A Federated Database for Obesity Research : An IMI-SOPHIA Study
(2024) In Life 14(2).- Abstract
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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2024-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- bioinformatics, federated database system, obesity, remote statistical analysis, risk prediction
- in
- Life
- volume
- 14
- issue
- 2
- article number
- 262
- publisher
- MDPI AG
- external identifiers
-
- scopus:85193278224
- pmid:38398771
- ISSN
- 0024-3019
- DOI
- 10.3390/life14020262
- language
- English
- LU publication?
- yes
- id
- 74bd0687-9fd0-433c-aa55-f7ae8cd24fd3
- date added to LUP
- 2024-06-28 13:10:53
- date last changed
- 2024-06-29 03:00:06
@article{74bd0687-9fd0-433c-aa55-f7ae8cd24fd3, abstract = {{<p>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.</p>}}, author = {{Delfin, Carl and Dragan, Iulian and Kuznetsov, Dmitry and Tajes, Juan Fernandez and Smit, Femke and Coral, Daniel E. and Farzaneh, Ali and Haugg, André and Hungele, Andreas and Niknejad, Anne and Hall, Christopher and Jacobs, Daan and Marek, Diana and Fraser, Diane P. and Thuillier, Dorothee and Ahmadizar, Fariba and Mehl, Florence and Pattou, Francois and Burdet, Frederic and Hawkes, Gareth and Arts, Ilja C.W. and Blanch, Jordi and Van Soest, Johan and Fernández-Real, José Manuel and Boehl, Juergen and Fink, Katharina and van Greevenbroek, Marleen M.J. and Kavousi, Maryam and Minten, Michiel and Prinz, Nicole and Ipsen, Niels and Franks, Paul W. and Ramos, Rafael and Holl, Reinhard W. and Horban, Scott and Duarte-Salles, Talita and Tran, Van Du T. and Raverdy, Violeta and Leal, Yenny and Lenart, Adam and Pearson, Ewan and Sparsø, Thomas and Giordano, Giuseppe N. and Ioannidis, Vassilios and Soh, Keng and Frayling, Timothy M. and Le Roux, Carel W. and Ibberson, Mark}}, issn = {{0024-3019}}, keywords = {{bioinformatics; federated database system; obesity; remote statistical analysis; risk prediction}}, language = {{eng}}, number = {{2}}, publisher = {{MDPI AG}}, series = {{Life}}, title = {{A Federated Database for Obesity Research : An IMI-SOPHIA Study}}, url = {{http://dx.doi.org/10.3390/life14020262}}, doi = {{10.3390/life14020262}}, volume = {{14}}, year = {{2024}}, }