BUILDING A METABOLIC TISSUE DATABASE: constitution, qualification, and use of a database of tissues from type 2 diabetes patients

AXLR



24 Octobre 2015

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Fields

Biology / Medical

Sectors

Health

Constitution, qualification, and use of a database of tissues from type 2 diabetes patients for industry and academic partners.

CONTEXT
According to the International Diabetes Foundation, 387 million adults around the world have diabetes today.

Type 2 diabetes is characterized by chronic hyperglycemia, which, when not treated properly, can lead to severe micro- and macro-vascular complications. The consequences cause incapacitation for some patients, while reducing life expectancy for others, thereby contributing significantly to the disease’s comorbidity, mortality, and high medical-social costs. The disease is related to malfunctioning of pancreatic β cells, causing an insufficient secretion of insulin as well as a modification of its action. This results in insulin-resistance in neighboring cells (adipose tissue and skeletal muscles) and the liver.

BENEFITS
This project consists of establishing a collection of human tissue samples from obese patients exhibiting metabolic abnormalities. The goal is to be able to identify and confirm new molecular entities associated with type 2 diabetes and its worsening.

In addition to containing a large number of patients, the established collection will offer a wide variety of tissues involved with metabolic dysfunction, notably in muscles, the liver, adipose tissues, and plasma/serum.

The potential for making discoveries thanks to this database is enormous, with data being transposable directly to human clinical applications. Generated results are therefore expected to help improve diabetes treatment, both through the emergence of innovative treatments and through better patient characterization and follow-up on their anti-diabetic treatments.

APPLICATIONS
• Understanding physiopathological mechanisms involved with the disease.

• Searching for new targets with therapeutic potential.

• Searching for diagnostic markers to track the disease’s evolution and the efficiency of pharmacological treatments.

• Better patient stratification for using anti-diabetic drugs.

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