DiGuMet

The DiGuMet project concludes having conducted one 12-month observational study with >500 participants at three timepoints generating multi-omics data as well as clinical, antropometric, diet and other lifestyle data and one randomizedcontrolled trial investigating SCFA-based metabotypes basd on inter- and intra-individual response to two differentfermentable fibers and their relation to cardiometabolic risk factors measured.

We highlight:

• The establishment of the MAX validation study of the DCH-NG cohort with ~500 Danish participants with data collected atbaseline, 6 and 12 months. This includes collection of urine-, blood-, fecal-, and saliva samples along with data collected onclinical and anthropometric variables, disease history, physical activity, socioeconomic factors and detailed dietary data thatincludes repeated 24h dietary recalls and food frequency questionnaires. The analysis conducted comprise untargeted- andtargeted plasma and targeted urine metabolomes and the gut microbiota. This study represents a goldmine for biomarkerdiscovery and validation. It has enabled a thorough investigation into the presence of metabotypes and studies of their relationto health-related outcomes. Furthermore, studies of the stability of metabolomes and the gut microbiota over time and theirdeterminants as well as diet-cardiometabolic risk factor relationships as well as the concentrations and presence of bioactivemetabolites, pesticides, and contaminants in the diet. The samples and generated data will continue to be a useful resource fornumerous studies to come including metabolite biomarkers for exposures, namely dietary exposures, diet x gut microbiotarelationships and cardiometabolic endpoints. Gut microbiota analysis and plasma metabolomics (exploratory outcomes) areused to explore and unravel if the gut microbiota composition and plasma metabolites are associated with the plasma SCFAresponse after the fiber challenge test and with potential improvements in cardiometabolic parameters, including plasmaglucose, insulin, blood lipids, blood pressure and free fatty acid levels.
• Investigations into the stability of both metabolites and the gut microbiota over one year (0, 6 and 12 months) and the roleof different determinants including diet on the metabolome and gut microbiome. Results can guide on which signals in themetabolome and microbiome that may be stable enough to be studied in relation to cardiometabolic risk factors.
• The thorough investigation into the discovery of metabotypes in the MAX study, based on clinical and anthropometricvariables, metabolomics and gut microbial data using machine learning and integrative clustering methods. In contrast to ourhypothesis, we did not observe any clusters in free-living participants that we deemed sufficiently distinct to guide thedevelopment into novel metabotype-based prevention strategies. Instead, we have observed metabotypes in postprandial datafrom two intervention studies (Skantze et al. 2022) and unpublished. These represented differential postprandial responses toprotein rich meals in key amino acids that related differently to plasma creatinine (Skanze et al. 2022) and differential plasmaSCFA AUCs, in response to developed dietary fibre challenges. Taken together, these results suggest that postprandial- andtimes series data may be more suited for metabotyping-strategies than static data and it remains to validate if it has a meaningfor cardiometabolic disease prevention. Our work resulted in novel methodological developments of metabotyping of time-series data reported in Skanze et al. (2022).
• The randomized controlled cross-over trial, Fiferm, conducted to evaluate the presence of metabotypes based onpostprandial sort-chain fatty acid (SCFA) response to two fermentable cereal fibers (solubilized arabinoxylans and wheat bran)and cellulose control in 20 individuals with high risk for cardiometabolic disease. The dietary fibers were first used to developnew food products and provided to participants as part of breakfast meals. The concentrations of the short chain fatty acidswere time dependent and varied across individuals, allowing for identification of butyratogenic and propiongenic metabotypes(responders and non-responders). Moreover the two fermentable fibers led to differential effects: the intake of solublizedarabinoxylans increased plasma levels of propionate by 44% and those of butyrate by 19%, corresponding increases for wheatbran were 7% and 21%, compared to cellulose. Arabinoxylans and wheat bran did not affect postprandial glycaemia in the 8 LEFT SUBHEADER RIGHT SUBHEADER
  hours following their intake on group level, but had a differential impact on insulin levels in the first few hours afterconsumption. The gut microbiota and plasma metabolome (exploratory outcomes) are explored to unravel if the gutmicrobiota composition and function and plasma metabolites are associated with the SCFA response to the fiber challenge testand with potential improvements in cardiometabolic parameters, including plasma glucose, insulin, blood lipids, free fattyacids as well as with postprandial incretin levels. In respect to methodology development of a challenge test, we have alsoinvestigated the number of time points required to identify fermentable fiber responders and non-responders. Taken together,this study has provided support for a novel metabotyping strategy to differentiate individuals based on SCFA response in theirplasma. This approach is now being validated in external studies to investigate wherther differential SCFA-metabotypes aredifferentially related to CMD risk factors after long-term interventions.

In addition, we have expanded our work to also include spin-off projects. We highlight: 

• Investigations into gut microbiota x diet interactions and their associated effects on cardiometabolic risk factors, as anexplorative analysis of data from a 12-week intervention study on fermented high-fibre cereal products (Liu et al. 2021). Thiswork was in collaboration with partners in Shanghai using novel food products developed by Lantmännen and Barilla. Theresults suggest that the intake of high-fibre rye products could modify specific gut bacterial genera and that these effects areparalleled with favorable modifications of short-chain fatty acid concentrations and associated with altered glycemic traits.(Liu et al. 2021)
• Establishment of the dietary intake of polyphenols in the MAX study population as well as the DCH-NG cohort. Consortiumpartners from Spain and Denmark collaborated closely to link an in-house food composition database on polyphenols(Barcelona) to the comprehensive dietary data collected by the Danish partner. The amount of polyphenols consumed for eachindividual and day (mg/day) has been estimated from the 1670 food items and >5000 ingredients from the 24h dietary recalland will next be estimated using the 475 food items and >1200 ingredients from the food frequency questionnaire. Phenolicintake will be related to cardiometabolic risk factors and metabolomics data.
• A portfolio of additionally 43 xenobiotic compounds, including 10 pesticides, 2 organoarsenic compounds and 31household chemicals and environmental pollutants was included in the targeted metabolomics methodology developed by theUB team for the analysis of biological samples from the MAX study. Many of these xenobiotics were detected and quantifiedin plasma and urine samples, normally at very low concentration levels (sub-ppb). In particular for pesticides, their accuratequantification was below the limits of detection and limits of quantification of our methodology, due to the low levels usuallyfound in the biological samples. Furthermore, due to their very high polarity, most of these pesticides were detected eluted inthe void volume of the chromatographic profile, which consequently hinder their unequivocal detection and separation frompotential coeluting interferences. The data on contaminants is also planned to be linked with organic vs. conventional foodconsumption and associated with cardiometabolic diseases.

We have also conducted substantial method development efforts. We have:

• Developed a challenge test with fermentable fibers to detect diet response metabotypes (manuscript in preparation)
• Developed new methods for metabotyping using dynamic (time series) data. This method was developed and applied toidentify metabotypes from postprandial metabolite date from diet challenge setting. Metabotypes were differently associatedwith plasma creatinine (Skantze et al. 2022)
• A method for untargeted metabolomics of fecal samples was developed and optimized for large scale freeze-drying of fecalsamples to precede gut microbiome analysis and metabolomics (Ken et al. Metabolomics, 2020)
• Conducted a comprehensive method investigation to derive general population metabotypes by combining anthropometric,biochemical and multiomics data. In addition, investigated approaches to improve metabotyping by adjusting Omics data to better reflect metabolic regulation, similar to adjusting dietary data for total energy intake using the residual method(manuscript)
• Expanded databases to include extensive polyphenol and exposure data. As part of this work, we developed and used amethod to estimate the intake of dietary polyphenolic compounds based on software developed by the Spanish partner
• Expanded an in-house database with targeted metabolite biomarkers. The UHPLC-MS metabolomics platform can identifyand quantify over 1000 metabolites commonly found in biological samples such as urine, serum and plasma. These includeabout 500 endogenous metabolites, 450 food-derived metabolites, 50 common pollutants, 50 drugs as well as microbiota-derived metabolites and biomarkers related to lifestyle habits e.g., smoking and alcohol consumption. This comprehensivemethod provides excellent opportunities to detect diet, gut microbiota and disease related biomarkers in the MAX study andbeyond. This targeted methodology was published in the Journal of Agricultural and Food Chemistry (JAFC) in 2020 and wasawarded the Best Research Article of the Year Award in 2021 for the Division of Agricultural and Food Chemistry
• Developed a web-based tool, POMAShiny, with a user-friendly workflow for the visualization, exploratory and statisticalanalysis of mass spectrometry data and a R/Bioconductor package, POMA, that integrates several statistical methods,including widely applied omics methods. Both POMAShiny and POMA are freely available online.
• Developed FOBI (Food-Biomarker Ontology), a novel ontology that describes food and their associated metabolite entitiesin a hierarchical way (Spanish partner, Database Journal, 2020). This ontology has enabled the analysis and integration of thetwo different datatypes and uses a formal naming system, category definitions, properties and relations between both types ofdata. FOBI is composed of two interconnected sub-ontologies; a ’Food Ontology’ consisting of raw foods and ‘multi-component foods’ and a ‘Biomarker Ontology’ containing food intake biomarkers classified by their chemical classes. Thisallows data and information regarding foods and food biomarkers to be visualized in a bidirectional way, going frommetabolomics to nutritional data or vice versa. Potential applications of this ontology include the annotation of foods andbiomarkers using a well-defined and consistent nomenclature, the standardized reporting of metabolomics workflows or theapplication of different enrichment analysis approaches to analyze nutrimetabolomic data. FOBI is freely available in bothOWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) formats in the project’s Github repository.
• Developed a novel open-source software (QualiMon – LaMa) for real-time quality monitoring in untargeted LC-MSmetabolomics (submitted). This tool fills an important gap since manual curation is largely inadequate and quality issues arefrequently not detected until later stages in the bioinformatics pipeline resulting in poor resource economy. QualiMon assessesdata quality from automated extraction of thousands of individual quality characteristics. Transgressing samples activate real-time warnings through web services, so measures can immediately be taken. QualiMon is freely available under MIT licenceat
  https://github.com/MetaboComp/QualiMon