The human gut microbiota has been linked with incidence and progression of noncommunicable diseases and their risk factors. Moreover, diet has been identified as an important modulator of microbiota composition and function, but responses vary between individuals. Underlying mechanisms of diet x microbiota interactions remain to be elucidated to provide a foundation for tailored dietary strategies for personalized/precision nutrition.

The overall aim of the DiGuMet project is to explore how gut microbiota interacts with diet and the role of such interactions for disease risk factors in humans. Our aim is to dissect the underlying mechanisms through extensive molecular phenotyping using metagenomics and metabolomics combined with lifestyle data from an established cohort as well as a dietary intervention study. We hypothesize that: (i) gut microbiota x diet interactions are a major determinant of specific metabotypes that are linked with cardiometabolic disease risk; (ii) distinct metabotypes can be identified in free-living subjects based on comprehensive assessments of health-related status, traditional blood-based biomarkers and multi-OMICs; and (iii) these metabotypes could be used as the basis for tailored dietary interventions to maximize health benefits for groups of individuals.

To address hypotheses i-ii, we have conducted a validation study, MAX, (n ~500) which is a sub-cohort of the Danish Diet, Cancer and Health- Next Generations cohort (DCH-NG; n = 39,554) where biological samples were collected at 0, 6 and 12 months across gender, age and fasting strata with the aim to explore whether we can group individuals into different metabotypes based on the data collected and explore if we can find metabolite biomarkers of such metabotypes. Untargeted and targeted metabolomics has been performed and gut microbiota analyses are in progress.

In parallel, we are preparing for a dietary intervention study to evaluate if individuals with signs of metabolic syndrome belonging to two different enterotypes will have differential responses to fermentable or non-fermentable cereal fiber. Individuals will be screened and enterotyped based on the high vs low presence of Prevotella. Participants will then undergo a 6 week cross-over intervention with fermentable- vs nonfermentable fiber and differential effects across enterotypes will be compared. Three fermentable fiber products and matched control products will be provided by industrial collaborators. Cardiometabolic risk factors are the primary outcomes and effects on the metabolome and gut microbiota will also be evaluated.

We expect that our project will i) provide insight in dietx microbiota interactions; ii) provide novel methods for grouping individuals according to their metabotype based on multiomics data; and iii) propose tailoring diet to distinct enterotypes as a novel disease prevention strategy. The concepts and implications have been presented in an opinion paper  in November 2019.

The DiGuMet project is progressing and we have concluded the targeted and untargeted analysis of the plasma metabolome, prepared dietary, anthropometric and clinical data for statistical analysis and performed several methodological advancements. In addition, gut microbiota analysis (16S rRNA) has been commenced and the urine metabolome has been analyzed. Due to the covid pandemic the intervention study has been delayed, but novel intervention foods have been developed and tested. We are also planning a new short-term trial which will test the impact of the intervention foods on formation of short-chain fatty acids and effects on postprandial glucose and hormone responses. The data will be used to develop a butyrate challenge test that can be used for screening of individual with high, medium and low butyrate concentrations in plasma after a challenge with dietary fiber, i.e. butyrate metabotypes. Such tool will be used in later large scale interventions to investigate differential effects on clinical outcomes across butyrate metabotypes. Moreover, we have expanded our project to include assessments of dietary polyphenols and contaminants.

Below are some of the DiGuMet project highlights to date:

*Plasma samples for the ~500 Danish participants from the MAX validation study of the DCH-NG cohort have been analyzed with untargeted and targeted metabolomics at all three time points (baseline, 6 and 12 months). Targeted urine metabolomics has also been performed. Data collected on clinical and anthropometric variables, disease history and 24h dietary recall data has been prepared for analysis. The remaining data on physical activity, socioeconomic factors and food frequency questionnaires and more is expected to be ready starting spring 2021.

*Metabotyping based on baseline clinical and anthropometric variables in the MAX valdation study has commenced. We have applied several different clustering algorithms and evaluated their performance and investigated differences in disease history, age and gender across clusters (metabotypes), in part using new methods that we have developed. We have also started to evaluate methods for data reduction and variable optimization. A paper will be drafted and submitted by the end of 2021. The DiGuMet consortium wrote a perspectives article on metabotyping and group-based dietary advice for precision nutrition and prevention of cardiometabolic disease. The article was published in November 2019 in the
journal Advances in Nutrition and highlighted by the American Society of Nutrition on their blog in January 2020.

*As a spin-out, we have also used metabolomics and clinical data from a previous intervention study with fish and meat intake and developed methodology for distinguishing different metabotypes in dynamic metabolite data from a postprandial diet challenge setting. We found that these biomarkers were differently associated with plasma creatinine. This methodology can be used for deriving metabotypes from dynamic (repeated measures) metabolomics data. A paper has been submitted for consideration to Bioinformatics 2021.

*As another spin-out with relevance to DiGUMET, the Swedish team has analyzed gut microbiota x diet interactions and their associated effects on cardiometabolic risk factors in a explorative analysis using samples conducted from a 12 wk intervention study together with partners in Shanghai and using fermented high-fibre cereal products developed by Lantmännen and Barilla (original study reported in Xue et al. Front Nutr. 2021 Jan 15;7:608623. doi: 10.3389/fnut.2020.608623.). At week 12, we observed a higher serum insulin concentration (P-value = 0.038) in FRB (n = 31) versus RW (n = 53), and this difference was corroborated with alterations in the genus-level relative abundances of gut microbiota, represented by an increase in Romboutsia and a reduction in Bilophila in FRB (n = 22) versus RW (n = 46).  Compared to RW (n = 53), fecal acetic acid concentration was significantly higher in FRB (n = 31) at week 12. We also found that fecal acetic and butyric acids positively, while isobutyric, isovaleric and 2-methylbutyric acids inversely, correlated with gut Romboutsia level among all participants (n = 68) at week 12. We found positive correlations of fecal isobutyric, isovaleric and 2-methylbutyric acids with gut Bilophila (n = 68). In conclusion, our results suggest the intake of high-fibre rye products could modify gut Romboutsia and Bilophila in a Chinese population. These effects are paralleled with favorable modifications of the SCFA concentration and associated with altered glycemic traits. (Liu et al. 2021. Revision submitted to food function)

*Spin-out of the Barcelona partner  based on the Digumet concept of the  Dietary fibre and its role as a health-promoting nutrient although its effects depend on the properties of each fibre. In the case of a beneficial effects of a mixture of fermentable fibres, using a daily dose extrapolated to human consumption, The UB Partner studied a  wide number of biometric and biochemical parameters and conducted a multi-omics approach based on transcriptomics, metagenomics and metabolomics analysis. To obtain deeper insights into the effects elicited by fibre supplementation, an integrative multivariate analysis was applied. Fibre intake also improved the intestinal health and had a clear effect over endotoxemia (i.e., increased caecal weight and small intestine length/weight ratio, reduced LPS serum levels and MPO activity in the colon), which was in turn reflected at the metabolomics (i.e., production of short chain fatty acids and phenolic acids), metagenomics (i.e., modulation of Ruminococcus species) and transcriptomics levels (i.e., expression of tight junctions). Furthermore, transcriptomics analysis showed downregulated proteolysis in response to fibre, in line with the decrease of amino acid levels observed in serum and urine and with the increase of Lactobacillus bacteria. The integrative multi-omics approach applied highlights the great potential of dietary fibres to ameliorate the impairments in metabolic and intestinal health triggered by obesity and related comorbidities.

*The untargeted and targeted plasma metabolome data has been pre-processed and made ready for analysis. The stability of the untargeted plasma metabolome over time and the impact of systematic and random variability is being assessed. This includes investigating how age, BMI and sex impact plasma metabolites. This will be informative for many parts of the DiGuMet project and beyond.

*The UHPLC-MS metabolomics platform has been expanded and can at present identify and quantify over 1000 metabolites commonly found in biological samples such as urine, serum and plasma. These include about 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 comprehensive method provides excellent opportunities to detect diet, gut microbiota and disease related biomarkers in the MAX study and beyond. This targeted methodology was published in the Journal of Agricultural and Food Chemistry (JAFC) in 2020 and was awarded the Best Research Article of the Year Award in 2021 for the Division of Agricultural and Food Chemistry.

*We have set up and evaluated a protocol for freeze-drying fecal samples for the subsequent gut microbiota and metabolomics analysis. The protocol and the metabolomics results were published in the journal Metabolomics in April 2020. The 16S rRNA analysis of the gut microbiota will be concluded in spring 2021.

*In order to discover individual or groups of biomarkers reflecting adherence to dietary patterns, supervised learning techniques will be used to find metabolites discriminating between high and low adherence to dietary pattern scores. Discover and validate metabolites resulting as biomarkers of dietary patterns and associate them with health data. Studies of associations between biomarkers of dietary patterns and clinical and anthropometric data will be performed using distinct types of correlation analysis (from standard correlations to mixed Gaussian graphical models).

*A new screening and stratification strategy has been set up for the intervention study to identify individuals with low or high Prevotella enterotypes. Moreover, several new intervention food products have been developed and have been/are being tested for feasibility and palatability. This includes a wheat bran breakfast cereal in collaboration with Örebro University and produced by Lantmännen, a bread containing arabinoxylan‐oligosaccharides (AXOS) in collaboration with Carbiotix (https://carbiotix.com/) and a resistant starch type 4 (RS4) -based product. In total, three food products rich in either fermentable fiber or nonfermentable fiber (control) will be made available specifically for the intervention. The products will also be tested in a separate trial investigating their postprandial effects on blood glucose, insulin and lipids as well as short-chain fatty acids. A study protocol for the DiGuMet intervention has also been drafted.

*The DiGuMet project has been expanded to include the investigation of the dietary intake of polyphenols in the MAX study population as well as the DCH-NG cohort. As part of this work, we have developed and used a method to estimate the intake of dietary polyphenolic compounds using a Barcelona in house-software. Consortium partners from Spain and Denmark have been in close collaboration 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 each individual and day (mg/day) has been estimated from the 1670 food items and >5000 ingredients from the 24h dietary recall and will next be estimated using the 475 food items and >1200 ingredients from the food frequency questionnaire. Phenolic intake will be related to cardiometabolic risk factors and metabolomics data.

*A portfolio of additionally 43 xenobiotic compounds, including 10 pesticides, 2 organoarsenic compounds and 31 household chemicals and environmental pollutants was included in the targeted metabolomics methodology developed by the UB team for the analysis of biological samples from the MAX study. Many of these xenobiotics were successfully detected and quantified in plasma and urine samples, normally at very low concentration levels (sub-ppb). In particular for pesticides, their accurate quantification was below the limits of detection and limits of quantification of our methodology, due to the low levels usually found in the biological samples. Furthermore, due to their very high polarity, most of these pesticides were detected eluted in the void volume of the chromatographic profile, which consequently hinder their unequivocal detection and separation from potential coeluting interferences. The data on contaminants is also planned to be linked with organic vs. conventional food consumption and associated with cardiometabolic diseases.

*Associations between targeted metabolomic biomarkers, clinical and anthropometric data, and gut microbiota data will be studied using distinct types of correlation analysis.The targeted metabolomic data will be used to discover and validate (1) direct markers resulting from dietary intake, (2) secondary markers resulting from gut microbiota metabolism and (3) markers from other biological exposure and their relation with cardiometabolic risk parameters, and gut microbiota. Additionally, searching  for changes in metabolites concentration over time (baseline, 6 months and 12 months). In order to discover individual or groups of biomarkers of food intake and biomarkers of exposure supervised learning techniques will be used to find metabolite discriminating between high and low adherence to intake and/or high and low exposition.

* A novel ontology that describes food and their associated metabolite entities in a hierarchical way has been published by the Barcelona partner in the Database Journal in 2020. This ontology has enabled the analysis and integration of the two different datatypes and uses a formal naming system, category definitions, properties and relations between both types of data. The ontology presented is called FOBI (Food-Biomarker Ontology) and is composed of two interconnected sub-ontologies. One is a ’Food Ontology’ consisting of raw foods and ‘multi-component foods’ while the second is a ‘Biomarker Ontology’ containing food intake biomarkers classified by their chemical classes. This allows data and information regarding foods and food biomarkers to be visualized in a bidirectional way, going from metabolomics to nutritional data or vice versa. Potential applications of this ontology include the annotation of foods and biomarkers using a well-defined and consistent nomenclature, the standardized reporting of metabolomics workflows or the application of different enrichment analysis approaches to analyze nutrimetabolomic data. FOBI is freely available in both OWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) formats in the project’s Github repository

*A user-friendly workflow for the visualization, exploratory and statistical analysis of mass spectrometry data has also been developed. A web-based tool, POMAShiny, is expected to be published in 2021. This tool integrates several statistical methods, some of them widely used in other types of omics, and it's based on the POMA R/Bioconductor package, which increases the reproducibility and exibility of analysis outside the web environment. POMAShiny and POMA are both freely available.