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. 2023 Jun 1;80(6):597-609.
doi: 10.1001/jamapsychiatry.2023.0685.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Najaf Amin  1 , Jun Liu  1 , Bruno Bonnechere  1   2   3 , Siamak MahmoudianDehkordi  4 , Matthias Arnold  4   5 , Richa Batra  6 , Yu-Jie Chiou  1   7 , Marco Fernandes  8 , M Arfan Ikram  9 , Robert Kraaij  10 , Jan Krumsiek  6 , Danielle Newby  8 , Kwangsik Nho  11 , Djawad Radjabzadeh  10 , Andrew J Saykin  11 , Liu Shi  8 , William Sproviero  8 , Laura Winchester  8 , Yang Yang  1   12 , Alejo J Nevado-Holgado  8 , Gabi Kastenmüller  5 , Rima Kaddurah-Daouk  4 , Cornelia M van Duijn  1
Affiliations

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Najaf Amin et al. JAMA Psychiatry. .

Abstract

Importance: Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression.

Objective: To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD.

Design, setting and participants: This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021.

Main outcomes and measures: Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform.

Results: The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not.

Conclusions and relevance: The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Arnold reported grants from the National Institute on Aging during the conduct of the study; other from Chymia (equity and principal investigator) and PsyProtix (principal investigator) outside the submitted work; and a patent for “methods for identification, stratification, and treatment of CNS diseases” pending. Dr Saykin reported grants from National Institutes of Health to Indiana University outside the submitted work and support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of positron emission tomography tracer precursor); Bayer Oncology (scientific advisory board); Eisai (scientific advisory board); Siemens (dementia advisory board); Springer-Nature Publishing (editorial office support as editor in chief, Brain Imaging and Behavior). Dr Nevado-Holgado reported grants from GSK, Johnson & Johnson, Ono Pharma, and Novo Nordisk outside the submitted work. Dr Kastenmüller reports grants from National Institute on Aging during the conduct of the study; equity from Chymia outside the submitted work; patents issued (WO2013006278A3, WO2018157014A1, WO2018157013A8, WO2018049268A8, WO2020185563A1, and WO2021016466A1); and is coinventor on several (provisional and issued) patents on the application of metabolomics in diseases of the central nervous system. Some of those are licensed to Chymia and Psyprotix/Atai that are investigating the potential for therapeutic applications targeting mitochondrial metabolism in treatment-resistant depression. Dr Kaddurah-Daouk in an inventor on a series of patents on use of metabolomics for the diagnosis and treatment of central nervous system diseases and holds equity in Metabolon, Chymia, and PsyProtix. M.A. and G.K. received funding (through their institutions) from the National Institutes of Health/National Institute on Aging through grants RF1AG058942, RF1AG059093, U01AG061359, U19AG063744, and R01AG069901. M.A. and G.K. are coinventors (through Duke University/Helmholtz Zentrum München) on patents on applications of metabolomics in diseases of the central nervous system. Dr Kaddurah-Daouk reported grants from National Institute on Aging during the conduct of the study; grants from National Institute on Aging outside the submitted work; a patent issued for use of metabolomics in Alzheimer disease; is an inventor on a series of patents on use of metabolomics for the diagnosis and treatment of Alzheimer disease; had a role on advising boards at Chymia and PsyProtix; had a role in leading various committees of the Metabolomics Society and the Metabolomics Association of North America; and holds equity in Metabolon, Chymia, and PsyProtix. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. A Conceptual Framework of the Study
Arrows depict the directionality of association, and darker shading indicates stronger associations. The green arrows depict associations that were neither studied nor used in the current study.
Figure 2.
Figure 2.. Association of Metabolic Profiles (z Scores) of Healthy Gut Microbiota and Major Depressive Disorder (MDD)
Scatterplots showing correlations between the metabolic signatures of microbial taxa and MDD. Taxa that show inverse correlation with the metabolic signatures of MDD are shown. Each dot represents a metabolite. The x-axis shows the association of the metabolite with microbial taxa, and the y-axis shows the association of the metabolite with MDD. Different colors of the dots represent the class of the metabolite, and the metabolites highlighted with black circles are significantly associated with MDD in model 4.
Figure 3.
Figure 3.. Association of Metabolic Profiles (z Scores) of Unhealthy Microbiota and Major Depressive Disorder (MDD)
Scatterplots showing the correlation between the metabolic signatures of microbial taxa and MDD. Taxa that show positive correlation with the metabolic signatures of MDD are shown. The x-axis shows the association of the metabolite with microbial taxa, and the y-axis shows the association of the metabolite with MDD. Different colors of the dots represent the class of the metabolite, and the metabolites highlighted with black circles are significantly associated with MDD in model 4.
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