Background Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma... Show moreBackground Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma biomarkers are strongly needed for specific diagnosis and potential treatment monitoring of FTD. We aimed to identify specific FTD plasma biomarker profiles discriminating FTD from AD and controls, and between FTD pathological subtypes. In addition, we compared plasma results with results in post-mortem frontal cortex of FTD cases to understand the underlying process. Methods Plasma proteins (n = 1303) from pathologically and/or genetically confirmed FTD patients (n = 56; FTLD-Tau n = 16; age = 58.2 +/- 6.2; 44% female, FTLD-TDP n = 40; age = 59.8 +/- 7.9; 45% female), AD patients (n = 57; age = 65.5 +/- 8.0; 39% female), and non-demented controls (n = 148; 61.3 +/- 7.9; 41% female) were measured using an aptamer-based proteomic technology (SomaScan). In addition, exploratory analysis in post-mortem frontal brain cortex of FTD (n = 10; FTLD-Tau n = 5; age = 56.2 +/- 6.9, 60% female, and FTLD-TDP n = 5; age = 64.0 +/- 7.7, 60% female) and non-demented controls (n = 4; age = 61.3 +/- 8.1; 75% female) were also performed. Differentially regulated plasma and tissue proteins were identified by global testing adjusting for demographic variables and multiple testing. Logistic lasso regression was used to identify plasma protein panels discriminating FTD from non-demented controls and AD, or FTLD-Tau from FTLD-TDP. Performance of the discriminatory plasma protein panels was based on predictions obtained from bootstrapping with 1000 resampled analysis. Results Overall plasma protein expression profiles differed between FTD, AD and controls (6 proteins; p = 0.005), but none of the plasma proteins was specifically associated to FTD. The overall tissue protein expression profile differed between FTD and controls (7-proteins; p = 0.003). There was no difference in overall plasma or tissue expression profile between FTD subtypes. Regression analysis revealed a panel of 12-plasma proteins discriminating FTD from AD with high accuracy (AUC: 0.99). No plasma protein panels discriminating FTD from controls or FTD pathological subtypes were identified. Conclusions We identified a promising plasma protein panel as a minimally-invasive tool to aid in the differential diagnosis of FTD from AD, which was primarily associated to AD pathophysiology. The lack of plasma profiles specifically associated to FTD or its pathological subtypes might be explained by FTD heterogeneity, calling for FTD studies using large and well-characterize cohorts. Show less
Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease characterised by the progressive degeneration of the frontal and temporal lobes, which results in behavioural (behavioural... Show moreFrontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease characterised by the progressive degeneration of the frontal and temporal lobes, which results in behavioural (behavioural variant FTD) and language (primary progressive aphasia) disorders. No effective therapies currently exist to cure FTD or slow disease progression. However, efforts are being made to develop disease modifying treatments, which aim to reverse or inhibit pathological processes leading up to neuronal cell death. Therefore, the ability to diagnose FTD before brain atrophy (i.e., irreversible brain damage) is crucial. Approximately 10–30% of all FTD patients have a familial form, often caused by mutations in the genes MAPT, GRN or a repeat expansion in the gene C9orf72. These families offer the unique opportunity to study mutation carriers in the presymptomatic stage, where early pathological changes may already occur, but subjects are cognitively healthy. In this dissertation, we used multimodal MRI and machine learning to investigate whether MRI biomarkers for FTD have diagnostic value on the single-subject level to detect FTD-related differences in the presymptomatic disease stage. Furthermore, we aimed to advance the combination of resting-state functional MRI data between scanners. Lastly, we studied potential biomarkers for the differentiation between early stages of FTD and Alzheimer’s disease. Show less
