The limbic‐predominant hypometabolic subtype of AD is not enriched for comorbid TDP‐43 pathology or hippocampal sclerosis

Abstract

AbstractBackgroundPrevious research has identified distinct subtypes of AD that differed with respect to the distribution of neurofibrillary tangles (NFT). One of them, a limbic‐predominant subtype, was enriched for comorbid TDP‐43 pathology. Our previous data‐driven analysis used FDG‐PET to identify a limbic‐predominant subtype of AD that may correspond to the limbic‐predominant NFT subtype. In the current study, we tested the expected associations between measures of neuropathology such as TDP‐43 pathology, hippocampal sclerosis, and Braak stages of NFT accumulation, and the limbic‐predominant hypometabolic subtype.MethodWe analyzed ante‐mortem FDG‐PET and neuropathological data of 74 individuals from the autopsy cohort of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including 7 cognitively normal participants, 12 participants with MCI, and 55 with dementia (as assessed at the last available clinical evaluation). Severity of TDP‐43 pathology was operationalized as the number of brain areas with TDP‐43 immunoreactive inclusions (including amygdala, hippocampus, entorhinal/inferior temporal cortex and neocortex). Hippocampal sclerosis was operationalized as a binary variable. We measured similarity of individual FDG‐PET profiles to the limbic‐predominant subtype. For that, we used Euclidean distances from each individual profile to the mean profile of the limbic‐predominant AD subtype identified previously in a larger ADNI sample using a data‐driven hierarchical clustering approach (Fig.1). Lower Euclidean distances indicated higher similarity to the subtype. Bayesian linear regressions in JASP were used to assess associations between neuropathological markers and similarity to the limbic‐predominant FDG‐PET pattern, controlling for Thal phase of amyloid accumulation, age, sex, and time delay between the FDG‐PET scan and death in the null model. We assessed Bayes factors representing evidence for a given model in comparison to the null model (BF10).ResultOur results suggest that similarity of individual FDG‐PET profiles to the limbic‐predominant FDG‐PET pattern was not related to severity of TDP‐43 pathology (BF10 = 0.449), presence of hippocampal sclerosis (BF10 = 0.544), or Braak NFT stages (BF10 = 0.525).ConclusionContrary to the expected effects, our current analysis indicates a lack of reliable associations between neuropathological markers such as TDP‐43 pathology, hippocampal sclerosis, or global NFT accumulation and the limbic‐predominant hypometabolic subtype. Future analyses will assess potential alternative contributors, such as region‐specific patterns of NFT distribution.