Di Angelantonio & Ieva Group
Nel Di Angelantonio & Ieva Group epidemiologi, statistici e data scientists lavorano insieme per colmare il vuoto nelle conoscenze tra genotipo e fenotipo andando a studiare vari livelli di dati molecolari per indagare lo stato di salute degli individui e della popolazione. Per raggiungere questo obiettivo, il Gruppo sviluppa studi innovativi che integrano i dati biomolecolari con i dati di cartelle cliniche, di imaging e di dispositivi medici portatili. Vengono utilizzati sia dati già esistenti (cartelle cliniche ospedaliere, prescrizioni mediche, studi di coorte) sia nuovi dati generati da studi di popolazione che vengono analizzati attraverso nuovi metodi analitici, integrando l’epidemiologia clinica con la ricerca sanitaria allo scopo di migliorare l’analisi e l’interpretazione dei dati.
Connettendo i dati molecolari con le cartelle cliniche, il Gruppo si pone l’obiettivo di generare progressi utili in biologia, eziologia delle malattie, previsione del rischio, diagnosi precoce e targeting terapeutico. I metodi sviluppati vedranno applicazione nella medicina personalizzata, con benefici sulla salute individuale dei pazienti, così come in studi di popolazione grazie all’uso di dati su larga scala, con importanti progressi in sanità pubblica, analisi di dati sanitari e sviluppo di politiche sanitarie mirate.
La ricerca del Gruppo si concentra sullo studio dei fattori di rischio che causano le malattie e per sviluppare modelli di previsione del rischio per lo sviluppo di malattie croniche usando diversi livelli di dati tra cui dati omici, genetici e da cartelle cliniche.
Membri del gruppo
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Emanuele Di Angelantonio
Head of Health Data Science Centre -
Francesca Ieva
Associate Head of Research centre -
Laura Bondi
Postdoc -
Solène Cadiou
Postdoc -
Andrea Corbetta
PhD Student -
Nicole Fontana
PhD Student -
Andrea Lampis
PhD Student -
Katherine Marie Logan
PhD Student -
Alessia Mapelli
PhD Student -
Michela Carlotta Massi
Staff Scientist -
Lucia Piubeni
PhD Student -
Carlo Andrea Pivato
Scientific Visitor -
Laura Savarè
Postdoc -
Piercesare Secchi
Scientific Visitor -
Luca Trizio
Scientific Visitor -
Andrea Mario Vergani
PhD Student
Pubblicazioni
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09/2022 - PloS CompBio
Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: an application to Breast Cancer time to diagnosis
Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation profiles, either ignore the explicit modeling of the time to diagnosis (TTD) as in a survival analysis setting, or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at […]
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08/2022 - European Journal of Preventive Cardiology
Obesity and cardiovascular disease: mechanistic insights and management strategies. A joint position paper by the World Heart Federation and World Obesity Federation
The ongoing obesity epidemic represents a global public health crisis that contributes to poor health outcomes, reduced quality of life, and >2.8 million deaths each year. Obesity is relapsing, progressive, and heterogeneous. It is considered a chronic disease by the World Obesity Federation (WOF) and a chronic condition by the World Heart Federation (WHF). People […]
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08/2022 - European Journal of Preventive Cardiology
Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP
Aims The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) […]
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08/2022 - European Journal of Nuclear Medicine and Molecular Imaging
PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival
Purpose Intrahepatic cholangiocarcinoma (IHC) is an aggressive disease with few reliable preoperative biomarkers. This study aims to elucidate if radiomics extracted from preoperative [18F]FDG PET/CT may grant a non-invasive biological characterization of IHC and predict outcome after complete resection of the tumor. Methods All patients preoperatively imaged by [18F]FDG PET/CT who underwent hepatectomy for mass-forming […]
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06/2022 - Statistical Methods & Applications
Modelling time-varying covariates effect on survival via functional data analysis: application to the MRC BO06 trial in osteosarcoma
Time-varying covariates are of great interest in clinical research since they represent dynamic patterns which reflect disease progression. In cancer studies biomarkers values change as functions of time and chemotherapy treatment is modified by delaying a course or reducing the dose intensity, according to patient’s toxicity levels. In this work, a Functional covariate Cox Model […]
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