@article{SiptrothMoskalenkoKrumbiegeletal.2023, author = {Siptroth, Julienne and Moskalenko, Olga and Krumbiegel, Carsten and Ackermann, J{\"o}rg and Koch, Ina and Pospisil, Heike}, title = {Variation of butyrate production in the gut microbiome in type 2 diabetes patients}, series = {International Microbiology}, volume = {26}, journal = {International Microbiology}, publisher = {Springer Nature}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17802}, pages = {601 -- 610}, year = {2023}, abstract = {Background Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student's t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive. Results By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection. Conclusions With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease.}, language = {en} } @article{SiptrothMoskalenkoKrumbiegeletal.2023, author = {Siptroth, Julienne and Moskalenko, Olga and Krumbiegel, Carsten and Ackermann, J{\"o}rg and Koch, Ina and Pospisil, Heike}, title = {Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks}, series = {Discover Artificial Intelligence}, volume = {3}, journal = {Discover Artificial Intelligence}, number = {1}, publisher = {Springer Nature}, issn = {2731-0809}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17497}, year = {2023}, abstract = {Background Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients. Methods Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways. Results It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674). Conclusion With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis.}, language = {en} }