Sphingolipids in Obesity and Correlated Co-Morbidities: The Contribution of Gender, Age and Environment

Torretta, Enrica; Barbacini, Pietro; Al-Daghri, Nasser M.; Gelfi, Cecilia

doi:10.3390/ijms20235901

Cited by 46 publications

(46 citation statements)

References 253 publications

(312 reference statements)

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“…Increased free fatty acids (FFA) produced from adipose tissue as well as secretion of hormones, cytokines, and pro-inflammatory markers, which are directly linked to obesity, induce reduced glucose uptake in muscle cells and increased hepatic glucose production. These metabolic dysfunctions lead to a glucose overflow in the circulation, which culminates in glucose intolerance and the installation of type 2 diabetes (T2D) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Sphingosine-1-Phosphate Metabolism in the Regulation of Obesity/Type 2 Diabetes

Guitton

Bandet

Mariko

et al. 2020

Cells

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Obesity is a pathophysiological condition where excess free fatty acids (FFA) target and promote the dysfunctioning of insulin sensitive tissues and of pancreatic β cells. This leads to the dysregulation of glucose homeostasis, which culminates in the onset of type 2 diabetes (T2D). FFA, which accumulate in these tissues, are metabolized as lipid derivatives such as ceramide, and the ectopic accumulation of the latter has been shown to lead to lipotoxicity. Ceramide is an active lipid that inhibits the insulin signaling pathway as well as inducing pancreatic β cell death. In mammals, ceramide is a key lipid intermediate for sphingolipid metabolism as is sphingosine-1-phosphate (S1P). S1P levels have also been associated with the development of obesity and T2D. In this review, the current knowledge on S1P metabolism in regulating insulin signaling in pancreatic β cell fate and in the regulation of feeding by the hypothalamus in the context of obesity and T2D is summarized. It demonstrates that S1P can display opposite effects on insulin sensitive tissues and pancreatic β cells, which depends on its origin or its degradation pathway.

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Section: Introductionmentioning

confidence: 99%

Sphingosine-1-Phosphate Metabolism in the Regulation of Obesity/Type 2 Diabetes

Guitton

Bandet

Mariko

et al. 2020

Cells

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“…In several studies, it was revealed that increased sphingosine-1-phosphate (S1P) occurs to obese animals and humans, which can lead to IR and overexpression of sphingolipid (SL) metabolism [46]. The sphingolipid metabolism is altered in T2DM and relating to cell death [26].…”

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confidence: 99%

An Integrated Fecal Microbiome and Metabolomics in T2DM Rats Reveal Antidiabetes Effects from Host-Microbial Metabolic Axis of EtOAc Extract from Sophora flavescens

Shao

Liu

Wang

et al. 2020

Oxidative Medicine and Cellular Longevity

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Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease. Sophora flavescens (S. flavescens), also named Kushen, is a famous Chinese herbal medicine that has been used to prevent and cure T2DM both in folk medicine and in medical institution. However, its mechanism of action remains unclear. In this study, the pharmacodynamic effects of S. flavescens EtOAc extract (SFE) on high-fat diet and low-dose streptozotocin-induced T2DM rats were examined. Fecal metabolomics analysis and 16S rRNA gene sequencing were applied to determine the influence of T2DM and SFE treatment on gut microbiota and host metabolism. Based on the consistency of the results of metabolic pathways in metabolomics analysis and phylogenetic investigation of communities by reconstruction of unobserved state (PICRUSt) analysis of 16S rRNA gene sequencing, the level of metabolites and the operational taxonomic units of gut bacteria were combined, and Spearman’s analysis was implemented. Our data showed that SFE significantly decreased fasted blood glucose levels and improved lipid profile, glycosylated serum protein, glycosylated hemoglobin index, and pancreas damage. Metabolomics and 16S rRNA gene sequencing analysis indicated gut bacteria disorder, disturbed lipid metabolism, carbohydrate metabolism, and especially amino acid metabolism in T2DM and that SFE can regulated these metabolic pathways through the influence on gut bacteria. Spearman’s analysis indicated that the amino acid metabolism that included tryptophan, branched chain amino acid, aromatic amino acid, beta-alanine, and glycine, serine and threonine metabolism, lipid metabolism, including lysophosphatidylcholines and lysophosphatidylethanolamines, primary bile acid and linoleic acid metabolism, carbohydrate metabolism, and nucleotide metabolism positively correlated with Faecalibacterium, Flexispira, Phascolarctobacterium, Prevotella, Roseburia, and [Prevotella]. In addition, arginine and proline metabolism, steroid hormone, steroid biosynthesis, and sphingolipid metabolism positively correlated with Lactobacillus, Oscillospira, Parabacteroides, Ruminococcus, and Streptococcus. Taken together, we speculated that SFE may have an effect on T2DM by mediating host-microbial metabolic axis. Exploration of SFE treatment for T2DM by multiomics is expected to provide a reference for clinical treatment.

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“…The altered levels of certain SM and PC species in hyperglycemic individuals under increased risk for CKD could be caused by fluctuations in their fatty acid profile, which influences the first rate-limiting step in de novo SM synthesis, due to nutritional oversupply, dyslipidemia (44) or gut microbiome (45). The severity of CKD correlates with increased levels of saturated and mono-unsaturated fatty acids (46) 11; 51; 52).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes

et al. 2020

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Early and precise identification of individuals with pre-diabetes and type 2 diabetes (T2D) at risk of progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in persons with pre-and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

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Sphingolipids in Obesity and Correlated Co-Morbidities: The Contribution of Gender, Age and Environment

Cited by 46 publications

References 253 publications

Sphingosine-1-Phosphate Metabolism in the Regulation of Obesity/Type 2 Diabetes

Sphingosine-1-Phosphate Metabolism in the Regulation of Obesity/Type 2 Diabetes

An Integrated Fecal Microbiome and Metabolomics in T2DM Rats Reveal Antidiabetes Effects from Host-Microbial Metabolic Axis of EtOAc Extract from Sophora flavescens

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes

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