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Introduction Our aim was to test risk factors for chronic and transient loneliness as well as cross-sectional and longitudinal associations of courses of loneliness with depression. Methods Responses from participants in Wave 5 (T1, 2013) and Wave 6 (T2, 2015) of The Survey of Health, Ageing and Retirement in Europe (SHARE) (N = 45,490) were analyzed. The existence of clinically significant symptoms of depression was defined as reporting a value greater than or equal to 4 on the Euro-D scale. Loneliness was measured through the 3-item UCLA loneliness scale and a single question. Both measures were tested in separate regression models to identify risk factors for transient (loneliness at T1) and chronic (loneliness at T1 and T2) loneliness as well as their associations with depression. Results Chronic loneliness was observed in 47%–40% of the cases of loneliness, according to the UCLA scale and the single question, respectively. Risk factors for chronic loneliness in both models were being female, not being married, having a low educational level, having poor mental and physical health, being limited in activities, having a poor social network, and living in a culturally individualistic country. Risk factors for transient loneliness were less robust and no significant effects were found for variables such as sex and physical health in both models, education level in the UCLA measure model, and social network size in the single question model. Chronic loneliness also showed a strong association with depression in the cross-sectional model and a marked one in the longitudinal model. Conclusion The courses of loneliness are relevant in the study of its risk factors and association with depression., Introduction: Our aim was to test risk factors for chronic and transient loneliness as well as cross-sectional and longitudinal associations of courses of loneliness with depression. Methods: Responses from participants in Wave 5 (T1, 2013) and Wave 6 (T2, 2015) of The Survey of Health, Ageing and Retirement in Europe (SHARE) (N = 45,490) were analyzed. The existence of clinically significant symptoms of depression was defined as reporting a value greater than or equal to 4 on the Euro-D scale. Loneliness was measured through the 3-item UCLA loneliness scale and a single question. Both measures were tested in separate regression models to identify risk factors for transient (loneliness at T1) and chronic (loneliness at T1 and T2) loneliness as well as their associations with depression. Results: Chronic loneliness was observed in 47%–40% of the cases of loneliness, according to the UCLA scale and the single question, respectively. Risk factors for chronic loneliness in both models were being female, not being married, having a low educational level, having poor mental and physical health, being limited in activities, having a poor social network, and living in a culturally individualistic country. Risk factors for transient loneliness were less robust and no significant effects were found for variables such as sex and physical health in both models, education level in the UCLA measure model, and social network size in the single question model. Chronic loneliness also showed a strong association with depression in the cross-sectional model and a marked one in the longitudinal model. Conclusion: The courses of loneliness are relevant in the study of its risk factors and association with depression.

BACKGROUND: Depression is a heterogeneous disease. Identification of latent depression subgroups and differential associations across these putative groups and sociodemographic and health-related factors might pave the way toward targeted treatment of individuals.METHODS: We used model-based clustering to identify relevant subgroups of 2900 individuals with moderate to severe depression (defined as scores ≥10 on the PHQ-9 instrument) from the NHANES cross-sectional survey. We used ANOVA and chi-squared tests to assess associations between cluster membership and sociodemographics, health-related variables, and prescription medication use.RESULTS: We identified six latent clusters of individuals, three based on depression severity and three differentially loaded by somatic and mental components of the PHQ-9. The Severe mental depression cluster had the most individuals with low education and income (P < 0.05). We observed differences in the prevalence of numerous health conditions, with the Severe mental depression cluster showing the worst overall physical health. We observed marked differences between the clusters regarding prescription medication use: the Severe mental depression cluster had the highest use of cardiovascular and metabolic agents, while the Uniform severe depression cluster showed the highest use of central nervous system and psychotherapeutic agents.LIMITATIONS: Due to the cross-sectional design we cannot make conclusions about causal relationships. We used self-reported data. We did not have access to a replication cohort.CONCLUSIONS: We show that socioeconomic factors, somatic diseases, and prescription medication use are differentially associated with distinct and clinically relevant clusters of individuals with moderate to severe depression.

Introduction Previous research indicates that social isolation, loneliness, physical dysfunction and depressive symptoms are interrelated factors, little is known about the potential pathways among them. The aim of the study is to analyse simultaneously reciprocal relationships that could exist between the four factors to clarify potential mediation effects. Methods Within a large representative sample of older people in the Longitudinal Aging Study Amsterdam (LASA), participants aged 75 and over were followed up over a period of 11 years (four waves). We tested cross-lagged and autoregressive longitudinal associations of social network size, loneliness, physical functioning and depressive symptoms using structural equation modelling (SEM). Results Several statistically significant cross-lagged associations were found: decreasing physical functioning (Coef. = -0.03; p < 0.05), as well as social network size (Coef. = -0.02; p < 0.05), predicted higher levels of loneliness, which predicted an increase in depressive symptoms (Coef. = 0.17; p < 0.05) and further reduction of social network (Coef. = -0.20; p < 0.05). Decreasing physical functioning also predicted an increase in depressive symptoms (Coef. = -0.08; p < 0.05). All autoregressive associations were statistically significant. Conclusion Interventions focused on promoting social activities among older adults after negative life events, such as loss of social contacts or declining physical function, may alleviate feelings of loneliness and act as mental health protector., Peer Reviewed, Postprint (published version)