Hyperglycemia with or without insulin resistance triggers different structural changes in brain microcirculation and perivascular matrix.

Both type-1 and type-2 DM are related to an increased risk of cognitive impairment, neurovascular complications, and dementia. The primary triggers for complications are hyperglycemia and concomitant insulin resistance in type-2 DM. However, the diverse mechanisms in the pathogenesis of diabetes-related neurovascular complications and extracellular matrix (ECM) remodeling in type-1 and 2 have not been elucidated yet. Here, we investigated the high fat-high sucrose (HFHS) feeding model and streptozotocin-induced type-1 DM model to study the early effects of hyperglycemia with or without insulin resistance to demonstrate the brain microcirculatory changes, perivascular ECM alterations in histological sections and 3D-reconstructed cleared brain tissues. One of the main findings of this study was robust rarefaction in brain microvessels in both models. Interestingly, the HFHS model leads to widespread non-functional angiogenesis, but the type-1 DM model predominantly in the rostral brain. Rarefaction was accompanied by basement membrane thickening and perivascular collagen accumulation in type-1 DM; more severe blood-brain barrier leakage, and disruption of perivascular ECM organization, mainly of elastin and collagen fibers' structural integrity in the HFHS model. Our results point out that the downstream mechanisms of the long-term vascular complications of hyperglycemia models are structurally distinctive and may have implications for appropriate treatment options.

Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host-microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.