Vitamin D metabolizing enzymes and vitamin D receptors are present in many cell types including various immune cells such as antigen-presenting-cells, T cells, B cells and monocytes. In vitro data show that, in addition to modulating innate immune cells, vitamin D also promotes a more tolerogenic immunological status. In vivo data from animals and from human vitamin D supplementation studies have shown beneficial effects of vitamin D on immune function, in particular in the context of autoimmunity. In this review, currently available data are summarized to give an overview of the effects of vitamin D on the immune system in general and on the regulation of inflammatory responses, as well as regulatory mechanisms connected to autoimmune diseases particularly in type 1 diabetes mellitus.

Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Purpose Vitamin D is well known for its effects on bone mineralisation but has also been attributed immunomodulatory properties. It positively influences human health, but in vivo data describing vitamin D effects on the human gut microbiome are missing. We aimed to investigate the effects of oral vitamin D₃ supplementation on the human mucosa-associated and stool microbiome as well as CD8⁺ T cells in healthy volunteers. Methods This was an interventional, open-label, pilot study. Sixteen healthy volunteers (7 females, 9 males) were endoscopically examined to access a total of 7 sites. We sampled stomach, small bowel, colon, and stools before and after 8 weeks of vitamin D₃ supplementation. Bacterial composition was assessed by pyrosequencing the 16S rRNA gene (V1–2), and CD8⁺ T cell counts were determined by flow cytometry. Results Vitamin D₃ supplementation changed the gut microbiome in the upper GI tract (gastric corpus, antrum, and duodenum). We found a decreased relative abundance of Gammaproteobacteria including Pseudomonas spp. and Escherichia/Shigella spp. and increased bacterial richness. No major changes occurred in the terminal ileum, appendiceal orifice, ascending colon, and sigmoid colon or in stools, but the CD8⁺ T cell fraction was significantly increased in the terminal ileum. Conclusion Vitamin D₃ modulates the gut microbiome of the upper GI tract which might explain its positive influence on gastrointestinal diseases, such as inflammatory bowel disease or bacterial infections. The local effects of vitamin D demonstrate pronounced regional differences in the response of the GI microbiome to external factors, which should be considered in future studies investigating the human microbiome.