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

Author:

Young Alexandra L12ORCID,Vogel Jacob W34ORCID,Robinson John L56,McMillan Corey T78,Ossenkoppele Rik4910,Wolk David A8,Irwin David J7811ORCID,Elman Lauren8,Grossman Murray78,Lee Virginia M Y56,Lee Edward B56ORCID,Hansson Oskar412ORCID

Affiliation:

1. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London , London, SE5 8AF , UK

2. Centre for Medical Image Computing, Department of Computer Science, University College London , London, WC1V 6LJ , UK

3. Department of Clinical Sciences, SciLifeLab, Lund University , SE-222 42 Lund , Sweden

4. Clinical Memory Research Unit, Lund University , SE-222 42 Lund , Sweden

5. Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine , Philadelphia, PA 19104 , USA

6. Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine , Philadelphia, PA 19104 , USA

7. Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 , USA

8. Department of Neurology, University of Pennsylvania School of Medicine , Philadelphia, PA 19104 , USA

9. Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc , 1081 HZ Amsterdam , The Netherlands

10. Amsterdam Neuroscience, Neurodegeneration , 1081 HV Amsterdam , The Netherlands

11. Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 , USA

12. Memory Clinic, Skåne University Hospital , SE-205 02 Malmö , Sweden

Abstract

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.

Funder

MRC Skills Development Fellowship

NIH

SciLifeLab & Wallenberg Data Driven Life Science Program

Penn Udall Center of Excellence in Parkinson's Disease Research

Penn Center for Neurodegenerative Disease Research

Department of Pathology and Laboratory Medicine of the Penn Alzheimer’s Disease Research Center

Swedish Research Council

Knut and Alice Wallenberg foundation

Marianne and Marcus Wallenberg foundation

Strategic Research Area MultiPark

Swedish Alzheimer Foundation

Swedish Brain Foundation

The Parkinson foundation of Sweden

Cure Alzheimer’s fund

Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse

Skåne University Hospital Foundation

Regionalt Forskningsstöd

Swedish federal government

Publisher

Oxford University Press (OUP)

Subject

Neurology (clinical)

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