A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Neural Network model achieved highest accuracy of 0.744

• Primary Outcome: Severe diabetes distress (≥40 points on Problem Areas in Diabetes scale)

• Nnt Or Sensitivity: AUC 0.728 indicates moderate discriminative ability

• Confidence Interval: AUC 0.728 (95% CI: 0.608-0.845)

Clinical Application

Requires CGM data and computational resources for model implementation. May be feasible in specialist diabetes centres Relevant to Australian diabetes services given PBS subsidised CGM access for T1DM patients. Could complement RACGP diabetes care guidelines for psychological screening Adults with type 1 diabetes using continuous glucose monitoring systems

Abstract

BACKGROUND: Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate prediction models for diabetes distress in adults with T1DM using continuous glucose monitoring (CGM) metrics. METHODS: The CGM metrics were collected from 259 adults with T1DM. Severe diabetes distress was defined as 40 points on the Problem Areas in Diabetes scale. Prediction models were developed based on ten machine learning algorithms: random forest (RF), support vector machine (SVM), Naive Bayes (NB), Neural Network (NN), k-nearest neighbor (k-NN), XGBoost (XGB), SGDClassifier (SGDC), XGB_limitet_depth (CGB_ld), L1LogisticRegression (L1), and LightGBM. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: Among the ten models, accuracy in the NN model was the highest (NN: 0.744, L1: 0.731, NB: 0.718, SVM: 0.692, SGDC: 0.654, RF: 0.628, k-NN: 0.628, LightGBM: 0.615, XGBoost: 0.564, and XGB_ld: 0.564). The NN model achieved the highest AUC of 0.728 (95% confidence interval: 0.608-0.845). CONCLUSIONS: This study developed a predictive model for severe diabetes distress using machine learning, incorporating both demographic and CGM metrics in adults with type 1 diabetes mellitus. The NN model demonstrated potential as a practical tool to assist clinicians in identifying individuals at risk of severe diabetes distress.

References

1. 1.Sakane, N., Hirota, Y., Yamamoto, A., Miura, J., Takaike, H., Hoshina, S., Toyoda, M., Saito, N., Hosoda, K., Matsubara, M., Tone, A., Kawashima, S., Sawaki, H., Matsuda, T., Domichi, M., Suganuma, A., Sakane, S., & Murata, T. (2026). A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus. Journal of Diabetes Investigation. https://doi.org/10.1111/jdi.70229