Assessing the Causal Effects of Human Serum Metabolites on 5 Major Psychiatric Disorders

Blood metabolomic and postpartum depression: a mendelian randomization study

BACKGROUND

Postpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR).

METHODS

This study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study's clinical expert groups, which included leading experts in their respective medical fields.

RESULTS

The MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation.

CONCLUSIONS

This study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Causal effect of immune cells on idiopathic pulmonary fibrosis: A mendelian randomization study

BACKGROUND

A key component of idiopathic pulmonary fibrosis (IPF) is the involvement of immune cells. However, the causal interaction between different immune cell signatures and IPF remain inconclusive.

OBJECTIVES

Based on publicly accessible data, our study utilized the Mendelian randomization (MR) approach to determine the causative relevance of complex immune cell phenotypes in IPF.

METHODS

We deployed a two-sample Mendelian randomization approach to evaluate the causal interaction between immune cell markers and IPF. All data regarding 731 immune signatures and IPF were acquired from two genome-wide association studies (GWAS) that are accessible to the public. The original study adopted the inverse variance weighted (IVW) method, followed by sensitivity analyses aimed at eliminating heterogeneity and pleiotropy. Additionally, Multivariate Mendelian randomization (MVMR) was utilized to identify the independent risk factors in our study.

RESULTS

The summary dataset for IPF was accessed from the Finnish Genetic Consortium R9, comprising 2018 patients and 373,064 controls. And the dataset for immune signatures was conducted in 3,757 Sardinian individuals. By conducting IVW and extensive sensitivity analyses, univariate Mendelian randomization (UVMR) identified one immunophenotype that remained causally associated with IPF after false discovery rate (FDR) correction: CD39 on CD39+ CD8+T cells (odd ratio [OR] = 0.850, 95 % confidence interval [CI] = 0.787-0.918, P = 3.68 × 10-5). The causal association with IPF was further validated using MVMR.

CONCLUSIONS

Based on rigorous MR analysis methods and FDR correction, our study demonstrated that CD39 on CD39+ CD8+T cells showed a protective effect against IPF, providing effective insights for preventing and diagnosing pulmonary fibrosis.

Roles of blood metabolites in mediating the relationship between vitiligo and autoimmune diseases: Evidence from a Mendelian randomization study

OBJECTIVE

This study employed Mendelian Randomization (MR) to investigate the causal relationship between genetic susceptibility to vitiligo and the risk of various autoimmune diseases, along with the mediating role of blood metabolites.

METHODS

We performed two-sample MR analyses using aggregated genome-wide association studies (GWAS) data on 486 blood metabolites, vitiligo, and nine autoimmune diseases to investigate blood metabolites' causal effects on the susceptibility of vitiligo and the associations of vitiligo with nine autoimmune comorbidities. We also applied multivariable MR to unravel metabolites by which vitiligo influences the pathogenesis of autoimmune diseases.

RESULTS

Our findings indicate that vitiligo amplified the risk of several autoimmune diseases, including rheumatoid arthritis (OR 1.17; 95 % CI 1.08-1.27), psoriasis (OR 1.10; 95 % CI 1.04-1.17), type 1 diabetes (OR 1.41; 95 % CI 1.23-1.63), pernicious anemia (OR 1.23; 95 % CI 1.12-1.36), autoimmune hypothyroidism (OR 1.19; 95 % CI 1.11-1.26), alopecia areata (OR 1.22; 95 % CI 1.10-1.35), and autoimmune Addison's disease (OR 1.22; 95 % CI 1.12-1.33). Additionally, our analysis identified correlations with vitiligo for 14 known (nine risk, five protective) and seven uncharacterized serum metabolites. After adjusting for genetically predicted levels of histidine and pyruvate, the associations between vitiligo and these diseases were attenuated.

CONCLUSIONS

We substantiated vitiligo's influence on susceptibility to seven autoimmune diseases and conducted a thorough investigation of serum metabolites correlated with vitiligo. Histidine and pyruvate are potential mediators of vitiligo associated with autoimmune diseases.By combining metabolomics with genomics, we provide new perspectives on the etiology of vitiligo and its immune comorbidities.

Blood metabolites and chronic kidney disease: a Mendelian randomization study

BACKGROUND

Human blood metabolites have demonstrated close associations with chronic kidney disease (CKD) in observational studies. Nonetheless, the causal relationship between metabolites and CKD is still unclear. This study aimed to assess the associations between metabolites and CKD risk.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to evaluate relationships between 1400 blood metabolites and eight phenotypes (outcomes) (CKD, estimated glomerular filtration rate(eGFR), urine albumin to creatinine ratio, rapid progress to CKD, rapid decline of eGFR, membranous nephropathy, immunoglobulin A nephropathy, and diabetic nephropathy). The inverse variance weighted (IVW), MR-Egger, and weighted median were used to investigate the causal relationship. Sensitivity analyses were performed with Cochran's Q, MR-Egger intercept, MR-PRESSO Global test, and leave-one-out analysis. Bonferroni correction was used to test the strength of the causal relationship.

RESULTS

Through the MR analysis of 1400 metabolites and eight clinical phenotypes, a total of 48 metabolites were found to be associated with various outcomes. Among them, N-acetylleucine (OR = 0.923, 95%CI: 0.89-0.957, PIVW = 1.450 × 10-5) has a strong causal relationship with lower risk of CKD after the Bonferroni-corrected test, whereas Glycine to alanine ratio has a strong causal relationship with higher risk of CKD (OR = 1.106, 95%CI: 1.063-1.151, PIVW = 5.850 × 10-7). No horizontal pleiotropy and heterogeneity were detected.

CONCLUSION

Our study offers groundbreaking insights into the integration of metabolomics and genomics to reveal the pathogenesis of and therapeutic strategies for CKD. It underscores 48 metabolites as potential causal candidates, meriting further investigation.

Dissecting causal relationships between immune cells, plasma metabolites, and COPD: a mediating Mendelian randomization study

Objective

This study employed Mendelian Randomization (MR) to investigate the causal relationships among immune cells, COPD, and potential metabolic mediators.

Methods

Utilizing summary data from genome-wide association studies, we analyzed 731 immune cell phenotypes, 1,400 plasma metabolites, and COPD. Bidirectional MR analysis was conducted to explore the causal links between immune cells and COPD, complemented by two-step mediation analysis and multivariable MR to identify potential mediating metabolites.

Results

Causal relationships were identified between 41 immune cell phenotypes and COPD, with 6 exhibiting reverse causality. Additionally, 21 metabolites were causally related to COPD. Through two-step MR and multivariable MR analyses, 8 cell phenotypes were found to have causal relationships with COPD mediated by 8 plasma metabolites (including one unidentified), with 1-methylnicotinamide levels showing the highest mediation proportion at 26.4%.

Conclusion

We have identified causal relationships between 8 immune cell phenotypes and COPD, mediated by 8 metabolites. These findings contribute to the screening of individuals at high risk for COPD and offer insights into early prevention and the precocious diagnosis of Pre-COPD.

Circulating metabolites and coronary heart disease: a bidirectional Mendelian randomization

Background

Numerous studies have established a link between coronary heart disease and metabolic disorders. Yet, causal evidence connecting metabolites and Coronary Heart Disease (CHD) remains scarce. To address this, we performed a bidirectional Mendelian Randomization (MR) analysis investigating the causal relationship between blood metabolites and CHD.

Methods

Data were extracted from published genome-wide association studies (GWASs) on metabolite levels, focusing on 1,400 metabolite summary data as exposure measures. Primary analyses utilized the GWAS catalog database GCST90199698 (60,801 cases and 123,504 controls) and the FinnGen cohort (43,518 cases and 333,759 controls). The primary method used for causality analysis was random inverse variance weighting (IVW). Supplementary analyses included MR-Egger, weighted mode, and weighted median methods. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Reverse MR analysis was employed to evaluate the direct impact of metabolites on coronary heart disease. Additionally, replication and meta-analysis were performed. We further conducted the Steiger test and colocalization analysis to reflect the causality deeply.

Results

This study identified eight metabolites associated with lipids, amino acids and metabolite ratios that may influence CHD risk. Findings include: 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels: OR = 1.08; 95% CI 1.04-1.12; P = 8.21E-06; 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels: OR = 1.07; 95% CI 1.04-1.11; P = 9.01E-05; Linoleoyl-arachidonoyl-glycerol (18:2/20:4): OR = 1.08; 95% CI 1.04-1.22; P = 0.0001; Glycocholenate sulfate: OR = 0.93; 95% CI 0.90-0.97; P = 0.0002; 1-stearoyl-2-arachidonoyl-GPE (OR = 1.07; 95% CI 1.03-1.11; P = 0.0002); N-acetylasparagine (OR = 1.04; 95% CI 1.02-1.07; P = 0.0030); Octadecenedioate (C18:1-DC) (OR = 0.93; 95% CI 0.90-0.97; P = 0.0004); Phosphate to linoleoyl-arachidonoyl-glycerol (18:2-20:4) (1) ratio (OR = 0.92; 95% CI 0.88-0.97; P = 0.0005).

Conclusion

The integration of genomics and metabolomics offers novel insights into the pathogenesis of CHD and holds significant importance for the screening and prevention of CHD.

Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico

Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (β = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.

Causal relationships between blood metabolites and diabetic retinopathy: a two-sample Mendelian randomization study

Background

Diabetic retinopathy (DR) is a microvascular complication of diabetes, severely affecting patients' vision and even leading to blindness. The development of DR is influenced by metabolic disturbance and genetic factors, including gene polymorphisms. The research aimed to uncover the causal relationships between blood metabolites and DR.

Methods

The two-sample mendelian randomization (MR) analysis was employed to estimate the causality of blood metabolites on DR. The genetic variables for exposure were obtained from the genome-wide association study (GWAS) dataset of 486 blood metabolites, while the genetic predictors for outcomes including all-stage DR (All DR), non-proliferative DR (NPDR) and proliferative DR (PDR) were derived from the FinnGen database. The primary analysis employed inverse variance weighted (IVW) method, and supplementary analyses were performed using MR-Egger, weighted median (WM), simple mode and weighted mode methods. Additionally, MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were also conducted to guarantee the accuracy and robustness of the results. Subsequently, we replicated the MR analysis using three additional datasets from the FinnGen database and conducted a meta-analysis to determine blood metabolites associated with DR. Finally, reverse MR analysis and metabolic pathway analysis were performed.

Results

The study identified 13 blood metabolites associated with All DR, 9 blood metabolites associated with NPDR and 12 blood metabolites associated with PDR. In summary, a total of 21 blood metabolites were identified as having potential causal relationships with DR. Additionally, we identified 4 metabolic pathways that are related to DR.

Conclusion

The research revealed a number of blood metabolites and metabolic pathways that are causally associated with DR, which holds significant importance for screening and prevention of DR. However, it is noteworthy that these causal relationships should be validated in larger cohorts and experiments.

Association of genetically predicted 486 blood metabolites on the risk of Alzheimer's disease: a Mendelian randomization study

Background

Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention.

Methods

We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary two-sample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings.

Results

After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data.

Conclusion

By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD.

The enigmatic interplay of immune cells and abnormal spermatozoa through Mendelian randomization

PURPOSE

Abnormal spermatozoa significantly impact reproductive health, affecting fertility rates, potentially prolonging conception time, and increasing the risk of miscarriages. This study employs Mendelian randomization to explore their potential link with immune cells, aiming to reveal their potential causal association and wider implications for reproductive health.

METHODS

We conducted forward and reverse Mendelian randomization analyses to explore the potential causal connection between 731 immune cell signatures and abnormal spermatozoa. Using publicly available genetic data, we investigated various immune signatures such as median fluorescence intensities (MFI), relative cell (RC), absolute cell (AC), and morphological parameters (MP). Robustness was ensured through comprehensive sensitivity analyses assessing consistency, heterogeneity, and potential horizontal pleiotropy. The MR study produced a statistically significant p-value of .0000684, Bonferroni-corrected for the 731 exposures.

RESULTS

The Mendelian randomization analysis revealed strong indications of a reciprocal relationship between immune cell pathways and sperm integrity. When examining immune cell exposure, a potential causal link with abnormal sperm was observed in 35 different types of immune cells. Conversely, the reverse Mendelian randomization results indicated that abnormal sperm might causally affect 39 types of immune cells. These outcomes suggest a potential mutual influence between alterations in immune cell functionality and the quality of spermatozoa.

CONCLUSION

This study highlights the close link between immune responses and sperm development, suggesting implications for reproductive health and immune therapies. Further research may offer crucial insights into male fertility and immune disorders.

Assessing the causal association between human blood metabolites and the risk of gout

BACKGROUND

The occurrence of gout is closely related to metabolism, but there is still a lack of evidence on the causal role of metabolites in promoting or preventing gout.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to assess the association between 486 serum metabolites and gout using genome-wide association study statistics. The inverse variance weighting method was used to generate the main results, while sensitivity analyses using MR-Egger, weighted median, Cochran's Q test, Egger intercept test, and leave-one-out analysis, were performed to assess the stability and reliability of the results. We also performed a metabolic pathway analysis to identify potential metabolic pathways.

RESULTS

After screening, 486 metabolites were retained for MR analysis. After screening by IVW and sensitivity analysis, 14 metabolites were identified with causal effect on gout (P < 0.05), among which hexadecanedioate was the most significant candidate metabolite associated with a lower risk of gout (IVW OR = 0.50; 95% CI = 0.38-0.67; P = 1.65 × 10-6 ). Metabolic pathway analysis identified one pathway that may be associated with the disease.

CONCLUSION

This MR study combining genomics with metabolomics provides a novel insight into the causal role of blood metabolites in the risk of gout, which implies that examination of certain blood metabolites would be a feasible strategy for screening populations with a higher risk of gout.

A Metabolome-Wide Mendelian Randomization Study Identifies Dysregulated Arachidonic Acid Synthesis as a Potential Causal Risk Factor for Bipolar Disorder

BACKGROUND

Bipolar disorder (BPD) is a debilitating mood disorder with an unclear etiology. A better understanding of the underlying pathophysiological mechanisms will help to identify novel targets for improved treatment options and prevention strategies. In this metabolome-wide Mendelian randomization study, we screened for metabolites that may have a causal role in BPD.

METHODS

We tested a total of 913 circulating metabolite exposures assessed in 14,296 Europeans using a mass spectrometry-based platform. For the BPD outcome, we used summary data from the largest and most recent genome-wide association study reported to date, including 41,917 BPD cases.

RESULTS

We identified 33 metabolites associated with BPD (padjusted < 5.48 × 10-5). Most of them were lipids, including arachidonic acid (β = -0.154, SE = 0.023, p = 3.30 × 10-11), a polyunsaturated omega-6 fatty acid, along with several complex lipids containing either an arachidonic or a linoleic fatty acid side chain. These associations did not extend to other closely related psychiatric disorders like schizophrenia or depression, although they may be involved in the regulation of lithium response. These lipid associations were driven by genetic variants within the FADS1/2/3 gene cluster, which is a robust BPD risk locus encoding a family of fatty acid desaturase enzymes that are responsible for catalyzing the conversion of linoleic acid into arachidonic acid. Statistical colocalization analyses indicated that 27 of the 33 metabolites shared the same genetic etiology with BPD at the FADS1/2/3 cluster, demonstrating that our findings are not confounded by linkage disequilibrium.

CONCLUSIONS

Overall, our findings support the notion that arachidonic acid and other polyunsaturated fatty acids may represent potential targets for BPD.

The causal association between serum metabolites and lung cancer based on multivariate Mendelian randomization

This study seeks to understand the causal association between serum metabolites and different lung cancer types, an area yet to be extensively studied. We Used a two-sample Mendelian randomization (TSMR) approach, utilizing 486 blood metabolites as exposures and 3 distinct lung cancer types genome-wide association studies datasets as outcomes. We employed inverse variance weighting, MR-Egger, weighted median, simple mode, and weighted mode to estimate causal effects. We performed sensitivity analyses using Cochran Q test, MR-Egger intercept test, and MR-pleiotropy residual sum and outlier (MR-PRESSO). Linkage disequilibrium score (LDSC) analysis was conducted on the selected metabolites, and common confounding single nucleotide polymorphisms were eliminated using the human genotype-phenotype association Database. Metabolic pathway analysis was performed with MetaboAnalyst 5.0 software. Subsequently, a multivariate Mendelian randomization analysis was conducted to ascertain independent risk exposures. Our findings suggest independent risk factors for specific types of lung cancer: 7-methylxanthine and isoleucine for lung adenocarcinoma, cysteine and 1-arachidonoylglycerophosphocholine are identified as independent protective and risk factors for squamous lung cancer. Undecanoate (11:0) with Linoleate (18:2n6) showed a protective effect for small cell lung cancer. Additionally, 11 metabolic pathways were associated with lung cancer. This novel perspective offers a multidimensional understanding of lung cancer phenotypes, providing valuable guidance for identifying and screening of diverse lung cancer phenotypes.

Inflammatory cytokines and their potential role in Sjogren's syndrome risk: insights from a mendelian randomization study

AIM

This study aimed to investigate the causal impact of inflammatory cytokines on Sjogren's Syndrome (SS) and to identify potential biomarkers for SS clinical management using Mendelian Randomization (MR).

MATERIALS AND METHODS

Leveraging GWAS summary data of inflammatory cytokines and SS, we executed the first two-sample MR analysis. Genetic variants from prior GWASs associated with circulating inflammatory cytokines served as instrumental variables (IVs). Data regarding cytokines were analyzed using the Olink Target-96 Inflammation panel, synthesizing data from 14,824 participants. GWAS summary statistics for SS were procured from the UK Biobank, focusing on samples of European ancestry. To discern the causal relationship between inflammatory cytokines and SS, several MR methodologies, including inverse variance weighted (IVW) and MR-Egger regression, were applied.

RESULTS

After rigorous IV quality control, 91 cytokines were incorporated into the MR analysis. The IVW analysis identified 8 cytokines with a positive association to SS: Axin-1 (OR 2.56, 95% CI 1.07-6.10), T-cell surface glycoprotein CD5 (OR 1.81, 95% CI 1.08-3.02), CUDP1 (OR 1.61, 95% CI 1.00-2.58), CXCL10 (OR 1.92, 95% CI 1.25-2.95), IL-4 (OR 2.18, 95% CI 1.22-3.91), IL-7 (OR 2.35, 95% CI 1.27-4.33), MCP-2 (OR 1.27, 95% CI 1.05-1.54), and TNFRSF9 (OR 1.83, 95% CI 1.03-3.24), suggesting their potential in increasing SS risk.

CONCLUSION

Our study conducted through MR, identified various inflammatory cytokines associated with SS risk, validating some previous research results and offering some new potential biomarkers for SS. However, these findings necessitate further research for validation and exploration of their precise role in the onset and progression of SS.

Untargeted Metabolomic Profiling Reveals Differentially Expressed Serum Metabolites and Pathways in Type 2 Diabetes Patients with and without Cognitive Decline: A Cross-Sectional Study

Diabetes is recognized as a risk factor for cognitive decline, but the underlying mechanisms remain elusive. We aimed to identify the metabolic pathways altered in diabetes-associated cognitive decline (DACD) using untargeted metabolomics. We conducted liquid chromatography-mass spectrometry-based untargeted metabolomics to profile serum metabolite levels in 100 patients with type 2 diabetes (T2D) (54 without and 46 with DACD). Multivariate statistical tools were used to identify the differentially expressed metabolites (DEMs), and enrichment and pathways analyses were used to identify the signaling pathways associated with the DEMs. The receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of a set of metabolites. We identified twenty DEMs, seven up- and thirteen downregulated in the DACD vs. DM group. Chemometric analysis revealed distinct clustering between the two groups. Metabolite set enrichment analysis found significant enrichment in various metabolite sets, including galactose metabolism, arginine and unsaturated fatty acid biosynthesis, citrate cycle, fructose and mannose, alanine, aspartate, and glutamate metabolism. Pathway analysis identified six significantly altered pathways, including arginine and unsaturated fatty acid biosynthesis, and the metabolism of the citrate cycle, alanine, aspartate, glutamate, a-linolenic acid, and glycerophospholipids. Classifier models with AUC-ROC > 90% were developed using individual metabolites or a combination of individual metabolites and metabolite ratios. Our study provides evidence of perturbations in multiple metabolic pathways in patients with DACD. The distinct DEMs identified in this study hold promise as diagnostic biomarkers for DACD patients.

The role of blood metabolites in oral cancer: insights from a Mendelian randomization approach

Aim

This research aimed to explore the causal impact of blood metabolites on oral cancer using a two-sample Mendelian randomization (MR) analysis. The study endeavored to identify potential biomarkers for oral cancer's clinical management.

Materials and methods

Based on the large individual-level datasets from UK Biobank as well as GWAS summary datasets, we first constructed genetic risk scores (GRSs) of 486 human blood metabolites and evaluated the effect on oral cancer. Various statistical methods, including inverse variance weighted (IVW), MR-Egger, and weighted median, among others, were employed to analyze the potential causal relationship between blood metabolites and oral cancer. The sensitivity analyses were conducted using Cochran's Q tests, funnel plots, leave-one-out analyses, and MR-Egger intercept tests.

Results

29 metabolites met the stringent selection criteria. Out of these, 14 metabolites demonstrated a positive association with oral cancer risk, while 15 metabolites indicated a protective effect against oral cancer. The IVW-derived estimates were significant, and the results were consistent across different statistical methodologies. Both the Cochran Q test and the MR-Egger intercept test indicated no heterogeneity and pleiotropy.

Conclusion

This MR study offers evidence of the role specific blood metabolites play in oral cancer, pinpointing several with potential risk or protective effects. These findings could be helpful for new diagnostic tools and treatments for oral cancer. While the results are promising, additional research is necessary to fully validate and refine these conclusions. This study serves as a foundational step towards more comprehensive understandings in the future.

The Relationship of Circulating Choline and Choline-Related Metabolite Levels with Health Outcomes: A Scoping Review of Genome-Wide Association Studies and Mendelian Randomization Studies

Choline is essential for proper liver, muscle, brain, lipid metabolism, cellular membrane composition, and repair. Understanding genetic determinants of circulating choline metabolites can help identify new determinants of choline metabolism, requirements, and their link to disease endpoints. We conducted a scoping review to identify studies assessing the association of genetic polymorphisms on circulating choline and choline-related metabolite concentrations and subsequent associations with health outcomes. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement scoping review extension. Literature was searched to September 28, 2022, in 4 databases: Embase, MEDLINE, Web of Science, and the Biological Science Index. Studies of any duration in humans were considered. Any genome-wide association study (GWAS) investigating genetic variant associations with circulating choline and/or choline-related metabolites and any Mendelian randomization (MR) study investigating the association of genetically predicted circulating choline and/or choline-related metabolites with any health outcome were considered. Qualitative evidence is presented in summary tables. From 1248 total reviewed articles, 53 were included (GWAS = 27; MR = 26). Forty-two circulating choline-related metabolites were tested in association with genetic variants in GWAS studies, primarily trimethylamine N-oxide, betaine, sphingomyelins, lysophosphatidylcholines, and phosphatidylcholines. MR studies investigated associations between 52 total unique choline metabolites and 66 unique health outcomes. Of these, 47 significant associations were reported between 16 metabolites (primarily choline, lysophosphatidylcholines, phosphatidylcholines, betaine, and sphingomyelins) and 27 health outcomes including cancer, cardiovascular, metabolic, bone, and brain-related outcomes. Some articles reported significant associations between multiple choline types and the same health outcome. Genetically predicted circulating choline and choline-related metabolite concentrations are associated with a wide variety of health outcomes. Further research is needed to assess how genetic variability influences choline metabolism and whether individuals with lower genetically predicted circulating choline and choline-related metabolite concentrations would benefit from a dietary intervention or supplementation.

Causal association between kynurenine and depression investigated using two-sample mendelian randomization

As research progresses, the intricate metabolic connections between depression and tryptophan, as well as kynurenine (KYN), have become increasingly evident. In studies investigating the relationship between KYN and depression, the conclusions reached thus far have been inconsistent. Therefore, we propose employing a two-sample mendelian randomization (MR) approach to further elucidate the relationship between KYN and depression. We utilized extensive data from large-scale genome-wide association studies to identify single nucleotide polymorphisms that act as instrumental variables for kynurenine and depression in European ancestry populations, ensuring compliance with MR assumptions. We employed five MR algorithms, namely, weighted median, MR-Egger, inverse variance weighted (IVW), simple mode, and weighted mode, with IVW as the primary analysis method. Sensitivity tests were conducted using Cochran's Q test, MR-Egger intercept test, MR Pleiotropy Residual Sum and Outlier, and Leave-one-out analysis.The IVW analysis revealed that each standard deviation increase in kynurenine corresponded to a 1.4-fold increase in the risk of depression (OR = 1.351, 95% CI 1.110-1.645, P = 0.003). The direction of the effect size (positive or negative) was consistent with the findings from the other four algorithms. Sensitivity tests indicated no heterogeneity or horizontal pleiotropy among the instrumental variables. Elevated levels of kynurenine have a causal relationship with an increased risk of developing depression.

Causal relationship between human blood metabolites and risk of ischemic stroke: a Mendelian randomization study

Background: Ischemic stroke (IS) is a major cause of death and disability worldwide. Previous studies have reported associations between metabolic disorders and IS. However, evidence regarding the causal relationship between blood metabolites and IS lacking. Methods: A two-sample Mendelian randomization analysis (MR) was used to assess the causal relationship between 1,400 serum metabolites and IS. The inverse variance-weighted (IVW) method was employed to estimate the causal effect between exposure and outcome. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode approaches were employed as supplementary comprehensive evaluations of the causal effects between blood metabolites and IS. Tests for pleiotropy and heterogeneity were conducted. Results: After rigorous selection, 23 known and 5 unknown metabolites were identified to be associated with IS. Among the 23 known metabolites, 13 showed significant causal effects with IS based on 2 MR methods, including 5-acetylamino-6-formylamino-3-methyluracil, 1-ribosyl-imidazoleacetate, Behenoylcarnitine (C22), N-acetyltyrosine, and N-acetylputrescine to (N (1) + N (8))-acetate,these five metabolites were positively associated with increased IS risk. Xanthurenate, Glycosyl-N-tricosanoyl-sphingadienine, Orotate, Bilirubin (E,E), Bilirubin degradation product, C17H18N2O, Bilirubin (Z,Z) to androsterone glucuronide, Bilirubin (Z,Z) to etiocholanolone glucuronide, Biliverdin, and Uridine to pseudouridine ratio were associated with decreased IS risk. Conclusion: Among 1,400 blood metabolites, this study identified 23 known metabolites that are significantly associated with IS risk, with 13 being more prominent. The integration of genomics and metabolomics provides important insights for the screening and prevention of IS.

Evaluating the causal relationship between human blood metabolites and gastroesophageal reflux disease

BACKGROUND

Gastroesophageal reflux disease (GERD) affects approximately 13% of the global population. However, the pathogenesis of GERD has not been fully elucidated. The development of metabolomics as a branch of systems biology in recent years has opened up new avenues for the investigation of disease processes. As a powerful statistical tool, Mendelian randomization (MR) is widely used to explore the causal relationship between exposure and outcome.

AIM

To analyze of the relationship between 486 blood metabolites and GERD.

METHODS

Two-sample MR analysis was used to assess the causal relationship between blood metabolites and GERD. A genome-wide association study (GWAS) of 486 metabolites was the exposure, and two different GWAS datasets of GERD were used as endpoints for the base analysis and replication and meta-analysis. Bonferroni correction is used to determine causal correlation features (P < 1.03 × 10-4). The results were subjected to sensitivity analysis to assess heterogeneity and pleiotropy. Using the MR Steiger filtration method to detect whether there is a reverse causal relationship between metabolites and GERD. In addition, metabolic pathway analysis was conducted using the online database based MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, four blood metabolites are negatively correlated with GERD: Levulinate (4-oxovalerate), stearate (18:0), adrenate (22:4n6) and p-acetamidophenylglucuronide. However, we also found a positive correlation between four blood metabolites and GERD: Kynurenine, 1-linoleoylglycerophosphoethanolamine, butyrylcarnitine and guanosine. And bonferroni correction showed that butyrylcarnitine (odd ratio 1.10, 95% confidence interval: 1.05-1.16, P = 7.71 × 10-5) was the most reliable causal metabolite. In addition, one significant pathways, the "glycerophospholipid metabolism" pathway, can be involved in the pathogenesis of GERD.

CONCLUSION

Our study found through the integration of genomics and metabolomics that butyrylcarnitine may be a potential biomarker for GERD, which will help further elucidate the pathogenesis of GERD and better guide its treatment. At the same time, this also contributes to early screening and prevention of GERD. However, the results of this study require further confirmation from both basic and clinical real-world studies.

Circulating Metabolites and Dental Traits: A Mendelian Randomization Study

It is of great importance to uncover causal biomarkers to gain insight into the pathogenesis of oral diseases and identify novel treatment targets for prevention and treatment thereof. This study aimed to systematically evaluate the causal effects of hundreds of metabolites on 10 dental traits using a 2-sample Mendelian randomization (MR) approach. Genetic variants from genome-wide association studies of 309 known metabolites were used as instrumental variables. We selected 10 dental traits, including clinical measures of dental diseases, from the Gene-Lifestyle Interactions in Dental Endpoints Consortium and self-reported oral health data from the UK Biobank. The causal relationships between metabolites and dental traits were inferred using the inverse variance-weighted approach and further controlled for horizontal pleiotropy using 5 additional MR methods. After correcting for multiple tests, 5 metabolites were identified as causal biomarkers. Genetically predicted increased levels of mannose were associated with lower risk of bleeding gums (odds ratio [OR] = 0.72; 95% confidence interval [CI], 0.61-0.85; P = 9.9 × 10-5). MR also indicated 4 metabolites on the causal pathway to dentures, with fructose (OR = 0.50; 95% CI, 0.36-0.70; P = 5.2 × 10-5) and 1-palmitoleoyl-glycerophosphocholine (OR = 0.67; 95% CI, 0.56-0.81; P = 4.8 × 10-5) as potential protective factors and glycine (OR = 1.22; 95% CI, 1.11-1.35; P = 5.6×10-5) and 1,5-anhydroglucitol (OR = 1.32; 95% CI, 1.14-1.52; P = 1.5 × 10-4) as risk factors. The causal associations were robust in various sensitivity analyses. We further observed some shared metabolites among different dental traits, implying similar biological mechanisms underlying the pathogenic processes. Finally, the pathway analysis revealed several significant metabolic pathways that may be involved in the development of dental disorders. Our study provides novel insights into the combination of metabolomics and genomics to reveal the pathogenesis of and therapeutic strategies for dental disorders. It highlighted 5 metabolites and several pathways as causal candidates, warranting further investigation.

Metabolome subtyping reveals multi-omics characteristics and biological heterogeneity in major psychiatric disorders

Growing evidence suggests that major psychiatric disorders (MPDs) share common etiologies and pathological processes. However, the diagnosis is currently based on descriptive symptoms, which ignores the underlying pathogenesis and hinders the development of clinical treatments. This highlights the urgency of characterizing molecular biomarkers and establishing objective diagnoses of MPDs. Here, we collected untargeted metabolomics, proteomics and DNA methylation data of 327 patients with MPDs, 131 individuals with genetic high risk and 146 healthy controls to explore the multi-omics characteristics of MPDs. First, differential metabolites (DMs) were identified and we classified MPD patients into 3 subtypes based on DMs. The subtypes showed distinct metabolomics, proteomics and DNA methylation signatures. Specifically, one subtype showed dysregulation of complement and coagulation proteins, while the DNA methylation showed abnormalities in chemical synapses and autophagy. Integrative analysis in metabolic pathways identified the important roles of the citrate cycle, sphingolipid metabolism and amino acid metabolism. Finally, we constructed prediction models based on the metabolites and proteomics that successfully captured the risks of MPD patients. Our study established molecular subtypes of MPDs and elucidated their biological heterogeneity through a multi-omics investigation. These results facilitate the understanding of pathological mechanisms and promote the diagnosis and prevention of MPDs.

Mendelian randomization studies of depression: evidence, opportunities, and challenges

BACKGROUND

Major depressive disorder (MDD) poses a significant social and economic burden worldwide. Identifying exposures, risk factors, and biological mechanisms that are causally connected to MDD can help build a scientific basis for disease prevention and development of novel therapeutic approaches.

METHODS

In this systematic review, we assessed the evidence for causal relationships between putative causal risk factors and MDD from Mendelian randomization (MR) studies, following PRISMA. We assessed methodological quality based on key elements of the MR design: use of a full instrumental variable analysis and validation of the three key MR assumptions.

RESULTS

We included methodological details and results from 52 articles. A causal link between lifestyle, metabolic, inflammatory biomarkers, particular pathological states and MDD is supported by MR investigations, although results for each category varied substantially.

CONCLUSIONS

While this review shows how MR can offer useful information for examining prospective treatment targets and better understanding the pathophysiology of MDD, some methodological flaws in the existing literature limit reliability of results and probably underlie their heterogeneity. We highlight perspectives and recommendations for future works on MR in psychiatry.

Assessing the causal relationship between immune traits and systemic lupus erythematosus by bi-directional Mendelian randomization analysis

Previous studies have observed relationships between immune cells and systemic lupus erythematosus (SLE), but their causal links remain undetermined. Based on the public available genome-wide association studies (GWAS) summary statistics, we conducted two-sample Mendelian randomization (MR) to evaluate the associations between 731 immune phenotypes and SLE pairs. Pairwise pleiotropy analysis was performed to identify pleiotropic genes for significant immunophenotype-SLE pairs. A comprehensive gene function analysis was undertaken to explore the mechanisms of immune cells in SLE. By using the instrumental variables extracted from GWAS data, we observed that increased levels of five immune phenotypes were causally associated with SLE risk (FDR < 0.05), that were CD20 on IgD+ CD38- naïve, BAFF-R on IgD+ CD38dim, CD39+ secreting Treg AC, CD14- CD16+ monocyte AC, and HLA DR on CD14+ monocyte. Pairwise gene-based analyses identified a total of 38 pleiotropic genes for 5 significant pairs identified and gene set enrichment analysis revealed the involvement of the identified pleiotropic genes in complex pathways (i.e., systemic lupus erythematosus, an integral component of luminal side of endoplasmic reticulum membrane, C-type lectin receptor signaling pathway and regulation of hormone secretion). This study demonstrates that the immune response influences the progression of SLE in a complex pattern. These findings greatly improve our understanding of the interaction between immune response and SLE risk and also aid in the design of therapeutic strategies from an immunological perspective.

Causal relationship between blood metabolites and risk of five infections: a Mendelian randomization study

OBJECTIVE

Infectious diseases continue to pose a significant threat in the field of global public health, and our understanding of their metabolic pathogenesis remains limited. However, the advent of genome-wide association studies (GWAS) offers an unprecedented opportunity to unravel the relationship between metabolites and infections.

METHODS

Univariable and multivariable Mendelian randomization (MR) was commandeered to elucidate the causal relationship between blood metabolism and five high-frequency infection phenotypes: sepsis, pneumonia, upper respiratory tract infections (URTI), urinary tract infections (UTI), and skin and subcutaneous tissue infection (SSTI). GWAS data for infections were derived from UK Biobank and the FinnGen consortium. The primary analysis was conducted using the inverse variance weighted method on the UK Biobank data, along with a series of sensitivity analyses. Subsequently, replication and meta-analysis were performed on the FinnGen consortium data.

RESULTS

After primary analysis and a series of sensitivity analyses, 17 metabolites were identified from UK Biobank that have a causal relationship with five infections. Upon joint analysis with the FinGen cohort, 7 of these metabolites demonstrated consistent associations. Subsequently, we conducted a multivariable Mendelian randomization analysis to confirm the independent effects of these metabolites. Among known metabolites, genetically predicted 1-stearoylglycerol (1-SG) (odds ratio [OR] = 0.561, 95% confidence interval [CI]: 0.403-0.780, P < 0.001) and 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) (OR = 0.780, 95%CI: 0.689-0.883, P < 0.001) was causatively associated with a lower risk of sepsis, and genetically predicted phenylacetate (PA) (OR = 1.426, 95%CI: 1.152-1.765, P = 0.001) and cysteine (OR = 1.522, 95%CI: 1.170-1.980, P = 0.002) were associated with an increased risk of UTI. Ursodeoxycholate (UDCA) (OR = 0.906, 95%CI: 0.829-0.990, P = 0.029) is a protective factor against pneumonia. Two unknown metabolites, X-12407 (OR = 1.294, 95%CI: 1.131-1.481, P < 0.001), and X-12847 (OR = 1.344, 95%CI: 1.152-1.568, P < 0.001), were also identified as independent risk factors for sepsis.

CONCLUSIONS

In this MR study, we demonstrated a causal relationship between blood metabolites and the risk of developing sepsis, pneumonia, and UTI. However, there was no evidence of a causal connection between blood metabolites and the risk of URTI or SSTI, indicating a need for larger-scale studies to further investigate susceptibility to certain infection phenotypes.

The causal effects of genetically determined human blood metabolites on the risk of atrial fibrillation

Background

Blood metabolites have been found related to atrial fibrillation (AF), but the causal role is still unclear. Mendel randomization (MR) can give information about the causality between blood metabolites and AF.

Methods

Two-sample MR analysis was used to evaluate the causality between 486 blood metabolites and AF. Firstly, the genome-wide association study (GWAS) data for AF (from Nielsen et al.) was analyzed and some metabolites were identified. Then another GWAS data for AF (from Roselli et al.) was repeatedly analyzed to verify the results. Inverse variance weighted method was mainly used to determine the causality, and MR-egger, Weighted Median, and MR-PRESSO models were used as supplements of MR. Cochran's Q test was used to assess heterogeneity. And MR-Egger intercept and MR-PRESSO global test were performed to measure pleiotropy.

Results

The study used Bonferroni's corrected P value (P < 1.03 × 10-4) as the significance threshold. After MR analysis and replication analysis, we found two overlapped metabolites. Among which tryptophan betaine was the most significant causal metabolite in both AF GWAS data (from Nielsen et al.) (odds ratio (OR) = 0.83, 95% confidence interval (CI) = 0.76-0.90, P = 9.37 × 10-6) and AF GWAS data (from Roselli et al.) (OR = 0.82, 95% CI = 0.76-0.88, P = 2.00 × 10-7), while uridine was nominally significant metabolites in both AF GWAS data (from Nielsen et al.) (OR = 0.58, 95% CI = 0.40-0.84, P = 0.004) and AF GWAS data (from Roselli et al.) (OR = 0.56, 95% CI = 0.35-0.88, P = 0.01). And the results of sensitivity analysis showed that none of them had obvious heterogeneity or pleiotropy.

Conclusion

The study identified several blood metabolites that were causally related to AF, which may provide new perspectives on the pathogenesis of AF.

A two-sample mendelian randomization analysis investigates associations between gut microbiota and infertility

Observational studies have provided evidence of a correlation between alterations in gut microbiota composition and infertility. However, concrete proof supporting the causal relationship is still lacking. We performed a Mendelian randomization study to assess whether genetically gut microbiota composition influences the risk of infertility. The genetic data pertaining to gut microbiota were obtained from a genome-wide association study meta-analysis, which was conducted among 24 cohorts (18,340 participants) from the international MiBioGen consortium. By the primary method of assessing causality, we have identified 2 family taxa, 2 genus taxa, and 1 order taxa that were linked to a low risk of male infertility, while 1 genus taxa were associated with a high risk of male infertility. Furthermore, we have discovered 6 genus taxa, 1 phylum taxa, 1 class taxa, 1 order taxa, and 1 family taxa that were associated with a low risk of female infertility, while 1 genus taxa were linked to a high risk of female infertility. This study successfully confirmed that there was a causal link between gut microbiota and infertility. The identification of these specific strains through genetic prediction offers a valuable insight for early diagnosis, prevention, and treatment of infertility.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Xanthine-induced deficits in hippocampal behavior and abnormal expression of hemoglobin genes

The prevalence of mental disorders such as depression and anxiety is high and often comorbid with other diseases. Chronic stress is a common risk factor for these disorders, but the mechanisms behind their development are not yet fully understood. Metabolomics has revealed a close association between purine and pyrimidine metabolism and depression and anxiety, with increased levels of serum xanthine observed in both humans and mice. Xanthine is known as purine metabolism, and this compound shows several biological activities, but the impact of xanthine on our brain function is still unclear. The hippocampus, which plays a crucial role in memory and learning, is also implicated in the pathophysiology of depression and anxiety. Here, we investigated the effects of xanthine intraperitoneal administration on spatial memory and anxiety-like behavior in mice. The findings indicated that xanthine administration induced a deficit of hippocampus-dependent spatial memory and a tendency to anxiety-like behavior in mice. RNA-seq analysis showed that xanthine administration upregulated hemoglobin (Hb) genes involved in oxygen transport in the hippocampus. The upregulated Hb genes occurred in the neuronal cells, and in vitro experiments revealed that both Hba-a1 derived from mice and HBA2 derived from humans were upregulated by xanthine treatment. These observations suggest that the xanthine-induced Hb in the hippocampus could be related to spatial memory deficit and anxiety. This study sheds light on the direct effects of xanthine on the brain and its potential role in the development of depression and anxiety symptoms caused by chronic stress.

Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders

BACKGROUND AND HYPOTHESIS

To identify promising drug targets for psychiatric disorders, we applied Mendelian randomization (MR) design to systematically screen blood metabolome for potential mediators of psychiatric disorders and further predict target-mediated side effects.

STUDY DESIGN

We selected 92 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with totally 147 827 participants. Summary statistics for bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), schizophrenia (SCZ), panic disorder (PD), autistic spectrum disorder (ASD), and anorexia nervosa (AN) originated from the Psychiatric Genomics Consortium, involving 1 143 340 participants. Mendelian randomization (MR) analyses were conducted to estimate associations of blood metabolites with psychiatric disorders. Phenome-wide MR analysis was further performed to predict side effects mediated by metabolite-targeted interventions.

RESULTS

Eight metabolites were identified associated with psychiatric disorders, including five established mediators: N-acetylornithine (BIP: OR, 0.72 [95% CI, 0.66-0.79]; SCZ: OR, 0.74 [0.64-0.84]), glycine (BIP: OR, 0.62 [0.50-0.77]), docosahexaenoic acid (MDD: OR, 0.96 [0.94-0.97]), 3-Hydroxybutyrate (MDD: OR, 1.14 [1.08-1.21]), butyrylcarnitine (SCZ: OR, 1.22 [1.12-1.32]); and three novel mediators: 1-arachidonoylglycerophosphocholine (1-arachidonoyl-GPC)(BIP: OR, 0.31 [0.23-0.41]), glycoproteins (BIP: OR, 0.94 [0.92-0.97]), sphingomyelins (AN: OR, 1.12 [1.06-1.19]). Phenome-wide MR analysis showed that all identified metabolites except for N-acetylornithine and 3-Hydroxybutyrate had additional effects on nonpsychiatric diseases, while glycine, 3-Hydroxybutyrate, N-acetylornithine, and butyrylcarnitine had no adverse side effects.

CONCLUSIONS

This MR study identified five established and three novel mediators for psychiatric disorders. N-acetylornithine, glycine, 3-Hydroxybutyrate, and butyrylcarnitine might be promising targets against psychiatric disorders with no predicted adverse side effects.

Causal relationship between human blood omega-3 fatty acids and the risk of epilepsy: A two-sample Mendelian randomization study

Background

Though omega-3 fatty acids reduce seizures in several animal models, considerable controversy exists regarding the association between omega-3 fatty acids and epilepsy in human.

Objective

To assess whether genetically determined human blood omega-3 fatty acids are causally associated with the risk of epilepsy outcomes.

Methods

We conducted a two-sample Mendelian randomization (MR) analysis by applying summary statistics of genome-wide association study datasets of both exposure and outcomes. Single nucleotide polymorphisms significantly associated with blood omega-3 fatty acids levels were selected as instrumental variables to estimate the causal effects on epilepsy. Five MR analysis methods were conducted to analyze the final results. The inverse-variance weighted (IVW) method was used as the primary outcome. The other MR analysis methods (MR-Egger, weighted median, simple mode, and weighted mode) were conducted as the complement to IVW. Sensitivity analyses were also conducted to evaluate heterogeneity and pleiotropy.

Results

Genetically predicted the increase of human blood omega-3 fatty acids levels was associated with a higher risk of epilepsy (OR = 1.160, 95%CI = 1.051–1.279, P = 0.003).

Conclusions

This study revealed a causal relationship between blood omega-3 fatty acids and the risk of epilepsy, thus providing novel insights into the development mechanism of epilepsy.

Serum metabolomic profiling revealed potential diagnostic biomarkers in patients with panic disorder

BACKGROUND

Currently, specific metabolites and diagnostic biomarkers of panic disorder (PD) patients have not been identified in clinical practice. The aim of this study was to explore metabolites and metabolic pathways in serum through a metabolomics method.

METHODS

Fifty-five PD patients who completed 2 weeks of inpatient treatment and 55 healthy control subjects (HCs) matched for age, sex and BMI were recruited. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used to detect metabolites in serum. Multivariate Statistical Analysis was used to identify differential metabolites. The relevant biometabolic pathways were further identified by the online tool MetaboAnalyst 5.0.

RESULTS

43 different metabolites in PD patients compared to HCs (P < 0.05) were screened. Pathway analysis showed that these small molecules were mainly associated with amino acid metabolism. 14 metabolites were significantly changed after 2 weeks of drug treatment (P < 0.05), which were mainly associated with tryptophan metabolism.

CONCLUSION

In conclusion, our analysis of metabolomics of PD patients at baseline and two weeks after treatment screened for differential metabolites that could be potential diagnostic biomarkers involved in PD pathogenesis and influence some biometabolic pathways such as phenylalanine metabolism and tryptophan metabolism. In the future, we can summarize and observe the dynamic changes of differential metabolites that appear more frequently in similar studies to further explore the underlying mechanisms of PD evolution.

The mechanism of palmatine-mediated intestinal flora and host metabolism intervention in OA-OP comorbidity rats

Background

ErXian decoction is a Chinese herbal compound that can prevent and control the course of osteoarthritis (OA) and osteoporosis (OP). OP and OA are two age-related diseases that often coexist in elderly individuals, and both are associated with dysregulation of the gut microbiome. In the initial study, Palmatine (PAL) was obtained by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and network pharmacological screening techniques, followed by 16S rRNA sequencing and serum metabolomics of intestinal contents, to explore the mechanism of PAL in the treatment of OA and OP.

Methods

The rats selected for this study were randomly divided into three groups: a sham group, an OA-OP group and a PAL group. The sham group was intragastrically administered normal saline solution, and the PLA group was treated with PAL for 56 days. Through microcomputed tomography (micro-CT), ELISA, 16S rRNA gene sequencing and non-targeted metabonomics research, we explored the potential mechanism of intestinal microbiota and serum metabolites in PAL treatment of OA-OP rats.

Results

Palmatine significantly repair bone microarchitecture of rat femur in OA-OP rats and improved cartilage damage. The analysis of intestinal microflora showed that PAL could also improve the intestinal microflora disorder of OA-OP rats. For example, the abundance of Firmicutes, Bacteroidota, Actinobacteria, Lactobacillus, unclassified_f_Lachnospiraceae, norank_f_Muribaculaceae, Lactobacillaceae, Lachnospiraceae and Muribaculaceae increased after PAL intervention. In addition, the results of metabolomics data analysis showed that PAL also change the metabolic status of OA-OP rats. After PAL intervention, metabolites such as 5-methoxytryptophol, 2-methoxy acetaminophen sulfate, beta-tyrosine, indole-3-carboxylic acid-O-sulfate and cyclodopa glucoside increased. Association analysis of metabolomics and gut microbiota (GM) showed that the communication of multiple flora and different metabolites played an important role in OP and OA.

Conclusion

Palmatine can improve cartilage degeneration and bone loss in OA-OP rats. The evidence we provided supports the idea that PAL improves OA-OP by altering GM and serum metabolites. In addition, the application of GM and serum metabolomics correlation analysis provides a new strategy for uncovering the mechanism of herbal treatment for bone diseases.

Identification of blood metabolites linked to the risk of cholelithiasis: a comprehensive Mendelian randomization study

BACKGROUND AND AIMS

Observational and Mendelian randomization (MR) studies have identified several modifiable risk factors of cholelithiasis. However, there is limited evidence about the causal effect of blood metabolites on the cholelithiasis risk.

METHODS

To have a comprehensive understanding to causal relations between blood metabolites and cholelithiasis, for the primary discovery, we applied two MR methods to explore the associations between 249 circulating metabolites and cholelithiasis. For secondary validations, we replicated the examinations using another metabolic dataset with 123 metabolites. The summary statistics of cholelithiasis were retrieved from FinnGen Consortium Release 5 and UK Biobank. Inverse-variance weighted, weight median and MR-egger methods were used for calculating causal estimates. Furthermore, Bayesian model averaging MR (MR-BMA) method was employed to detect the dominant causal metabolic traits with adjustment for pleiotropy effects.

RESULTS

In the primary analysis, sphingomyelin showed consistent protective causal associations with cholelithiasis; while plasma cholesterol-associated traits showed generally inverse correlation with cholelithiasis risk. Notably, large numbers of traits within the (un)saturated fatty acid category demonstrated significant causal effects. Secondary analyses demonstrated similar results, with traits related to the levels of bisallylic groups in fatty acids showing protective effects. Lastly, MR-BMA analyses discovered that the degree of unsaturation plays a predominant role in reducing the risk of cholelithiasis.

CONCLUSION

Our MR study provides a complete atlas of associations between plasma metabolites on cholelithiasis risk. It highlighted that genetically predicted sphingomyelin and degree of unsaturation of fatty acid were causally associated with the reduced risk of cholelithiasis.

Applications of Mendelian randomization in psychiatry: a comprehensive systematic review

Psychiatric diseases exact a heavy socioeconomic toll, and it is particularly difficult to identify their risk factors and causative mechanisms due to their multifactorial nature, the limited physiopathological insight, the many confounding factors, and the potential reverse causality between the risk factors and psychiatric diseases. These characteristics make Mendelian randomization (MR) a precious tool for studying these disorders. MR is an analytical method that employs genetic variants linked to a certain risk factor, to assess if an observational association between that risk factor and a health outcome is compatible with a causal relationship. We report the first systematic review of all existing applications and findings of MR in psychiatric disorders, aiming at facilitating the identification of risk factors that may be common to different psychiatric diseases, and paving the way to transdiagnostic MR studies in psychiatry, which are currently lacking. We searched Web of Knowledge, Scopus, and Pubmed databases (until 3 May 2022) for articles on MR in psychiatry. The protocol was preregistered in PROSPERO (CRD42021285647). We included methodological details and results from 50 articles, mainly on schizophrenia, major depression, autism spectrum disorders, and bipolar disorder. While this review shows how MR can offer unique opportunities for unraveling causal links in risk factors and etiological elements of specific psychiatric diseases and transdiagnostically, some methodological flaws in the existing literature limit reliability of results and probably underlie their heterogeneity. We highlight perspectives and recommendations for future works on MR in psychiatry.

Viral metagenomics combined with metabolomics reveals the role of gut viruses in mouse model of depression

Depression is a heterogeneous mental disorder that has been linked to disturbances in the gut microbiome. As an essential part of the gut microbiome, gut virome may play critical roles in disease progression and development. However, the relationship between the effect of gut virome on neurotransmitter metabolism and depression is unknown. We evaluated the alterations of gut virome and neurotransmitters in chronic restraint stress (CRS)-induced mouse model of depression based on viral metagenomics and LC-MS/MS metabolomics analyses. The results reveal that the gut virome profile of CRS group differed significantly from CON group. Microviridae was the most abundant differential viral family in both groups, followed by Podoviridae, while Siphoviridae was only enriched in CRS group of the top 100 differential viruses. The differential viruses that predicted to Enterobacteriaceae phage, Gammaproteobacteria phage and Campylobacteraceae phage were enriched in CRS group. Furthermore, 12 differential neurotransmitters primarily involved in the tryptophan metabolism pathway were altered in depressive-like mice. Besides, tryptamine and 5-methoxytryptamine hydrochloride were strongly associated with differential viruses belonging to Podoviridae and Microviridae. Our findings provide new insight into understanding the potential role of the gut virome and metabolites in depression.

Causality of genetically determined metabolites on anxiety disorders: a two-sample Mendelian randomization study

BACKGROUND

Although anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms.

METHODS

A two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features (P < 1.03 × 10^-4). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study.

RESULTS

In MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (OR_{fixed-effect IVW} = 1.04; 95% CI 1.021-1.06; P_{fixed-effect IVW} = 4.3 × 10^-5) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a "leave-one-out" analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = - 0.0013, SE = 0.0006, P = 0.06). No heterogeneity was detected by Cochran's Q test (MR-Egger. Q = 7.68, P = 0.742; IVW. Q = 12.12, P = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < 0.001). In addition, two significant pathways, the "primary bile acid biosynthesis" pathway (P = 0.008) and the "valine, leucine, and isoleucine biosynthesis" pathway (P = 0.03), were identified through metabolic pathway analysis.

CONCLUSION

This study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.

Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling.

RESULTS

In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, β-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (β-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction.

CONCLUSIONS

This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.

Multi-omics analyses of serum metabolome, gut microbiome and brain function reveal dysregulated microbiota-gut-brain axis in bipolar depression

The intricate processes of microbiota-gut-brain communication in modulating human cognition and emotion, especially in the context of mood disorders, have remained elusive. Here we performed faecal metagenomic, serum metabolomics and neuroimaging studies on a cohort of 109 unmedicated patients with depressed bipolar disorder (BD) patients and 40 healthy controls (HCs) to characterise the microbial-gut-brain axis in BD. Across over 12,000 measured metabolic features, we observed a large discrepancy (73.54%) in the serum metabolome between BD patients and HCs, spotting differentially abundant microbial-derived neuroactive metabolites including multiple B-vitamins, kynurenic acid, gamma-aminobutyric acid and short-chain fatty acids. These metabolites could be linked to the abundance of gut microbiota presented with corresponding biosynthetic potentials, including Akkermansia muciniphila, Citrobacter spp. (Citrobacter freundii and Citrobacter werkmanii), Phascolarctobacterium spp., Yersinia spp. (Yersinia frederiksenii and Yersinia aleksiciae), Enterobacter spp. (Enterobacter cloacae and Enterobacter kobei) and Flavobacterium spp. Based on functional neuroimaging, BD-related neuroactive microbes and metabolites were discovered as potential markers associated with BD-typical features of functional connectivity of brain networks, hinting at aberrant cognitive function, emotion regulation, and interoception. Our study combines gut microbiota and neuroactive metabolites with brain functional connectivity, thereby revealing potential signalling pathways from the microbiota to the gut and the brain, which may have a role in the pathophysiology of BD.

Assessing the causal association between human blood metabolites and the risk of epilepsy

Background

Metabolic disturbance has been reported in patients with epilepsy. Still, the evidence about the causal role of metabolites in facilitating or preventing epilepsy is lacking. Systematically investigating the causality between blood metabolites and epilepsy would help provide novel targets for epilepsy screening and prevention.

Methods

We conducted two-sample Mendelian randomization (MR) analysis. Data for 486 human blood metabolites came from a genome-wide association study (GWAS) comprising 7824 participants. GWAS data for epilepsy were obtained from the International League Against Epilepsy (ILAE) consortium for primary analysis and the FinnGen consortium for replication and meta-analysis. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy.

Results

482 out of 486 metabolites were included for MR analysis following rigorous genetic variants selection. After IVW and sensitivity analysis filtration, six metabolites with causal effects on epilepsy were identified from the ILAE consortium. Only four metabolites remained significant associations with epilepsy when combined with the FinnGen consortium [uridine: odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.48–3.71, P = 0.0003; 2-hydroxystearate: OR = 1.61, 95% CI = 1.19–2.18, P = 0.002; decanoylcarnitine: OR = 0.82, 95% CI = 0.72–0.94, P = 0.004; myo-inositol: OR = 0.77, 95% CI = 0.62–0.96, P = 0.02].

Conclusion

The evidence that the four metabolites mentioned above are associated with epilepsy in a causal way provides a novel insight into the underlying mechanisms of epilepsy by integrating genomics with metabolism, and has an implication for epilepsy screening and prevention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03648-5.

Evidence of genetic overlap and causal relationships between blood-based biochemical traits and human cortical anatomy

Psychiatric disorders such as schizophrenia are commonly associated with structural brain alterations affecting the cortex. Recent genetic evidence suggests circulating metabolites and other biochemical traits play a causal role in many psychiatric disorders which could be mediated by changes in the cerebral cortex. Here, we leveraged publicly available genome-wide association study data to explore shared genetic architecture and evidence for causal relationships between a panel of 50 biochemical traits and measures of cortical thickness and surface area. Linkage disequilibrium score regression identified 191 genetically correlated biochemical-cortical trait pairings, with consistent representation of blood cell counts and other biomarkers such as C-reactive protein (CRP), haemoglobin and calcium. Spatially organised patterns of genetic correlation were additionally uncovered upon clustering of region-specific correlation profiles. Interestingly, by employing latent causal variable models, we found strong evidence suggesting CRP and vitamin D exert causal effects on region-specific cortical thickness, with univariable and multivariable Mendelian randomization further supporting a negative causal relationship between serum CRP levels and thickness of the lingual region. Our findings suggest a subset of biochemical traits exhibit shared genetic architecture and potentially causal relationships with cortical structure in functionally distinct regions, which may contribute to alteration of cortical structure in psychiatric disorders.

Systematic evaluation for the causal effects of blood metabolites on osteoporosis: Genetic risk score and Mendelian randomization

Purpose

Osteoporosis is associated with metabolic alterations, but the causal roles of serum metabolites on osteoporosis have not been identified.

Methods

Based on the large individual-level datasets from UK Biobank as well as GWAS summary datasets, we first constructed genetic risk scores (GRSs) for 308 of 486 human serum metabolites and evaluated the effect of each GRS on 2 major osteoporosis phenotypes, i.e., estimated bone miner density (eBMD) and fracture, respectively. Then, two-sample Mendelian Randomization (MR) was performed to validate the casual metabolites on osteoporosis. Multivariable MR analysis tested whether the effects of metabolites on osteoporosis are independent of possible confounders. Finally, we conducted metabolic pathway analysis for the metabolites involved in bone metabolism.

Results

We identified causal effects of 18 metabolites on eBMD and 1 metabolite on fracture with the GRS method after adjusting for multiple tests. Then, 9 of them were further validated with MR as replication, where comprehensive sensitive analyses proved robust of the causal associations. Although not identified in GRS, 3 metabolites were associated with at least three osteoporosis traits in MR results. Multivariable MR analysis determined the independent causal effect of several metabolites on osteoporosis. Besides, 23 bone metabolic pathways were detected, such as valine, leucine, isoleucine biosynthesis (p = 0.053), and Aminoacyl-tRNA biosynthesis (p = 0.076), and D-glutamine and D-glutamate metabolism (p = 0.004).

Conclusions

The systematic causal analyses strongly suggested that blood metabolites have causal effects on osteoporosis risk.

Integrated network pharmacology and hepatic metabolomics to reveal the mechanism of Acanthopanax senticosus against major depressive disorder

Objective:Acanthopanax senticosus (Rupr. et Maxim.) Harms (ASH) is a traditional herbal medicine widely known for its antifatigue and antistress effects, as well as tonifying qi, invigorating spleen and kidney, and tranquilizing the mind. Recent evidence suggests that ASH has a therapeutic effect on major depressive disorder (MDD), but its mechanism is still unclear. The current study aimed to investigate the effect of ASH on MDD and potential therapeutic mechanisms.

Materials and Methods: The chemical compound potential target network was predicted based on network pharmacology. Simultaneously, chronic unpredictable mild stress (CUMS) model mice were orally administrated ASH with three dosages (400, 200, and 100 mg/kg) for 6 weeks, and hepatic metabolomics based on gas chromatography–mass spectrometry (GC–MS) was carried out to identify differential metabolites and related metabolic pathways. Next, the integrated analysis of metabolomics and network pharmacology was applied to find the key target. Finally, molecular docking technology was employed to define the combination of the key target and the corresponding compounds.

Results: A total of 13 metabolites and four related metabolic pathways were found in metabolomics analysis. From the combined analysis of network pharmacology and metabolomics, six targets (DAO, MAOA, MAOB, GAA, HK1, and PYGM) are the overlapping targets and two metabolic pathways (glycine, serine, and threonine metabolism and starch and sucrose metabolism) are the most related pathways. Finally, DAO, MAOA, MAOB, GAA, HK1, and PYGM were verified bounding well to their corresponding compounds including isofraxidin, eleutheroside B1, eleutheroside C, quercetin, kaempferol, and acacetin.

Conclusion: Based on these results, it was implied that the potential mechanism of ASH on MDD was related to the regulation of metabolism of several excitatory amino acids and carbohydrates, as well as the expression of DAO, MAOA, MAOB, GAA, HK1, and PYGM.

The tRNA regulome in neurodevelopmental and neuropsychiatric disease

Transfer (t)RNAs are 70-90 nucleotide small RNAs highly regulated by 43 different types of epitranscriptomic modifications and requiring aminoacylation ('charging') for mRNA decoding and protein synthesis. Smaller cleavage products of mature tRNAs, or tRNA fragments, have been linked to a broad variety of noncanonical functions, including translational inhibition and modulation of the immune response. Traditionally, knowledge about tRNA regulation in brain is derived from phenotypic exploration of monogenic neurodevelopmental and neurodegenerative diseases associated with rare mutations in tRNA modification genes. More recent studies point to the previously unrecognized potential of the tRNA regulome to affect memory, synaptic plasticity, and affective states. For example, in mature cortical neurons, cytosine methylation sensitivity of the glycine tRNA family (tRNA^Gly) is coupled to glycine biosynthesis and codon-specific alterations in ribosomal translation together with robust changes in cognition and depression-related behaviors. In this Review, we will discuss the emerging knowledge of the neuronal tRNA landscape, with a focus on epitranscriptomic tRNA modifications and downstream molecular pathways affected by alterations in tRNA expression, charging levels, and cleavage while mechanistically linking these pathways to neuropsychiatric disease and provide insight into future areas of study for this field.

Ergothioneine and central nervous system diseases

Ergothioneine (ERGO) is a thiol contained in the food that exhibits an excellent antioxidant effect similar to that of glutathione. Although mammals lack a biosynthetic pathway for ERGO, the carnitine/organic cation transporter OCTN1/SLC22A4, which transports ERGO in vivo, is expressed throughout the body, and ERGO is distributed to various organs after oral intake. ERGO is a stable compound that remains in the body for a long time after ingestion. OCTN1 is also expressed in brain parenchymal cells, including neurons, and ERGO in the blood permeates the blood-brain barrier and is distributed to the brain, exhibiting a neuroprotective effect. Recently, the association between central nervous system (CNS) diseases and ERGO has become a research focus. ERGO concentrations in the blood components are lower in patients with cognitive impairment, Parkinson's disease, and frailty than in healthy subjects. ERGO exerts a protective effect against various neurotoxins and improves the symptoms of cognitive impairment, depression, and epilepsy in animal models. The promotion of neurogenesis and induction of neurotrophic factors, in addition to the antioxidant and anti-inflammatory effects, may be involved in the neuroprotective effect of ERGO. This review shows the association between ERGO and CNS diseases, discusses the possible biomarkers of peripheral ERGO in CNS diseases, and the possible preventive and improvement effects of ERGO on CNS diseases.

Antidepressive-like Behavior-Related Metabolomic Signatures of Sigma-1 Receptor Knockout Mice

Sigma-1 receptor (Sig1R) has been proposed as a therapeutic target for neurological, neurodegenerative, and psychiatric disorders, including depression and anxiety. Identifying metabolites that are affected by Sig1R absence and cross-referencing them with specific mood-related behaviors would be helpful for the development of new therapies for Sig1R-associated disorders. Here, we examined metabolic profiles in the blood and brains of male CD-1 background Sig1R knockout (KO) mice in adulthood and old age and correlated them with the assessment of depression- and anxiety-related behaviors. The most pronounced changes in the metabolic profile were observed in the plasma of adult Sig1R KO mice. In adult mice, the absence of Sig1R significantly influenced the amino acid, sphingolipid (sphingomyelin and ceramide (18:1)), and serotonin metabolic pathways. There were higher serotonin levels in plasma and brain tissue and higher histamine levels in the plasma of Sig1R KO mice than in their age-matched wild-type counterparts. This increase correlated with the reduced behavioral despair in the tail suspension test and lack of anhedonia in the sucrose preference test. Overall, these results suggest that Sig1R regulates behavior by altering serotonergic and histaminergic systems and the sphingolipid metabolic pathway.

Peripheral blood and urine metabolites and biological functions in post-stroke depression

Post-stroke depression (PSD) is the most common and severe neuropsychiatric complication after stroke. However, the molecular mechanism of PSD is still unclear. Previous studies have identified peripheral blood and urine metabolites associated with PSD using metabolomics techniques. We searched and systematically summarized metabolites that may be involved in metabolic changes in peripheral blood and urine of patients with PSD from the Metabolite Network of Depression Database (MENDA) and other biomedical databases. MetaboAnalyst5.0 software was used for pathway analysis and enrichment analysis of differential metabolites, and subgroup analyses were performed according to tissue types and metabolomics techniques. We identified 47 metabolites that were differentially expressed between patients with and without PSD. Five differential metabolites were found in both plasma and urine, including L-glutamic acid, pyroglutamic acid, palmitic acid, L-phenylalanine, and L-tyrosine. We integrated these metabolites into metabolic pathways, and six pathways were significantly altered. These pathways could be roughly divided into three modules including amino acid metabolism, nucleotide metabolism, and glucose metabolism. Among them, the most significantly altered pathway was "phenylalanine metabolism" and the pathway containing the most associated metabolites was "aminoacyl-tRNA biosynthesis", which deserve further study to elucidate their role in the molecular mechanism of PSD. In summary, metabolic changes in peripheral blood and urine are associated with PSD, especially the disruption of "phenylalanine metabolism" and "aminoacyl-tRNA biosynthesis" pathways. This study provides clues to the metabolic characteristics of patients with PSD, which may help to elucidate the molecular pathogenesis of PSD.

Socioeconomic Status, Individual Behaviors and Risk for Mental Disorders: A Mendelian Randomization Study

Background

There is increasing attention on the association of socioeconomic status and individual behaviors (SES/IB) with mental health. However, the impacts of SES/IB on mental disorders are still unclear. To provide evidence for establishing feasible strategies on disease screening and prevention, we implemented Mendelian randomization (MR) design to appraise causality between SES/IB and mental disorders.

Methods

We conducted a two-sample MR study to assess the causal effects of SES and IB (dietary habits, habitual physical activity, smoking behaviors, drinking behaviors, sleeping behaviors, leisure sedentary behaviors, risky behaviors, and reproductive behaviors) on three mental disorders, including bipolar disorder, major depressive disorder and schizophrenia. A series of filtering steps were taken to select eligible genetic instruments robustly associated with each of the traits. Inverse variance weighted was used for primary analysis, with alternative MR methods including MR-Egger, weighted median, and weighted mode estimate. Complementary methods were further used to detect pleiotropic bias.

Results

After Bonferroni correction and rigorous quality control, we identified that SES (educational attainment), smoking behaviors (smoking initiation, number of cigarettes per day), risky behaviors (adventurousness, number of sexual partners, automobile speeding propensity) and reproductive behavior (age at first birth) were causally associated with at least one of the mental disorders.

Conclusions

MR study provides robust evidence that SES/IB play broad impacts on mental disorders.

Genome Guided Personalized Drug Therapy in Attention Deficit Hyperactivity Disorder

ADHD is a common behavioral syndrome with a heritability of 70-80%. Genome wide sequencing and association studies indicate that ADHD risk variants are distributed across a wide range of allele frequencies and relative risks. Several common single nucleotide variants (SNPs) have been identified that increase the risk of ADHD with a few percent. Many of the reported risk genes and copy number variants are shared with other neuropsychiatric disorders. Moreover, ADHD often coexists with common or rare somatic diseases, including rare Mendelian neurometabolic diseases that can affect normal brain development and function. Some genetic/metabolic syndromes masquerading as common ADHD may lead to irreversible brain damage if not properly identified and treated during early childhood. As ADHD is such a heterogeneous condition in terms of severity, clinical features and most probably also underlying biology, it is crucial to offer individualized treatments. Recent progress in ADHD genetics is reviewed, prospects of using this information for targeted pharmacotherapy are discussed and critical knowledge gaps are identified. It is suggested that genome guided therapies could be introduced gradually, starting with rare ADHD syndromes with highly penetrant risk genes. Routine diagnostic application of whole exome or whole genome sequencing combined with metabolomic screening, and brain imaging may be needed in cases with suspected neurometabolic disorders. Identification and treatment of ADHD patients with defined neurometabolic aberrations could be a first step toward genome guided personalized treatment of ADHD. Possibly, screening for relevant biomarkers may gradually be implemented to guide treatment choices in larger patient groups.