Glutamate, NAA and Energy Metabolism in Clinical High Risk and First Episode Psychosis

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Kempton, Shue Kit Man, Alice Egerton, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7052494/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Regulation of brain glutamate is closely related to brain energy metabolism. Changes in both central glutamatergic function and peripheral energy metabolism have been implicated in psychosis risk, onset and long-term illness, but there is a lack of empirical evidence to link these processes. We investigated the relationships between glutamate and N-acetyl-aspartate (NAA, a potential marker of neuronal metabolic integrity) in the anterior cingulate cortex (ACC), measured using proton magnetic resonance spectroscopy ( 1 H-MRS), and peripheral markers of energy metabolism (mitochondrial I-V activity, pyruvate and lactate) in individuals either at clinical high risk for psychosis or in the first episode of psychosis (N = 36) and healthy controls (N = 20). ACC Glx (glutamate + glutamine) levels were positively related with principal components relating to mitochondrial complex activity, and this relationship did not differ between groups. These findings are consistent with the importance of mitochondrial ATP generation in regulating glutamatergic neurotransmission. While we did not find evidence that this relationship is disrupted in clinical high risk or first episode psychosis, further work is required to understand the mechanisms linking glutamate and energy metabolism in psychosis, including studies in larger cohorts, later stages of illness or in individuals with greater illness burden. Health sciences/Diseases Health sciences/Neurology Biological sciences/Neuroscience Glutamate NAA energy metabolism mitochondrial function clinical high risk first episode psychosis Figures Figure 1 Introduction The psychosis clinical high risk (CHR) state is defined as an early disease stage prior to the onset of overt psychosis and is characterised by sub-threshold psychotic or non-specific psychiatric symptoms [ 1 ]. Individuals meeting CHR criteria have a 25% probability of transitioning to first episode psychosis (FEP) within 3 years, and a 35% probability within 10 years [ 2 ]. Understanding the active biological mechanisms at the early stages of psychosis is crucial for elucidating the pathophysiology underlying psychosis risk and onset, and for identifying potential targets for early intervention. Recent reviews have highlighted the potential importance of the interplay between regulation of brain glutamate, mitochondrial dysfunction and energy metabolism in psychosis pathophysiology and onset [ 3 – 5 ]. Glutamatergic neurotransmission has been extensively associated with schizophrenia pathophysiology [ 6 – 7 ]. The regulation of glutamate is the most energy-intensive process in the brain, consuming the majority of ATP produced via cortical glucose metabolism, estimated to account for 75–80% of total glucose use [ 8 – 10 ]. In addition to the energy requirements of glutamate homeostasis through the glutamate-glutamine cycle [ 11 – 12 ], high concentrations of glutamate can inhibit mitochondrial complexes [ 13 – 15 ] thus disrupting ATP production which may increase neuronal vulnerability to excitotoxicity [ 16 ]. In response, lactate and pyruvate levels may rise, serving as alternative energy substrates and neuroprotective factors by sustaining ATP production and buffering against metabolic stress [ 17 – 18 ]. It has been proposed that during the early stages of psychosis, genetic and environmental risks converge to impair ATP production from glucose through oxidative phosphorylation in mitochondria, which may progress to compensatory increases in aerobic glycolysis and lactate formation at later illness stages [ 4 – 5 , 19 ]. However, most of the empirical evidence for mitochondrial dysfunction and disrupted energy metabolism in psychosis is derived from studies in chronic schizophrenia, (including postmortem), with relatively little in vivo investigation of the active mechanisms at the early stages of psychosis. Glutamate can be measured in vivo using proton magnetic resonance spectroscopy ( 1 H-MRS). 1 H-MRS meta-analyses have shown that compared to healthy volunteers, individuals with psychosis-related conditions, including schizophrenia, show an overall reduction in glutamate in the medial frontal cortex (mFC), including the anterior cingulate cortex (ACC) [ 20 – 22 ]. However, mFC/ACC glutamate levels may be increased at earlier illness stages, including in CHR individuals [ 23 ] or FEP [ 20 , 22 ]. Moreover, psychosis is associated with greater variability in MFC glutamate, and this is more pronounced in younger individuals [ 21 ]. This may suggest greater disturbances in energy-expensive glutamate regulatory mechanisms at earlier illness stages. 1 H-MRS also provides measurement of N-acetyl aspartate (NAA), which is primarily synthesised in neuronal mitochondria [ 24 ] and may provide a marker of neuronal metabolic integrity [ 25 – 26 ] and mitochondrial energy output [ 27 ]. NAA levels in the frontal lobe are reduced in CHR [ 28 – 29 ], FEP and chronic schizophrenia [ 30 ]. Together, these studies therefore indicate increased and more variable levels of MFC glutamate and decreased NAA, which may indicate impaired neuronal mitochondrial activity, during the clinical high risk and early stages of psychosis. In early psychosis, elevations in peripheral markers of mitochondrial dysfunction are associated with greater symptom severity, poorer functioning and neurocognitive abilities [ 31 ]. Mitochondrial complex I activity may be elevated [ 32 ] and positively correlated with positive and cognitive symptom severity [ 33 ]. In CHR individuals, while mitochondrial complex activity does not differ compared to in healthy controls, complex III activity appears negatively associated with prodromal negative and total symptom severity scores, and complex V activity positively correlated with disorganisation severity score [ 19 , 34 ]. Although brain lactate has not yet been investigated in CHR or FEP individuals, analysis of peripheral lactate and pyruvate suggests no change [ 19 ] or decreases during the CHR stage [ 35 – 36 ] but increases during FEP [ 37 ]. However, the relationships between brain glutamate and NAA with peripheral markers of energy metabolism have not yet been investigated. In this study we tested the hypothesis that in CHR and FEP individuals compared to healthy controls, brain glutamate, measured using 1 H-MRS, would be negatively associated with peripheral mitochondrial complex activity and positively associated with lactate and pyruvate levels; and that NAA, as a marker of metabolic integrity, would show the opposite relationships. In exploratory analyses we tested for associations between these markers, symptom severity and cognitive performance. Methods Regulatory approvals The study was performed in collaboration with Clinical and Translational Sciences CaTS BioBank under a repository protocol that allowed a re-analysis of previously acquired data approved by the Centre for Addiction and Mental Health Research Ethics Board and now approved under Clinical and Translational Sciences (CaTS) BioBank by the Research Ethics Board (REB) of the Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal—Mental Health and Neuroscience subcommittee for secondary analyses. Participants included in the study were subset of a larger dataset recruited from July 20, 2011, to March 12, 2019 (Ontario, Canada). The study was performed in accordance with Good Clinical Practice guidelines, regulatory requirements, and the Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from all participants after a full explanation of study procedures. All CHR and FEP individuals had capacity to provide informed consent, as assessed by MacArthur Competence Assessment Tool for Clinical Research (MacCAT). The dataset reported here partially overlaps with previously published cohorts, including 1H-MRS ACC [ 38 ], mitochondrial complex analyses [ 19 ] and C4A expression [ 39 ]. The analytic sample comprised 56 participants: 26 at CHR, 10 with FEP, and 20 healthy controls (HCs). HCs included in this study did not meet the diagnostic criteria for cannabis use disorder [ 38 ]. To maximise statistical power and availability of both 1 H-MRS and peripheral energy measures, CHR and FEP participants were combined into a single CHR + FEP group. Participants in the CHR + FEP and HC groups were then matched using propensity score matching (via the MatchIt package in R), based on age and sex. Participants and clinical assessments CHR participants met the diagnostic criteria for prodromal risk syndrome which was assessed using the criteria of prodromal syndromes (COPS) [ 40 ]. CHR participants were excluded if they currently met the criteria for any Axis I disorder, assessed by the structured clinical interview for DSM-IV (SCID-I) [ 41 ]. The severity of prodromal symptoms in the CHR group was assessed using the structured interview for prodromal syndromes (SIPS) and scale of prodromal symptoms (SOPS) [ 40 ]. The SOPS scores consist of four categories: positive, negative, general and disorganised symptoms [ 40 ]. FEP participants met the diagnostic criteria of schizophrenia, schizophreniform disorder, delusional disorder or psychosis, as determined by the SCID-I [ 41 ] and were within 36 months of their initial diagnosis. In FEP participants, symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) [ 42 ]. FEP participants were excluded if their psychotic symptoms were better explained by bipolar disorder or another concurrent DSM-IV Axis I Disorder. HCs were defined as having no history of psychiatric illness or first degree relative with a psychotic disorder, determined by the SCID-I [ 41 ]. Exclusion criteria for all participants included pregnancy or breastfeeding, meeting criteria for alcohol and/or substance abuse disorder, and standard contraindications to MRI. Functioning was assessed using the Global Assessment of Functioning scale (GAF) [ 43 ]. Neurocognitive performance was assessed using the Wisconsin Card Sorting Test (WCST). All participants underwent urine drug screens for recreational substances at the time of MRI and blood sample collection. Symptom severity scores Since the positive items in the SOPS are derived directly from the positive PANSS scale [ 40 ], we calculated a combined positive score for the CHR and FEP groups, following the approach described previously [ 44 ]. Proton magnetic resonance spectroscopy As previously described [ 38 ], 1 H-MRS scans were performed at the CAMH Research Imaging Centre in Toronto, Canada with a 3 Tesla General Electric scanner and 8-channel head coil. To minimise head motion, participants were positioned with a soft restraint padding placed around the head and with tape strapped across the forehead. T1-weighted fast spoiled-gradient-echo 3-dimensional sagittal acquisition scans were acquired for each participant (FSPGR sequence, TE = 3.0 ms, TR = 6.7 ms, TI = 650 ms, flip angle = 8°, FOV = 28 cm, acquisition matrix 256 × 256 matrix, slice thickness = 0.9 mm). The 1 H-MRS voxel was positioned in the bilaterial supragenual anterior cingulate cortex (ACC, 30 x 20 x 15 mm). 1 H-MRS spectra were acquired using the standard GE Proton Brain Examination (PROBE) sequence using PRESS (Point Resolved Spectroscopy Sequence), at TE = 35 ms, TR = 2000 ms, number of excitations = 8, bandwidth = 5,000 Hz, 4,096 data points, 128 water-suppressed, and 16 water-unsuppressed averages. The target water linewidths after shimming were 12 Hz or less. Voxel placement and example spectra are shown in Fig. 1 . Spectra were analysed with LCModel Version 6.3–1N [ 45 ] using a standard LC model basis set acquired using PRESS at 3T and TE = 35msec. Gannet 2.0 software (version 2.0, http://gabamrs.com/ ) co-registered the 1 H-MRS voxel onto the corresponding segmented T1-weighted image to extract the grey matter, white matter and cerebrospinal fluid in the voxel [ 46 ]. Metabolite values were corrected using the voxel tissue contents using the formula: M corr = M * (WM + 1.21 * GM + 1.55 * CSF) / (WM + GM) whereby M = uncorrected metabolite and WM, GM and CSF indicating the white and grey matter and cerebrospinal fluid content. This equation assumes a CSF concentration of 55.556 mol/L [ 47 – 48 ]. 1 H-MRS spectral quality was determined by review of LCModel estimates of spectral full-width-half-maximum (FWHM) and signal-to-noise (SNR) ratio. Predefined criteria for data exclusion were spectra associated with a FWHM or SNR 2 standard deviations respectively above or below the mean values for the dataset, and individual metabolite concentration estimates associated with a Cramer Rao Lower Bounds (CRLB) > 20%. No data were excluded based on these quality criteria. The primary 1 H-MRS outcome variables were glutamate, Glx and NAA. Due to overlapping resonances at 3 Tesla, NAA is reported as the sum of N-Acetyl aspartate (NAA) and N-Acetyl aspartyl glutamate (NAAG). Peripheral energy measures Mitochondrial complex I–V function was assessed in monocyte samples using a multiplex ELISA assay, as previously described [ 19 ]. Complex function was expressed as a percentage relative to nicotinamide nucleotide transhydrogenase (%NNT), a mitochondrial protein involved in oxidative phosphorylation. Lactate and pyruvate were measured in plasma samples using colorimetric L-Lactate and Pyruvate Assay Kits, also as previously described [ 19 ] and are reported in nmol/µL. Statistical analysis Data were analysed using RStudio Version 2024.4.2.764 [ 49 ], using the packages tidyverse [ 50 ], ppcor [ 51 ], car [ 52 ], boot [ 53 ], and Hmisc [ 54 ]. Figures were made using ggplot2 [ 55 ]. To address multicollinearity among mitochondrial complexes I–V, a principal component analysis (PCA) was conducted. Variables were standardised prior to analysis by conversion to Z scores. The number of components retained was determined based on inspection of the scree plot and the eigenvalue > 1 criterion (presented in supplementary materials). Component scores from the retained components were extracted and used in subsequent analyses. Initial analyses tested for group differences in demographic and clinical characteristics, 1 H-MRS metabolites and peripheral energy measures using T-tests for continuous variables and Chi-Squared Tests for categorical variables, or for non-parametric data Mann-Whitney U Tests and Fisher’s Exact Test respectively. General linear models tested the association between 1 H-MRS metabolites and peripheral energy measures across the whole sample, including a group by 1 H-MRS metabolite interaction term to test for group differences. Non-parametric data were analysed using Mann-Whitney U tests for group differences and bootstrapped general linear models for associations between 1 H-MRS metabolites and peripheral energy measures, including a group by 1 H-MRS metabolite interaction term to test for group differences. Follow up analyses controlled for group differences in tobacco use with ANCOVAs and additional general linear models, as appropriate. In exploratory analyses, bivariate correlations tested for associations between 1 H-MRS metabolites and peripheral energy markers with symptom severity and cognitive performance. Statistical significance was defined as P < 0.05 for associations between 1 H-MRS metabolites and peripheral energy measures. Benjamini-Hochberg false discovery rate (FDR) using a Q threshold of 10% was applied to analyses of associations between 1 H-MRS metabolites and peripheral energy measures with symptom severity and cognitive performance to control for multiple comparisons. Results Participant characteristics Clinical and demographic information is provided in Table 1 . The dataset for analysis comprised of 36 CHR + FEP participants and 20 HCs. Peripheral lactate and pyruvate levels were available 29 CHR + FEP participants and 14 HCs. In the CHR + FEP group, three individuals were receiving risperidone, and one was receiving quetiapine. Four participants in the CHR + FEP group and 2 HCs had a positive drug screen for cannabis. Compared to HCs, the CHR + FEP group had significantly higher tobacco use and lower GAF scores. There were no significant differences between the CHR + FEP group and HCs in the remaining demographic characteristics and cognitive scales (Table 1 ). Table 1 Clinical and Demographic Information CHR + FEP (N = 36) HCs (N = 20) P value CHR / FEP: n group 26 / 10 - - Sex (M / F) 22 / 14 7 / 13 P = 0.111 Age: 21.64 (3.62) 21.15 (1.87) P = 0.576 BMI 24.03 (5.75) 23.73 (5.21) P = 0.855 Positive urine for cannabis (Y / N) 4 / 32 2 / 18 P = 1.000 Cumulative cannabis exposure 467.76 (191.46) 168.21 (475.56) P = 0.239 Tobacco (Y / N) 10 / 26 0 / 20 P = 0.009* Current antipsychotic use in FEP (Y / N) 4 / 6 - - Symptoms and functioning : Positive symptom severity 13.70 (5.21) - GAF 50.92 (8.08) 83.4 (5.95) P < .001* Cognitive function (WCST) : Correct mean categories 19.39 (4.45) 18.40 (3.02) P = 0.420 Perseverative errors 2.69 (2.48) 2.12 (0.88) P = 0.364 Note : Values are presented as Mean ± SD. The CHR + FEP group comprised individuals meeting criteria for clinical high risk (CHR) of psychosis or first episode psychosis (FEP). BMI: body mass index; GAF: global assessment of functioning; WCST: Wisconsin card sorting test. * Denotes significance at P < 0.05 Group differences in 1 H-MRS metabolites and energy measures The PCA for mitochondrial complexes I–V identified two principal components with eigenvalues greater than 1. PC1 explained 48.51% of the variance and showed strong positive loadings from complexes I, II, and IV. PC2 explained 28.50% of the variance, with a strong positive loading from complex V and a strong negative loading from complex III (presented in supplementary materials). There were no significant group differences in glutamate, Glx, NAA, mitochondrial complex activity (PC1 and PC2), lactate, pyruvate or lactate/pyruvate (LP) ratio between the CHR + FEP group and HCs (Table 2 ). After covarying for group differences in tobacco use, NAA (F (1,53) = 4.254, P = 0.044) and pyruvate (F (1,40) = 4.30, P = 0.045) were higher in the CHR + FEP group compared to HCs. Table 2 1H-MRS data quality, voxel tissue contents, metabolites, energy measures CHR + FEP (N = 36) HCs (N = 20) T or U statistic df P value 1 H-MRS Data quality SNR 27.00 (5) 30.00 (5) T = -2.25 54 P = 0.028* FWHM 0.03 (0.01) 0.03 (0.01) T = 1.62 54 P = 0.110 Voxel tissue contents GM 0.70 (0.04) 0.70 (0.04) T = -0.11 54 P = 0.913 WM 0.16 (0.03) 0.15 (0.03) T = 0.29 54 P = 0.773 CSF 0.16 (0.03) 0.16 (0.04) T = -0.11 54 P = 0.913 1 H-MRS metabolites in the ACC Glutamate corr 16.65 (1.54) 16.38 (1.36) T = 0.67 54 P = 0.514 Glx corr 21.84 (2.53) 21.69 (2.67) T = 0.22 54 P = 0.830 NAA corr 15.16 (0.88) 15.02 (0.87) T = 0.59 54 P = 0.558 Peripheral markers of energy metabolism Mitochondrial complex PC1 0.27 (1.45) -0.51(1.69) T = 1.78 53 P = 0.080 Mitochondrial complex PC2 -0.09 (1.25) 0.18 (1.08) T = -0.80 53 P = 0.430 Pyruvate † 0.08 (0.04) 0.07 (0.03) U = 234 - P = 0.415 Lactate 2.78 (0.84) 2.71 (0.99) T = 0.20 41 P = 0.842 LP ratio 38.56 (10.12) 42.87 (11.52) T = 1.25 41 P = 0.218 Note : 1 H-MRS metabolite values are corrected for voxel tissue content and expressed as Mean (SD). † Indicates that the variable did not meet parametric assumptions and is expressed as the Median (interquartile range). Group differences in parametric variables were tested using independent samples t-tests and non-parametric variables were tested using Mann-Whitney U tests. T statistics are reported for parametric variables and U statistics are reported for non-parametric variables. Abbreviations: CSF: voxel cerebrospinal fluid fraction; FWHM: full width at half maximum; Glu: glutamate; Glx: glutamate + glutamine; GM: voxel grey matter fraction; LP ratio: lactate-to-pyruvate ratio; NAA: N-acetylaspartate plus N-acetylaspartyl glutamate; SNR: signal to noise ratio; WM: voxel white matter fraction. * Denotes significance at P < 0.05 Relationships between 1 H-MRS metabolites and peripheral energy measures Across the whole sample, ACC Glx was positively associated with both mitochondrial complex activity PC1 (Estimate = 0.632, T = 2.16, P = 0.036) and PC2 (Estimate = 0.734, T = 2.22, P = 0.03) (Fig. 1 ). These associations remained significant after controlling for tobacco use (PC1: Estimate = 0.666, T = 2.24, P = 0.029; PC2: Estimate = 0.753, T = 2.23, P = 0.029). Critically, there were no significant group by PC1 (Estimate = -0.419, T = -0.920, P = 0.363) or group by PC2 (Estimate = 0.061, T = 0.10, P = 0.923) interaction effects. Exploratory post hoc analysis indicated that Complex I (rho (56) = 0.329, P = 0.013) and Complex V (rho (56) = 0.346, P = 0.009) activity individually correlated with Glx, indicating that these complexes may contribute most strongly to the associations of PC1 and PC2 respectively with Glx. There were no further significant overall associations between 1 H-MRS metabolites and peripheral energy measures, or group by metabolite interactions, including when covarying for group differences in tobacco use (see supplementary material). Associations between Glx corr levels in the anterior cingulate cortex (ACC) and mitochondrial complex activity PC1 (Estimate = 0.632, T = 2.155, P = 0.036) and PC2 (Estimate = 0.734, T = 2.22, P = 0.031). The solid black line represents the line of best fit for the total sample. The dashed line represents the fit for the CHR + FEP group and the dotted line represents the fit for the HC group. Data points are plotted by group, with solid circles indicating CHR + FEP participants and unfilled circles indicating HC participants. Relationships with cognition and symptoms There were no significant correlations between 1H-MRS metabolites or peripheral energy-related measures and the mean number of categories or perseverative errors on the WCST across the whole sample (presented in supplementary materials). In the CHR + FEP group, there were no significant associations between combined positive symptom severity scores and 1 H-MRS metabolites or peripheral energy-related measures (presented in supplementary materials). Discussion The aim of this study was to investigate the relationships between anterior cingulate cortex (ACC) glutamate and N-acetylaspartate (NAA) levels and peripheral markers of energy-metabolism in individuals at clinical high risk of psychosis (CHR) or in the first episode of psychosis (FEP) and healthy controls (HCs). Contrary to our hypotheses, that ACC glutamate metabolites would be negatively associated with peripheral mitochondrial complex activity in the CHR + FEP group, we found significant positive associations between Glx (glutamate + glutamine) levels with principal components relating to mitochondrial complex activity, which did not differ between groups. NAA, which may provide a marker of metabolic integrity in the ACC, was not significantly associated with peripheral energy measures. As prior evidence overall indicates a decrease in mitochondrial activity [ 56 ] and an increase in ACC glutamate metabolites [ 20 , 22 – 23 ] in the clinical high risk or early stages of psychosis, we hypothesised that these markers would be negatively correlated. Instead, we found a significant positive relationship across all participants (CHR, FEP and HCs) between ACC Glx and PC1, reflecting increased activity of mitochondrial complexes I, II and IV and Glx and PC2, reflecting the balance between Complex V and III activity. Follow-up analysis suggested that positive relationships between activity of Complex I (NADH:ubiquinone oxidoreductase) and Complex V (ATP synthase) with ACC Glx may contribute most to these relationships, representing the first and last steps of the mitochondrial respiratory chain. There were no detectable differences in ACC glutamate metabolites or mitochondrial complex activity PCs in our CHR + FEP sample compared to HCs, or in the relationship between the mitochondrial PCs and Glx. Our results therefore indicate that peripheral mitochondrial complex activity is mainly positively associated with ACC Glx in the absence of marked dysregulation of either marker. This positive relationship is broadly consistent with mechanistic associations between glutamate homeostasis and cellular energy metabolism [ 12 , 57 ]. It may be that disrupted relationships between peripheral markers of mitochondrial complex activity and brain glutamate become more apparent during later stages of psychosis / schizophrenia and may differ in studies examining cohorts with greater average illness burden. In contrast to the positive associations between ACC Glx and mitochondrial complex PCs, we did not detect associations between ACC glutamatergic metabolites or NAA with peripheral lactate or pyruvate levels. This contrasts with a recent finding of a positive correlation between ACC Glx and NAA and peripheral lactate across patients with schizophrenia and HCs [ 58 ]. It is possible that this may be due to the more limited sample size of our study, in which both lactate and ACC glutamate metabolites were available in a total of 43 participants, compared to 96 participants in [ 58 ], although our data indicated a non-significant negative relationship. It is also possible that relationships between peripheral lactate and ACC glutamate or NAA only become apparent at later illness stages. According to a recent review [ 5 ], shifts in metabolic processes may occur in chronic stages of schizophrenia following excessive glutamate signalling and redox dysregulation within early illness stages, which “burn out” in chronic schizophrenia, leading to a shift from oxidative phosphorylation to glycolysis. Shifts in metabolic processes occurring in chronic stages of illness aligns with the evidence showing increases in lactate in chronic schizophrenia [ 59 – 60 ] but not in CHR or FEP, compared to HCs [ 19 , 61 – 62 ] as well as increases in the lactate to pyruvate ratio in post-mortem studies [ 63 – 64 ]. Future work should examine associations between lactate and glutamate metabolites in psychosis directly within the same brain region, using lactate-optimised 1 H-MRS. In exploratory analysis, we tested whether peripheral energy measures and 1 H-MRS metabolites were associated with symptom severity and cognitive function and found no significant associations. In early psychosis, greater mitochondrial dysfunction is associated with higher positive symptom burden and worse cognitive functioning [ 31 ], however our study mainly included CHR participants and utilised a different set of mitochondrial markers. Some studies have detected relationships between performance on the WCST in established psychosis and glutamate [ 65 – 66 ] or NAA [ 67 – 68 ]. However, due to null or opposite findings and methodological heterogeneity, the overall relationships between 1 H-MRS glutamate or NAA and cognition are not well understood [ 69 ]. Strengths and limitations Strengths of our study include the use of ex-vivo measurements of mitochondrial complex activity in peripheral blood cells. Most of our sample were unmedicated, which minimised the potential impact of antipsychotic medication on glutamate and NAA [ 70 – 74 ] as well as peripheral energy measures [ 75 – 76 ]. Limitations of our study include the relatively small sample size, and we may have lacked power to detect further associations or group differences in associations between 1 H-MRS metabolites and peripheral energy measures. A retrospective power analysis based on our dataset indicated that a sample size of 159 participants would be required to detect a small interaction effect (f² = 0.05) between metabolite levels and group, at a significance level of 0.05 (two-tailed) with 80% power. To maximise the available data, we combined CHR and FEP participants into one group for analysis, but our 1 H-MRS and peripheral measures, or their relationships, may change after the onset of psychosis compared to in the clinical high-risk stage. 1 H-MRS is limited in that it measures the total amount of MR-visible glutamate and glutamine in the voxel, whereas neuronal glutamate and glutamine are more tightly and directly coupled to mitochondrial ATP production [ 57 ]. Finally, we measured mitochondrial function, pyruvate and lactate in peripheral samples, while glutamate and NAA were assessed in the brain using 1 H-MRS. The strength of the association between peripheral measurements of energy metabolism and brain energy metabolism are largely unknown, although previous studies in Parkinson’s disease have shown that alterations in mitochondrial complex I activity are the same in the periphery and in the brain [ 77 – 78 ]. Conclusion In conclusion, this study found a significant positive relationship between ACC Glx and principal components relating to peripheral mitochondrial complex activity, which did not differ between CHR + FEP participants compared to HCs, and was potentially driven by complex I and V. Future studies in larger samples might investigate whether differential relationships between 1 H-MRS metabolites and peripheral or central energy measures evolve over the course of psychosis / schizophrenia, potentially in relation to illness burden. Declarations Author Contributions All authors critically reviewed the manuscript and approved the final version. B.K. contributed to conceptualisation, methodology, formal analysis, and writing – original draft. M.J.K. contributed to supervision and writing – review and editing. S.K.M. contributed to project administration and writing – review and editing. A.E. contributed to conceptualisation, methodology, supervision, and writing – review and editing. R.M. contributed to funding acquisition, methodology, supervision, and writing – review and editing. A.E. and R.M. jointly supervised the project and share senior authorship. Data availability statement The datasets generated and/or analysed during the current study are available from the corresponding authors upon reasonable request Additional information Competing Interests: A.E. has received consultancy fees from Leal Therapeutics. All other authors declare no conflicts of interest related to the subject of this study. Funding: This work was partially funded by a Canadian Institutes of Health Research (CIHR) grant (APP400704), and also represents independent research partly supported by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. B.K. is supported by a UK Medical Research Council PhD studentship (MR/N013700/1). For the purposes of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Correspondence and requests for materials should be addressed to A.E or R.M References Fusar-Poli, P. et al. The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry 70 , 107–120 (2013). De Pablo, G. S. et al. Probability of transition to psychosis in individuals at clinical high risk: an updated meta-analysis. JAMA Psychiatry 78 , 970–978 (2021). Cuenod, M. et al. Caught in vicious circles: a perspective on dynamic feed-forward loops driving oxidative stress in schizophrenia. Mol. Psychiatry 27 , 1886–1897 (2022). Sarnyai, Z. & Ben-Shachar, D. Schizophrenia, a disease of impaired dynamic metabolic flexibility: a new mechanistic framework. Psychiatry Res. 328 , 116220 (2024). Stein, A., Zhu, C., Du, F. & Öngür, D. Magnetic resonance spectroscopy studies of brain energy metabolism in schizophrenia: progression from prodrome to chronic psychosis. Curr. Psychiatry Rep. 25 , 659–669 (2023). Egerton, A. et al. Glutamate in schizophrenia: neurodevelopmental perspectives and drug development. Schizophr. Res. 223 , 59–70 (2020). McCutcheon, R. A., Krystal, J. H. & Howes, O. D. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry 19 , 15–33 (2020). Hertz, L. & Rothman, D. L. Glucose, lactate, β-hydroxybutyrate, acetate, GABA, and succinate as substrates for synthesis of glutamate and GABA in the glutamine–glutamate/GABA cycle. Adv. Neurobiol. 13 , 9–42 (2016). Sibson, N. R. et al. Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity. Proc. Natl Acad. Sci. USA 95 , 316–321 (1998). Yu, Y., Herman, P., Rothman, D. L., Agarwal, D. & Hyder, F. Evaluating the gray and white matter energy budgets of human brain function. J. Cereb. Blood Flow Metab. 38 , 1339–1353 (2018). Attwell, D. & Laughlin, S. B. An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21 , 1133–1145 (2001). Lezmy, J., Harris, J. J. & Attwell, D. Optimising the energetic cost of the glutamatergic synapse. Neuropharmacology 198 , 108727 (2021). Nicholls, D. & Åkerman, K. Mitochondrial calcium transport. Biochim. Biophys. Acta Rev. Bioenerg. 683 , 57–88 (1982). Nicholls, D. G. A role for the mitochondrion in the protection of cells against calcium overload? Prog. Brain Res. 63 , 97–106 (1985). Tiwari, K. D. et al. Effects of high glutamate concentrations on mitochondria of human neuroblastoma SH-SY5Y cells. Ann. Pharm. Fr. 81 , 457–465 (2023). Isaev, N. K., Andreeva, N. A., Stel’mashuk, E. V. & Zorov, D. B. Role of mitochondria in the mechanisms of glutamate toxicity. Biochemistry (Moscow) 70 , 611–618 (2005). Ros, J., Pecinska, N., Alessandri, B., Landolt, H. & Fillenz, M. Lactate reduces glutamate‐induced neurotoxicity in rat cortex. J. Neurosci. Res. 66 , 790–794 (2001). Izumi, Y. & Zorumski, C. F. Neuroprotective effects of pyruvate following NMDA-mediated excitotoxic insults in hippocampal slices. Neurosci. Lett. 478 , 131–135 (2010). Da Silva, T. et al. Mitochondrial function in individuals at clinical high risk for psychosis. Sci. Rep. 8 , 6216 (2018). Merritt, K. et al. Association of age, antipsychotic medication, and symptom severity in schizophrenia with proton magnetic resonance spectroscopy brain glutamate level: a mega-analysis of individual participant-level data. JAMA Psychiatry 78 , 667–681 (2021). Merritt, K. et al. Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis. Mol. Psychiatry 28 , 2039–2048 (2023). Nakahara, T. et al. Glutamatergic and GABAergic metabolite levels in schizophrenia-spectrum disorders: a meta-analysis of 1H-magnetic resonance spectroscopy studies. Mol. Psychiatry 27 , 744–757 (2022). Merritt, K., Egerton, A., Kempton, M. J., Taylor, M. J. & McGuire, P. K. Nature of glutamate alterations in schizophrenia: a meta-analysis of proton magnetic resonance spectroscopy studies. JAMA Psychiatry 73 , 665–674 (2016). Rae, C. D. A guide to the metabolic pathways and function of metabolites observed in human brain 1H magnetic resonance spectra. Neurochem. Res. 39 , 1–36 (2014). Moffett, J. R., Ross, B., Arun, P., Madhavarao, C. N. & Namboodiri, A. M. N-Acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog. Neurobiol. 81 , 89–131 (2007). Maddock, R. J. & Buonocore, M. H. MR spectroscopic studies of the brain in psychiatric disorders. In Brain Imaging in Behavioral Neuroscience, – 199–251 (2012). Clark, J. B. N-acetyl aspartate: a marker for neuronal loss or mitochondrial dysfunction. Dev. Neurosci. 20 , 271 (1998). Brugger, S., Davis, J. M., Leucht, S. & Stone, J. M. Proton magnetic resonance spectroscopy and illness stage in schizophrenia—a systematic review and meta-analysis. Biol. Psychiatry 69 , 495–503 (2011). Mondino, M., Brunelin, J. & Saoud, M. N-acetyl-aspartate level is decreased in the prefrontal cortex in subjects at-risk for schizophrenia. Front. Psychiatry 4 , 99 (2013). Whitehurst, T. S. et al. Proton magnetic resonance spectroscopy of N-acetyl aspartate in chronic schizophrenia, first episode of psychosis and high-risk of psychosis: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 119 , 255–267 (2020). Khadimallah, I. et al. Mitochondrial, exosomal miR137-COX6A2 and gamma synchrony as biomarkers of parvalbumin interneurons, psychopathology, and neurocognition in schizophrenia. Mol. Psychiatry 27 , 1192–1204 (2022). Ben-Shachar, D. et al. Increased mitochondrial complex I activity in platelets of schizophrenic patients. Int. J. Neuropsychopharmacol. 2 , 245–253 (1999). Dror, N. et al. State-dependent alterations in mitochondrial complex I activity in platelets: a potential peripheral marker for schizophrenia. Mol. Psychiatry 7 , 995–1001 (2002). Wu, A. et al. Validating mitochondrial electron transport chain content in individuals at clinical high risk for psychosis. Sci. Rep. 9 , 12695 (2019). Huang, J. T. J. et al. CSF metabolic and proteomic profiles in patients prodromal for psychosis. PLoS One 2 , e756 (2007). Onozato, M. et al. Serum D‐ and L‐lactate, pyruvate and glucose levels in individuals with at‐risk mental state and correlations with clinical symptoms. Early Interv. Psychiatry 14 , 410–417 (2020). Jiang, J. et al. Leukocyte proteomic profiling in first-episode schizophrenia patients: does oxidative stress play central roles in the pathophysiology network of schizophrenia? (2019). Watts, J. J. et al. Evidence that cannabis exposure, abuse, and dependence are related to glutamate metabolism and glial function in the anterior cingulate cortex: a 1H-magnetic resonance spectroscopy study. Front. Psychiatry 11 , 764 (2020). Da Silva, T. et al. Genetically predicted brain C4A expression is associated with TSPO and hippocampal morphology. Biol. Psychiatry 90 , 652–660 (2021). Miller, T. J. et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr. Bull. 29 , 703–715 (2003). First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. W. Structured Clinical Interview for DSM-IV Axis I Disorders – Patient Edition (SCID-I/P, Version 2.0). Biometric Research Department, New York State Psychiatric Institute (1995). Kay, S. R., Fiszbein, A. & Opler, L. A. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13 , 261–276 (1987). Hall, R. C. Global assessment of functioning: a modified scale. Psychosomatics 36 , 267–275 (1995). Schobel, S. A. et al. Differential targeting of the CA1 subfield of the hippocampal formation by schizophrenia and related psychotic disorders. Arch. Gen. Psychiatry 66 , 938–946 (2009). Provencher, S. W. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 30 , 672–679 (1993). Egerton, A. et al. Dopamine and glutamate in antipsychotic-responsive compared with antipsychotic-nonresponsive psychosis: a multicenter positron emission tomography and magnetic resonance spectroscopy study (STRATA). Schizophr. Bull. 47 , 505–516 (2021). Kreis, R., Ernst, T. & Ross, B. D. Development of the human brain: in vivo quantification of metabolite and water content with proton magnetic resonance spectroscopy. Magn. Reson. Med. 30 , 424–437 (1993). Gasparovic, C. et al. Use of tissue water as a concentration reference for proton spectroscopic imaging. Magn. Reson. Med. 55 , 1219–1226 (2006). Posit Team. RStudio: Integrated development environment for R (Version 2024.4.2.764). Posit Software, PBC (2024). https://posit.co/ Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4 , 1686 (2019). Gordon, M. ppcor: an R package for a fast calculation to semipartial and partial correlations. R package version 1.1 (2019). Fox, J. & Weisberg, S. An R Companion to Applied Regression (3rd ed.). Sage Publications (2019). Canty, A. & Ripley, B. D. boot: Bootstrap R (S-Plus) functions. R package version 1.3-28 (2022). Harrell, F. E., Jr. Hmisc: Harrell miscellaneous. R package version 5.1-3 (2024). Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag (2016). Rajasekaran, A., Venkatasubramanian, G., Berk, M. & Debnath, M. Mitochondrial dysfunction in schizophrenia: pathways, mechanisms and implications. Neurosci. Biobehav. Rev. 48 , 10–21 (2015). Andersen, J. V. et al. Glutamate metabolism and recycling at the excitatory synapse in health and neurodegeneration. Neuropharmacology 196 , 108719 (2021). Krzyściak, W. et al. Association of blood metabolomics biomarkers with brain metabolites and patient-reported outcomes as a new approach in individualized diagnosis of schizophrenia. Int. J. Mol. Sci. 25 , 2294 (2024). Rowland, L. M. et al. Elevated brain lactate in schizophrenia: a 7 T magnetic resonance spectroscopy study. Transl. Psychiatry 6 , e967 (2016). Wijtenburg, S. A. et al. Metabolite alterations in adults with schizophrenia, first degree relatives, and healthy controls: a multi-region 7T MRS study. Front. Psychiatry 12 , 656459 (2021). Wang, A. M. et al. Assessing brain metabolism with 7-T proton magnetic resonance spectroscopy in patients with first-episode psychosis. JAMA Psychiatry 76 , 314–323 (2019). Wang, M. et al. Longitudinal changes in brain metabolites in healthy controls and patients with first episode psychosis: a 7-Tesla MRS study. Mol. Psychiatry 28 , 2018–2029 (2023). Dean, B., Thomas, N., Scarr, E. & Udawela, M. Evidence for impaired glucose metabolism in the striatum, obtained postmortem, from some subjects with schizophrenia. Transl. Psychiatry 6 , e949 (2016). Park, H. J., Choi, I. & Leem, K. H. Decreased brain pH and pathophysiology in schizophrenia. Int. J. Mol. Sci. 22 , 8358 (2021). Dempster, K. et al. Glutamatergic metabolite correlations with neuropsychological tests in first episode schizophrenia. Psychiatry Res. Neuroimaging 233 , 180–185 (2015). Shirayama, Y. et al. Specific metabolites in the medial prefrontal cortex are associated with the neurocognitive deficits in schizophrenia: a preliminary study. Neuroimage 49 , 2783–2790 (2010). Braus, D. F. et al. Functioning and neuronal viability of the anterior cingulate neurons following antipsychotic treatment: MR-spectroscopic imaging in chronic schizophrenia. Eur. Neuropsychopharmacol. 12 , 145–152 (2002). Galińska, B. et al. Relationship between frontal N-acetylaspartate and cognitive deficits in first-episode schizophrenia. Med. Sci. Monit. 13 (Suppl 1), 11–16 (2007). Reddy-Thootkur, M., Kraguljac, N. V. & Lahti, A. C. The role of glutamate and GABA in cognitive dysfunction in schizophrenia and mood disorders – a systematic review of magnetic resonance spectroscopy studies. Schizophr. Res. 249 , 74–84 (2022). Arun, P., Madhavarao, C. N., Moffett, J. R. & Namboodiri, A. M. Antipsychotic drugs increase N‐acetylaspartate and N‐acetylaspartylglutamate in SH‐SY5Y human neuroblastoma cells. J. Neurochem. 106 , 1669–1680 (2008). Bustillo, J. R. et al. Longitudinal follow-up of neurochemical changes during the first year of antipsychotic treatment in schizophrenia patients with minimal previous medication exposure. Schizophr. Res. 58 , 313–321 (2002). Egerton, A., Modinos, G., Ferrera, D. & McGuire, P. Neuroimaging studies of GABA in schizophrenia: a systematic review with meta-analysis. Transl. Psychiatry 7 , e1147 (2017). Goto, N. et al. Six-month treatment with atypical antipsychotic drugs decreased frontal-lobe levels of glutamate plus glutamine in early-stage first-episode schizophrenia. Neuropsychiatr. Dis. Treat. 8 , 119 (2012). Kubota, M., Moriguchi, S., Takahata, K., Nakajima, S. & Horita, N. Treatment effects on neurometabolite levels in schizophrenia: a meta-analysis dataset of proton magnetic resonance spectroscopy. Data Brief 31 , 105862 (2020). Casademont, J. et al. Neuroleptic treatment effect on mitochondrial electron transport chain: peripheral blood mononuclear cells analysis in psychotic patients. J. Clin. Psychopharmacol. 27 , 284–288 (2007). Halim, N. D. et al. Increased lactate levels and reduced pH in postmortem brains of schizophrenics: medication confounds. J. Neurosci. Methods 169 , 208–213 (2008). Parker, W. D., Boyson, S. J. & Parks, J. K. Abnormalities of the electron transport chain in idiopathic Parkinson’s disease. Ann. Neurol. 26 , 719–723 (1989). Schapira, A. et al. Anatomic and disease specificity of NADH CoQ1 reductase (complex I) deficiency in Parkinson’s disease. J. Neurochem. 55 , 2142–2145 (1990). Additional Declarations Competing interest reported. A.E. has received consultancy fees from Leal Therapeutics. All other authors declare no conflicts of interest related to the subject of this study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7052494","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488604939,"identity":"c4a21a4d-47bb-437e-a220-5f5addcbc1a9","order_by":0,"name":"Bridget King","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Bridget","middleName":"","lastName":"King","suffix":""},{"id":488604941,"identity":"8dc14013-b6f8-43de-93d8-c3d506984eb2","order_by":1,"name":"Matthew J. Kempton","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"J.","lastName":"Kempton","suffix":""},{"id":488604942,"identity":"7a7501b4-43e7-4a88-a08f-3f87a56709e9","order_by":2,"name":"Shue Kit Man","email":"","orcid":"","institution":"Douglas Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Shue","middleName":"Kit","lastName":"Man","suffix":""},{"id":488604943,"identity":"a86e3c48-e284-4d01-aee2-d0c021495f09","order_by":3,"name":"Alice Egerton","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Alice","middleName":"","lastName":"Egerton","suffix":""},{"id":488604944,"identity":"c91c1883-bd58-411f-84b1-88ba63f51f32","order_by":4,"name":"Romina Mizrahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYJCCAyCCDYiZGQxsgBRj44EHJGhJA2lpOJBArHXMDAyHIYbg02Le3p14uKCGIZqP/+zDzwUF56P5+xeDbLGTx6VF5szZDYdnHGPIbWM4biw9w+B27owbD0Fakg0bcGiRkMjdcJiHDaiFsY1BmgeoZYPEQZCWA4z4tfwDamFmY/7NY3AOrsUerxbeNqAWNjY2oC0HcjfwN4K1JOLUwgP0C2+fRG4bDxubNY9BMtAvoEA2SE7GqYW9d/Nnnm82ufP7jzHf5vljl9vff/zhgw8Vdra4tMB0IrMTgIQBfvVogP8AScpHwSgYBaNg+AMAGPVaKvPgMMcAAAAASUVORK5CYII=","orcid":"","institution":"Douglas Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Romina","middleName":"","lastName":"Mizrahi","suffix":""}],"badges":[],"createdAt":"2025-07-05 10:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7052494/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7052494/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22845-y","type":"published","date":"2025-11-25T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87297902,"identity":"729c633e-a4cb-4813-be97-07741b4213a1","added_by":"auto","created_at":"2025-07-22 12:50:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59037,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between Glx\u003csub\u003ecorr \u003c/sub\u003elevels in the anterior cingulate cortex (ACC) and mitochondrial complex activity PC1 (Estimate = 0.632, T = 2.155, P = 0.036) and PC2 (Estimate = 0.734, T = 2.22, P = 0.031). The\u0026nbsp;solid black line\u0026nbsp;represents the line of best fit for the\u0026nbsp;total sample. The\u0026nbsp;dashed line\u0026nbsp;represents the fit for the\u0026nbsp;CHR+FEP group and the\u0026nbsp;dotted line\u0026nbsp;represents the fit for the\u0026nbsp;HC group. Data points are plotted by group, with\u0026nbsp;solid circles\u0026nbsp;indicating CHR+FEP participants and\u0026nbsp;unfilled circles\u0026nbsp;indicating HC participants.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7052494/v1/be59044218aaf9757843817c.jpg"},{"id":97178343,"identity":"90863136-5211-40da-b335-82fff80e6885","added_by":"auto","created_at":"2025-12-01 16:08:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1005679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7052494/v1/8f221346-abf5-4bdc-83ff-76c4c7ae422c.pdf"},{"id":87297909,"identity":"371f1d3d-a6ac-48f5-8bc6-27bf62ccf315","added_by":"auto","created_at":"2025-07-22 12:50:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1812675,"visible":true,"origin":"","legend":"","description":"","filename":"Energymetabolitessupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7052494/v1/e68d4dd77679be1aeff55c5d.docx"}],"financialInterests":"Competing interest reported. A.E. has received consultancy fees from Leal Therapeutics. All other authors declare no conflicts of interest related to the subject of this study.","formattedTitle":"Glutamate, NAA and Energy Metabolism in Clinical High Risk and First Episode Psychosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe psychosis clinical high risk (CHR) state is defined as an early disease stage prior to the onset of overt psychosis and is characterised by sub-threshold psychotic or non-specific psychiatric symptoms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Individuals meeting CHR criteria have a 25% probability of transitioning to first episode psychosis (FEP) within 3 years, and a 35% probability within 10 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Understanding the active biological mechanisms at the early stages of psychosis is crucial for elucidating the pathophysiology underlying psychosis risk and onset, and for identifying potential targets for early intervention.\u003c/p\u003e\u003cp\u003eRecent reviews have highlighted the potential importance of the interplay between regulation of brain glutamate, mitochondrial dysfunction and energy metabolism in psychosis pathophysiology and onset [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Glutamatergic neurotransmission has been extensively associated with schizophrenia pathophysiology [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The regulation of glutamate is the most energy-intensive process in the brain, consuming the majority of ATP produced via cortical glucose metabolism, estimated to account for 75–80% of total glucose use [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition to the energy requirements of glutamate homeostasis through the glutamate-glutamine cycle [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], high concentrations of glutamate can inhibit mitochondrial complexes [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] thus disrupting ATP production which may increase neuronal vulnerability to excitotoxicity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In response, lactate and pyruvate levels may rise, serving as alternative energy substrates and neuroprotective factors by sustaining ATP production and buffering against metabolic stress [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIt has been proposed that during the early stages of psychosis, genetic and environmental risks converge to impair ATP production from glucose through oxidative phosphorylation in mitochondria, which may progress to compensatory increases in aerobic glycolysis and lactate formation at later illness stages [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, most of the empirical evidence for mitochondrial dysfunction and disrupted energy metabolism in psychosis is derived from studies in chronic schizophrenia, (including postmortem), with relatively little \u003cem\u003ein vivo\u003c/em\u003e investigation of the active mechanisms at the early stages of psychosis.\u003c/p\u003e\u003cp\u003eGlutamate can be measured \u003cem\u003ein vivo\u003c/em\u003e using proton magnetic resonance spectroscopy (\u003csup\u003e1\u003c/sup\u003eH-MRS). \u003csup\u003e1\u003c/sup\u003eH-MRS meta-analyses have shown that compared to healthy volunteers, individuals with psychosis-related conditions, including schizophrenia, show an overall reduction in glutamate in the medial frontal cortex (mFC), including the anterior cingulate cortex (ACC) [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, mFC/ACC glutamate levels may be increased at earlier illness stages, including in CHR individuals [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] or FEP [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, psychosis is associated with greater variability in MFC glutamate, and this is more pronounced in younger individuals [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This may suggest greater disturbances in energy-expensive glutamate regulatory mechanisms at earlier illness stages.\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eH-MRS also provides measurement of N-acetyl aspartate (NAA), which is primarily synthesised in neuronal mitochondria [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and may provide a marker of neuronal metabolic integrity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and mitochondrial energy output [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. NAA levels in the frontal lobe are reduced in CHR [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], FEP and chronic schizophrenia [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Together, these studies therefore indicate increased and more variable levels of MFC glutamate and decreased NAA, which may indicate impaired neuronal mitochondrial activity, during the clinical high risk and early stages of psychosis.\u003c/p\u003e\u003cp\u003eIn early psychosis, elevations in peripheral markers of mitochondrial dysfunction are associated with greater symptom severity, poorer functioning and neurocognitive abilities [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Mitochondrial complex I activity may be elevated [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and positively correlated with positive and cognitive symptom severity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In CHR individuals, while mitochondrial complex activity does not differ compared to in healthy controls, complex III activity appears negatively associated with prodromal negative and total symptom severity scores, and complex V activity positively correlated with disorganisation severity score [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although brain lactate has not yet been investigated in CHR or FEP individuals, analysis of peripheral lactate and pyruvate suggests no change [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or decreases during the CHR stage [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] but increases during FEP [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, the relationships between brain glutamate and NAA with peripheral markers of energy metabolism have not yet been investigated.\u003c/p\u003e\u003cp\u003eIn this study we tested the hypothesis that in CHR and FEP individuals compared to healthy controls, brain glutamate, measured using \u003csup\u003e1\u003c/sup\u003eH-MRS, would be negatively associated with peripheral mitochondrial complex activity and positively associated with lactate and pyruvate levels; and that NAA, as a marker of metabolic integrity, would show the opposite relationships. In exploratory analyses we tested for associations between these markers, symptom severity and cognitive performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eRegulatory approvals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study was performed in collaboration with Clinical and Translational Sciences CaTS BioBank under a repository protocol that allowed a re-analysis of previously acquired data approved by the Centre for Addiction and Mental Health Research Ethics Board and now approved under Clinical and Translational Sciences (CaTS) BioBank by the Research Ethics Board (REB) of the Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal—Mental Health and Neuroscience subcommittee for secondary analyses.\u003c/p\u003e\u003cp\u003eParticipants included in the study were subset of a larger dataset recruited from July 20, 2011, to March 12, 2019 (Ontario, Canada). The study was performed in accordance with Good Clinical Practice guidelines, regulatory requirements, and the Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from all participants after a full explanation of study procedures. All CHR and FEP individuals had capacity to provide informed consent, as assessed by MacArthur Competence Assessment Tool for Clinical Research (MacCAT).\u003c/p\u003e\u003cp\u003eThe dataset reported here partially overlaps with previously published cohorts, including 1H-MRS ACC [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], mitochondrial complex analyses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and C4A expression [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The analytic sample comprised 56 participants: 26 at CHR, 10 with FEP, and 20 healthy controls (HCs). HCs included in this study did not meet the diagnostic criteria for cannabis use disorder [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo maximise statistical power and availability of both \u003csup\u003e1\u003c/sup\u003eH-MRS and peripheral energy measures, CHR and FEP participants were combined into a single CHR + FEP group. Participants in the CHR + FEP and HC groups were then matched using propensity score matching (via the \u003cem\u003eMatchIt\u003c/em\u003e package in R), based on age and sex.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants and clinical assessments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCHR participants met the diagnostic criteria for prodromal risk syndrome which was assessed using the criteria of prodromal syndromes (COPS) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. CHR participants were excluded if they currently met the criteria for any Axis I disorder, assessed by the structured clinical interview for DSM-IV (SCID-I) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The severity of prodromal symptoms in the CHR group was assessed using the structured interview for prodromal syndromes (SIPS) and scale of prodromal symptoms (SOPS) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The SOPS scores consist of four categories: positive, negative, general and disorganised symptoms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. FEP participants met the diagnostic criteria of schizophrenia, schizophreniform disorder, delusional disorder or psychosis, as determined by the SCID-I [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and were within 36 months of their initial diagnosis. In FEP participants, symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. FEP participants were excluded if their psychotic symptoms were better explained by bipolar disorder or another concurrent DSM-IV Axis I Disorder. HCs were defined as having no history of psychiatric illness or first degree relative with a psychotic disorder, determined by the SCID-I [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Exclusion criteria for all participants included pregnancy or breastfeeding, meeting criteria for alcohol and/or substance abuse disorder, and standard contraindications to MRI. Functioning was assessed using the Global Assessment of Functioning scale (GAF) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Neurocognitive performance was assessed using the Wisconsin Card Sorting Test (WCST). All participants underwent urine drug screens for recreational substances at the time of MRI and blood sample collection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSymptom severity scores\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSince the positive items in the SOPS are derived directly from the positive PANSS scale [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], we calculated a combined positive score for the CHR and FEP groups, following the approach described previously [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eProton magnetic resonance spectroscopy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs previously described [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], \u003csup\u003e1\u003c/sup\u003eH-MRS scans were performed at the CAMH Research Imaging Centre in Toronto, Canada with a 3 Tesla General Electric scanner and 8-channel head coil. To minimise head motion, participants were positioned with a soft restraint padding placed around the head and with tape strapped across the forehead. T1-weighted fast spoiled-gradient-echo 3-dimensional sagittal acquisition scans were acquired for each participant (FSPGR sequence, TE = 3.0 ms, TR = 6.7 ms, TI = 650 ms, flip angle = 8°, FOV = 28 cm, acquisition matrix 256 × 256 matrix, slice thickness = 0.9 mm).\u003c/p\u003e\u003cp\u003eThe \u003csup\u003e1\u003c/sup\u003eH-MRS voxel was positioned in the bilaterial supragenual anterior cingulate cortex (ACC, 30 x 20 x 15 mm). \u003csup\u003e1\u003c/sup\u003eH-MRS spectra were acquired using the standard GE Proton Brain Examination (PROBE) sequence using PRESS (Point Resolved Spectroscopy Sequence), at TE = 35 ms, TR = 2000 ms, number of excitations = 8, bandwidth = 5,000 Hz, 4,096 data points, 128 water-suppressed, and 16 water-unsuppressed averages. The target water linewidths after shimming were 12 Hz or less. Voxel placement and example spectra are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eSpectra were analysed with LCModel Version 6.3–1N [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] using a standard LC model basis set acquired using PRESS at 3T and TE = 35msec. Gannet 2.0 software (version 2.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gabamrs.com/\u003c/span\u003e\u003cspan address=\"http://gabamrs.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) co-registered the \u003csup\u003e1\u003c/sup\u003eH-MRS voxel onto the corresponding segmented T1-weighted image to extract the grey matter, white matter and cerebrospinal fluid in the voxel [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Metabolite values were corrected using the voxel tissue contents using the formula: M\u003cem\u003ecorr\u003c/em\u003e = M * (WM + 1.21 * GM + 1.55 * CSF) / (WM + GM) whereby M = uncorrected metabolite and WM, GM and CSF indicating the white and grey matter and cerebrospinal fluid content. This equation assumes a CSF concentration of 55.556 mol/L [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eH-MRS spectral quality was determined by review of LCModel estimates of spectral full-width-half-maximum (FWHM) and signal-to-noise (SNR) ratio. Predefined criteria for data exclusion were spectra associated with a FWHM or SNR 2 standard deviations respectively above or below the mean values for the dataset, and individual metabolite concentration estimates associated with a Cramer Rao Lower Bounds (CRLB) \u0026gt; 20%. No data were excluded based on these quality criteria.\u003c/p\u003e\u003cp\u003eThe primary \u003csup\u003e1\u003c/sup\u003eH-MRS outcome variables were glutamate, Glx and NAA. Due to overlapping resonances at 3 Tesla, NAA is reported as the sum of N-Acetyl aspartate (NAA) and N-Acetyl aspartyl glutamate (NAAG).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePeripheral energy measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMitochondrial complex I–V function was assessed in monocyte samples using a multiplex ELISA assay, as previously described [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Complex function was expressed as a percentage relative to nicotinamide nucleotide transhydrogenase (%NNT), a mitochondrial protein involved in oxidative phosphorylation. Lactate and pyruvate were measured in plasma samples using colorimetric L-Lactate and Pyruvate Assay Kits, also as previously described [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and are reported in nmol/µL.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData were analysed using RStudio Version 2024.4.2.764 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], using the packages tidyverse [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], ppcor [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], car [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], boot [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and Hmisc [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Figures were made using ggplot2 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo address multicollinearity among mitochondrial complexes I–V, a principal component analysis (PCA) was conducted. Variables were standardised prior to analysis by conversion to Z scores. The number of components retained was determined based on inspection of the scree plot and the eigenvalue \u0026gt; 1 criterion (presented in supplementary materials). Component scores from the retained components were extracted and used in subsequent analyses.\u003c/p\u003e\u003cp\u003eInitial analyses tested for group differences in demographic and clinical characteristics, \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy measures using T-tests for continuous variables and Chi-Squared Tests for categorical variables, or for non-parametric data Mann-Whitney U Tests and Fisher’s Exact Test respectively. General linear models tested the association between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy measures across the whole sample, including a group by \u003csup\u003e1\u003c/sup\u003eH-MRS metabolite interaction term to test for group differences. Non-parametric data were analysed using Mann-Whitney U tests for group differences and bootstrapped general linear models for associations between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy measures, including a group by \u003csup\u003e1\u003c/sup\u003eH-MRS metabolite interaction term to test for group differences.\u003c/p\u003e\u003cp\u003eFollow up analyses controlled for group differences in tobacco use with ANCOVAs and additional general linear models, as appropriate. In exploratory analyses, bivariate correlations tested for associations between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy markers with symptom severity and cognitive performance.\u003c/p\u003e\u003cp\u003eStatistical significance was defined as P \u0026lt; 0.05 for associations between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy measures. Benjamini-Hochberg false discovery rate (FDR) using a Q threshold of 10% was applied to analyses of associations between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy measures with symptom severity and cognitive performance to control for multiple comparisons.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eParticipant characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eClinical and demographic information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The dataset for analysis comprised of 36 CHR\u0026thinsp;+\u0026thinsp;FEP participants and 20 HCs. Peripheral lactate and pyruvate levels were available 29 CHR\u0026thinsp;+\u0026thinsp;FEP participants and 14 HCs. In the CHR\u0026thinsp;+\u0026thinsp;FEP group, three individuals were receiving risperidone, and one was receiving quetiapine. Four participants in the CHR\u0026thinsp;+\u0026thinsp;FEP group and 2 HCs had a positive drug screen for cannabis.\u003c/p\u003e\u003cp\u003eCompared to HCs, the CHR\u0026thinsp;+\u0026thinsp;FEP group had significantly higher tobacco use and lower GAF scores. There were no significant differences between the CHR\u0026thinsp;+\u0026thinsp;FEP group and HCs in the remaining demographic characteristics and cognitive scales (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical and Demographic Information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHR\u0026thinsp;+\u0026thinsp;FEP (N\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHCs (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHR / FEP: n group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 / 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (M / F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 / 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 / 13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.64 (3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.15 (1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.03 (5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.73 (5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive urine for cannabis (Y / N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 / 32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 / 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative cannabis exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e467.76 (191.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.21 (475.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTobacco (Y / N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 / 26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 / 20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.009*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent antipsychotic use in FEP (Y / N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 / 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSymptoms and functioning\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive symptom severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.70 (5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.92 (8.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.4 (5.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCognitive function (WCST)\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrect mean categories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.39 (4.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.40 (3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerseverative errors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.69 (2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: Values are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The CHR\u0026thinsp;+\u0026thinsp;FEP group comprised individuals meeting criteria for clinical high risk (CHR) of psychosis or first episode psychosis (FEP). BMI: body mass index; GAF: global assessment of functioning; WCST: Wisconsin card sorting test. * Denotes significance at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup differences in\u003c/b\u003e \u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eH-MRS metabolites and energy measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PCA for mitochondrial complexes I\u0026ndash;V identified two principal components with eigenvalues greater than 1. PC1 explained 48.51% of the variance and showed strong positive loadings from complexes I, II, and IV. PC2 explained 28.50% of the variance, with a strong positive loading from complex V and a strong negative loading from complex III (presented in supplementary materials).\u003c/p\u003e\u003cp\u003eThere were no significant group differences in glutamate, Glx, NAA, mitochondrial complex activity (PC1 and PC2), lactate, pyruvate or lactate/pyruvate (LP) ratio between the CHR\u0026thinsp;+\u0026thinsp;FEP group and HCs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After covarying for group differences in tobacco use, NAA (F (1,53)\u0026thinsp;=\u0026thinsp;4.254, P\u0026thinsp;=\u0026thinsp;0.044) and pyruvate (F (1,40)\u0026thinsp;=\u0026thinsp;4.30, P\u0026thinsp;=\u0026thinsp;0.045) were higher in the CHR\u0026thinsp;+\u0026thinsp;FEP group compared to HCs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e1H-MRS data quality, voxel tissue contents, metabolites, energy measures\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHR\u0026thinsp;+\u0026thinsp;FEP (N\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHCs (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT or U statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eH-MRS Data quality\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.00 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.00 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT = -2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.028*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFWHM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVoxel tissue contents\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT = -0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT = -0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eH-MRS metabolites in the ACC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlutamate \u003csub\u003ecorr\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.65 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.38 (1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlx \u003csub\u003ecorr\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.84 (2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.69 (2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.830\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNAA \u003csub\u003ecorr\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.16 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.02 (0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePeripheral markers of energy metabolism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMitochondrial complex PC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27 (1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.51(1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMitochondrial complex PC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.09 (1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18 (1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT = -0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePyruvate\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU\u0026thinsp;=\u0026thinsp;234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.78 (0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71 (0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLP ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.56 (10.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.87 (11.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: \u003csup\u003e1\u003c/sup\u003eH-MRS metabolite values are corrected for voxel tissue content and expressed as Mean (SD). \u003csup\u003e\u0026dagger;\u003c/sup\u003e Indicates that the variable did not meet parametric assumptions and is expressed as the Median (interquartile range). Group differences in parametric variables were tested using independent samples t-tests and non-parametric variables were tested using Mann-Whitney U tests. T statistics are reported for parametric variables and U statistics are reported for non-parametric variables. Abbreviations: CSF: voxel cerebrospinal fluid fraction; FWHM: full width at half maximum; Glu: glutamate; Glx: glutamate\u0026thinsp;+\u0026thinsp;glutamine; GM: voxel grey matter fraction; LP ratio: lactate-to-pyruvate ratio; NAA: N-acetylaspartate plus N-acetylaspartyl glutamate; SNR: signal to noise ratio; WM: voxel white matter fraction. * Denotes significance at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationships between\u003c/b\u003e \u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eH-MRS metabolites and peripheral energy measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAcross the whole sample, ACC Glx was positively associated with both mitochondrial complex activity PC1 (Estimate\u0026thinsp;=\u0026thinsp;0.632, T\u0026thinsp;=\u0026thinsp;2.16, P\u0026thinsp;=\u0026thinsp;0.036) and PC2 (Estimate\u0026thinsp;=\u0026thinsp;0.734, T\u0026thinsp;=\u0026thinsp;2.22, P\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These associations remained significant after controlling for tobacco use (PC1: Estimate\u0026thinsp;=\u0026thinsp;0.666, T\u0026thinsp;=\u0026thinsp;2.24, P\u0026thinsp;=\u0026thinsp;0.029; PC2: Estimate\u0026thinsp;=\u0026thinsp;0.753, T\u0026thinsp;=\u0026thinsp;2.23, P\u0026thinsp;=\u0026thinsp;0.029). Critically, there were no significant group by PC1 (Estimate = -0.419, T = -0.920, P\u0026thinsp;=\u0026thinsp;0.363) or group by PC2 (Estimate\u0026thinsp;=\u0026thinsp;0.061, T\u0026thinsp;=\u0026thinsp;0.10, P\u0026thinsp;=\u0026thinsp;0.923) interaction effects.\u003c/p\u003e\u003cp\u003eExploratory post hoc analysis indicated that Complex I (rho (56)\u0026thinsp;=\u0026thinsp;0.329, P\u0026thinsp;=\u0026thinsp;0.013) and Complex V (rho (56)\u0026thinsp;=\u0026thinsp;0.346, P\u0026thinsp;=\u0026thinsp;0.009) activity individually correlated with Glx, indicating that these complexes may contribute most strongly to the associations of PC1 and PC2 respectively with Glx.\u003c/p\u003e\u003cp\u003eThere were no further significant overall associations between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy measures, or group by metabolite interactions, including when covarying for group differences in tobacco use (see supplementary material).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAssociations between Glx\u003csub\u003ecorr\u003c/sub\u003e levels in the anterior cingulate cortex (ACC) and mitochondrial complex activity PC1 (Estimate\u0026thinsp;=\u0026thinsp;0.632, T\u0026thinsp;=\u0026thinsp;2.155, P\u0026thinsp;=\u0026thinsp;0.036) and PC2 (Estimate\u0026thinsp;=\u0026thinsp;0.734, T\u0026thinsp;=\u0026thinsp;2.22, P\u0026thinsp;=\u0026thinsp;0.031). The solid black line represents the line of best fit for the total sample. The dashed line represents the fit for the CHR\u0026thinsp;+\u0026thinsp;FEP group and the dotted line represents the fit for the HC group. Data points are plotted by group, with solid circles indicating CHR\u0026thinsp;+\u0026thinsp;FEP participants and unfilled circles indicating HC participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationships with cognition and symptoms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere were no significant correlations between 1H-MRS metabolites or peripheral energy-related measures and the mean number of categories or perseverative errors on the WCST across the whole sample (presented in supplementary materials). In the CHR\u0026thinsp;+\u0026thinsp;FEP group, there were no significant associations between combined positive symptom severity scores and \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites or peripheral energy-related measures (presented in supplementary materials).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to investigate the relationships between anterior cingulate cortex (ACC) glutamate and N-acetylaspartate (NAA) levels and peripheral markers of energy-metabolism in individuals at clinical high risk of psychosis (CHR) or in the first episode of psychosis (FEP) and healthy controls (HCs). Contrary to our hypotheses, that ACC glutamate metabolites would be negatively associated with peripheral mitochondrial complex activity in the CHR\u0026thinsp;+\u0026thinsp;FEP group, we found significant positive associations between Glx (glutamate\u0026thinsp;+\u0026thinsp;glutamine) levels with principal components relating to mitochondrial complex activity, which did not differ between groups. NAA, which may provide a marker of metabolic integrity in the ACC, was not significantly associated with peripheral energy measures.\u003c/p\u003e\u003cp\u003eAs prior evidence overall indicates a decrease in mitochondrial activity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and an increase in ACC glutamate metabolites [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] in the clinical high risk or early stages of psychosis, we hypothesised that these markers would be negatively correlated. Instead, we found a significant positive relationship across all participants (CHR, FEP and HCs) between ACC Glx and PC1, reflecting increased activity of mitochondrial complexes I, II and IV and Glx and PC2, reflecting the balance between Complex V and III activity. Follow-up analysis suggested that positive relationships between activity of Complex I (NADH:ubiquinone oxidoreductase) and Complex V (ATP synthase) with ACC Glx may contribute most to these relationships, representing the first and last steps of the mitochondrial respiratory chain. There were no detectable differences in ACC glutamate metabolites or mitochondrial complex activity PCs in our CHR\u0026thinsp;+\u0026thinsp;FEP sample compared to HCs, or in the relationship between the mitochondrial PCs and Glx. Our results therefore indicate that peripheral mitochondrial complex activity is mainly positively associated with ACC Glx in the absence of marked dysregulation of either marker. This positive relationship is broadly consistent with mechanistic associations between glutamate homeostasis and cellular energy metabolism [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. It may be that disrupted relationships between peripheral markers of mitochondrial complex activity and brain glutamate become more apparent during later stages of psychosis / schizophrenia and may differ in studies examining cohorts with greater average illness burden.\u003c/p\u003e\u003cp\u003eIn contrast to the positive associations between ACC Glx and mitochondrial complex PCs, we did not detect associations between ACC glutamatergic metabolites or NAA with peripheral lactate or pyruvate levels. This contrasts with a recent finding of a positive correlation between ACC Glx and NAA and peripheral lactate across patients with schizophrenia and HCs [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. It is possible that this may be due to the more limited sample size of our study, in which both lactate and ACC glutamate metabolites were available in a total of 43 participants, compared to 96 participants in [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], although our data indicated a non-significant negative relationship. It is also possible that relationships between peripheral lactate and ACC glutamate or NAA only become apparent at later illness stages. According to a recent review [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], shifts in metabolic processes may occur in chronic stages of schizophrenia following excessive glutamate signalling and redox dysregulation within early illness stages, which \u0026ldquo;burn out\u0026rdquo; in chronic schizophrenia, leading to a shift from oxidative phosphorylation to glycolysis. Shifts in metabolic processes occurring in chronic stages of illness aligns with the evidence showing increases in lactate in chronic schizophrenia [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] but not in CHR or FEP, compared to HCs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] as well as increases in the lactate to pyruvate ratio in post-mortem studies [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Future work should examine associations between lactate and glutamate metabolites in psychosis directly within the same brain region, using lactate-optimised \u003csup\u003e1\u003c/sup\u003eH-MRS.\u003c/p\u003e\u003cp\u003eIn exploratory analysis, we tested whether peripheral energy measures and \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites were associated with symptom severity and cognitive function and found no significant associations. In early psychosis, greater mitochondrial dysfunction is associated with higher positive symptom burden and worse cognitive functioning [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], however our study mainly included CHR participants and utilised a different set of mitochondrial markers. Some studies have detected relationships between performance on the WCST in established psychosis and glutamate [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] or NAA [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. However, due to null or opposite findings and methodological heterogeneity, the overall relationships between \u003csup\u003e1\u003c/sup\u003eH-MRS glutamate or NAA and cognition are not well understood [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStrengths of our study include the use of \u003cem\u003eex-vivo\u003c/em\u003e measurements of mitochondrial complex activity in peripheral blood cells. Most of our sample were unmedicated, which minimised the potential impact of antipsychotic medication on glutamate and NAA [\u003cspan additionalcitationids=\"CR71 CR72 CR73\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] as well as peripheral energy measures [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLimitations of our study include the relatively small sample size, and we may have lacked power to detect further associations or group differences in associations between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral energy measures. A retrospective power analysis based on our dataset indicated that a sample size of 159 participants would be required to detect a small interaction effect (f\u0026sup2; = 0.05) between metabolite levels and group, at a significance level of 0.05 (two-tailed) with 80% power. To maximise the available data, we combined CHR and FEP participants into one group for analysis, but our \u003csup\u003e1\u003c/sup\u003eH-MRS and peripheral measures, or their relationships, may change after the onset of psychosis compared to in the clinical high-risk stage. \u003csup\u003e1\u003c/sup\u003eH-MRS is limited in that it measures the total amount of MR-visible glutamate and glutamine in the voxel, whereas neuronal glutamate and glutamine are more tightly and directly coupled to mitochondrial ATP production [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Finally, we measured mitochondrial function, pyruvate and lactate in peripheral samples, while glutamate and NAA were assessed in the brain using \u003csup\u003e1\u003c/sup\u003eH-MRS. The strength of the association between peripheral measurements of energy metabolism and brain energy metabolism are largely unknown, although previous studies in Parkinson\u0026rsquo;s disease have shown that alterations in mitochondrial complex I activity are the same in the periphery and in the brain [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study found a significant positive relationship between ACC Glx and principal components relating to peripheral mitochondrial complex activity, which did not differ between CHR\u0026thinsp;+\u0026thinsp;FEP participants compared to HCs, and was potentially driven by complex I and V. Future studies in larger samples might investigate whether differential relationships between \u003csup\u003e1\u003c/sup\u003eH-MRS metabolites and peripheral or central energy measures evolve over the course of psychosis / schizophrenia, potentially in relation to illness burden.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed the manuscript and approved the final version. B.K. contributed to conceptualisation, methodology, formal analysis, and writing \u0026ndash; original draft. M.J.K. contributed to supervision and writing \u0026ndash; review and editing. S.K.M. contributed to project administration and writing \u0026ndash; review and editing. A.E. contributed to conceptualisation, methodology, supervision, and writing \u0026ndash; review and editing. R.M. contributed to funding acquisition, methodology, supervision, and writing \u0026ndash; review and editing. \u003cstrong\u003eA.E. and R.M. jointly supervised the project and share senior authorship.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding authors upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eA.E. has received consultancy fees from Leal Therapeutics. All other authors declare no conflicts of interest related to the subject of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was partially funded by a Canadian Institutes of Health Research (CIHR) grant (APP400704), and also represents independent research partly supported by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King\u0026rsquo;s College London.\u0026nbsp;The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.\u0026nbsp;B.K. is supported by a UK Medical Research Council PhD studentship (MR/N013700/1). For the purposes of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to A.E or R.M\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFusar-Poli, P. et al. The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry \u003cstrong\u003e70\u003c/strong\u003e, 107\u0026ndash;120 (2013).\u003c/li\u003e\n\u003cli\u003eDe Pablo, G. S. et al. Probability of transition to psychosis in individuals at clinical high risk: an updated meta-analysis. JAMA Psychiatry \u003cstrong\u003e78\u003c/strong\u003e, 970\u0026ndash;978 (2021).\u003c/li\u003e\n\u003cli\u003eCuenod, M. et al. Caught in vicious circles: a perspective on dynamic feed-forward loops driving oxidative stress in schizophrenia. Mol. Psychiatry \u003cstrong\u003e27\u003c/strong\u003e, 1886\u0026ndash;1897 (2022).\u003c/li\u003e\n\u003cli\u003eSarnyai, Z. \u0026amp; Ben-Shachar, D. Schizophrenia, a disease of impaired dynamic metabolic flexibility: a new mechanistic framework. Psychiatry Res. \u003cstrong\u003e328\u003c/strong\u003e, 116220 (2024).\u003c/li\u003e\n\u003cli\u003eStein, A., Zhu, C., Du, F. \u0026amp; \u0026Ouml;ng\u0026uuml;r, D. Magnetic resonance spectroscopy studies of brain energy metabolism in schizophrenia: progression from prodrome to chronic psychosis. Curr. Psychiatry Rep. \u003cstrong\u003e25\u003c/strong\u003e, 659\u0026ndash;669 (2023).\u003c/li\u003e\n\u003cli\u003eEgerton, A. et al. Glutamate in schizophrenia: neurodevelopmental perspectives and drug development. Schizophr. Res. \u003cstrong\u003e223\u003c/strong\u003e, 59\u0026ndash;70 (2020).\u003c/li\u003e\n\u003cli\u003eMcCutcheon, R. A., Krystal, J. H. \u0026amp; Howes, O. D. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry \u003cstrong\u003e19\u003c/strong\u003e, 15\u0026ndash;33 (2020).\u003c/li\u003e\n\u003cli\u003eHertz, L. \u0026amp; Rothman, D. L. Glucose, lactate, \u0026beta;-hydroxybutyrate, acetate, GABA, and succinate as substrates for synthesis of glutamate and GABA in the glutamine\u0026ndash;glutamate/GABA cycle. Adv. Neurobiol. \u003cstrong\u003e13\u003c/strong\u003e, 9\u0026ndash;42 (2016).\u003c/li\u003e\n\u003cli\u003eSibson, N. R. et al. Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity. Proc. Natl Acad. Sci. USA \u003cstrong\u003e95\u003c/strong\u003e, 316\u0026ndash;321 (1998).\u003c/li\u003e\n\u003cli\u003eYu, Y., Herman, P., Rothman, D. L., Agarwal, D. \u0026amp; Hyder, F. Evaluating the gray and white matter energy budgets of human brain function. J. Cereb. Blood Flow Metab. \u003cstrong\u003e38\u003c/strong\u003e, 1339\u0026ndash;1353 (2018).\u003c/li\u003e\n\u003cli\u003eAttwell, D. \u0026amp; Laughlin, S. B. An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. \u003cstrong\u003e21\u003c/strong\u003e, 1133\u0026ndash;1145 (2001).\u003c/li\u003e\n\u003cli\u003eLezmy, J., Harris, J. J. \u0026amp; Attwell, D. Optimising the energetic cost of the glutamatergic synapse. Neuropharmacology \u003cstrong\u003e198\u003c/strong\u003e, 108727 (2021).\u003c/li\u003e\n\u003cli\u003eNicholls, D. \u0026amp; \u0026Aring;kerman, K. Mitochondrial calcium transport. Biochim. Biophys. Acta Rev. Bioenerg. \u003cstrong\u003e683\u003c/strong\u003e, 57\u0026ndash;88 (1982).\u003c/li\u003e\n\u003cli\u003eNicholls, D. G. A role for the mitochondrion in the protection of cells against calcium overload? Prog. Brain Res. \u003cstrong\u003e63\u003c/strong\u003e, 97\u0026ndash;106 (1985).\u003c/li\u003e\n\u003cli\u003eTiwari, K. D. et al. Effects of high glutamate concentrations on mitochondria of human neuroblastoma SH-SY5Y cells. Ann. Pharm. Fr. \u003cstrong\u003e81\u003c/strong\u003e, 457\u0026ndash;465 (2023).\u003c/li\u003e\n\u003cli\u003eIsaev, N. K., Andreeva, N. A., Stel\u0026rsquo;mashuk, E. V. \u0026amp; Zorov, D. B. Role of mitochondria in the mechanisms of glutamate toxicity. Biochemistry (Moscow) \u003cstrong\u003e70\u003c/strong\u003e, 611\u0026ndash;618 (2005).\u003c/li\u003e\n\u003cli\u003eRos, J., Pecinska, N., Alessandri, B., Landolt, H. \u0026amp; Fillenz, M. Lactate reduces glutamate‐induced neurotoxicity in rat cortex. J. Neurosci. Res. \u003cstrong\u003e66\u003c/strong\u003e, 790\u0026ndash;794 (2001).\u003c/li\u003e\n\u003cli\u003eIzumi, Y. \u0026amp; Zorumski, C. F. Neuroprotective effects of pyruvate following NMDA-mediated excitotoxic insults in hippocampal slices. Neurosci. Lett. \u003cstrong\u003e478\u003c/strong\u003e, 131\u0026ndash;135 (2010).\u003c/li\u003e\n\u003cli\u003eDa Silva, T. et al. Mitochondrial function in individuals at clinical high risk for psychosis. Sci. Rep. \u003cstrong\u003e8\u003c/strong\u003e, 6216 (2018).\u003c/li\u003e\n\u003cli\u003eMerritt, K. et al. Association of age, antipsychotic medication, and symptom severity in schizophrenia with proton magnetic resonance spectroscopy brain glutamate level: a mega-analysis of individual participant-level data. JAMA Psychiatry \u003cstrong\u003e78\u003c/strong\u003e, 667\u0026ndash;681 (2021).\u003c/li\u003e\n\u003cli\u003eMerritt, K. et al. Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis. Mol. Psychiatry \u003cstrong\u003e28\u003c/strong\u003e, 2039\u0026ndash;2048 (2023).\u003c/li\u003e\n\u003cli\u003eNakahara, T. et al. Glutamatergic and GABAergic metabolite levels in schizophrenia-spectrum disorders: a meta-analysis of 1H-magnetic resonance spectroscopy studies. Mol. Psychiatry \u003cstrong\u003e27\u003c/strong\u003e, 744\u0026ndash;757 (2022).\u003c/li\u003e\n\u003cli\u003eMerritt, K., Egerton, A., Kempton, M. J., Taylor, M. J. \u0026amp; McGuire, P. K. Nature of glutamate alterations in schizophrenia: a meta-analysis of proton magnetic resonance spectroscopy studies. JAMA Psychiatry \u003cstrong\u003e73\u003c/strong\u003e, 665\u0026ndash;674 (2016).\u003c/li\u003e\n\u003cli\u003eRae, C. D. A guide to the metabolic pathways and function of metabolites observed in human brain 1H magnetic resonance spectra. Neurochem. Res. \u003cstrong\u003e39\u003c/strong\u003e, 1\u0026ndash;36 (2014).\u003c/li\u003e\n\u003cli\u003eMoffett, J. R., Ross, B., Arun, P., Madhavarao, C. N. \u0026amp; Namboodiri, A. M. N-Acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog. Neurobiol. \u003cstrong\u003e81\u003c/strong\u003e, 89\u0026ndash;131 (2007).\u003c/li\u003e\n\u003cli\u003eMaddock, R. J. \u0026amp; Buonocore, M. H. MR spectroscopic studies of the brain in psychiatric disorders. In Brain Imaging in Behavioral Neuroscience, \u003cstrong\u003e\u0026ndash;\u003c/strong\u003e199\u0026ndash;251 (2012).\u003c/li\u003e\n\u003cli\u003eClark, J. B. N-acetyl aspartate: a marker for neuronal loss or mitochondrial dysfunction. Dev. Neurosci. \u003cstrong\u003e20\u003c/strong\u003e, 271 (1998).\u003c/li\u003e\n\u003cli\u003eBrugger, S., Davis, J. M., Leucht, S. \u0026amp; Stone, J. M. Proton magnetic resonance spectroscopy and illness stage in schizophrenia\u0026mdash;a systematic review and meta-analysis. Biol. Psychiatry \u003cstrong\u003e69\u003c/strong\u003e, 495\u0026ndash;503 (2011).\u003c/li\u003e\n\u003cli\u003eMondino, M., Brunelin, J. \u0026amp; Saoud, M. N-acetyl-aspartate level is decreased in the prefrontal cortex in subjects at-risk for schizophrenia. Front. Psychiatry \u003cstrong\u003e4\u003c/strong\u003e, 99 (2013).\u003c/li\u003e\n\u003cli\u003eWhitehurst, T. S. et al. Proton magnetic resonance spectroscopy of N-acetyl aspartate in chronic schizophrenia, first episode of psychosis and high-risk of psychosis: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. \u003cstrong\u003e119\u003c/strong\u003e, 255\u0026ndash;267 (2020).\u003c/li\u003e\n\u003cli\u003eKhadimallah, I. et al. Mitochondrial, exosomal miR137-COX6A2 and gamma synchrony as biomarkers of parvalbumin interneurons, psychopathology, and neurocognition in schizophrenia. Mol. Psychiatry \u003cstrong\u003e27\u003c/strong\u003e, 1192\u0026ndash;1204 (2022).\u003c/li\u003e\n\u003cli\u003eBen-Shachar, D. et al. Increased mitochondrial complex I activity in platelets of schizophrenic patients. Int. J. Neuropsychopharmacol. \u003cstrong\u003e2\u003c/strong\u003e, 245\u0026ndash;253 (1999).\u003c/li\u003e\n\u003cli\u003eDror, N. et al. State-dependent alterations in mitochondrial complex I activity in platelets: a potential peripheral marker for schizophrenia. Mol. Psychiatry \u003cstrong\u003e7\u003c/strong\u003e, 995\u0026ndash;1001 (2002).\u003c/li\u003e\n\u003cli\u003eWu, A. et al. Validating mitochondrial electron transport chain content in individuals at clinical high risk for psychosis. Sci. Rep. \u003cstrong\u003e9\u003c/strong\u003e, 12695 (2019).\u003c/li\u003e\n\u003cli\u003eHuang, J. T. J. et al. CSF metabolic and proteomic profiles in patients prodromal for psychosis. PLoS One \u003cstrong\u003e2\u003c/strong\u003e, e756 (2007).\u003c/li\u003e\n\u003cli\u003eOnozato, M. et al. Serum D‐ and L‐lactate, pyruvate and glucose levels in individuals with at‐risk mental state and correlations with clinical symptoms. Early Interv. Psychiatry \u003cstrong\u003e14\u003c/strong\u003e, 410\u0026ndash;417 (2020).\u003c/li\u003e\n\u003cli\u003eJiang, J. et al. Leukocyte proteomic profiling in first-episode schizophrenia patients: does oxidative stress play central roles in the pathophysiology network of schizophrenia? (2019).\u003c/li\u003e\n\u003cli\u003eWatts, J. J. et al. Evidence that cannabis exposure, abuse, and dependence are related to glutamate metabolism and glial function in the anterior cingulate cortex: a 1H-magnetic resonance spectroscopy study. Front. Psychiatry \u003cstrong\u003e11\u003c/strong\u003e, 764 (2020).\u003c/li\u003e\n\u003cli\u003eDa Silva, T. et al. Genetically predicted brain C4A expression is associated with TSPO and hippocampal morphology. Biol. Psychiatry \u003cstrong\u003e90\u003c/strong\u003e, 652\u0026ndash;660 (2021).\u003c/li\u003e\n\u003cli\u003eMiller, T. J. et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr. Bull. \u003cstrong\u003e29\u003c/strong\u003e, 703\u0026ndash;715 (2003).\u003c/li\u003e\n\u003cli\u003eFirst, M. B., Spitzer, R. L., Gibbon, M. \u0026amp; Williams, J. B. W. Structured Clinical Interview for DSM-IV Axis I Disorders \u0026ndash; Patient Edition (SCID-I/P, Version 2.0). Biometric Research Department, New York State Psychiatric Institute (1995).\u003c/li\u003e\n\u003cli\u003eKay, S. R., Fiszbein, A. \u0026amp; Opler, L. A. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. \u003cstrong\u003e13\u003c/strong\u003e, 261\u0026ndash;276 (1987).\u003c/li\u003e\n\u003cli\u003eHall, R. C. Global assessment of functioning: a modified scale. Psychosomatics \u003cstrong\u003e36\u003c/strong\u003e, 267\u0026ndash;275 (1995).\u003c/li\u003e\n\u003cli\u003eSchobel, S. A. et al. Differential targeting of the CA1 subfield of the hippocampal formation by schizophrenia and related psychotic disorders. Arch. Gen. Psychiatry \u003cstrong\u003e66\u003c/strong\u003e, 938\u0026ndash;946 (2009).\u003c/li\u003e\n\u003cli\u003eProvencher, S. W. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. \u003cstrong\u003e30\u003c/strong\u003e, 672\u0026ndash;679 (1993).\u003c/li\u003e\n\u003cli\u003eEgerton, A. et al. Dopamine and glutamate in antipsychotic-responsive compared with antipsychotic-nonresponsive psychosis: a multicenter positron emission tomography and magnetic resonance spectroscopy study (STRATA). Schizophr. Bull. \u003cstrong\u003e47\u003c/strong\u003e, 505\u0026ndash;516 (2021).\u003c/li\u003e\n\u003cli\u003eKreis, R., Ernst, T. \u0026amp; Ross, B. D. Development of the human brain: in vivo quantification of metabolite and water content with proton magnetic resonance spectroscopy. Magn. Reson. Med. \u003cstrong\u003e30\u003c/strong\u003e, 424\u0026ndash;437 (1993).\u003c/li\u003e\n\u003cli\u003eGasparovic, C. et al. Use of tissue water as a concentration reference for proton spectroscopic imaging. Magn. Reson. Med. \u003cstrong\u003e55\u003c/strong\u003e, 1219\u0026ndash;1226 (2006).\u003c/li\u003e\n\u003cli\u003ePosit Team. RStudio: Integrated development environment for R (Version 2024.4.2.764). Posit Software, PBC (2024). https://posit.co/\u003c/li\u003e\n\u003cli\u003eWickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. \u003cstrong\u003e4\u003c/strong\u003e, 1686 (2019).\u003c/li\u003e\n\u003cli\u003eGordon, M. ppcor: an R package for a fast calculation to semipartial and partial correlations. R package version 1.1 (2019).\u003c/li\u003e\n\u003cli\u003eFox, J. \u0026amp; Weisberg, S. An R Companion to Applied Regression (3rd ed.). Sage Publications (2019).\u003c/li\u003e\n\u003cli\u003eCanty, A. \u0026amp; Ripley, B. D. boot: Bootstrap R (S-Plus) functions. R package version 1.3-28 (2022).\u003c/li\u003e\n\u003cli\u003eHarrell, F. E., Jr. Hmisc: Harrell miscellaneous. R package version 5.1-3 (2024).\u003c/li\u003e\n\u003cli\u003eWickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag (2016).\u003c/li\u003e\n\u003cli\u003eRajasekaran, A., Venkatasubramanian, G., Berk, M. \u0026amp; Debnath, M. Mitochondrial dysfunction in schizophrenia: pathways, mechanisms and implications. Neurosci. Biobehav. Rev. \u003cstrong\u003e48\u003c/strong\u003e, 10\u0026ndash;21 (2015).\u003c/li\u003e\n\u003cli\u003eAndersen, J. V. et al. Glutamate metabolism and recycling at the excitatory synapse in health and neurodegeneration. Neuropharmacology \u003cstrong\u003e196\u003c/strong\u003e, 108719 (2021).\u003c/li\u003e\n\u003cli\u003eKrzyściak, W. et al. Association of blood metabolomics biomarkers with brain metabolites and patient-reported outcomes as a new approach in individualized diagnosis of schizophrenia. Int. J. Mol. Sci. \u003cstrong\u003e25\u003c/strong\u003e, 2294 (2024).\u003c/li\u003e\n\u003cli\u003eRowland, L. M. et al. Elevated brain lactate in schizophrenia: a 7 T magnetic resonance spectroscopy study. Transl. Psychiatry \u003cstrong\u003e6\u003c/strong\u003e, e967 (2016).\u003c/li\u003e\n\u003cli\u003eWijtenburg, S. A. et al. Metabolite alterations in adults with schizophrenia, first degree relatives, and healthy controls: a multi-region 7T MRS study. Front. Psychiatry \u003cstrong\u003e12\u003c/strong\u003e, 656459 (2021).\u003c/li\u003e\n\u003cli\u003eWang, A. M. et al. Assessing brain metabolism with 7-T proton magnetic resonance spectroscopy in patients with first-episode psychosis. JAMA Psychiatry \u003cstrong\u003e76\u003c/strong\u003e, 314\u0026ndash;323 (2019).\u003c/li\u003e\n\u003cli\u003eWang, M. et al. Longitudinal changes in brain metabolites in healthy controls and patients with first episode psychosis: a 7-Tesla MRS study. Mol. Psychiatry \u003cstrong\u003e28\u003c/strong\u003e, 2018\u0026ndash;2029 (2023).\u003c/li\u003e\n\u003cli\u003eDean, B., Thomas, N., Scarr, E. \u0026amp; Udawela, M. Evidence for impaired glucose metabolism in the striatum, obtained postmortem, from some subjects with schizophrenia. Transl. Psychiatry \u003cstrong\u003e6\u003c/strong\u003e, e949 (2016).\u003c/li\u003e\n\u003cli\u003ePark, H. J., Choi, I. \u0026amp; Leem, K. H. Decreased brain pH and pathophysiology in schizophrenia. Int. J. Mol. Sci. \u003cstrong\u003e22\u003c/strong\u003e, 8358 (2021).\u003c/li\u003e\n\u003cli\u003eDempster, K. et al. Glutamatergic metabolite correlations with neuropsychological tests in first episode schizophrenia. Psychiatry Res. Neuroimaging \u003cstrong\u003e233\u003c/strong\u003e, 180\u0026ndash;185 (2015).\u003c/li\u003e\n\u003cli\u003eShirayama, Y. et al. Specific metabolites in the medial prefrontal cortex are associated with the neurocognitive deficits in schizophrenia: a preliminary study. Neuroimage \u003cstrong\u003e49\u003c/strong\u003e, 2783\u0026ndash;2790 (2010).\u003c/li\u003e\n\u003cli\u003eBraus, D. F. et al. Functioning and neuronal viability of the anterior cingulate neurons following antipsychotic treatment: MR-spectroscopic imaging in chronic schizophrenia. Eur. Neuropsychopharmacol. \u003cstrong\u003e12\u003c/strong\u003e, 145\u0026ndash;152 (2002).\u003c/li\u003e\n\u003cli\u003eGalińska, B. et al. Relationship between frontal N-acetylaspartate and cognitive deficits in first-episode schizophrenia. Med. Sci. Monit. \u003cstrong\u003e13\u003c/strong\u003e(Suppl 1), 11\u0026ndash;16 (2007).\u003c/li\u003e\n\u003cli\u003eReddy-Thootkur, M., Kraguljac, N. V. \u0026amp; Lahti, A. C. The role of glutamate and GABA in cognitive dysfunction in schizophrenia and mood disorders \u0026ndash; a systematic review of magnetic resonance spectroscopy studies. Schizophr. Res. \u003cstrong\u003e249\u003c/strong\u003e, 74\u0026ndash;84 (2022).\u003c/li\u003e\n\u003cli\u003eArun, P., Madhavarao, C. N., Moffett, J. R. \u0026amp; Namboodiri, A. M. Antipsychotic drugs increase N‐acetylaspartate and N‐acetylaspartylglutamate in SH‐SY5Y human neuroblastoma cells. J. Neurochem. \u003cstrong\u003e106\u003c/strong\u003e, 1669\u0026ndash;1680 (2008).\u003c/li\u003e\n\u003cli\u003eBustillo, J. R. et al. Longitudinal follow-up of neurochemical changes during the first year of antipsychotic treatment in schizophrenia patients with minimal previous medication exposure. Schizophr. Res. \u003cstrong\u003e58\u003c/strong\u003e, 313\u0026ndash;321 (2002).\u003c/li\u003e\n\u003cli\u003eEgerton, A., Modinos, G., Ferrera, D. \u0026amp; McGuire, P. Neuroimaging studies of GABA in schizophrenia: a systematic review with meta-analysis. Transl. Psychiatry \u003cstrong\u003e7\u003c/strong\u003e, e1147 (2017).\u003c/li\u003e\n\u003cli\u003eGoto, N. et al. Six-month treatment with atypical antipsychotic drugs decreased frontal-lobe levels of glutamate plus glutamine in early-stage first-episode schizophrenia. Neuropsychiatr. Dis. Treat. \u003cstrong\u003e8\u003c/strong\u003e, 119 (2012).\u003c/li\u003e\n\u003cli\u003eKubota, M., Moriguchi, S., Takahata, K., Nakajima, S. \u0026amp; Horita, N. Treatment effects on neurometabolite levels in schizophrenia: a meta-analysis dataset of proton magnetic resonance spectroscopy. Data Brief \u003cstrong\u003e31\u003c/strong\u003e, 105862 (2020).\u003c/li\u003e\n\u003cli\u003eCasademont, J. et al. Neuroleptic treatment effect on mitochondrial electron transport chain: peripheral blood mononuclear cells analysis in psychotic patients. J. Clin. Psychopharmacol. \u003cstrong\u003e27\u003c/strong\u003e, 284\u0026ndash;288 (2007).\u003c/li\u003e\n\u003cli\u003eHalim, N. D. et al. Increased lactate levels and reduced pH in postmortem brains of schizophrenics: medication confounds. J. Neurosci. Methods \u003cstrong\u003e169\u003c/strong\u003e, 208\u0026ndash;213 (2008).\u003c/li\u003e\n\u003cli\u003eParker, W. D., Boyson, S. J. \u0026amp; Parks, J. K. Abnormalities of the electron transport chain in idiopathic Parkinson\u0026rsquo;s disease. Ann. Neurol. \u003cstrong\u003e26\u003c/strong\u003e, 719\u0026ndash;723 (1989).\u003c/li\u003e\n\u003cli\u003eSchapira, A. et al. Anatomic and disease specificity of NADH CoQ1 reductase (complex I) deficiency in Parkinson\u0026rsquo;s disease. J. Neurochem. \u003cstrong\u003e55\u003c/strong\u003e, 2142\u0026ndash;2145 (1990).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glutamate, NAA, energy metabolism, mitochondrial function, clinical high risk, first episode psychosis ","lastPublishedDoi":"10.21203/rs.3.rs-7052494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7052494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRegulation of brain glutamate is closely related to brain energy metabolism. Changes in both central glutamatergic function and peripheral energy metabolism have been implicated in psychosis risk, onset and long-term illness, but there is a lack of empirical evidence to link these processes. We investigated the relationships between glutamate and N-acetyl-aspartate (NAA, a potential marker of neuronal metabolic integrity) in the anterior cingulate cortex (ACC), measured using proton magnetic resonance spectroscopy (\u003csup\u003e1\u003c/sup\u003eH-MRS), and peripheral markers of energy metabolism (mitochondrial I-V activity, pyruvate and lactate) in individuals either at clinical high risk for psychosis or in the first episode of psychosis (N = 36) and healthy controls (N = 20). ACC Glx (glutamate + glutamine) levels were positively related with principal components relating to mitochondrial complex activity, and this relationship did not differ between groups. These findings are consistent with the importance of mitochondrial ATP generation in regulating glutamatergic neurotransmission. While we did not find evidence that this relationship is disrupted in clinical high risk or first episode psychosis, further work is required to understand the mechanisms linking glutamate and energy metabolism in psychosis, including studies in larger cohorts, later stages of illness or in individuals with greater illness burden.\u003c/p\u003e","manuscriptTitle":"Glutamate, NAA and Energy Metabolism in Clinical High Risk and First Episode Psychosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 12:50:28","doi":"10.21203/rs.3.rs-7052494/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-13T04:44:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-10T20:46:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-30T18:42:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T20:56:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329300473773588566931481439859889056893","date":"2025-07-21T12:52:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332751519518103367870969346225033192466","date":"2025-07-20T16:26:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128177040059576655217255277192714589587","date":"2025-07-18T15:06:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16892756849633373675628531880000871377","date":"2025-07-18T14:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204266951018211994155971406412187828340","date":"2025-07-18T14:43:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-18T14:22:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-18T11:33:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T17:02:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T06:55:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-05T10:34:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b349ef2b-22fe-4b07-84dd-b6589e0bee5d","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51867206,"name":"Health sciences/Diseases"},{"id":51867207,"name":"Health sciences/Neurology"},{"id":51867208,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-12-01T16:00:58+00:00","versionOfRecord":{"articleIdentity":"rs-7052494","link":"https://doi.org/10.1038/s41598-025-22845-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-25 15:57:23","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-07-22 12:50:28","video":"","vorDoi":"10.1038/s41598-025-22845-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22845-y","workflowStages":[]},"version":"v1","identity":"rs-7052494","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7052494","identity":"rs-7052494","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}