A mission to
transform obesity care

SOPHIA is a Public-Private Partnership that brings together healthcare professionals, academia, industry leaders, and patient organizations to optimize the future of obesity care.

Our mission is to better predict the risks of obesity and responses to treatment – making care more personalized and patient centric.

Using machine learning, federated health data, and a unique approach to shared value, we want to inspire a whole new approach to obesity care and management worldwide.

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    year project span

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    completion date

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    million euros in funding

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    consortium partners

One step closer to improved disease management

Our vision for obesity care is to better understand an individual’s risk and the overall health impact so we can personalize and prioritize care.

This means targeted interventions to manage and treat obesity and its associated health complications. This takes us one step closer to identifying the right treatment for the right patient at the right time.

Our key aims

  • Creating a federated database

    Analyzing data securely across multiple servers in Europe and around the world


  • Defining subpopulations

    Using obesity subclusters to identify obesity risks and responses to treatment


  • Changing attitudes and perspectives

    Gaining insights from those living with obesity to build a common understanding


  • Transforming research into action

    Using our results to map opportunities for improved healthcare delivery and policy


8 ways we’re working to

optimize obesity treatment

SOPHIA is working to deliver evidence-based insights across the healthcare system to

redefine obesity and optimize future treatment.

This will improve our ability to predict who experiences which complications and who

responds best to different treatments.

Unfold and explore our eight work packages below.


Dissemination & communication

WP1 Objectives

This work package focuses on ensuring the management, communication, and promotion of the project runs smoothly. This involves effective organization of activities in every work package, the timely delivery of project deliverables, reports and results, as well as the dissemination of its results and planning for the future use of SOPHIA results.


Data federation & analysis

WP2 Objectives

This work package aims to harmonize and standardize obesity cohorts into a federated database. This involves employing federated machine-learning tools for data analysis and identification of biomarkers relevant to patient stratification and treatment response, all while maintaining strict data management protocols for security, confidentiality, and access control.


Risks & response in people with obesity without diabetes

WP3 Objectives

The activities of this work package use large population-based datasets to describe cohorts of people living with obesity that include genetic and other molecular phenotype information to investigate risk factors for developing obesity and identify biomarkers that:

  1. Predict the risks of obesity
  2. Predict response to obesity treatments (lifestyle, surgery, pharmacotherapy)
  3. Subclassify obesity into etiological (cause) and pathogenic (consequence) subclasses


Obesity & type 1 diabetes

WP4 Objectives

This work package seeks to investigate the link between obesity and Type 1 Diabetes (T1D) by analyzing distinct cohorts, including individuals with T1D with and without obesity, those who have received obesity treatments, and those at risk for T1D with and without obesity.


Obesity & type 2 diabetes

WP5 Objectives

This work package utilizes extensive population-based cohorts of Type 2 Diabetes (T2D) patients, including genetic and molecular characterization, to examine the impact of obesity and its subtypes on T2D phenotype and outcomes. The objectives are to explore differences in risk among obesity subpopulations within T2D, investigate variations in obesity phenotypes and outcomes in Indian Asians with T2D, and identify predictors of response to obesity treatments, including diet, exercise, diabetes medication, and surgery.


Biomarker validation & predictive algorithms

WP6 Objectives

This work package focuses on leveraging large cohorts of T2D patients to study the influence of obesity and its subtypes on disease phenotype and outcomes. It aims to identify differences in risk among obesity subpopulations, explore variations in obesity phenotypes in specific demographics, and predict response to various obesity treatments including diet, exercise, medication, and surgery.


Patient perceptions & preferences

WP7 Objectives

This work package aims to understand patient perspectives on the risks and challenges of living with obesity and their desired responses to treatment. The insights gained from workshops, focus groups, interviews, and surveys will inform the future of personalized medicine, healthcare strategies, and approaches to patient-centered care.


Shared value analysis

WP8 Objectives

This work package focuses on establishing shared value among stakeholders. Workshops and focus groups will align research results, assess stakeholder needs, and map potential pathways toward future healthcare solutions and policy objectives.