Mots-Clés
type 1 diabetes
glycemic variability
continuous glucose monitoring
funtional genomics
multi-omics
molQTL
GWAS
causal inference
biostatistics
epidemiological genetics
translational medecine
Description
Project title: Glycemic Variability, Multi-Omics Integration, and Translational Perspectives across the Disease Spectrum
Project summary
Type 1 diabetes (T1D) affects millions of people worldwide and remains associated with severe acute and chronic complications, particularly cardiovascular disease, despite major therapeutic advances including continuous glucose monitoring (CGM) and hybrid closed-loop systems. While poor glycemic control is a well-established risk factor, the standard biomarker HbA1c fails to capture day-to-day glucose fluctuations — what we call glycemic variability (GV). Whether GV causally drives complications, and through which molecular mechanisms, remains largely unknown. No genome-wide association study (GWAS) has yet explored CGM-derived metrics in T1D, and no formal causal link between GV and chronic complications has been established.
This PhD project is embedded in the ANR GLYRISK consortium and builds on an ongoing GWAS in the large multicenter SFDT1 cohort (French-speaking Society of Diabetes, >3,000 patients). It aims to dissect the molecular and cellular mechanisms underlying glycemic variability in T1D through functional genomics and epidemiological genetics approaches, and to translate this understanding to populations with distinct physiopathological profiles — children with T1D and pre-diabetic individuals.
Part 1 — Molecular and cellular determinants of glycemic variability in adults
Building on CGM-derived glycemic risk signatures and GWAS results being established in the SFDT1 cohort, the student will integrate transcriptomic (bulk and single-nucleus RNA-seq) and proteogenomic data from peripheral immune cells and serums to identify molecular and inflammatory signatures associated with distinct glycemic profiles, using multi-omics integration frameworks and dimensionality reduction approaches. The student will then map molecular quantitative trait loci (molQTLs, including eQTLs and pQTLs) to provide functional context to genetic associations and reveal the regulatory and cellular mechanisms connecting GV to T1D complications — through colocalization analyses, gene set enrichment, and regulatory network inference. The resulting genetic instruments will serve as the basis for Mendelian Randomization analyses conducted in collaboration with our statistical genetics partners.
Part 2 — Glycemic variability in children and pre-diabetic individuals
Co-supervised by Dr Kevin Perge (Hospices Civils de Lyon; Institut Cochin, Paris), this second part extends the investigation of GV to two populations using multivariate epidemiological analyses. In a pediatric cohort available at the Hospices Civils de Lyon, CGM-derived metrics will be systematically benchmarked across key clinical dimensions: developmental age groups (very young children under 5–7 years, school-age children, adolescents), sex, and treatment modality (before and after hybrid closed-loop initiation), alongside additional covariates. Primary endpoints will focus on acute complications — severe hypoglycemia including nocturnal episodes, and diabetic ketoacidosis. In pre-diabetic individuals, we will characterize how glycemic fluctuations evolve across the successive immunological stages of T1D (Stage 1: ≥2 autoantibodies with normal glycemia; Stage 2: dysglycemia; Stage 3: clinical onset), with potential diagnostic and predictive value for disease onset.
Funding
The research project is funded by the ANR collaborative project GLYRISK. The doctoral fellowship is subject to competitive selection through Doctoral School BioSPC (Université Paris Cité).
Team and supervision
This PhD will be supervised by Dr Claire Vandiedonck (HDR) (https://cvandiedonck.github.io/) at IMMEDIAB, Institut Necker Enfants Malades (Inserm U1151, Paris; https://www.institut-necker-enfants-malades.fr/). Dr Kevin Perge (Hospices Civils de Lyon; Institut Cochin, Paris) will co-supervise all clinical aspects of the project and lead Part 2. Additional collaborations include the EGID platform (Dr Amélie Bonnefond, Lille) for genotyping and GWAS, and the Institut Curie (Dr Marie Verbanck, Paris) for statistical genetics. The PhD student will benefit from regular meetings with the supervisors and from a rich interdisciplinary network across genomics, clinical diabetology, and statistical genetics.
Candidate profile
We are looking for a highly motivated candidate holding a Master’s degree (or equivalent) in bioinformatics, statistical genetics, biostatistics, computational biology, or applied mathematics, with:
- Strong background in statistics and/or data analysis applied to biological data
- Programming proficiency in R and/or Python
- Interest in human genetics, genomics, and precision medicine
Experience in omics data analysis, multi-omics integration, or genetic epidemiology would be a strong asset.
How to apply
Interested candidates should first contact Dr Claire Vandiedonck directly by sending a CV, Master grade reports (M1, M2S1) and cover letter to: [adresse email]. Pre-selected candidates will then be invited to apply through the doctoral school competitive process (ED BioSPC, Université Paris Cité). Only one candidate will be presented to the competition.