Analysis Group Co-launches Consortium to Advance AI-Driven Precision Health and Evidenced Medicine Research in Cardiometabolic Conditions

May 5, 2026

Improvements in care for patients with cardiometabolic conditions, as well as decisions related to their treatment and management, increasingly depend on integrated insights drawn from diverse public and private data sources. Harnessing medical big data through AI-driven methodologies offers a promising path to meet this need.

Analysis Group and Harvard T.H. Chan School of Public Health co-organized the Consortium for Obesity & Cardiometabolic Research & Evidence (CORE). CORE is designed to bridge fragmented data silos and break down traditional barriers to collaboration across the health care ecosystem. It enables the generation of more robust, timely, and actionable insights and speeds up the conversion of research findings into real-world implementation.

About the Consortium for Obesity & Cardiometabolic Research & Evidence (CORE)

CORE is a collaborative network of academic researchers, leading medical and payer experts, and industry partners focused on strengthening the scientific rigor, policy relevance, and real-world impact of obesity and cardiometabolic research.

In parallel with the Harvard Chan School–Analysis Group Initiative on Precision Health (HAPI), CORE aims to leverage cutting-edge AI methodologies to connect a spectrum of data sources and deliver a comprehensive and consistent view of disease progression, treatment impacts, and outcomes.

About the Harvard Chan School—Analysis Group Initiative on Precision Health (HAPI)

HAPI was created to bridge academic and private-sector data sources and research. Through HAPI, researchers will leverage rich datasets from Harvard Chan School’s landmark prospective cohorts along with AI and machine learning technology to develop a comprehensive understanding of diseases and treatments, uncover novel disease pathways, engineer risk stratification tools, and advance personalized medical intervention strategies.

At its inaugural annual meeting on April 6, 2026, CORE convened leading global experts to consider critical evidence gaps, high-impact research opportunities, and emerging therapeutic trends. Attendees also explored how AI can accelerate evidence generation, strengthen value demonstration, and inform clinical, access, and policy decision making. Participating experts presented the validation and applications of the Dynamic Evaluation of Cardiometabolic and Obesity Disease (DECODETM) model, a digital twin AI that simulates long-term disease trajectories and treatment effects.

About the Dynamic Evaluation of Cardiometabolic and Obesity Disease (DECODETM) model

DECODETM, trained and validated in various real-world data sources, aims to achieve cross-dataset generalizability, enabling risk prediction across diverse populations and supporting personalized prevention strategies rooted in real-world data. It predicts disease progression, patient journeys, prognoses, and treatment outcomes for patients with cardiometabolic conditions.

Dr. Frank Hu, Conference Chair and Chair of the Department of Nutrition and Fredrick J. Stare Professor of Nutrition and Epidemiology at Harvard Chan School, noted, “In cardiometabolic health, the question is no longer only what works, but how to make what works reach patients at scale. CORE exists to unite academia, health care systems, industry, and payers to generate actionable evidence and translate innovation into real-world impact.”

Analysis Group Managing Principal Eric Wu stated, “With CORE and GenAI models like DECODE, we’re creating a scalable, AI-enabled knowledge network to shape the future of personalized health care and empower informed, data-driven decisions.”

A full list of CORE 2026 Annual Meeting Presentations is included below.

  • Frank B. Hu, M.D., Ph.D., Chair, Department of Nutrition, Harvard T.H. Chan School of Public Health and Professor of Medicine, Brigham and Women’s Hospital and Harvard Medical School – Pharmacotherapy, Lifestyle, and Cardiometabolic Risk in Obesity: A Framework for Evidence-based Precision Health: Integrating pharmacotherapy and lifestyle approaches for precision health in obesity and cardiometabolic risk
  • Peter Libby, M.D., Ph.D., Mallinckrodt Professor of Medicine, Harvard Medical School and Cardiovascular medicine specialist, Brigham and Women’s Hospital – Inflammation, Lipid Treatment, and the Future of Cardiovascular Prevention: Role of inflammation and lipid treatment in advancing cardiovascular prevention
  • Robert W. Platt, Ph.D., Director, McGill Centre for Clinical Epidemiology and Professor of Epidemiology, Biostatistics, and Occupational Health, McGill University – Artificial Intelligence, Medical Big Data, and the Dynamic Evaluation of Cardiometabolic and Obesity Disease (DECODE) Model: Use of AI and big data in modeling and evaluating cardiometabolic disease
  • Liming Liang, Ph.D., Professor of Statistical Genetics, Department of Biostatistics and Director, Program in Genetic Epidemiology and Statistical Genetics, Harvard Chan School – Connecting Data Islands: Building Comprehensive Understanding of Disease Through Collaborative Global Cardiometabolic Research: Leveraging global collaboration and data integration to understand cardiometabolic disease
  • Eric Wu, Ph.D., Managing Principal, Analysis Group – Holistic Understanding of Cardiometabolic Disease Through DECODE: AI Powered Medical Insight: AI-driven insights for comprehensive understanding of cardiometabolic disease
  • Steven B. Heymsfield, M.D., Boyd Professor and Director, Metabolism and Body Composition Laboratory, Pennington and Biomedical Research Center, Louisiana State University – Beyond the Scale: Protective Weight Loss in the Era of GLP-1 Receptor Agonists: Benefits of weight loss beyond weight metrics, especially with GLP-1 therapies
  • Steven K. Grinspoon, M.D., Professor of Medicine, Harvard Medical School and Director, Boston Area Nutrition Obesity Research Center – Redefining Obesity and Pre-obesity in Cardiometabolic Risk Stratification: New frameworks for defining obesity and assessing cardiometabolic risk
  • Sébastien Czernichow, M.D., Ph.D., Head, Department of Nutrition, Hôpital Européen Georges-Pompidou, Paris and Professor of Nutrition, Université Paris Cité – Epidemiology of Obesity in France
  • Olga V. Demler, Ph.D., Assistant Professor (part-time), Brigham and Women’s Hospital and Harvard Medical School and Senior Research Scientist (part-time), Computer Science Department, ETH Zurich – Advancing Precision Cardiovascular Medicine: Integrating Clinical Trials, Biomarkers, and AI-driven Risk Prediction: Combining trials, biomarkers, and AI for precision cardiovascular medicine
  • Richard Wagner, Pharm.D., Former Pharmacy Director, Kaiser Permanente (retired) – Coverage and Formulary Management of Novel Obesity and Cardiovascular Therapies: Opportunities, Challenges, and the Role of Personalized Medicine: Payer perspectives on access, coverage, and personalization of new therapies 
  • Frank B. Hu, M.D., Ph.D., Chair, Department of Nutrition, Harvard T.H. Chan School of Public Health and Professor of Medicine, Brigham and Women’s Hospital and Harvard Medical School; Peter Libby, M.D., Ph.D., Mallinckrodt Professor of Medicine, Harvard Medical School and Cardiovascular medicine specialist, Brigham and Women’s Hospital; Richard Wagner, Pharm.D., Former Pharmacy Director, Kaiser Permanente (retired); Rafael Alfonso, M.D., Ph.D., Executive Director TA Head Evidence Generation Cardio Renal Metabolic, Novartis – Panel Discussion: Advancing Cardiometabolic Research and Patient Care through Partnerships: Collaborative approaches across academia, industry, and healthcare to advance research and patient care 

Learn more about CORE in our ISPOR 2026 Presentation Guide