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H2020

RHAPSODY Report Summary

Project ID: 115881
Funded under: H2020-EU.3.1.7.6.

Periodic Reporting for period 1 - RHAPSODY (Assessing risk and progression of prediabetes and type 2 diabetes to enable disease modification)

Reporting period: 2016-04-01 to 2017-03-31

Summary of the context and overall objectives of the project

Diabetes is subdivided into Type 1 Diabetes (T1D) and Type 2 Diabetes mellitus (T2D). T1D accounts for about 10% of all patients. T2D, which accounts for about 80-90% of all patients, is very heterogeneous, but the extent of this diversity is not known. RHAPSODY explores whether diverse sub-forms of T2D are characterized by different rates of progression from pre-diabetes to T2D and by differences in disease progression, e.g. need for insulin treatment. Establishing a better patient stratification from diagnosis of diabetes will help design novel strategies for precision therapy and prevention of diabetes and for more efficient clinical trials.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Biomarker selection
So far diabetes is mainly diagnosed by measuring one biomarker in blood, glucose. We think increasing the number of biomarkers can help dissect the diversity of the disease and strive to generate a common definition of “biomarkers”, procedures to identify them, and evaluate their use in disease stratification and clinical management of diabetes. Biomarker candidates have been selected and a plan to identify new biomarkers using RHAPSODY’s quantitative analytic platforms has been established.

Data federation and systems biology
We developed a secure central database, which authorized partners can access from their own locations. To establish such a federated database we have
- completed the harmonization and formatting of 6 primary cohorts to a suitable, standardised data format.
- set up the computational infrastructure for a federated database with several clinical cohort nodes located across Europe.
- set out a data management plan describing how data will be stored and accessed.
- set up a secure RHAPSODY project database, which is accessible to all partners via individual username and password.
- provided a computational framework for prioritizing biomarker candidates.

Predictive biomarkers of glycaemic deterioration
Prospective cohorts of people with and without T2D have been identified. Those have been selected based upon availability of biomarkers and repeated-measures follow-up data, suitable for the detection/discovery of biomarkers. We are now focusing on modelling available variables that will be used in the downstream biomarker analyses.

Multi-omics biomarker discovery and assay development
Plasma biomarkers reflect the metabolic activity of different tissues (mainly liver, fat, muscle) and their deregulations during disease progression. Large data sets of genetic, epigenetic, genomic and protein expression will be collected from these tissues. Quality control (QC) assessment of the chemical properties of plasma available from the selected cohorts has been carried out. We created a central hub for the receipt and transmission of samples to four analytical platforms for quantification of polar metabolites, lipids, peptides/proteins, and micro RNAs. We confirmed that 4 diabetes and 3 pre-diabetes cohorts meet the QC criteria for all platforms.

Predictive biomarkers of beta cell dysfunction
Diabetes develops when insulin secretion no longer meet the demand imposed by the progression of insulin resistance. To discovery and validate biomarker candidates of impaired insulin secretion in humans, we have
- established a robust infrastructure for standardized protocols and documentation and sharing samples.
- initiated genomic and epigenetic investigation of human islets from non-diabetic, pre-diabetic and diabetic subjects and obtained paired blood samples from a subset of these islet samples for ‘omic’ analyses.
- initiated studies of morphology if islets at different stage of the development of T2D.
- initiated the characterization of the cellular, genomic, and epigenetic responses of human islets to specific physiological factors that may affect their function.

Predictive biomarkers of insulin target tissue dysfunction
Sphingolipids, in particular dihydroceramides, are biomarker candidates for T2D development. We have established a list of 26 genes involved in their biosynthesis and degradation and searched for genetic variants in genome-wide association studies of pre-diabetes and T2D cohorts. We are performing in vitro experiments to assess the effect of selected sphingolipids on the function of fat and liver cells.
Mouse models of pre-diabetes have been investigated to identify changes in gene expression and epigenetic marks in insulin target tissues and to assess islets function in order to correlate changes in insulin secretion with insulin target tissue gene expression.

Regulatory consensus for diabetes disease modification
RHAPSODY has established a regulatory f

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

During year 1, the major advances beyond state of the art are:
- Almost completion of the harmonization of the cohorts’ data annotation and their transfer in node specific databases and beyond firewalls.
- Establishment of a RHAPSODY database with analysis and visualization tools for cross cohort data interrogation at an EU level; establishment of all regulatory/administrative requirements for data sharing. This will represent a research database for T2D with a tremendous potential impact on science.
- Quality control analysis of biosamples from all identified cohorts to prepare for network-wide analysis of candidate biomarkers using four different “omics” platforms; the establishment of platforms (polar metabolites, lipidomics) allowing quantitative measurements of plasma metabolites. This represents an important improvement as most previous studies of metabolites have measured relative rather than absolute values.
- Quality control of islet-derived human data and the possibility to co-analyse data generated at different sites provide unprecedented tools for research in T2D. Availability of a first series of paired plasmas and islets tissue from control and diabetic patients to facilitate identification of plasma biomarkers of beta-cell dysfunction.
- Identification of novel candidate biomarkers of deregulated insulin target tissue functions that may lead to increased sphingolipid biomarker production.

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