Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus.

Background: Neuregulin 4 (NRG4) is a novel metabolic regulator closely associated with insulin resistance and thyroid dysfunction. However, its role in the pathogenesis of comorbid type 2 diabetes mellitus and hyperthyroidism (T2DM-FT) remains to be systematically elucidated. Given the complex clinical characteristics of T2DM-FT patients, traditional statistical methods are often insufficient to effectively analyze nonlinear relationships among multiple variables. Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.

Objective: This study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.

Methods: Studied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. Collected data on demographics, disease characteristics, NRG4, and thyroid indices. Pearson correlation was used to identify features correlated with NRG4. A parameter-optimized SVM model (C=1, linear kernel) was constructed for structured data modeling. Additionally, a CNN+LSTM network was employed to extract spatial (thyroid morphology) and temporal (hemodynamics) features from ultrasound sequences. These features were then fused with biochemical indicators, such as NRG4, to develop the final SVM-CNN+LSTM multimodal predictive model.

Results: Serum NRG4 levels in T2DM+FT patients were significantly higher than those in the healthy Ctrl group (4.44 ± 1.25 2.17 ± 0.48 μg/L, < 0.05). NRG4 levels were positively correlated with HOMA-IR ( = 0.593), FT3 ( = 0.773), FT4 ( = 0.683), thyroid volume ( = 0.652), and the resistance index (RI) ( = 0.473) (< 0.05). The optimized SVM model demonstrated a sensitivity of 86.23%, specificity of 90.33%, and an area under the curve (AUC) of 0.887. In contrast, the fusion model SVM-CNN+LSTM outperformed the SVM model across all metrics, achieving a sensitivity of 91.32%, specificity of 94.18%, and an AUC of 0.943 (< 0.05).

Conclusion: The SVM-CNN+LSTM multimodal model, which integrates serum NRG4 levels with ultrasound features, significantly enhances the predictive accuracy of hyperthyroidism in T2DM patients. This approach effectively reveals the multifactorial mechanisms underlying T2DM-FT comorbidity, providing a powerful tool for early clinical intervention.