Org, E. | bioRxiv Science

bioRxiv2021

Using fecal immunochemical tubes for the analysis of gut microbiome has potential to improve colorectal cancer screening

BackgroundColorectal cancer (CRC) is an important and challenging public health problem which successful treatment depends on the early detection of the disease. Recently, colorectal cancer specific microbiome signatures have been proposed as an additional marker for CRC detection. A desirable aim would be the possibility to analyze microbiome from the fecal samples collected during CRC screening programs into FIT tubes for fecal occult blood testing. MethodsWe investigated the impact of the Fecal Immunohistochemical Test (FIT) and stabilization buffer on the microbial community structure in stool samples from 30 volunteers and compared their communities to fresh-frozen samples highlighting also the previously published cancer-specific communities. Altogether 214 samples were analyzed including positive and negative controls using 16S rRNA gene sequencing. ResultsThe variation between individuals is greater than the differences introduced by collection strategy. The vast majority of the genera are stable for up to 7 days. None of the changes observed between fresh frozen samples and FIT tubes are related to previously shown colorectal-cancer specific bacteria. ConclusionsOverall, our results show that FIT tubes can be used for profiling the gut microbiota in colorectal cancer screening programs as the community is similar to fresh frozen samples and stable at least for 7 days. ImpactSample material from FIT tubes could be used in addition to fecal immunochemical tests for future investigations into the role of gut microbiota in colorectal cancer screening programs circumventing the need to collect additional samples and possibly improving the sensitivity of FIT.

cancer biology↗

bioRxiv2020

Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation

The incidence of type 2 diabetes (T2D) has been increasing globally and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long pre-diabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and pre-diabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome In Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4.5 year) period. Our results show that the inclusion of gut microbiome improves prediction accuracy for modelling T2D associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and non-linear associations. Additionally, the modelling strategy carried out allowed us to compare the stability of model performances and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modelling strategies and the value of interpretable machine learning. ImportanceRecent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using microbiome in personalized medicine promising.

microbiology↗