Background <p>Obesity and overweight have a wide range of adverse clinical outcomes. Predictive models could help clinicians identify individuals with obesity and overweight at greater risk for obesity-related complications, enabling personalized shared decision-making about obesity treatment. We previously developed a set of predictive models of adverse clinical outcomes of obesity and overweight using electronic health record (EHR) data of a large health system in Massachusetts. However, these models have not been validated on data from other sources.</p> Methods <p>We validated 12 predictive models for cardiometabolic, oncologic and musculoskeletal complications of excess weight using a linked EHR and claims dataset of a national patient cohort (Optum^® Market Clarity) that included adults 18–80 years of age with body mass index (BMI) ≥ 25&#xa0;kg/m² between 2007 and 2023.</p> Results <p>A 5% randomly selected validation dataset included 437,223 patients (51.9% women) with a mean age of 51.4 years and a mean BMI of 31.9&#xa0;kg/m² who were followed for a mean of 5.7 years. Most (11/12) models achieved Harrell C-index ≥ 0.70. Most (11/12) models had either 5-year or 10-year calibration slope within 0.3 of the ideal value of 1.0; 7 out of 12 had either 5-year or 10-year calibration slope that was not significantly different from 1.0; all had intercepts very close to 0.</p> Conclusions <p>A set of EHR-based predictive models of adverse outcomes of obesity and overweight was generalizable to a large national cohort. These findings suggest potential clinical and population health applications for predictive models in treatment of individual patients with obesity / overweight, as well as in population management and quality improvement.</p> Clinical trial number <p>Not applicable.</p>