Supervisor: Giacomo Cappon
Co-supervisor: Jason Chai
Co-supervisor Department/Company: Radcliffe Department of Medicine, Univerisity of Oxford
Creation Date: 10/12/2025 08:48
What we know:
* Hyperglycaemia induced trained immunity (HITI), defined by a pro-inflammatory and/or anti-reparative state of immune cells, is more prevalent in patients with type 2 diabetes (T2D)
* HITI drives atherogenesis and impedes plaque regression or stability and hence associate with more complex coronary artery disease
* Characterisation of T2D by current clinical criteria such as the level of glycated haemoglobin (HbA1c) is insufficient to identify patients who have HITI but instead patterns of dysregulated glycaemia captured by continuous glucose monitoring (CGM) coupled with clinically available information such as duration of type 2 diabetes provides better resolution to do so
Aim of the thesis:
* To explore the metabolic drivers and prevalence of HITI in patients with T2D presenting with acute coronoary syndrome (ACS)
Dataset type: Already acquired data
Dataset description: Overall, we have data of 73 participants. Details: * Clinical characteristics and experimental results * Average diet * Glucose traces collected with continuous glucose monitoring systems (CGM) of: - 18 diabetes & pre-diabetes vs 14 without [1-10 days] (in-hospital) - 35 diabetes & pre-diabetes vs 26 without [10-30 days] (at home) - 10 diabetes & pre-diabetes vs 9 without (paired in-hospital and home)
List of Methods: Correlation analysis Machine learning-based modeling AGATA (https://github.com/gcappon/py_agata)
Machine Learning Analisi di Dati Biologici