%0 Journal Article %J J Alzheimers Dis %D 2021 %T The Right Temporal Variant of Frontotemporal Dementia Is Not Genetically Sporadic: A Case Series. %A Ulugut Erkoyun, Hulya %A van der Lee, Sven J %A Nijmeijer, Bas %A van Spaendonk, Rosalina %A Nelissen, Anne %A Scarioni, Marta %A Dijkstra, Anke %A Samancı, Bedia %A Gürvit, Hakan %A Yıldırım, Zerrin %A Tepgeç, Fatih %A Bilgic, Basar %A Barkhof, Frederik %A Rozemuller, Annemieke %A van der Flier, Wiesje M %A Scheltens, Philip %A Cohn-Hokke, Petra %A Pijnenburg, Yolande %K Aphasia, Primary Progressive %K DNA-Binding Proteins %K Female %K Frontotemporal Dementia %K Functional Laterality %K Genetic Testing %K Genetic Variation %K Gyrus Cinguli %K Humans %K Male %K Middle Aged %K Progranulins %K tau Proteins %X

BACKGROUND: Right temporal variant frontotemporal dementia (rtvFTD) has been generally considered as a right sided variant of semantic variant primary progressive aphasia (svPPA), which is a genetically sporadic disorder. Recently, we have shown that rtvFTD has a unique clinical syndrome compared to svPPA and behavioral variant frontotemporal dementia.

OBJECTIVE: We challenge the assumption that rtvFTD is a sporadic, non-familial variant of FTD by identifying potential autosomal dominant inheritance and related genes in rtvFTD.

METHODS: We collected all subjects with a diagnosis of FTD or primary progressive aphasia who had undergone genetic screening (n = 284) and subsequently who had a genetic variant (n = 48) with a diagnosis of rtvFTD (n = 6) in 2 specialized memory clinics.

RESULTS: Genetic variants in FTD related genes were found in 33% of genetically screened rtvFTD cases; including MAPT (n = 4), GRN (n = 1), and TARDBP (n = 1) genes, whereas only one svPPA case had a genetic variant in our combined cohorts. Additionally, 4 out of 6 rtvFTD subjects had a strong family history for dementia.

CONCLUSION: Our results demonstrate that rtvFTD, unlike svPPA, is not a pure sporadic, but a heterogeneous potential genetic variant of FTD, and screening for genetic causes for FTD should be performed in patients with rtvFTD.

%B J Alzheimers Dis %V 79 %P 1195-1201 %8 2021 %G eng %N 3 %1 https://www.ncbi.nlm.nih.gov/pubmed/33427744?dopt=Abstract %R 10.3233/JAD-201191 %0 Journal Article %J J Alzheimers Dis %D 2016 %T Integrating Biomarkers for Underlying Alzheimer's Disease in Mild Cognitive Impairment in Daily Practice: Comparison of a Clinical Decision Support System with Individual Biomarkers. %A Rhodius-Meester, Hanneke F M %A Koikkalainen, Juha %A Mattila, Jussi %A Teunissen, Charlotte E %A Barkhof, Frederik %A Lemstra, Afina W %A Scheltens, Philip %A Lötjönen, Jyrki %A van der Flier, Wiesje M %K Aged %K Aged, 80 and over %K Algorithms %K Alzheimer Disease %K Area Under Curve %K Biomarkers %K Cognitive Dysfunction %K Cohort Studies %K Decision Support Systems, Clinical %K Disease Progression %K Female %K Humans %K Image Processing, Computer-Assisted %K Magnetic Resonance Imaging %K Male %K Mental Status Schedule %K Middle Aged %K Neuropsychological Tests %K Outcome Assessment (Health Care) %K Predictive Value of Tests %X

BACKGROUND: Recent criteria allow biomarkers to provide evidence of Alzheimer's disease (AD) pathophysiology. How they should be implemented in daily practice remains unclear, especially in mild cognitive impairment (MCI) patients.

OBJECTIVE: We evaluated how a clinical decision support system such as the PredictAD tool can aid clinicians to integrate biomarker evidence to support AD diagnosis.

METHODS: With available data on demographics, cerebrospinal fluid (CSF), and MRI, we trained the PredictAD tool on a reference population of 246 controls and 491 AD patients. We then applied the identified algorithm to 211 MCI patients. For comparison, we also classified patients based on individual biomarkers (MRI; CSF) and the NIA-AA criteria. Progression to dementia was used as outcome measure.

RESULTS: After a median follow up of 3 years, 72 (34%) MCI patients remained stable and 139 (66%) progressed to AD. The PredictAD tool assigned a likelihood of underlying AD to each patient (AUC 0.82). Excluding patients with missing data resulted in an AUC of 0.87. According to the NIA-AA criteria, half of the MCI patients had uninformative biomarkers, precluding an assignment of AD likelihood. A minority (41%) was assigned to high or low AD likelihood with good predictive value. The individual biomarkers showed best value for CSF total tau (AUC 0.86).

CONCLUSION: The ability of the PredictAD tool to identify AD pathophysiology was comparable to individual biomarkers. The PredictAD tool has the advantage that it assigns likelihood to all patients, regardless of missing or conflicting data, allowing clinicians to integrate biomarker data in daily practice.

%B J Alzheimers Dis %V 50 %P 261-70 %8 2016 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/26577521?dopt=Abstract %R 10.3233/JAD-150548