ArticleBMC psychiatry2025
Using network analysis to identify central symptoms of depression and anxiety in different profiles of infertility patients.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Exploring the interrelationships between athlete burnout, depression, and anxiety: a network analysis approach.Frontiers in psychology · 2026Article
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11 authors.
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Abstract
backgroundDepression and anxiety were not only common but also with serious consequence in infertility patients. The current study endeavors to define distinct depression and anxiety profiles of infertility patients and identify central symptoms within different profiles to facilitate targeted interventions.
methodThe research employed K-means Clustering to delineate the depression and anxiety profiles, followed by a repetition of the analysis using Latent Class Analysis (LCA). Furthermore, network analysis was utilized to identify central symptoms within the various profiles.
resultK‑means Clustering identified Cluster 1 (16.15%), Cluster 2 (37.08%) and Cluster 3 (46.77%), while LCA yielded the low-risk group (47.23%), the mild-risk group (34.46%) and the high-risk group (18.31%). A majority of patients in the three clusters were predominantly in a single LCA-derived patient class (88.38-100%). Network analysis revealed that connections within each symptom in PHQ-9 and GAD-7 were stronger than those between symptoms. Furthermore, PHQ 2 ("sad mood"), GAD 1 ("nervousness") and GAD 2 ("uncontrollable worry") were identified as the central symptoms in Cluster 1 GAD 3 ("excessive worry"), GAD 2 ("uncontrollable worry") and GAD 5 ("restlessness") emerged as the central symptoms in Cluster 2) Additionally, PHQ 4 ("fatigue"), GAD 6 ("irritability") and GAD 3 ("excessive worry") were identified as the central symptoms in Cluster 3.
conclusionsWe defined three distinct depression and anxiety profiles among infertility 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.
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