β‐amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline

Abstract

Background Over the past several decades, treatment development for Alzheimer’s disease (AD) has been largely focused on modifying amyloid‐beta (Aβ), but no drugs that modify the pathophysiological processes underlying the disease have been FDA approved; it is therefore possible that Aβ may not be the optimal target for treating AD. The NIA‐AA consortium has proposed the use of amyloid, tau, and neurodegenerative (A/T/N) biomarkers in diagnosis and treatment of AD. However, it remains unclear whether each arm of the A/T/N framework has an equally weighted contribution to the progression of AD or rather a stage‐dependent importance to AD development. Methods Here we use random forest, a machine learning algorithm, in participants from the ADNI dataset (Table 1) to predict AD cognitive decline using integrated biomarkers from the A/T/N framework: Aβ‐PET, CSF‐pTau, and FDG‐PET and MRI‐Structural, respectively (Table 2). We chose random forest for its high prediction accuracy and interpretability. We also analyzed the relationship between A/T/N biomarkers and memory composite and executive functioning composite scores. Results We show that the A/T/N biomarkers have stage‐dependent importance to AD development, with Aβ and phosphorylated‐tau (pTau) better predicting early dementia status (i.e. mild cognitive impairment) and neurodegeneration, especially low glucose uptake, better predicting later dementia status (i.e. clinical AD) (Table 3). We show a similar pattern when correlating markers to performance on memory and executive functioning tests. Conclusions Our results provide evidence that AD treatments may need to be stage‐oriented to match the natural disease progression. (Figure 1) Aβ and tau may be appropriate targets early in the disease course, but brain metabolic restoration should be explored as a treatment target later on in the disease process.

BACE1: More than just a β‐secretase

“… 5 AD is the most common form of dementia and presents with several physiological changes in addition to the accumulation of Aβ‐42 into extracellular amyloid plaques, including neurofibrillary tangles (NFTs), chronic inflammation, synapse loss, neuronal death, and hypometabolism. 11 , 12 The accumulation Aβ‐plaques disrupts neuronal and synaptic functions leading to detrimental cognitive effects. 5 However, there is increasing evidence for non‐neuronal functions of Aβ and consequences of altered BACE1 activity.…”

Human Gray and White Matter Metabolomics to Differentiate APOE and Stage Dependent Changes in Alzheimer’s Disease

“…APOE is a lipid transport carrier with a direct impact on metabolism whose function is dependent on the structure of the protein variant (whether it is ε2, ε3, or ε4). Those carriers of the APOE ε4 allele have a two-to four-fold increased risk for developing AD [3,4]. AD progresses differently in the white matter than the gray matter.…”

Stage dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

“…While impaired glucose uptake in the ANG is consistently shown to be an important feature for predicting memory and executive functioning performance in the later stages of AD 63,64 , our present findings provide further insights into the early critical role of ANG-based metabolic covariance network for intact memory (i.e., earlier peak of beta) in the preclinical AD stage. The ANG, located in the posterior part of the inferior parietal lobule, is one of the major connector hubs that links different subsystems such as the DMN 20,65 that are affected by AD pathophysiology, and is involved in verbal working memory 66,67 and episodic memory retrieval 68 .…”