Opioids and insulin secretion: a new avenue to fight type 2 diabetes

Introduction

Type 2 diabetes (T2D) and obesity are leading causes of morbidity and mortality, becoming a major burden on public health. Failure to understand their pathophysiology has frustrated efforts to develop improved therapeutic strategies. T2D and obesity are complex polygenic disorders.

Current Research Landscape

Genome-wide association studies have identified hundreds of loci associated with metabolic traits, but they have not led to promising new drug targets so far. In contrast, the identification and characterization of rare mutations causing monogenic metabolic disorders have been instrumental in repositioning or developing drugs.

Challenge Ahead

Beyond this proof of concept, the challenge is to use genomic medicine for innovative molecules restoring impaired insulin secretion that characterizes T2D.

Hypothesis

OO has stemmed from the old but forgotten correlation between opioids consumption and metabolic trait abnormalities. Opioids classically act through delta opioid receptor (DOP encoded by OPRD1), kappa, and mu opioid receptors. Based on my preliminary data that include large-scale human functional genetics of OPRD1 mutations, my hypothesis is that DOP is a major link between opioids and metabolism in humans.

Proposed Mechanism

More specifically, I propose that DOP and opioid signaling have a crucial direct role in insulin secretion from pancreatic cells, making DOP a promising new drug target against T2D.

Proposed Tasks

In this context, I suggest four tasks in OO:

1. The first two tasks will decipher the role of DOP and opioid signaling in metabolism with a specific focus on islets and β cells.
2. The two following tasks will decipher the systemic contribution of opioids and opioid receptors to metabolism.

Methodologies

The methodologies that I proposed are truly comprehensive and innovative as they combine:

• Preclinical animal studies (with a humanized mouse model)
• Deep phenotyping of islets
• Pharmacology
• Multi-omic analyses
• Genome editing
• Functional genetics
• Genetic epidemiology (including Mendelian randomization)
• Clinical intervention study