DEVELOPMENT AND VALIDATION OF AN INDIVIDUAL PATIENT-LEVEL SIMULATION OBESITY MODEL TO ESTIMATE OBESITY PROGRESSION
Author(s)
Giovanni Esposito, PhD1, Laetitia Gerlier, MSc1, Andrew H. Briggs, DPhil2, Carel W. le Roux, PhD3, Andrew Walker, PhD4, Ahmad Alhussein, BSPharm5, Anindit Chhibber, MS, PhD6, Maria de Lourdes Rodriguez, BSc, MSc7, Sean D. Sullivan, PhD8, Anastasia Uster, PhD, MD5.
1IQVIA, Zaventem, Belgium, 2London School of Hygiene & Tropical Medicine, London, United Kingdom; and Occam Research, London, United Kingdom, 3Ulster University, Coleraine, UK; University College Dublin, Dublin, Ireland, 4Salus Alba, Glasgow, United Kingdom, 5Boehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany, 6Boehringer Ingelheim, Ridgefield, CT, USA, 7Boehringer Ingelheim, Bracknell, United Kingdom, 8University of Washington School of Pharmacy, Seattle, WA, USA; and London School of Economics and Political Science, London, United Kingdom.
1IQVIA, Zaventem, Belgium, 2London School of Hygiene & Tropical Medicine, London, United Kingdom; and Occam Research, London, United Kingdom, 3Ulster University, Coleraine, UK; University College Dublin, Dublin, Ireland, 4Salus Alba, Glasgow, United Kingdom, 5Boehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany, 6Boehringer Ingelheim, Ridgefield, CT, USA, 7Boehringer Ingelheim, Bracknell, United Kingdom, 8University of Washington School of Pharmacy, Seattle, WA, USA; and London School of Economics and Political Science, London, United Kingdom.
OBJECTIVES: Obesity represents an urgent and long-term global health challenge, and microsimulation modelling has become a key methodology informing public health approaches worldwide. We describe development and validation of an obesity disease‑progression model.
METHODS: A patient-level microsimulation model was developed to predict evolution of body mass index (BMI) and associated risk factors in patients with obesity, informed by clinical guidelines, literature reviews and expert opinion. For each patient in the modelled cohort, baseline sociodemographic, genetic, lifestyle, anthropometric and clinical characteristics are generated by a stepwise sampling process using clinical trial or real-world data. BMI progression is based on an Organisation for Economic Cooperation and Development model; obesity-related risk factors and mortality are predicted using existing BMI-adjusted risk equations. The model predicts cardiorenal-metabolic, respiratory, musculoskeletal, inflammatory, oncology and mental health complications using BMI‑dependent and risk‑factor-mediated equations informed by cohort studies. For a simulated cohort of 1000 patients based on baseline patient characteristics from the real-world All of Us (AoU) study, 10-year predicted incidence of obesity‑related complications across BMI strata were externally validated against estimates from AoU (5-year data extrapolated to 10 years), before and after calibration.
RESULTS: Baseline characteristics of the simulated cohort were aligned with the AoU cohort (mean age, 51.7 years; mean BMI, 29.9 kg/m²; 62.7% female; 15.4% had type 2 diabetes; 20.8%, 11.4% and 9.6% were obesity class I, II and III, respectively). Before calibration, agreement between 10-year predicted complications and complications estimated using AoU data was moderate overall and across BMI strata (R2 range: 0.74-0.83). Following calibration, predicted complications demonstrated excellent agreement with AoU estimates, with R2 >0.99 overall and across BMI strata.
CONCLUSIONS: This validated disease‑progression model provides a robust and flexible framework for simulating obesity‑related complications. Model inputs are modifiable including treatment effects, utilities and costs to enable cost-effectiveness evaluations of weight‑management interventions.
METHODS: A patient-level microsimulation model was developed to predict evolution of body mass index (BMI) and associated risk factors in patients with obesity, informed by clinical guidelines, literature reviews and expert opinion. For each patient in the modelled cohort, baseline sociodemographic, genetic, lifestyle, anthropometric and clinical characteristics are generated by a stepwise sampling process using clinical trial or real-world data. BMI progression is based on an Organisation for Economic Cooperation and Development model; obesity-related risk factors and mortality are predicted using existing BMI-adjusted risk equations. The model predicts cardiorenal-metabolic, respiratory, musculoskeletal, inflammatory, oncology and mental health complications using BMI‑dependent and risk‑factor-mediated equations informed by cohort studies. For a simulated cohort of 1000 patients based on baseline patient characteristics from the real-world All of Us (AoU) study, 10-year predicted incidence of obesity‑related complications across BMI strata were externally validated against estimates from AoU (5-year data extrapolated to 10 years), before and after calibration.
RESULTS: Baseline characteristics of the simulated cohort were aligned with the AoU cohort (mean age, 51.7 years; mean BMI, 29.9 kg/m²; 62.7% female; 15.4% had type 2 diabetes; 20.8%, 11.4% and 9.6% were obesity class I, II and III, respectively). Before calibration, agreement between 10-year predicted complications and complications estimated using AoU data was moderate overall and across BMI strata (R2 range: 0.74-0.83). Following calibration, predicted complications demonstrated excellent agreement with AoU estimates, with R2 >0.99 overall and across BMI strata.
CONCLUSIONS: This validated disease‑progression model provides a robust and flexible framework for simulating obesity‑related complications. Model inputs are modifiable including treatment effects, utilities and costs to enable cost-effectiveness evaluations of weight‑management interventions.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MSR68
Topic
Economic Evaluation, Methodological & Statistical Research
Disease
Diabetes/Endocrine/Metabolic Disorders (including obesity), No Additional Disease & Conditions/Specialized Treatment Areas