Obesity is considered one of the greatest risk to human health and is associated with several factors including genetic components, diet, and physical inactivity. Recently, the relationship between obesity and numerous progressive and aging-related neurodegenerative diseases such as Parkinson's disease (PD) and Alzheimer's disease (AD) have been observed. Thus, the involvement of the most abundant and heterogeneous group of glial cells in neurodegenerative diseases, the astrocytes, is caused by a combination of the failure on their normal homeostatic functions and the increase of toxic metabolites upon pathological event. Upon brain damage, molecular signals induce astrocyte activation and migration to the site of injury, entering in a highly active state, with the aim to contribute to ameliorating or worsening the pathology. In this regard, the aim of this review is to elucidate the relationship between obesity, Alzheimer's disease, and Parkinson's disease and highlight the role of astrocytes in these pathologies.

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Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.

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