff

Rabinovici

et al. 2016

ObjectiveTo compare the values of arterial spin‐labeled (ASL) MRI and fluorodeoxyglucose (FDG) PET in the diagnosis of behavioral variant of frontotemporal dementia (bvFTD) and Alzheimer's disease (AD).MethodsPartial least squares logistic regression was used to identify voxels with diagnostic value in cerebral blood flow (CBF) and cerebral metabolic rate of glucose (CMRgl) maps from patients with bvFTD (n = 32) and AD (n = 28), who were compared with each other and with cognitively normal controls (CN, n = 15). Diagnostic values of these maps were compared with each other.ResultsRegions that differentiated each disorder from controls were similar for CBF and CMRgl. For differentiating AD from CN, the areas under the curve (AUC) for CBF (0.89) and CMRgl (0.91) were similar, with similar sensitivity (CBF: 86%, CMRgl: 78%) and specificity (CBF: 92%, CMRgl: 100%). Likewise, for differentiating bvFTD from CN performances of CBF (AUC = 0.83) and CMRgl (AUC = 0.85) were equivalent, with similar sensitivity (CBF: 78%, CMRgl: 79%) and specificity (CBF: 92%, CMRgl: 100%). In differentiating bvFTD from AD, classification was again similar for CBF (AUC = 0.87) and CMRgl (AUC = 0.79), as were sensitivity (CBF: 83%, CMRgl: 89%) and specificity (CBF: 93%, CMRgl: 78%). None of the differences in any performance measure were statistically significant.InterpretationASL‐MRI has similar diagnostic utility as FDG‐PET in the diagnosis of AD and bvFTD. Continued development of ASL‐MRI as a diagnostic tool for neurodegenerative dementias is warranted.