Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study

Discover Mental Health - Tập 4 Số 1
Jon Berner1, Animesh Acharjee2
1Woodinville Psychiatric Associates, 18500 156Th Ave NE #100, Woodinville, WA, 98072, USA
2Institute of Cancer and Genomics Sciences, University of Birmingham, Birmingham, UK

Tóm tắt

AbstractDepression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting for ~ 32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.

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Tài liệu tham khảo

Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62:593–602.

Gonda X, Hullam G, Antal P, Eszlari N, Petschner P, Hokfelt TG, et al. Significance of risk polymorphisms for depression depends on stress exposure. Sci Rep. 2018;8:3946.

Dunn EC, Brown RC, Dai Y, Rosand J, Nugent NR, Amstadter AB, et al. Genetic determinants of depression: recent findings and future directions. Harv Rev Psychiatry. 2015;23:1–18.

Conway CR, George MS, Sackeim HA. Toward an evidence-based, operational definition of treatment-resistant depression: when enough is enough. JAMA Psychiat. 2017;74:9–10.

Yoshimi N, Futamura T, Bergen SE, Iwayama Y, Ishima T, Sellgren C, et al. Cerebrospinal fluid metabolomics identifies a key role of isocitrate dehydrogenase in bipolar disorder: evidence in support of mitochondrial dysfunction hypothesis. Mol Psychiatry. 2016;21:1504–10.

Pan LA, Martin P, Zimmer T, Segreti AM, Kassiff S, McKain BW, et al. Neurometabolic disorders: potentially treatable abnormalities in patients with treatment-refractory depression and suicidal behavior. Am J Psychiatry. 2017;174:42–50.

Berner J, Elsea S. Cerebrospinal fluid metabolomics uniquely identifies pathways suggesting risk for anesthesia reactions during electroconvulsive therapy for bipolar disorder. Bipolar Disord. 2020;22:874–5.

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13.

Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166:1092–7.

Ustun TB, Chatterji S, Kostanjsek N, Rehm J, Kennedy C, Epping-Jordan J, et al. Developing the world health organization disability assessment schedule 2.0. Bull World Health Organ. 2010;88:815–23.

Konecky B, Meyer EC, Marx BP, Kimbrel NA, Morissette SB. Using the WHODAS 2.0 to assess functional disability associated with DSM-5 mental disorders. Am J Psychiatry. 2014;171:818–20.

Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord. 2001;31:5–17.

Gardner A, Boles RG. Symptoms of somatization as a rapid screening tool for mitochondrial dysfunction in depression. Biopsychosoc Med. 2008;2:7.

Berner JE. Cost Effectiveness of IV Ketamine for Treatment-Resistant Bipolar Depression. Arch Gen Psychiatry. 2010;67:Comment.

Kennedy AD, Pappan KL, Donti TR, Evans AM, Wulff JE, Miller LAD, et al. Elucidation of the complex metabolic profile of cerebrospinal fluid using an untargeted biochemical profiling assay. Mol Genet Metab. 2017;121:83–90.

Arning E, Bottiglieri T. Quantitation of 5-methyltetrahydrofolate in cerebrospinal fluid using liquid chromatography-electrospray tandem mass spectrometry. Methods Mol Biol. 2016;1378:175–82.

Office for Human Research Protections. Subpart A of 45 CFR Part 46: basic policy for protection of human subjects. Rockville: US Department of Health and Human Services; 2018.

R core team. The R project for statistical computing. Version 4. 2022. https://www.r-project.org/

Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37:W652–60.

Breiman L. Random forests. Mach Learn. 2001;45:5–32.

Leurgans SE, Moyeed RA, Silverman BW. Canonical correlation analysis when the data are curves. J R Statist Soc B. 1993;55:725–40.

Rohart F, Gautier B, Singh A, Le Cao KA. mixOmics: an R package for ’omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752.

Acharjee A, Hazeldine J, Bazarova A, Deenadayalu L, Zhang J, Bentley C, et al. Integration of metabolomic and clinical data improves the prediction of intensive care unit length of stay following major traumatic injury. Metabolites. 2021. https://doi.org/10.3390/metabo12010029.

Agarwal P, Wicklow BA, Dart AB, Hizon NA, Sellers EAC, McGavock JM, et al. Integrative analysis reveals novel associations between DNA methylation and the serum metabolome of adolescents with type 2 diabetes: a cross-sectional study. Front Endocrinol (Lausanne). 2022;13:934706.

Smits B, van den Heuvel L, Knoop H, Kusters B, Janssen A, Borm G, et al. Mitochondrial enzymes discriminate between mitochondrial disorders and chronic fatigue syndrome. Mitochondrion. 2011;11:735–8.

Frye RE, Lionnard L, Singh I, Karim MA, Chajra H, Frechet M, et al. Mitochondrial morphology is associated with respiratory chain uncoupling in autism spectrum disorder. Transl Psychiatry. 2021;11:527.

Beech RD, Leffert JJ, Lin A, Sylvia LG, Umlauf S, Mane S, et al. Gene-expression differences in peripheral blood between lithium responders and non-responders in the Lithium Treatment-Moderate dose Use Study (LiTMUS). Pharmacogenomics J. 2014;14:182–91.

Morgan PG, Hoppel CL, Sedensky MM. Mitochondrial defects and anesthetic sensitivity. Anesthesiology. 2002;96:1268–70.

Li N, Lee B, Liu RJ, Banasr M, Dwyer JM, Iwata M, et al. mTOR-dependent synapse formation underlies the rapid antidepressant effects of NMDA antagonists. Science. 2010;329:959–64.

Huang H, Tang S, Ji M, Tang Z, Shimada M, Liu X, et al. p300-mediated lysine 2-hydroxyisobutyrylation regulates glycolysis. Mol Cell. 2018;70(663–78):e6.

Margolis LM, Karl JP, Wilson MA, Coleman JL, Ferrando AA, Young AJ, et al. Metabolomic profiles are reflective of hypoxia-induced insulin resistance during exercise in healthy young adult males. Am J Physiol Regul Integr Comp Physiol. 2021;321:R1–11.

Moaddel R, Zanos P, Farmer CA, Kadriu B, Morris PJ, Lovett J, et al. Comparative metabolomic analysis in plasma and cerebrospinal fluid of humans and in plasma and brain of mice following antidepressant-dose ketamine administration. Transl Psychiatry. 2022;12:179.

Johnston KJA, Adams MJ, Nicholl BI, Ward J, Strawbridge RJ, Ferguson A, et al. Genome-wide association study of multisite chronic pain in UK Biobank. PLoS Genet. 2019;15:e1008164.

Yao W, Cao Q, Luo S, He L, Yang C, Chen J, et al. Microglial ERK-NRBP1-CREB-BDNF signaling in sustained antidepressant actions of (R)-ketamine. Mol Psychiatry. 2022;27:1618–29.

Glynn EL, Fry CS, Drummond MJ, Timmerman KL, Dhanani S, Volpi E, et al. Excess leucine intake enhances muscle anabolic signaling but not net protein anabolism in young men and women. J Nutr. 2010;140:1970–6.

Tajerian M, Leu D, Yang P, Huang TT, Kingery WS, Clark JD. Differential efficacy of ketamine in the acute versus chronic stages of complex regional pain syndrome in mice. Anesthesiology. 2015;123:1435–47.

Ma L, Zhang J, Fujita Y, Qu Y, Shan J, Wan X, et al. Nuclear factor of activated T cells 4 in the prefrontal cortex is required for prophylactic actions of (R)-ketamine. Transl Psychiatry. 2022;12:27.

Ting SM, Zhao X, Zheng X, Aronowski J. Excitatory pathway engaging glutamate, calcineurin, and NFAT upregulates IL-4 in ischemic neurons to polarize microglia. J Cereb Blood Flow Metab. 2020;40:513–27.

Castillo-Quan JI, Tain LS, Kinghorn KJ, Li L, Gronke S, Hinze Y, et al. A triple drug combination targeting components of the nutrient-sensing network maximizes longevity. Proc Natl Acad Sci U S A. 2019;116:20817–9.

Kaddurah-Daouk R, Yuan P, Boyle SH, Matson W, Wang Z, Zeng ZB, et al. Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile. Sci Rep. 2012;2:667.

Power RA, Kyaga S, Uher R, MacCabe JH, Langstrom N, Landen M, et al. Fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa, or substance abuse vs their unaffected siblings. JAMA Psychiat. 2013;70:22–30.

Campos D, Marques A, Nguyen T, Kurtz M, Zhai C. Sparse*BERT: sparse models generalize to new tasks and domains. arXiv. 2022. https://doi.org/10.48550/arXiv.2205.12452.

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