Meta-Analysis of the Efficacy of Adjunctive NMDA Receptor Modulators in Chronic Schizophrenia

Abstract

Background: Based on the glutamatergic NMDA receptor hypofunction theory of schizophrenia, NMDA receptor modulators (NMDARMs) may have therapeutic potential in the treatment of schizophrenia.

Objective: This meta-analysis aimed to evaluate the potential of modulators of the NMDA receptor as adjunctive therapy for schizophrenia, using the results from published trials.

Data Sources: A primary electronic search for controlled clinical trials using NMDARMs in schizophrenia was conducted on the PubMed, Cochrane Library, EMBASE, CINAHL® and PsycINFO databases. A secondary manual search of references from primary publications was also performed.

Study Selection: Inclusion criteria were the application of an established method of diagnosis, randomized case assignment, comparison of NMDARM add-on therapy with placebo, and double-blind assessment of symptoms in chronic schizophrenia using standardized rating scales. Results were based on a total sample size of 1253 cases from 29 trials that fulfilled the specified criteria.

Data Extraction: Scores on rating scales or on their relevant subscales were obtained for all selected studies from published results for the minimum dataset to compute the difference between post- and pre-trial scores and their pooled standard deviation for NMDARM add-on therapy and placebo groups for negative, positive and total symptoms.

Results: A negative standardized mean difference (SMD) indicates therapeutic benefit in favour of NMDARM add-on therapy and all SMD results mentioned here are statistically significant. The overall effect size for NMDARMs as a group was small for negative (SMD −0.27) and medium for total (SMD −0.40) symptoms of chronic schizophrenia. Subgroup analysis revealed medium effect sizes for D-serine and N-acetyl-cysteine (NAC) for negative (SMD −0.53 and −0.45, respectively) and total (SMD −0.40 and −0.64, respectively) symptoms, and for glycine (SMD −0.66) and sarcosine (SMD −0.41) for total symptoms. As adjuvants to non-clozapine antipsychotics, additional therapeutic benefits were observed for NMDARM as a group (SMD −0.14) and glycine (SMD −0.54) for positive symptoms; D-serine (SMD −0.54), NAC (SMD −0.45) and sarcosine (SMD −0.39) for negative symptoms; and NMDARM as a group (SMD −0.38), D-serine (SMD −0.40), glycine (SMD −1.12), NAC (SMD −0.64) and sarcosine (SMD −0.53) for total symptoms. When added to clozapine, none of the drugs demonstrated therapeutic potential, while addition of glycine (SMD +0.56) worsened positive symptoms.

Conclusions: Taking into consideration the number of trials and sample size in subgroup analyses, D-serine, NAC and sarcosine as adjuncts to non-clozapine antipsychotics have therapeutic benefit in the treatment of negative and total symptoms of chronic schizophrenia. While glycine improves positive and total symptoms as an adjuvant to non-clozapine antipsychotics, it worsens them when added to clozapine.

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Acknowledgements

The authors are thankful to and acknowledge the valuable advice from Prof. Kelvin Chan (University of Wolverhampton, Wolverhampton, West Midlands, UK) at the stage of conception of this project, and for help offered by Dr Sushmit Roy and Ms Rederi Grobler (Mental Health Directorate, Wolverhampton City Primary Care Trust, Wolverhampton, West Midlands, UK) for proofreading of this manuscript. No external funding support was received to conduct the study or prepare the article. The authors have no financial interest or other relationship relevant to the subject matter of this article.

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Correspondence to Dr Surendra P. Singh.

Appendix 1

Appendix 1

Summarizing data

Weighted mean (WM) of two samples

$$WM_{ij}={n_i \bullet m_i +\bullet m_j\over n_i +n_j}$$
(1)

Standarised weighted mean (SWM) of two samples

$$SWM_{ij}={WM_{ij}\over n_j+n_j}$$
(2)

Pooled standard deviation (SD) using covariance method

$$SD_{ij}={\sqrt{(SD_{i}^{2}+SD^{2}_{J}+2\bullet r_{ij}\bullet SD_{i}\bullet SD_{j})}}$$
(3)

Unbiased SD of one sample

$$SD_{i}={\sqrt{{\Sigma x^{2}_{i}-nm^{2}}\over n-1}}$$
(4)

Pooled SD of two samples

$$SD_{ij}=\sqrt{\sum x_i^2+\sum y_i^2-(n_x+n_y)\bigg[{n_xm_x+n_ym_y\over n_x+n_y}\bigg]^2\over n_x+n_y-1}$$
(5)

Pooled SD of two samples

$$\sqrt{{(n_x-1)s_x^2+n_xm_x^2+(n_y-1)s_y^2+n_ym_y^2-(n_x+n_y)\bigg[{n_xm_x+n_ym_y\over n_x+n_y}\bigg]^2\over n_x+n_y-1}}$$
(6)

Where n is sample size of i or j; and m is mean of study i or j; and SD, WM, SWM can belong to i, j, or both i and j.

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Singh, S.P., Singh, V. Meta-Analysis of the Efficacy of Adjunctive NMDA Receptor Modulators in Chronic Schizophrenia. CNS Drugs 25, 859–885 (2011). https://doi.org/10.2165/11586650-000000000-00000

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Keywords

  • Schizophrenia
  • NMDA Receptor
  • Clozapine
  • Negative Symptom
  • Memantine