Evidence map›Paper›PMID 37150879›Full record

ArticleBrain : a journal of neurology2023

Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies.

Alexandra L Young, Jacob W Vogel, John L Robinson, Corey T McMillan, Rik Ossenkoppele, David A Wolk, David J Irwin, Lauren Elman, Murray Grossman, Virginia M Y Lee and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
13.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 64 citations in OpenAlex.

  1. Review
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  7. Biological classification of memory clinic patients.Brain : a journal of neurology · 2026
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  10. Reframing TDP-43 in Alzheimer's disease: From co-pathology to neurovascular culprit.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
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  15. Development of [Nature communications · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 4 countries.

Alexandra L YoungDepartment of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK.ORCID 0000-0002-7772-781X
Jacob W VogelDepartment of Clinical Sciences, SciLifeLab, Lund University, SE-222 42 Lund, Sweden.ORCID 0000-0001-6394-9940
John L RobinsonPenn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
Corey T McMillanPenn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Rik OssenkoppeleClinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden.
David A WolkDepartment of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
David J IrwinPenn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-5599-5098
Lauren ElmanDepartment of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
Murray GrossmanPenn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Virginia M Y LeePenn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
Edward B LeePenn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.ORCID 0000-0002-4589-1180
Oskar HanssonClinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden.ORCID 0000-0001-8467-7286
University of Pennsylvania · USLund University · SEKing's College London · GB

Funding

Research Education ComponentP30AG072979 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVID A WOLK · 2021 to 2026
$24.8M
Understanding Environmental Contributions to Heterogeneity in bvFTD.P01AG066597 · NIA · UNIVERSITY OF PENNSYLVANIA · PI David John Irwin, Corey T McMillan · 2020 to 2026
$18.8M
Project IV "Tackling Heterogeneity of Cognitive Trajectory in LBD"U19AG062418 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LEE, VIRGINIA M · 2019 to 2023
$18.1M
SCHIZOPHRENIA: A NEUROPSYCHIATRIC PERSPECTIVET32MH019112 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RAQUEL E · 1990 to 2024
$6.2M
Microscopic and Large-Scale Networks of Molecular Pathology in Frontotemporal Lobar DegenerationR01NS109260 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI IRWIN, DAVID JOHN · 2019 to 2023
$3.8M
Medical Research Council MR/T027800/1NIA NIH HHS P01 AG066597NIA NIH HHS P30 AG072979NIA NIH HHS U19 AG062418NIMH NIH HHS T32 MH019112NINDS NIH HHS R01 NS109260
6 · The paper itself

Abstract

TAR DNA-binding protein-43 (TDP-43) accumulation is the primary pathology underlying several neurodegenerative diseases. Charting the progression and heterogeneity of TDP-43 accumulation is necessary to better characterize TDP-43 proteinopathies, but current TDP-43 staging systems are heuristic and assume each syndrome is homogeneous. Here, we use data-driven disease progression modelling to derive a fine-grained empirical staging system for the classification and differentiation of frontotemporal lobar degeneration due to TDP-43 (FTLD-TDP, n = 126), amyotrophic lateral sclerosis (ALS, n = 141) and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) with and without Alzheimer's disease (n = 304). The data-driven staging of ALS and FTLD-TDP complement and extend previously described human-defined staging schema for ALS and behavioural variant frontotemporal dementia. In LATE-NC individuals, progression along data-driven stages was positively associated with age, but negatively associated with age in individuals with FTLD-TDP. Using only regional TDP-43 severity, our data driven model distinguished individuals diagnosed with ALS, FTLD-TDP or LATE-NC with a cross-validated accuracy of 85.9%, with misclassifications associated with mixed pathological diagnosis, age and genetic mutations. Adding age and SuStaIn stage to this model increased accuracy to 92.3%. Our model differentiates LATE-NC from FTLD-TDP, though some overlap was observed between late-stage LATE-NC and early-stage FTLD-TDP. We further tested for the presence of subtypes with distinct regional TDP-43 progression patterns within each diagnostic group, identifying two distinct cortical-predominant and brainstem-predominant subtypes within FTLD-TDP and a further two subcortical-predominant and corticolimbic-predominant subtypes within ALS. The FTLD-TDP subtypes exhibited differing proportions of TDP-43 type, while there was a trend for age differing between ALS subtypes. Interestingly, a negative relationship between age and SuStaIn stage was seen in the brainstem/subcortical-predominant subtype of each proteinopathy. No subtypes were observed for the LATE-NC group, despite aggregating individuals with and without Alzheimer's disease and a larger sample size for this group. Overall, we provide an empirical pathological TDP-43 staging system for ALS, FTLD-TDP and LATE-NC, which yielded accurate classification. We further demonstrate that there is substantial heterogeneity amongst ALS and FTLD-TDP progression patterns that warrants further investigation in larger cross-cohort studies.

Indexed as

Alzheimer DiseaseAmyotrophic Lateral SclerosisFrontotemporal DementiaFrontotemporal Lobar DegenerationTDP-43 ProteinopathiesDementiaDNA-Binding ProteinsHumansDNA-Binding Proteinsamyotrophic lateral sclerosisfrontotemporal lobar degenerationlimbic-predominant age-related TDP-43 encephalopathymachine learningneuropathological staging

Identifiers

PMID37150879
PMCPMC10317181
OpenAlexW4375856581

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.