Through the past decade of research, the correlation between depression and metabolic diseases has been noticed. More and more studies have confirmed that depression is comorbid with a variety of metabolic diseases, such as obesity, diabetes, metabolic syndrome and so on. Studies showed that the underlying mechanisms of both depression and metabolic diseases include chronic inflammatory state, which is significantly related to the severity. In addition, they also involve endocrine, immune systems. At present, the effects of clinical treatments of depression is limited. Therefore, exploring the co-disease mechanism of depression and metabolic diseases is helpful to find a new clinical therapeutic intervention strategy. Herein, focusing on the relationship between depression and metabolic diseases, this manuscript aims to provide an overview of the comorbidity of depression and metabolic.
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http://dx.doi.org/10.3389/fnins.2021.728810 | DOI Listing |
Cardiol Rev
January 2025
Ghulam Muhammad Mahar Medical College, Sukkur, Pakistan.
Obstructive sleep apnea (OSA), a highly prevalent and serious disorder with significant complications, causes considerable daytime and nighttime symptoms as well as long-term consequences and is yet an underdiagnosed and inadequately treated condition. Patients with OSA undergo frequent awakenings during the sleep cycle and find it impossible to get restorative sleep. Individuals are extremely fatigued, sleepy, and irritable throughout the day.
View Article and Find Full Text PDFQ Rev Biophys
January 2025
Faculty of Medicine, Department of Biophysics and Neuroscience, Wroclaw Medical University, Wrocław, Poland.
The GABA type A receptor (GABAR) belongs to the family of pentameric ligand-gated ion channels and plays a key role in inhibition in adult mammalian brains. Dysfunction of this macromolecule may lead to epilepsy, anxiety disorders, autism, depression, and schizophrenia. GABAR is also a target for multiple physiologically and clinically relevant modulators, such as benzodiazepines (BDZs), general anesthetics, and neurosteroids.
View Article and Find Full Text PDFGenet Sel Evol
January 2025
GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet-Tolosan, France.
Background: The magnitude of inbreeding depression depends on the recessive burden of the individual, which can be traced back to the hidden (recessive) inbreeding load among ancestors. However, these ancestors carry different alleles at potentially deleterious loci and therefore there is individual variability of this inbreeding load. Estimation of the additive genetic value for inbreeding load is possible using a decomposition of inbreeding in partial inbreeding components due to ancestors.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Histology and Embryology, Ankara University School of Medicine, Ankara, Turkey.
NMDAR antagonists, such as memantine and ketamine, have shown efficacy in treating neurodegenerative diseases and major depression. The mechanism by which these drugs correct the aforementioned diseases is still unknown. Our study reveals that these antagonists significantly enhance 20S proteasome activity, crucial for degrading intrinsically disordered, oxidatively damaged, or misfolded proteins, factors pivotal in neurodegenerative diseases like Alzheimer's and Parkinson's.
View Article and Find Full Text PDFCMAJ
January 2025
Schools of Health and Wellbeing (Nakada, Pell, Ho), and Cardiovascular and Metabolic Health (Welsh, Celis-Morales), University of Glasgow, Glasgow, UK; Human Performance Laboratory, Education, Physical Activity and Health Research Unit (Celis-Morales), Universidad Católica del Maule, Talca, Chile; Centro de Investigación en Medicina de Altura (CEIMA) (Celis-Morales), Universidad Arturo Prat, Iquique, Chile.
Background: Anxiety and depression are associated with cardiovascular disease (CVD). We aimed to investigate whether adding measures of anxiety and depression to the American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) predictors improves the prediction of CVD risk.
Methods: We developed and internally validated risk prediction models using 60% and 40% of the cohort data from the UK Biobank, respectively.
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