Gut Metabotypes as Biomarkers for Nutrition and Health
Background and aim
In recent decades, nutritional science and biomedicine have made enormous progress in the development of modern molecular and cellular research technologies. This has led to the awareness that health is a state of homeostasis, not only for our various eukaryotic cells, but also for the millions of living microorganisms that live in symbiosis on (e.g. skin) or in (e.g. gut) our human bodies. The aim of the project described here was to identify useful microbiome-host metabolic axes and to use biological patterns in the gut microbiome and of metabolites in human serum as markers for nutrition and health. Identified patterns reflecting a symbiotically balanced state will provide a better indication of "good" nutrition and health compared to a single molecule biomarker.
The gut microbiome is essential for human health and gastrointestinal tract homeostasis as it plays a role in the development and activity of the immune system, regulates the renewal of the intestinal epithelium and the maintenance of mucosal integrity, and is essential for dietary energy production. It is now widely recognised that disorders in the gut microbiome are associated with many different diseases, including:
- metabolic disorders (e.g. type 2 diabetes and obesity),
- cardiovascular diseases (e.g. atherosclerosis and heart failure),
- chronic inflammatory diseases (e.g. rheumatoid arthritis and chronic inflammatory bowel diseases) and
- defined malignancies (e.g. stomach and colon cancer).
Because disorders of the gut microbiome are associated with so many different disease entities, a health-promoting microbiome is likely to be a reliable reflection of the state of symbiotic homeostasis associated with health. Furthermore, it is known that human health status and the gut microbiome are strongly influenced by different diets. The gut microbiome intervenes in human metabolism through the uptake and exchange of certain dietary components and the production of new metabolites.
Various bioinformatic methods were used to evaluate the data generated in the project. The classification of probands according to health status and the identification of relevant biological patterns was done using random forests, among other statistical methods. Multi-Omics analysis of metabolome, lipidome and dietary and microbiome data led to the identification of a biopattern consisting of 10 microbiome ASVs, several hydrophilic and lipophilic metabolites and few nutrition components that is indicative of a homeostatic and healthy human state.
In the past biomarker development has been focused mainly on serum samples using defined proteins. While markers for diseases such as type 2 diabetes (HbA1c) and rheumatoid arthritis (CCP-antibody) are reliable for diagnoses and treatment, no suitable serum marker for healthy nutrition and human health have been developed.
With our approach we are now extending the biomarker research on the human gut microbiome which is heavily influenced by diet and therefore might be more suitable as a biomarker for nutrition and health compared to serum markers. Also, we are not focusing on one certain bacterium (like on one certain serum protein) but on patterns of metabolites of microbiome populations. From our point of view this approach is more suitable to identify a biomarker for health for the following reason:
Usually biomarkers for diseases are specifically up-regulated and are somehow related to the pathophysiology. Thus disease might be the extreme imbalance of one or maybe only a few important factors which then might serve as biomarkers. However, in health, which is the focus of this proposal, one would not expect that one distinct protein is altered in an extreme way – and might serve as a biomarker - but rather that cells, proteins, and microorganisms are in a stable and balanced condition. Thus, from our point of view examining complete populations of several microbiota and their patterns of bacterial metabolites in human serum are more likely to identify a biomarker or “biopattern” for health compared to the search for a single serum factor.
From a scientific point of view, the data obtained by the consortium will be important for (A) prevention of nutrition/life-style associated diseases and (B) will be suitable for the monitoring of recovery of a disease into health:
- Biopattern for Nutrition and Health in prevention:
- A biopattern for nutrition and health can be used in screening programs in routine medical care
- A biopattern for nutrition and health can be used to monitor the compliance of an individual to a dietary advice for a healthy nutrition
- A biopattern for a healthy type of nutrition can be used to monitor a nutritional intervention to test for the patients/probands compliance in scientific programs
- A biopattern for a healthy type of the microbiome might be of use in studies on the effect of pre- and probiotics on human health in translational studies
- Biopattern for Nutrition and Health in clinical care (e. g. recovery of health after disease)
Besides being an indicator for a healthy nutrition and health in general in prevention, a biopattern of gut metabolites could also be of interest as indicators for the recovery of health after severe diseases. A biopattern for a healthy type of the microbiome could not only be important in screening and prevention programs but might also be of interest in clinical care. For example: if a patient receives an antibiotic treatment and his bacterial metabolites in the serum remain stable, this might suggest that this patient is not at risk for an antibiotics associated diarrhea. However, if a patient is getting treated with antibiotics and the bacterial metabolites in the serum are shifted to a non-favorable pattern, this could indicate susceptibility for an antibiotic associated diarrhea and would suggest consumption e. g. of probiotics to prevent the diarrhea and preserve gastrointestinal health.
University Hospital of Schleswig Holstein
|Human Nutrition and Food Science
|University of Calgary
|Medizinische Universität Graz
The consortium successfully established a human dietary intervention study as well as recalls of the FoCus and ATP Tomorrow cohorts. A biopattern which is associated with human health and consists of 10 microbiome ASVs, several hydrophilic and lipophilic metabolites and few nutrition components has been identified. Multi- Omics and machine learning analysis revealed that the gut microbiome influences human health more than other omics levels. A variety of secondary research projects have been achieved within this platform, for example regarding gut microbial community metabolism and bile acids, secondary plant compounds, the gut microbial species Parasutterella as a biomarker for metabolic disease and the development of a new platform for Lipidomics data analysis.
The aim of the project was to identify useful microbiome-host metabolic axes and to use biological patterns of bacterial metabolites in human serum as markers of nutrition and health. Microbiome, metabolome, and lipidome data, as well as clinical phenotypes and dietary patterns were evaluated in three population-based cohorts, as well as a dietary intervention study. Subjects in the cohorts were stratified into healthy and cardiovascular, metabolic, inflammatory, and cancer groups. Participants' dietary patterns were assessed using a modified Mediterranean Dietary Score (mMDS). Here, a high score indicates a dietary pattern generally considered healthy.
While only isolated associations of the score with the different disease groups were found in this study, a high score was associated with higher alpha and beta diversity of the microbiome. Several candidate species were identified in the gut microbiome that were related to with good health status and showed high potential to be influenced by dietary changes. Furthermore, univariate and multivariate statistical methods, as well as machine learning methods (random forests), identified potential biomarker profiles in metabolome and lipidome that were also associated with subject health. Ultimately, the profiles from the different omics levels were merged to investigate, among other aspects, the metabolic interaction of candidate species of the gut microbiome with the host. The biomarker profiles defined in this way show a high suitability as markers for health in clinical practice and have a higher predictive power than individual markers.