An exploratory study of metabolomics in endogenous and cannabis-use-associated psychotic-like experiences in adolescence

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Metabolomic studies have shown promise in providing valuable insights into predicting psychosis with enhanced precision compared to conventional clinical features. This study investigated metabolomic alterations associated with PLE in 76 depressed adolescents aged 14–20 years. Serum concentrations of 92 metabolites were analyzed with liquid chromatography–mass spectrometry. PLE were assessed using the Youth Experiences and Health (YEAH) questionnaire. The associations between PLE symptom dimensions (delusions, paranoia, hallucinations, negative symptoms, thought disorder, and dissociation) and metabolite concentrations were analyzed in linear regression models adjusted for different covariates. The symptom dimensions consistently correlated with the metabolome in different models, except those adjusted for cannabis use. Specifically, the hallucination dimension was associated with 13 metabolites (acetoacetic acid, allantoin, asparagine, decanoylcarnitine, D-glucuronic acid, guanidinoacetic acid, hexanoylcarnitine, homogentidic acid, leucine, NAD + , octanoylcarnitine, trimethylamine-N-oxide, and valine) in the various linear models. However, when adjusting for cannabis use, eight metabolites were associated with hallucinations (adenine, AMP, cAMP, chenodeoxycholic acid, cholic acid, L-kynurenine, neopterin, and D-ribose-5-phosphate). The results suggest diverse mechanisms underlying PLE in adolescence; hallucinatory experiences may be linked to inflammatory functions, while cannabis use may engage an alternative metabolic pathway related to increased energy demand and ketogenesis in inducing PLE. The limited sample of individuals with depression restricts the generalizability of these findings. Future research should explore whether various experiences and related metabolomic changes jointly predict the onset of psychoses and related disorders. Health sciences/Biomarkers/Prognostic markers Biological sciences/Psychology Biological sciences/Molecular biology metabolomics psychotic-like experiences prodromal psychotic symptoms psychosis cannabis Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Psychosis is a devastating condition, and there is growing interest in identifying biomarkers that can predict its occurrence during the prodromal stage, preceding actual onset (Chan et al., 2015 ; Couttas et al., 2022 ). For instance, plasma proteomics have been found to predict psychosis with greater accuracy than clinical features (Mongan et al., 2021 ). Early diagnosis is particularly important for implementing interventions that result in better recovery from the first psychotic episode (McGorry et al., 2007 ). Based on earlier studies, prodromal, first-episode, and chronic stages of psychosis share similar alterations in lipid and glucose metabolism, which may thus form promising biomarkers (Leppik et al., 2020 ; O’Gorman et al., 2017 ). The physiological phenomena associated with psychotic-like experiences and prodromal psychotic symptoms have been extensively studied, yet a clear consensus remains to be established. For example, lysophosphatidylcholines, lipids found to be altered in the prodromal stages of psychosis, have been implicated in promoting inflammation, which is another system associated with a higher risk of psychosis in adolescence (O’Gorman et al., 2017 ). Additionally, cAMP signaling, important in the integration of information from neurotransmitter receptors (e.g., glutamatergic, dopaminergic, and GABAergic receptors), has been implicated in the pathophysiology of psychosis (Funk et al., 2012 ). Recognizing the interconnectedness of various systems and their impact on each other’s functions may unveil layers of development of disorder within the nervous system and at the systemic level. Conversely, subtypes such as autoimmune-related psychosis have been proposed, suggesting diverse mechanisms underlying psychoses (Najjar et al., 2018 ). Alterations in the plasma lipidome in children have been observed to precede psychotic-like experiences (PLE) (Madrid-Gambin et al., 2019 ) and psychosis (O’Gorman et al., 2017 ) in adolescence, and dysregulated lipid metabolites have been found to predict psychosis in young adults (Dickens et al., 2021 ; Li et al., 2022 ). Furthermore, psychotic experiences in early adulthood have been associated with disturbances in lipid metabolism (Yin et al., 2022 ). In addition, altered lipid levels in red blood cell membranes have been associated with an increased risk of psychosis (Frajerman et al., 2023 ). Specifically, a disturbed biosynthesis of unsaturated fatty acid pathway and altered triacylglycerol levels have been found in both serum and plasma samples in patients clinically at high risk of psychosis (Dickens et al., 2021 ; Li et al., 2022 ; Yin et al., 2022 ). Other common findings related to the prodromal stages of psychosis are altered serum and plasma levels of phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins (Dickens et al., 2021 ; O’Gorman et al., 2017 ; Yin et al., 2022 ). In particular, alterations in phosphatidylcholines and lysophosphatidylcholines during childhood have been observed to precede the manifestation of PLE in adolescence (Madrid-Gambin et al., 2019 ). Similarly, phospho- and sphingolipids have been found to be altered in first-episode psychosis (FEP) when compared to healthy controls (Leppik et al., 2020 ). Apolipoprotein E, an important protein in cholesterol metabolism, has been present at greater levels in adolescents undergoing persistent psychotic experiences compared to those whose experiences did not persist (Sabherwal et al., 2019 ). Findings in individuals with an interview-assessed clinical high risk of psychosis (CHR) include altered catecholamine dopamine and noradrenaline metabolite alterations in saliva samples (Cui et al., 2021 ). Additionally, PLEs have been associated with changes in gene expression, observed as altered DNA methylation (Roberts et al., 2019 ), as well as alterations in the proteome (Föcking et al., 2021 ; Sabherwal et al., 2019 ) in children and adolescents when compared to healthy age-matched controls. To our knowledge, no metabolomic research, except for lipidomic studies, has been conducted in relation to PLE. While metabolomic changes appear to be associated with the physiological process of psychosis, the chronicity of a disease and medications can also impact the metabolome (Kriisa et al., 2017 ; Leppik et al., 2020 ). Therefore, the investigation of unmedicated patients at risk of or in the early stages of a disorder is crucial for a better understanding of the early disease etiology. In order to identify metabolomic changes linked to the initial stages of the psychotic process, we conducted an exploratory study to investigate the associations between PLEs and the metabolome in a cohort of 14–20-year-old, mainly unmedicated depressed psychiatric outpatients. 2 Methods 2.1 Study population The present study formed part of the SMART (Systemic Metabolomic Alterations Related To different psychiatric disease categories in adolescent outpatients) project, which has recruited 14–20-year-old patients from the Adolescent Psychiatry Outpatient Clinic at Kuopio University Hospital. When the current study was performed, 445 patients had been interviewed using the clinician version of the Structured Clinical Interview for DSM-IV (First et al. SCID-CV; First et al., 1996 ) as part of the SMART project, all of whom responded to the questionnaires. The first 76 enrolled individuals diagnosed with a depressive disorder were included in this cross-sectional baseline study. The Research Ethics Committee of the Kuopio University Hospital reviewed and approved the SMART project in 2017. 2.2 Questionnaires and clinical assessments The psychotic-like experiences of the patients were assessed with the novel Youth Experiences and Health (YEAH) questionnaire (Therman & Lindgren, 2017 ), which incorporates 39 items previously shown to be predictive of psychosis or correlated with concurrent CHR symptoms ( Supplementary table 7 ), reformulated to a six-point frequency scale (from many times/day to more rarely or never ). The 21-item Beck Depression Inventory (BDI-1A) was used to assess the severity of depressive symptoms, such as alterations in cognition, feelings, and physical symptoms, on a scale from 0–63 (Beck et al., 1979). Adverse childhood events were assessed with the Trauma and Distress Scale (TADS), with a total raw score ranging from 0 to 100 (Patterson et al., 2002 ). Quality of sleep was measured with the Pittsburg Sleep Quality Index (PSQI; Buysse et al., 1989 ) and the severity of insomnia with the Insomnia Severity Index (ISI; Bastien et al., 2001 ). Alcohol use was evaluated with the first three questions of the Alcohol Use Disorders Identification Test (AUDIT-C), scored from 0–12 (Saunders et al., 1993 ). The tobacco (scored 0–31) and cannabis (scored 0–39) use scales of the ASSIST 3.1 interview were used to assess tobacco and cannabis use during the previous three months (Humeniuk et al., 2010 ). Higher scores indicate greater disturbance on all the above scales. Diet quality was evaluated with an adjusted 16-item version of the Index of Diet Quality (IDQ), with higher scores indicating a healthier diet. The IDQ has been developed according to Nordic nutrition recommendations to depict diet quality and health-promoting aspects of the diet, and it has been validated in the Finnish population (Leppälä et al., 2010 ). To assess the overall use of medications, a dichotomized variable was used. The use of any medications was considered as ongoing medication in the variable. In addition, antipsychotic medication and SSRI use were included as separate dichotomized variables in the analyses. 2.3 Blood sampling Blood samples were obtained between 7–10 am after 12 hours of fasting, rested for 30 min, and centrifuged at 2500 x g for 10 min. After preparation, serum samples were stored at -70°C. Analysis was conducted in one batch after the sample collection had been completed. Blood samples were collected and stored by the Kuopio University Hospital (KUH) laboratory unit ISLAB. 2.4 Targeted metabolomics analysis Metabolomics analysis was carried out at the Institute of Molecular Medicine Finland in Helsinki, Finland. Targeted metabolomics analysis was performed with ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS). Ten microliters of radioisotopically labeled internal standard was added to 100 µL of sample, and the resulting mixture was allowed to equilibrate, after which 400 µL of extraction solvent (1% formic acid in acetonitrile) was inserted into the mixture and the resulting supernatant was collected. The supernatant was divided between the wells of a 96-well plate and filtered on a Hamilton Robotics vacuum station (300–400 mbar for 2.5 minutes). Five microliters of preprocessed sample was inserted into an ACQUITY UPLC® system coupled to a Xevo® TQ-S triple quadrupole mass spectrometer (Waters Corporation, Milford, MA, USA). UPLC-MS was run in positive and negative polarities, with the polarity switching time being 20 ms for the separation and quantification of the metabolites. The multiple reaction monitoring (MRM) acquisition mode was used for the quantification. MassLynx 4.1 software was used for data collection, handling, and instrument control, and Target Lynx software for data processing. The resulting 92 metabolite concentrations provided us a targeted summary of each patient’s metabolism. The metabolites included acylcarnitines, amino acids and their derivatives, bile acids, carbohydrates and carbohydrate conjugates, cholesterol and steroid metabolites, choline metabolites, citric acid cycle metabolites, enzyme cofactors, ethanol amines, methylation cycle metabolites, neurotransmitter metabolites, nucleobases, nucleosides, nucleotides, organic substances, and products of the urea cycle. The results reflect the homeostatic state of biological processes, such as functions of the immune system and the energy metabolism of cells. 2.5 Statistical analysis 2.5.1 Factor analysis of PLEs The larger questionnaire data set (n = 445) was used to estimate a confirmatory item factor model of the YEAH questionnaire responses, resulting in six factors: delusions, paranoia hallucinations, negative symptoms, thought disorder, and dissociative symptoms. Factor analysis was performed with Mplus 8.3 software (Muthén & Muthén, 2017 ) using the default settings with the WLSMV estimator and theta parameterization. The pairwise coverage among the items ranged from 97.7–98.6% on average, indicating a minimal impact of missing data assuming that it occurred randomly. The model demonstrated an acceptable fit: CFI .974, RMSEA .052, and SRMR .057. Standardized factor loadings and response thresholds are detailed in Supplementary Tables 2b–c , with item-wise categorization provided in Supplementary Table 2a . Factor scores for subsequent analyses were derived using the maximum a posteriori method. 2.5.2 Regression models The associations between factor scores of the six YEAH PLE dimensions and the individual metabolites were estimated in linear regression models with a custom script in the R statistical software environment (v 4.3.1; R Core Team, 2023 ) using R packages stats (v 3.6.2; R builtin) and lm.beta (v 1.7-2; Behrendt, 2023 ). The metabolite concentrations were standardized to z-scores before linear regression modeling. Background variables with possible effects on metabolism were used as covariates in the regression models, which were based on previous findings indicating relevance, as described in our previous article (Kurkinen et al., 2021 ). These included gender, age, BMI, IDQ, ongoing medications, depression chronicity, BDI, TADS (Kurkinen et al., 2023 ), ISI, PSQI, tobacco smoking, cannabis use (Hinckley et al., 2022 ), and alcohol use (ASSIST and AUDIT-C). In addition to the unadjusted Model 1, five adjusted linear models were estimated with the covariates correlating with the six symptom dimensions ( Supplementary Table 3 ). As there were multiple variables to consider, overfitting was avoided by dividing the variables into five models. Model 2 was adjusted for the participants’ lifestyle effects with BMI and IDQ scores, Model 3 with ASSIST Tobacco, and Model 4 with ASSIST Cannabis scores. Model 5 was adjusted for mental health variables, considering TADS and BDI scores, and Model 6 was adjusted for sleep quality and insomnia severity with ISI and PSQI scores. Each resulting regression beta coefficient was hierarchically clustered in a heat plot with the R packages gplots (v 3.1.3; Gaili, 2022 ) and RColorBrewer (v1.1-3; Neuwirth, 2022 ). As a post hoc analysis, we performed linear regression analyses predicting metabolite concentrations with cannabis use ( Supplementary Table 4 ). 2.5.3 Sensitivity analyses Considering the non-normal distribution of some of the metabolites ( Supplementary Table 5 ), we performed a sensitivity analysis in which rank transformed metabolite concentrations were analyzed with the linear model predicting the hallucination symptom dimension. The comparison of resulting estimates and 95% confidence intervals of original and rank normalized data was illustrated in a dot-and-whisker plot generated using the R package ggplot2 (v 3.4.4; Wickham, 2023 ). 2.5.4 Principal component analysis of metabolite concentrations In addition to linear regression analyses with individual metabolites, a multivariate principal component analysis (PCA) of metabolite concentrations was computed with SIMCA (Version 17; Sartorius Stedim Data Analytics AB) for data reduction, and primary components were correlated with PLE factor scores. As metabolites tend to correlate with each other in targeted metabolomic analyses, multiple testing was used to adjust the α level (Würtz et al., 2016 ). The α level of .05 was divided by the number of PCA components explaining at least 95% of the variation in the data. Metabolomic associations with p- values of less than .05 and more than the α adjusted for multiple testing were considered as trends. Metabolites with over 10% missing cases were excluded from the analyses, except for cotinine, a molecule naturally absent in nicotine-naïve people. 3 Results 3.1 Demographic and clinical variables The demographic and clinical characteristics of the sample and their associations with the six PLE factors (1. delusions, 2. paranoia, 3. hallucinations, 4. negative symptoms, 5. thought disorder, and 6. dissociation) are presented in Table 1 . None of the characteristics were associated with all six factors. Delusions were associated with a higher BMI and lower IDQ, higher BDI, TADS, ISI, and PSQI scores, and chronicity of depression. Paranoia was associated with tobacco smoking and cannabis use, as well as higher BDI and TADS scores. Hallucinations were associated with cannabis use and with higher BDI, TADS, ISI, and PSQI scores. Negative symptoms were associated with tobacco smoking, episodic MDD, and higher PSQI scores. Thought disorder was associated with cannabis use and higher BDI, ISI, and PSQI scores. Dissociation was only associated with higher BDI scores. We did not observe any of the factors to correlate with gender, age, alcohol consumption, or medication use (Table 1 ). Of the sample population, 20% were taking SSRIs, 17% antipsychotics, 5% agomelatine, tricyclic antidepressants or vortioxetine, 4% mirtazapine, and 13% other medications, including melatonin, mini-pills, or oxazepam. The 95% confidence intervals of the betas are additionally presented in Supplementary Table 1 . 3.2 Linear regressions predicting YEAH factors with metabolite concentrations The results of linear regression analyses with six regression models and six PLE factors against 92 metabolites are displayed in Fig. 1 . The results were similar for each factor in the various regression models, except for Model 4, which was adjusted for cannabis use, as can be seen on the left side of Fig. 1 . The results are presented in greater detail ( β , p , 95% CI) for each model and each factor in Supplementary Table 3 . Factor 6, dissociation, had the smallest number of significant results in the linear regression, from one metabolite to three depending on the model, which is already expected by chance. In contrast, the number of significant metabolites varied between 8–13 in the six regression models with Factor 3, hallucinations. Table 1 Characteristics of the participants, including distributions of multivariate model covariates, with standardized linear regression coefficients in models predicting PLE dimensions. Variable Cohort Delusions Paranoia Hallucinations Negative symptoms Thought disorder Dissociation β p β p β p β p β p β p Gender; Male n (%) 12 (16) .22 .057 .07 .556 .16 .165 − .15 .188 − .02 .855 − .02 .839 Age, mean (SD) 16.4 (1.6) .06 .624 − .14 .241 − .08 .476 − .02 .883 − .04 .756 .19 .097 Body mass index, mean (SD) 22.3 (5.7) .29 .012 − .01 .943 .09 .427 .03 .772 .09 .454 .22 .062 Diet quality (IDQ), mean (SD) 25.6 (5.7) − .27 .020 .18 .117 − .10 .403 .12 .319 − .04 .744 − .17 .147 Tobacco use (ASSIST), mean (SD) 6.5 (9) − .08 .475 .24 .037 .09 .433 .29 .012 .17 .141 − .02 .866 Cannabis use (ASSIST), mean (SD) 2.3 (3.4) .08 .134 .56 .031 .72 .003 .36 .185 .54 .040 .47 .075 Alcohol use (ASSIST), mean (SD) 8.1 (8.3) − .07 .627 .21 .115 .02 .879 .09 .525 .06 .638 − .18 .166 Alcohol use (AUDIT-C), mean (SD) 2.7 (2.9) − .22 .057 .06 .620 − .13 .277 .22 .054 − .01 .960 − .08 .507 Medication, n (%) 45 (59) 0.1 .652 .01 .297 − .01 .607 .01 .640 .11 .352 − .06 .600 SSRI, n (%) 25 (33) − .05 .696 .06 .612 − .07 .573 .06 .599 .09 .436 .00 .995 AP medication, n (%) 13 (17.1) − .22 .054 − .01 .908 − .14 .227 .08 .491 − .04 .752 − .07 .525 Chronic depression, n (%) 43 (56.6) .22 .017 − .09 .462 .07 .526 − .24 .034 − .06 .714 .12 .307 Depression (BDI), mean (SD) 30.2 (7.6) .30 .008 .32 .005 .43 < .001 .14 .229 .43 < .001 .27 .019 Childhood adversity (TADS), mean (SD) 43.8 (18.6) .27 .017 .34 .003 .32 .007 .11 .337 .21 .071 .14 .235 Insomnia (ISI), mean (SD) 12.6 (5.4) .27 .019 .15 .208 .27 .018 .20 .081 .17 .018 .19 .097 Sleep quality (PSQI), mean (SD) 10.7 (3.9) .27 .020 .19 .101 .33 .004 .29 .010 .33 .004 .21 .074 Legend: AP, antipsychotic; ASSIST, Alcohol, Smoking and Substance Involvement Screening Test; AUDIT-C, Alcohol Use Disorder Identification Test; BDI, Beck Depression Inventory; IDQ, Index of Diet Quality subset; ISI, Insomnia Severity Index; medication coded as having any of agomelatine, mirtazapine, SSRI, antipsychotic medication , or other medications ; β , standardized regression coefficient; p , p- value of linear regression model (statistical significance); PSQI, Pittsburg Sleep Quality Index; SD, standard deviation; SSRI, medication with only selective serotonin reuptake inhibitors; TADS, Trauma and Distress Score. The metabolites associated with hallucinations (Factor 3) that were consistently significant throughout all models (except Model 4, adjusted for cannabis use) were acetoacetic acid, allantoin, hexanoylcarnitine, nicotinamine adenine dinucleotide (NAD + ), valine, and octanoylcarnitine. On the other hand, the metabolites that were only significant in Model 4 when predicting hallucinations were adenine, adenosine monophosphate (AMP), cyclic adenosine monophosphate (cAMP), chenodeoxycholic acid, cholic acid, L-kynurenine, neopterin, and D-ribose-5-phosphate (Fig. 2 ). Other metabolites that were only found significant in some of the models when predicting hallucinations were asparagine, decanoylcarnitine, D-glucuronic acid, guanidinoacetic acid, homogentisic acid, leucine, and trimethylamine-N-oxide. The results for all six factors can be found in Supplementary Table 3. 3.3 Sensitivity analyses After implementing a distribution adjustment method (rank ordering) to even the data distribution and mitigate the effect of outliers, most metabolite associations remained consistent, although some variations were observed. Notably, cholic acid lost statistical significance in the model adjusted for cannabis use when employing rank normalization (Fig. 3 A). However, chenodeoxycholic acid, AMP, D-ribose-5-phosphate, adenine, neopterin, L-kynurenine, and cAMP retained statistical significance after ranking. Some additional metabolites, on the other hand, reached statistical significance, including negative associations of carnitine, propionylcarnitine, normetanephrine, and deoxycytidine. In models not adjusted for cannabis use, the ranking affected the results to some extent, as can be seen in Fig. 3 B. Hexanoylcarnitine and NAD + remained significant across all five models. Acetoacetic acid retained statistical significance in all models except those adjusted for BDI and TADS, while allantoin remained significant across all models except for one adjusted for PSQI and ISI. Octanoylcarnitine maintained statistical significance in models considering BMI and IDQ, as well as tobacco smoking, but not in models considering BDI and TADS or PSQI and ISI, nor the model without background variables. Valine lost statistical significance following rank normalization. All values from the sensitivity analysis in six models are presented in Supplementary Table 6 . 3.4 Principal component analysis In the PCA analyses, similar associations were observed between the YEAH PLE dimensions and metabolites as in linear regression models (Fig. 4 ). Some metabolites grouped very close to the hallucination dimension, for instance octanoyl- and hexanoylcarnitines, acetoacetic acid and cholic acid. Furthermore, the multiple-testing-adjusted α level was set to 0.0012, as 95% of the variation in the data was explained by 42 principal components. In this study, none of the results were below this level, and they should therefore be considered preliminary. 4 Discussion In this exploratory study, metabolomic alterations related to six psychotic-like experience dimensions were investigated. The highest number of associated metabolites were found to be linked to the frequency of hallucinations. Metabolites associated with non-cannabis-induced, endogenous hallucinatory experiences were related to inflammation, oxidative stress, cellular signaling, and fat and energy metabolism. The results also indicated that metabolites associated with ketogenesis and oxidative stress were linked to cannabis use or cannabis-induced PLEs. When cannabis use was taken into account, we presume to have observed metabolomic alterations related to endogenous PLEs, whereas metabolomic alterations found in other models may reflect the direct effects of cannabis use or PLEs induced by cannabis use. An association between cannabis use, hallucinations, and schizophrenia-like psychoses has previously been observed (Hietala, 2018 ), and cannabis use is a well-established risk factor for psychotic disorders (Marconi et al., 2016 ). This indicates that cannabis use may trigger an alternative pathophysiological pathway to psychotic symptoms in high-risk individuals. In fact, serum metabolomic profiles have been found in a preliminary report to differ between persons with schizophrenia, cannabis use disorder, or both (Uriguen et al., 2023 ). 4.1 Metabolomic alterations related to hallucinations 4.1.1 Non-cannabis-related alterations Chenodeoxycholic and cholic acids, as well as AMP, associated negatively with hallucinatory experiences in the model adjusted for cannabis use, suggesting that altered fat and energy metabolism is associated with non-cannabis-related hallucinatory experiences. However, cholic acid did not remain significant in the sensitivity analysis. These bile acids have a role in lipid absorption in the gut and cholesterol catabolism in the liver (Staels & Fonseca, 2009 ). Levels of the same bile acids, namely cholic acid and chenodeoxycholic acid (Qing et al., 2022 ), have been found to be lower in schizophrenic patients. Furthermore, AMP is a nucleotide able to store energy in mitochondrial oxidative phosphorylation. Disturbances in oxidative phosphorylation have been suggested to play a role in schizophrenia pathology (Henkel et al., 2022 ). Moreover, carnitine and propionylcarnitine displayed negative associations with hallucinatory experiences in a model employing more robust normalization. Both compounds play roles in energy metabolism and the transportation of lipids into mitochondria (Ferrari et al., 2004 ). Moreover, metabolites related to oxidative stress and inflammation were found altered in the model adjusted for cannabis use. The decrease in D-ribose-5-phosphate, an intermediate of the pentose-phosphate pathway (PPP; Huck et al., 2003 ), and adenine, a purine catabolized to allantoin, may reflect an increased need for NADPH against oxidative stress (Kloska et al., 2022 ; Yao et al., 2010 ). On the other hand, neopterin is an indicator of immune system activity (Gieseg et al., 2018 ) and the kynurenine pathway has been associated with inflammation (Pedraz-Petrozzi et al., 2020 ). High serum neopterin has been found in persons with schizophrenia when compared to healthy controls, and antipsychotic medication has been found to significantly reduce the blood levels of neopterin (Chittiprol et al., 2010 ). A recent meta-analysis on schizophrenia revealed that the serum kynurenine/tryptophan ratio may be the only useful peripheral biomarker within the kynurenine pathway (Almulla et al., 2022 ). However, recent literature indicates the existence of a specific subtype of psychosis associated with autoimmune conditions (Najjar et al., 2018 ). Finally, cAMP was positively correlated with hallucinations only when cannabis use was considered. cAMP signaling is part of the information integration from neurotransmitter receptors, and is found to be altered in patients with psychosis (Funk et al., 2012 ; Kamath et al., 2012 ). Furthermore, disruptions in cAMP signaling in young adults with a clinical high risk of psychosis (Kamath et al., 2012 , 2018 ) and increased cAMP levels in the olfactory neuronal precursor cells of persons with schizophrenia and bipolar disorder have been observed when compared to healthy controls (Muñoz-Estrada et al., 2015 ). 4.1.2 Alterations related to cannabis use Hexanoylcarnitine, octanoylcarnitine, and acetoacetic acid had positive trend-level associations with hallucinations in every model except for the one adjusted for cannabis use. Medium-chain acylcarnitines, such as hexanoyl-, octanoyl-, and decanoylcarnitines, support fat beta-oxidation, resulting in increased ketones in cells (Dambrova et al., 2022 ). Acetoacetic acid is the stable form of its conjugate base acetoacetate, a ketone body. High levels of hexanoylcarnitine and octanoylcarnitine have been reported in first-episode psychosis and schizophrenia patients (Kriisa et al., 2017 ; Mednova et al., 2021 ). Furthermore, antipsychotic treatment appears to alleviate high acylcarnitine levels (Kriisa et al., 2017 ). Increased saliva acetoacetic acid levels have been detected before the onset of schizophrenia (Cui et al., 2021 ), and high blood levels have been observed in patients with schizophrenia (Wang et al., 2022 ), suggesting that observed alterations may be associated with PLE in this this study. However, it is possible that altered levels of acylcarnitines are caused by cannabis use per se. Hexanoylcarnitine had a direct positive association with cannabis use in this sample ( Supplementary Table 4 ), and cannabis use has previously been suggested to alter carnitine synthesis pathways (Alasmari et al., 2022 ). Cannabinoid effects on cellular energy metabolism have previously been reported (Farokhnia et al., 2020 ). However, these phenomena are not mutually exclusive. For instance, a dysregulated endocannabinoid system, which is closely connected to lipid metabolism, has been observed in FEP (Bioque et al., 2013 ; Hietala, 2018 ), suggesting its potential involvement in the underlying pathophysiology of psychosis. Alterations related to acylcarnitines and acetoacetic acid may suggest a pathway for prodromal psychotic symptomology induced by cannabis use via the mitochondrial production of ketone bodies for energy. Ketogenesis is an alternative energy source, especially in the liver and astrocytes (Metna-Laurent & Marsicano, 2015 ). The type-1 cannabinoid (CB 1 ) receptor has been found to modulate astrocytic ketogenesis, and in a rat model, cannabinoids have been found to stimulate the production of ketone bodies, such as acetoacetic acid (Metna-Laurent & Marsicano, 2015 ). A ketogenic diet has also been studied as a possible augmentation treatment for schizophrenia (Kraft & Westman, 2009 ). Furthermore, the purine metabolite allantoin had a positive, and the oxidant NAD + a negative trend-level association with the hallucinations dimension in all the models except for the one adjusted for cannabis use. Allantoin is generated from uric acid when reactive oxidative species (ROS) are present (Xuan et al., 2011 ), and NAD + can inhibit the production of reactive oxygen species in cells (Kim et al., 2017 ). Furthermore, increased allantoin and decreased NAD + have been reported in patients with schizophrenia, in line with our results (Xuan et al., 2011 ; Zhang et al., 2021 ). An increase in ROS while lacking antioxidants results in an increase in lipid peroxidation (Money & Bousman, 2013 ), while the beta-oxidation of lipids, discussed above, increases the formation of ROS in various CNS disorders (Adibhatla & Hatcher, 2010 ). In this study, molecules related to beta-oxidation and oxidative stress were coincidentally found to be altered. Finally, the branched-chain amino acid (BCAA) valine displayed a positive trend-level association with the hallucination symptom dimension in all models except when cannabis use was considered, but this association did not remain significant after rank normalization. High levels of valine have been found in the plasma of unmedicated patients with schizophrenia (Bjerkenstedt et al., 1985 ). On the contrary, low valine levels have been reported in first-episode psychotic patients (Leppik et al., 2018 ). However, a positive association between cannabis use and serum valine levels has also been reported (Alasmari et al., 2022 ), suggesting that high valine levels could be associated with cannabis use. 4.2 Strengths and limitations This study used a novel approach of investigating prodromal psychotic symptoms from a symptom dimension perspective. The patients were young, and our findings for some of them may therefore reflect metabolomic changes at a rather early stage before any psychosis onset. The sample was small, especially the subset reporting any cannabis use (n = 15, 20%), increasing type II bias in the results and highlighting that these results should be considered preliminary. The study should be replicated with a larger sample size and with a healthy control group. The choice of transformation method for metabolomics data is important, and in this study, more robust rank ordering was used as a sensitivity analysis in addition to the original data. Ranking changes the nature of the data, but the results were relatively similar in this study (Fig. 3 ). Other factors (e.g., perinatal complications) have also been associated with psychotic symptoms, which could not be controlled for in this study. However, several covariates, such as childhood adversity, either considered or potentially considered in this study, are likely to induce psychotic-like experiences. Therefore, including them as covariates in the analyses may be overly conservative. Behavioral changes resulting from either cannabis use or PLEs may also account for some of the observed alterations, although they were considered in the analyses (e.g., diet and sleep quality). Finally, females were overrepresented in this sample, reflecting the natural incidence of depression and a higher tendency to seek treatment and take part in studies among females. In future metabolomic studies considering psychosis-related phenomena, it will be important to consider cannabis use by patients. In addition, future analyses could include broader lipidomic assays, considering that numerous studies investigating psychotic-like experiences or prodromal stages of psychosis have reported alterations in lipids. 4.3 Conclusions In this exploratory study, we detected metabolomic alterations related to six different PLE dimensions. The degree to which these dimensions were associated with the peripheral markers varied, and we observed cannabis use to have an impact on the associations. Based on these preliminary results, we hypothesize that PLEs develop via various pathophysiological mechanisms, one being inflammation. Cannabis use, on the other hand, was associated with hallucinatory experiences via increased energy demand and ketogenesis. In the future, the prevalence of psychotic disorders later in life could be examined in the light of endogenous and cannabis-use-related prodromal metabolomic alterations, following participants via comprehensive national registries to determine how well the observed metabolomic alterations predict psychotic episodes or disorder onset later in life, both as such and together with clinical indicators. Declarations Acknowledgements We would especially like to thank the FIMM Metabolomics Unit funded by Biocenter Finland and HiLIFE for the metabolite analysis, the ISLAB laboratory nurses for sample collection, the patients for participating in this study, and Roy Siddall for revising the language of the text. This study was supported by the Strategic Research Council within the Academy of Finland [SchoolWell, grant number 352509, work package 352511], the Foundation for Pediatric Research [190162, 2020], and the Foundation of Helena Vuorenmies [2022]. 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Supplementary Files SupplementarytablesYEAH240408.xlsx Supplementary tables 1-6 Supplement7YEAHEnglishv12017.docx Supplement 7: YEAH Questionnaire, version 1 (2017) textsummarysupplementarymaterial.pdf Summary of supplementary materials Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2024 Read the published version in Translational Psychiatry → Version 1 posted Unknown event 01 Oct, 2024 Editorial decision: Reject after peer review 15 Aug, 2024 Review # 2 received at journal 05 Aug, 2024 Reviewer # 2 agreed at journal 25 Jul, 2024 Review # 1 received at journal 23 Jun, 2024 Reviewer # 1 agreed at journal 13 Jun, 2024 Reviewers invited by journal 12 Jun, 2024 Editor assigned by journal 09 Apr, 2024 Submission checks completed at journal 09 Apr, 2024 First submitted to journal 08 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4237477","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":289250083,"identity":"952a6662-8128-406f-aa16-2f2b3a8979d1","order_by":0,"name":"Karoliina Kurkinen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie2PP0sDMRiH33IQl+qtbwjoVzjpYqEfJqEQlyqFLk5HpnMpuN4g+hXapWsPCu1yxTVu1+Xmm6RDB5OrCgpJV4c8kOQ35Hn/AAQC/xC0V5XYh5hT2BAVAAOIiU/hvxXCASTQzKu0sVVs6CatohwKfdzuKj6+uY8VkeyhNIFNP7DhgBcOhXVve4kZbIIFWdNS44Q+bxc0N4prl0uQBI0iVHGWUdWgmOm7BTvfQ+pU4vqovH4rSz2q2cHTheFXl5kdTGkTcEQYeBSa15HdRcxXZNhXJYpcy15/ytGp4JvsVM0hFS+b7PpdrVPxlA93es8HeKUczg/Rn1qn/gcCgUDAwycMJlLoFZMYzAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Eastern Finland","correspondingAuthor":true,"prefix":"","firstName":"Karoliina","middleName":"","lastName":"Kurkinen","suffix":""},{"id":289250084,"identity":"0652d120-5c9b-4b2c-98c8-40c0c39708cc","order_by":1,"name":"Olli Kärkkäinen","email":"","orcid":"https://orcid.org/0000-0003-0825-4956","institution":"University of Eastern Finland","correspondingAuthor":false,"prefix":"","firstName":"Olli","middleName":"","lastName":"Kärkkäinen","suffix":""},{"id":289250085,"identity":"cf6b52af-020b-438f-9516-e5366f6e8353","order_by":2,"name":"Soili Lehto","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Soili","middleName":"","lastName":"Lehto","suffix":""},{"id":289250086,"identity":"065d0994-73e2-4cc5-a02f-2337cd8deff9","order_by":3,"name":"Ilona Luoma","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ilona","middleName":"","lastName":"Luoma","suffix":""},{"id":289250087,"identity":"70838ab8-5bb9-4b51-8c66-da8dc13d2cca","order_by":4,"name":"Siiri-Liisi Kraav","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Siiri-Liisi","middleName":"","lastName":"Kraav","suffix":""},{"id":289250088,"identity":"c8946541-6d98-400e-9254-cc81058e98f4","order_by":5,"name":"Petri Kivimäki","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Petri","middleName":"","lastName":"Kivimäki","suffix":""},{"id":289250089,"identity":"507b1e2a-f72d-4603-8d54-fe9c3f7aaf20","order_by":6,"name":"Sebastian Therman","email":"","orcid":"https://orcid.org/0000-0001-9407-4905","institution":"Finnish Institute for Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Therman","suffix":""},{"id":289250090,"identity":"c564021d-a313-4083-a02c-bcdc1d897790","order_by":7,"name":"Tommi Tolmunen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tommi","middleName":"","lastName":"Tolmunen","suffix":""}],"badges":[],"createdAt":"2024-04-08 15:26:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4237477/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4237477/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41398-024-03163-9","type":"published","date":"2024-11-07T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56963612,"identity":"5eba972b-8d3b-4d3c-901c-8511189c5738","added_by":"auto","created_at":"2024-05-22 18:57:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":290209,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the linear regression results as a heatmap where \u003cem\u003eβ\u003c/em\u003e-values from the linear regression between each factor in each model and measured metabolites have been reordered and presented as z-scores with different colors. Both metabolites and models were hierarchically clustered. M, model; F, factor\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/be42a21e74991dc0da1f594d.png"},{"id":56962933,"identity":"9494ae4d-d482-4226-a640-ac8bf347935e","added_by":"auto","created_at":"2024-05-22 18:49:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1386061,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the results not observed in other models except for when cannabis use was adjusted for (A) and of the results observed in five models but not in M4 adjusted for cannabis use (B). AMP, adenosine monophosphate; cAMP, cyclic AMP; BCAA, branched-chain amino acid. The associations that were the most significant are presented, if found in multiple models (*).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/b3e7f3e29defe89b4f627475.png"},{"id":56963611,"identity":"b6b8b7f4-ea8a-4470-856b-2112f01e50aa","added_by":"auto","created_at":"2024-05-22 18:57:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":275431,"visible":true,"origin":"","legend":"\u003cp\u003eDot-and-whisker plot of sensitivity analyses where estimates (standardized betas, β) are plotted with confidence intervals (95% CI) in the models using original and rank normalized data for the dimension hallucinations. A) models adjusted for cannabis use, B) models without adjustment for cannabis use.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/f952071c886de89b0270b041.png"},{"id":56962934,"identity":"129bbeea-314b-40e9-a618-9898ffe3effd","added_by":"auto","created_at":"2024-05-22 18:49:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1940202,"visible":true,"origin":"","legend":"\u003cp\u003eFactor means and individual metabolites plotted on the two first principal components of the 92 metabolites. Only the metabolites found associated with the factor hallucinations are named in this illustration. 1. Delusions, 2. Paranoia, 3. Hallucinations, 4. Negative symptoms, 5. Thought disorder, and 6. Dissociative symptoms. AMP, adenosine monophosphate; cAMP, cyclic AMP; NAD\u003csup\u003e+\u003c/sup\u003e, nicotinamide adenine dinucleotide.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/e51446410e14a34d01e67736.png"},{"id":68523238,"identity":"b28c17a6-d89f-4921-939b-a1d68c46ca12","added_by":"auto","created_at":"2024-11-08 08:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4206147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/58f3e551-8f22-4b04-83c4-6a020ade26af.pdf"},{"id":56962928,"identity":"243e1cd2-d6ef-43ac-b70d-b3157fe8113a","added_by":"auto","created_at":"2024-05-22 18:49:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":308937,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary tables 1-6\u003c/p\u003e","description":"","filename":"SupplementarytablesYEAH240408.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/48c81add12c90a2f2dbbfc40.xlsx"},{"id":56962927,"identity":"a40e3b95-489a-4893-9cd3-3627b8ab578a","added_by":"auto","created_at":"2024-05-22 18:49:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25552,"visible":true,"origin":"","legend":"\u003cp\u003eSupplement 7: YEAH Questionnaire, version 1 (2017)\u003c/p\u003e","description":"","filename":"Supplement7YEAHEnglishv12017.docx","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/e904e2c719573291227216e8.docx"},{"id":56962932,"identity":"55d2f30d-c656-4edd-97ab-c75225e39eff","added_by":"auto","created_at":"2024-05-22 18:49:43","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":34077,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of supplementary materials\u003c/p\u003e","description":"","filename":"textsummarysupplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4237477/v1/05cd24d4f00b2cc17169e4b5.pdf"}],"financialInterests":"\nOlli Kärkkäinen is a co-founder of a company providing global metabolomics analysis services, Afekta Technologies Ltd. (not used in this study).","formattedTitle":"An exploratory study of metabolomics in endogenous and cannabis-use-associated psychotic-like experiences in adolescence","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePsychosis is a devastating condition, and there is growing interest in identifying biomarkers that can predict its occurrence during the prodromal stage, preceding actual onset (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Couttas et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, plasma proteomics have been found to predict psychosis with greater accuracy than clinical features (Mongan et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Early diagnosis is particularly important for implementing interventions that result in better recovery from the first psychotic episode (McGorry et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Based on earlier studies, prodromal, first-episode, and chronic stages of psychosis share similar alterations in lipid and glucose metabolism, which may thus form promising biomarkers (Leppik et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; O\u0026rsquo;Gorman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe physiological phenomena associated with psychotic-like experiences and prodromal psychotic symptoms have been extensively studied, yet a clear consensus remains to be established. For example, lysophosphatidylcholines, lipids found to be altered in the prodromal stages of psychosis, have been implicated in promoting inflammation, which is another system associated with a higher risk of psychosis in adolescence (O\u0026rsquo;Gorman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, cAMP signaling, important in the integration of information from neurotransmitter receptors (e.g., glutamatergic, dopaminergic, and GABAergic receptors), has been implicated in the pathophysiology of psychosis (Funk et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Recognizing the interconnectedness of various systems and their impact on each other\u0026rsquo;s functions may unveil layers of development of disorder within the nervous system and at the systemic level. Conversely, subtypes such as autoimmune-related psychosis have been proposed, suggesting diverse mechanisms underlying psychoses (Najjar et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlterations in the plasma lipidome in children have been observed to precede psychotic-like experiences (PLE) (Madrid-Gambin et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and psychosis (O\u0026rsquo;Gorman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in adolescence, and dysregulated lipid metabolites have been found to predict psychosis in young adults (Dickens et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, psychotic experiences in early adulthood have been associated with disturbances in lipid metabolism (Yin et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, altered lipid levels in red blood cell membranes have been associated with an increased risk of psychosis (Frajerman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specifically, a disturbed biosynthesis of unsaturated fatty acid pathway and altered triacylglycerol levels have been found in both serum and plasma samples in patients clinically at high risk of psychosis (Dickens et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther common findings related to the prodromal stages of psychosis are altered serum and plasma levels of phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins (Dickens et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; O\u0026rsquo;Gorman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, alterations in phosphatidylcholines and lysophosphatidylcholines during childhood have been observed to precede the manifestation of PLE in adolescence (Madrid-Gambin et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, phospho- and sphingolipids have been found to be altered in first-episode psychosis (FEP) when compared to healthy controls (Leppik et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Apolipoprotein E, an important protein in cholesterol metabolism, has been present at greater levels in adolescents undergoing persistent psychotic experiences compared to those whose experiences did not persist (Sabherwal et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Findings in individuals with an interview-assessed clinical high risk of psychosis (CHR) include altered catecholamine dopamine and noradrenaline metabolite alterations in saliva samples (Cui et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, PLEs have been associated with changes in gene expression, observed as altered DNA methylation (Roberts et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as alterations in the proteome (F\u0026ouml;cking et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sabherwal et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in children and adolescents when compared to healthy age-matched controls. To our knowledge, no metabolomic research, except for lipidomic studies, has been conducted in relation to PLE.\u003c/p\u003e \u003cp\u003eWhile metabolomic changes appear to be associated with the physiological process of psychosis, the chronicity of a disease and medications can also impact the metabolome (Kriisa et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Leppik et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the investigation of unmedicated patients at risk of or in the early stages of a disorder is crucial for a better understanding of the early disease etiology. In order to identify metabolomic changes linked to the initial stages of the psychotic process, we conducted an exploratory study to investigate the associations between PLEs and the metabolome in a cohort of 14\u0026ndash;20-year-old, mainly unmedicated depressed psychiatric outpatients.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe present study formed part of the SMART (Systemic Metabolomic Alterations Related To different psychiatric disease categories in adolescent outpatients) project, which has recruited 14\u0026ndash;20-year-old patients from the Adolescent Psychiatry Outpatient Clinic at Kuopio University Hospital. When the current study was performed, 445 patients had been interviewed using the clinician version of the Structured Clinical Interview for DSM-IV (First et al. SCID-CV; First et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) as part of the SMART project, all of whom responded to the questionnaires. The first 76 enrolled individuals diagnosed with a depressive disorder were included in this cross-sectional baseline study. The Research Ethics Committee of the Kuopio University Hospital reviewed and approved the SMART project in 2017.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Questionnaires and clinical assessments\u003c/h2\u003e \u003cp\u003eThe psychotic-like experiences of the patients were assessed with the novel Youth Experiences and Health (YEAH) questionnaire (Therman \u0026amp; Lindgren, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which incorporates 39 items previously shown to be predictive of psychosis or correlated with concurrent CHR symptoms (\u003cem\u003eSupplementary table 7\u003c/em\u003e), reformulated to a six-point frequency scale (from \u003cem\u003emany times/day\u003c/em\u003e to \u003cem\u003emore rarely or never\u003c/em\u003e). The 21-item Beck Depression Inventory (BDI-1A) was used to assess the severity of depressive symptoms, such as alterations in cognition, feelings, and physical symptoms, on a scale from 0\u0026ndash;63 (Beck et al., 1979). Adverse childhood events were assessed with the Trauma and Distress Scale (TADS), with a total raw score ranging from 0 to 100 (Patterson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Quality of sleep was measured with the Pittsburg Sleep Quality Index (PSQI; Buysse et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and the severity of insomnia with the Insomnia Severity Index (ISI; Bastien et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Alcohol use was evaluated with the first three questions of the Alcohol Use Disorders Identification Test (AUDIT-C), scored from 0\u0026ndash;12 (Saunders et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The tobacco (scored 0\u0026ndash;31) and cannabis (scored 0\u0026ndash;39) use scales of the ASSIST 3.1 interview were used to assess tobacco and cannabis use during the previous three months (Humeniuk et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Higher scores indicate greater disturbance on all the above scales. Diet quality was evaluated with an adjusted 16-item version of the Index of Diet Quality (IDQ), with higher scores indicating a healthier diet. The IDQ has been developed according to Nordic nutrition recommendations to depict diet quality and health-promoting aspects of the diet, and it has been validated in the Finnish population (Lepp\u0026auml;l\u0026auml; et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To assess the overall use of medications, a dichotomized variable was used. The use of any medications was considered as ongoing medication in the variable. In addition, antipsychotic medication and SSRI use were included as separate dichotomized variables in the analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Blood sampling\u003c/h2\u003e \u003cp\u003eBlood samples were obtained between 7\u0026ndash;10 am after 12 hours of fasting, rested for 30 min, and centrifuged at 2500 x g for 10 min. After preparation, serum samples were stored at -70\u0026deg;C. Analysis was conducted in one batch after the sample collection had been completed. Blood samples were collected and stored by the Kuopio University Hospital (KUH) laboratory unit ISLAB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Targeted metabolomics analysis\u003c/h2\u003e \u003cp\u003eMetabolomics analysis was carried out at the Institute of Molecular Medicine Finland in Helsinki, Finland. Targeted metabolomics analysis was performed with ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS). Ten microliters of radioisotopically labeled internal standard was added to 100 \u0026micro;L of sample, and the resulting mixture was allowed to equilibrate, after which 400 \u0026micro;L of extraction solvent (1% formic acid in acetonitrile) was inserted into the mixture and the resulting supernatant was collected. The supernatant was divided between the wells of a 96-well plate and filtered on a Hamilton Robotics vacuum station (300\u0026ndash;400 mbar for 2.5 minutes). Five microliters of preprocessed sample was inserted into an ACQUITY UPLC\u0026reg; system coupled to a Xevo\u0026reg; TQ-S triple quadrupole mass spectrometer (Waters Corporation, Milford, MA, USA). UPLC-MS was run in positive and negative polarities, with the polarity switching time being 20 ms for the separation and quantification of the metabolites. The multiple reaction monitoring (MRM) acquisition mode was used for the quantification. MassLynx 4.1 software was used for data collection, handling, and instrument control, and Target Lynx software for data processing.\u003c/p\u003e \u003cp\u003eThe resulting 92 metabolite concentrations provided us a targeted summary of each patient\u0026rsquo;s metabolism. The metabolites included acylcarnitines, amino acids and their derivatives, bile acids, carbohydrates and carbohydrate conjugates, cholesterol and steroid metabolites, choline metabolites, citric acid cycle metabolites, enzyme cofactors, ethanol amines, methylation cycle metabolites, neurotransmitter metabolites, nucleobases, nucleosides, nucleotides, organic substances, and products of the urea cycle. The results reflect the homeostatic state of biological processes, such as functions of the immune system and the energy metabolism of cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Factor analysis of PLEs\u003c/h2\u003e \u003cp\u003eThe larger questionnaire data set (n\u0026thinsp;=\u0026thinsp;445) was used to estimate a confirmatory item factor model of the YEAH questionnaire responses, resulting in six factors: delusions, paranoia hallucinations, negative symptoms, thought disorder, and dissociative symptoms. Factor analysis was performed with Mplus 8.3 software (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) using the default settings with the WLSMV estimator and theta parameterization. The pairwise coverage among the items ranged from 97.7\u0026ndash;98.6% on average, indicating a minimal impact of missing data assuming that it occurred randomly. The model demonstrated an acceptable fit: CFI .974, RMSEA .052, and SRMR .057. Standardized factor loadings and response thresholds are detailed in \u003cem\u003eSupplementary Tables\u0026nbsp;2b\u0026ndash;c\u003c/em\u003e, with item-wise categorization provided in \u003cem\u003eSupplementary Table\u0026nbsp;2a\u003c/em\u003e. Factor scores for subsequent analyses were derived using the maximum \u003cem\u003ea posteriori\u003c/em\u003e method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Regression models\u003c/h2\u003e \u003cp\u003eThe associations between factor scores of the six YEAH PLE dimensions and the individual metabolites were estimated in linear regression models with a custom script in the R statistical software environment (v 4.3.1; R Core Team, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) using R packages \u003cem\u003estats\u003c/em\u003e (v 3.6.2; R builtin) and \u003cem\u003elm.beta\u003c/em\u003e (v 1.7-2; Behrendt, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The metabolite concentrations were standardized to z-scores before linear regression modeling. Background variables with possible effects on metabolism were used as covariates in the regression models, which were based on previous findings indicating relevance, as described in our previous article (Kurkinen et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These included gender, age, BMI, IDQ, ongoing medications, depression chronicity, BDI, TADS (Kurkinen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), ISI, PSQI, tobacco smoking, cannabis use (Hinckley et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and alcohol use (ASSIST and AUDIT-C). In addition to the unadjusted Model 1, five adjusted linear models were estimated with the covariates correlating with the six symptom dimensions (\u003cem\u003eSupplementary Table\u0026nbsp;3\u003c/em\u003e). As there were multiple variables to consider, overfitting was avoided by dividing the variables into five models. Model 2 was adjusted for the participants\u0026rsquo; lifestyle effects with BMI and IDQ scores, Model 3 with ASSIST Tobacco, and Model 4 with ASSIST Cannabis scores. Model 5 was adjusted for mental health variables, considering TADS and BDI scores, and Model 6 was adjusted for sleep quality and insomnia severity with ISI and PSQI scores. Each resulting regression beta coefficient was hierarchically clustered in a heat plot with the R packages \u003cem\u003egplots\u003c/em\u003e (v 3.1.3; Gaili, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cem\u003eRColorBrewer\u003c/em\u003e (v1.1-3; Neuwirth, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a \u003cem\u003epost hoc\u003c/em\u003e analysis, we performed linear regression analyses predicting metabolite concentrations with cannabis use (\u003cem\u003eSupplementary Table\u0026nbsp;4\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Sensitivity analyses\u003c/h2\u003e \u003cp\u003eConsidering the non-normal distribution of some of the metabolites (\u003cem\u003eSupplementary Table\u0026nbsp;5\u003c/em\u003e), we performed a sensitivity analysis in which rank transformed metabolite concentrations were analyzed with the linear model predicting the hallucination symptom dimension. The comparison of resulting estimates and 95% confidence intervals of original and rank normalized data was illustrated in a dot-and-whisker plot generated using the R package \u003cem\u003eggplot2\u003c/em\u003e (v 3.4.4; Wickham, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Principal component analysis of metabolite concentrations\u003c/h2\u003e \u003cp\u003eIn addition to linear regression analyses with individual metabolites, a multivariate principal component analysis (PCA) of metabolite concentrations was computed with SIMCA (Version 17; Sartorius Stedim Data Analytics AB) for data reduction, and primary components were correlated with PLE factor scores. As metabolites tend to correlate with each other in targeted metabolomic analyses, multiple testing was used to adjust the \u003cem\u003eα\u003c/em\u003e level (W\u0026uuml;rtz et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u003cem\u003eα\u003c/em\u003e level of .05 was divided by the number of PCA components explaining at least 95% of the variation in the data. Metabolomic associations with \u003cem\u003ep-\u003c/em\u003evalues of less than .05 and more than the \u003cem\u003eα\u003c/em\u003e adjusted for multiple testing were considered as trends. Metabolites with over 10% missing cases were excluded from the analyses, except for cotinine, a molecule naturally absent in nicotine-na\u0026iuml;ve people.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Demographic and clinical variables\u003c/h2\u003e\n\u003cp\u003eThe demographic and clinical characteristics of the sample and their associations with the six PLE factors (1. delusions, 2. paranoia, 3. hallucinations, 4. negative symptoms, 5. thought disorder, and 6. dissociation) are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. None of the characteristics were associated with all six factors. Delusions were associated with a higher BMI and lower IDQ, higher BDI, TADS, ISI, and PSQI scores, and chronicity of depression. Paranoia was associated with tobacco smoking and cannabis use, as well as higher BDI and TADS scores. Hallucinations were associated with cannabis use and with higher BDI, TADS, ISI, and PSQI scores. Negative symptoms were associated with tobacco smoking, episodic MDD, and higher PSQI scores. Thought disorder was associated with cannabis use and higher BDI, ISI, and PSQI scores. Dissociation was only associated with higher BDI scores. We did not observe any of the factors to correlate with gender, age, alcohol consumption, or medication use (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the sample population, 20% were taking SSRIs, 17% antipsychotics, 5% agomelatine, tricyclic antidepressants or vortioxetine, 4% mirtazapine, and 13% other medications, including melatonin, mini-pills, or oxazepam. The 95% confidence intervals of the betas are additionally presented in \u003cem\u003eSupplementary Table\u0026nbsp;1\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Linear regressions predicting YEAH factors with metabolite concentrations\u003c/h2\u003e\n\u003cp\u003eThe results of linear regression analyses with six regression models and six PLE factors against 92 metabolites are displayed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The results were similar for each factor in the various regression models, except for Model 4, which was adjusted for cannabis use, as can be seen on the left side of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The results are presented in greater detail (\u003cem\u003e\u0026beta;\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e, 95% CI) for each model and each factor in \u003cem\u003eSupplementary Table\u0026nbsp;3\u003c/em\u003e. Factor 6, dissociation, had the smallest number of significant results in the linear regression, from one metabolite to three depending on the model, which is already expected by chance. In contrast, the number of significant metabolites varied between 8\u0026ndash;13 in the six regression models with Factor 3, hallucinations.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristics of the participants, including distributions of multivariate model covariates, with standardized linear regression coefficients in models predicting PLE dimensions.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCohort\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDelusions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eParanoia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHallucinations\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNegative symptoms\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eThought disorder\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDissociation\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender; Male n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.855\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.839\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.097\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBody mass index, mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.29\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.012\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.943\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.772\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiet quality (IDQ), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.27\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.020\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.147\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTobacco use (ASSIST), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.475\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.24\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.037\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.433\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.29\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.012\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.866\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCannabis use (ASSIST), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.56\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.031\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.72\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.54\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.040\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.075\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol use (ASSIST), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(8.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.627\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.879\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.638\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.166\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol use (AUDIT-C), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.960\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.507\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedication, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.652\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.640\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.352\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.600\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSRI, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.696\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.573\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.995\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAP medication, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(17.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.525\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic depression, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(56.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.22\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.017\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.24\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.034\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.714\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.307\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression (BDI), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.30\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.32\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.43\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.43\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.27\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.019\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChildhood adversity (TADS), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(18.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.27\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.017\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.34\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.32\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.337\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.235\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInsomnia (ISI), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.27\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.019\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.27\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.018\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.17\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.018\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.097\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSleep quality (PSQI), mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.27\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.020\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.33\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.29\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.33\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.074\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend: AP, antipsychotic; ASSIST, Alcohol, Smoking and Substance Involvement Screening Test; AUDIT-C, Alcohol Use Disorder Identification Test; BDI, Beck Depression Inventory; IDQ, Index of Diet Quality subset; ISI, Insomnia Severity Index; medication coded as having any of \u003cem\u003eagomelatine, mirtazapine, SSRI, antipsychotic medication\u003c/em\u003e, or \u003cem\u003eother medications\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e, standardized regression coefficient; \u003cem\u003ep\u003c/em\u003e, \u003cem\u003ep-\u003c/em\u003evalue of linear regression model (statistical significance); PSQI, Pittsburg Sleep Quality Index; SD, standard deviation; SSRI, medication with only selective serotonin reuptake inhibitors; TADS, Trauma and Distress Score.\u003c/p\u003e\n\u003cp\u003eThe metabolites associated with hallucinations (Factor 3) that were consistently significant throughout all models (except Model 4, adjusted for cannabis use) were acetoacetic acid, allantoin, hexanoylcarnitine, nicotinamine adenine dinucleotide (NAD\u003csup\u003e+\u003c/sup\u003e), valine, and octanoylcarnitine. On the other hand, the metabolites that were only significant in Model 4 when predicting hallucinations were adenine, adenosine monophosphate (AMP), cyclic adenosine monophosphate (cAMP), chenodeoxycholic acid, cholic acid, L-kynurenine, neopterin, and D-ribose-5-phosphate (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Other metabolites that were only found significant in some of the models when predicting hallucinations were asparagine, decanoylcarnitine, D-glucuronic acid, guanidinoacetic acid, homogentisic acid, leucine, and trimethylamine-N-oxide. The results for all six factors can be found in \u003cem\u003eSupplementary Table\u0026nbsp;3.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Sensitivity analyses\u003c/h2\u003e\n\u003cp\u003eAfter implementing a distribution adjustment method (rank ordering) to even the data distribution and mitigate the effect of outliers, most metabolite associations remained consistent, although some variations were observed. Notably, cholic acid lost statistical significance in the model adjusted for cannabis use when employing rank normalization (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). However, chenodeoxycholic acid, AMP, D-ribose-5-phosphate, adenine, neopterin, L-kynurenine, and cAMP retained statistical significance after ranking. Some additional metabolites, on the other hand, reached statistical significance, including negative associations of carnitine, propionylcarnitine, normetanephrine, and deoxycytidine.\u003c/p\u003e\n\u003cp\u003eIn models not adjusted for cannabis use, the ranking affected the results to some extent, as can be seen in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB. Hexanoylcarnitine and NAD\u0026thinsp;+\u0026thinsp;remained significant across all five models. Acetoacetic acid retained statistical significance in all models except those adjusted for BDI and TADS, while allantoin remained significant across all models except for one adjusted for PSQI and ISI. Octanoylcarnitine maintained statistical significance in models considering BMI and IDQ, as well as tobacco smoking, but not in models considering BDI and TADS or PSQI and ISI, nor the model without background variables. Valine lost statistical significance following rank normalization. All values from the sensitivity analysis in six models are presented in \u003cem\u003eSupplementary Table\u0026nbsp;6\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Principal component analysis\u003c/h2\u003e\n\u003cp\u003eIn the PCA analyses, similar associations were observed between the YEAH PLE dimensions and metabolites as in linear regression models (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Some metabolites grouped very close to the hallucination dimension, for instance octanoyl- and hexanoylcarnitines, acetoacetic acid and cholic acid. Furthermore, the multiple-testing-adjusted \u0026alpha; level was set to 0.0012, as 95% of the variation in the data was explained by 42 principal components. In this study, none of the results were below this level, and they should therefore be considered preliminary.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this exploratory study, metabolomic alterations related to six psychotic-like experience dimensions were investigated. The highest number of associated metabolites were found to be linked to the frequency of hallucinations. Metabolites associated with non-cannabis-induced, endogenous hallucinatory experiences were related to inflammation, oxidative stress, cellular signaling, and fat and energy metabolism. The results also indicated that metabolites associated with ketogenesis and oxidative stress were linked to cannabis use or cannabis-induced PLEs.\u003c/p\u003e \u003cp\u003eWhen cannabis use was taken into account, we presume to have observed metabolomic alterations related to endogenous PLEs, whereas metabolomic alterations found in other models may reflect the direct effects of cannabis use or PLEs induced by cannabis use. An association between cannabis use, hallucinations, and schizophrenia-like psychoses has previously been observed (Hietala, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and cannabis use is a well-established risk factor for psychotic disorders (Marconi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This indicates that cannabis use may trigger an alternative pathophysiological pathway to psychotic symptoms in high-risk individuals. In fact, serum metabolomic profiles have been found in a preliminary report to differ between persons with schizophrenia, cannabis use disorder, or both (Uriguen et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Metabolomic alterations related to hallucinations\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Non-cannabis-related alterations\u003c/h2\u003e \u003cp\u003eChenodeoxycholic and cholic acids, as well as AMP, associated negatively with hallucinatory experiences in the model adjusted for cannabis use, suggesting that altered fat and energy metabolism is associated with non-cannabis-related hallucinatory experiences. However, cholic acid did not remain significant in the sensitivity analysis. These bile acids have a role in lipid absorption in the gut and cholesterol catabolism in the liver (Staels \u0026amp; Fonseca, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Levels of the same bile acids, namely cholic acid and chenodeoxycholic acid (Qing et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), have been found to be lower in schizophrenic patients. Furthermore, AMP is a nucleotide able to store energy in mitochondrial oxidative phosphorylation. Disturbances in oxidative phosphorylation have been suggested to play a role in schizophrenia pathology (Henkel et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, carnitine and propionylcarnitine displayed negative associations with hallucinatory experiences in a model employing more robust normalization. Both compounds play roles in energy metabolism and the transportation of lipids into mitochondria (Ferrari et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, metabolites related to oxidative stress and inflammation were found altered in the model adjusted for cannabis use. The decrease in D-ribose-5-phosphate, an intermediate of the pentose-phosphate pathway (PPP; Huck et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and adenine, a purine catabolized to allantoin, may reflect an increased need for NADPH against oxidative stress (Kloska et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). On the other hand, neopterin is an indicator of immune system activity (Gieseg et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the kynurenine pathway has been associated with inflammation (Pedraz-Petrozzi et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). High serum neopterin has been found in persons with schizophrenia when compared to healthy controls, and antipsychotic medication has been found to significantly reduce the blood levels of neopterin (Chittiprol et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A recent meta-analysis on schizophrenia revealed that the serum kynurenine/tryptophan ratio may be the only useful peripheral biomarker within the kynurenine pathway (Almulla et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, recent literature indicates the existence of a specific subtype of psychosis associated with autoimmune conditions (Najjar et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, cAMP was positively correlated with hallucinations only when cannabis use was considered. cAMP signaling is part of the information integration from neurotransmitter receptors, and is found to be altered in patients with psychosis (Funk et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kamath et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, disruptions in cAMP signaling in young adults with a clinical high risk of psychosis (Kamath et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and increased cAMP levels in the olfactory neuronal precursor cells of persons with schizophrenia and bipolar disorder have been observed when compared to healthy controls (Mu\u0026ntilde;oz-Estrada et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Alterations related to cannabis use\u003c/h2\u003e \u003cp\u003eHexanoylcarnitine, octanoylcarnitine, and acetoacetic acid had positive trend-level associations with hallucinations in every model except for the one adjusted for cannabis use. Medium-chain acylcarnitines, such as hexanoyl-, octanoyl-, and decanoylcarnitines, support fat beta-oxidation, resulting in increased ketones in cells (Dambrova et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Acetoacetic acid is the stable form of its conjugate base acetoacetate, a ketone body. High levels of hexanoylcarnitine and octanoylcarnitine have been reported in first-episode psychosis and schizophrenia patients (Kriisa et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mednova et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, antipsychotic treatment appears to alleviate high acylcarnitine levels (Kriisa et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Increased saliva acetoacetic acid levels have been detected before the onset of schizophrenia (Cui et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and high blood levels have been observed in patients with schizophrenia (Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggesting that observed alterations may be associated with PLE in this this study.\u003c/p\u003e \u003cp\u003eHowever, it is possible that altered levels of acylcarnitines are caused by cannabis use per se. Hexanoylcarnitine had a direct positive association with cannabis use in this sample (\u003cem\u003eSupplementary Table\u0026nbsp;4\u003c/em\u003e), and cannabis use has previously been suggested to alter carnitine synthesis pathways (Alasmari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cannabinoid effects on cellular energy metabolism have previously been reported (Farokhnia et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, these phenomena are not mutually exclusive. For instance, a dysregulated endocannabinoid system, which is closely connected to lipid metabolism, has been observed in FEP (Bioque et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hietala, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), suggesting its potential involvement in the underlying pathophysiology of psychosis.\u003c/p\u003e \u003cp\u003eAlterations related to acylcarnitines and acetoacetic acid may suggest a pathway for prodromal psychotic symptomology induced by cannabis use via the mitochondrial production of ketone bodies for energy. Ketogenesis is an alternative energy source, especially in the liver and astrocytes (Metna-Laurent \u0026amp; Marsicano, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The type-1 cannabinoid (CB\u003csub\u003e1\u003c/sub\u003e) receptor has been found to modulate astrocytic ketogenesis, and in a rat model, cannabinoids have been found to stimulate the production of ketone bodies, such as acetoacetic acid (Metna-Laurent \u0026amp; Marsicano, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A ketogenic diet has also been studied as a possible augmentation treatment for schizophrenia (Kraft \u0026amp; Westman, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the purine metabolite allantoin had a positive, and the oxidant NAD\u003csup\u003e+\u003c/sup\u003e a negative trend-level association with the hallucinations dimension in all the models except for the one adjusted for cannabis use. Allantoin is generated from uric acid when reactive oxidative species (ROS) are present (Xuan et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and NAD\u003csup\u003e+\u003c/sup\u003e can inhibit the production of reactive oxygen species in cells (Kim et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, increased allantoin and decreased NAD\u003csup\u003e+\u003c/sup\u003e have been reported in patients with schizophrenia, in line with our results (Xuan et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). An increase in ROS while lacking antioxidants results in an increase in lipid peroxidation (Money \u0026amp; Bousman, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), while the beta-oxidation of lipids, discussed above, increases the formation of ROS in various CNS disorders (Adibhatla \u0026amp; Hatcher, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, molecules related to beta-oxidation and oxidative stress were coincidentally found to be altered.\u003c/p\u003e \u003cp\u003eFinally, the branched-chain amino acid (BCAA) valine displayed a positive trend-level association with the hallucination symptom dimension in all models except when cannabis use was considered, but this association did not remain significant after rank normalization. High levels of valine have been found in the plasma of unmedicated patients with schizophrenia (Bjerkenstedt et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). On the contrary, low valine levels have been reported in first-episode psychotic patients (Leppik et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, a positive association between cannabis use and serum valine levels has also been reported (Alasmari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggesting that high valine levels could be associated with cannabis use.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis study used a novel approach of investigating prodromal psychotic symptoms from a symptom dimension perspective. The patients were young, and our findings for some of them may therefore reflect metabolomic changes at a rather early stage before any psychosis onset. The sample was small, especially the subset reporting any cannabis use (n\u0026thinsp;=\u0026thinsp;15, 20%), increasing type II bias in the results and highlighting that these results should be considered preliminary. The study should be replicated with a larger sample size and with a healthy control group. The choice of transformation method for metabolomics data is important, and in this study, more robust rank ordering was used as a sensitivity analysis in addition to the original data. Ranking changes the nature of the data, but the results were relatively similar in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Other factors (e.g., perinatal complications) have also been associated with psychotic symptoms, which could not be controlled for in this study. However, several covariates, such as childhood adversity, either considered or potentially considered in this study, are likely to induce psychotic-like experiences. Therefore, including them as covariates in the analyses may be overly conservative. Behavioral changes resulting from either cannabis use or PLEs may also account for some of the observed alterations, although they were considered in the analyses (e.g., diet and sleep quality). Finally, females were overrepresented in this sample, reflecting the natural incidence of depression and a higher tendency to seek treatment and take part in studies among females. In future metabolomic studies considering psychosis-related phenomena, it will be important to consider cannabis use by patients. In addition, future analyses could include broader lipidomic assays, considering that numerous studies investigating psychotic-like experiences or prodromal stages of psychosis have reported alterations in lipids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Conclusions\u003c/h2\u003e \u003cp\u003eIn this exploratory study, we detected metabolomic alterations related to six different PLE dimensions. The degree to which these dimensions were associated with the peripheral markers varied, and we observed cannabis use to have an impact on the associations. Based on these preliminary results, we hypothesize that PLEs develop via various pathophysiological mechanisms, one being inflammation. Cannabis use, on the other hand, was associated with hallucinatory experiences via increased energy demand and ketogenesis. In the future, the prevalence of psychotic disorders later in life could be examined in the light of endogenous and cannabis-use-related prodromal metabolomic alterations, following participants via comprehensive national registries to determine how well the observed metabolomic alterations predict psychotic episodes or disorder onset later in life, both as such and together with clinical indicators.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would especially like to thank the FIMM Metabolomics Unit funded by Biocenter Finland and HiLIFE for the metabolite analysis, the ISLAB laboratory nurses for sample collection, the patients for participating in this study, and Roy Siddall for revising the language of the text. This study was supported by the Strategic Research Council within the Academy of Finland [SchoolWell, grant number 352509, work package 352511], the Foundation for Pediatric Research [190162, 2020], and the Foundation of Helena Vuorenmies [2022].\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eOlli K\u0026auml;rkk\u0026auml;inen is a co-founder of Afekta Technologies Ltd, a company providing global metabolomics analysis services (not used in this study).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdibhatla, R. M., \u0026amp; Hatcher, J. F. (2010). Lipid Oxidation and Peroxidation in CNS Health and Disease: From Molecular Mechanisms to Therapeutic Opportunities. \u003cem\u003eAntioxidants \u0026amp; Redox Signaling\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 125\u0026ndash;169. https://doi.org/10.1089/ars.2009.2668\u003c/li\u003e\n\u003cli\u003eAlasmari, F., Assiri, M. A., Ahamad, S. R., Aljumayi, S. 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Kynurenine metabolism and metabolic syndrome in patients with schizophrenia. \u003cem\u003eJournal of Psychiatric Research\u003c/em\u003e, \u003cem\u003e139\u003c/em\u003e, 54\u0026ndash;61. https://doi.org/10.1016/j.jpsychires.2021.05.004\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"metabolomics, psychotic-like experiences, prodromal psychotic symptoms, psychosis, cannabis","lastPublishedDoi":"10.21203/rs.3.rs-4237477/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4237477/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn adolescence, psychotic-like experiences (PLE) may indicate potential prodromal symptoms preceding the onset of psychosis. Metabolomic studies have shown promise in providing valuable insights into predicting psychosis with enhanced precision compared to conventional clinical features. This study investigated metabolomic alterations associated with PLE in 76 depressed adolescents aged 14\u0026ndash;20 years. Serum concentrations of 92 metabolites were analyzed with liquid chromatography\u0026ndash;mass spectrometry. PLE were assessed using the Youth Experiences and Health (YEAH) questionnaire. The associations between PLE symptom dimensions (delusions, paranoia, hallucinations, negative symptoms, thought disorder, and dissociation) and metabolite concentrations were analyzed in linear regression models adjusted for different covariates. The symptom dimensions consistently correlated with the metabolome in different models, except those adjusted for cannabis use. Specifically, the hallucination dimension was associated with 13 metabolites (acetoacetic acid, allantoin, asparagine, decanoylcarnitine, D-glucuronic acid, guanidinoacetic acid, hexanoylcarnitine, homogentidic acid, leucine, NAD\u003csup\u003e+\u003c/sup\u003e, octanoylcarnitine, trimethylamine-N-oxide, and valine) in the various linear models. However, when adjusting for cannabis use, eight metabolites were associated with hallucinations (adenine, AMP, cAMP, chenodeoxycholic acid, cholic acid, L-kynurenine, neopterin, and D-ribose-5-phosphate). The results suggest diverse mechanisms underlying PLE in adolescence; hallucinatory experiences may be linked to inflammatory functions, while cannabis use may engage an alternative metabolic pathway related to increased energy demand and ketogenesis in inducing PLE. The limited sample of individuals with depression restricts the generalizability of these findings. Future research should explore whether various experiences and related metabolomic changes jointly predict the onset of psychoses and related disorders.\u003c/p\u003e","manuscriptTitle":"An exploratory study of metabolomics in endogenous and cannabis-use-associated psychotic-like experiences in adolescence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 18:49:38","doi":"10.21203/rs.3.rs-4237477/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"transferred","content":"Translational Psychiatry","date":"2024-10-01T09:46:11+00:00","index":"","fulltext":""},{"type":"decision","content":"Reject after peer review","date":"2024-08-15T11:56:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-08-05T16:48:14+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-07-25T15:55:23+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-06-23T12:15:35+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-13T19:47:36+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-06-12T17:21:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-09T11:30:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-09T11:30:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2024-04-08T15:22:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5c0fc77-c63c-45be-abdc-76a00958b765","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30456918,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":30456919,"name":"Biological sciences/Psychology"},{"id":30456920,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2024-11-08T08:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-4237477","link":"https://doi.org/10.1038/s41398-024-03163-9","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2024-11-07 05:00:00","publishedOnDateReadable":"November 7th, 2024"},"versionCreatedAt":"2024-05-22 18:49:38","video":"","vorDoi":"10.1038/s41398-024-03163-9","vorDoiUrl":"https://doi.org/10.1038/s41398-024-03163-9","workflowStages":[]},"version":"v1","identity":"rs-4237477","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4237477","identity":"rs-4237477","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}