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Burghardt KJ, Kajy M, Ward KM, Burghardt PR. Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review. Biomedicines 2023; 11:3295. [PMID: 38137517 PMCID: PMC10741000 DOI: 10.3390/biomedicines11123295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/06/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.
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Affiliation(s)
- Kyle J. Burghardt
- Department of Pharmacy Practice, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University Detroit, Detroit, MI 48201, USA;
| | - Megan Kajy
- Department of Pharmacy Practice, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University Detroit, Detroit, MI 48201, USA;
| | - Kristen M. Ward
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan Ann Arbor, Detroit, MI 48109, USA;
| | - Paul R. Burghardt
- Department of Nutrition and Food Science, Wayne State University Detroit, Detroit, MI 48201, USA;
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Prince N, Stav M, Cote M, Chu SH, Vyas CM, Okereke OI, Palacios N, Litonjua AA, Vokonas P, Sparrow D, Spiro A, Lasky-Su JA, Kelly RS. Metabolomics and Self-Reported Depression, Anxiety, and Phobic Symptoms in the VA Normative Aging Study. Metabolites 2023; 13:851. [PMID: 37512558 PMCID: PMC10383599 DOI: 10.3390/metabo13070851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/27/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Traditional approaches to understanding metabolomics in mental illness have focused on investigating a single disorder or comparisons between diagnoses, but a growing body of evidence suggests substantial mechanistic overlap in mental disorders that could be reflected by the metabolome. In this study, we investigated associations between global plasma metabolites and abnormal scores on the depression, anxiety, and phobic anxiety subscales of the Brief Symptom Inventory (BSI) among 405 older males who participated in the Normative Aging Study (NAS). Our analysis revealed overlapping and distinct metabolites associated with each mental health dimension subscale and four metabolites belonging to xenobiotic, carbohydrate, and amino acid classes that were consistently associated across all three symptom dimension subscales. Furthermore, three of these four metabolites demonstrated a higher degree of alteration in men who reported poor scores in all three dimensions compared to men with poor scores in only one, suggesting the potential for shared underlying biology but a differing degree of perturbation when depression and anxiety symptoms co-occur. Our findings implicate pathways of interest relevant to the overlap of mental health conditions in aging veterans and could represent clinically translatable targets underlying poor mental health in this high-risk population.
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Affiliation(s)
- Nicole Prince
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (N.P.); (M.S.); (M.C.); (S.H.C.); (O.I.O.); (J.A.L.-S.)
- Harvard Medical School, Boston, MA 02115, USA;
| | - Meryl Stav
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (N.P.); (M.S.); (M.C.); (S.H.C.); (O.I.O.); (J.A.L.-S.)
| | - Margaret Cote
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (N.P.); (M.S.); (M.C.); (S.H.C.); (O.I.O.); (J.A.L.-S.)
| | - Su H. Chu
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (N.P.); (M.S.); (M.C.); (S.H.C.); (O.I.O.); (J.A.L.-S.)
- Harvard Medical School, Boston, MA 02115, USA;
| | - Chirag M. Vyas
- Harvard Medical School, Boston, MA 02115, USA;
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Olivia I. Okereke
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (N.P.); (M.S.); (M.C.); (S.H.C.); (O.I.O.); (J.A.L.-S.)
- Harvard Medical School, Boston, MA 02115, USA;
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA;
| | - Natalia Palacios
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA;
- Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA
- Geriatric Research Education Clinical Center, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA 01730, USA
| | - Augusto A Litonjua
- Division of Pediatric Pulmonary Medicine, Golisano Children’s Hospital at Strong, University of Rochester Medical Center, Rochester, NY 14642, USA;
| | - Pantel Vokonas
- Department of Veterans Affairs, Boston, MA 02114, USA; (P.V.); (D.S.)
- VA Normative Aging Study, VA Boston Healthcare System, Boston, MA 02130, USA;
| | - David Sparrow
- Department of Veterans Affairs, Boston, MA 02114, USA; (P.V.); (D.S.)
- VA Normative Aging Study, VA Boston Healthcare System, Boston, MA 02130, USA;
- Department of Medicine, Boston University Chobanian and Avidisian School of Medicine, Boston, MA 02118, USA
| | - Avron Spiro
- VA Normative Aging Study, VA Boston Healthcare System, Boston, MA 02130, USA;
- Department of Medicine, Boston University Chobanian and Avidisian School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
- Department of Psychiatry, Boston University Chobanian and Avidisian School of Medicine, Boston, MA 02118, USA
| | - Jessica A. Lasky-Su
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (N.P.); (M.S.); (M.C.); (S.H.C.); (O.I.O.); (J.A.L.-S.)
- Harvard Medical School, Boston, MA 02115, USA;
| | - Rachel S. Kelly
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (N.P.); (M.S.); (M.C.); (S.H.C.); (O.I.O.); (J.A.L.-S.)
- Harvard Medical School, Boston, MA 02115, USA;
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3
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Amdanee N, Shao M, Hu X, Fang X, Zhou C, Chen J, Ridwan Chattun M, Wen L, Pan X, Zhang X, Xu Y. Serum Metabolic Profile in Schizophrenia Patients With Antipsychotic-Induced Constipation and Its relationship With Gut Microbiome. Schizophr Bull 2023; 49:646-658. [PMID: 36723169 PMCID: PMC10154739 DOI: 10.1093/schbul/sbac202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND AND HYPOTHESIS Antipsychotics (APs), the cornerstone of schizophrenia treatment, confer a relatively high risk of constipation. However, the mechanisms underpinning AP-induced constipation are poorly understood. Thus, we hypothesized that (1) schizophrenia patients with AP-induced constipation have distinct metabolic patterns; (2) there is more than one mechanism at play in producing this adverse drug effect; and (3) AP-associated changes in the gut microbiome are related to the altered metabolic profiles. STUDY DESIGN Eighty-eight schizophrenia patients, including 44 with constipation (C) and 44 matched patients without constipation (NC), were enrolled in this study. Constipation was diagnosed by Rome IV criteria for constipation and colonic transit time using radiopaque markers (ROMs) while severity was evaluated with the Bristol Stool Form Scale (BSS) and Constipation Assessment Scale (CAS). Fasting blood samples were drawn from all participants and were subjected to non-targeted liquid chromatography-mass spectrometry (LC-MS) metabolomic analysis. STUDY RESULTS Eleven metabolites were significantly altered in AP-induced constipation which primarily disturbed sphingolipid metabolism, choline metabolism, and sphingolipid signaling pathway (P value < .05, FDR < 0.05). In the C group, changes in the gut bacteria showed a certain degree of correlation with 2 of the significantly altered serum metabolites and were associated with alterations in choline metabolism. CONCLUSIONS Our findings indicated that there were disturbances in distinct metabolic pathways that were associated with AP-induced constipation. In addition, this study presents evidence of a link between alterations in the gut microbiome and host metabolism which provides additional mechanistic insights on AP-induced constipation.
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Affiliation(s)
- Nousayhah Amdanee
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Miaomiao Shao
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Department of Psychiatry, The Second People’s Hospital of Jiangning District, Nanjing, Jiangsu, China
| | - Xiuxiu Hu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Department of Psychiatry, The Second People’s Hospital of Jiangning District, Nanjing, Jiangsu, China
| | - Xinyu Fang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Lu Wen
- Department of Psychiatry, The Second People’s Hospital of Jiangning District, Nanjing, Jiangsu, China
| | - Xinming Pan
- Department of Psychiatry, The Second People’s Hospital of Jiangning District, Nanjing, Jiangsu, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- The Affiliated Xuzhou Oriental Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yue Xu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Lee J, Costa-Dookhan K, Panganiban K, MacKenzie N, Treen QC, Chintoh A, Remington G, Müller DJ, Sockalingam S, Gerretsen P, Sanches M, Karnovsky A, Stringer KA, Ellingrod VL, Tso IF, Taylor SF, Agarwal SM, Hahn MK, Ward KM. Metabolomic signatures associated with weight gain and psychosis spectrum diagnoses: A pilot study. Front Psychiatry 2023; 14:1169787. [PMID: 37168086 PMCID: PMC10164938 DOI: 10.3389/fpsyt.2023.1169787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 03/27/2023] [Indexed: 05/13/2023] Open
Abstract
Psychosis spectrum disorders (PSDs), as well as other severe mental illnesses where psychotic features may be present, like bipolar disorder, are associated with intrinsic metabolic abnormalities. Antipsychotics (APs), the cornerstone of treatment for PSDs, incur additional metabolic adversities including weight gain. Currently, major gaps exist in understanding psychosis illness biomarkers, as well as risk factors and mechanisms for AP-induced weight gain. Metabolomic profiles may identify biomarkers and provide insight into the mechanistic underpinnings of PSDs and antipsychotic-induced weight gain. In this 12-week prospective naturalistic study, we compared serum metabolomic profiles of 25 cases within approximately 1 week of starting an AP to 6 healthy controls at baseline to examine biomarkers of intrinsic metabolic dysfunction in PSDs. In 17 of the case participants with baseline and week 12 samples, we then examined changes in metabolomic profiles over 12 weeks of AP treatment to identify metabolites that may associate with AP-induced weight gain. In the cohort with pre-post data (n = 17), we also compared baseline metabolomes of participants who gained ≥5% baseline body weight to those who gained <5% to identify potential biomarkers of antipsychotic-induced weight gain. Minimally AP-exposed cases were distinguished from controls by six fatty acids when compared at baseline, namely reduced levels of palmitoleic acid, lauric acid, and heneicosylic acid, as well as elevated levels of behenic acid, arachidonic acid, and myristoleic acid (FDR < 0.05). Baseline levels of the fatty acid adrenic acid was increased in 11 individuals who experienced a clinically significant body weight gain (≥5%) following 12 weeks of AP exposure as compared to those who did not (FDR = 0.0408). Fatty acids may represent illness biomarkers of PSDs and early predictors of AP-induced weight gain. The findings may hold important clinical implications for early identification of individuals who could benefit from prevention strategies to reduce future cardiometabolic risk, and may lead to novel, targeted treatments to counteract metabolic dysfunction in PSDs.
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Affiliation(s)
- Jiwon Lee
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Kenya Costa-Dookhan
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Kristoffer Panganiban
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Nicole MacKenzie
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Quinn Casuccio Treen
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Araba Chintoh
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Gary Remington
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Daniel J. Müller
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
- Pharmacogenetics Research Clinic, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sanjeev Sockalingam
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Education, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Philip Gerretsen
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Geriatric Mental Health Services, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Marcos Sanches
- Biostatistics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Kathleen A. Stringer
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, United States
| | - Vicki L. Ellingrod
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, United States
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Ivy F. Tso
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Psychiatry & Behavioral Health, Ohio State University, Columbus, OH, United States
| | - Stephan F. Taylor
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Sri Mahavir Agarwal
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Banting and Best Diabetes Centre, University of Toronto, Toronto, ON, Canada
| | - Margaret K. Hahn
- Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Banting and Best Diabetes Centre, University of Toronto, Toronto, ON, Canada
| | - Kristen M. Ward
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
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Song M, Liu Y, Zhou J, Shi H, Su X, Shao M, Yang Y, Wang X, Zhao J, Guo D, Liu Q, Zhang L, Zhang Y, Lv L, Li W. Potential plasma biomarker panels identification for the diagnosis of first-episode schizophrenia and monitoring antipsychotic monotherapy with the use of metabolomics analyses. Psychiatry Res 2023; 321:115070. [PMID: 36706560 DOI: 10.1016/j.psychres.2023.115070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 01/17/2023] [Accepted: 01/22/2023] [Indexed: 01/25/2023]
Abstract
Schizophrenia (SCZ) is a severe mental disorder. Using liquid chromatography mass spectrometry, we performed comprehensive metabolomics analyses of plasma samples from healthy controls (HC) and first episode SCZ patients before and after an acute period of medication. Ten lipid metabolites and 27 soluble small molecules were identified as potential biomarkers associated with the diagnosis and treatment of SCZ. These metabolites were significantly reduced in SCZ, and lipids and sulfate were significantly increased after treatment. Of the metabolites identified, four showed significant correlations with the Positive and Negative Syndrome Scale total scores. A biomarker panel composed of alpha-dimorphecolic, Phosphatidylcholine (PC) (16:0/18:1(11Z)), 1-methylnicotinamide, Phosphatidylethanolamine (PE) (20:2(11Z,14Z)/18:2(9Z,12Z)), sulfate, and L-tryptophan was selected to distinguish SCZ from HC; this provided the maximum classification performance with an AUC of 0.972. A biomarker panel including C16 sphinganine, gamma-linolenic acid, linoleic acid, PC(16:0/18:1(11Z)), PE(20:2(11Z,14Z)/18:2(9Z,12Z)), and sulfate, was selected for discrimination between SCZ before and after medication, and produced the optimal classification performance with an AUC of 0.905. Disturbances in lipid metabolism, sulfation modification, tryptophan metabolism, anti-inflammatory and antioxidant systems, and unsaturated fatty acids metabolism, were identified in SCZ. Our findings could facilitate the development of objective diagnostic or drug treatment monitoring tools for schizophrenia.
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Affiliation(s)
- Meng Song
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China.
| | - Ya Liu
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China
| | - Jiahui Zhou
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China
| | - Han Shi
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China
| | - Xi Su
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China
| | - Minglong Shao
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yongfeng Yang
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China
| | - Xiujuan Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China
| | - Jingyuan Zhao
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China
| | - Dong Guo
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China
| | - Qing Liu
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China
| | - Luwen Zhang
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yan Zhang
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China
| | - Luxian Lv
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China.
| | - Wenqiang Li
- The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, China.
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6
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Tkachev A, Stekolshchikova E, Vanyushkina A, Zhang H, Morozova A, Zozulya S, Kurochkin I, Anikanov N, Egorova A, Yushina E, Vogl T, Senner F, Schaupp SK, Reich-Erkelenz D, Papiol S, Kohshour MO, Klöhn-Saghatolislam F, Kalman JL, Heilbronner U, Heilbronner M, Gade K, Comes AL, Budde M, Anderson-Schmidt H, Adorjan K, Wiltfang J, Reininghaus EZ, Juckel G, Dannlowski U, Fallgatter A, Spitzer C, Schmauß M, von Hagen M, Zorkina Y, Reznik A, Barkhatova A, Lisov R, Mokrov N, Panov M, Zubkov D, Petrova D, Zhou C, Liu Y, Pu J, Falkai P, Kostyuk G, Klyushnik T, Schulze TG, Xie P, Schulte EC, Khaitovich P. Lipid Alteration Signature in the Blood Plasma of Individuals With Schizophrenia, Depression, and Bipolar Disorder. JAMA Psychiatry 2023; 80:250-259. [PMID: 36696101 PMCID: PMC9878436 DOI: 10.1001/jamapsychiatry.2022.4350] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/31/2022] [Indexed: 01/26/2023]
Abstract
Importance No clinically applicable diagnostic test exists for severe mental disorders. Lipids harbor potential as disease markers. Objective To define a reproducible profile of lipid alterations in the blood plasma of patients with schizophrenia (SCZ) independent of demographic and environmental variables and to investigate its specificity in association with other psychiatric disorders, ie, major depressive disorder (MDD) and bipolar disorder (BPD). Design, Setting, and Participants This was a multicohort case-control diagnostic analysis involving plasma samples from psychiatric patients and control individuals collected between July 17, 2009, and May 18, 2018. Study participants were recruited as consecutive and volunteer samples at multiple inpatient and outpatient mental health hospitals in Western Europe (Germany and Austria [DE-AT]), China (CN), and Russia (RU). Individuals with DSM-IV or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnoses of SCZ, MDD, BPD, or a first psychotic episode, as well as age- and sex-matched healthy controls without a mental health-related diagnosis were included in the study. Samples and data were analyzed from January 2018 to September 2020. Main Outcomes and Measures Plasma lipidome composition was assessed using liquid chromatography coupled with untargeted mass spectrometry. Results Blood lipid levels were assessed in 980 individuals (mean [SD] age, 36 [13] years; 510 male individuals [52%]) diagnosed with SCZ, BPD, MDD, or those with a first psychotic episode and in 572 controls (mean [SD] age, 34 [13] years; 323 male individuals [56%]). A total of 77 lipids were found to be significantly altered between those with SCZ (n = 436) and controls (n = 478) in all 3 sample cohorts. Alterations were consistent between cohorts (CN and RU: [Pearson correlation] r = 0.75; DE-AT and CN: r = 0.78; DE-AT and RU: r = 0.82; P < 10-38). A lipid-based predictive model separated patients with SCZ from controls with high diagnostic ability (area under the receiver operating characteristic curve = 0.86-0.95). Lipidome alterations in BPD and MDD, assessed in 184 and 256 individuals, respectively, were found to be similar to those of SCZ (BPD: r = 0.89; MDD: r = 0.92; P < 10-79). Assessment of detected alterations in individuals with a first psychotic episode, as well as patients with SCZ not receiving medication, demonstrated only limited association with medication restricted to particular lipids. Conclusions and Relevance In this study, SCZ was accompanied by a reproducible profile of plasma lipidome alterations, not associated with symptom severity, medication, and demographic and environmental variables, and largely shared with BPD and MDD. This lipid alteration signature may represent a trait marker of severe psychiatric disorders, indicating its potential to be transformed into a clinically applicable testing procedure.
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Affiliation(s)
- Anna Tkachev
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Elena Stekolshchikova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Anna Vanyushkina
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Hanping Zhang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Anna Morozova
- Department Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Moscow, Russia
- Moscow Psychiatric Hospital No. 1, named after N.A. Alekseev, Moscow, Russia
| | | | - Ilia Kurochkin
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Nickolay Anikanov
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Alina Egorova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Ekaterina Yushina
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- FSBSI N.P. Bochkov Research Center of Medical Genetics, Moscow, Russia
| | - Thomas Vogl
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Fanny Senner
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Sabrina K. Schaupp
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Daniela Reich-Erkelenz
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Mojtaba Oraki Kohshour
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Immunology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Farahnaz Klöhn-Saghatolislam
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Janos L. Kalman
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Maria Heilbronner
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Katrin Gade
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Ashley L. Comes
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Heike Anderson-Schmidt
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Kristina Adorjan
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- German Center for Neurodegenerative Diseases, Göttingen, Germany
- Neurosciences and Signaling Group, Institute of Medicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Eva Z. Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Neurobiology and Anthropometrics in Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Georg Juckel
- Department of Psychiatry, Ruhr University Bochum, LWL University Hospital, Bochum, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Fallgatter
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University Tübingen, Tübingen, Germany
| | - Carsten Spitzer
- Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Rostock, Rostock, Germany
| | - Max Schmauß
- Department of Psychiatry and Psychotherapy, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Martin von Hagen
- Clinic for Psychiatry and Psychotherapy, Clinical Center Werra-Meißner, Eschwege, Germany
| | - Yana Zorkina
- Department Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Moscow, Russia
- Moscow Psychiatric Hospital No. 1, named after N.A. Alekseev, Moscow, Russia
| | - Alexander Reznik
- Moscow Psychiatric Hospital No. 1, named after N.A. Alekseev, Moscow, Russia
- Moscow State University of Food Production, Moscow, Russia
| | | | - Roman Lisov
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Nikita Mokrov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Center for Artificial Intelligence Technologies, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Maxim Panov
- Technology Innovation Institute, Abu Dhabi, United Arab Emirates
| | - Dmitri Zubkov
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Daria Petrova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Chanjuan Zhou
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China
| | - Yiyun Liu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Juncai Pu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Georgiy Kostyuk
- Moscow Psychiatric Hospital No. 1, named after N.A. Alekseev, Moscow, Russia
| | | | - Thomas G. Schulze
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, New York
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Peng Xie
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
| | - Eva C. Schulte
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, medical Faculty University of Bonn, Bonn, Germany
| | - Philipp Khaitovich
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
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7
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Glucose and Lipid Profiles Predict Anthropometric Changes in Drug-Naïve Adolescents Starting Treatment with Risperidone or Sertraline: A Pilot Study. Biomedicines 2022; 11:biomedicines11010048. [PMID: 36672556 PMCID: PMC9855642 DOI: 10.3390/biomedicines11010048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/15/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
Psychiatric disorders are associated with cardiometabolic diseases, partly due to adverse drug effects with individual risk variabilities. Risperidone and sertraline are widely used for youths. Although they may be exposed to anthropometric changes, few data about this population exist. We evaluated the correlation between several blood parameters and body changes in a very small group of drug-naïve adolescents who had started risperidone or sertraline. We examined weight, waist circumference (WC), WC/height ratio and body mass index (BMI) at baseline (T0) and after at least three months of therapy (T1), and blood glucose and lipid profiles at T0. Here, we show significant increases in several anthropometric parameters in both groups, a negative correlation between HDL and ΔWC in the risperidone group and positive correlations between insulin and ΔBMI and between HOMA-IR and ΔBMI in the sertraline group. Despite the sample size, these results are important because it is difficult to study adolescents who are long-term-compliant with psychotropic drugs. This pilot study supports the importance of future large-scale investigations to understand the metabolic risk profiles of psychotropic drugs, their individual vulnerabilities and their underlying mechanisms. Simultaneous guideline-based psychiatric and metabolic interventions should be part of daily practice.
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8
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Khan MM. Role of de novo lipogenesis in insulin resistance in first-episode psychosis and therapeutic options. Neurosci Biobehav Rev 2022; 143:104919. [DOI: 10.1016/j.neubiorev.2022.104919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 10/07/2022] [Accepted: 10/15/2022] [Indexed: 11/06/2022]
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9
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Yin X, Mongan D, Cannon M, Zammit S, Hyötyläinen T, Orešič M, Brennan L, Cotter DR. Plasma lipid alterations in young adults with psychotic experiences: A study from the Avon Longitudinal Study of Parents and Children cohort. Schizophr Res 2022; 243:78-85. [PMID: 35245705 DOI: 10.1016/j.schres.2022.02.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 01/12/2022] [Accepted: 02/18/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Psychotic experiences (PEs) are associated with an increased risk of future psychotic and non-psychotic mental disorders. The identification of biomarkers of PEs may provide insights regarding the underlying pathophysiology. METHODS The current study applied targeted lipidomic approaches to compare plasma lipid profiles in participants from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort who did (n = 206) or did not (n = 206) have PEs when aged approximately 24 years. RESULTS In total, 202 lipids including 8 lipid classes were measured by using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS). Eight lipid clusters were generated. Thirteen individual lipids were nominally significantly higher in the PEs group compared to the control group. After correction for multiple comparisons, 9 lipids comprising 3 lysophosphatidylcholines (LPCs), 2 phosphatidylcholines (PCs) and 4 triacylglycerols (TGs) remained significant. In addition, PEs cases had increased levels of TGs and LPCs with a low double bond count. CONCLUSIONS These findings indicate plasma lipidomic abnormalities in individuals experiencing PEs. The lipidomic profile measures could aid our understanding of the underlying pathophysiological mechanisms.
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Affiliation(s)
- Xiaofei Yin
- Institute of Food and Health, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland.
| | - David Mongan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Beaumont Hospital, Dublin, Ireland
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Beaumont Hospital, Dublin, Ireland
| | - Stanley Zammit
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK; Centre for Academic Mental Health, School of Social & Community Medicine, University of Bristol, Bristol, UK
| | | | - Matej Orešič
- School of Medical Sciences, Örebro University, Örebro, Sweden; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Lorraine Brennan
- Institute of Food and Health, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland
| | - David R Cotter
- Department of Psychiatry, Royal College of Surgeons in Ireland, Beaumont Hospital, Dublin, Ireland.
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10
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Kim S, Okazaki S, Otsuka I, Shinko Y, Horai T, Shimmyo N, Hirata T, Yamaki N, Tanifuji T, Boku S, Sora I, Hishimoto A. Searching for biomarkers in schizophrenia and psychosis: Case-control study using capillary electrophoresis and liquid chromatography time-of-flight mass spectrometry and systematic review for biofluid metabolites. Neuropsychopharmacol Rep 2021; 42:42-51. [PMID: 34889082 PMCID: PMC8919119 DOI: 10.1002/npr2.12223] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/20/2021] [Accepted: 11/27/2021] [Indexed: 11/10/2022] Open
Abstract
Metabolomics has been attracting attention in recent years as an objective method for diagnosing schizophrenia. In this study, we analyzed 378 metabolites in the serum of schizophrenia patients using capillary electrophoresis‐ and liquid chromatography‐time‐of‐flight mass spectrometry. Using multivariate analysis with the orthogonal partial least squares method, we observed significantly higher levels of alanine, glutamate, lactic acid, ornithine, and serine and significantly lower levels of urea, in patients with chronic schizophrenia compared to healthy controls. Additionally, levels of fatty acids (15:0), (17:0), and (19:1), cis‐11‐eicosenoic acid, and thyroxine were significantly higher in patients with acute psychosis than in those in remission. Moreover, we conducted a systematic review of comprehensive metabolomics studies on schizophrenia over the last 20 years and observed consistent trends of increase in some metabolites such as glutamate and glucose, and decrease in citrate in schizophrenia patients across several studies. Hence, we provide substantial evidence for metabolic biomarkers in schizophrenia patients through our metabolomics study. In this study, we analyzed 378 metabolites in the serum of schizophrenia patients using CE and LC‐TOFMS. With a systematic review, here we provide substantial evidence for metabolic biomarkers in schizophrenia patients through our metabolomics study.![]()
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Affiliation(s)
- Saehyeon Kim
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Satoshi Okazaki
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Ikuo Otsuka
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yutaka Shinko
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tadasu Horai
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Naofumi Shimmyo
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takashi Hirata
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Naruhisa Yamaki
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takaki Tanifuji
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Shuken Boku
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan.,Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Ichiro Sora
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Akitoyo Hishimoto
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan.,Department of Psychiatry, Yokohama City University Graduate School of Medicine, Yokohama, Japan
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11
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Lenski M, Sidibé J, Gholam M, Hennart B, Dubath C, Augsburger M, von Gunten A, Conus P, Allorge D, Thomas A, Eap CB. Metabolomic alteration induced by psychotropic drugs: Short-term metabolite profile as a predictor of weight gain evolution. Clin Transl Sci 2021; 14:2544-2555. [PMID: 34387942 PMCID: PMC8604229 DOI: 10.1111/cts.13122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/26/2021] [Accepted: 07/10/2021] [Indexed: 11/28/2022] Open
Abstract
Psychotropic drugs can induce strong metabolic adverse effects, potentially increasing morbidity and/or mortality of patients. Metabolomic profiling, by studying the levels of numerous metabolic intermediates and products in the blood, allows a more detailed examination of metabolism dysfunctions. We aimed to identify blood metabolomic markers associated with weight gain in psychiatric patients. Sixty-two patients starting a treatment known to induce weight gain were recruited. Two hundred and six selected metabolites implicated in various pathways were analyzed in plasma, at baseline and after 1 month of treatment. Additionally, 15 metabolites of the kynurenine pathway were quantified. This latter analysis was repeated in a confirmatory cohort of 24 patients. Among the 206 metabolites, a plasma metabolomic fingerprint after 1 month of treatment embedded 19 compounds from different chemical classes (amino acids, acylcarnitines, carboxylic acids, catecholamines, nucleosides, pyridine, and tetrapyrrole) potentially involved in metabolic disruption and inflammation processes. The predictive potential of such early metabolite changes on 3 months of weight evolution was then explored using a linear mixed-effects model. Of these 19 metabolites, short-term modifications of kynurenine, hexanoylcarnitine, and biliverdin, as well as kynurenine/tryptophan ratio at 1 month, were associated with 3 months weight evolution. Alterations of the kynurenine pathway were confirmed by quantification, in both exploratory and confirmatory cohorts. Our metabolomic study suggests a specific metabolic dysregulation after 1 month of treatment with psychotropic drugs known to induce weight gain. The identified metabolomic signature could contribute in the future to the prediction of weight gain in patients treated with psychotropic drugs.
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Affiliation(s)
- Marie Lenski
- Univ. LilleCHU LilleInstitut Pasteur de LilleULR 4483 – IMPECS – IMPact de l’Environnement Chimique sur la Santé humaineLilleFrance
| | - Jonathan Sidibé
- Unit of Forensic Toxicology and ChemistryCURMLLausanne University HospitalGeneva University HospitalsLausanne, GenevaSwitzerland
| | - Mehdi Gholam
- Department of PsychiatryCenter for Psychiatric Epidemiology and PsychopathologyLausanne University HospitalUniversity of LausannePrillySwitzerland
| | - Benjamin Hennart
- Univ. LilleCHU LilleInstitut Pasteur de LilleULR 4483 – IMPECS – IMPact de l’Environnement Chimique sur la Santé humaineLilleFrance
| | - Céline Dubath
- Unit of Pharmacogenetics and Clinical PsychopharmacologyDepartment of PsychiatryCenter for Psychiatric NeuroscienceLausanne University HospitalUniversity of LausannePrillySwitzerland
| | - Marc Augsburger
- Unit of Forensic Toxicology and ChemistryCURMLLausanne University HospitalGeneva University HospitalsLausanne, GenevaSwitzerland
| | - Armin von Gunten
- Service of Old Age PsychiatryDepartment of PsychiatryLausanne University HospitalUniversity of LausannePrillySwitzerland
| | - Philippe Conus
- Service of General PsychiatryDepartment of PsychiatryLausanne University HospitalUniversity of LausannePrillySwitzerland
| | - Delphine Allorge
- Univ. LilleCHU LilleInstitut Pasteur de LilleULR 4483 – IMPECS – IMPact de l’Environnement Chimique sur la Santé humaineLilleFrance
| | - Aurelien Thomas
- Unit of Forensic Toxicology and ChemistryCURMLLausanne University HospitalGeneva University HospitalsLausanne, GenevaSwitzerland
- Faculty Unit of ToxicologyFaculty of Biology and MedicineCURML, Lausanne University HospitalUniversity of LausanneLausanneSwitzerland
| | - Chin B. Eap
- Unit of Pharmacogenetics and Clinical PsychopharmacologyDepartment of PsychiatryCenter for Psychiatric NeuroscienceLausanne University HospitalUniversity of LausannePrillySwitzerland
- Center for Research and Innovation in Clinical Pharmaceutical SciencesUniversity of LausanneSwitzerland
- School of Pharmaceutical SciencesUniversity of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western SwitzerlandUniversity of GenevaUniversity of LausanneLausanneSwitzerland
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12
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Molina JD, Avila S, Rubio G, López-Muñoz F. Metabolomic connections between schizophrenia, antipsychotic drugs and metabolic syndrome: A variety of players. Curr Pharm Des 2021; 27:4049-4061. [PMID: 34348619 DOI: 10.2174/1381612827666210804110139] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 07/02/2021] [Indexed: 12/08/2022]
Abstract
BACKGROUND Diagnosis of schizophrenia lacks of reliable medical diagnostic tests and robust biomarkers applied to clinical practice. Schizophrenic patients undergoing treatment with antipsychotics suffer a reduced life expectancy due to metabolic disarrangements that co-exist with their mental illness and predispose them to develop metabolic syndrome, also exacerbated by medication. Metabolomics is an emerging and potent technology able to accelerate this biomedical research. <P> Aim: This review focus on a detailed vision of the molecular mechanisms involved both in schizophrenia and antipsychotic-induced metabolic syndrome, based on innovative metabolites that consistently change in nascent metabolic syndrome, drug-naïve, first episode psychosis and/or schizophrenic patients compared to healthy subjects. <P> Main lines: Supported by metabolomic approaches, although not exclusively, noteworthy variations are reported mainly through serum samples of patients and controls in several scenes: 1) alterations in fatty acids, inflammatory response indicators, amino acids and biogenic amines, biometals and gut microbiota metabolites (schizophrenia); 2) alterations in metabolites involved in carbohydrate and gut microbiota metabolism, inflammation and oxidative stress (metabolic syndrome), some of them shared with the schizophrenia scene; 3) alterations of cytokines secreted by adipose tissue, phosphatidylcholines, acylcarnitines, Sirtuin 1, orexin-A and changes in microbiota composition (antipsychotic-induced metabolic syndrome). <P> Conclusion: Novel insights into the pathogenesis of schizophrenia and metabolic side-effects associated to its antipsychotic treatment, represent an urgent request for scientifics and clinicians. Leptin, carnitines, adiponectin, insulin or interleukin-6 represent some examples of candidate biomarkers. Cutting-edge technologies like metabolomics have the power of strengthen research for achieving preventive, diagnostic and therapeutical solutions for schizophrenia.
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Affiliation(s)
- Juan D Molina
- Clinical Management Area of Psychiatry and Mental Health, Psychiatric Service, 12 de Octubre University Hospital, Madrid. Spain
| | - Sonia Avila
- Department of Psychiatry, Faculty of Medicine, Complutense University of Madrid. Spain
| | - Gabriel Rubio
- Clinical Management Area of Psychiatry and Mental Health, Psychiatric Service, 12 de Octubre University Hospital, Madrid. Spain
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13
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Tkachev A, Stekolshchikova E, Anikanov N, Zozulya S, Barkhatova A, Klyushnik T, Petrova D. Shorter Chain Triglycerides Are Negatively Associated with Symptom Improvement in Schizophrenia. Biomolecules 2021; 11:biom11050720. [PMID: 34064997 PMCID: PMC8151512 DOI: 10.3390/biom11050720] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 04/30/2021] [Accepted: 05/08/2021] [Indexed: 12/29/2022] Open
Abstract
Schizophrenia is a serious mental disorder requiring lifelong treatment. While medications are available that are effective in treating some patients, individual treatment responses can vary, with some patients exhibiting resistance to one or multiple drugs. Currently, little is known about the causes of the difference in treatment response observed among individuals with schizophrenia, and satisfactory markers of poor response are not available for clinical practice. Here, we studied the changes in the levels of 322 blood plasma lipids between two time points assessed in 92 individuals diagnosed with schizophrenia during their inpatient treatment and their association with the extent of symptom improvement. We found 20 triglyceride species increased in individuals with the least improvement in Positive and Negative Syndrome Scale (PANSS) scores, but not in those with the largest reduction in PANSS scores. These triglyceride species were distinct from the rest of the triglyceride species present in blood plasma. They contained a relatively low number of carbons in their fatty acid residues and were relatively low in abundance compared to the principal triglyceride species of blood plasma.
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Affiliation(s)
- Anna Tkachev
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (E.S.); (N.A.); (D.P.)
- Correspondence:
| | - Elena Stekolshchikova
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (E.S.); (N.A.); (D.P.)
| | - Nickolay Anikanov
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (E.S.); (N.A.); (D.P.)
| | - Svetlana Zozulya
- Mental Health Research Center, 115522 Moscow, Russia; (S.Z.); (A.B.); (T.K.)
| | | | - Tatiana Klyushnik
- Mental Health Research Center, 115522 Moscow, Russia; (S.Z.); (A.B.); (T.K.)
| | - Daria Petrova
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (E.S.); (N.A.); (D.P.)
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14
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Dickens AM, Sen P, Kempton MJ, Barrantes-Vidal N, Iyegbe C, Nordentoft M, Pollak T, Riecher-Rössler A, Ruhrmann S, Sachs G, Bressan R, Krebs MO, Amminger GP, de Haan L, van der Gaag M, Valmaggia L, Hyötyläinen T, Orešič M, McGuire P. Dysregulated Lipid Metabolism Precedes Onset of Psychosis. Biol Psychiatry 2021; 89:288-297. [PMID: 32928501 DOI: 10.1016/j.biopsych.2020.07.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 06/16/2020] [Accepted: 07/19/2020] [Indexed: 01/21/2023]
Abstract
BACKGROUND A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group. METHODS Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy control subjects, who were then clinically monitored for up to 5 years. Machine learning was used to identify lipid profiles that discriminated between CHR and control subjects, and between subgroups of CHR subjects with distinct clinical outcomes. RESULTS At baseline, compared with control subjects, CHR subjects (independent of outcome) had higher levels of triacylglycerols with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n = 50) were distinguished from those that did not (n = 213) on the basis of lipid profile at baseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p < .01). CONCLUSIONS Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.
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Affiliation(s)
- Alex M Dickens
- Turku Bioscience Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Partho Sen
- Turku Bioscience Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Neus Barrantes-Vidal
- Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Fundació Sanitària Sant Pere Claver, Spanish Mental Health Research Network, Barcelona, Spain
| | - Conrad Iyegbe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Merete Nordentoft
- Mental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Glostrup, Denmark
| | - Thomas Pollak
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rodrigo Bressan
- Lab Interdisciplinar Neurociências Clínicas, Departimento Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Marie-Odile Krebs
- University of Paris, Groupe Hospitalier Universitaire Paris Sainte-Anne, Centre d'Évaluation Pour Jeunes Adultes et Adolescents, Institut National de la Santé et de la Recherche Médicale U1266, Institut de Psychiatrie, Centre National de la Recherche Scientifique 3557, Paris, France
| | - G Paul Amminger
- Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam University Medical Center, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands
| | - Mark van der Gaag
- Department of Clinical Psychology and EMGO+ Institute for Health and Care Research, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands; Department of Psychosis Research, Parnassia Psychiatric Institute, The Hague, The Netherlands
| | - Lucia Valmaggia
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | | | - Matej Orešič
- Turku Bioscience Center, University of Turku and Åbo Akademi University, Turku, Finland; School of Medical Sciences, Örebro University, Örebro, Sweden.
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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15
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Lamichhane S, Dickens AM, Sen P, Laurikainen H, Borgan F, Suvisaari J, Hyötyläinen T, Howes O, Hietala J, Orešič M. Association Between Circulating Lipids and Future Weight Gain in Individuals With an At-Risk Mental State and in First-Episode Psychosis. Schizophr Bull 2021; 47:160-169. [PMID: 32609372 PMCID: PMC7825089 DOI: 10.1093/schbul/sbaa087] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Patients with schizophrenia have a lower than average life span, largely due to the increased prevalence of cardiometabolic comorbidities. There is an unmet public health need to identify individuals with psychotic disorders who have a high risk of rapid weight gain and who are at risk of developing metabolic complications. Here, we applied mass spectrometry-based lipidomics in a prospective study comprising 48 healthy controls (CTR), 44 first-episode psychosis (FEP) patients, and 22 individuals at clinical high risk (CHR) for psychosis, from 2 study centers (Turku, Finland and London, UK). Baseline serum samples were analyzed using lipidomics, and body mass index (BMI) was assessed at baseline and after 12 months. We found that baseline triacylglycerols (TGs) with low double-bond counts and carbon numbers were positively associated with the change in BMI at follow-up. In addition, a molecular signature comprised of 2 TGs (TG[48:0] and TG[45:0]) was predictive of weight gain in individuals with a psychotic disorder, with an area under the receiver operating characteristic curve (AUROC) of 0.74 (95% CI: 0.60-0.85). When independently tested in the CHR group, this molecular signature predicted said weight change with AUROC = 0.73 (95% CI: 0.61-0.83). We conclude that molecular lipids may serve as a predictor of weight gain in psychotic disorders in at-risk individuals and may thus provide a useful marker for identifying individuals who are most prone to developing cardiometabolic comorbidities.
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Affiliation(s)
- Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Alex M Dickens
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Heikki Laurikainen
- Department of Psychiatry, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Faith Borgan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
| | - Jaana Suvisaari
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland
| | | | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London, UK
| | - Jarmo Hietala
- Department of Psychiatry, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,School of Medical Sciences, Örebro University, Örebro, Sweden
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16
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Hylén U, McGlinchey A, Orešič M, Bejerot S, Humble MB, Särndahl E, Hyötyläinen T, Eklund D. Potential Transdiagnostic Lipid Mediators of Inflammatory Activity in Individuals With Serious Mental Illness. Front Psychiatry 2021; 12:778325. [PMID: 34899431 PMCID: PMC8661474 DOI: 10.3389/fpsyt.2021.778325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/21/2021] [Indexed: 11/28/2022] Open
Abstract
Mental disorders are heterogeneous and psychiatric comorbidities are common. Previous studies have suggested a link between inflammation and mental disorders. This link can manifest as increased levels of proinflammatory mediators in circulation and as signs of neuroinflammation. Furthermore, there is strong evidence that individuals suffering from psychiatric disorders have increased risk of developing metabolic comorbidities. Our group has previously shown that, in a cohort of low-functioning individuals with serious mental disorders, there is increased expression of genes associated with the NLRP3 inflammasome, a known sensor of metabolic perturbations, as well as increased levels of IL-1-family cytokines. In the current study, we set out to explore the interplay between disease-specific changes in lipid metabolism and known markers of inflammation. To this end, we performed mass spectrometry-based lipidomic analysis of plasma samples from low-functioning individuals with serious mental disorders (n = 39) and matched healthy controls (n = 39). By identifying non-spurious immune-lipid associations, we derived a partial correlation network of inflammatory markers and molecular lipids. We identified levels of lipids as being altered between individuals with serious mental disorders and controls, showing associations between lipids and inflammatory mediators, e.g., osteopontin and IL-1 receptor antagonist. These results indicate that, in low-functioning individuals with serious mental disorders, changes in specific lipids associate with immune mediators that are known to affect neuroinflammatory diseases.
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Affiliation(s)
- Ulrika Hylén
- University Health Care Research Center, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,Inflammatory Response and Infection Susceptibility Centre, Örebro University, Örebro, Sweden
| | - Aidan McGlinchey
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Matej Orešič
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Susanne Bejerot
- University Health Care Research Center, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,Inflammatory Response and Infection Susceptibility Centre, Örebro University, Örebro, Sweden
| | - Mats B Humble
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,Inflammatory Response and Infection Susceptibility Centre, Örebro University, Örebro, Sweden
| | - Eva Särndahl
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,Inflammatory Response and Infection Susceptibility Centre, Örebro University, Örebro, Sweden
| | - Tuulia Hyötyläinen
- Man-Technology-Environment Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden
| | - Daniel Eklund
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,Inflammatory Response and Infection Susceptibility Centre, Örebro University, Örebro, Sweden
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17
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Alves MA, Lamichhane S, Dickens A, McGlinchey A, Ribeiro HC, Sen P, Wei F, Hyötyläinen T, Orešič M. Systems biology approaches to study lipidomes in health and disease. Biochim Biophys Acta Mol Cell Biol Lipids 2020; 1866:158857. [PMID: 33278596 DOI: 10.1016/j.bbalip.2020.158857] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/15/2022]
Abstract
Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.
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Affiliation(s)
- Marina Amaral Alves
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Alex Dickens
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | | | - Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Fang Wei
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
| | | | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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18
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Metabolomic profiles associated with a mouse model of antipsychotic-induced food intake and weight gain. Sci Rep 2020; 10:18581. [PMID: 33122657 PMCID: PMC7596057 DOI: 10.1038/s41598-020-75624-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 10/09/2020] [Indexed: 12/12/2022] Open
Abstract
Antipsychotic drugs (AP) are used to treat a multitude of psychiatric conditions including schizophrenia and bipolar disorder. However, APs also have metabolic side effects including increased food intake and body weight, but the underlying mechanisms remain unknown. We previously reported that minocycline (MINO) co-treatment abrogates olanzapine (OLZ)-induced hyperphagia and weight gain in mice. Using this model, we investigated the changes in the pharmacometabolome in the plasma and hypothalamus associated with OLZ-induced hyperphagia and weight gain. Female C57BL/6 mice were divided into groups and fed either i) control, CON (45% fat diet) ii) CON + MINO, iii) OLZ (45% fat diet with OLZ), iv) OLZ + MINO. We identified one hypothalamic metabolite indoxylsulfuric acid and 389 plasma metabolites (including 19 known metabolites) that were specifically associated with AP-induced hyperphagia and weight gain in mice. We found that plasma citrulline, tricosenoic acid, docosadienoic acid and palmitoleic acid were increased while serine, asparagine and arachidonic acid and its derivatives were decreased in response to OLZ. These changes were specifically blocked by co-treatment with MINO. These pharmacometabolomic profiles associated with AP-induced hyperphagia and weight gain provide candidate biomarkers and mechanistic insights related to the metabolic side effects of these widely used drugs.
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19
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Links between central CB1-receptor availability and peripheral endocannabinoids in patients with first episode psychosis. NPJ SCHIZOPHRENIA 2020; 6:21. [PMID: 32848142 PMCID: PMC7450081 DOI: 10.1038/s41537-020-00110-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 07/07/2020] [Indexed: 01/23/2023]
Abstract
There is an established, link between psychosis and metabolic abnormalities, such as altered glucose metabolism and dyslipidemia, which often precede the initiation of antipsychotic treatment. It is known that obesity-associated metabolic disorders are promoted by activation of specific cannabinoid targets (endocannabinoid system (ECS)). Our recent data suggest that there is a change in the circulating lipidome at the onset of first episode psychosis (FEP). With the aim of characterizing the involvement of the central and peripheral ECSs, and their mutual associations; here, we performed a combined neuroimaging and metabolomic study in patients with FEP and healthy controls (HC). Regional brain cannabinoid receptor type 1 (CB1R) availability was quantified in two, independent samples of patients with FEP (n = 20 and n = 8) and HC (n = 20 and n = 10), by applying three-dimensional positron emission tomography, using two radiotracers, [11C]MePPEP and [18F]FMPEP-d2. Ten endogenous cannabinoids or related metabolites were quantified in serum, drawn from these individuals during the same imaging session. Circulating levels of arachidonic acid and oleoylethanolamide (OEA) were reduced in FEP individuals, but not in those who were predominantly medication free. In HC, there was an inverse association between levels of circulating arachidonoyl glycerol, anandamide, OEA, and palmitoyl ethanolamide, and CB1R availability in the posterior cingulate cortex. This phenomenon was, however, not observed in FEP patients. Our data thus provide evidence of cross talk, and dysregulation between peripheral endocannabinoids and central CB1R availability in FEP.
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20
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Profiling of lipidomics before and after antipsychotic treatment in first-episode psychosis. Eur Arch Psychiatry Clin Neurosci 2020; 270:59-70. [PMID: 30604052 DOI: 10.1007/s00406-018-0971-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Accepted: 12/19/2018] [Indexed: 12/20/2022]
Abstract
Alterations in complex lipids may be involved in pathophysiology of schizophrenia spectrum disorders. Previously, we demonstrated importance of detecting lipid metabolism dysregulation by acylcarnitine (ACs) profile analysis in patients with first-episode psychosis (FEP). The aim of this study was to adopt lipidomics to identify serum glycerophospholipids (GPLs) and sphingomyelins (SMs) for describing FEP status before and after 7-month antipsychotic treatment. Using mass spectrometry and liquid chromatography technique, we profiled 105 individual lipids [14 lysophosphatidylcholines (LysoPCs), 76 phosphatidylcholines (PCs) and 15 SMs] in serum samples from 53 antipsychotic-naïve FEP patients, 44 of them were studied longitudinally and from 37 control subjects (CSs). Among the identified and quantified metabolites one LysoPC was elevated, and contrary the levels of 16 PCs as well as the level of one SM were significantly (p ≤ 0.0005) reduced in antipsychotic-naïve FEP patients compared to CSs. Comparison of serum lipids profiles of FEP patients before and after 7-month antipsychotic treatment revealed that 11 GPLs (2 LysoPCs, 9 PCs), and 2 SMs were found to be significantly changed (p ≤ 0.0005) in which GPLs were up-regulated, and SMs were down-regulated. However, no significant differences were noted when treated patient's serum lipid profiles were compared with CSs. Our findings suggest that complex lipid profile abnormalities are specifically associated with FEP and these discrepancies reflect two different disease-related pathways. Our findings provide insight into lipidomic information that may be used for monitoring FEP status and impact of the treatment in the early stage of the schizophrenia spectrum disorder.
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21
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Ahonen L, Jäntti S, Suvitaival T, Theilade S, Risz C, Kostiainen R, Rossing P, Orešič M, Hyötyläinen T. Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients. Metabolites 2019; 9:metabo9090184. [PMID: 31540069 PMCID: PMC6780060 DOI: 10.3390/metabo9090184] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 08/30/2019] [Accepted: 09/11/2019] [Indexed: 12/13/2022] Open
Abstract
Several small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities, and complications. Here, we report the development and validation of a novel, quantitative method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. We selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R2), and intra- and inter-day repeatability of each metabolite. The method’s performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.
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Affiliation(s)
- Linda Ahonen
- Steno Diabetes Center Copenhagen, 2820 Gentofte, Denmark.
| | - Sirkku Jäntti
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, 00014 Helsinki, Finland.
| | | | | | - Claudia Risz
- Steno Diabetes Center Copenhagen, 2820 Gentofte, Denmark.
| | - Risto Kostiainen
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, 00014 Helsinki, Finland.
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, 2820 Gentofte, Denmark.
- Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark.
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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22
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Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders. Prog Neuropsychopharmacol Biol Psychiatry 2019; 93:182-188. [PMID: 30904564 DOI: 10.1016/j.pnpbp.2019.03.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 02/21/2019] [Accepted: 03/20/2019] [Indexed: 01/14/2023]
Abstract
Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.
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23
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Misiak B, Bartoli F, Stramecki F, Samochowiec J, Lis M, Kasznia J, Jarosz K, Stańczykiewicz B. Appetite regulating hormones in first-episode psychosis: A systematic review and meta-analysis. Neurosci Biobehav Rev 2019; 102:362-370. [PMID: 31121198 DOI: 10.1016/j.neubiorev.2019.05.018] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 05/13/2019] [Accepted: 05/18/2019] [Indexed: 12/13/2022]
Abstract
We aimed to perform a systematic review and meta-analysis of appetite regulating hormones in patients with first-episode psychosis (FEP). Meta-analyses were conducted using random-effects models with Hedges' g as the effect size estimate. We identified 31 eligible studies, investigating the levels of 7 appetite regulating hormones (adiponectin, insulin, leptin, ghrelin, orexin, resistin and visfatin) in 1792 FEP patients and 1364 controls. The insulin levels in FEP patients were higher than in controls (g = 0.34, 95%CI: 0.19 - 0.49, p < 0.001), even considering only antipsychotic-naïve patients (g = 0.39, 95%CI: 0.12 - 0.66, p = 0.005). The severity of negative symptoms was positively associated with the effect size estimates (β = 0.08, 95%CI: 0.01 - 0.16, p = 0.030). Moreover, we found lower levels of leptin in antipsychotic-naïve FEP patients (g = -0.62, 95%CI: -1.11 - 0.12, p = 0.015). Impaired appetite regulation, in terms of elevated insulin levels and decreased leptin levels, occurs in early psychosis, before antipsychotic treatment. Hyperinsulinemia might be related to negative symptoms.
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Affiliation(s)
- Błażej Misiak
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1 Street, 50-368 Wroclaw, Poland.
| | - Francesco Bartoli
- Department of Medicine and Surgery, University of Milano Bicocca, Monza, Italy; Department of Mental Health, ASST Nord Milano, Milano, Italy
| | - Filip Stramecki
- Department of Psychiatry, Wroclaw Medical University, Pasteura 10 Street, 50-367 Wroclaw, Poland
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Broniewskiego 26 Street, 71-460 Szczecin, Poland
| | - Michał Lis
- Clinical Department of Internal Diseases, Endocrinology and Diabetology, The Central Clinical Hospital of the Ministry of the Interior in Warsaw, Wołoska 137 Street, 02-507 Warsaw, Poland
| | - Justyna Kasznia
- Inpatient Psychiatric Unit, Municipal General Hospital, Limanowskiego 20/22 Street, 63-400 Ostrów Wielkopolski, Poland
| | - Konrad Jarosz
- Department of Clinical Nursing, Pomeranian Medical University, Żołnierska 48 Street, 71-210 Szczecin, Poland
| | - Bartłomiej Stańczykiewicz
- Department of Nervous System Diseases, Wroclaw Medical University, Bartla 5 Street, 51-618 Wroclaw, Poland
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24
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Cao B, Jin M, Brietzke E, McIntyre RS, Wang D, Rosenblat JD, Ragguett RM, Zhang C, Sun X, Rong C, Wang J. Serum metabolic profiling using small molecular water-soluble metabolites in individuals with schizophrenia: A longitudinal study using a pre-post-treatment design. Psychiatry Clin Neurosci 2019; 73:100-108. [PMID: 30156046 DOI: 10.1111/pcn.12779] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 07/24/2018] [Accepted: 08/21/2018] [Indexed: 12/12/2022]
Abstract
AIM We sought to compare alterations in serum bioenergetic markers within a well-characterized sample of adults with schizophrenia at baseline and after 8 weeks of pharmacological treatment with the hypothesis that treatment would be associated with significant changes in bioenergetic markers given the role of bioenergetic dysfunction in schizophrenia. METHODS We recruited adults with schizophrenia (n = 122) who had not received pharmacological treatment for at least 1 month prior to enrollment, including drug-naïve (i.e., first-episode) participants and treatment non-adherent participants. Pre- and post-treatment serum samples were analyzed using liquid chromatography-tandem mass spectrometry. RESULTS Metabolites with the greatest change, when comparing pre- and post-treatment levels, were identified revealing 14 water-soluble metabolites of interest. The composition of these metabolites was: amino acids (n = 6), carnitines (n = 4), polar lipids (n = 3), and organic acid (n = 1). All amino acids and lysophosphatidylcholines (LysoPC) were increased, while the four carnitines - oleoylcarnitine, L-palmitoylcarnitine, linoleyl carnitine, and L-acetylcarnitine - were decreased post-treatment. Of these metabolite biomarkers, six - oleoylcarnitine, linoleyl carnitine, L-acetylcarnitine, LysoPC(15:0), D-glutamic acid, and L-arginine - were identified as having most consistently and predictably changed after 8 weeks of treatment. CONCLUSION The current study identified several bioenergetic markers that consistently change with pharmacological treatment. These bioenergetic changes may provide further insights into the pathophysiology of schizophrenia along with furthering our understanding of the mechanisms subserving both the effects (e.g., antipsychotic effects) and side-effects (e.g., metabolic syndrome) of antipsychotics.
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Affiliation(s)
- Bing Cao
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Min Jin
- School of Public Health, Baotou Medical College, Baotou, China
| | - Elisa Brietzke
- Mood Disorders Psychopharmacology Unit, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, Toronto Western Hospital, University Health Network, Toronto, Canada.,The Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Dongfang Wang
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Joshua D Rosenblat
- Mood Disorders Psychopharmacology Unit, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Renee-Marie Ragguett
- Mood Disorders Psychopharmacology Unit, Toronto Western Hospital, University Health Network, Toronto, Canada
| | | | - Xiaoyu Sun
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Carola Rong
- Mood Disorders Psychopharmacology Unit, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Jingyu Wang
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China.,Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing, China
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25
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Characterizing acyl-carnitine biosignatures for schizophrenia: a longitudinal pre- and post-treatment study. Transl Psychiatry 2019; 9:19. [PMID: 30655505 PMCID: PMC6336814 DOI: 10.1038/s41398-018-0353-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/18/2018] [Accepted: 11/08/2018] [Indexed: 12/21/2022] Open
Abstract
Subjects with schizophrenia have high risks of metabolic abnormalities and bioenergetic dysfunction. Acyl-carnitines involved in bioenergetic pathways provide potential biomarker targets for identifying early changes and onset characteristics in subjects with schizophrenia. We measured 29 acyl-carnitine levels within well-characterized plasma samples of adults with schizophrenia and healthy controls using liquid chromatography-mass spectrometry (LC-MS). Subjects with schizophrenia were measured at baseline and after 8 weeks of treatment. A total of 225 subjects with schizophrenia and 175 age- and gender-matched healthy controls were enrolled and 156 subjects completed the 8-week follow-up. With respect to plasma acyl-carnitines, the individuals with schizophrenia at baseline showed significantly higher levels of C4-OH (C3-DC) and C16:1, but lower concentrations of C3, C8, C10, C10:1, C10:2, C12, C14:1-OH, C14:2, and C14:2-OH when compared with healthy controls after controlling for age, sex, body mass index (BMI), smoking, and drinking. For the comparison between pretreatment and posttreatment subjects, all detected acyl-carnitines were significantly different between the two groups. Only the concentration of C3 and C4 were increased after selection by variable importance in projection (VIP) value >1.0 and false discovery rate (FDR) q value <0.05. A panel of acyl-carnitines were selected for the ability to differentiate subjects of schizophrenia at baseline from controls, pre- from post-treatment, and posttreatment from controls. Our data implicated acyl-carnitines with abnormalities in cellular bioenergetics of schizophrenia. Therefore, acyl-carnitines can be potential targets for future investigations into their roles in the pathoetiology of schizophrenia.
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26
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Metabolomics Biomarkers for Precision Psychiatry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1161:101-113. [PMID: 31562625 DOI: 10.1007/978-3-030-21735-8_10] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The treatment of psychiatric disorders remains a significant challenge in part due to imprecise diagnostic criteria and incomplete understanding of the molecular pathology involved. Current diagnostic and pharmacological treatment guidelines use a uniform approach to address each disorder even though psychiatric clinical presentation and prognosis within a disorder are known to be heterogeneous. Limited therapeutic success highlights the need for a precision medicine approach in psychiatry, termed precision psychiatry. To practice precision psychiatry, it is essential to research and develop multiple omics-based biomarkers that consider environmental factors and careful phenotype determination. Metabolomics, which lies at the endpoint of the "omics cascade," allows for detection of alterations in systems-level metabolites within biological pathways, thereby providing insights into the mechanisms that underlie various physiological conditions and pathologies. The eicosanoids, a family of metabolites derived from oxygenated polyunsaturated fatty acids, play a key role in inflammatory mechanisms and have been implicated in psychiatric disorders such as anorexia nervosa and depression. This review (1) provides background on the current clinical challenges of psychiatric disorders, (2) gives an overview of metabolomics application as a tool to develop improved biomarkers for precision psychiatry, and (3) summarizes current knowledge on metabolomics and lipidomic findings in common psychiatric disorders, with a focus on eicosanoids. Metabolomics is a promising tool for precision psychiatry. This research has great potential for both discovering biomarkers and elucidating molecular mechanisms underlying psychiatric disorders.
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27
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Burghardt KJ, Ward KM, Sanders EJ, Howlett BH, Seyoum B, Yi Z. Atypical Antipsychotics and the Human Skeletal Muscle Lipidome. Metabolites 2018; 8:metabo8040064. [PMID: 30322152 PMCID: PMC6316471 DOI: 10.3390/metabo8040064] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/05/2018] [Accepted: 10/12/2018] [Indexed: 12/21/2022] Open
Abstract
Atypical antipsychotics (AAPs) are a class of medications associated with significant metabolic side effects, including insulin resistance. The aim of this study was to analyze the skeletal muscle lipidome of patients on AAPs, compared to mood stabilizers, to further understand the molecular changes underlying AAP treatment and side effects. Bipolar patients on AAPs or mood stabilizers underwent a fasting muscle biopsy and assessment of insulin sensitivity. A lipidomic analysis of total fatty acids (TFAs), phosphatidylcholines (PCs) and ceramides (CERs) was performed on the muscle biopsies, then lipid species were compared between treatment groups, and correlation analyses were performed with insulin sensitivity. TFAs and PCs were decreased and CERs were increased in the AAP group relative to those in the mood stabilizer group (FDR q-value <0.05). A larger number of TFAs and PCs were positively correlated with insulin sensitivity in the AAP group compared to those in the mood stabilizer group. In contrast, a larger number of CERs were negatively correlated with insulin sensitivity in the AAP group compared to that in the mood stabilizer group. The findings here suggest that AAPs are associated with changes in the lipid profiles of human skeletal muscle when compared to mood stabilizers and that these changes correlate with insulin sensitivity.
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Affiliation(s)
- Kyle J Burghardt
- Department of Pharmacy Practice, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48202, USA.
| | - Kristen M Ward
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Elani J Sanders
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Bradley H Howlett
- Department of Pharmacy Practice, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48202, USA.
| | - Berhane Seyoum
- Division of Endocrinology, School of Medicine, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48202, USA.
| | - Zhengping Yi
- Department of Pharmaceutical Science, Wayne State University, Detroit, MI 48202, USA.
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28
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Dickens AM, Posti JP, Takala RSK, Ala-Seppälä H, Mattila I, Coles JP, Frantzén J, Hutchinson PJ, Katila AJ, Kyllönen A, Maanpää HR, Newcombe V, Outtrim J, Tallus J, Carpenter KLH, Menon DK, Hyötyläinen T, Tenovuo O, Orešic M. Serum Metabolites Associated with Computed Tomography Findings after Traumatic Brain Injury. J Neurotrauma 2018; 35:2673-2683. [PMID: 29947291 DOI: 10.1089/neu.2017.5272] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
There is a need to rapidly detect patients with traumatic brain injury (TBI) who require head computed tomography (CT). Given the energy crisis in the brain following TBI, we hypothesized that serum metabolomics would be a useful tool for developing a set of biomarkers to determine the need for CT and to distinguish among different types of injuries observed. Logistical regression models using metabolite data from the discovery cohort (n = 144, Turku, Finland) were used to distinguish between patients with traumatic intracranial findings and those with negative findings on head CT. The resultant models were then tested in the validation cohort (n = 66, Cambridge, United Kingdom). The levels of glial fibrillary acidic protein and ubiquitin C-terminal hydrolase-L1 were also quantified in the serum from the same patients. Despite there being significant differences in the protein biomarkers in patients with TBI, the model that determined the need for a CT scan validated poorly (area under the curve [AUC] = 0.64: Cambridge patients). However, using a combination of six metabolites (two amino acids, three sugar derivatives, and one ketoacid) it was possible to discriminate patients with intracranial abnormalities on CT and patients with a normal CT (AUC = 0.77 in Turku patients and AUC = 0.73 in Cambridge patients). Further, a combination of three metabolites could distinguish between diffuse brain injuries and mass lesions (AUC = 0.87 in Turku patients and AUC = 0.68 in Cambridge patients). This study identifies a set of validated serum polar metabolites, which associate with the need for a CT scan. Additionally, serum metabolites can also predict the nature of the brain injury. These metabolite markers may prevent unnecessary CT scans, thus reducing the cost of diagnostics and radiation load.
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Affiliation(s)
- Alex M Dickens
- 1 Turku Centre for Biotechnology, University of Turku , Turku, Finland
| | - Jussi P Posti
- 2 Turku Brain Injury Centre, Turku University Hospital , Turku, Finland .,3 Department of Neurology, University of Turku , Turku, Finland .,4 Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital , Turku, Finland
| | - Riikka S K Takala
- 5 Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku , Turku, Finland
| | | | - Ismo Mattila
- 6 Steno Diabetes Center Copenhagen , Gentofte, Denmark
| | - Jonathan P Coles
- 7 Division of Anaesthesia, Department of Medicine, University of Cambridge , Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Janek Frantzén
- 2 Turku Brain Injury Centre, Turku University Hospital , Turku, Finland .,3 Department of Neurology, University of Turku , Turku, Finland .,4 Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital , Turku, Finland
| | - Peter J Hutchinson
- 8 Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge , Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Ari J Katila
- 5 Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku , Turku, Finland
| | - Anna Kyllönen
- 3 Department of Neurology, University of Turku , Turku, Finland
| | | | - Virginia Newcombe
- 7 Division of Anaesthesia, Department of Medicine, University of Cambridge , Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Joanne Outtrim
- 7 Division of Anaesthesia, Department of Medicine, University of Cambridge , Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Jussi Tallus
- 3 Department of Neurology, University of Turku , Turku, Finland
| | - Keri L H Carpenter
- 8 Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge , Addenbrooke's Hospital, Cambridge, United Kingdom
| | - David K Menon
- 7 Division of Anaesthesia, Department of Medicine, University of Cambridge , Addenbrooke's Hospital, Cambridge, United Kingdom
| | | | - Olli Tenovuo
- 2 Turku Brain Injury Centre, Turku University Hospital , Turku, Finland .,3 Department of Neurology, University of Turku , Turku, Finland
| | - Matej Orešic
- 1 Turku Centre for Biotechnology, University of Turku , Turku, Finland .,10 Schools of Medical Science, Örebro University , Örebro, Sweden
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29
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Ward KM, Yeoman L, McHugh C, Kraal AZ, Flowers SA, Rothberg AE, Karnovsky A, Das AK, Ellingrod VL, Stringer KA. Atypical Antipsychotic Exposure May Not Differentiate Metabolic Phenotypes of Patients with Schizophrenia. Pharmacotherapy 2018; 38:638-650. [PMID: 29722909 PMCID: PMC6014920 DOI: 10.1002/phar.2119] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
STUDY OBJECTIVE Patients with schizophrenia are known to have higher rates of metabolic disease than the general population. Contributing factors likely include lifestyle and atypical antipsychotic (AAP) use, but the underlying mechanisms are unknown. The objective of this study was to identify metabolomic variability in adult patients with schizophrenia who were taking AAPs and grouped by fasting insulin concentration, our surrogate marker for metabolic risk. DESIGN Metabolomics analysis PARTICIPANTS: Ninety-four adult patients with schizophrenia who were taking an AAP for at least 6 months, with no changes in their antipsychotic regimen for the previous 8 weeks, and who did not require treatment with insulin, participated in the study. Twenty age- and sex-matched nonobese (10 subjects) and obese (10 subjects) controls without cardiovascular disease or mental health diagnoses were used to match the body mass index (BMI) range of the patients with schizophrenia to account for metabolite concentration differences attributable to BMI. MEASUREMENTS AND MAIN RESULTS Existing serum samples were used to identify aqueous metabolites (to differentiate fasting insulin concentration quartiles) and fatty acids with quantitative nuclear magnetic resonance and gas chromatography methods, respectively. To exclude metabolites from our pathway mapping analysis that were due to variability in weight, we also subjected serum samples from the nonobese and obese controls to the same analyses. Patients with schizophrenia had a median age of 47.0 years (interquartile range 41.0-52.0 years). Using a false discovery rate threshold of less than 25%, 10 metabolites, not attributable to weight, differentiated insulin concentration quartiles in patients with schizophrenia and identified variability in one-carbon metabolism between groups. Patients with higher fasting insulin concentrations (quartiles 3 and 4) also trended toward higher levels of saturated fatty acids compared with patients with lower fasting insulin concentrations (quartiles 1 and 2). CONCLUSION Our results illustrate the utility of metabolomics to identify pathways underlying variable fasting insulin concentration in patients with schizophrenia. Importantly, no significant difference in AAP exposure was observed among groups, suggesting that current antipsychotic use may not be a primary factor that differentiates middle-aged adult patients with schizophrenia by fasting insulin concentration.
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Affiliation(s)
- Kristen M Ward
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan
| | - Larisa Yeoman
- NMR Metabolomics Laboratory, College of Pharmacy, University of Michigan, Ann Arbor, Michigan
| | - Cora McHugh
- NMR Metabolomics Laboratory, College of Pharmacy, University of Michigan, Ann Arbor, Michigan
| | - A Zarina Kraal
- Psychology Department, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan
| | - Stephanie A Flowers
- Department of Pharmacy Practice, College of Pharmacy, University of Illinois, Chicago, Illinois
| | - Amy E Rothberg
- Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, Michigan
| | - Alla Karnovsky
- Department of Bioinformatics and Computational Medicine, School of Medicine, University of Michigan, Ann Arbor, Michigan
- The Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan
| | - Arun K Das
- Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, Michigan
- The Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan
| | - Vicki L Ellingrod
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan
- Department of Psychiatry, School of Medicine, University of Michigan, Ann Arbor, Michigan
| | - Kathleen A Stringer
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan
- NMR Metabolomics Laboratory, College of Pharmacy, University of Michigan, Ann Arbor, Michigan
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, Michigan
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30
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Frank E, Maier D, Pajula J, Suvitaival T, Borgan F, Butz-Ostendorf M, Fischer A, Hietala J, Howes O, Hyötyläinen T, Janssen J, Laurikainen H, Moreno C, Suvisaari J, Van Gils M, Orešič M. Platform for systems medicine research and diagnostic applications in psychotic disorders-The METSY project. Eur Psychiatry 2018; 50:40-46. [PMID: 29361398 DOI: 10.1016/j.eurpsy.2017.12.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 11/30/2017] [Accepted: 12/01/2017] [Indexed: 12/12/2022] Open
Abstract
Psychotic disorders are associated with metabolic abnormalities including alterations in glucose and lipid metabolism. A major challenge in the treatment of psychosis is to identify patients with vulnerable metabolic profiles who may be at risk of developing cardiometabolic co-morbidities. It is established that both central and peripheral metabolic organs use lipids to control energy balance and regulate peripheral insulin sensitivity. The endocannabinoid system, implicated in the regulation of glucose and lipid metabolism, has been shown to be dysregulated in psychosis. It is currently unclear how these endocannabinoid abnormalities relate to metabolic changes in psychosis. Here we review recent research in the field of metabolic co-morbidities in psychotic disorders as well as the methods to study them and potential links to the endocannabinoid system. We also describe the bioinformatics platforms developed in the EU project METSY for the investigations of the biological etiology in patients at risk of psychosis and in first episode psychosis patients. The METSY project was established with the aim to identify and evaluate multi-modal peripheral and neuroimaging markers that may be able to predict the onset and prognosis of psychiatric and metabolic symptoms in patients at risk of developing psychosis and first episode psychosis patients. Given the intrinsic complexity and widespread role of lipid metabolism, a systems biology approach which combines molecular, structural and functional neuroimaging methods with detailed metabolic characterisation and multi-variate network analysis is essential in order to identify how lipid dysregulation may contribute to psychotic disorders. A decision support system, integrating clinical, neuropsychological and neuroimaging data, was also developed in order to aid clinical decision making in psychosis. Knowledge of common and specific mechanisms may aid the etiopathogenic understanding of psychotic and metabolic disorders, facilitate early disease detection, aid treatment selection and elucidate new targets for pharmacological treatments.
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Affiliation(s)
| | | | - Juha Pajula
- VTT Technical Research Centre of Finland Ltd., FI-33720 Tampere, Finland
| | | | - Faith Borgan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London WC2R 2LS, UK; Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London W12 0HS, UK
| | | | | | - Jarmo Hietala
- Department of Psychiatry, University of Turku, FI-20520 Turku, Finland; Turku PET Centre, Turku University Hospital, FI-20521 Turku, Finland
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London WC2R 2LS, UK; Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London W12 0HS, UK
| | | | - Joost Janssen
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - Heikki Laurikainen
- Department of Psychiatry, University of Turku, FI-20520 Turku, Finland; Turku PET Centre, Turku University Hospital, FI-20521 Turku, Finland
| | - Carmen Moreno
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - Jaana Suvisaari
- National Institute for Health and Welfare (THL), FI-00300 Helsinki, Finland
| | - Mark Van Gils
- VTT Technical Research Centre of Finland Ltd., FI-33720 Tampere, Finland
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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31
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Suvisaari J, Mantere O, Keinänen J, Mäntylä T, Rikandi E, Lindgren M, Kieseppä T, Raij TT. Is It Possible to Predict the Future in First-Episode Psychosis? Front Psychiatry 2018; 9:580. [PMID: 30483163 PMCID: PMC6243124 DOI: 10.3389/fpsyt.2018.00580] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 10/23/2018] [Indexed: 12/26/2022] Open
Abstract
The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.
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Affiliation(s)
- Jaana Suvisaari
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Outi Mantere
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Psychiatry, McGill University, Montreal, QC, Canada.,Bipolar Disorders Clinic, Douglas Mental Health University Institute, Montreal, QC, Canada.,Department of Psychiatry, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jaakko Keinänen
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Psychiatry, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Teemu Mäntylä
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Center, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland.,Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Eva Rikandi
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Center, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland.,Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Maija Lindgren
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Tuula Kieseppä
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Psychiatry, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Tuukka T Raij
- Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Center, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
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32
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Hyötyläinen T, Ahonen L, Pöhö P, Orešič M. Lipidomics in biomedical research-practical considerations. Biochim Biophys Acta Mol Cell Biol Lipids 2017; 1862:800-803. [PMID: 28408341 DOI: 10.1016/j.bbalip.2017.04.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 04/06/2017] [Accepted: 04/08/2017] [Indexed: 02/06/2023]
Abstract
Lipids have many central physiological roles including as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Lipid-related disturbances are known to underlie many diseases and their co-morbidities. The emergence of lipidomics has empowered researchers to study lipid metabolism at the cellular as well as physiological levels at a greater depth than was previously possible. The key challenges ahead in the field of lipidomics in medical research lie in the development of experimental protocols and in silico techniques needed to study lipidomes at the systems level. Clinical questions where lipidomics may have an impact in healthcare settings also need to be identified, both from the health outcomes and health economics perspectives. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.
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Affiliation(s)
| | - Linda Ahonen
- Steno Diabetes Center A/S, DK-2820 Gentofte, Denmark
| | - Päivi Pöhö
- Faculty of Pharmacy, University of Helsinki, FI-00014 Helsinki, Finland
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
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