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Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders.

ABSTRACT: 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. 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. 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. 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.

SUBMITTER: Jia Y

PROVIDER: S-EPMC10016401 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders.

Jia Yiming Y Hui Li L Sun Lulu L Guo Daoxia D Shi Mengyao M Zhang Kaixin K Yang Pinni P Wang Yu Y Liu Fanghua F Shen Ouxi O Zhu Zhengbao Z

Schizophrenia bulletin 20230301 2

<h4>Background and hypothesis</h4>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.<h4>Study design</h4>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 defici ...[more]

PMID: 36124769

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Project description:Myocarditis is a common disease of the cardiovascular and immune systems, but the relationship between relevant blood metabolites and the risk of myocarditis has not been well-established. To identify potential biometabolic markers associated with myocarditis, we conducted a two-sample Mendelian randomization (MR) study. We performed preliminary MR analysis using the inverse variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode methods to adjust for false discovery rate (FDR). Confounders were screened using the GWAS Catalog website. Sensitivity analyses included Cochrane Q-test, Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), scatterplots, funnel plots, and forest plots. For genetic and directional analysis, we employed co-localization analysis and the Steiger test. MR analysis was performed using the FinnGen database and meta-analysis was performed using the IEU database. MR analysis identified significant correlations for five metabolic biomarkers after FDR correction. These included four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, and 5-acetylamino-6-formylamino-3-methyluracil, as well as one unknown metabolite, X-25,422. Among these, kynurenine (OR = 1.441, 95%CI = 1.089-1.906, p-value = 0.018) and 1-stearoyl-GPE (18:0) (OR = 1.263, 95%CI = 1.029-1.550, p-value = 0.029) were identified as risk factors for myocarditis, while deoxycarnitine (OR = 0.813, 95%CI = 0.676-0.979, p-value = 0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.864, 95% CI = 0.775-0.962, p-value = 0.018), and X-25,422 (OR = 0.721, 95%CI = 0.587-0.886, p-value = 0.009) were found to be protective factors. No evidence of heterogeneity, horizontal pleiotropy, or sensitivity issues was observed, and no shared genetic factors between exposure and outcome were detected. The causality was in the correct direction. Meta-analysis further confirmed the causal relationship between the five metabolites and myocarditis. This study identifies a causal relationship between five circulating metabolites and myocarditis. Kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, X-25,422, and 5-acetylamino-6-formylamino-3-methyluracil may serve as potential drug targets for myocarditis, providing a theoretical basis for the prevention, diagnosis, and treatment of the condition.

Project description:BackgroundDelirium significantly contributes to both mortality and morbidity among hospitalized older adults. Furthermore, delirium leads to escalated healthcare expenditures, extended hospital stays, and enduring cognitive deterioration, all of which are acknowledged detrimental outcomes. Nonetheless, the current strategies for predicting and managing delirium remain constrained. Our aim was to employ Mendelian randomization (MR) to investigate the potential causal relationship between metabolites and delirium, as well as to identify potential therapeutic targets.MethodsWe identified 129 distinct blood metabolites from three genome-wide association studies (GWASs) conducted on the metabolome, involving a total of 147,827 participants of European descent. Genetic information pertaining to delirium was sourced from the ninth iteration of the Finngen Biobank, encompassing 359,699 individuals of Finnish ancestry. We conducted MR analyses to evaluate the connections between blood metabolites and delirium. Additionally, we extended our analysis to encompass the entire phenome using MR, aiming to uncover potential on-target consequences resulting from metabolite interventions.ResultsIn our investigation, we discovered three metabolites serving as causal mediators in the context of delirium: clinical low density lipoprotein cholesterol (LDL-C) (odds ratio [OR]: 1.47, 95% confidence interval [CI]: 1.25-1.73, p = 3.92 x 10-6), sphingomyelin (OR: 1.47, 95% CI: 1.25-1.74, p = 5.97 x 10-6), and X-11593-O-methylascorbate (OR: 0.21, 95% CI: 0.10-0.43, p = 1.86 x 10-5). Furthermore, utilizing phenome-wide MR analysis, we discerned that clinical LDL-C, sphingomyelin, and O-methylascorbate not only mediate delirium susceptibility but also impact the risk of diverse ailments.Limitations(1) Limited representation of the complete blood metabolome, (2) reliance on the PheCode system based on hospital diagnoses may underrepresent conditions with infrequent hospital admissions, and (3) limited to European ancestry.ConclusionThe genetic prediction of heightened O-methylascorbate levels seems to correspond to a diminished risk of delirium, in contrast to the association of elevated clinical LDL-C and sphingomyelin levels with an amplified risk. A comprehensive analysis of side-effect profiles has been undertaken to facilitate the prioritization of drug targets. Notably, O-methylascorbate emerges as a potentially auspicious target for mitigating and treating delirium, offering the advantage of lacking predicted adverse side effects.

Project description:BackgroundThough epidemiological studies show increased cardiovascular disease (CVD) risks among individuals with psychiatric disorders, findings on sex differences in comorbidity have been inconsistent.MethodsThis genetic epidemiology study examined the sex-specific association between the genetic risk of 3 psychiatric disorders (major depression [MD], schizophrenia, and bipolar disorder), estimated using polygenic scores (PGSs), and risks of 3 CVDs (atrial fibrillation [AF], coronary artery disease [CAD], and heart failure [HF]) in 345 169 European-ancestry individuals (UK Biobank), with analyses replicated in an independent BioVU cohort (n=49 057). Mediation analysis was conducted to determine whether traditional CVD risk factors could explain any observed sex difference.ResultsIn the UK Biobank, a 1-SD increase in PGSMD was significantly associated with the incident risks of all 3 CVDs in females after multiple testing corrections (hazard ratio [HR]AF-female=1.04 [95% CI, 1.02-1.06]; P=1.5×10-4; HRCAD-female=1.07 [95% CI, 1.04-1.11]; P=2.6×10-6; and HRHF-female=1.09 [95% CI, 1.06-1.13]; P=9.7×10-10), but not in males. These female-specific associations remained even in the absence of any psychiatric disorder diagnosis or psychiatric medication use. Although mediation analysis demonstrated that the association between PGSMD and CVDs in females was partly mediated by baseline body mass index, hypercholesterolemia, hypertension, and smoking, these risk factors did not explain the higher risk compared with males. The association between PGSMD and CAD was consistent between females who were premenopausal and postmenopausal at baseline, while the association with AF and HF was only observed in the baseline postmenopausal cohort. No significant association with CVD risks was observed for the PGS of schizophrenia or bipolar disorder. The female-specific positive association of PGSMD with CAD risk was replicated in BioVU.ConclusionsGenetic predisposition to MD confers a greater risk of CVDs in females versus males, even in the absence of any depression diagnosis. This study warrants further investigation into whether genetic predisposition to depression could be useful for improving cardiovascular risk prediction, especially in women.

Project description:IntroductionPatients with chronic health conditions, particularly chronic kidney disease, are at heightened risk for psychiatric disorders; yet, there are limited data on those with primary glomerular disease.MethodsThis study included patients with glomerular disease enrolled in the kidney research network multisite patient registry. Registry data include encounter, diagnoses, medication, laboratory, and vital signs data extracted from participants' electronic health records. ICD-9/10 diagnosis codes were used to identify a subset of psychiatric disorders focused on anxiety, mood, and behavioral disorders. Time-varying Cox proportional hazard models were used to analyze time from the onset of kidney disease to diagnosis of psychiatric disorder. Adjusted models retained significant covariates from the full list of potential confounders, including age, sex, race, ethnicity, time-varying treatment, the estimated glomerular filtration rate, and proteinuria (urine protein-to-creatinine ratio [UPCR]). Analogous models examined diagnosis of psychiatric disorder as a predictor of time to end-stage kidney disease (ESKD).ResultsData were available for 950 participants, with a median of 58 months of follow-up. 110 (12%) participants were diagnosed with psychiatric disorder during the follow-up. The estimated rate of psychiatric diagnosis after kidney disease was 14.7 cases per 1,000 person-years and was highest among those of adolescent age at the time of kidney disease diagnosis. Adjusted analyses found adolescent age (vs. adult, hazard ratio [HR] = 3.11, 95% confidence interval [CI] 1.87-5.17) and Asian race (vs. white, HR = 0.34, 95% CI 0.16-0.71) were associated with psychiatric diagnosis. A higher UPCR per 1 log unit (HR 1.13, 95% CI 1.01-1.27) and a higher total number of oral medications were associated with psychiatric disorder (p < 0.001). Psychiatric diagnosis was also associated with progression to ESKD (HR = 2.45, 95% CI 1.53-3.92) in adjusted models.Discussion/conclusionPsychiatric disorders were documented in approximately one-eighth of patients with glomerular disease and correlated with clinical disease characteristics such as age, race, proteinuria, and oral medication burden. These findings suggest mental health screening is warranted in patients of all ages with glomerular disease.

Project description:ImportanceUrban residence has been highlighted as an environmental risk factor for schizophrenia and, to a lesser extent, several other psychiatric disorders. However, few studies have explored genetic effects on the choice of residence.ObjectiveTo investigate whether individuals with genetic predisposition to a range of psychiatric disorders have an increased likelihood to live in urban areas.Design, setting, and participantsA cross-sectional retrospective cohort study including genotypes, address history, and geographic distribution of population density in the UK based on census data from 1931-2011 was conducted. Polygenic risk score (PRS) analyses, genome-wide association studies, genetic correlation, and 2-sample mendelian randomization analyses were applied to 385 793 UK Biobank participants with self-reported or general practitioner registration-based address history. The study was conducted from February 2018 to May 2021, and data analysis was performed from April 2018 to May 2021.Main outcomes and measuresPopulation density of residence at different ages and movement during the life span between urban and rural environments.ResultsIn this cohort study of 385 793 unrelated UK Biobank participants (207 963 [54%] were women; age, 37-73 years; mean [SD], 56.7 [8] years), PRS analyses showed significant associations with higher population density across adult life (age 25 to >65 years) reaching highest significance at the 45- to 55-year age group for schizophrenia (88 people/km2; 95% CI, 65-98 people/km2), bipolar disorder (44 people/km2; 95% CI, 34-54 people/km2), anorexia nervosa (36 people/km2; 95% CI, 22-50 people/km2), and autism spectrum disorder (35 people/km2; 95% CI, 25-45 people/km2). The schizophrenia PRS was also significantly associated with higher birthplace population density (37 people/km2; 95% CI, 19-55 people/km2; P = 8 × 10-5). Attention-deficit/hyperactivity disorder PRS was significantly associated with reduced population density in adult life (-31 people/km2; 95% CI, -42 to -20 people/km2 at age 35-45 years). Individuals with higher PRS for schizophrenia, bipolar disorder, anorexia nervosa, and autism spectrum disorder and lower PRS for attention-deficit/hyperactivity disorder preferentially moved from rural environments to cities (difference in PRS with Tukey pairwise comparisons for schizophrenia: 0.05; 95% CI, 0.03 to 0.60; bipolar disorder: 0.10; 95% CI, 0.08 to 0.13; anorexia nervosa: 0.05; 95% CI, 0.03 to 0.07; autism spectrum disorder: 0.04; 95% CI 0.03 to 0.06; and attention-deficit/hyperactivity disorder: -0.09, 95% CI, -0.12 to -0.06). Genetic correlation results were largely consistent with PRS analyses, whereas mendelian randomization provided support for associations between schizophrenia and bipolar disorder and living in high population-density areas.Conclusions and relevanceThese findings suggest that a high genetic risk for a variety of psychiatric disorders may affect an individual's choice of residence. This result supports the hypothesis of genetic selection of an individual's environment, which intersects the traditional gene-environment dichotomy.

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Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders.

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Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders.

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