Anguish as a Clinical Marker of Depressive Vulnerability: Evidence from Outpatient Populations

Author(s): Fernando Filipe Paulos Vieira, Francisco Lotufo Neto.

Background: Anguish is characterized by a sensation of precordial tightness or oppression with an emotional origin. Unlike anxiety, which is oriented toward the future, anguish is experienced in the present as a state of mental pain and agony. It is believed to involve distinct autonomic, physiological, and biochemical responses compared to anxiety and depression. Objective: To investigate whether depression is more strongly associated with anguish than anxiety, and to identify the variables or symptoms most predictive of the state of anguish. Methods: A binomial logistic regression model was applied to examine associations between anguish, depression, and anxiety. Diagnostic and psychometric variables were included, such as the MINI Depression module, Hamilton scores, and Brief Symptom Inventory (BSI) subscales. Correspondence analysis was also performed to explore relationships among categorical variables. Results: The logistic model revealed statistically significant evidence that depression is more closely related to anguish than anxiety. The diagnostic variable MINI Depression was significant at the 10% level, supporting the central hypothesis. Additionally, under the same model, the following variables were significantly associated with the state of anguish: Gender, Reduced Hamilton Score, BSI Somatization, BSI Hostility, BSI Obsession-Compulsion, Age, and MINI Depression. Correspondence analysis provided further evidence consistent with these findings. Conclusions: Depression appears to be more strongly associated with the experience of anguish than anxiety. Several demographic and psychometric factors, particularly depressive symptoms and somatization-related dimensions, may help identify individuals prone to states of anguish. These results suggest the need to differentiate anguish from anxiety and depression in both clinical assessment and theoretical models.