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

Abstract

Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ and tau better predicting early dementia status, and biomarkers of neurodegeneration, especially glucose hypometabolism, better predicting later dementia status. Our results suggest that AD treatments may also need to be disease stage-oriented with Aβ and tau as targets in early AD and glucose metabolism as a target in later AD.

Apolipoprotein E genotype-dependent nutrigenetic effects to prebiotic inulin for modulating systemic metabolism and neuroprotection in mice via gut-brain axis

“…Though we studied the mice at pre-symptomatic stage, our study implies that inulin may reduce risk for developing AD for both APOE3 and APOE4 carriers. Emerging evidence further shows that AD is associated with brain metabolic impairment [44], gut microbiota dysbiosis [45], and bile acid profile alterations [46]. Further, tryptophan metabolism is also seen to be altered in patients with AD [47], which impedes the capacity to inhibit Aβ fibril formation in neurons and neuroblastoma cells [27].…”

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

“…Hammond et al. revealed different roles of Aβ, tau, and neurodegeneration in Alzheimer's disease development by using the random forest machine learning method [19] . In the selected datasets, machine learning methods are capable of identifying statistically significant patterns that could provide predictions with good accuracy.…”

Predicting Progression & Cognitive Decline in Amyloid-Positive Patients with Alzheimer's Disease

“…However, individuals with significant levels of Aβ plaques do not necessarily exhibit any cognitive impairments [13,14]. Therefore, it can be concluded that Aβ pathology alone is not sufficient as a predictor of disease progression [15,16].…”

“…In this context, most ML models focused on binary classification methods predicting with high accuracy whether individuals diagnosed with MCI will decline or remain stable in their diagnosis [27]. The classification techniques such as random forest [15,28,29,25], logistic regression (LR) [30,31,32,25] and support vector machines (SVM) [33,29] have been used. Most previous AD studies based on ML focus on predicting conversion between the CN, MCI and AD diagnosis [20,34] based on diagnosis classification using various neuroimaging tech-niques such as magnetic resonance imaging (MRI).…”