Network Analysis of Symptom Clusters and Core Symptoms in Patients with Type 2 Diabetes Mellitus

This study aimed to identify symptom clusters in hospitalized patients with type 2 diabetes mellitus (T2DM), construct a symptom network, and determine core symptoms.

A cross-sectional study was conducted among 331 hospitalized patients with type 2 diabetes mellitus recruited by convenience sampling from the Department of Endocrinology at a tertiary Grade A hospital in Sanya City. Demographic and clinical data were collected using a self-designed questionnaire, and symptoms were assessed with the Diabetes Symptom Checklist-Revised. Exploratory factor analysis was used to extract symptom clusters, and a Gaussian graphical model with graphical LASSO regularization was applied in R to construct the symptom network and calculate centrality indices.

Five clinically meaningful symptom clusters were identified. Network analysis showed strong symptom interconnections, particularly between reduced appetite and drowsiness or fatigue, polydipsia and polyuria, abnormal sensations in the legs or feet and blurred vision, and chest tightness and shortness of breath. Reduced appetite, polydipsia, and abnormal sensations in the legs or feet had the highest strength centrality, whereas reduced appetite, abnormal sensations in the legs or feet, and constipation showed relatively high betweenness centrality, suggesting potential bridging roles. Palpitations and shortness of breath had high positive expected influence values, indicating that they may serve as influential symptoms within the network.

The clinical symptoms of hospitalized T2DM patients do not exist in isolation but co-occur in specific symptom clusters. In clinical practice, healthcare providers can use symptom clusters and core symptoms as key focal points for rapid assessment and intervention. Identifying these clusters and their core symptoms may allow clinicians to streamline assessment and target interventions more efficiently.

This study provides a novel framework for understanding symptom interconnections in T2DM patients through network analysis. These findings are intended to provide theoretical guidance for stratified management and comprehensive interventions in clinical practice.