2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Metformin for early comorbid glucose dysregulation and schizophrenia spectrum disorders: a pilot double-blind randomized clinical trial

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Patients with schizophrenia have exceedingly high rates of metabolic comorbidity including type 2 diabetes and lose 15–20 years of life due to cardiovascular diseases, with early accrual of cardiometabolic disease. In this study, thirty overweight or obese (Body Mass Index (BMI) > 25) participants under 40 years old with schizophrenia spectrum disorders and early comorbid prediabetes or type 2 diabetes receiving antipsychotic medications were randomized, in a double-blind fashion, to metformin 1500 mg/day or placebo (2:1 ratio; n = 21 metformin and n = 9 placebo) for 4 months. The primary outcome measures were improvements in glucose homeostasis (HbA1c, fasting glucose) and insulin resistance (Matsuda index—derived from oral glucose tolerance tests and homeostatic model of insulin resistance (HOMA-IR)). Secondary outcome measures included changes in weight, MRI measures of fat mass and distribution, symptom severity, cognition, and hippocampal volume. Twenty-two patients ( n = 14 metformin; n = 8 placebo) completed the trial. The metformin group had a significant decrease over time in the HOMA-IR ( p = 0.043) and fasting blood glucose ( p = 0.007) vs. placebo. There were no differences between treatment groups in the Matsuda index, HbA1c, which could suggest liver-specific effects of metformin. There were no between group differences in other secondary outcome measures, while weight loss in the metformin arm correlated significantly with decreases in subcutaneous, but not visceral or hepatic adipose tissue. Our results show that metformin improved dysglycemia and insulin sensitivity, independent of weight loss, in a young population with prediabetes/diabetes and psychosis spectrum illness, that is at extremely high risk of early cardiovascular mortality. Trial Registration: This protocol was registered with clinicaltrials.gov (NCT02167620).

          Related collections

          Most cited references40

          • Record: found
          • Abstract: found
          • Article: not found

          3D Slicer as an image computing platform for the Quantitative Imaging Network.

          Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer. Copyright © 2012 Elsevier Inc. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018.

            (2017)
            The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Use and abuse of HOMA modeling.

              Homeostatic model assessment (HOMA) is a method for assessing beta-cell function and insulin resistance (IR) from basal (fasting) glucose and insulin or C-peptide concentrations. It has been reported in >500 publications, 20 times more frequently for the estimation of IR than beta-cell function. This article summarizes the physiological basis of HOMA, a structural model of steady-state insulin and glucose domains, constructed from physiological dose responses of glucose uptake and insulin production. Hepatic and peripheral glucose efflux and uptake were modeled to be dependent on plasma glucose and insulin concentrations. Decreases in beta-cell function were modeled by changing the beta-cell response to plasma glucose concentrations. The original HOMA model was described in 1985 with a formula for approximate estimation. The computer model is available but has not been as widely used as the approximation formulae. HOMA has been validated against a variety of physiological methods. We review the use and reporting of HOMA in the literature and give guidance on its appropriate use (e.g., cohort and epidemiological studies) and inappropriate use (e.g., measuring beta-cell function in isolation). The HOMA model compares favorably with other models and has the advantage of requiring only a single plasma sample assayed for insulin and glucose. In conclusion, the HOMA model has become a widely used clinical and epidemiological tool and, when used appropriately, it can yield valuable data. However, as with all models, the primary input data need to be robust, and the data need to be interpreted carefully.
                Bookmark

                Author and article information

                Contributors
                Margaret.Hahn@camh.ca
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                14 April 2021
                14 April 2021
                2021
                : 11
                : 219
                Affiliations
                [1 ]GRID grid.155956.b, ISNI 0000 0000 8793 5925, Centre for Addiction and Mental Health, ; Toronto, ON Canada
                [2 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Psychiatry, , University of Toronto, ; Toronto, ON Canada
                [3 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Institute of Medical Science, Faculty of Medicine, University of Toronto, ; Toronto, ON Canada
                [4 ]GRID grid.415502.7, St. Michael’s Hospital, ; Toronto, ON Canada
                [5 ]GRID grid.416166.2, ISNI 0000 0004 0473 9881, Mount Sinai Hospital, ; Toronto, ON Canada
                [6 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Medicine, , Division of Endocrinology and Metabolism, University of Toronto, ; Toronto, ON Canada
                [7 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Banting and Best Diabetes Centre, University of Toronto, ; Toronto, ON Canada
                Author information
                http://orcid.org/0000-0002-2705-5146
                http://orcid.org/0000-0003-0156-0395
                http://orcid.org/0000-0001-8884-9946
                Article
                1338
                10.1038/s41398-021-01338-2
                8046796
                33854039
                ebd59489-8ed5-4c4f-8583-7dbd2334ea76
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 December 2020
                : 8 March 2021
                : 26 March 2021
                Funding
                Funded by: Slaight Family Foundation Grant Award
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Clinical Psychology & Psychiatry
                physiology,schizophrenia
                Clinical Psychology & Psychiatry
                physiology, schizophrenia

                Comments

                Comment on this article

                scite_

                Similar content268

                Cited by6

                Most referenced authors782