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Tkachev A, Stekolshchikova E, Golubova A, Serkina A, Morozova A, Zorkina Y, Riabinina D, Golubeva E, Ochneva A, Savenkova V, Petrova D, Andreyuk D, Goncharova A, Alekseenko I, Kostyuk G, Khaitovich P. Screening for depression in the general population through lipid biomarkers. EBioMedicine 2024; 110:105455. [PMID: 39571307 PMCID: PMC11617895 DOI: 10.1016/j.ebiom.2024.105455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 10/29/2024] [Accepted: 10/31/2024] [Indexed: 12/08/2024] Open
Abstract
BACKGROUND Anxiety and depression significantly contribute to the overall burden of mental disorders, with depression being one of the leading causes of disability. Despite this, no biochemical test has been implemented for the diagnosis of these mental disorders, while recent studies have highlighted lipids as potential biomarkers. METHODS Using a streamlined high-throughput lipidome analysis method, direct-infusion mass spectrometry, we evaluated blood plasma lipid levels in 604 individuals from a general urban population and analysed their association with self-reported anxiety and depression symptoms. We also assessed lipidome profiles in 32 patients with clinical depression, matched to 21 healthy controls. FINDINGS We found a significant correlation between lipid abundances and the severity of self-reported depression symptoms. Moreover, lipid alterations detected in high scoring volunteers mirrored the lipidome profiles identified in patients with clinical depression included in our study. Based on these findings, we developed a lipid-based predictive model distinguishing individuals reporting severe depressive symptoms from non-depressed subjects with high accuracy. INTERPRETATION This study demonstrates the possibility of generalizing lipid alterations from a clinical cohort to the general population and underscores the potential of lipid-based biomarkers in assessing depressive states. FUNDING This study was sponsored by the Moscow Center for Innovative Technologies in Healthcare, №2707-2, №2102-11.
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Affiliation(s)
- Anna Tkachev
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia; LLC NeurOmix, Moscow, 119571, Russia
| | - Elena Stekolshchikova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Anastasia Golubova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Anna Serkina
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Anna Morozova
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034, Moscow, Russia
| | - Yana Zorkina
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034, Moscow, Russia
| | - Daria Riabinina
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia
| | - Elizaveta Golubeva
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia
| | - Aleksandra Ochneva
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034, Moscow, Russia
| | - Valeria Savenkova
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia
| | - Daria Petrova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Denis Andreyuk
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Economy Faculty, M.V. Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Anna Goncharova
- Moscow Center for Healthcare Innovations, Moscow, 123473, Russia
| | - Irina Alekseenko
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow Region, 142290, Russia
| | - Georgiy Kostyuk
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia.
| | - Philipp Khaitovich
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia; LLC NeurOmix, Moscow, 119571, Russia.
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Gu X, Zhang G, Wang Q, Song J, Li Y, Xia C, Zhang T, Yang L, Sun J, Zhou M. Integrated network pharmacology and hepatic metabolomics to reveal the mechanism of Acanthopanax senticosus against major depressive disorder. Front Cell Dev Biol 2022; 10:900637. [PMID: 35990602 PMCID: PMC9389016 DOI: 10.3389/fcell.2022.900637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 07/11/2022] [Indexed: 11/23/2022] Open
Abstract
Objective:Acanthopanax senticosus (Rupr. et Maxim.) Harms (ASH) is a traditional herbal medicine widely known for its antifatigue and antistress effects, as well as tonifying qi, invigorating spleen and kidney, and tranquilizing the mind. Recent evidence suggests that ASH has a therapeutic effect on major depressive disorder (MDD), but its mechanism is still unclear. The current study aimed to investigate the effect of ASH on MDD and potential therapeutic mechanisms. Materials and Methods: The chemical compound potential target network was predicted based on network pharmacology. Simultaneously, chronic unpredictable mild stress (CUMS) model mice were orally administrated ASH with three dosages (400, 200, and 100 mg/kg) for 6 weeks, and hepatic metabolomics based on gas chromatography–mass spectrometry (GC–MS) was carried out to identify differential metabolites and related metabolic pathways. Next, the integrated analysis of metabolomics and network pharmacology was applied to find the key target. Finally, molecular docking technology was employed to define the combination of the key target and the corresponding compounds. Results: A total of 13 metabolites and four related metabolic pathways were found in metabolomics analysis. From the combined analysis of network pharmacology and metabolomics, six targets (DAO, MAOA, MAOB, GAA, HK1, and PYGM) are the overlapping targets and two metabolic pathways (glycine, serine, and threonine metabolism and starch and sucrose metabolism) are the most related pathways. Finally, DAO, MAOA, MAOB, GAA, HK1, and PYGM were verified bounding well to their corresponding compounds including isofraxidin, eleutheroside B1, eleutheroside C, quercetin, kaempferol, and acacetin. Conclusion: Based on these results, it was implied that the potential mechanism of ASH on MDD was related to the regulation of metabolism of several excitatory amino acids and carbohydrates, as well as the expression of DAO, MAOA, MAOB, GAA, HK1, and PYGM.
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Affiliation(s)
- Xinyi Gu
- Institute for Interdisciplinary Medicine Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guanying Zhang
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qixue Wang
- Institute for Interdisciplinary Medicine Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing Song
- Institute for Interdisciplinary Medicine Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ying Li
- Institute for Interdisciplinary Medicine Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chenyi Xia
- Department of Physiology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ting Zhang
- Institute for Interdisciplinary Medicine Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Li Yang
- Institute for Interdisciplinary Medicine Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jijia Sun
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jijia Sun, ; Mingmei Zhou,
| | - Mingmei Zhou
- Institute for Interdisciplinary Medicine Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jijia Sun, ; Mingmei Zhou,
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