# Oxidative stress markers during the psychotic transition

Victoria Rambaud, Ariel Frajerman, Margot Fournier, Anton Iftimovici, Daniella Dwir, Ines Khadimallah, Oussama Kebir, Aude Marzo, Marie-Odile Krebs, Boris Chaumette

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Victoria Rambaud, Ariel Frajerman, Margot Fournier, Anton Iftimovici, Daniella Dwir, et al. Oxidative stress markers during the psychotic transition. Journal of Psychiatric Research, 2025, 186, pp.137-144. 10.1016/j.jpsychires.2025.04.002. inserm-05058748

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Oxidative stress markers during the psychotic transition

Victoria Rambaud¹, Ariel Frajerman¹,³, Margot Fournier², Anton Iftimovici¹,³, Daniella Dwir², Ines Khadimallah², Oussama Kebir¹,³, Aude Marzo¹, Marie-Odile Krebs¹,³, Boris Chaumette¹,³,⁴

1. Université Paris Cité, INSERM U1266, Institute of Psychiatry and Neuroscience of Paris,
Paris, France

2. Center for Psychiatric Neuroscience, Lausanne University Hospital (CHUV), Department of Psychiatry, Lausanne, Switzerland

3. GHU-Paris Psychiatrie et Neurosciences, Paris, France

4. Department of Psychiatry, McGill University, Montreal, France

Corresponding author:

Dr Boris Chaumette - INSERM U1266 - 108 rue de la Santé - 75014 Paris - France

Phone : +33 1 45 65 81 79

Email : boris.chaumette@inserm.fr
Ultra-high-risk state (UHR) concept was initially applied to promote the early detection of young help-seeking patients with higher risk of psychotic transition. However, most UHR individuals do not evolve to psychosis, stressing the need for biomarkers allowing the prediction of the transition. Substantial evidence suggest that redox dysregulation plays a major role in the pathophysiology of psychotic disorders. The aim of this study is to explore the relationship between the evolution of blood oxidative stress markers in UHR individuals. Blood samples were collected from 48 UHR individuals at their first visit and 12 months later for those who did not convert to psychosis (UHR-NC), or at the time of the transition for the converters (UHR-C). Markers for redox dysregulation, including the glutathione antioxidant system, superoxide dismutase, thioredoxin, TBARS, macrophage migration inhibitory factor, peroxiredoxin-4, MMP9 and sRAGE, were assessed in erythrocytes, serum and plasma. Statistical analyses revealed a combination of peripheral redox markers associated with the risk of transition to psychosis. These markers were able to discriminate between UHR-C and UHR-NC subjects at baseline. A decrease in blood levels of peroxiredoxin-4, an antioxidant enzyme, was associated with a lower risk of transition. GPx activity and TBARS levels were associated with the later severity of symptoms during the course of psychosis. These findings suggest the interest of peripheral biomarkers of oxidative stress to monitor the risk of psychosis. Overall, these findings hold promises for early detection and argue for the development of treatments targeting redox pathways in psychosis.
Introduction

The development of psychiatric disorders progresses in a clinical trajectory which can be divided in five main phases: the prodromal phase, the ultra-high risk (UHR) phase, the transition, the recurrence (or relapse), and the chronic phase (McGorry et al., 2006). During the prodromal phase, young individuals at UHR have subthreshold psychotic symptoms and are at increased risk of developing psychosis (Yung et al., 2008). Amongst this population, only one quarter will evolve toward psychosis after three years of follow-up (Salazar de Pablo et al., 2021). Therefore, neurobiological research of these groups could provide biomarkers of a UHR state in psychosis, along with relevant information about the causal mechanisms leading to the development of schizophrenia that could serve as therapeutic targets. In addition, early onset psychosis has been proposed to underlie schizophrenia pathophysiology. This study is driven by the present need for biomarkers in order to improve the distinction between young UHR individuals who will convert to psychosis (UHR-Converters or UHR-C), and those who will not (UHR-Non-Converters or UHR-NC).

Studies have highlighted a dysregulation in redox metabolism during the onset of psychosis and increased oxidative damage is observed in a consistent manner in young UHR individuals who subsequently develop psychosis (Do et al., 2009; Do et al., 2000; Lavoie et al., 2017; Perkins et al., 2015). Our previous longitudinal study during the onset of psychosis has identified changes in DNA methylation (Kebir et al., 2017) and RNA expression (Chaumette et al., 2019) for genes involved in oxidative stress, such as the *GSTM5* gene, which is part of the glutathione transferases family and is involved in the detoxification of electrophilic compounds, including byproducts of oxidative stress (Hayes et al., 2005). Thus, the oxidative stress theory, as a link between the multiple molecular changes observed in psychosis, can be harnessed to identify predictive biomarkers in UHR individuals and could shed light on the pathophysiology of the onset of psychosis. Oxidative stress is defined as an imbalance between pro-oxidative factors and antioxidant, which can lead to damage to lipids, proteins and DNA. Reactive oxygen species (ROS) are produced as a physiological process by mitochondria, and mitochondrial dysfunction can cause and is exacerbated by oxidative stress. Additionally, ROS are produced by the brain for redox signaling (Murphy et al., 2011). In the present study, longitudinal oxidant and antioxidant status changes were investigated in young UHR individuals at different time points. The activity of antioxidant enzymes,
including glutathione peroxidase (GPx), that reduces hydrogen peroxide to water (Lubos et al., 2011), glutathione reductase (GR), which is responsible for maintaining supplies of reduced GSH (Couto et al., 2016), and superoxide dismutase (SOD), that scavenges oxygen radicals through oxidation/reduction cycles (McCord and Fridovich, 1988), were assessed in UHR individuals. Levels of non-enzymatic antioxidants glutathione (GSH) and thioredoxin (TRX) and enzymatic antioxidant peroxiredoxin 4 (PRX4), which regulates redox balance and oxidative folding by reducing hydrogen peroxide ($H_2O_2$) level in the endoplasmic reticulum (Jia et al., 2019), were also assessed. The markers of oxidative stress measured included (i) the macrophage migration inhibitory factor (MIF), a protein whose secretion is induced by oxidative stress, (ii) the soluble receptor for advanced glycation end products (sRAGE), which forms in a pro-oxidant environment, (iii) the matrix metalloproteinase (MMP9), which is activated by ROS, and (iv) thiobarbituric acid reactive substances (TBARS), a marker of lipid peroxidation.

The aim is to reveal a relationship between blood oxidative stress markers and the risk of developing psychotic disorders in UHR individuals. Although previous studies with similar aims have already demonstrated redox dysregulation in UHR individuals, we include in our study new markers that have not been investigated (Guler et al., 2021; Martínez-Cengotitabengoa et al., 2012). In addition, our study is a unique longitudinal design that will provide insight into the evolution of oxidative stress in UHR-C and UHR-NC. This has clinical implications, as it could facilitate the identification of UHR individuals with a heightened risk for transitioning to psychosis, distinguishing them from those with similar symptoms but lower risk.

# Materials and Methods

## 1 Study participants

This study included 48 ultra-high-risk (UHR) individuals from the ICAAR cohort: 30 non-converters (UHR-N) and 18 converters (UHR-C). The ICAAR study (Influence of Cannabis use on the emergence of psychopathological symptoms in Adolescents and young Adults presenting an at-Risk mental state) is a large prospective study for help-seeking individuals conducted in the specialised clinic in early detection/intervention of psychosis (C'JAAD) at GHU Paris Psychiatrie & Neurosciences (Sainte-Anne Hospital) in Paris (Oppetit et al., 2018).
Participants were selected for the present study based on whether their blood samples were available. The study procedures were in accordance with French regulations and were approved by the Ethics Committee of Paris Ile de France 3 and the CNIL and CTTIRS. Details about the study design, including patients' characteristics and diagnostic definitions, have been reported elsewhere (Chaumette et al., 2016). Briefly, UHR and transition status were determined with the CAARMS in its French-translated version (Krebs et al., 2014). The symptoms severity was measured according to (i) the Positive and Negative Syndrome Scale (PANSS), with separate scores for the positive, negative and general psychotic symptoms, (ii) the Montgomery-Asberg Depression Rating Scale (MADRS), which assess the severity of depressive symptoms in adults, and (iii) the Brief Psychiatric Rating Scale (BPRS-24) which measures several psychiatric symptoms including depression, anxiety, psychotic symptoms and unusual behaviour. Exclusion criteria for this cohort were manifest psychosis for more than one week, previous schizophrenia bipolar disorder, or pervasive developmental, current antipsychotic treatment (>100 mg/day Chlorpromazine equivalent) for more than 12 weeks; psychoactive substance dependence or abuse during the previous year and/or >5 years; severe somatic and neurological disorders; head injury; IQ lower than 70; and participants who were not native French speakers.

## 2 Oxidative Stress Evaluation

Blood samples were collected at the date of inclusion (M0) and the last visit (MF). The latter corresponds to either the date of transition to psychosis for the UHR-C or a one-year follow-up for UHR-NC. Blood was collected on an EDTA tube, and centrifuged at 3000g for 5 min at 4°C. The pellet, which contains the erythrocytes, was washed twice with 0.9% NaCl and the aliquots were frozen at -80°C and stored in EDTA tubes at the Biological Resource Center NSPN (GHU Paris Psychiatrie & Neurosciences). The frozen aliquots were sent to an independent laboratory (Unit for Research in Schizophrenia, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), CH-1008 Prilly-Lausanne, Switzerland) for the quantification of the oxidative markers, or to the *Centre histologies, d'Imagerie et de Cytologie (CHIC) of the Centre de Recherche des Cordeliers* (CRC) by cytokine quantifications. Hemolysates were prepared by dilution of blood cells at 1:10 in water. Antioxidant enzyme activity, including GPx and GR, and levels of non-enzymatic antioxidant GSH and TRX were measured in the erythrocytes. Enzymatic antioxidants SOD and
PRX4 were measured in the plasma, along with MIF. The activity of MMP9 and the contents of sRAGE and TBARS were assessed in the serum.

### GPx and GR enzymatic activity in erythrocytes

GPx and GR activities were measured as previously described (Xin et al., 2016). Briefly, GPx activity was assessed in 8µL of hemolysate in PBS (100mM, pH 7.5) with EDTA (0.6mM), GSH (2.5mM), NADP+ (0.25 mM), GR (0.84 U/ml; Fluka) and Tert-butyl hydroperoxide (0.8mM, Fluka). GR activity was assessed in the same amount of hemolysate incubated with EDTA (0.6mM), oxidised GSH (GSSG; 2.5mM), and NADPH (0.25mM). GPx and GR activity was determined in triplicates as a function of the change in NADPH measured at 340nm. GPx and GR enzymatic activity in hemolysates was normalised to the haemoglobin content. Thus, it was expressed in enzymatic units per gram of haemoglobin.

### GSH levels in erythrocytes

Blood levels of reduced GSH were assessed in 45µL of whole blood using Calbiochem's Glutathione Assay Kit 354102-100T and according to manufacturer instructions. The concentration of reduced GSH was normalised to blood volume.

### TRX activity in erythrocytes

Levels of active TRX were measured as previously described (Alameda et al., 2018) with an adapted version of a previously described protocol (Arnér and Holmgren, 2005). Briefly, TRX levels were assessed in 10µL hemolysates by end-point measurement of TRX-reducing activity, using insulin as a substrate. The levels of TRX in the blood samples were calculated based on a standard curve of recombinant TRX. TRX levels in hemolysates were normalised to haemoglobin content.

### Plasma SOD activity and serum levels of MIF, sRAGE and TBARS

SOD enzymatic activity was measured using Cayman Chemical's SOD Assay Kit 706002-480 (Ann Arbor, MI, USA) following the instructions on the manufacturer's protocol. Serum MIF concentrations were determined using the Bio-Technie ELISA kits as specified in the manufacturer's protocol. Plasma levels of sRAGE were measured using the Human RAGE ELISA (R&D systems, DY1145) kits as specified in the manufacturer's protocol. Serum levels of TBARS were measured using Cayman Chemical's TBARS Assay Kit 10009055-96 (Ann Arbor, MI, USA), which assays lipid peroxidation according to the manufacturer's protocol.
### Plasma PRX4 levels

Levels of PRX in plasma were quantified using the PRDX4 (Human) ELISA kit (Abnova, KA2121).

### Serum MMP9 activity

The activity of MMP9 in the serum was measured as previously described (Dwir et al., 2020). A gelatinase assay kit (EnzChek® Gelatinase/Collagenase Assay Kit E12055, Life Technology) was used following the manufacturer's protocol.

### Inflammatory markers

Additionally, peripheral markers for inflammation were previously assessed and available for this cohort. Because oxidative stress and inflammation are intricately linked processes, inflammatory marker levels were included as exploratory analyses. Indeed, at the molecular level, oxidative stress induces inflammation (Bitanihirwe and Woo, 2011), and conversely, inflammatory responses are a major source of ROS (Block et al., 2007). The reciprocal relationship between immune activation and oxidative stress in psychosis development is evident, as seen in studies demonstrating imbalances between pro-oxidants and anti-oxidants in models like maternal immune activation (MIA), where the effects were reversible with NAC administration (Lanté et al., 2007). The markers of inflammation included interleukins (IL-1α, IL-1β, IL-1RA, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12p70, IL-13, IL-15, IL-16, IL-17A, IL-18, IL-21, IL-22, IL-23, and IL-27), interferons (IFN-γ and IFN-α), tumor necrosis factors (TNF-α and TNF-β), tumor growth factors (TGF-b1, TGF-b2, and TGF-b3), vascular endothelial growth factors (VEGF-D and VEGF-A), complements (C1q, C3, C3b-iC3b, and C4), soluble intercellular adhesion molecule-1 (sICAM-1), and C-Reactive Protein (CRP). These markers were quantified in plasma with multiplex ELISA assays at the Centre d'Histologie, d'Imagerie et de Cytologie (CHIC) of the Centre de Recherche des Cordeliers (CRC) using Luminex technology and Thermofisher® kits.

## 3 Data analysis

All statistical analyses were performed with Python (version 3.8.11). Data in the figures and the text are expressed as mean and standard deviation (SD). The normality of the group distributions for conducting parametric statistical analyses was checked for in the preliminary step using the Shapiro-Wilk test. Due to the non-parametric distribution of the data, the group
differences between UHR-C and UHR-NC in redox markers and sociodemographic and clinical characteristics were tested with Mann-Whitney U tests. Comparisons of multiple categorical groups were made with Chi-squared or Fisher's Exact tests.

The difference in the longitudinal evolution of redox markers between groups was tested statistically using the time-adjusted delta for each marker, calculated as the difference between the last visit and the first visit adjusted for the time of the final visit ($\Delta = \frac{MF-M0}{tF-t0}$).

The time normalisation was applied to account for the varying follow-up durations between UHR-C and UHR-NC. As noted, the UHR-NC group had a scheduled one-year follow-up, but in practice, the re-test occurred on average after 14 months. In contrast, UHR-C transitioned at different points within the year, with an average re-test after 8 months. Given these differences, normalization ensures that observed changes in oxidative markers reflect biological evolution rather than differences in observation periods.

Importantly, the follow-up durations between groups are not randomly distributed, with systematically longer intervals for UHR-NC. However, failing to account for time differences could lead to misleading interpretations, as biomarker fluctuations are likely influenced by the duration of observation. While normalizing by time may introduce a systematic downward bias for UHR-NC, it provides a more standardized measure of biomarker change across participants.

Logistic regressions were performed with the transition status as the independent variable to investigate the predictive value of the redox markers. Because the redox markers are not independent, a principal component analysis (PCA) was performed to reduce the dimensionality of the redox variables in two principal components (PC). First, a logistic regression with the principal components of the redox markers at M0 as predictors. Potential confounders such as age, sex, body mass index (BMI) and levels of inflammation were included in the regression analysis to control for their effect. A PCA analysis was also conducted to reduce the dimensionality of the inflammatory markers. The two principal components defined by the model were included as confounders in the logistic regressions.

A second regression analysis was performed to investigate the predictive value of the evolution of redox markers overtime on the transition to psychosis. In this regression, age, sex, and BMI were included to control for their potential confounding effect. Multicollinearity
between evolutions of the redox markers was verified using the variance inflation factor method (VIF < 2). The VIF method performs, for each feature, a regression against all the other features.

In addition, clustering was performed to reveal associations between redox markers at M0 in UHR-C and UHR-NC. The loadings for the PC were calculated as the weight of the original variables in each PC. Clustering was performed using the K-means method, which groups observations by minimising Euclidean distances of all points with their nearest cluster centres; hence, it minimises the within-cluster sum of squared errors (WCSS) (MacQueen, 1967). Using the elbow method(Kodinariya and Makwana, 2013), we choose to set the number of clusters to two. A chi-square test was performed to determine each cluster's difference in UHR-C and UHR-NC proportions.

Lastly, Spearman rank correlation analyses were performed between the clinical measures at MF and the redox markers at M0 in the UHR individuals. A Bonferroni-corrected p-value was calculated based on the correlations (corrected p ≤ .0031).

For demographic and clinical comparisons, a Bonferroni-corrected p-value was calculated based on the total number of group comparisons (corrected p ≤ .0033). The uncorrected p-values for baseline comparison are presented in the results to prevent neglecting clinical differences that could bias the findings. Standardisation using the mean and standard deviation was applied to all the measures for the statistical analyses. Removing the outliers did not change the significance of the results; therefore, they were kept in the analyses. All statistical tests were two-tailed; p < .05 was considered statistically significant.

# Results

## 1 Description of the cohort

A summary of the characteristics of the UHR-NC (n = 30, 17 men and 13 women) and the UHR-C (n = 18, 11 men and 7 women) at baseline is presented in Table 1. At baseline, UHR-C and UHR-NC were comparable in age, sex, and clinical dimensions. At MF, UHR-C showed significantly higher scores for the psychiatric symptoms assessed by the BPRS24 (U = 231.5, p = .049) and lower functional scores on the SOFAS (U = 115.0, p = .034).

Although no difference in the levels of redox markers was found at baseline, the levels of GPx activity (U = 124, p = .021) and SOD (U = 104, p = .022) in the blood of UHR-C decreased
significantly more over time compared to UHR-NC. At MF, we detected a statistically significant difference between UHR-C and UHR-NC in sRAGE levels (U = 415.5, p = .0019), with UHR-C having higher levels of peripheral sRAGE.

Additional correlation analysis revealed that none of the redox markers was associated with the patient's age. Although none of the differences between UHR-C and UHR-NC mentioned above met the Bonferroni-adjusted statistical threshold, this indicates differences in the evolution of the antioxidant system and higher symptoms and lower functioning in UHR-C compared to UHR-NC after follow-up.

<table><caption>**Table 1:** Oxidative stress markers, demographic and clinical information for ultra-high risk non-converters and ultra-high-risk converters at the first visit M0, clinical information at the last visit MF and longitudinal evolution of oxidative stress markers (*p value < .05). Values are means +/- SD?</caption><thead><tr><th>Subjects</th><th>UHR-NC</th><th>n UHR-C</th><th>n</th><th>p (unadjusted)</th></tr></thead><tbody><tr><td>Sex (n (%), male)</td><td>17 (56.67)</td><td>30 11 (61.11)</td><td>18</td><td>1.00</td></tr><tr><td>Mean Age (years)</td><td>21.47 ± 3.49</td><td>30 19.78 ± 2.62</td><td>18</td><td>.078</td></tr><tr><td>Mean Size (cm)</td><td>172.73 ± 8.54</td><td>30 171.19 ± 5.76</td><td>18</td><td>-</td></tr><tr><td>Mean Weight (kg)</td><td>65.35 ± 11.38</td><td>30 66.53 ± 11.9</td><td>18</td><td>-</td></tr><tr><td>Mean BMI</td><td>21.9 ± 3.14</td><td>30 22.69 ± 3.71</td><td>18</td><td>.655</td></tr><tr><td><strong>Oxidative Stress Markers</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean GPx Activity at M0 (µmol/min/g Hb)</td><td>16.01 ± 3.51</td><td>30 18.15 ± 5.19</td><td>18</td><td>.259</td></tr><tr><td>Delta GPx Activity (µmol/min/g Hb/month)</td><td>-0.01 ± 0.08</td><td>29 -0.18 ± 0.39</td><td>17</td><td>.021*</td></tr><tr><td>Mean GR Activity at M0 (µmol/min/g Hb)</td><td>1.96 ± 0.57</td><td>30 2.19 ± 0.47</td><td>18</td><td>.148</td></tr><tr><td>Delta GR Activity (µmol/min/g Hb/month)</td><td>-0.01 ± 0.03</td><td>29 -0.01 ± 0.07</td><td>17</td><td>.776</td></tr><tr><td>Mean TRX levels at M0 (ng/µg Hb)</td><td>0.45 ± 0.25</td><td>30 0.59 ± 0.36</td><td>18</td><td>.173</td></tr><tr><td>Delta TRX levels (ng/µg Hb/month)</td><td>0 ± 0.01</td><td>29 -0.02 ± 0.11</td><td>17</td><td>.407</td></tr><tr><td>Mean GSH levels at M0 (µmol/mL)</td><td>1.18 ± 0.51</td><td>30 1.06 ± 0.39</td><td>18</td><td>.302</td></tr><tr><td>Delta GSH levels (µmol/mL/month)</td><td>0 ± 0.03</td><td>29 0 ± 0.12</td><td>17</td><td>.26</td></tr><tr><td>Mean MIF levels at M0 (ng/ml)</td><td>52.23 ± 21.1</td><td>30 68.51 ± 36.26</td><td>18</td><td>.201</td></tr><tr><td>Delta MIF levels (ng/ml/month)</td><td>0.46 ± 3.2</td><td>29 -2.41 ± 8.93</td><td>16</td><td>.422</td></tr><tr><td>Mean PRX4 levels at M0 (pg/ml)</td><td>2131.19 ± 783.92</td><td>30 3428.43 ± 3292.5</td><td>18</td><td>.456</td></tr><tr><td>Delta PRX4 levels (pg/ml/month)</td><td>-59.66 ± 56.58</td><td>29 -318.67 ± 657.28</td><td>16</td><td>.162</td></tr></tbody></table>
<table><tbody><tr><td>Mean SOD levels at M0 (U/ml)</td><td>3.16 ± 1.8</td><td>30 3.27 ± 1.47</td><td>18</td><td>.428</td></tr><tr><td><strong>Delta SOD levels (U/ml/month)</strong></td><td><strong>-0.09 ± 0.1</strong></td><td><strong>29 -0.4 ± 0.78</strong></td><td><strong>15</strong></td><td><strong>.022*</strong></td></tr><tr><td>Mean sRAGE levels at M0 (ng/ml)</td><td>0.47 ± 0.34</td><td>30 0.62 ± 0.39</td><td>18</td><td>.12</td></tr><tr><td>Delta sRAGE levels (ng/ml/month)</td><td>0 ± 0.03</td><td>30 0 ± 0.07</td><td>18</td><td>.518</td></tr><tr><td>Mean MMP9 activity at M0</td><td>8.74 ± 3.86</td><td>30 7.29 ± 4.02</td><td>18</td><td>.113</td></tr><tr><td>Delta MMP9 activity</td><td>-0.02 ± 0.41</td><td>30 0.02 ± 1.1</td><td>18</td><td>.249</td></tr><tr><td>Mean TBARS levels at M0 (µM)</td><td>6.17 ± 1.8</td><td>25 5.69 ± 1.31</td><td>16</td><td>.47</td></tr><tr><td>Delta TBARS levels (µM/month)</td><td>-0.02 ± 0.17</td><td>23 -0.15 ± 0.67</td><td>14</td><td>.808</td></tr><tr><td><strong>Clinical Assessment</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>PANSS (Negative Symptoms) at M0</td><td>16.83 ± 7.91</td><td>30 16.5 ± 8.15</td><td>18</td><td>.839</td></tr><tr><td>PANSS (Negative Symptoms) at MF</td><td>13 ± 6.6</td><td>30 14.2 ± 5.13</td><td>11</td><td>.229</td></tr><tr><td>PANSS (Positive Symptoms) at M0</td><td>14.03 ± 4.65</td><td>30 16.22 ± 4.62</td><td>18</td><td>.15</td></tr><tr><td>PANSS (Positive Symptoms) at MF</td><td>10.47 ± 3.59</td><td>30 12.27 ± 5.15</td><td>11</td><td>.106</td></tr><tr><td>PANSS (Total Score) at M0</td><td>71.23 ± 17.43</td><td>30 74.28 ± 16.69</td><td>18</td><td>.558</td></tr><tr><td>PANSS (Total Score) at MF</td><td>55 ± 15.37</td><td>30 62.07 ± 12</td><td>11</td><td>.075</td></tr><tr><td>MADRS (Total Score) at M0</td><td>20.67 ± 9.09</td><td>30 20.94 ± 9.72</td><td>18</td><td>.881</td></tr><tr><td>MADRS (Total Score) at MF</td><td>12.27 ± 8.31</td><td>30 13.24 ± 5.59</td><td>11</td><td>.338</td></tr><tr><td>SOFAS (Total Score) at M0</td><td>46.8 ± 9.55</td><td>30 46.56 ± 11.07</td><td>18</td><td>.915</td></tr><tr><td><strong>SOFAS (Total Score) at MF</strong></td><td><strong>62.07 ± 12.55</strong></td><td><strong>30 52.99 ± 13.81</strong></td><td><strong>13</strong></td><td><strong>.034*</strong></td></tr><tr><td>BPRS24 (Total Score) at M0</td><td>54.57 ± 11.72</td><td>30 53.57 ± 13.03</td><td>15</td><td>.485</td></tr><tr><td><strong>BPRS24 (Total Score) at MF</strong></td><td><strong>42.23 ± 11.12</strong></td><td><strong>30 47.05 ± 9.14</strong></td><td><strong>11</strong></td><td><strong>.049*</strong></td></tr></tbody></table>

## 2 Longitudinal evolution of PRX4 is associated with the risk of transition

The logistic regression analysis with the longitudinal evolution of redox markers as predictor variables of the psychotic transition identified delta PRX4 levels as the only significant predictor of psychotic transition among the ten markers assessed, with age at M0 and sex showing no significance (Table 2). Whilst Table 1 shows no statistically significant differences between groups for baseline or delta PRX4 values, the regression model demonstrates that delta PRX4 levels independently predict the risk of transition when accounting for covariates. Specifically, for every unit decrease in delta PRX4, the odds of
transition are reduced by approximately 99.23% (Table 2). This model was statistically significant (LLR p-value = .034). A post hoc power analysis was conducted to evaluate the power of the logistic regression model. Using the pwr package in R, the analysis was based on the effect size (f²) of 0.54, derived from the model's McFadden's R-squared value of 0.351. With a sample size of 47 participants, including 30 UHR-NC and 17 UHR-C, the analysis indicated a power of 82.3% at a significance level of 0.05. This suggests that the study had sufficient power to detect meaningful effects in the logistic regression model.

To further examine the impact of time normalization, an additional logistic regression analysis was conducted using delta MF – M0 without time normalization (Supplementary Table). Again, this model identified PRX4 MF – PRX4 M0 as the only significant predictor of psychotic transition (β = -4.484, p = .007, 95% CI [-7.76, -1.21]). The model was statistically significant (McFadden's R-squared = 0.440, LLR p-value = .006), indicating a strong association between PRX4 change and transition risk, regardless the normalization applied. However, the other markers remained non-significant, and the overall pattern of results remained consistent with the primary analysis, reinforcing the robustness of the findings.

<table><caption>**Table 2:** Logistic Regression with the evolution of the oxidative stress markers as predictors, controlling for age at M0, sex, and BMI at M0.</caption><thead><tr><th></th><th>Coef.</th><th>Std. Err.</th><th>z</th><th>P>|z|</th><th>CI 95%</th></tr></thead><tbody><tr><td>Age at M0</td><td>-0.684</td><td>0.478</td><td>-1.433</td><td>.152</td><td>[-1.62 ; 0.252]</td></tr><tr><td>Sex</td><td>0.002</td><td>0.447</td><td>0.004</td><td>.997</td><td>[-0.874 ; 0.877]</td></tr><tr><td>BMI at M0</td><td>0.043</td><td>0.404</td><td>0.107</td><td>.915</td><td>[-0.749 ; 0.835]</td></tr><tr><td>Delta GPx</td><td>-0.107</td><td>0.97</td><td>-0.11</td><td>.912</td><td>[-2.008 ; 1.794]</td></tr><tr><td>Delta GR</td><td>0.748</td><td>0.697</td><td>1.074</td><td>.283</td><td>[-0.618 ; 2.114]</td></tr><tr><td>Delta TRX</td><td>0.951</td><td>2.988</td><td>0.319</td><td>.75</td><td>[-4.904 ; 6.807]</td></tr><tr><td>Delta GSH</td><td>2.114</td><td>1.118</td><td>1.891</td><td>.059</td><td>[-0.077 ; 4.304]</td></tr><tr><td>Delta MIF</td><td>0.074</td><td>0.777</td><td>0.095</td><td>.924</td><td>[-1.449 ; 1.597]</td></tr><tr><td>Delta SOD</td><td>-1.769</td><td>1.808</td><td>-0.979</td><td>.328</td><td>[-5.312 ; 1.774]</td></tr><tr><td>Delta sRAGE</td><td>0.356</td><td>0.599</td><td>0.595</td><td>.552</td><td>[-0.818 ; 1.53]</td></tr><tr><td>Delta PRX4</td><td><strong>-4.866</strong></td><td>2.266</td><td>-2.147</td><td><strong>.032</strong></td><td><strong>[-9.308 ; -0.425]</strong></td></tr><tr><td>Delta MMP9</td><td>-0.089</td><td>0.747</td><td>-0.119</td><td>.905</td><td>[-1.554 ; 1.375]</td></tr><tr><td>Delta TBARS</td><td>-0.421</td><td>0.952</td><td>-0.442</td><td>.658</td><td>[-2.288 ; 1.445]</td></tr></tbody></table>

Significant results are in bold: McFadden's R-squared = 0.351, LLR p-value = .034
3 Peripheral redox markers at M0 can distinguish UHR-C from UHR-NC

Antioxidant systems work in concert to maintain the redox balance. Therefore, a PCA and clustering analysis were performed to determine whether a combination of the redox markers at M0 could discriminate UHR-C from UHR-NC. The PCA reduces the dimensions of independent variables into two PCs (PC1 and PC2) based on similarities across variables (Figure 1, A). Loadings allow us to identify which variables have the largest effect on the PC that contributed to the separation of UHR-NC and UHR-C (Suppl. Fig 1).

Clustering analysis groups the observations based on similarities across subjects. Fisher's exact test was used to determine if there was a significant association between the transition status and the cluster. Using the two first PCs, we identified two significantly different clusters in their composition of UHR-C and UHR-NC (p = .016). Indeed, cluster 0 was enriched in UHR-NC (72.2% of total cluster 0) and cluster 1 in UHR-C (66.7% of total cluster 1). Therefore, there seems to be a combination of redox markers at M0 that can distinguish UHR-C from UHR-NC (Figure 1, B). However, the sum of the squared error is large (SSE = 192.65), indicating heterogeneity between individuals in each cluster.

To assess the effects of a combination of redox marker levels at M0 on the likelihood of transitioning to psychosis in UHR individuals, a logistic regression was performed. Holding all other predictor variables constant, the odds of transitioning to psychosis increase by 145.5% (OR = 2.455, p = .025) for a one-unit increase in PC1 of the redox markers blood levels at M0 (Table 3). From the loading matrix (Supplementary Figure 1), it appears that MMP9 activity (r = -0.449) and TBARS levels (r = -0.397) at M0 have considerable negative projections on PC1. In contrast, MIF levels (r = 0.518) and PRX4 levels (r = 0.497) at M0 have positive projections on PC1. However, the model was not significant (LLR p-value = .218).

Taken together, these results indicate an association between a combination of redox markers at M0 and the risk of transitioning to chronic psychosis in UHR individuals. On the other hand, inflammatory markers were not significantly associated with the risk of transition in this study (Table 3).
<figure><img src="image_2.png" /><figcaption>**Figure 1:** (A) Score Plot for the projection of the UHR individuals into the two principal components. PC1 and PC2, respectively, explain 19.96% and 15.32% of the variance. (B) The cluster analysis of redox markers at M0 using the K-mean method. The identified clusters are coloured in purple circles (0) and dark green crosses (1). Cluster 0 was significantly enriched in UHR-C (n = 26). Cluster 1 was significantly enriched in UHR-NC (n = 8). A Fisher Exact test assessed the difference between the two clusters in their proportion of UHR-C and UHR-NC (p = .016).</figcaption></figure>

<table><caption>**Table 3:** Logistic Regression with predictors Age at M0, Sex, BMI at M0, PC1 and PC2 Inflammation at M0, and PC1 and PC2 redox at M0.</caption><thead><tr><th></th><th>Coef.</th><th>Std. Err.</th><th>z</th><th>P>|z|</th><th>CI 95%</th></tr></thead><tbody><tr><td>Age at M0</td><td>-0.741</td><td>0.394</td><td>-1.882</td><td>.06</td><td>[-1.512 ; 0.031]</td></tr><tr><td>Sex</td><td>0.108</td><td>0.363</td><td>0.297</td><td>.767</td><td>[-0.604 ; 0.82]</td></tr><tr><td>BMI at M0</td><td>0.088</td><td>0.329</td><td>0.267</td><td>.79</td><td>[-0.557 ; 0.733]</td></tr><tr><td><strong>PC1 Redox M0</strong></td><td><strong>0.898</strong></td><td>0.402</td><td>2.236</td><td><strong>.025</strong></td><td><strong>[0.111 ; 1.685]</strong></td></tr><tr><td>PC2 Redox M0</td><td>0.413</td><td>0.355</td><td>1.164</td><td>.245</td><td>[-0.283 ; 1.109]</td></tr><tr><td>PC1 Inflammation M0</td><td>-0.151</td><td>0.382</td><td>-0.396</td><td>.692</td><td>[-0.901 ; 0.598]</td></tr><tr><td>PC2 Inflammation M0</td><td>0.313</td><td>0.355</td><td>0.882</td><td>.378</td><td>[-0.382 ; 1.007]</td></tr></tbody></table>

Significant results are in bold: McFadden's R-squared = 0.147, LLR p-value = .156

## 4 Peripheral redox markers at M0 correlate with the severity of symptoms at MF

Then, we investigated the statistical relationship between peripheral levels of redox markers at M0 and clinical dimensions at MF. In UHR individuals, GPx activity at M0 was positively correlated with total scores at MF on the PANSS (r = 0.29, p < .0029), and especially the positive symptoms subscale (r = 0.35, p < .0029), with the MADRS, indicative of the depression levels (r = 0.17, p < .0015), and the BPRS24 (r = 0.19, p < .0029) at MF (Table 4). It is also interesting to note that in UHR individuals, TBARS levels at M0 were negatively correlated
with the negative symptom scores (r = -0.33, p-value < .0015) on the PANSS at MF (Table 4). Overall, these results indicate that GPx activity and TBARS level markers at M0 are good indicators of the severity of symptoms at MF.

<table><caption>**Table 4:** Correlation coefficients between Redox markers at M0 and symptoms severity at MF in UHR-C (only significant tests).</caption><thead><tr><th>Clinical scales at MF</th><th>PANSS Total Score</th><th>PANSS Negative Symptoms</th><th>PANSS Positive Symptoms</th><th>MADRS Score</th><th>BPRS24 Score</th></tr></thead><tbody><tr><td>GPx activity at M0</td><td>0.29</td><td>-</td><td>0.35</td><td>0.17</td><td>0.29</td></tr><tr><td>TBARS levels at M0</td><td>-</td><td>-0.33</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

Discussion

In this longitudinal study, the role of peripheral redox markers in young UHR individuals was investigated as potential indicators of the psychotic transition.

The main finding revealed that, over time, a decrease in delta PRX4 levels is associated with a substantial decrease in the odds of transitioning to psychosis. The PRX system uses TRX as an electron donor to detoxify H₂O₂ (Jia et al., 2019). Thus, the PRX antioxidant system appears more active over time in UHR-C, which could result from increased oxidative stress. In addition, the activity of GPX and SOD antioxidant enzymes decreased significantly more over time in UHR-C than in UHR-NC despite the lack of significance after adjustments for multiple testing. To our knowledge, only one other study so far has compared the levels of antioxidants and oxidative stress in antipsychotic naïve UHR individuals and healthy controls (Zeni-Graiff et al., 2019) and found that GPx and SOD activity was reduced in this UHR group but without longitudinal assessment. Altered antioxidant status has also been reported in untreated patients with a first episode of psychosis (FEP), leading to lipid peroxidation (Khan et al., 2002; Mahadik et al., 1998; Sarandol et al., 2015). Increased lipid peroxidation, in association with decreased blood levels of catalase, SOD, GPx and GSH in the blood of antipsychotic-naïve FEP patients (Ruiz-Litago et al., 2012), is in line with our results and argues for increased oxidative stress and a defect in GPx and SOD antioxidant systems during the emergence of psychosis.

Our study also indicated that a combination of oxidative stress markers at baseline could help separate UHR individuals depending on their future clinical outcome. When combined in a single predictor variable, the levels of redox markers in the blood can distinguish UHR-C from
UHR-NC but also inform on the risk for transition to psychosis in a logistic regression model. Before the transition, there appears to be a difference in the balance of proteins, such as MMP9, TBARS, PRX4 and MIF, between converters and non-converters. Indeed, our findings from the PCA suggest that, at baseline, higher PRX4 and MIF levels combined with lower MMP9 activity contribute to the transition risk. In line with our finding, homeostatic networks of correlated proteins involved in inflammatory processes and synaptic plasticity, such as MMP9 (Lepeta and Kaczmarek, 2015; Reijerkerk et al., 2008), were shown to be affected in UHR subjects who convert to psychosis (Jeffries et al., 2018). Therefore, MMP9 activity and MIF levels could indicate intricated inflammatory and oxidation processes during the psychotic transition. However, the heterogeneity within the clusters (SSE = 192.65) indicates individual variability. Therefore, the role of baseline MMP9 activity, PRX4 and MIF levels in the transition to psychosis should be considered carefully. Notably, the longitudinal decrease in PRX4 levels, rather than baseline levels, emerged as a significant factor associated with the transition to psychosis.

We also found that, in UHR-C individuals, GPx activity at M0 positively correlates with the severity of later positive symptoms. It could be that GPx activity is initially increased to compensate for high oxidative stress. Indeed, high GPx activity reflects high oxidation status and has been associated with smaller hippocampal volumes and more severe positive symptoms in patients with early psychosis who have experienced childhood trauma (Alameda et al., 2018). A collapse of the compensatory mechanism during the disorder may lead to more severe symptoms over time, which may explain why GPx activity decreased significantly over time in UHR-C. Regarding negative symptoms, we found that, in UHR-C, blood levels of TBARS at M0 negatively correlate with the severity of negative symptoms at MF. Although a previous study found similar results (González-Blanco et al., 2018), another one reports that higher levels of lipid peroxidation were associated with more severe negative symptoms in chronic schizophrenia (Guidara et al., 2020). These differences underline the importance of phase-specific markers for early detection in the prodromal phase.

In addition to the variability in clinical phenotypes of schizophrenia, there seems to be a high variability in the biochemical mechanisms at play between individuals. Indeed, neurodevelopmental abnormalities and neuroinflammation have been suggested as pathophysiological mechanisms underlying the emergence of psychosis (Rambaud et al., 2022). In addition, pathological alterations in cortical inhibitory circuits and imbalance in
neurotransmitters such as N-methyl-D-aspartate and dopamine are also implicated in the emergence of symptoms (Rambaud et al., 2022). This might indicate that in chronic schizophrenia patients with high positive symptoms, oxidative stress is not the main mechanism underlying the physiopathology of psychosis. Whereas in others who present with more severe negative symptoms, oxidative stress could be the main mechanism at play. Understanding these processes would be useful to identify biological subgroups. A recent meta-analysis found only 3 studies investigating oxidative stress biomarkers in pediatric psychosis and psychosis risk with heterogeneous results (Taylor et al., 2024).

It is important to address the limitations of our study—first, the small size of our cohort, which is only 48 UHR subjects. Second, individuals who present to the hospital may not be representative of all UHR individuals in the population. This can introduce bias, as those seeking hospital care may have different characteristics or severity of symptoms than those who do not seek care. This selection bias could reduce the generalizability of our findings and may also contribute to the lack of significance after Bonferroni correction for the larger decrease in GPx and SOD activity over time observed in UHR-C compared to UHR-NC by decreasing the power of the analyses. In addition, only a tendency has been identified in UHR-C for the antioxidant enzymes GPx, GR, PRX4, and SOD to be more active.

Furthermore, the normalization of biomarker evolutions over time ($\Delta = \frac{MF-M0}{tF-t0}$) introduces a potential limitation. Since the follow-up duration was systematically longer for UHR-NC (14 months on average) than for UHR-C (8 months on average), the time normalization can introduce a systematic bias if biomarker changes do not occur at a constant rate over time. While this approach aimed to account for variability in follow-up times, it assumes a constant rate of biomarker change over time, which may not accurately reflect biological reality. To address this, we conducted an additional analysis using absolute biomarker differences ($MF - M0$) without time-normalization, and we present these results as supplementary material.

Then, it is known that UHR individuals at an older age are at lower risk of transitioning to psychosis (Addington et al., 2019). However, we tested the association between redox differences and age and did not detect any correlation. Lastly, it is important to note that, while we decided to use a two-cluster solution, the WCSS value did not show an abrupt
change for this number of clusters using the Elbow method. However, using two clusters
seemed appropriate considering the small sample size and the hypothesis driving this project.

## Conclusion

Our findings suggested that oxidative stress could play a role in the development of psychosis
in UHR individuals. Combining the use of oxidative stress markers in the blood and clinical
data might improve the prediction of transition in UHR individuals. Additional related
markers, such as lipids (Frajerman et al., 2023) or cytokines (Mondelli et al., 2023), could be
added to the predictive model to increase the prediction accuracy. Our results suggested that
a progressive failure in the antioxidant system may contribute to the onset of psychosis. If
confirmed, this could benefit the implementation of informed prevention strategies in these
groups by providing more understanding of the pathophysiological mechanism underlying the
development of psychosis. Further research, including larger cohorts, is required to validate
the use of oxidative stress markers as a diagnostical tool and for the development of
prevention strategies. As N-acetylcysteine treatment has been proposed in the early phases
of psychosis (Conus et al., 2018), it would be interesting to test phase-specific treatments
since the UHR state, maybe with a biologically stratified approach.

## Acknowledgements

We would like to thank the patients and their families. We thank the colleagues from the CRC,
especially Macarena Cuenca, Amel Aloulou and Yveline Lepaicheux. We acknowledge using
the Biological Resource Center NSPN, GHU Paris Psychiatrie & Neurosciences Biobank (BB-
0033-00026). We are grateful to the colleagues from the Centre d'Histologie, d'Imagerie et
de Cytologie (CHIC) of the Centre de Recherche des Cordeliers who carried on the
inflammatory analyses. We are grateful to Adeline Cottier, Gloria Reuteler and Morgane
Baumgartner for their expert technical assistance in measuring blood oxidative stress
markers.

## Funding

Ariel Frajerman was supported by ARS contract 2020-10-37. Daniella Dwir is supported by the
Adrian and Simone Frutiger Foundation. Boris Chaumette received a grant from the Fondation
Bettencourt Schueller (CCA-INSERM-Bettencourt program). This work has been supported by the PHRC ICAAR (AOM 07_118) grant and the French government's "Investissements d'Avenir" programme, which is managed by the Agence Nationale de la Recherche (ANR), under the reference PsyCARE ANR-18-RHUS-0014.

## Author contribution

OK, MOK and BC designed the study. AF, AI, OK, AM, MOK and BC supervised the study. AF, MF, AI, DD, and IK collected the data. VR and AF analysed the data. VR, AF, MF, DD, and BC interpreted the data. VR, AF, AM and BC drafted the report. All authors read and approved the final manuscript.

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<figure><img src="image_3.png" /></figure>
Supplementary Materials

<table><caption>**Supp. Table 1:** Logistic Regression of evolution of the oxidative stress markers as predictors, controlling for age at M0, sex, and BMI at M0, without time-normalization</caption><thead><tr><th>Predictor</th><th>Coef.</th><th>Std. Err.</th><th>z</th><th>P>|z|</th><th>95% CI</th></tr></thead><tbody><tr><td>Age at M0</td><td>-1,329</td><td>0,600</td><td>-2,216</td><td>0,027</td><td>[-2.50 ; -0.15]</td></tr><tr><td>Sex</td><td>-0,787</td><td>0,555</td><td>-1,418</td><td>0,156</td><td>[-1.88 ; 0.30]</td></tr><tr><td>BMI at M0</td><td>0,421</td><td>0,520</td><td>0,809</td><td>0,419</td><td>[-0.60 ; 1.44]</td></tr><tr><td>GPx MF – GPx M0</td><td>-0,379</td><td>1,047</td><td>-0,362</td><td>0,718</td><td>[-2.43 ; 1.67]</td></tr><tr><td>GR MF – GR M0</td><td>-1,027</td><td>1,181</td><td>-0,869</td><td>0,385</td><td>[-3.34 ; 1.29]</td></tr><tr><td>TRX MF – TRX M0</td><td>-0,140</td><td>0,595</td><td>-0,234</td><td>0,815</td><td>[-1.31 ; 1.03]</td></tr><tr><td>GSH MF – GSH M0</td><td>0,900</td><td>0,647</td><td>1,392</td><td>0,164</td><td>[-0.37 ; 2.17]</td></tr><tr><td>MIF MF – MIF M0</td><td>0,979</td><td>0,687</td><td>1,425</td><td>0,154</td><td>[-0.37 ; 2.32]</td></tr><tr><td>SOD MF – SOD M0</td><td>-0,191</td><td>0,504</td><td>-0,380</td><td>0,704</td><td>[-1.18 ; 0.80]</td></tr><tr><td>sRAGE MF – sRAGE M0</td><td>1,126</td><td>0,662</td><td>1,703</td><td>0,089</td><td>[-0.17 ; 2.42]</td></tr><tr><td><strong>PRX4 MF – PRX4 M0</strong></td><td><strong>-4,484</strong></td><td>1,672</td><td>-2,682</td><td><strong>0,007</strong></td><td><strong>[-7.76 ; -1.21]</strong></td></tr><tr><td>MMP9 MF – MMP9 M0</td><td>-1,090</td><td>0,776</td><td>-1,405</td><td>0,160</td><td>[-2.61 ; 0.43]</td></tr><tr><td>TBARS MF – TBARS M0</td><td>0,802</td><td>0,598</td><td>1,342</td><td>0,180</td><td>[-0.37 ; 1.97]</td></tr></tbody></table>

Significant results are in bold: McFadden's R-squared = 0.440, LLR p-value = .006
A

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /><figcaption>Supplementary Figure 1: (A) Loading plot obtained by principal component analysis (PCA) from the redox markers at M0 in UHR individuals. (B) Correlation matrix between the principal components (PC) and the redox markers at M0.</figcaption></figure>