Development and validation of a nomogram for predicting comorbid anxiety and depression in patients with gestational diabetes mellitus

Abstract

BACKGROUND

Gestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy closely associated with elevated risks of maternal perinatal anxiety and depression. Psychological comorbidities worsen glycemic control and adversely affect maternal and fetal outcomes. Currently, standardized risk prediction tools for anxiety and depression among women with GDM remain scarce, and most existing prediction tools ignore easily accessible sociodemographic and psychosocial predictors. Therefore, a visual prediction tool is needed to screen high-risk populations, and age, miscarriage history, education, and social support are hypothesized as independent risk factors.

AIM

To validate a nomogram for predicting anxiety-depression risk in GDM patients and evaluate its discrimination, calibration, and net clinical benefit.

METHODS

This retrospective study retrospectively enrolled 238 GDM patients diagnosed at our hospital between February 2022 and January 2024, categorized into comorbid (38 cases) and control (200 cases) groups based on Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) scores. General data were collected using a custom-made questionnaire. SAS and SDS scores were analyzed and univariate analysis performed on general data of the groups. Multivariate logistic regression was used to identify risk factors, and a prediction model was developed accordingly.

RESULTS

The comorbidity group had significantly older age, higher rates of miscarriage history, lower education (junior high school or below), and poorer social support than controls. Multivariate regression identified age, miscarriage history, and low education and social support as independent risk factors for anxiety and depression in GDM patients. Receiver operating characteristic analysis yielded areas under the curve of 0.718, 0.635, 0.666, 0.667, and 0.853 for age, miscarriage history, low education, social support, and the combined predictor, respectively, with corresponding sensitivities of 0.579, 0.395, 0.553, 0.684, and 0.816. The model exhibited favorable screening efficacy for anxiety-depression in GDM patients. Bootstrap internal validation confirmed well-calibrated curves; decision curve analysis showed positive net clinical benefit across threshold probabilities of 0.02-0.93, superior to reference curves.

CONCLUSION

The identified risk factors independently elevate anxiety-depression risk in GDM patients. The constructed prediction model exhibits robust discrimination and calibration, enabling early identification of high-risk GDM patients for psychological intervention.

Keywords: Gestational diabetes mellitus; Anxiety; Depression; Risk prediction model; Model performance

Core Tip: This study explored the factors associated with combined anxiety and depression in pregnant women with gestational diabetes mellitus (GDM) and constructed a targeted risk prediction nomogram. The results emphasized that older maternal age, prior miscarriage, lower educational attainment, and limited social support were key contributors to psychological comorbidities. The prediction model showed good diagnostic performance. Therefore, it may serve as a practical tool for early detection and timely intervention of emotional disorders in GDM patients. Future research should aim for external validation and further refinement of this model to enhance its accuracy and clinical applicability.