Modulation of glucose metabolism and metabolic connectivity by β-amyloid

Abstract

Glucose hypometabolism in the pre-clinical stage of Alzheimer's disease (AD) has been primarily associated with the APOE e4 genotype, rather than fibrillar b-amyloid. In contrast, aberrant patterns of metabolic connectivity are more strongly related to b-amyloid burden than APOE e4 status. A major limitation of previous studies has been the dichotomous classification of subjects as amyloid-positive or amyloid-negative. Dichotomous treatment of a continuous variable, such as b-amyloid, potentially obscures the true relationship with metabolism and reduces the power to detect significant changes in connectivity. In the present work, we assessed alterations of glucose metabolism and metabolic connectivity as continuous function of b-amyloid burden using positron emission tomography scans from the Alzheimer's Disease Neuroimaging Initiative study. Modeling b-amyloid as a continuous variable resulted in better model fits and improved power compared to the dichotomous model. Using this continuous model, we found that both APOE e4 genotype and b-amyloid burden are strongly associated with glucose hypometabolism at early stages of Alzheimer's disease. We also determined that the cumulative effects of b-amyloid deposition result in a particular pattern of altered metabolic connectivity, which is characterized by global, synchronized hypometabolism at early stages of the disease process, followed by regionally heterogeneous, progressive hypometabolism.

Spatially Distributed Amyloid-β Reduces Glucose Metabolism in Mild Cognitive Impairment

“…In agreement with our previous findings [21], our present results also suggest that the statistically significant association between A␤ deposition and glucose metabolism can only be detected across a heterogeneous population of subjects representing the continuous spectrum of the AD pathology, rather than in discrete segments of the AD progression trajectories represented by homogeneous subpopulations (e.g., CN, EMCI). As shown in Figure 2, statistically significant relationship between SVD-based A␤ and metabolism could be detected only in the LMCI subpopulation, perhaps due to the heterogeneity of the A␤ scores distribution across subjects (as shown in Fig.…”

“…The different spatial patterns observed in the first eigenimage corresponding to the SVD analysis across the different cognitive subpopulations ( Supplementary Figure 1) are in agreement with previous studies suggesting that global fibrillar A␤ load and cerebral glucose metabolism follow spatiotemporally-divergent [10,13,15,21] evolution paths across AD progression. Indeed, our analysis showed that the bilateral angular gyri are common (across the three subpopulations) regions of strong reduced metabolism as revealed by the first FDG eigenimage, while they only have strong contribution to the A␤ eigenimage in the CN group.…”

“…In contrast, Yi et al [19] found that, for CN subjects carrying the APOE 4 genotype, hypometabolism in AD-signature regions is primarily mediated by global cortical A␤ deposition. More general results suggest that, independently of the APOE 4 genotype, A␤ accumulation and hypometabolism are more likely to occur simultaneously through the spectrum of brain A␤ levels in a dose-dependent manner [20,21].…”

Gaussian Graphical Models Reveal Inter-Modal and Inter-Regional Conditional Dependencies of Brain Alterations in Alzheimer's Disease

“…We expected adjacent brain regions to form clusters with high inter-cluster similarity for amyloid-β deposition (Figure 3), as it is known to have low variability in spatial distribution and, therefore, is often used as a dichotomic variable after applying a certain threshold to the global amyloid tracer uptake [Chételat et al, 2013, Landau et al, 2013 or as four-stage variable derived from a linear spreading pattern [Grothe et al, 2017, Sakr et al, 2019. We also found such clustering patterns for metabolism ( Figure 4) and gray matter volume ( Figure 5), matching previous studies on metabolism and gray matter covariance networks based on Pearson correlation [Yao et al, 2010, Carbonell et al, 2016, Pereira et al, 2016 or principal component analysis [Di and Biswal, 2012, Spetsieris et al, 2015, Savio et al, 2017. Clusters of high covariance have been found in the lateral and medial parietal lobe, lateral frontal lobe, and lateral and medial temporal lobe, and had been associated with simultaneous growth during brain development, functional co-activation, and axonal connectivity in the literature [Gong et al, 2012, Alexander-Bloch et al, 2013.…”

A Potential Role for α-Amylase in Amyloid-β-Induced Astrocytic Glycogenolysis and Activation

“…Nevertheless, the hypothesis has gained attention within the research field of Alzheimer's disease (AD), a dementia disorder neuropathologically characterized foremost by the presence of amyloid-␤ (A␤) plaques and neurofibrillary tangles (NFT) [14]. Interestingly, the disorder is also characterized by an early impaired glucose utilization and hypometabolism [15][16][17], which is associated with the A␤ 42 plaque burden [18]. Moreover, patients with AD commonly show an increased load of large abnormal aggregates of polysaccharides in astrocytes in form of polyglucosan bodies (PGB) or corpora amylacea [19][20][21].…”