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DT‐02‐02: TOMMORROW: RESULTS FROM A PHASE 3 TRIAL TO DELAY THE ONSET OF MCI DUE TO AD AND QUALIFY A GENETIC BIOMARKER ALGORITHM
Author(s) -
Alexander Robert,
Burns Daniel K.,
Welsh-Bohmer Kathleen A.,
Burke James R.,
Chiang Carl,
Culp Meredith,
Plassman Brenda L.,
Wu Jingtao,
Lutz Michael W.,
Rubens Robert,
Evans Rebecca,
Saunders Ann M.,
Ratti Emilianelo
Publication year - 2019
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.08.011
Subject(s) - placebo , medicine , clinical endpoint , population , biomarker , randomized controlled trial , incidence (geometry) , pioglitazone , adverse effect , clinical trial , diabetes mellitus , type 2 diabetes , pathology , endocrinology , biochemistry , chemistry , physics , alternative medicine , environmental health , optics
with Alzheimer’s disease (AD). At present, TDP-43-positive status can only be determined at post-mortem. Given the strong associations between amnestic dementia, hippocampal atrophy, and TDP-43 (Josephs KA, 2008), ante-mortem diagnostic methods should be explored. Methods: We conducted a cross-sectional neuroimaging-histological analysis of ante-mortem FDG-PET and post-mortem brain tissue from an autopsy cohort of 756 participants with an AD spectrum pathological diagnosis from Mayo Clinic Alzheimer Disease Researcher Center or Study of Aging brain bank. TDP-43-positive (TDP-43(+)) status was assigned when TDP-43 immunoreactive inclusions were identified in amygdala (stage 1). Statistical Parametric Mapping analyses comparing TDP-43(+) to TDP-43(-) cases, accounting for gender, field strength, age at death, Braak neurofibrillary tangle (NFT) and CERAD neuritic plaque stages, were performed to determine differences in FDG-PET patterns. Multivariate logistic regression analysis was explored to determine how regional FDG-PET values predict TDP-43(+) status. We specifically assessed the ratio of inferior temporal (IT) to medial temporal (MT) hypometabolism as this has been proposed as a biomarker of hippocampal sclerosis (Botha H, 2018). Results: Of 74 cases with ante-mortem FDG-PET, 27 (36%) were TDP-43(+). Six cases (8%) with hippocampal sclerosis (all TDP-43(+)) were identified. Voxel-level analysis showed TDP43(+) cases having greater hypometabolism of MT, frontal superior medial (Fsm) and supraorbital (Fso) regions, compared to TDP43(-) cases. Multivariate regression modelling including Fso, Fsm, and IT/MTas covariates showed only Fso and IT/MTas being strongly associated with TDP-43(+) status. Using coefficients, we generated a formula for TDP-43 status prediction: TDP-43(+)1⁄4 9.21*(IT/MT)–10.44*Fso + 2.436. The formula allowed for identification of 82% of TDP-43(+) cases, (p<0.0001). A ratio of IT/ (MT*Fso) also showed good performance, separating 78% of TDP-43(+) cases (p<0.0001), outperforming the IT/MT ratio which only separated 48% (p1⁄40.0189). Conclusions: Alzheimer’srelated TDP-43-proteinopathy (ARTP) is associated with hypometabolism in medial temporal, Fsm and Fso regions. Given the very good separation of TDP-43(+) from TDP-43(-) cases, combining FDG-PET measures from medial temporal, inferior temporal and frontal regions may provide for a cut-point that is predictive of ARTP.