An exploratory study of metabolomics in endogenous and cannabis-use-associated psychotic-like experiences in adolescence
https://ror.org/00cyydd11grid.9668.10000 0001 0726 2490Institute of Clinical Medicine, University of Eastern Finland, Yliopistonranta 1, FI-70210 Kuopio, Finland
https://ror.org/00cyydd11grid.9668.10000 0001 0726 2490School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, FI-70210 Kuopio, Finland
https://ror.org/01xtthb56grid.5510.10000 0004 1936 8921Institute of Clinical Medicine, University of Oslo, P.O. Box 1171, Blindern, 0318 Oslo, Norway
https://ror.org/0331wat71grid.411279.80000 0000 9637 455XR&D Department, Division of Mental Health Services, Akershus University Hospital, PB 1000, 1478 Lørenskog, Norway
https://ror.org/040af2s02grid.7737.40000 0004 0410 2071Department of Psychiatry, Faculty of Medicine, University of Helsinki, Yliopistonkatu 3, 00014 Helsinki, Finland
https://ror.org/00fqdfs68grid.410705.70000 0004 0628 207XDepartment of Child Psychiatry, Kuopio University Hospital, Kaartokatu 9, Kuopio, Finland
https://ror.org/00cyydd11grid.9668.10000 0001 0726 2490Department of Social Sciences, University of Eastern Finland, Yliopistonranta 1, 70210 Kuopio, Finland
https://ror.org/03tf0c761grid.14758.3f0000 0001 1013 0499Mental Health Team, Finnish Institute for Health and Welfare, P.O. Box 30, FI-00271 Helsinki, Finland
https://ror.org/00fqdfs68grid.410705.70000 0004 0628 207XKuopio University Hospital, Department of Adolescent Psychiatry, Kaartokatu 9, Kuopio, Finland
Abstract
In 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 14–20 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, homogentisic 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.
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 [1, 2]. For instance, plasma proteomics have been found to predict psychosis with greater accuracy than clinical features [3]. Early diagnosis is particularly important for implementing interventions that result in better recovery from the first psychotic episode [4]. 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 [5, 6].
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 [6]. 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 [7]. 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 [8].
Alterations in the plasma lipidome in children have been observed to precede psychotic-like experiences (PLE) [9] and psychosis [6] in adolescence, and dysregulated lipid metabolites have been found to predict psychosis in young adults [10, 11]. Furthermore, psychotic experiences in early adulthood have been associated with disturbances in lipid metabolism [12]. In addition, altered lipid levels in red blood cell membranes have been associated with an increased risk of psychosis [13]. 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 [10–12].
Other common findings related to the prodromal stages of psychosis are altered serum and plasma levels of phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins [6, 10, 12]. In particular, alterations in phosphatidylcholines and lysophosphatidylcholines during childhood have been observed to precede the manifestation of PLE in adolescence [9]. Similarly, phospho- and sphingolipids have been found to be altered in first-episode psychosis (FEP) when compared to healthy controls [5]. 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 [14]. Findings in individuals with an interview-assessed clinical high risk of psychosis (CHR) include altered catecholamine dopamine and noradrenaline metabolite alterations in saliva samples [15]. Additionally, PLEs have been associated with changes in gene expression, observed as altered DNA methylation [16], as well as alterations in the proteome [14, 17] 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 [5, 18]. 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.
Methods
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 (SCID-IV) [19] 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.
Questionnaires and clinical assessments
The psychotic-like experiences of the patients were assessed with the novel Youth Experiences and Health (YEAH) questionnaire [20], 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 [21]. Adverse childhood events were assessed with the Trauma and Distress Scale (TADS), with a total raw score ranging from 0 to 100 [22]. Quality of sleep was measured with the Pittsburgh Sleep Quality Index (PSQI) [23] and the severity of insomnia with the Insomnia Severity Index (ISI) [24]. Alcohol use was evaluated with the first three questions of the Alcohol Use Disorders Identification Test (AUDIT-C), scored from 0–12 [25]. 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 [26]. 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 [27]. 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.
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.
Statistical analysis
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 [28] using the default settings with the WLSMV estimator and theta parameterization. The pairwise coverage among the items ranged from 97.7% to 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 Table 2b, c, with item-wise categorization provided in Supplementary Table 2a. Factor scores for subsequent analyses were derived using the maximum a posteriori method.
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) [29] using R packages stats (v 3.6.2; R builtin) and lm.beta (v 1.7-2) [30]. 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 [31]. These included gender, age, BMI, IDQ, ongoing medications, depression chronicity, BDI, TADS [32], ISI, PSQI, tobacco smoking, cannabis use [33], 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) [34] and RColorBrewer (v 1.1-3) [35]. As a post hoc analysis, we performed linear regression analyses predicting metabolite concentrations with cannabis use (Supplementary Table 4).
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) [36].
Results
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. From this sample, 65.8% of the patients had recently had experiences of delusions, 96.1% paranoia, 78.9% hallucinations, 96.1% negative symptoms, 89.5% thought disorder and 94.7% dissociation at least occasionally. 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, cannabis use and antipsychotic medication, 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 overall medication use (Table 1). Of the sample population, 32% were taking SSRIs, 17% antipsychotics, 5% agomelatine, tricyclic antidepressants or vortioxetine, 1% mood stabilizers, 8% mirtazapine, and 32% other medications, including melatonin, mini-pills, beta blockers, insulin or oxazepam. The 95% confidence intervals of the betas are additionally presented in Supplementary Table 1.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) 0.22 0.057 0.07 0.556 0.16 0.165 −0.15 0.188 −0.02 0.855 −0.02 0.839 Age, mean (SD) 16.4 (1.6) 0.06 0.624 −0.14 0.241 −0.08 0.476 −0.02 0.883 −0.04 0.756 0.19 0.097 Body mass index, mean (SD) 22.3 (5.7) 0.29 0.012 −0.01 0.943 0.09 0.427 0.03 0.772 0.09 0.454 0.22 0.062 Diet quality (IDQ), mean (SD) 25.6 (5.7) −0.27 0.020 0.18 0.117 −0.10 0.403 0.12 0.319 −0.04 0.744 −0.17 0.147 Tobacco use (ASSIST), mean (SD) 6.5 (9) −0.08 0.475 0.24 0.037 0.09 0.433 0.29 0.012 0.17 0.141 −0.02 0.866 Cannabis use (ASSIST), mean (SD) 2.3 (3.4) 0.08 0.134 0.56 0.031 0.72 0.003 0.36 0.185 0.54 0.040 0.47 0.075 Alcohol use (ASSIST), mean (SD) 8.1 (8.3) −0.07 0.627 0.21 0.115 0.02 0.879 0.09 0.525 0.06 0.638 −0.18 0.166 Alcohol use (AUDIT-C), mean (SD) 2.7 (2.9) −0.22 0.057 0.06 0.620 −0.13 0.277 0.22 0.054 −0.01 0.960 −0.08 0.507 Medication, n (%) 43 (56.6) 0.21 0.914 −0.08 0.608 −0.22 0.255 0.01 0.936 0.33 0.055 −0.08 0.588 SSRI, n (%) 24 (31.6) 0.11 0.508 −0.11 0.508 −0.20 0.306 0.02 0.926 0.31 0.079 −0.03 0.829 AP medication, n (%) 13 (17.1) 0.14 0.471 −0.33 0.038 0.15 0.935 0.13 0.402 0.24 0.151 0.03 0.831 Chronic depression, n (%) 43 (56.6) 0.22 0.017 −0.09 0.462 0.07 0.526 −0.24 0.034 −0.06 0.714 0.12 0.307 Depression (BDI), mean (SD) 30.2 (7.6) 0.30 0.008 0.32 0.005 0.43 <0.001 0.14 0.229 0.43 <0.001 0.27 0.019 Childhood adversity (TADS), mean (SD) 43.8 (18.6) 0.27 0.017 0.34 0.003 0.32 0.007 0.11 0.337 0.21 0.071 0.14 0.235 Insomnia (ISI), mean (SD) 12.6 (5.4) 0.27 0.019 0.15 0.208 0.27 0.018 0.20 0.081 0.17 0.018 0.19 0.097 Sleep quality (PSQI), mean (SD) 10.7 (3.9) 0.27 0.020 0.19 0.101 0.33 0.004 0.29 0.010 0.33 0.004 0.21 0.074
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.
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.
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. 3A). 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. 3B. 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.
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.
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 [38], and cannabis use is a well-established risk factor for psychotic disorders [39]. 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 [40].
Strengths and limitations
This study used a novel approach of investigating potential 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. However, the study did not include follow-up data to confirm the prodromal nature of the psychotic-like experiences, which is an important limitation to be addressed in the following research. 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. Another limitation was that the study did not include a healthy control group. PLE has been suggested to be limited as they may not predict only psychosis but onset of a variety of psychiatric conditions [70], however, some dimensions have been found to more accurately predict psychotic conditions than others [71]. 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.
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.
Supplementary information
Supplementary information
The online version contains supplementary material available at 10.1038/s41398-024-03163-9.
Acknowledgements
We would especially like to thank the patients participating in this study, the ISLAB laboratory nurses for sample collection, 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].
Data availability
The data supporting the findings of this study are available upon request from the corresponding author, KK. However, due to the sensitive nature of the information and to protect the privacy of the research participants, the data are not publicly accessible. The study’s ethical approval and the consent terms agreed upon by the participants specifically prohibit public sharing of the data, even in anonymized form.
Competing interests
OK is a co-founder of Afekta Technologies Ltd, a company providing global metabolomics analysis services (not used in this study).
Ethics approval and consent to participate
The project was approved by the Research Ethics Committee of Kuopio University Hospital (238/13.02.00/2017). Informed consent was obtained from all participants. All methods were carried out in accordance with relevant guidelines and regulations.