Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver · 2026
Identifying central symptoms of depression and anxiety in different profiles of inflammatory bowel disease patients: A network analysis approach.
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Xie Xiangying, Su Juan, Zhou Qian, Wang Wei, Su Ying, An Ping, Zhang Lili
Аннотация
BACKGROUND: Depression and anxiety were not only common but also with serious consequence in inflammatory bowel diseases (IBD) patients. The current study endeavors to define distinct depression and anxiety profiles of IBD patients and identify central symptoms within different profiles to facilitate targeted interventions.
METHODS: The research employed K-means Clustering to delineate the depression and anxiety profiles, followed by a repetition of the analysis using Latent Profile Analysis (LPA). Furthermore, network analysis was utilized to identify central symptoms within the various profiles.
RESULTS: K‑means Clustering identified Cluster 1 (38.89%), Cluster 2 (45.33%) and Cluster 3 (15.78%), while LPA yielded the low-risk group (39.56%), the mild-risk group (44.22%) and the high-risk group (16.22%). A majority of patients in the three clusters were predominantly in a single LPA-derived patient class (96.1-99.0%). Network analysis revealed that connections within each symptom in PHQ-9 and GAD-7 were stronger than those between symptoms. Furthermore, PHQ 6 ("guilt"), PHQ2 ("sad mood")and GAD 7 ("feeling afraid") were identified as the central symptoms in Cluster 1. PHQ2 ("sad mood"), GAD 3("excessive worry") and GAD 1 ("nervousness") emerged as the central symptoms in Cluster 2. Additionally, GAD3 ("excessive worry"), GAD 4 ("trouble relaxing") and GAD 6("irritability") were identified as the central symptoms in Cluster 3.
CONCLUSION: We defined three distinct depression and anxiety profiles among IBD patients and pinpointed central symptoms within each profile. These findings underscore the importance of directing research towards those central symptoms within each profile in order to develop targeted intervention strategies.
METHODS: The research employed K-means Clustering to delineate the depression and anxiety profiles, followed by a repetition of the analysis using Latent Profile Analysis (LPA). Furthermore, network analysis was utilized to identify central symptoms within the various profiles.
RESULTS: K‑means Clustering identified Cluster 1 (38.89%), Cluster 2 (45.33%) and Cluster 3 (15.78%), while LPA yielded the low-risk group (39.56%), the mild-risk group (44.22%) and the high-risk group (16.22%). A majority of patients in the three clusters were predominantly in a single LPA-derived patient class (96.1-99.0%). Network analysis revealed that connections within each symptom in PHQ-9 and GAD-7 were stronger than those between symptoms. Furthermore, PHQ 6 ("guilt"), PHQ2 ("sad mood")and GAD 7 ("feeling afraid") were identified as the central symptoms in Cluster 1. PHQ2 ("sad mood"), GAD 3("excessive worry") and GAD 1 ("nervousness") emerged as the central symptoms in Cluster 2. Additionally, GAD3 ("excessive worry"), GAD 4 ("trouble relaxing") and GAD 6("irritability") were identified as the central symptoms in Cluster 3.
CONCLUSION: We defined three distinct depression and anxiety profiles among IBD patients and pinpointed central symptoms within each profile. These findings underscore the importance of directing research towards those central symptoms within each profile in order to develop targeted intervention strategies.