Objective: To investigate the association between diabetes mellitus (DM) and incidence of depressive episodes among men and women.
Methods: Data were used from 12,730 participants (5866 men and 6864 women) at baseline (2008-2010) and follow-up 1 (2012-2014) of the Longitudinal Study of Adult Health (ELSA-Brasil), a multicenter cohort of Brazilian civil servants. Participants were classified for diabetes using self-reported and clinical information, and evaluated for presence of depressive episodes by the Clinical Interview Schedule-Revised (CIS-R). Associations were estimated by means of logistic regression models (crude and adjusted for socio-demographic variables).
Results: Women classified as with DM prior to the baseline were at 48% greater risk (95% confidence interval (CI) = 1.03-2.07) of depressive episodes in the crude model and 54% greater risk (95% CI = 1.06-2.19) in the final adjusted model . No significant associations were observed for men. The regression models for duration of DM and incidence of depressive episodes ( = 2143 participants; 1160 men and 983 women) returned no significant associations.
Conclusion: In women classified as with prior DM, the greater risk of depressive episodes suggests that more frequent screening for depression may be beneficial as part of a multi-factorial approach to care for DM.
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http://dx.doi.org/10.1177/20420188221093212 | DOI Listing |
Innov Clin Neurosci
December 2024
All authors are with Ocean University Medical Center in Brick, New Jersey.
Introduction: The demographics of the United States (US) are evolving as time progresses. The geriatric population is growing, with many elderly people dealing with mental health issues. Major depressive episodes affect 1 to 5 percent of those aged 65 years or older, which emphasizes the importance of addressing mental health concerns in this populace.
View Article and Find Full Text PDFIndian J Psychiatry
November 2024
Department of Psychiatry, Murshidabad Medical College and Hospital, Murshidabad, West Bengal, India.
Background: There is lack of data on bipolar disorder (BD) type II from India.
Aim: To compare the demographic and clinical characteristics of patients with BD-I and BD-II using the data of the Bipolar Disorder Course and Outcome study from India (BiD-CoIN study).
Methodology: Using the data of the BiD-CoIN study, patients with BD-I and BD-II were compared for demographic and clinical variables.
Addiction
January 2025
Center for Studies on Justice and Society (CJS), Pontificia Universidad Católica de, Chile.
Background And Aims: Evidence from high-income countries has linked duration and compliance with treatment for substance use disorders (SUDs) with reductions in substance use and improvements in mental health. Generalizing these findings to other regions like South America, where opioid and injection drug use is uncommon, is not straightforward. We examined if length of time in treatment and compliance with treatment reduced subsequent substance use and presence of psychiatric comorbidities.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Psychotherapy and Psychosomatic Medicine, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
The Dermatology Life Quality Index (DLQI) should be used to assess treatment success in psoriasis (PSO). However, the DLQI does not assess the importance and achievement of treatment goals. The Patient Benefit Index (PBI) is a questionnaire that takes both into account.
View Article and Find Full Text PDFBr J Anaesth
January 2025
Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Outcomes Research Consortium, Cleveland, OH, USA. Electronic address:
Background: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aimed to identify and independently validate endotypes of hypotension in big datasets of surgical and critically ill patients using unsupervised deep learning.
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