BACKGROUND: Overweight and obesity is a growing health problem worldwide. The most effective strategy to reduce weight is energy restriction (ER). ER has been shown to be beneficial in disease prevention and it reduces chronic inflammation. Recent studies suggest that reducing the protein quantity of a diet contributes to the beneficial effects by ER. The organ most extensively affected during ER is white adipose tissue (WAT).OBJECTIVE: The first objective was to assess changes in gene expression between a high-protein diet and a normal protein diet during ER. Second, the total effect of ER on changes in gene expression in WAT was assessed.METHODS: In a parallel double-blinded controlled study, overweight older participants adhered to a 25% ER diet, either combined with high-protein intake (HP-ER, 1.7 g kg-1 per day), or with normal protein intake (NP-ER, 0.9 g kg-1 per day) for 12 weeks. From 10 HP-ER participants and 12 NP-ER participants subcutaneous WAT biopsies were collected before and after the diet intervention. Adipose tissue was used to isolate total RNA and to evaluate whole-genome gene expression changes upon a HP-ER and NP-ER diet.RESULTS: A different gene expression response between HP-ER and NP-ER was observed for 530 genes. After NP-ER, a downregulation in expression of genes linked to immune cell infiltration, adaptive immune response and inflammasome was found, whereas no such effect was found after HP-ER. HP-ER resulted in upregulation in expression of genes linked to cell cycle, GPCR signalling, olfactory signalling and nitrogen metabolism. Upon 25% ER, gene sets related to energy metabolism and immune response were decreased.CONCLUSIONS: Based on gene expression changes, we concluded that consumption of normal protein quantity compared with high-protein quantity during ER has a more beneficial effect on inflammation-related gene expression in WAT.
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The thoracic and peritoneal cavities are lined by serous membranes and are home of the serosal immune system. This immune system fuses innate and adaptive immunity, to maintain local homeostasis and repair local tissue damage, and to cooperate closely with the mucosal immune system. Innate lymphoid cells (ILCs) are found abundantly in the thoracic and peritoneal cavities, and they are crucial in first defense against pathogenic viruses and bacteria. Nanomaterials (NMs) can enter the cavities intentionally for medical purposes, or unintentionally following environmental exposure; subsequent serosal inflammation and cancer (mesothelioma) has gained significant interest. However, reports on adverse effects of NMon ILCs and other components of the serosal immune systemare scarce or even lacking. As ILCs are crucial in the first defense against pathogenic viruses and bacteria, it is possible that serosal exposure to NMmay lead to a reduced resistance against pathogens. Additionally, affected serosal lymphoid tissues and cells may disturb adipose tissue homeostasis. This review aims to provide insight into key effects of NMon the serosal immune system.
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Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
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