Abstract

This study presents the development of an online nomogram and calculator designed to estimate the likelihood of depression among obese Americans. Depression is a prevalent mental health concern associated with obesity, influenced by various psychosocial and physiological factors. The nomogram integrates key predictors such as demographic characteristics, BMI (Body Mass Index), comorbidities, and psychosocial variables to provide personalized risk assessments. Using data from national health surveys and clinical databases, we constructed and validated the nomogram through statistical modeling and machine learning techniques. The predictive accuracy of the model was assessed using measures of discrimination and calibration, demonstrating its utility in clinical settings and public health interventions. Findings underscore the significance of obesity-related factors in predicting depression risk, highlighting the complex interactions between physical health, mental health, and sociodemographic variables. The nomogram offers a practical tool for healthcare providers to identify individuals at heightened risk of depression among obese populations, facilitating early intervention and tailored treatment strategies. Implications for healthcare policy and practice include the integration of predictive tools into routine clinical assessments, enhancing mental health screening efforts in obesity management programs. By addressing depression risk proactively, healthcare providers can improve patient outcomes and mitigate the broader societal burden associated with obesity-related mental health disorders. Continued refinement and validation of the nomogram are essential to optimize its accuracy and applicability across diverse demographic and clinical settings.