Empirical pathological staging and subtyping of TDP‐43 proteinopathies. (20th December 2022)

• Record Type: Journal Article
• Authors: Young, Alexandra L.; Vogel, Jacob W; Robinson, John; McMillan, Corey T; Ossenkoppele, Rik; Wolk, David A.; Irwin, David J.; Elman, Lauren; Grossman, Murray; Lee, Virginia M‐Y; Lee, Eddie B; Trojanowski, John Q; Hansson, Oskar
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.063390
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Pathological aggregation of tar DNA‐binding protein 43 (TDP‐43) in the brain is the primary cause of many cases of frontotemporal lobar degeneration (FTLD), amyotrophic lateral sclerosis (ALS) and limbic‐predominant age‐related TDP‐43 encephalopathy (LATE). It is therefore imperative to establish empirical staging systems to characterize and distinguish stereotypical patterns and commonplace deviations of different TDP‐43 proteinopathies. Method: We use ordinal ratings of TDP‐43 burden from 19 brain regions to perform data‐driven disease progression modeling (SuStaIn) to find the most likely trajectories for FTLD‐TDP (n = 108), ALS (n = 137) and LATE (n = 283) from the CNDR Brain Bank at the University of Pennsylvania. Subtype number was defined using cross‐validated information criterion. Each individual was assigned a subtype and stage. Multivariate OLS models tested differences between subtypes. Stages were compared to age and existing staging schemes. Cross‐validated logistic regression was used for 3‐way classification using SuStaIn information only. Result: SuStaIn provided data‐driven staging of TDP‐43 proteinopathies complementing previously described human‐defined staging schema, further providing additional detail (Fig1A‐C; Fig3A‐C). SuStaIn also identified two distinct subtypes within FTLD‐TDP and a further two within ALS (Fig1D). FTLD‐TDP subtypes differed in TDP‐43 type and Alzheimer's disease pathology (Table1); ALS subtypes were differentiated by age (Table 2) and by antemortem clinical characteristics. No subtypes were observed for the LATE group. Progression along data‐driven stages was positively associated with age in LATE individuals, but negatively associated with age in individuals with FTLD‐TDP (Fig2). Using only regional TDP‐43 severity, our data driven model could distinguish individuals diagnosed with ALS, FTD or LATE with a cross‐validated balanced precision of 0.93 and balanced recall of 0.92, and these metrics improved to 0.95 and 0.96 when combined with a logistic regression model (Fig3). Very little stage overlap was found between FTLD‐TDP and LATE, but stages that did overlap showed subtly different patterns (Fig4) Conclusion: We provide an empirical pathological staging system for ALS, FTLD‐TDP and LATE, which is sufficient for staging and accurate classification. We demonstrate that there is substantial heterogeneity amongst ALS and FTLD‐TDP progression patterns, whilst LATE exhibits a homogeneous progression pattern.