Skip to main content
Download PDF
Download ePub

Relationship of metabolites and metabolic ratios with schizophrenia: a mendelian randomization study

Annals of General Psychiatry volume 23, Article number: 34 (2024) Cite this article

Abstract

Background

This study aims to investigate the causal relationship of human plasma metabolites and metabolic ratios with schizophrenia (SCZ).

Methods

We employed Mendelian Randomization (MR) approach to comprehensively analyze two large-scale metabolomics and schizophrenia Genome-Wide Association Study (GWAS) datasets, incorporating a total of 1091 metabolites and 309 metabolic ratios, with 52017 schizophrenia patients and 75889 healthy controls. The inverse variance-weighted (IVW) method was utilized to estimate the causal relationship between exposure and outcome. To provide a more comprehensive evaluation, additional Mendelian Randomization (MR) approaches were employed, including MR-Egger regression, weighted median, simple mode, and weighted mode methods. These analyses assessed the causal effects between blood metabolites, metabolic ratios, and schizophrenia. Tests for pleiotropy and heterogeneity were conducted. False Discovery Rate (FDR) correction was applied to account for multiple comparisons and heterogeneity, ensuring the robustness and reliability of our findings. Consistent with previous studies, an FDR threshold of < 0.2 was considered suggestive of a causal relationship, while an FDR of < 0.05 was considered to indicate a significant causal relationship.

Results

The final results revealed that a significant causal association was found between the levels of two metabolites and schizophrenia, Alliin (OR = 0.915, 95%CI = 0.879–0.953, P = 1.93 × 10− 5, FDR = 0.013) was associated with a decreased risk of schizophrenia, N-actylcitrulline (OR = 1.058, 95%CI = 1.034–1.083, P = 1.4 × 10− 6, FDR = 0.002) was associated with increased risk of schizophrenia. When adjusting FDR to 0.2, the results showed that 4 metabolite levels and 2 metabolite ratios were suggestively causally associated with a reduced risk of schizophrenia including 2-aminooctanoate (OR = 0.904, 95%CI = 0.847–0.964, P = 0.002, FDR = 0.160), N-lactoylvaline (OR = 0.853, 95%CI = 0.775–0.938, P = 0.001,FDR = 0.122), X − 21310 (OR = 0.917, 95%CI = 0.866–0.971, P = 0.003,FDR = 0.195), X − 26111 (OR = 0.932, 95%CI = 0.890–0.976, P = 0.003,FDR = 0.189), Arachidonate (20:4n6) to oleate to vaccenate (18:1) ratio (OR = 0.945, 95%CI = 0.914–0.977, P = 8.2 × 10− 4, FDR = 0.104), and Citrulline to ornithine ratio (OR = 0.924, 95%CI = 0.881–0.969, P = 0.001, FDR = 0.122), while 4 metabolite levels and 2 metabolite ratios were suggestively causally associated with an increased risk of schizophrenia including N2, N5-diacetylornithine (OR = 1.090, 95%CI = 1.031–1.153, P = 0.003, FDR = 0.185), N − acetyl − 2−aminooctanoate (OR = 1.069, 95%CI=(1.027–1.114, P = 0.001, FDR = 0.127), N − acetyl − 2−aminoadipate (OR = 1.081, 95%CI = 1.030–1.133, P = 0.001, FDR = 0.128), X − 13844 (OR = 1.110, 95%CI = 1.036–1.190, P = 0.003, FDR = 0.196), X − 24556 (OR = 1.083, 95%CI = 1.036–1.132, P = 4.5 × 10− 4, FDR = 0.098), X − 24736 (OR = 1.065, 95%CI = 1.028–1.104, P = 5.6 × 10− 4, FDR = 0.098), N − acetylasparagine (OR = 1.048, 95%CI = 1.021–1.075, P = 4.5 × 10− 4, FDR = 0.098), N − acetylarginine (OR = 1.060, 95%CI = 1.028–1.092, P = 1.8 × 10− 4, FDR = 0.083), Cysteine to alanine ratio (OR = 1.086, 95%CI = 1.036–1.138, P = 6.5 × 10− 4, FDR = 0.101), and Benzoate to linoleoyl − arachidonoyl − glycerol (18:2 to 20:4) ratio (OR = 1.070, 95%CI = 1.025–1.117, P = 0.002, FDR = 0.158).

Conclusion

Our study results provide valuable insights for identifying diagnostic biomarkers related to schizophrenia and offer preliminary research findings for further exploration of the mechanisms linking schizophrenia and metabolism.

Introduction

Schizophrenia (SCZ) is a severe mental disorder, with a lifetime prevalence of approximately 1% and a suicide rate of around 5% among patients [1]. The symptoms of SCZ are typically divided into positive symptoms (such as hallucinations and delusions) and negative symptoms (such as social dysfunction and emotional withdrawal) [2]. Additionally, some patients may experience depressive moods and cognitive impairments [3, 4]. Patients with SCZ typically have a lifelong illness, and even those with well-controlled symptoms face adverse outcomes such as social isolation, stigma, and increased unemployment rates [5]. In consequence, treatment for SCZ is particularly important. Currently, antipsychotic medication remains the preferred method, while non-pharmacological therapies such as psychological interventions have relatively minor effects on symptoms [1]. Currently, there are still many issues with antipsychotic drugs in clinical practice [6], so early diagnosis and treatment of SCZ may help control symptoms and improve prognosis [7]. Nevertheless, the diagnosis of SCZ still relies on subjective interpretation of symptoms and social functioning, without an objective biological marker as a diagnostic standard [8]. Therefore, this study makes the link between biomarkers and treatment of SCZ clearer.

There are some studies on SCZ and metabolites to prove that there is an important link between the two. One study showed that serum samples of patients with SCZ had elevated levels of branched chain amino acids, phenylalanine and tyrosine, proline, glutamic, lactic acid, and pyruvic acids compared with those of patients with other psychiatric disorders. This study may potentially provide assistance in the diagnosis of SCZ [9]. Another retrospective systematic study showed that some metabolites may be involved in the pathogenesis of SCZ. The increased levels of peroxide metabolites, arachidonic acid and pyruvate in patients with SCZ [8]. may be related to oxidative stress, PUFA metabolism and glucose metabolism malfunction in SCZ. The decrease of phospholipid level is closely related to the membrane phospholipid hypothesis. However, these studies appear to have relatively small sample sizes and appear to lack evidence of relevance to the wider population.

Mendelian randomization (MR) uses genetic variation as an instrumental variable to infer whether risk factors causally influence health outcomes and has been used to combine summary data from genome-wide association studies (GWAS) involving a large number of genetic variations [10]. MR studies use single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) based on the assumption that the SNP is associated with the risk factor but not with the disease or confounding factors [11]. In recent years, several MR studies have been conducted in the field of mental illness, and some progress has been made [12]. Jia et al. [13]. investigated the association between 92 blood metabolites and the risk of various mental illnesses, finding associations between eight metabolites and psychiatric disorders. However, the study results seem to be limited in their relevance to SCZ due to constraints related to the types of metabolites and the specific types of disorders. Yang et al. [14] further investigated the relationship between 486 metabolites and psychiatric disorders using MR methods, identifying 22 metabolites with potential causal associations with SCZ. In contrast, the present study utilized new metabolite GWAS data based on previous studies to investigate the association of a wider range of metabolites (1091) with schizophrenia. Additionally, for the first time,301 metabolite ratios were included in the study.This study aims to determine the causal relationship of human blood metabolites and metabolic ratios with SCZ through comprehensive two-sample MR analysis.

Materials and methods

GWAS data sources and IV selection

In this study, we consider metabolites and metabolic ratios as the exposure factors, and disease (SCZ) as the outcome factor, and follow the two-sample Mendelian randomization (2SMR) method (Fig. 1).

Fig. 1
figure 1

Flowchart of Mendelian randomization study

Full size image

Summary statistics for each metabolite were obtained from the GWAS Catalog (accession numbers from GCST90199621 to GCST90201020) [15]. The data comes from Cheng et al.and is published in Nat.Genet in 2023.01, in the largest human metabolomics GWAS study to date, comprising 1,091 metabolites and 309 metabolite ratios in 8,299 individuals from the Canadian Longitudinal Study on Aging (CLSA) cohort. These metabolites were detected by biomarker analysis in biological samples, such as blood and urine, using a variety of assays including mass spectrometry, nuclear magnetic resonance, immunoassay and liquid chromatography. A total of 248 loci associated with 690 metabolites and 69 loci associated with 143 metabolite ratios were identified. After integrating metabolic genes and gene expression information, 94 effector genes were identified, involving 109 metabolites and 48 metabolite proportions. Of the 1,091 plasma metabolites tested, 850 had known properties in eight super pathways (i.e., lipids, amino acids, xenobiotics, nucleotides, cofactors and vitamins, carbohydrates, peptides and energy). The remaining 241 molecules were classified as unknown or “partially” characteristic molecules. The current study included 81 metabolites, which had not been detected in previously representative large metabolomic GWAS [16,17,18,19,20]. These metabolites may include previously unnamed metabolites. Detailed information on this GWAS cohort (mean age, BMI, sex) and metabolites compared with previously published metabolite GWAS data is available in Supplementary Table 1.

Based on a large number of recent studies of the same type [21,22,23,24] the significance level of IVs for each metabolite was set to 1 × 10− 5. For the inverse MR analysis, the IV significance threshold for schizophrenia was defined as 5 × 10− 8and linkage disequilibrium (LD) was mainly determined by R2 = 0.001 and distance (kb = 10000) [25]. Further, F values were calculated, and only those with F > 10 were retained as instrumental variables (IV) [26]. Based on these criteria, we conducted the screening using R software (version 4.3.2).

Recently, Trubetskoy et al. [27] conducted a comprehensive analysis of the GWAS data from the Psychiatric Genomics Consortium (PGC). This study utilized GWAS data from a cohort of 127,906 participants of European ((Ncase = 52,017 Ncontrol = 75,889) from their dataset. The original data can be downloaded from the IEU Open GWAS project at https://gwas.mrcieu.ac.uk/ (ID: ieu-b-5102). Detailed information on study design, phenotype definition, quality control, and genetic data estimation has been previously described in a prior study by Trubetskoy et al. [27].

Mendelian randomization analysis

MR analyses were performed using the 2SMR approach with the “TwoSampleMR” (version 0.5.7) package of R software 4.3.2. The analysis was performed using the IVW, MR-Egger, simple mode, weighted median, and weighted mode methods. The inverse variance weighting (IVW) method is currently the most important method in two-sample MR analysis, providing an overall estimate of the causal effect [28]. The MR-Egger regression method is mainly used to address horizontal pleiotropy, with an intercept < 0.05 being considered as evidence of horizontal pleiotropy [29]. Simple mode, weighted median, and weighted mode are used to assess the stability of MR results [30]. This study primarily uses the IVW method to infer the causal relationship between metabolites and SCZ, with other methods serving as supplementary analyses to the IVW method. The obtained results are subjected to FDR correction to ensure stability, including: (1) IVW with P < 0.05 and PFDR<0.2, (2) Consistency in the direction of the MR analysis results (β values) from the three methods, and (3) MR-Egger with P > 0.05 to eliminate pleiotropy of each IV. Finally, the effect of individual SNPs on the results was analyzed using the leave-one-out method of analysis.

Results

Information on instrumental variables (SNP)

After screening 1400 metabolites or ratio of all SNPs, a total of 34,843 SNPs were found to meet the IV selection criteria (Supplementary Table 2) with R2 = 0.001 and distance (kb = 10000), F > 10. These SNPs were then merged with the GWAS data of SCZ, resulting in a final inclusion of 30,184 SNPs for subsequent analysis (Supplementary Table 3).

MR analyses results

We selected the IVW, MR-Egger, Simple mode, Weighted median, and Weighted mode methods for two-sample MR analysis and visualized the results. Results with p > 0.05 for all five methods were excluded. The final results are shown in Fig. 2 (complete results can be found in Supplementary Table 4).

Fig. 2
figure 2

Causal relationship results of blood metabolites and metabolic ratios with SCZ

Full size image

To obtain more accurate and effective results, we subsequently performed FDR correction. Data meeting the following three criteria were retained, with the IVW method as the main analytical method: (1) IVW P < 0.05, FDR < 0.2 is considered suggestive of a causal relationship, while FDR < 0.05 is considered to indicate a significant causal relationship, (2) Consistency in OR values across all methods (either all greater than 1 or all less than 1, Figs. 3), (3) Elimination of heterogeneity in each IV with MR-Egger P > 0.05.

Fig. 3
figure 3

Scatter plot of the genetic correlation between metabolites, metabolic ratios, and SCZ using different MR methods

Full size image

The final 2 metabolites were found to be significantly associated with SCZ in a causal relationship. And 16 metabolites and ratios were found to be suggestively associated with SCZ in a causal relationship.

Among these 18 metabolites and ratios, the levels of 5 metabolites and 2 metabolite ratios were found to be associated with a decreased risk of schizophrenia (SCZ) per 1-SD increase in the genetically determined Alliin (OR = 0.915, 95%CI = 0.879–0.953, P = 1.93 × 10− 5, FDR = 0.013), 2-aminooctanoate (OR = 0.904, 95%CI = 0.847–0.964, P = 0.002, FDR = 0.160), N-lactoylvaline (OR = 0.853, 95%CI = 0.775–0.938, P = 0.001,FDR = 0.122), X − 21,310 (OR = 0.917, 95%CI = 0.866–0.971, P = 0.003,FDR = 0.195), X − 26,111 (OR = 0.932, 95%CI = 0.890–0.976, P = 0.003,FDR = 0.189), Arachidonate (20:4n6) to oleate to vaccenate (18:1) ratio (OR = 0.945, 95%CI = 0.914–0.977, P = 8.2 × 10− 4, FDR = 0.104), and Citrulline to ornithine ratio (OR = 0.924, 95%CI = 0.881–0.969, P = 0.001, FDR = 0.122).

On the other hand, the levels of 9 metabolites and 2 metabolite ratios were found to be associated with an increased risk of SCZ per 1-SD increase in the genetically determined N-actylcitrulline (OR = 1.058, 95%CI = 1.034–1.083, P = 1.4 × 10− 6, FDR = 0.002), N2, N5-diacetylornithine (OR = 1.090, 95%CI = 1.031–1.153, P = 0.003, FDR = 0.185), N − acetyl − 2−aminooctanoate (OR = 1.069, 95%CI=(1.027–1.114, P = 0.001, FDR = 0.127), N − acetyl − 2−aminoadipate (OR = 1.081, 95%CI = 1.030–1.133, P = 0.001, FDR = 0.128), X − 13844 (OR = 1.110, 95%CI = 1.036–1.190, P = 0.003, FDR = 0.196), X − 24556 (OR = 1.083, 95%CI = 1.036–1.132, P = 4.5 × 10− 4, FDR = 0.098), X − 24736 (OR = 1.065, 95%CI = 1.028–1.104, P = 5.6 × 10− 4, FDR = 0.098), N − acetylasparagine (OR = 1.048, 95%CI = 1.021–1.075, P = 4.5 × 10− 4, FDR = 0.098), N − acetylarginine (OR = 1.060, 95%CI = 1.028–1.092, P = 1.8 × 10− 4, FDR = 0.083), Cysteine to alanine ratio (OR = 1.086, 95%CI = 1.036–1.138, P = 6.5 × 10− 4, FDR = 0.101), and Benzoate to linoleoyl − arachidonoyl − glycerol (18:2 to 20:4) ratio (OR = 1.070, 95%CI = 1.025–1.117, P = 0.002, FDR = 0.158). (Fig. 4 for details, full table available in Supplementary Table 5).

Fig. 4
figure 4

Results of Mendelian randomization analysis of the causal association between metabolites and metabolic ratios with SCZ using 5 methods

Full size image

We plotted scatter plots and funnel plots to illustrate the MR estimates of SCZ at different metabolite levels or ratios and analyzed the effect of individual SNPs on the results using leave-one-out sensitivity analysis, whereas the funnel plots showed minimal heterogeneity (Supplementary Fig. S1), and the results of the leave-one-out method indicated that there were no individual SNPs that significantly affected the results (Supplementary Fig. S2). In conclusion, our results are stable, reliable, and accurate.

Reverse analysis

A reverse Mendelian randomization analysis was conducted on the two metabolites that demonstrated significant causal associations with schizophrenia. In this analysis, 154 instrumental variables (IVs) (Supplementary Table 6) strongly associated with schizophrenia were identified based on previously established threshold criteria. The findings indicated no evidence of reverse causality between the two metabolites and schizophrenia (Fig. 5).

Fig. 5
figure 5

Results of reverse Mendelian randomization analysis

Full size image

Discussion

SCZ is considered a severe mental disorder and has received widespread attention. Diagnosing SCZ has long relied on subjective scales, with the widely used Diagnostic and Statistical Manual of Mental Disorders (DSM-5) being a common reference. However, unfortunately, a definitive biochemical marker for diagnosing SCZ has yet to be found. In recent years, a significant amount of research has been conducted by numerous researchers in an effort to identify such a marker. For example, Rodrigues et al. [31] conducted a retrospective study indicating that the current biomarkers for SCZ primarily fall into five main categories: (1) Neuroimaging biomarkers, (2) Genetic biomarkers, (3) Epigenetic biomarkers, (4) Protein biomarkers, and (5) Metabolic biomarkers. While these studies are abundant, the majority of them are observational. With the development of MR methods, a new approach has been provided for studying the causal relationships between SCZ and various exposure factors. There are already some MR studies for SCZ, and they have achieved promising results [12, 32, 33]. Currently, most of the research on the relationship between metabolite levels and schizophrenia is observational. For example, Koike et al. [34] observed a significant increase in free bile acids in the plasma of SCZ patients, and Liu et al. [35] identified 182 metabolites with significant differences in levels between SCZ patients and healthy controls using metabolomics methods. The finding of Jia et al. [13] found two metabolites that were causally related to SCZ. Yang et al. [14] further investigated the relationship between 486 metabolites and psychiatric disorders using MR methods, and found 22 metabolites that appeared to have a potential causal relationship with SCZ. However, a recent study by Lu et al. [36] revealed causal relationships between metabolite abundance and psychiatric disorders. Notably, the findings of both studies showed no overlap with the results of the present study. The GWAS data used by Yang et al. were derived from an earlier metabolite GWAS study that included only 486 metabolites, fewer than those examined in the current study. While Lu et al. used the same schizophrenia GWAS data as the present study, their MR analysis covered only around 600 metabolites. Building on these previous studies, the current research expands the scope by analyzing a larger set of 1091 metabolites and, for the first time, performing MR analysis on 309 metabolic ratios in relation to schizophrenia. These findings contribute further evidence for future research in this field.

Our study is based on two large-scale GWAS summary datasets, and we conducted rigorous two-sample MR analyses to assess blood metabolites and metabolic ratios. Our research suggests that there is a causal relationship of 14 metabolite levels and 4 metabolic ratios with schizophrenia. This includes 9 known metabolites and 5 unknown metabolites. Alliin is a non-protein amino acid and an important precursor for the synthesis of Allicin, a sulfur-containing compound derived from garlic. Numerous studies have demonstrated that Allicin is a neuroprotective agent [37, 38]. Neuroinflammation can be suppressed by inhibiting brain inflammation factors and the activation of microglial cells [39]. The vulnerability-stress-inflammation model for schizophrenia currently suggests that neuroinflammation is one of the potential causes of schizophrenia [40]. Numerous studies have indicated that inhibiting neuroinflammation and microglial cell activation can reverse symptoms of schizophrenia [41]. Therefore, Alliin may be associated with the inflammatory pathways of schizophrenia.

2-aminooctanoate is an alpha-amino acid. In a metabolomics study on depression, it was found to be negatively correlated with depression [42]. N-lactoyl valine is a N-acyl-amino acid and is widely used in diabetes research [43]. Interestingly, in a metabolomics study on diabetes and cognitive impairment, N-lactoyl valine was found to be associated with the cognitive impairment that accompanies diabetes [44]. As is well known, cognitive impairment has long been considered a key symptom of schizophrenia, which is consistent with our findings. N-acetylarginine is believed to be associated with oxidative stress in the brain [45, 46]. Research has shown that the cognitive impairment in schizophrenia is associated with increased oxidative stress. Levels of thio-barbituric acid reactive substances, a measure of oxidative stress, are elevated in the bodies of schizophrenia patients, and this elevation is significantly correlated with poor performance on working memory tests [47]. According to previous MR results, N-acetylarginine is negatively correlated with schizophrenia, which may differ from our findings [13]. This difference may be related to the criteria for IV selection and the FDR correction standards. N2, N5-Diacetylornithine is a metabolic by-product of the urea cycle, and there is currently limited research on its role. A related study shows its association with heart function [48]. Similarly, there are few reports on the roles of N-acetyl-2-aminooctanoate, N-acetyl-2-aminoadipate, N-acetylasparagine, and N-acetylcitrulline in psychiatric disorders.

As mentioned above, there is still no stable, reliable, and objective standard for the diagnosis of schizophrenia. In recent years, thanks to the development of metabolomics, an increasing number of studies have demonstrated the association between human metabolic compounds and schizophrenia [49,50,51]. Unfortunately, to date, we have yet to find a reliable physiological marker to serve as a diagnostic standard.

However, the emergence of MR has provided us with a new approach. Our study explores the causal relationship between metabolites, metabolic ratios, and schizophrenia from the perspective of genetic polymorphism. Compared to descriptive studies, our research results are more reliable. Our study is based on genetic exploration of the entire metabolome and its causal relationship with schizophrenia. To our knowledge, this is currently the study with the largest number of metabolites included in the research on the causal relationship of schizophrenia. After FDR correction, our study excluded pleiotropy and heterogeneity. Therefore, compared to observational studies, we have eliminated confounding factors, making our research results more reliable.

Our study has several limitations. Firstly, the population in this study consisted only of individuals of European. The results obtained may not be applicable to populations of Asian or African. Secondly, our study only explores the causal relationship between the metabolome and schizophrenia, without further investigation into the underlying mechanisms. Additionally, because schizophrenia is a mental illness significantly associated with brain function, it may have minimal impact on plasma. Therefore, in future studies, it would be important to collect more cerebrospinal fluid or brain tissue samples from individuals with schizophrenia to obtain more data from within the brain. Lastly, the results of MR depend on sample size and enlarging the sample size would make the results more stable and reliable.

Overall, our study is the most comprehensive MR study to date, incorporating a total of 1091 metabolites and 309 metabolic ratios, investigating their impact on schizophrenia, ultimately identifying 14 metabolites and 4 metabolic ratios with a causal link to schizophrenia. Our study analyzes from the perspective of genetic genes and metabolomics, providing strong evidence for the search for diagnostic biomarkers for schizophrenia and offering preliminary evidence for further research on metabolomics and schizophrenia. In consequence, our results are stable and reliable.

Conclusion

Our study has identified the relationship between blood metabolites and metabolic ratios based on genetic polymorphisms with SCZ. Our research provides a potential discovery for diagnostic biomarkers of SCZ. Subsequently, further research is needed to assess the universality of the results and to explore the underlying mechanisms between metabolites and SCZ.

Data availability

The datasets generated and analysed during the current study are available in the GWAS Catalog (accession numbers from GCST90199621 to GCST90201020) and EU Open GWAS project at https://gwas.mrcieu.ac.uk/ (ID: ieu-b-5102).

Abbreviations

SCZ:

Schizophrenia

MR:

Mendelian randomization

GWAS:

genome-wide association study

PGC:

Psychiatric Genomics Consortium

CLDS:

Canadian Longitudinal Study

IV:

Instrumental Variable

IVW:

Inverse variance weighted

References

  1. Jauhar S, Johnstone M, McKenna PJ, Schizophrenia. Lancet. 2022;399(10323):473–86.

    Article  CAS  PubMed  Google Scholar 

  2. McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia—An overview. JAMA Psychiatry. 2020;77(2):201–10.

    Article  PubMed  Google Scholar 

  3. Blum LH, Vakhrusheva J, Saperstein A, Khan S, Chang RW, Hansen MC, et al. Depressed mood in individuals with schizophrenia: a comparison of retrospective and real-time measures. Psychiatry Res. 2015;227(2):318–23.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lise Docx MS, Sabbe B, Fransen MDPDE, Bervoets MSPDC, Hulstijn MDW, Maarten MSPD, Van Den Bossche JA. Longitudinal evaluation of the Psychomotor Syndrome in Schizophrenia. J Neuropsychiatry Clin Neurosci. 2014;26(4):359–68.

    Article  PubMed  Google Scholar 

  5. Marwaha S, Johnson S, Bebbington P, Stafford M, Angermeyer MC, Brugha T, et al. Rates and correlates of employment in people with schizophrenia in the UK, France and Germany. Br J Psychiatry. 2007;191(1):30–7.

    Article  PubMed  Google Scholar 

  6. Leucht S, Corves C, Arbter D, Engel RR, Li C, Davis JM. Second-generation versus first-generation antipsychotic drugs for schizophrenia: a meta-analysis. Lancet. 2009;373(9657):31–41.

    Article  CAS  PubMed  Google Scholar 

  7. Lieberman JA, Small SA, Girgis RR. Early detection and preventive intervention in Schizophrenia: from Fantasy to reality. Am J Psychiatry. 2019;176(10):794–810.

    Article  PubMed  Google Scholar 

  8. Davison J, O’Gorman A, Brennan L, Cotter DR. A systematic review of metabolite biomarkers of schizophrenia. Schizophr Res. 2018;195:32–50.

    Article  PubMed  Google Scholar 

  9. Orešič M, Tang J, Seppänen-Laakso T, Mattila I, Saarni SE, Saarni SI, et al. Metabolome in schizophrenia and other psychotic disorders: a general population-based study. Genome Med. 2011;3(3):19.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bowden J, Holmes MV. Meta-analysis and mendelian randomization: a review. Res Synth Methods. 2019;10(4):486–96.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Boehm FJ, Zhou X. Statistical methods for mendelian randomization in genome-wide association studies: a review. Comput Struct Biotechnol J. 2022;20:2338–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang C, Zhu D, Zhang D, Zuo X, Yao L, Liu T, et al. Causal role of immune cells in schizophrenia: mendelian randomization (MR) study. BMC Psychiatry. 2023;23(1):590.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Jia Y, Hui L, Sun L, Guo D, Shi M, Zhang K, et al. Association between Human Blood Metabolome and the risk of Psychiatric disorders. Schizophr Bull. 2023;49(2):428–43.

    Article  PubMed  Google Scholar 

  14. Yang J, Yan B, Zhao B, Fan Y, He X, Yang L, et al. Assessing the Causal effects of human serum metabolites on 5 Major Psychiatric disorders. Schizophr Bull. 2020;46(4):804–13.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet. 2023;55(1):44–53.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Long T, Hicks M, Yu H-C, Biggs WH, Kirkness EF, Menni C, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet. 2017;49(4):568–78.

    Article  CAS  PubMed  Google Scholar 

  17. Lotta LA, Pietzner M, Stewart ID, Wittemans LBL, Li C, Bonelli R, et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat Genet. 2021;53(1):54–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yin X, Chan LS, Bose D, Jackson AU, VandeHaar P, Locke AE, et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun. 2022;13(1):1644.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hysi PG, Mangino M, Christofidou P, Falchi M, Karoly ED, Investigators NB, et al. Metabolome Genome-Wide Association Study identifies 74 novel genomic regions influencing plasma metabolites levels. Metabolites. 2022;12(1):61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Dong T, Wang X, Jia Z, Yang J, Liu Y. Assessing the associations of 1,400 blood metabolites with major depressive disorder: a mendelian randomization study. Front Psychiatry. 2024;15:1391535.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Li Q, Wei Z, Zhang Y, Zheng C. Causal role of metabolites in non-small cell lung cancer: mendelian randomization (MR) study. PLoS ONE. 2024;19(3):e0300904.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ling K, Hong M, Jin L, Wang J. Blood metabolomic and postpartum depression: a mendelian randomization study. BMC Pregnancy Childbirth. 2024;24(1):429.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Tang W, Jiang H, Wu X, Wu G, Zhao C, Lin W, et al. Metabolomic insights into pulmonary fibrosis: a mendelian randomization study. BMC Pulm Med. 2024;24(1):271.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Maciejewski ML, Dowd BE, Norton EC. Instrumental variables and Heterogeneous Treatment effects. JAMA. 2022;327(12):1177–8.

    Article  PubMed  Google Scholar 

  27. Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Gkatzionis A, Burgess S, Newcombe PJ. Statistical methods for cis-mendelian randomization with two-sample summary-level data. Genet Epidemiol. 2023;47(1):3–25.

    Article  CAS  PubMed  Google Scholar 

  30. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Rodrigues-Amorim D, Rivera-Baltanás T, López M, Spuch C, Olivares JM, Agís-Balboa RC. Schizophrenia: a review of potential biomarkers. J Psychiatr Res. 2017;93:37–49.

  32. Dang X, Song M, Lv L, Yang Y, Luo X-J. Proteome-wide mendelian randomization reveals the causal effects of immune-related plasma proteins on psychiatric disorders. Hum Genet. 2023;142(6):809–18.

    Article  CAS  PubMed  Google Scholar 

  33. Yang Z, Li D, He Y, Chen X, Li Z. Unrevealing the shared genetic mechanisms underlying C-reactive protein and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2023;126:110785.

    Article  CAS  PubMed  Google Scholar 

  34. Koike S, Miyaji Y, Suzuki K, Miyashita M, Itokawa M, Arai M, et al. Plasma unconjugated bile acids as novel biomarker for schizophrenia. Biochem Biophys Res Commun. 2022;634:70–4.

    Article  CAS  PubMed  Google Scholar 

  35. Liu Y, Song X, Liu X, Pu J, Gui S, Xu S, et al. Alteration of lipids and amino acids in plasma distinguish schizophrenia patients from controls: a targeted metabolomics study. J Neuropsychiatry Clin Neurosci. 2021;75(4):138–44.

    CAS  Google Scholar 

  36. Lu T, Chen Y, Yoshiji S, Ilboudo Y, Forgetta V, Zhou S, et al. Circulating metabolite abundances associated with risks of bipolar disorder, schizophrenia, and depression: a mendelian randomization study. Biol Psychiatry. 2024.

  37. Salehi B, Zucca P, Orhan IE, Azzini E, Adetunji CO, Mohammed SA, et al. Allicin and health: a comprehensive review. Trends Food Sci Technol. 2019;86:502–16.

    Article  CAS  Google Scholar 

  38. Kong X, Gong S, Su L, Li C, Kong Y. Neuroprotective effects of allicin on ischemia-reperfusion brain injury. Oncotarget. 2017;8(61):104492–507.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Nadeem MS, Kazmi I, Ullah I, Muhammad K, Anwar F. Allicin, an antioxidant and neuroprotective Agent, ameliorates cognitive impairment. Antioxidants. 2022;11(1):87.

    Article  CAS  Google Scholar 

  40. Müller N. Inflammation in Schizophrenia: pathogenetic aspects and therapeutic considerations. Schizophr Bull. 2018;44(5):973–82.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zhu H, Guan A, Liu J, Peng L, Zhang Z, Wang S. Noteworthy perspectives on microglia in neuropsychiatric disorders. J Neuroinflamm. 2023;20(1):223.

    Article  Google Scholar 

  42. van der Spek A, Stewart ID, Kühnel B, Pietzner M, Alshehri T, Gauß F et al. Circulating metabolites modulated by diet are associated with depression. Mol Psychiatry. 2023;28(9)3874–3887.

  43. Fernandes Silva L, Hokkanen J, Vangipurapu J, Oravilahti A, Laakso M. Metabolites as risk factors for Diabetic Retinopathy in patients with type 2 diabetes: a 12-Year follow-up study. The Journal of Clinical Endocrinology & Metabolism; 2023.

  44. Song L, Zhuang P, Lin M, Kang M, Liu H, Zhang Y, et al. Urine Metabonomics reveals early biomarkers in Diabetic Cognitive Dysfunction. J Proteome Res. 2017;16(9):3180–9.

    Article  CAS  PubMed  Google Scholar 

  45. Terezinha de Souza Wyse A, Serra Bavaresco C, Elisabeth Kienzle Hagen M, Delwing D, Milton Duval Wannmacher C, Severo Dutra-Filho C, et al. In vitro stimulation of oxidative stress in cerebral cortex of rats by the guanidino compounds accumulating in hyperargininemia. Brain Res. 2001;923(1):50–7.

    Article  Google Scholar 

  46. Sasso S, Dalmedico L, Delwing-Dal Magro D, Wyse ATS, Delwing-de Lima D. Effect of N-acetylarginine, a metabolite accumulated in hyperargininemia, on parameters of oxidative stress in rats: protective role of vitamins and L-NAME. Cell Biochem Funct. 2014;32(6):511–9.

    Article  CAS  PubMed  Google Scholar 

  47. Ansari Z, Pawar S, Seetharaman R. Neuroinflammation and oxidative stress in schizophrenia: are these opportunities for repurposing? Postgrad Med. 2022;134(2):187–99.

    Article  PubMed  Google Scholar 

  48. Razavi AC, Bazzano LA, He J, Fernandez C, Whelton SP, Krousel-Wood M, et al. Novel findings from a Metabolomics Study of Left ventricular diastolic function: the Bogalusa Heart Study. J Am Heart Assoc. 2020;9(3):e015118.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Zhou S, Huang Y, Kuang Q, Yan S, Li H, Wu K, et al. Kynurenine pathway metabolites are associated with gray matter volume in subjects with schizophrenia. Front Psychiatry. 2022;13:941479.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Jiang Y, Sun X, Hu M, Zhang L, Zhao N, Shen Y, et al. Plasma metabolomics of schizophrenia with cognitive impairment: a pilot study. Front Psychiatry. 2022;13:950602.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wang D, Sun X, Yan J, Ren B, Cao B, Lu Q, et al. Alterations of eicosanoids and related mediators in patients with schizophrenia. J Psychiatr Res. 2018;102:168–78.

    Article  PubMed  Google Scholar 

Download references

Funding

The composition of this manuscript was financially supported by funding from Macau Science and Technology Development fund(FDCT(0102-2023-AMJ,0023-2023-RIB1,0089/2021/A,0088/2021/A)) The funders had no role in the study design; collection, analysis, and interpretation of data; writing of the report; and decision to submit the article for publication.

Author information

Authors and Affiliations

  1. School of Pharmacy, Macau University of Science and Technology, Macau, 999078, China

    Na Zhou

  2. State Key Laboratory of Quality Research in Chinese Medicine, Macau, 999078, China

    Yu Huang, Jiayu Zheng & Na Zhou

  3. Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, 999078, China

    Yu Huang, Hanxuan Wang & Jiayu Zheng

Authors
  1. Yu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanxuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiayu Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Na Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yu Huang and Hanxuan Wang provided the concept and designed the study and conducted the analyses and wrote the manuscript. Jiayu Zheng participated in data collection and the analysis of the data. NaZhou revised and proof-read the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Na Zhou.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1: Supplementary Fig. S1

: Funnel plot for metabolites

Supplementary Material 2: Supplementary Fig. S2

: Leave-one-out analysis of SNPs

Supplementary Material 3: Supplementary Table 1

: Metabolite GWAS data Details

Supplementary Material 4: Supplementary Table 2

: Detailed information on all metabolite SNPs

Supplementary Material 5: Supplementary Table 3

: Detailed information on all metabolite SNPs finally used after screening

Supplementary Material 6: Supplementary Table 4

: Complete results of MR

Supplementary Material 7: Supplementary Table 5

: MR results after screening

Supplementary Material 8: Supplementary Table 6

: Detailed information on all SCZ SNPs finally used after screening

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Y., Wang, H., Zheng, J. et al. Relationship of metabolites and metabolic ratios with schizophrenia: a mendelian randomization study. Ann Gen Psychiatry 23, 34 (2024). https://doi.org/10.1186/s12991-024-00521-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12991-024-00521-1

Annals of General Psychiatry

ISSN: 1744-859X