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Brain GABA and glutamate levels across pain conditions: A systematicliterature review and meta-analysis of 1H-MRS studies using the MRS-Qquality assessment toolAimie Laura Peeka , * , Trudy Rebbeck a , Nicolaas AJ. Puts b , Julia Watson c , Maria-Eliza R. Aguila d ,Andrew M. Leaveraa Faculty of Health Sciences, University of Sydney, 75 East Street, Lidcombe, NSW, 2141, Australiab Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 600 N. Wolfe Street, 21287,Baltimore, MD, USAc School of Clinical Sciences, Queensland University of Technology, 2 George St, Brisbane, QLD, 4000, Australiad University of the Philippines, Pedro Gil Street, Ermita, Manila, 1000, PhilippinesA R T I C L E I N F OKeywords:PainBiomarkerMR spectroscopyGABAGlutamateGlutamineA B S T R A C TBackground: A proposed mechanism of chronic pain is dysregulation between the main inhibitory (GABA) andexcitatory (glutamate) neurometabolites of the central nervous system. The level of these neurometabolites ap-pears to differ in individual studies of people with pain compared to pain-free controls across different painconditions. However, this has yet to be systematically investigated.Aims: To establish whether GABA, glutamate, glutamine and Glx levels differ across pain conditions whencompared to pain-free controls.Methods: Five databases were searched. Studies were included if they investigated: 1) A pain condition comparedto control. 2) Reported GABA, glutamate, glutamine or glutamate/glutamine level. 3) Used 1H-Magnetic Reso-nance Spectroscopy (Prospero Project ID CRD42018092170). Data extracted included neurometabolite level, paindiagnosis, and spectroscopy parameters. Meta-analyses were conducted to establish the difference in neuro-metabolite level between participants with pain and pain-free controls for different pain conditions. The MRS-Qwas developed from existing clinical consensus to allow for the assessment of quality in the included studies.Results: Thirty-f i ve studies were included investigating combinations of migraine (n¼ 11), musculoskeletal pain(n ¼ 8), chronic pain syndromes (n¼ 9) and miscellaneous pain (n ¼ 10). Higher GABA levels were found inparticipants with migraine compared to controls (Hedge's G 0.499, 95%CI: 0.2 to 0.798). In contrast, GABA levelsin musculoskeletal pain conditions (Hedge's G ?0.189, 95%CI: 0.530 to 0.153) and chronic pain syndromes(Hedge's G 0.077, 95%CI: 1.612 to 1.459) did not differ from controls. Results for other brain neurometabolitesrevealed signif i cantly higher levels for glutamate in participants with migraine and Glx in chronic pain syndromescompared to controls.Conclusion: These results support the theory that underlying neurometabolite levels may be unique in differentpain conditions and therefore representative of biomarkers for specif i c pain conditions.1. IntroductionTwo key neurometabolites implicated in the pathophysiology of painare glutamate and gamma-aminobutyric acid (GABA). Glutamate is theprincipal excitatory neurometabolite in the central nervous system, andis involved in many metabolic pathways (Rae, 2014; Ramadan et al.,2013; Zhou and Danbolt, 2014). GABA is the most abundant inhibitoryneurometabolite in the central nervous system (Enna and McCarson,2006; Rae, 2014) and is considered an important regulator of the balancebetween excitation and inhibition in the brain (Petroff, 2002). Bothglutamate and GABA are critical for many centrally regulated physio-logical functions, including pain processing and pain modulation.* Corresponding author. Faculty of Health Sciences, University of Sydney, 75 East Street, Lidcombe, NSW, 2141, Australia.E-mail addresses: apee6909@uni.sydney.edu.au, aimie.peek@yahoo.com (A.L. Peek), trudy.rebbeck@sydney.edu.au (T. Rebbeck), nputs1@jhmi.edu (N.AJ. Puts),julia.watson@tri.edu.au (J. Watson), mraguila1@up.edu.ph (M.-E.R. Aguila), andrew.leaver@sydney.edu.au (A.M. Leaver).Contents lists available at ScienceDirectNeuroImagejournal homepage: www.elsevier.com/locate/neuroimagehttps://doi.org/10.1016/j.neuroimage.2020.116532Received 22 August 2019; Received in revised form 6 December 2019; Accepted 8 January 2020Available online 18 January 20201053-8119/© 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ ).NeuroImage 210 (2020) 116532