Type 2 diabetes currently impacts the lives of some 400 million people worldwide. It is already considered as a global epidemic due to the constant increase in the number of patients and the challenges faced by families, partners and caregivers.
Unfortunately, the effectiveness of current therapies varies considerably between individuals. Using precision medicine approaches that take into account the patient's biological characteristics can optimise disease prevention and treatment, thereby improving overall prognosis and reducing costs and side effects.As supported by the World Health Organisation, “effective measures for the surveillance, prevention and control of diabetes and its complications” need be developed. This is where RHAPSODY comes in.
Joined RHAPSODY publication in Diabetologia
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Publication in Diabetes, Obesity and Metabolism on prediction of mortality and major cardiovascular complicationsin type 2 diabetes
Currently, risk and diagnosis of type 2 diabetes are confirmed by testing blood sugar levels. Hence, blood glucose acts as a ‘biomarker’ for the risk and presence of diabetes. In RHAPSODY, it is our aim to identify novel biomarkers, other than blood glucose, that can be used to identify individuals most at risk of developing type 2 diabetes, and predict its individual clinical course (disease progression) and its complications.
Importantly, type 2 diabetes is a highly heterogeneous disease, leading to various degrees of deficient insulin production, and/or insulin resistance and finally hyperglycemia. The heterogeneous nature of type 2 diabetes in patients makes it difficult to determine the most efficient treatment for a given patient, as patients often respond differently to the same treatment.
In this context, the characterization of groups of individual patients whose diabetes behaves similarly (so-called “patient clustering”) and the identification of novel biomarkers specific to these groups are of great importance for tailored diagnosis and disease monitoring for the individual patients. This is the first step towards precision medicine in type 2 diabetes: by enabling this step so, RHAPSODY aims at improving overall prognosis.
Summary for publication
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In RHAPSODY, we have access to data from large groups of patients from across Europe (study cohorts, learn more about RHAPSODY cohorts here), including individual clinical, biochemical, genetic and multi-omics data (discover more about the omics here). We combine powerful bioinformatics analysis and various experimental approaches, using blood samples and key diabetes-relevant tissues and organs from humans and mouse preclinical models (learn more here) to identify novel biomarkers for type 2 diabetes susceptibility and progression for individual patients, as well as to clarify their link with the alterations of pancreatic insulin secretion and of insulin action on liver, fat and muscle.
In order to validate the novel biomarkers identified in RHAPSODY for clinical use and pharmacological applications, experts in regulatory approval support scientists in ensuring that biomarker characterization and development are in line and further developed with the requirements by the European Medicines Agency (EMA). Health economists perform cost-benefit assessments, ensuring that the discovery process of RHAPSODY minimizes the time between biomarker identification and their potential clinical and commercial use (more information available here).
Clinical Data Interchange Standards Consortium. A set of widely used standards for clinical data.
A research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes (source: https://grants.nih.gov/).
Patient cohort: A group of individuals affected by common diseases, environmental or temporal influences, treatments, or other traits whose progress is assessed in a research study (source: https://medical-dictionary.thefreedictionary.com/)
Computer simulation models are a common tool to predict how different treatments may affect disease progression and outcomes. They are used to extrapolate the findings of clinical trials into lifetime costs and health benefits.
Diabetes is a chronic disease characterised by chronically elevated levels of blood sugar (hyperglycemia), due to alterations in the production or the use of insulin by the body. Insulin action is vital to ensure that glucose (sugar, basic source of energy) from our food is properly utilised by cells of the human body.
Insulin is a hormone produced by the pancreas (more precisely by pancreatic beta cells), which is crucial to control blood glucose (glycemia). Once secreted into the blood stream, insulin orchestrates a coordinated response to lower blood glucose by acting on several tissues (namely insulin-target tissues). Notably, insulin stimulates glucose uptake by muscle and fat (adipose tissue) and prevents glucose production by the liver.
For a long time, diabetes has been labelled either ‘type 1’ or ‘type 2’ diabetes. Type 2 diabetes is the result of failures across several complex biological systems. Compared to those with type 1 diabetes, patients with type 2 diabetes can still produce insulin, at least during the first stages of the disease (prediabetes and early type 2 diabetes). However, their insulin-target tissues stop responding to the normal amounts of insulin produced after eating: this so-called “insulin resistance” process prevents glucose uptake by cells, resulting in rise of blood glucose. Chronic hyperglycemia tricks the pancreas into over-producing insulin. If untreated, this vicious circle of events can spiral out of control leading to an exhaustion of the pancreas (“pancreatic beta cell failure”) to produce insulin (insulin deficiency).
Diabetes is a major public health problem since over time, it can damage heart and blood vessels (heart attacks and strokes), nerves (neuropathy resulting in foot ulcers and possibly limb amputation), eyes (retinopathy leading to blindness) and kidneys (nephropathy).
Epigenetics is the study of how behaviors and environment can cause changes that affect the way genes work. Unlike genetic changes, epigenetic changes are reversible and do not change DNA sequence, but they can change how the body reads a DNA sequence (source: https://www.cdc.gov/genomics/disease/epigenetics.htm).
Elovl2: this gene encodes for the protein called "Elongation of very long chain fatty acids protein 2" which catalyzes the first and rate-limiting reaction of the four reactions that constitute the long-chain fatty acids elongation cycle. This endoplasmic reticulum-bound enzymatic process allows the addition of 2 carbons to the chain of long- and very long-chain fatty acids (VLCFAs) per cycle (source: https://www.uniprot.org/uniprot/Q9JLJ4)
A server containing a clinical database which can be connected to from a central computer using web services (HTTPS)
A surgical bypass operation that typically involves reducing the size of the stomach and reconnecting the smaller stomach to bypass the first portion of the small intestine so as to restrict food intake and reduce caloric absorption in cases of severe obesity (source: https://merriam-webster.com/).
Organs where insulin exerts its blood glucose lowering action. Liver: insulin decreases glucose production; muscles and adipose tissue: insulin stimulates glucose uptake.
Multi-omics refer to biological information in the form of any molecules in our bodies which are part of our everyday functioning and can change during diseases.
Genomics: study of the full genetics components of an organism (the genome) which includes the entire DNA sequence. It also considers the inter-relationships between the genes and their interaction with environmental factors.
Transcriptomics: transcriptomics is used to identify the qualitative and quantitative RNA levels in the whole genome. It can tell us which transcripts are present together with the levels of their expression. Almost 80% of the genome is transcribed to RNA.
Proteomics: study of the structure, function and physiological role of the entire proteins in a cell or tissue of an organism.
Metabolomics:study of the metabolites present in a cell or tissue/fluids of an organism. This includes small molecules, carbohydrates, peptides, lipids, nucleosides and metabolism products.
A set of concepts and categories in a subject area or domain that shows their properties and the relations between them.
A phenotypic trait is an obvious, observable, and measurable trait; it is the expression of genes in an observable way. An example of a phenotypic trait is a specific hair color. Underlying genes, which make up the genotype, determine the hair color, but the hair color observed is the phenotype. The phenotype is dependent on the genetic make-up of the organism, and also influenced by the environmental conditions (source: https://en.wikipedia.org/wiki/Phenotypic_trait)
PNLIPRP1: this gene encodes for the protein called "pancreatic lipase-related protein 1" which may function as inhibitor of dietary triglyceride digestion (source: https://www.uniprot.org/uniprot/P54315)
Precision medicine refers to the tailoring of medical treatment to the individual characteristics of each patient, […] the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease, in the biology or prognosis of those diseases they may develop, or in their response to a specific treatment (definition from the US National Research Council, https://en.wikipedia.org/wiki/Precision_medicine).
When fasting blood glucose is raised beyond normal levels, but is not high enough to warrant a diabetes diagnosis (source: https://diabetes.co.uk/)
Health benefits are often expressed as quality-adjusted life years (QALYs). QALYs combine longevity and quality of life in a single metric and allow comparisons across different treatments and diseases. As a result, the impact on QALYs are required by several European agencies to make decisions about whether to adopt and reimburse medicines at a national level.
A programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis (source: https://en.wikipedia.org/wiki/R_(programming_language)).
An integrated development environment for R that runs on Windows, Mac or Linux operating systems.
Sensitivity is the proportion of true-positives which actually test positive, and how well a test is able to detect positive individuals in a population (source: https://www.fws.gov/aah/PDF/SandS.pdf).
Specificity is the proportion of true-negatives which actually test negative, and reflects how well an assay performs in a group of disease negative individuals (source: https://www.fws.gov/aah/PDF/SandS.pdf).
This work is supported by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0097-2.
The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies.
