Details

Autor(en) / Beteiligte

• Delfin, Carl
• Dragan, Iulian
• Kuznetsov, Dmitry
• Tajes, Juan Fernandez
• Smit, Femke
• Coral, Daniel E
• Farzaneh, Ali
• Haugg, André
• Hungele, Andreas
• Niknejad, Anne
• Hall, Christopher
• Jacobs, Daan
• Marek, Diana
• Fraser, Diane P
• Thuillier, Dorothee
• Ahmadizar, Fariba
• Mehl, Florence
• Pattou, Francois
• Burdet, Frederic
• Hawkes, Gareth
• Arts, Ilja C W
• Blanch, Jordi
• Van Soest, Johan
• Fernández-Real, José-Manuel
• Boehl, Juergen
• Fink, Katharina
• van Greevenbroek, Marleen M J
• Kavousi, Maryam
• Minten, Michiel
• Prinz, Nicole
• Ipsen, Niels
• Franks, Paul W
• Ramos, Rafael
• Holl, Reinhard W
• Horban, Scott
• Duarte-Salles, Talita
• Tran, Van Du T
• Raverdy, Violeta
• Leal, Yenny
• Lenart, Adam
• Pearson, Ewan
• Sparsø, Thomas
• Giordano, Giuseppe N
• Ioannidis, Vassilios
• Soh, Keng
• Frayling, Timothy M
• Le Roux, Carel W
• Ibberson, Mark

Titel

A Federated Database for Obesity Research: An IMI-SOPHIA Study

Ist Teil von

• Life (Basel, Switzerland), 2024-02, Vol.14 (2), p.262

Ort / Verlag

Switzerland: MDPI AG

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.