Metabolic pathway modulation by olanzapine: Multitarget approach for treating violent aggression in patients with schizophrenia

Abstract

BACKGROUND

The use of network pharmacology and blood metabolomics to study the pathogenesis of violent aggression in patients with schizophrenia and the related drug mechanisms of action provides new directions for reducing the risk of violent aggression and optimizing treatment plans.

AIM

To explore the metabolic regulatory mechanism of olanzapine in treating patients with schizophrenia with a moderate to high risk of violent aggression.

METHODS

Metabolomic technology was used to screen differentially abundant metabolites in patients with schizophrenia with a moderate to high risk of violent aggression before and after olanzapine treatment, and the related metabolic pathways were identified. Network pharmacology was used to establish protein-protein interaction networks of the core targets of olanzapine. Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were subsequently performed.

RESULTS

Compared with the healthy group, the patients with schizophrenia group presented significant changes in the levels of 24 metabolites related to the disruption of 9 metabolic pathways, among which the key pathways were the alanine, aspartate and glutamate metabolism and arginine biosynthesis pathways. After treatment with olanzapine, the levels of 10 differentially abundant metabolites were significantly reversed in patients with schizophrenia. Olanzapine effectively regulated six metabolic pathways, among which the key pathways were alanine, aspartate and glutamate metabolism and arginine biosynthesis pathways. Ten core targets of olanzapine were involved in several key pathways.

CONCLUSION

The metabolic pathways of alanine, aspartate, and glutamate metabolism and arginine biosynthesis are the key pathways involved in olanzapine treatment for aggressive schizophrenia.

Key Words: Schizophrenia; Violent aggression; Olanzapine; Metabolomics; Network pharmacology

Core Tip: Violent aggression is an important symptom of schizophrenia that causes serious harm to society and public health. This study integrated network pharmacology with blood metabolomics to explore the pathogenesis of violent aggression in patients with schizophrenia and the metabolic regulatory mechanism of olanzapine, establishing theoretical basis for fundamental research and clinical application of olanzapine in the treatment of violent aggression in patients with schizophrenia.

INTRODUCTION

Schizophrenia (SCZ) is one of the most common and severe mental illnesses; it is characterized by a lack of coordination between consciousness and behavior, and neurotransmitter dysfunction is a typical pathological change. Patients often experience symptoms such as auditory hallucinations and paranoid ideation, accompanied by emotional instability, irritability, and impulsive behavior, especially violent aggression[1]. Violent aggressive behavior in SCZ patients is highly explosive and destructive and can cause considerable damage in a short period. Compared with the general population, individuals with SCZ are 4 to 7 times more likely to commit violent crimes, such as assault and homicide[2], and 4 to 6 times more likely to exhibit general aggressive behavior, such as verbal and physical threats[3]. At present, there is no clear conclusion on the mechanism underlying violence in SCZ patients[4], but it is believed that violent behavior is the result of multiple factors working together[5]. Substance abuse, alcohol abuse, neurological impairment, genetic factors and social burdens can all increase the risk of aggressive behavior[6-10]. Among the multiple risk factors, comorbid substance abuse and the presence of positive symptoms, such as persecutory ideation, hallucinations, and delusions, have been duplicated in several studies[11-17]. In addition, SCZ patients have significant cognitive impairment[18], which is considered the third major core symptom after positive and negative symptoms and another indicator of a high risk for violent behavior in SCZ patients. Genetic, neurobiochemical, and metabolic factors[19-22] play important roles in violent and aggressive behavior in patients with SCZ. The study of the biological mechanisms underlying violent and aggressive behavior in patients with SCZ is highly important for revealing the etiology of SCZ, seeking effective therapeutic targets, and treating it clinically. Many studies have confirmed that multiple neurotransmitters in the central nervous system are closely related to violent and aggressive behavior. The neurotransmitters involved in violent and aggressive behavior mainly include 5-HT, norepinephrine, dopamine, glutamate (Glu), GABA, the endocannabinoid system, androgens (such as testosterone), and inflammatory factors [interleukin (IL)-2, IL-6, IL-8, Tumor necrosis factor (TNF)], among others. The rapid development of neuroimaging technology has made certain progress in the study of the neurobiological mechanisms of aggressive behavior in patients with SCZ. Previous studies have shown that structural or functional imbalances in the cortex limbic system related to emotional regulation and decision-making processes are closely associated with the occurrence of aggressive behavior, including regions such as the orbitofrontal gyrus, dorsolateral prefrontal cortex, amygdala, hippocampus, and cingulate gyrus[23-25]. In addition, dysfunction of the frontotemporal lobe circuit plays an important role in aggressive behavior in patients with SCZ, and drug therapy may be effective through this circuit[26].

Owing to the sudden and destructive nature of violent attacks in patients with mental disorders, the most effective measure to prevent violent behavior is the regular use of antipsychotic medication[27-29]. Some antipsychotic drugs, especially clozapine, seem to have specific anti-aggressive effects[30-32]. However, clozapine is rarely used as a first-line treatment because of its hematological side effects. Full blood count monitoring is required throughout the treatment, placing a burden on the patient. This tedious monitoring may increase the already high level of nonadherence observed in SCZ patients. Olanzapine, a first-line drug used to treat various mental disorders, such as SCZ or bipolar disorder, is a chemical analog of clozapine and has similar pharmacological properties. However, in contrast to clozapine, olanzapine causes fewer autonomic side effects and does not increase the risk of neutropenia. For many years, olanzapine has been used to treat patients with bipolar disorder and SCZ accompanied by acute agitation. Its partial function is to antagonize 5-HT_1A, 5-HT₃, 5-HT₆, and 5-HT₇ receptors and act as antagonist agonists of 5-HT_2B and 5-HT_2C receptors[33]. Clinical studies have shown that high-dose olanzapine has clinical efficacy in treating agitation or aggressive behavior in SCZ patients[34,35]. The improvement in cognitive function in SCZ patients caused by olanzapine treatment is related to a reduction in aggressive behavior[36]. However, the therapeutic mechanism of olanzapine in treating violent aggressive behavior in SCZ patients is still unclear. The identification of sensitive and specific biochemical indicators can not only be used to accurately diagnose and evaluate a patient’s condition but also objectively and accurately reflect the treatment effect, which is highly important for improving patient prognosis[37].

Metabolomics can be used to identify molecular markers under specific physiological and pathological conditions and analyze the overall changes in endogenous metabolites in the body after stimulation or interference using multivariate statistical analysis. Metabolomics has been used for diagnosis, identification of disease mechanisms, identification of new drug targets and monitoring of treatment outcomes[38]. In recent years, the establishment and improvement of psychopharmacological metabolomics platforms have provided theoretical and practical support for studying the effects of antipsychotic drugs at the small-molecule metabolite level[39,40]. Network pharmacology is an emerging discipline that studies the pathogenesis of diseases and the mechanisms of drug action in the context of biological networks. By connecting disease-related genes with drug targets, a network of interactions between genes and targets can be constructed to systematically explain the relationships between drugs and diseases and the mechanisms of action at multiple levels, providing a theoretical basis for different drug treatments for diseases. At present, some studies have applied metabolomics to the diagnosis of violent SCZ patients[41], indicating that the dysregulation of lipid and amino acid metabolism might provide information for the etiological understanding of violence in SCZ patients. However, relatively few studies on the metabolomics of antipsychotic drugs exist, and explorations of the mechanisms and drug effects of violent SCZ through the combination of metabolomics with network pharmacology are lacking. This study aims to fill this theoretical gap. Therefore, this study aimed to explore the potential biomarkers and metabolic pathways associated with olanzapine in the treatment of SCZ patients with a moderate to high risk of violent aggression through the integration of metabolomics and network pharmacology and to investigate the potential mechanism of action.

MATERIALS AND METHODS

General information

A total of 48 hospitalized patients who were diagnosed with SCZ at risk of violent attacks at The Affiliated Encephalopathy Hospital of Zhengzhou University (Zhumadian Second People's Hospital) from January to December 2023 were selected as the patient group. The patient group included 28 males and 20 females, aged 19-59 years, with an average age of 35.2 ± 12.08 years. Additionally, 94 healthy individuals who underwent health examinations at our hospital during the same period were selected as the control group. The control group included 58 males and 36 females aged 18-57 years, with an average age of 36.60 ± 10.49 years. There was no statistically significant difference in general information such as sex, age, race, socioeconomic status, lifestyle, or comorbidities (such as drug abuse) between the two groups (P > 0.05). This study was approved by the Medical Ethics Committee of Zhumadian Second People's Hospital (Approval No. Keshen-2023-001-01).

The inclusion criteria for SCZ patients at risk of violent attacks were as follows: (1) Met the International Classification of Diseases-10 diagnostic criteria for SCZ; (2) Were at moderate to high risk of violent attacks according to the Violence Risk Screening-10; (3) Aged 18-60 years; and (4) Had first-onset SCZ or had not taken any antipsychotic medication within the past three months; and provided informed consent. The exclusion criteria were as follows: (1) Patients who had recently taken antipsychotic drugs; (2) Patients who had used corticosteroid drugs in the past two weeks; (3) Patients with drug dependence; patients with any other mental disorders; (4) Patients with serious physical illnesses; and (5) Pregnant or lactating women.

Main instruments and reagents

The ultrahigh-performance liquid chromatography-quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) system used included an Ultimate 3000 ultrahigh-performance liquid chromatograph (Dionex, United States), a Q Exactive high-resolution mass spectrometer (Thermo Fisher Scientific, United States), and an Acquity UPLC^®BEH C18 (50 mm × 2.1 mm, 1.7 μm) chromatographic column (Waters Corporation, United States). The multivariate data processing software used was SIMCA14.0 (Umetrics, Sweden). A New Classic MS type one hundred thousandth analytical balance (Mettler Toledo Shanghai Co., Ltd., Switzerland) was used. Methanol, acetonitrile, and formic acid were all chromatographically pure (Fisher, United States), the water used was ultrapure water (with a conductivity of 0.1-0.055 microsecond/cm), and the other reagents were of analytical grade (all purchased from Tianjin Kemio Chemical Reagent Co., Ltd.).

Chromatographic conditions

The chromatographic column used was a Waters ACQUITY UPLC^®BEH C18 chromatographic column (2.1 mm × 50 mm, 1.7 μm). The mobile phases were 0.1% formic acid aqueous solution (A) and acetonitrile (B), and the gradient elution conditions were as follows: 0-1 minute, 5% A; 1-2 minutes, 5%-35% A; 2-5 minutes, 35%-45% A; 5-8 minutes, 45%-70% A; 8-13 minutes, 70%-85% A; 13-15 minutes, 85-100% A; 15-18 minutes, 100% A; and 18-20 minutes, 5% A. The volume flow rate was 0.2 mL/minute, the injection volume was 10 μL, and the column temperature was 40 °C.

Mass spectrometry conditions

The UHPLC-Q Exactive liquid chromatography-mass spectrometry system was as follows: The ion source was a heated electrospray ionization source, the auxiliary gas temperature was 300 °C, the auxiliary gas flow rate was 10 μL/minute, and the ion transport tube temperature was 320 °C. In positive ion mode, the spray voltage was 3.50 kV, and the sheath gas flow rate was 40 μL/minute. In negative ion mode, the spray voltage was 2.80 kV, and the sheath gas flow rate was 38 μL/minute. The mass spectrometry analysis methods used were as follows: Full MS/dd-MS²; mass-charge ratio window width, 2; collision energy gradients, 20, 50, and 80 eV; and scanning range (m/z), 80-1200.

Therapeutic protocol

The patients in the experimental group received routine treatment with olanzapine at a dosage of 5-40 mg/day, depending on the patients' clinical condition[42], for four weeks.

Preparation and pretreatment of plasma samples

Before and after treatment, 3 mL of venous blood was collected from the median cubital vein of fasted patients in both the healthy group and the disease group and then transferred to EDTA anticoagulant tubes. The tubes were centrifuged at 4 °C and 3000 r/minute for 10 minutes, and the supernatant was collected and stored in a -80 °C freezer. Before testing, the serum was thawed at 4 °C and homogenized by vortex oscillation. A total of 200 μL of serum was extracted, and 800 μL of precooled acetonitrile solution was added. After vortexing, the mixture was centrifuged at 13000 r/minute at 4 °C for 15 minutes. Then, 800 μL of the supernatant was removed and centrifuged at low temperature at 4 °C until drying. The freeze-dried material was dissolved in 200 μL of an acetonitrile-water mixture (8:2, v/v) via ultrasonication, vortexed, and centrifuged at 13000 r/minute for 15 minutes. Finally, the supernatant was removed and injected for detection.

Statistical analysis

The raw data were obtained via UHPLC-Q-Orbitrap HRMS, and peak extraction, alignment, filtering, normalization, and other related preprocessing were performed using Compound Discoverer 3.3.1.111 software. Using SIMCA V14.1 and MetaboAnalyst 6.0 for multivariate analysis of the data, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed, with variable importance in projection (VIP) > 1 and P < 0.05 as evaluation indicators. The HMDB online database (http://www.hmdb.ca/) was used to screen differentially abundant metabolites (DMs) between groups. The diagnostic ability of the DMs was evaluated through receiver operating characteristic (ROC) curve analysis. The online software MetaboAnalyst 6.0 (http://www.metaboanalyst.ca) was used to identify relevant metabolic pathways.

SPSS 25 statistical software was used to analyze the data. The quantitative data are expressed as mean ± SD. Binary logistic regression analysis and t tests were used to statistically analyze the DMs, with P < 0.05 indicating statistical significance. Boxplots of the DMs were created using SPSS 25, and the data are presented as the median ± min to max.

Network pharmacology analysis

The PubChem database was used to obtain the structures of olanzapine and the DMs, which were imported into the Swiss Target Prediction platform (http://www.swisstargetprediction.ch/) to identify targets. A probability > 0 was used as the screening criterion, and duplicate targets were removed to obtain effective targets for olanzapine and DMs. Disease target screening was conducted using the Online Mendelian Inheritance in Man (OMIM) database (https://omim.org) and the Gene Cards database (https://www.genecards.org/). The obtained drug component targets were mapped to disease targets, Venny 2.1 (http://bioinfogp.cnb.csic.es/) was used to create a Venn diagram (Veen), and common drug-disease targets were screened. The STRING database (https://string-db.org/) and Cytoscape 3.8.2 software were used to construct a protein-protein interaction (PPI) network to identify drug-disease intersection targets. The connections between nodes represent PPI, and the size and color of nodes were adjusted according to the degree value. The larger the degree is, the larger and darker the nodes. The CytoHubba plugin was used to obtain hub genes, and the top ten genes were selected as the core targets for olanzapine in the treatment of SCZ patients at risk for violent attacks. Gene Ontology (GO) functional analysis of the drug-disease intersection targets was performed using the DAVID database (https://david.ncifcrf.gov/summary.jsp). The roles of the target proteins in the gene function of drug therapy for diseases were annotated in three categories: Biological process (BP), cellular component (CC), and molecular function (MF). The DAVID database was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on intersecting targets; the top 20 pathways with P values were selected, and a bubble chart was drawn. A network file and tape file were constructed on the basis of the intersection targets, and Cytoscape 3.8.2 was used to construct an olanzapine metabolite-target-disease network diagram.

RESULTS

Metabolomics analysis results

Multivariate statistical analysis: To determine metabolic differences among the groups, multivariate data analysis was performed using Simca 14.1 and MetaboAnalyst 6.0 on the metabolomics data of the control group and disease group before and after treatment. As shown in Figure 1, there was a significant separation between the control group and the disease group before and after treatment on the PCA scoring graph, indicating that biochemical disturbances occurred in the disease group before and after treatment with olanzapine. To better reveal intergroup differences and metabolic profiles, OPLS-DA was performed. This analysis revealed two principal components in positive ion mode (R2X = 0.508, R2Y = 0.83, and Q2 = 0.654). Two principal components were also obtained in negative ion mode (R2X = 0.404, R2Y = 0.731, and Q2 = 0.56), indicating that the models in both positive and negative ion modes were reliable and could explain the differences in metabolites between groups. As shown in Figure 1, the metabolites of the healthy group and the disease group before and after treatment were significantly different, indicating significant differences in the plasma metabolic profiles among the three groups. Compared with those before treatment, the metabolite levels in the disease group were closer to those in the healthy group after treatment, indicating that olanzapine improved metabolic disorders in the disease group. The metabolite levels in some patients returned to the levels in the healthy group after treatment, and the identified metabolites of the two groups were similar.

Figure 1 Metabolomic profiling analysis of the two groups. A: Positive ion modes; B: Negative ion modes. Principal component analysis and orthogonal partial least squares discriminant analysis score plots and permutation tests of the two groups in positive (A) and negative (B) ion modes.

DMs: This study screened small-molecule DMs between the control group and the disease group using screening criteria of VIP > 1 and P < 0.05. Moreover, ROC curves and areas under the curve (AUCs) were used to evaluate the DMs for disease diagnosis and drug efficacy. Compared with those in the healthy group, there were significant changes in the levels of 24 metabolites in the disease group (15 in positive ion mode and 11 in negative ion mode, with 2 duplicates in both modes), and the AUC was > 0.7. Among them, the plasma levels of 16 DMs were upregulated, and those of 8 DMs were downregulated. For detailed information, please refer to Table 1. After 4 weeks of treatment with olanzapine, the plasma concentrations of 10 metabolites, namely, N-undecanoylglycine, cortisol, palmitoyl ethanolamide, L-glutamic acid, D-(+)-pyroglutamic acid, platelet-activating factor, D-fructose, 4-oxoproline, dihydrosphingosine 1-phosphate, and sphingosine 1-phosphate, were significantly reversed in the disease group, as shown in Table 2 and Figure 2.

Figure 2 Relative peak areas of differentially abundant metabolites in the experimental group regulated by olanzapine. A: Positive ion mode; B: Negative ion mode. All data are presented as the median ± min to max (n = 11).

Table 1 Differentially abundant metabolites in patients with schizophrenia with violent aggression.

No.	Metabolites	Retention time/minute	m/z	Formula	VIP	P value	Trend	Scan mode
1	Platelet-activating factor	9.44	524.36951	C26 H54 N O7 P	2.09169	0.000	↓	+
2	2-amino-1,3,4-octadecanetriol	6.441	318.29908	C18 H39 N O3	1.88102	0.000	↑	+
3	Palmitic acid	6.402	274.27291	C16 H32 O2	1.86839	0.000	↑	+
4	Cortisol	5.742	363.21555	C21 H30 O5	1.91193	0.000	↑	+
5	Stearic acid	6.99	302.30423	C18 H36 O2	1.59533	0.000	↑	+
6	Hexadecanamide	10.073	256.26277	C16 H33 N O	1.47424	0.000	↑	+
7	Arachidic acid	7.54	330.33567	C20 H40 O2	1.17354	0.000	↑	+
8	N-undecanoylglycine	6.134	266.17185	C13 H25 N O3	1.97952	0.000	↑	+
9	Dihydrosphingosine 1-phosphate	7.693	382.27068	C18 H40 N O5 P	1.37863	0.000	↑	+
10	Palmitoyl ethanolamide	9.734	300.28892	C18 H37 N O2	1.70707	0.000	↑	+
11	D-(+)-pyroglutamic acid	1.01	130.04979	C5 H7 N O3	1.21094	0.000	↓	+
12	Oleoylethanolamide	9.932	326.30444	C20 H39 N O2	1.45824	0.000	↑	+
13	D-erythro-sphingosine 1-phosphate	7.519	380.25492	C18 H38 N O5 P	1.36048	0.000	↑	+
14	Hypoxanthine	1.111	137.04556	C5 H4 N4 O	1.33242	0.000	↓	+
15	Glutamine	0.96	147.07623	C5 H10 N2 O3	1.24123	0.000	↑	+
16	N-undecanoylglycine	6.181	242.17601	C13 H25 N O3	2.20108	0.000	↑	-
17	L-glutamic acid	0.893	146.0449	C5 H9 N O4	1.9134	0.000	↓	-
18	Sphingosine 1-phosphate	7.587	378.24163	C18 H38 N O5 P	1.81005	0.000	↑	-
19	N-phenylacetylglutamine	4.125	263.10371	C13 H16 N2 O4	1.68847	0.000	↑	-
20	D-(-)-fructose	0.853	179.05538	C6 H12 O6	1.63729	0.000	↑	-
21	4-oxoproline	0.968	128.03417	C5 H7 N O3	1.54629	0.000	↓	-
22	L-(+)-lactic acid	1.093	89.02311	C3 H6 O3	1.16621	0.000	↓	-
23	Levothyroxine	6.053	775.68055	C15 H11 I4 N O4	1.51385	0.000	↑	-
24	3-hydroxybutyric acid	1.784	103.03886	C4 H8 O3	1.43635	0.000	↑	-
25	Arachidonic acid	10.197	303.23285	C20 H32 O2	1.12287	0.000	↓	-
26	Cis-5,8,11,14,17-eicosapentaenoic acid	9.785	301.21728	C20 H30 O2	1.09833	0.000	↓	-

“↑” or ”↓” indicates that the metabolite significantly increased or decreased, respectively, in the disease group compared with the control group. VIP: Variable importance in projection.

Table 2 Differentially abundant metabolites in patients with schizophrenia treated with olanzapine.

No.	Formula	Metabolites	Control group vs pretherapy in the disease group			Control group vs post therapy in the disease group			Adjust
			VIP	Fold change	P value	VIP	Fold change	P value
1	C26 H54 N O7 P	Platelet-activating factor	2.09169	0.620166	1.64E-21	1.79868	0.645408	7.75E-19	Reverse
2	C21 H30 O5	Cortisol	1.91193	2.14094	1.21E-17	1.63334	1.72288	2.07E-13	Reverse
3	C18 H40 N O5 P	Dihydrosphingosine 1-phosphate	1.37863	1.79149	1.55E-08	1.11856	1.4172	9.58E-06	Reverse
4	C18 H37 N O2	Palmitoyl ethanolamide	1.70707	1.6449	6.51E-14	1.00437	1.27934	7.33E-06	Reverse
5	C5 H7 N O3	D-(+)-pyroglutamic acid	1.21094	0.586656	9.25E-07	1.06578	0.591467	8.40E-06	Reverse
6	C13 H25 N O3	N-undecanoylglycine	2.20108	4.90093	2.62E-15	1.58901	4.27589	1.64E-09	Reverse
7	C5 H9 N O4	L-glutamic acid	1.9134	0.630722	1.87E-11	1.41269	0.712935	9.56E-08	Reverse
8	C18 H38 N O5 P	Sphingosine 1-phosphate	1.81005	1.38203	4.22E-11	1.45196	1.27835	1.04E-06	Reverse
9	C6 H12 O6	D-(-)-fructose	1.63729	1.77875	6.54E-09	1.25196	1.61936	9.64E-06	Reverse
10	C5 H7 N O3	4-oxoproline	1.54629	0.592293	1.21E-07	1.33034	0.615205	3.25E-06	Reverse

VIP: Variable importance in projection.

Metabolic pathway analysis: Metabolic pathway analysis was performed using the online database MetaboAnalyst 5.0 (http://www.Metaboanalyst.ca). The 24 DMs in Table 1 and the 10 DMs in Table 2 were imported into MetaboAnalyst 5.0 for pathway analysis. In this study, the screening criterion was -lg P > 0.1. Pathways with an impact value greater than 0.01 were considered relevant metabolic pathways, and pathways with an impact value greater than 0.1 were considered critical metabolic pathways. The results indicated that metabolic disorders in SCZ patients with a moderate to high risk of violent attacks were associated with 9 metabolic pathways, including sphingolipid metabolism; arginine biosynthesis; alanine, aspartate and Glu metabolism; glutathione metabolism; purine metabolism; fructose and mannose metabolism; arachidonic acid metabolism; fatty acid biosynthesis; and steroid hormone biosynthesis. Among them, the critical metabolic pathways were alanine, aspartate and Glu metabolism and arginine biosynthesis. Olanzapine can effectively regulate six metabolic pathways in these patients, including glutathione metabolism; sphingolipid metabolism; arginine biosynthesis; fructose and mannose metabolism; alanine, aspartate and Glu metabolism; and steroid hormone biosynthesis. The critical metabolic pathways involved alanine, aspartate and Glu metabolism and arginine biosynthesis (Figure 3). These results indicate that metabolic disorders in SCZ patients with a moderate to high risk of violent attacks may be one of the causes of the onset of violent attacks and that olanzapine has a significant regulatory effect on metabolic disorders in these patients.

Figure 3 Metabolic pathway analysis. A: The disordered metabolic pathways in Schizophrenia patients with a moderate to high risk of violent attacks; B: The metabolic pathways regulated by olanzapine. The size and color of each circle indicate the significance of the pathway ranked by the P value and the pathway impact score, respectively. Red represents higher P values, and yellow represents lower P values. The larger the circle is, the higher the impact score.

Network pharmacology analysis results

Target selection: The structures of olanzapine and 10 DMs were obtained from the PubChem database and imported into the Swiss Target Prediction database. Targets with a probability > 0 were selected as drug targets, and 336 drug targets were screened after removing duplicate targets.

The keywords "aggressive behavior in schizophrenia" and "violence in schizophrenia" were used to search the OMIM and GeneCards databases. Disease targets were collected and summarized, and a total of 5395 disease targets were obtained after deduplication. On the Venny2.1 platform, 336 drug targets and 5395 disease targets were input, and a Venn diagram was drawn. The intersection of the two diagrams revealed 203 shared drug-disease targets, as shown in Figure 4.

Figure 4 Venn diagram of common targets.

PPI network construction and core target screening: The 203 intersecting target genes were imported into the String database, the species was set to Homo sapiens, and the confidence level was set to 0.4. The network files were saved in TSV format, and the TSV files were imported into Cytoscape 3.8.2 software to construct the protein interaction network shown in Figure 5A. The CytoHubba plugin was used to screen the top 10 hub nodes as hub genes, including AKT1, IL6, TNF, BCL2, PPARG, ESR1, PTGS2, MAPK3, GSK3B and MTOR, as shown in Figure 5B. These targets may play important roles in the target network of SCZ patients with a moderate to high risk of violent attacks during olanzapine treatment.

Figure 5 Protein-protein interaction network and hub genes. A: Protein-protein interaction network; B: Hub genes.

Enrichment analysis of core targets: Using the DAVID database, GO functional analysis and KEGG pathway enrichment analysis were performed on the core targets of olanzapine treatment in SCZ patients with a moderate to high risk of violent aggression. The BP, CC, and MF terms from the GO analysis were sorted in descending order of -lg P, and the top 10 pathways with P values were selected to draw a bubble chart. Additionally, the top 20 KEGG pathways according to the P value rankings were selected, and a bubble chart was drawn, as shown in Figure 6.

Figure 6 Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A: Gene Ontology function; B: Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.

The results revealed a correlation (P < 0.01) between a high risk of violent aggression in patients with SCZ treated with olanzapine and 302 BP, 52 CC, and 76 MF terms. The BP terms were involved mainly in chemical synaptic transmission, the G protein-coupled receptor signaling pathway, coupled to cyclic nucleotide second messengers, the adenylate cyclase-activating adrenergic receptor signaling pathway, the G protein-coupled serotonin receptor signaling pathway, and the phospholipase C-activating G protein-coupled receptor signaling pathway. The CC terms were related mainly to glutamatergic synapses, neuronal cell bodies, synapses, and receptor complexes. The MF terms were related mainly to G-protein coupled serotonin receptor activity, serotonin binding, transmembrane receptor protein tyrosine kinase activity, protein tyrosine kinase activity, and neurotransmitter receptor activity. KEGG enrichment analysis revealed a total of 154 signaling pathways, including neuroactive ligand-receptor interaction, the phospholipase D signaling pathway, glutamatergic synapses, serotonergic synapses, the cAMP signaling pathway, the PI3K-Akt signaling pathway, the thyroid hormone signaling pathway, pathways related to neurodegeneration-multiple diseases, and retrograde endocannabinoid signaling. The above signaling pathways are closely related to amino acid metabolism, energy metabolism, neurotransmitter metabolism, and endocrine hormone metabolism.

Construction of the olanzapine metabolite-target-disease network diagram: Using Cytoscape 3.8.2 software, an olanzapine metabolite-target-disease network diagram was constructed. The DMs were ranked from high to low impact as follows: N-undecanoylglycine, cortisol, palmitoyl ethanolamide, L-glutamic acid, D-(+)-pyroglutamic acid, platelet-activating factor, D-fructose, 4-oxoproline, dihydrosphingosine 1-phosphate, and sphingosine 1-phosphate. The core targets affected by olanzapine and the DMs were DRD2, CHRM1, CHRM2, HTR1A, ACE, HMGCR, HSD11B1, HSD17B3, ITGA2B, PYGL, PRKCA, PLA2G5, and PLA2G2A. These findings indicate that olanzapine has multitarget and multipathway effects in the treatment of highly aggressive SCZ patients (Figure 7).

Figure 7 Metabolite-target-disease network diagram.

DISCUSSION

Although the biological basis of violent aggression in SCZ patients is poorly understood, dopamine hyperfunction, stress vulnerability, glutamatergic hypofunction, GABAergic deficits, neurodevelopmental disruption and cholinergic system dysfunction have been linked to the etiology or pathophysiology of this disease[43]. This study used nontargeted metabolomics to analyze the changes in metabolites and metabolic pathways in patients in the disease group. Compared with healthy controls, SCZ patients at a moderate to high risk of violent aggression presented a total of 24 DMs involving 9 disrupted metabolic pathways: Sphingolipid metabolism; arginine biosynthesis; alanine, aspartate and Glu metabolism; glutathione metabolism; purine metabolism; fructose and mannose metabolism; arachidonic acid metabolism; fatty acid biosynthesis; and steroid hormone biosynthesis. These pathways may be involved in the pathogenesis of violent aggression in SCZ patients.

Metabolomic analysis revealed that olanzapine significantly affected 10 DMs in patients in the disease group involved in 6 important metabolic pathways: Glutathione metabolism; sphingolipid metabolism; arginine biosynthesis; fructose and mannose metabolism; alanine, aspartate and Glu metabolism; and steroid hormone biosynthesis. The two most critical metabolic pathways were alanine, aspartate and Glu metabolism and arginine biosynthesis. Network pharmacology analysis revealed that olanzapine can regulate signaling pathways such as neuroactive ligand-receptor interactions, the phospholipase D signaling pathway, and glutamatergic synapses through core targets such as AKT1, IL6, and TNF, improving processes such as amino acid metabolism, energy metabolism, neurotransmitter metabolism, and endocrine hormone metabolism and thereby reducing violent aggression in SCZ patients.

In recent years, the hypothesis of Glu dysfunction in SCZ has been increasingly supported by the reported results, suggesting that dysfunction of Glu NMDA receptors plays an important role in the pathophysiology of this disease. Glu is a major neurotransmitter in the brain that is synthesized from glucose through the tricarboxylic acid cycle and glutamine (Gln). Compared with those in the healthy controls, the levels of Glu in the cerebrospinal fluid[44] and brain[45] of SCZ patients were decreased, whereas the levels of Glu in the cortex were increased[46]. However, there is controversy over the levels of Glu and Gln in the peripheral blood. For example, previous studies have shown that peripheral blood Glu levels are elevated in patients with chronic SCZ[47] but not in patients with acute SCZ[48]. Palomino et al[49] reported that lower plasma levels of Glu were associated with the onset of psychosis, particularly SCZ[49,50]. Similarly, plasma Gln levels in SCZ patients are also lower[51]. A new perspective suggests that Gln/Glu levels during SCZ are dynamic, with the Gln/Glu ratio increasing at the onset of SCZ and decreasing as the condition progresses[52]. There were significant differences in the levels and ratios of Glu and Gln in the central nervous system and peripheral nervous system between the healthy control group and SCZ patients, as well as during disease progression, indicating that they are potential diagnostic biomarkers for glutamatergic dysfunction in SCZ patients[53]. This study revealed that the concentration of Glu in the disease group was significantly lower than that in the healthy group, whereas the concentration of Gln was significantly greater than that in the healthy group, indicating that Glu and Gln are DMs. Indeed, several studies have reported increased concentrations of Glu in the plasma/serum of patients with SCZ who had been treated with antipsychotics[54,55]. Atypical antipsychotic drugs are believed to increase plasma Glu levels in SCZ patients, but the mechanism by which they regulate Glu levels is not yet clear. In this study, patients in the disease group presented significantly increased Glu levels after treatment with olanzapine, which is consistent with previous research. Normally, metabolic abnormalities in the body can disturb the metabolism of alanine, aspartate, and Glu. This metabolic pathway analysis revealed that the alanine, aspartate, and Glu metabolic pathways were among the main metabolic pathways regulated by olanzapine and that Glu and Gln were important metabolites. There is relatively sufficient evidence to suggest that Glu receptor gene polymorphisms are associated with SCZ. Metabolic Glu receptors are widely distributed on the presynaptic or postsynaptic membranes of neurons and glial cells. Research has shown that mGluR3 can reduce the release of excitatory neurotransmitters and the production of inhibitory amino acids, monoamines, and neuropeptides. By regulating the activity of Ca²⁺ and K⁺ ion channels, mGluR3 affects Ca²⁺ ion influx and regulates the efficacy of presynaptic membranes[56]. Olanzapine is an atypical antipsychotic medication with multiple receptor effects. In addition to its strong antagonistic effect on dopamine receptors, it can selectively act on mGluR3 and effectively bind to some sites on the receptor, thereby enhancing its regulation of neurotransmitters[57] and reducing the release of excitatory neurotransmitters and violent aggression in SCZ patients. Research has confirmed that the cortical limbic Glu gamma aminobutyric acid pathway in patients with SCZ is functionally impaired. When it decreases to a certain extent, the dopamine D2 receptor function in the limbic system becomes disinhibited and hyperactive, resulting in positive symptoms such as hallucinations, delusions, thinking disorders, and stereotyped symptoms[58]. The glutamatergic effect of olanzapine can enhance the cortical limbic Glu gamma aminobutyric acid pathway, inhibit the D2 receptor function in the limbic system, treat positive symptoms, and reduce violent aggressive behavior[59].

Arginine is a versatile alkaline amino acid with numerous bioactive metabolites that have neuroprotective and cognitive effects. Its mechanism may be similar to that of Glu, and it is mediated by the activation of NMDA receptors. Research has shown that arginine can serve as a biological marker for predicting or evaluating cognitive function in SCZ patients. Impulsive aggressive behavior has long been associated with cognitive impairment in patients with mental disorders. In this study, there was no significant difference in the level of arginine between the two groups before treatment, which was similar to the results of Liu et al[60]. Metabolic pathway analysis revealed that the arginine biosynthesis metabolic pathway was another critical metabolic pathway involved in the treatment of highly aggressive SCZ with olanzapine. On the one hand, the activation of NMDA receptors by Glu results in calcium influx into the cell, which binds to calmodulin and stimulates the neuronal nitric oxide synthase (nNOS) enzyme to produce NO in the nervous system. NO activates guanylate cyclase, which increases the levels of the second messenger cyclic guanosine monophosphate (cyclic GMP). This NMDA-NO-cyclic GMP pathway has been demonstrated to modulate the release of neurotransmitters such as Glu and dopamine and has been repeatedly implicated in SCZ[61]. On the other hand, NO has been shown to influence the release of neurotransmitters, learning, memory, and neurodevelopment. Previous investigations have indicated that NO upregulation augments memory and learning tasks and that mental focus and acuity are improved through increases in NO levels. Improved NO levels potentially enhance blood flow to the brain, resulting in increased glucose uptake[62]. The decreased synthesis of NO in the brain has also been discussed as a pathogenic factor of mental disorders. nNOS deficiency in patients with SCZ has been shown to result in cognitive and behavioral disturbances[63]. A meta-analysis by Maia-de-Oliveira et al[64] revealed that the level of NO does not differ significantly between SCZ patients and healthy controls. However, when they examined only the five studies with patients on antipsychotic treatment, a significant difference between patients and healthy volunteers was found, showing that patients taking antipsychotics had higher levels of plasma NO than controls did[64]. Owing to limitations in conditions, the NO level was not measured in this study. However, arginine is a substrate for the endogenous synthesis of NO. After treatment, the arginine level in the disease group significantly increased. Therefore, it is speculated that olanzapine can act on the arginine-NO pathway, generate NO under the catalysis of NO synthase, improve patients' cognitive impairment, and reduce violent aggression in patients with SCZ.

This study constructed a network diagram of "olanzapine metabolite disease targets". Through network topology analysis, it was found that the degree values of the AKT1, IL6, BCL2, MAPK3, GSK3B, and MTOR core targets were much greater than those of the other targets and were important key nodes. These genes are enriched in the PI3K/Akt signaling pathway, suggesting that olanzapine may activate the PI3K/Akt pathway to treat SCZ and exert neuroprotective effects[65]. The PI3K/Akt signaling pathway is the main signaling pathway that regulates cellular transcription, translation, metabolism, proliferation, migration, and survival. It plays a crucial role in many pathophysiological activities, especially neuroprotection. Overall, these effects improve cell replication and survival ability and weaken growth arrest and apoptosis. Research has shown that PI3K/Akt is involved in the downstream signaling of multiple SCZ-related genes, such as dopamine and serotonin, that mediate the onset and progression of SCZ. In the PI3K/Akt signaling pathway, PI3K activation can activate AKT, further activating mTOR[66]. The mammalian target protein of mTOR is an important downstream signaling factor of PI3K that participates in and regulates intracellular protein synthesis and plays an important role in regulating neuronal morphology and synaptic plasticity[67,68]. In this metabolomics study, olanzapine increased peripheral blood Glu levels in patients with SCZ, and the binding of Glu to NR2B receptors can activate the PI3K/Akt/mTOR signaling pathway. Olanzapine regulates protein and mRNA expression levels in the PI3K/Akt signaling pathway by increasing mTOR protein and mRNA expression levels and reducing phosphorylation product expression levels. Delaying the aging of brain nerve cells, protecting brain tissue cells, improving cognitive levels, and alleviating symptoms of SCZ. In addition, PI3K can phosphorylate and activate Akt via various upstream signaling factors, acting on the Ser-9 position of the downstream protein GSK-3β, thereby inhibiting GSK-3 activity and reducing its overexpression, reversing symptoms of mental excitement, regulating the balance between proinflammatory and anti-inflammatory factors, improving neuronal damage caused by neuroinflammation and oxidative stress, and playing a crucial role in the occurrence, development, and treatment of SCZ[69]. Brain-derived neurotrophic factor is activated by NMDA receptors and transports postsynaptic density protein-95 (PSD-95) to dendrites through the PI3K/Akt signaling pathway, regulating dendritic size and shape and synaptic plasticity[70]. Olanzapine can inhibit the downregulation of the Akt/GSK-3β signaling pathway induced by phencyclidine (PCP), an NMDA receptor antagonist, reducing PCP-induced downregulation of synaptophysin and PSD-95 expression, as well as neuronal damage such as shortening of neurite length and a reduction in the number of neurites and branches[71]. This study further confirmed the neuroprotective effect of olanzapine through the PI3K/Akt signaling pathway.

In addition, changes in stress hormone levels in SCZ patients were also observed in this study. Previous studies have suggested that aggressive behavior in SCZ patients is closely related to the serum cortisol level[72]. Raine's low arousal theory suggests that patients with lower basal cortisol levels are not afraid of punishment for bad behavior, making them more likely to engage in aggressive behavior[73]. However, it is generally believed that the more severe the mental symptoms are, the higher the serum cortisol level is[74]. A meta-analysis by Hubbard and Miller[75] revealed that, compared with control patients, first-episode psychosis patients had significantly elevated blood cortisol levels, with a small to moderate effect[75], which was consistent with our findings. Olanzapine affects the steroid hormone biosynthesis and cortisol metabolic pathways, reduces cortisol levels in patients, and improves violent aggressive behavior in SCZ patients. However, considering the following limitations, the results and conclusions of this study should be interpreted with caution. One limitation was that the sample size of each group was relatively small, which may have prevented the detection of some DMs. Considering the inaccuracy of diagnostic analysis with small sample sizes, these analyses were not conducted in this study to avoid misleading information. Second, all recruited participants were of the same race and from the same location, and bias based on race and specific location cannot be ruled out. Furthermore, because of the inherent limitations of using metabolomics in psychiatric research, the results may be influenced by various confounding factors such as exercise, diet, circadian rhythm, and disease course. Finally, this study included only SCZ patients with a moderate to high risk of violent aggression as the disease group and did not include SCZ patients with no violent aggressive behavior as controls. Therefore, multicenter and large-scale controlled experiments are needed to validate our findings.

CONCLUSION

In summary, this study revealed that olanzapine improved disrupted amino acid metabolism, energy metabolism, neurotransmitter metabolism, and endocrine hormone metabolism in SCZ patients at moderate to high risk of violent attacks by regulating their metabolites and metabolic pathways, reflecting its multitarget and multipathway therapeutic effects. The alanine, aspartate, and Glu metabolism pathways, as well as the biosynthetic metabolic pathway of arginine, are critical pathways involved in the treatment of highly aggressive SCZ with olanzapine. These findings provide a reference for studying the mechanism of olanzapine in the treatment of patients with aggressive SCZ.

ACKNOWLEDGEMENTS

I would like to thank all the medical staff of the Department of Pharmacy in The Affiliated Encephalopathy Hospital of Zhengzhou University, Zhumadian First People's Hospital and The First Affiliated Hospital of Zhengzhou University for providing convenient conditions for implementing this study.