Emma L. Van Der Ende, Erasmus MC
Esther E. Bron, Erasmus MC
Jackie M. Poos, Erasmus MC
Lize C. Jiskoot, Erasmus MC
Jessica L. Panman, Erasmus MC
Janne M. Papma, Erasmus MC
Lieke H. Meeter, Erasmus MC
Elise G.P. Dopper, Erasmus MC
Carlo Wilke, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V.
Matthis Synofzik, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V.
Carolin Heller, UCL Queen Square Institute of Neurology
Imogen J. Swift, UCL Queen Square Institute of Neurology
Aitana Sogorb-Esteve, UCL Queen Square Institute of Neurology
Arabella Bouzigues, UCL Queen Square Institute of Neurology
Barbara Borroni, Università degli Studi di Brescia
Raquel Sanchez-Valle, Universitat de Barcelona
Fermin Moreno, Osakidetza, Donostia University Hospital
Caroline Graff, Karolinska Institutet
Robert Laforce, CHU de Québec - Université Laval
Daniela Galimberti, Università degli Studi di Milano
Mario Masellis, University of Toronto
Maria Carmela Tartaglia, Tanz Centre for Research in Neurodegenerative Diseases
Elizabeth Finger, Western UniversityFollow
Rik Vandenberghe, Departement Neurowetenschappen
James B. Rowe, University of Cambridge
Alexandre De Mendoncą, Faculdade de Medicina, Universidade de Lisboa
Fabrizio Tagliavini, Foundation IRCCS Neurological Institute "C. Besta"
Isabel Santana, University of Coimbra, Center for Neuroscience and Cell Biology
Simon Ducharme, McConnell Brain Imaging Centre
Christopher R. Butler, University of Oxford Medical Sciences Division

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Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.