A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
Authors: van der Ende Emma L , Bron Esther E , Poos Jackie M , Jiskoot Lize C , Panman Jessica L , Papma Janne M , Meeter Lieke H , Dopper Elise G P , Wilke Carlo , Synofzik Matthis , Heller Carolin , Swift Imogen J , Sogorb-Esteve Aitana , Bouzigues Arabella , Borroni Barbara , Sanchez-Valle Raquel , Moreno Fermin , Graff Caroline , Laforce Robert , Galimberti Daniela , Masellis Mario , Tartaglia Maria Carmela , Finger Elizabeth , Vandenberghe Rik , Rowe James B , de Mendonça Alexandre , Tagliavini Fabrizio , Santana Isabel , Ducharme Simon , Butler Christopher R , Gerhard Alexander , Levin Johannes , Danek Adrian , Otto Markus , Pijnenburg Yolande A L , Sorbi Sandro , Zetterberg Henrik , Niessen Wiro J , Rohrer Jonathan D , Klein Stefan , van Swieten John C , Venkatraghavan Vikram , Seelaar Harro ,   GENFI consortium, Afonso Sónia , Almeida Maria Rosario , Anderl-Straub Sarah , Andersson Christin , Antonell Anna , Archetti Silvana , Arighi Andrea , Balasa Mircea , Barandiaran Myriam , Bargalló Nuria , Bartha Robart , Bender Benjamin , Benussi Alberto , Benussi Luisa , Bessi Valentina , Binetti Giuliano , Black Sandra , Bocchetta Martina , Borrego-Ecija Sergi , Bras Jose , Bruffaerts Rose , Cañada Marta , Cantoni Valentina , Caroppo Paola , Cash David , Castelo-Branco Miguel , Convery Rhian , Cope Thomas , Di Fede Giuseppe , Díez Alina , Duro Diana , Fenoglio Chiara , Ferrari Camilla , Ferreira Catarina B , Fox Nick , Freedman Morris , Fumagalli Giorgio , Gabilondo Alazne , Gasparotti Roberto , Gauthier Serge , Gazzina Stefano , Giaccone Giorgio , Gorostidi Ana , Greaves Caroline , Guerreiro Rita , Hoegen Tobias , Indakoetxea Begoña , Jelic Vesna , Karnath Hans-Otto , Keren Ron , Langheinrich Tobias , Leitão Maria João , Lladó Albert , Lombardi Gemma , Loosli Sandra , Maruta Carolina , Mead Simon , Miltenberger Gabriel , van Minkelen Rick , Mitchell Sara , Moore Katrina , Nacmias Benedetta , Nicholas Jennifer , Öijerstedt Linn , Olives Jaume , Ourselin Sebastien , Padovani Alessandro , Peakman Georgia , Pievani Michela , Pijnenburg Yolande , Polito Cristina , Premi Enrico , Prioni Sara , Prix Catharina , Rademakers Rosa , Redaelli Veronica , Rittman Tim , Rogaeva Ekaterina , Rosa-Neto Pedro , Rossi Giacomina , Rosser Martin , Santiago Beatriz , Scarpini Elio , Schönecker Sonja , Semler Elisa , Shafei Rachelle , Shoesmith Christen , Tábuas-Pereira Miguel , Tainta Mikel , Taipa Ricardo , Tang-Wai David , Thomas David L , Thompson Paul , Thonberg Hakan , Timberlake Carolyn , Tiraboschi Pietro , Todd Emily , Van Damme Philip , Vandenbulcke Mathieu , Veldsman Michele , Verdelho Ana , Villanua Jorge , Warren Jason , Woollacott Ione , Wlasich Elisabeth , Zulaica Miren 
Published: October 2021

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.