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      Augmentation therapy with minocycline in treatment-resistant depression patients with low-grade peripheral inflammation: results from a double-blind randomised clinical trial

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          Abstract

          This study aimed to investigate the role of baseline levels of peripheral inflammation when testing the efficacy of antidepressant augmentation with minocycline in patients with treatment-resistant depression. We conducted a 4-week, placebo-controlled, randomised clinical trial of minocycline (200 mg/day) added to antidepressant treatment in 39 patients selected for elevated levels of serum C-reactive protein (CRP ≥ 1 mg/L), n = 18 randomised to minocycline (M) and n = 21 to placebo (P). The main outcome was the change in Hamilton Depression Rating Scale (HAM-D-17) score from baseline to week 4, expressed both as mean and as full or partial response, in the overall sample and after further stratification for baseline CRP≥3 mg/L. Secondary outcomes included changes in other clinical and inflammatory measures. Changes in HAM-D-17 scores and the proportion of partial responders did not differ between study arms. After stratification for CRP levels <3 mg/L (CRP ) or ≥3 mg/L (CRP +), CRP +/M patients showed the largest changes in HAM-D-17 scores (mean ± SD = 12.00 ± 6.45) compared with CRP - /M (2.42 ± 3.20, p < 0.001), CRP + /P (3.50 ± 4.34, p = 0.003) and CRP /P (2.11 ± 3.26, p = 0.006) patients, and the largest proportion (83.3%, p = 0.04) of partial treatment response at week 4. The threshold point for baseline CRP to distinguish responders from non-responders to minocycline was 2.8 mg/L. Responders to minocycline had higher baseline IL-6 concentrations than non-responders ( p = 0.03); IFNγ was significantly reduced after treatment with minocycline compared with placebo ( p = 0.03). Our data show some evidence of efficacy of add-on treatment with minocycline in MDD patients but only in those with low-grade inflammation defined as CRP ≥3 mg/L.

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          A RATING SCALE FOR DEPRESSION

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            The role of inflammation in depression: from evolutionary imperative to modern treatment target.

            Crosstalk between inflammatory pathways and neurocircuits in the brain can lead to behavioural responses, such as avoidance and alarm, that are likely to have provided early humans with an evolutionary advantage in their interactions with pathogens and predators. However, in modern times, such interactions between inflammation and the brain appear to drive the development of depression and may contribute to non-responsiveness to current antidepressant therapies. Recent data have elucidated the mechanisms by which the innate and adaptive immune systems interact with neurotransmitters and neurocircuits to influence the risk for depression. Here, we detail our current understanding of these pathways and discuss the therapeutic potential of targeting the immune system to treat depression.
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              When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts

              Background Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention. Methods The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials. Results Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial. Conclusions We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical. Electronic supplementary material The online version of this article (10.1186/s12874-017-0442-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                valeria.mondelli@kcl.ac.uk
                Journal
                Neuropsychopharmacology
                Neuropsychopharmacology
                Neuropsychopharmacology
                Springer International Publishing (Cham )
                0893-133X
                1740-634X
                28 January 2021
                28 January 2021
                April 2021
                : 46
                : 5
                : 939-948
                Affiliations
                [1 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, King’s College London, , Institute of Psychiatry Psychology and Neuroscience, Department of Psychological Medicine, ; London, UK
                [2 ]GRID grid.37640.36, ISNI 0000 0000 9439 0839, National Institute for Health Research Mental Health Biomedical Research Centre, , South London and Maudsley NHS Foundation Trust and King’s College London, ; London, UK
                [3 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Division of Neurogeriatrics, Department of Neurobiology, , Care Sciences and Society, Karolinska Institute, ; Stockholm, Sweden
                Author information
                http://orcid.org/0000-0002-5401-8391
                http://orcid.org/0000-0002-5214-305X
                http://orcid.org/0000-0003-2291-6952
                http://orcid.org/0000-0002-9132-5091
                http://orcid.org/0000-0001-8690-6839
                Article
                948
                10.1038/s41386-020-00948-6
                8096832
                33504955
                a11d94fb-daee-47b7-b5a0-ceec9562cacd
                © 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
                : 24 July 2020
                : 27 November 2020
                : 14 December 2020
                Funding
                Funded by: NIHR Biomedical Research Centre South London and Maudsley NHS Foundation Trust
                Funded by: NIHR Biomedical Research Centre South London and Maudsley NHS Foundation Trust and KIng’s College London
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to American College of Neuropsychopharmacology 2021

                Pharmacology & Pharmaceutical medicine
                depression,predictive markers,translational research

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