Systems medicine approach will change the paradigm of CMD

MetaCardis aims to change the paradigm of CMD via the application of systems medicine approaches towards disease development. This will be achieved by using high-throughput sequencing and screening technologies and a systems biology approach to better understand CMD in terms of the early to late stages of disease progression. CMD constitute a group of heterogeneous diseases and current classification of clinical risk and stages has attained its limits. The complex nature of the natural evolution of CMD will be approached by building a large cohort of relevant patients and through the application of advanced and harmonised phenotyping approaches will monitor the progressive stages of CMD evolution. The consortium will also analyse gut microbiota and its microbiome, which are likely crucial environmental components in CMD. Combination of these complex datasets will lead to unparalleled insights into CMD and the potential role of gut microbiota in these diseases. This approach should contribute considerable knowledge that can be used in the development of innovative personalised therapeutic approaches. It will also contribute to a considerable scale-up in cardiometabolic research in Europe, which will be important for the continued development of better, safer medicines and prevention/treatment approaches in CMD – a disease that is predicted to become more widespread and costly in Europe as populations age.

Beyond patient genomics by exploring deeper host environment interaction via the study of gut microbiota

The MetaCardis consortium will go beyond human genomics and extend knowledge in CMD pathophysiology through an investigation into the contribution of gut microbiota and their collective genome - the gut microbiome - in CMD cohorts. Gut microbiota is found altered in metabolic diseases (i.e. there may be dysbiosis) and may link lifestyle patterns to the host metabolome in part via immuno-inflammation and altered hormonal function.

Gut microbiome: a source of new shared pathophysiological targets for CMD?

MetaCardis aims to firmly establish associations between the gut microbiome and CMD intermediate phenotypes underlying important co-morbidities. This will be primarily achieved through a systems medicine approach based on clinical, genomic and metagenomic studies. Importantly this will be followed by validation stages in experimental systems and models. This initially descriptive process will ultimately provide solid foundations for future developments of diagnostic tools and therapeutic strategies for CMD patients.

Advanced lifestyle phenotyping in CMD

MetaCardis will contribute new knowledge on the role of gut microbiota (i.e. bacterial diversity, enterotypes, specific bacterial species, gut-derived metabolome) in health and CMD, and its link with lifestyle aspects (unbalanced diet, inactive lifestyles, psychological stress) in CMD populations. This will be done in basal situations (i.e. the different CMD stages) and in obese and diabetic patients before and after gastric surgery. In this case, this model is unique in that combined changes of lifestyle, gut microbiome, and metabolomic, inflammatory and hormonal phenotypes are induced and can be monitored closely in a clinical situation.

Advanced host biological phenotyping in CMD

MetaCardis will provide extensive phenotyping data well beyond usual bioclinical data through incorporation of metagenomic, and molecular phenotyping (metabolomic, transcriptomic) information. This will be combined with patient clinical data (i.e. metabolic and heart phenotypes), data relating to inflammatory state (both systemic and tissular) and hormonal state information. Taken together this will lead to the development of a systems medicine programme of study. The integration of state-of-the-art metagenomics, metabolomics, transcriptomics and metabolic network modelling with provide details of patient phenotypes. This represents a truly novel approach towards integrative systems medicine. Importantly, information provided by detailed metabolic network models will improve subjects’ phenotyping and in turn increase the predictive power of the approach for biomarker identification.

Several researchers have called for a “marriage” to be made between metagenomics and metabolomics. In MetaCardis, 2,000 subjects will have their metagenome and their metabolome profiled. This will provide a unique and unmatched opportunity to seal this marriage and will be implemented via the first large-scale ‘Metagenome-Wide and Metabolome-wide Association Study’ (called MW²AS, i.e. correlating metagenome sequence abundance with metabolite abundance levels). This MW²AS will enable the consortium to identify the metagenome-wide determinants of human metabolism with a state-of-the-art integrative systems biology approach. This will be similar to a metabolomic genome-wide association study, but focussing on the prokaryote metagenome and not the human genome. Findings will lay the groundwork for future therapeutic approaches aimed at changing the composition and functions of the commensal microbiota so as to act on the energy storage capacity and complications associated with obesity, diabetes and their heart and vascular comorbidities. Furthermore, the detailed metabolic models will allow for simulation of disease onset and progression and in turn enable identification of novel treatment strategies.

Beyond the “state of the art” in systems biology

We will extend the current state of the art by developing (i) understanding of global, microbiome-based metabolic cascades in the human intestine, (ii) model consortia of multiple, interacting species, in conjunction with (iii) the modelling of relevant host tissues and (iv) global models of intestinal flux. This integrated, multi-level modelling is unique, unprecedented and can be performed thanks to the complementary experience of the partners of MetaCardis and the integrated datasets that will be compiled using a broad range of technologies. The integration of such models within a clinical research programme will undoubtedly advance our understanding of disease mechanisms and allow the prediction of crucial components in disease onset and progression.

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