A new future
for obesity care
Welcome to SOPHIA – Stratification of Obesity Phenotypes to Optimize Future Therapy. This Public-Private Partnership brings together healthcare professionals, academia, industry leaders, and patient organizations to change the future of obesity care.
Tackling the challenge of obesity
Obesity rates are rising around the world. Today, more than one billion people are living with this serious and complex chronic disease. By 2030, it’s projected that 20% of the global population could be affected.
Obesity is a gateway disease to many other health complications, such as heart disease, diabetes, and cancer.1 A fundamental change is needed in how we approach obesity care to uncover new solutions to alleviate this growing burden.
The power to predict
Obesity is associated with more than 230 health complications.1 But healthcare professionals cannot yet predict who will develop these complications. On top of this, there are only a few predictors for who will respond best to different obesity treatments.
The mission of SOPHIA is to help healthcare professionals reliably predict the complications of obesity and who will respond to treatment. Using new research, we aim to better stratify patients, optimize treatment, and ultimately change the narrative around this disease.
This initiative will take us one step closer to identifying the right treatment for the right patient at the right time”
Professor Carel le Roux
SOPHIA Coordinator & Professor of Chemical Pathology,
Diabetes Complications Research Centre at University College Dublin
Turning aims
into actions
The overall goal of SOPHIA is to deliver evidence-based insights across the healthcare
system to redefine obesity and optimize future treatment. We aim to make this a reality through:
STRATIFYING
obesity phenotypes
Defining different subtypes or clusters of obesity through phenotypes helps us better understand obesity and its complex underlying mechanisms.
HARMONIZING
patient data in a federated database
Our federated database harmonizes and combines diverse patient datasets for safe and secure clinical analysis on a global scale.
MODELLING
disease progression and treatment response
Machine learning models trained on patient data help us identify predictors of obesity risk and response among different disease subpopulations.
CREATING
a common understanding of obesity
Gaining perspectives from people living with obesity to build a shared narrative, tackle stigma, improve care, and enhance adherence.
DEMONSTRATING
actionable objectives for all stakeholders
Aligning needs, challenges, and opportunities across stakeholders to promote innovative solutions to obesity management across healthcare systems.
References
1. Czernichow S, Bain SC, Capehorn M, Bøgelund M, Madsen ME, Yssing C, et al. Costs of the COVID-19 pandemic associated with obesity in Europe: A health-care cost model. Clin Obes [Internet]. 2021 Apr 1 [cited 2023 Nov 24];11(2). Available from: https://pubmed.ncbi.nlm.nih.gov/33554456/