Glycaemic sugar metabolism and the gut microbiota: past, present and future.

Non-communicable diseases (NCDs), such as type 2 diabetes (T2D) and metabolic dysfunction-associated fatty liver disease, have reached epidemic proportions worldwide. The global increase in dietary sugar consumption, which is largely attributed to the production and widespread use of cheap alternatives such as high-fructose corn syrup, is a major driving factor of NCDs. Therefore, a comprehensive understanding of sugar metabolism and its impact on host health is imperative to rise to the challenge of reducing NCDs. Notably, fructose appears to exert more pronounced deleterious effects than glucose, as hepatic fructose metabolism induces de novo lipogenesis and insulin resistance through distinct mechanisms. Furthermore, recent studies have demonstrated an intricate relationship between sugar metabolism and the small intestinal microbiota (SIM). In contrast to the beneficial role of colonic microbiota in complex carbohydrate metabolism, sugar metabolism by the SIM appears to be less beneficial to the host as it can generate toxic metabolites. These fermentation products can serve as a substrate for fatty acid synthesis, imposing negative health effects on the host. Nevertheless, due to the challenging accessibility of the small intestine, our knowledge of the SIM and its involvement in sugar metabolism remains limited. This review presents an overview of the current knowledge in this field along with implications for future research, ultimately offering potential therapeutic avenues for addressing NCDs.

DNA Methylation Profiling Enables Accurate Classification of Nonductal Primary Pancreatic Neoplasms.

BACKGROUND & AIMS: Cytologic and histopathologic diagnosis of non-ductal pancreatic neoplasms can be challenging in daily clinical practice, whereas it is crucial for therapy and prognosis. The cancer methylome is successfully used as a diagnostic tool in other cancer entities. Here, we investigate if methylation profiling can improve the diagnostic work-up of pancreatic neoplasms. METHODS: DNA methylation data were obtained for 301 primary tumors spanning 6 primary pancreatic neoplasms and 20 normal pancreas controls. Neural Network, Random Forest, and extreme gradient boosting machine learning models were trained to distinguish between tumor types. Methylation data of 29 nonpancreatic neoplasms (n = 3708) were used to develop an algorithm capable of detecting neoplasms of non-pancreatic origin. RESULTS: After benchmarking 3 state-of-the-art machine learning models, the random forest model emerged as the best classifier with 96.9% accuracy. All classifications received a probability score reflecting the confidence of the prediction. Increasing the score threshold improved the random forest classifier performance up to 100% with 87% of samples with scores surpassing the cutoff. Using a logistic regression model, detection of nonpancreatic neoplasms achieved an area under the curve of >0.99. Analysis of biopsy specimens showed concordant classification with their paired resection sample. CONCLUSIONS: Pancreatic neoplasms can be classified with high accuracy based on DNA methylation signatures. Additionally, non-pancreatic neoplasms are identified with near perfect precision. In summary, methylation profiling can serve as a valuable adjunct in the diagnosis of pancreatic neoplasms with minimal risk for misdiagnosis, even in the pre-operative setting.

Prognostic and Predictive Biomarkers for Pancreatic Neuroendocrine Tumors.

Pancreatic neuroendocrine tumors (PanNETs) represent a clinically challenging disease because these tumors vary in clinical presentation, natural history, and prognosis. Novel prognostic biomarkers are needed to improve patient stratification and treatment options. Several putative prognostic and/or predictive biomarkers (eg, alternative lengthening of telomeres, alpha-thalassemia/mental retardation, X-linked (ATRX)/Death Domain Associated Protein (DAXX) loss) have been independently validated. Additionally, recent transcriptomic and epigenetic studies focusing on endocrine differentiation have identified PanNET subtypes that display similarities to either α-cells or ß-cells and differ in clinical outcomes. Thus, future prospective studies that incorporate genomic and epigenetic biomarkers are warranted and have translational potential for individualized therapeutic and surveillance strategies.