The metabolome-wide signature of major depressive disorder

MacDonald K, Krishnan A, Cervenka E, Hu G, Guadagno E, Trakadis Y. Biomarkers for major depressive and bipolar disorders using metabolomics: A systematic review. Am J Med Genet B Neuropsychiatr Genet. 2019;180:122–37.

Article  PubMed  Google Scholar 

Bot M, Milaneschi Y, Al-Shehri T, Amin N, Garmaeva S, Onderwater GLJ, et al. Metabolomics Profile in Depression: A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls. Biological Psychiatry. 2020;87:409–18.

Article  CAS  PubMed  Google Scholar 

Julkunen H, Cichońska A, Tiainen M, Koskela H, Nybo K, Mäkelä V, et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun. 2023;14:604.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pan A, Keum N, Okereke OI, Sun Q, Kivimaki M, Rubin RR, et al. Bidirectional association between depression and metabolic syndrome: a systematic review and meta-analysis of epidemiological studies. Diabetes Care. 2012;35:1171–80.

Article  PubMed  PubMed Central  Google Scholar 

Milaneschi Y, Simmons WK, van Rossum EFC, Penninx BW. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24:18–33.

Article  CAS  PubMed  Google Scholar 

Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care. 2008;31:2383–90.

Article  PubMed  PubMed Central  Google Scholar 

Van der Kooy K, van Hout H, Marwijk H, Marten H, Stehouwer C, Beekman A. Depression and the risk for cardiovascular diseases: systematic review and meta analysis. Int J Geriatr Psychiatry. 2007;22:613–26.

Article  PubMed  Google Scholar 

Zacharias HU, Hertel J, Johar H, Pietzner M, Lukaschek K, Atasoy S, et al. A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression. Mol Psychiatry. 2021;26:7372–83.

Article  CAS  PubMed  PubMed Central  Google Scholar 

van der Spek A, Stewart ID, Kühnel B, Pietzner M, Alshehri T, Gauß F, et al. Circulating metabolites modulated by diet are associated with depression. Mol Psychiatry. 2023;28:3874–87.

Milaneschi Y, Kappelmann N, Ye Z, Lamers F, Moser S, Jones PB, et al. Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts. Mol Psychiatry. 2021;26:7393–402.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Milaneschi Y, Arnold M, Kastenmüller G, Dehkordi SM, Krishnan RR, Dunlop BW, et al. Genomics-based identification of a potential causal role for acylcarnitine metabolism in depression. J Affect Disord. 2022;307:254–63.

Article  CAS  PubMed  Google Scholar 

Milaneschi Y, Peyrot WJ, Nivard MG, Mbarek H, Boomsma DI, Penninx WJH. B. A role for vitamin D and omega-3 fatty acids in major depression? An exploration using genomics. Transl Psychiatry. 2019;9:219.

Article  PubMed  PubMed Central  Google Scholar 

Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med. 2022;28:2321–32.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience. 2019;22:343.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Penninx BWJH, Eikelenboom M, Giltay EJ, van Hemert AM, Riese H, Schoevers RA, et al. Cohort profile of the longitudinal Netherlands Study of Depression and Anxiety (NESDA) on etiology, course and consequences of depressive and anxiety disorders. J Affect Disord. 2021;287:69–77.

Article  PubMed  Google Scholar 

Penninx BWJH, Beekman ATF, Smit JH, Zitman FG, Nolen WA, Spinhoven P, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res. 2008;17:121–40.

Article  PubMed  PubMed Central  Google Scholar 

Do KT, Wahl S, Raffler J, Molnos S, Laimighofer M, Adamski J, et al. Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics. 2018;14:128.

Article  PubMed  PubMed Central  Google Scholar 

Rush AJ, Giles DE, Schlesser MA, Fulton CL, Weissenburger J, Burns C. The Inventory for Depressive Symptomatology (IDS): preliminary findings. Psychiatry Res. 1986;18:65–87.

Article  CAS  PubMed  Google Scholar 

Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exercise. 2003;35:1381–95.

Article  Google Scholar 

Gerrits MM, van Oppen P, van Marwijk HW, van der Horst H, Penninx BW. The impact of chronic somatic diseases on the course of depressive and anxiety disorders. Psychother Psychosom. 2013;82:64–66.

Article  PubMed  Google Scholar 

Gaspersz R, Lamers F, Beekman ATF, van Hemert AM, Schoevers RA, Penninx BWJH. The Impact of Depressive Disorder Symptoms and Subtypes on 6-Year Incidence of Somatic Diseases. Psychother Psychosom. 2018;87:308–10.

Article  PubMed  Google Scholar 

Liu J, Lahousse L, Nivard MG, Bot M, Chen L, van Klinken JB, et al. Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas. Nat Med. 2020;26:110–7.

Article  CAS  PubMed  Google Scholar 

Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40:597–608.

Article  PubMed  PubMed Central  Google Scholar 

Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.

Article  PubMed  PubMed Central  Google Scholar 

Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304–14.

Article  PubMed  PubMed Central  Google Scholar 

Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

Article  PubMed  PubMed Central  Google Scholar 

Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36:1783–802.

Article  PubMed  PubMed Central  Google Scholar 

Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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

Article  PubMed  PubMed Central  Google Scholar 

Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–52.

Article  PubMed  Google Scholar 

Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51:1339–48.

Article  CAS  PubMed 

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