JOURNAL ARTICLE

Optimising antidepressant therapy in type 2 diabetes mellitus.

  • Published In: Psychiatry / Psychiatria, 2025, v. 22. P. 9 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Niedziela, Michał; Maruszak, Monika; Bystroń, Adrian; Matuszewska, Karolina; Zasadzińska, Maria; Borowski, Grzegorz 3 of 3

Abstract

Introduction: The co-occurrence of depression and diabetes mellitus, particularly type 2 diabetes, represents a significant challenge in clinical practice due to its impact on both metabolic control and mental health management. Antidepressants, including selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and tricyclic antidepressants (TCAs), are commonly used in managing depression, yet their effects on glycaemic control remain complex. Some antidepressants may influence insulin sensitivity, body weight, and glucose metabolism, thereby potentially complicating diabetes management. Literature review: The aim of this review was to examine the pharmacological treatment of depression in the context of diabetes, focusing on the effects of antidepressants on patients’ metabolic profile and clinical outcomes. We also discuss the impact of antidepressant therapy on quality of life, stress levels, and overall emotional well-being in diabetic patients, highlighting both the benefits and risks associated with pharmacological treatments. Results: Given the potential for both therapeutic benefits and adverse interactions, a careful, individualised approach is necessary to optimise treatment strategies for patients coping with both depression and diabetes. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Psychiatry / Psychiatria. 2025/01, Vol. 22, p9
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:1732-9841
  • DOI:10.5603/psych.105153
  • Accession Number:191361311
  • Copyright Statement:Copyright of Psychiatry / Psychiatria is the property of VM Medica-VM Group (Via Medica) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.