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A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

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 pro...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166533/
https://www.ncbi.nlm.nih.gov/pubmed/34633446
http://dx.doi.org/10.1093/brain/awab382
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author 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
author_facet 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
author_sort van der Ende, Emma L
collection PubMed
description 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.
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spelling pubmed-91665332022-06-06 A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia 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 Brain Original Article 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. Oxford University Press 2021-10-11 /pmc/articles/PMC9166533/ /pubmed/34633446 http://dx.doi.org/10.1093/brain/awab382 Text en © The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
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
A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
title A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
title_full A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
title_fullStr A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
title_full_unstemmed A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
title_short A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
title_sort data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166533/
https://www.ncbi.nlm.nih.gov/pubmed/34633446
http://dx.doi.org/10.1093/brain/awab382
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