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Yuan Y, Huang L, Yu L, Yan X, Chen S, Bi C, He J, Zhao Y, Yang L, Ning L, Jin H, Yang R, Li Y. Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls. Diabetes Metab Res Rev 2024; 40:e3789. [PMID: 38501707 DOI: 10.1002/dmrr.3789] [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] [Received: 07/11/2023] [Revised: 01/01/2024] [Accepted: 01/31/2024] [Indexed: 03/20/2024]
Abstract
AIMS Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD. MATERIALS AND METHODS We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis. RESULTS The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered. CONCLUSIONS We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.
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Affiliation(s)
- Yu Yuan
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Liping Huang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lulu Yu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xingxu Yan
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Siyu Chen
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Chenghao Bi
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Junjie He
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yiqing Zhao
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Liu Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Li Ning
- Department Clinical Laboratory, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Hua Jin
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Rongrong Yang
- Public Health Science and Engineering College, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yubo Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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2
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Wei Q, Yu Z, Zhou X, Gong R, Jiang R, Xu G, Liu W. Metabolomic Profiling of Aqueous Humor from Pathological Myopia Patients with Choroidal Neovascularization. Metabolites 2023; 13:900. [PMID: 37623844 PMCID: PMC10456621 DOI: 10.3390/metabo13080900] [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: 06/05/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Choroidal neovascularization (CNV) is a severe complication observed in individuals with pathological myopia (PM). Our hypothesis is that specific metabolic alterations occur during the development of CNV in patients with PM. To investigate this, an untargeted metabolomics analysis was conducted using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) on aqueous humor (AH) samples obtained from meticulously matched PM patients, including those with CNV (n = 11) and without CNV (n = 11). The analysis aimed to identify differentially expressed metabolites between the two groups. Furthermore, the discriminative ability of each metabolite was evaluated using the area under the receiver operating characteristic curve (AUC). Enriched metabolic pathways were determined using the KEGG and MetaboAnalyst databases. Our results revealed the detection of 272 metabolites using GC-MS and 1457 metabolites using LC-MS in AH samples. Among them, 97 metabolites exhibited significant differential expression between the CNV and non-CNV groups. Noteworthy candidates, including D-citramalic acid, biphenyl, and isoleucylproline, demonstrated high AUC values ranging from 0.801 to 1, indicating their potential as disease biomarkers. Additionally, all three metabolites showed a strong association with retinal cystoid edema in CNV patients. Furthermore, the study identified 12 altered metabolic pathways, with five of them related to carbohydrate metabolism, suggesting their involvement in the occurrence of myopic CNV. These findings provide possible disease-specific biomarkers of CNV in PM and suggest the role of disturbed carbohydrate metabolism in its pathogenesis. Larger studies are needed to validate these findings.
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Affiliation(s)
- Qiaoling Wei
- Department of Ophthalmology, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai 200031, China
- NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China
- Ocular Trauma Center, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
| | - Zhiqiang Yu
- Department of Ophthalmology, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai 200031, China
- NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China
| | - Xianjin Zhou
- Department of Ophthalmology, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai 200031, China
- NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China
| | - Ruowen Gong
- Department of Ophthalmology, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai 200031, China
- NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China
| | - Rui Jiang
- Department of Ophthalmology, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai 200031, China
- NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China
- Ocular Trauma Center, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
| | - Gezhi Xu
- Department of Ophthalmology, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai 200031, China
- NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China
| | - Wei Liu
- Department of Ophthalmology, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
- Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai 200031, China
- NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China
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Khan SR, Rost H, Cox B, Razani B, Alexeeff S, Wheeler MB, Gunderson EP. Heterogeneity in Early Postpartum Metabolic Profiles Among Women with GDM Who Progressed to Type 2 Diabetes During 10-Year Follow-Up: The SWIFT Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.13.23291346. [PMID: 37398098 PMCID: PMC10312884 DOI: 10.1101/2023.06.13.23291346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
GDM is a strong risk factor for progression to T2D after pregnancy. Although both GDM and T2D exhibit heterogeneity, the link between the distinct heterogeneity of GDM and incident T2D has not been established. Herein, we evaluate early postpartum profiles of women with recent GDM who later developed incident T2D using a soft clustering method, followed by the integration of both clinical phenotypic variables and metabolomics to characterize these heterogeneous clusters/groups clinically and their molecular mechanisms. We identified three clusters based on two indices of glucose homeostasis at 6-9 weeks postpartum - HOMA-IR and HOMA-B among women who developed incident T2D during the 12-year follow-up. The clusters were classified as follows: pancreatic beta-cell dysfunction group (cluster-1), insulin resistant group (cluster-3), and a combination of both phenomena (cluster-2) comprising the majority of T2D. We also identified postnatal blood test parameters to distinguish the three clusters for clinical testing. Moreover, we compared these three clusters in their metabolomics profiles at the early stage of the disease to identify the mechanistic insights. A significantly higher concentration of a metabolite at the early stage of a T2D cluster than other clusters indicates its essentiality for the particular disease character. As such, the early-stage characters of T2D cluster-1 pathology include a higher concentration of sphingolipids, acyl-alkyl phosphatidylcholines, lysophosphatidylcholines, and glycine, indicating their essentiality for pancreatic beta-cell function. In contrast, the early-stage characteristics of T2D cluster-3 pathology include a higher concentration of diacyl phosphatidylcholines, acyl-carnitines, isoleucine, and glutamate, indicating their essentiality for insulin actions. Notably, all these biomolecules are found in the T2D cluster-2 with mediocre concentrations, indicating a true nature of a mixed group. In conclusion, we have deconstructed incident T2D heterogeneity and identified three clusters with their clinical testing procedures and molecular mechanisms. This information will aid in adopting proper interventions using a precision medicine approach.
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Affiliation(s)
- Saifur R Khan
- Department of Cardiology, University of Pittsburgh, PA, USA
- Vascular Medicine Institute, University of Pittsburgh, PA, USA
- Departments of Physiology and Medicine, University of Toronto, Ontario, Canada
| | - Hannes Rost
- Donnelly Centre, University of Toronto, Ontario, Canada
| | - Brian Cox
- Department of Obstetrics and Gynaecology, University of Toronto, Ontario, Canada
| | - Babak Razani
- Department of Cardiology, University of Pittsburgh, PA, USA
- Vascular Medicine Institute, University of Pittsburgh, PA, USA
| | - Stacey Alexeeff
- Kaiser Permanente Northern California, Division of Research, Oakland, CA
| | - Michael B Wheeler
- Departments of Physiology and Medicine, University of Toronto, Ontario, Canada
| | - Erica P Gunderson
- Kaiser Permanente Northern California, Division of Research, Oakland, CA
- Kaiser Permanente Bernard J. Tyson School of Medicine, Department of Health Systems Science, Pasadena, CA
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Mahajan UM, Oehrle B, Sirtl S, Alnatsha A, Goni E, Regel I, Beyer G, Vornhülz M, Vielhauer J, Chromik A, Bahra M, Klein F, Uhl W, Fahlbusch T, Distler M, Weitz J, Grützmann R, Pilarsky C, Weiss FU, Adam MG, Neoptolemos JP, Kalthoff H, Rad R, Christiansen N, Bethan B, Kamlage B, Lerch MM, Mayerle J. Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis. Gastroenterology 2022; 163:1407-1422. [PMID: 35870514 DOI: 10.1053/j.gastro.2022.07.047] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 06/28/2022] [Accepted: 07/14/2022] [Indexed: 12/19/2022]
Abstract
BACKGROUND & AIMS Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature. METHODS We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance. RESULTS The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively. CONCLUSIONS The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.
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Affiliation(s)
- Ujjwal M Mahajan
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Bettina Oehrle
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Simon Sirtl
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Ahmed Alnatsha
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Elisabetta Goni
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Ivonne Regel
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Georg Beyer
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Marlies Vornhülz
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Jakob Vielhauer
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany
| | - Ansgar Chromik
- Department of General and Visceral Surgery, Asklepios Klinikum Hamburg, Hamburg, Germany
| | - Markus Bahra
- Zentrum für Onkologische Oberbauchchirurgie und Robotik, Krankenhaus Waldfriede, Berlin, Germany
| | - Fritz Klein
- Department of General, Visceral and Transplantation Surgery, Charité, Campus Virchow Klinikum, Berlin, Germany
| | - Waldemar Uhl
- Department of General and Visceral Surgery, Katholisches Klinikum Bochum, Bochum, Germany
| | - Tim Fahlbusch
- Department of General and Visceral Surgery, Katholisches Klinikum Bochum, Bochum, Germany
| | - Marius Distler
- Department for Visceral, Thoracic and Vascular Surgery, University Hospital, Technical University Dresden, Dresden, Germany
| | - Jürgen Weitz
- Department for Visceral, Thoracic and Vascular Surgery, University Hospital, Technical University Dresden, Dresden, Germany
| | - Robert Grützmann
- Department of Surgery, Erlangen University Hospital, Erlangen, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Erlangen, Germany
| | - Christian Pilarsky
- Department of Surgery, Erlangen University Hospital, Erlangen, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Erlangen, Germany
| | - Frank Ulrich Weiss
- Department of Medicine A, University Medicine Greifswald, Greifswald, Germany
| | - M Gordian Adam
- Metanomics Health GmbH, Berlin, Germany; biocrates life sciences ag, Innsbruck, Austria
| | - John P Neoptolemos
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
| | - Holger Kalthoff
- Section for Molecular Oncology, Institut for Experimental Cancer Research (IET), Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Roland Rad
- Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany; Institute of Molecular Oncology and Functional Genomics, TUM School of Medicine and Center for Translational Cancer Research (TranslaTUM), Technische Universität München, Munich, Germany
| | - Nicole Christiansen
- Metanomics Health GmbH, Berlin, Germany; TrinamiX GmbH, Ludwigshafen am Rhein, Rheinland-Pfalz, Germany
| | | | | | - Markus M Lerch
- Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany; Department of Medicine A, University Medicine Greifswald, Greifswald, Germany; Ludwig Maximilian University Klinikum, Munich, Germany
| | - Julia Mayerle
- Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany.
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Metabolomics and Biomarkers in Retinal and Choroidal Vascular Diseases. Metabolites 2022; 12:metabo12090814. [PMID: 36144219 PMCID: PMC9503269 DOI: 10.3390/metabo12090814] [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: 07/26/2022] [Revised: 08/25/2022] [Accepted: 08/27/2022] [Indexed: 11/17/2022] Open
Abstract
The retina is one of the most important structures in the eye, and the vascular health of the retina and choroid is critical to visual function. Metabolomics provides an analytical approach to endogenous small molecule metabolites in organisms, summarizes the results of “gene-environment interactions”, and is an ideal analytical tool to obtain “biomarkers” related to disease information. This study discusses the metabolic changes in neovascular diseases involving the retina and discusses the progress of the study from the perspective of metabolomics design and analysis. This study advocates a comparative strategy based on existing studies, which encompasses optimization of the performance of newly identified biomarkers and the consideration of the basis of existing studies, which facilitates quality control of newly discovered biomarkers and is recommended as an additional reference strategy for new biomarker discovery. Finally, by describing the metabolic mechanisms of retinal and choroidal neovascularization, based on the results of existing studies, this study provides potential opportunities to find new therapeutic approaches.
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6
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Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity. Pharmaceuticals (Basel) 2022; 15:ph15091051. [PMID: 36145272 PMCID: PMC9504187 DOI: 10.3390/ph15091051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/11/2022] [Accepted: 08/19/2022] [Indexed: 02/07/2023] Open
Abstract
The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.
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7
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Ding X, Yang F, Chen Y, Xu J, He J, Zhang R, Abliz Z. Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics. Anal Chem 2022; 94:7500-7509. [PMID: 35584098 DOI: 10.1021/acs.analchem.1c05502] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Large-scale and long-period metabolomics study is more susceptible to various sources of systematic errors, resulting in nonreproducibility and poor data quality. A reliable and robust batch correction method removes unwanted systematic variations and improves the statistical power of metabolomics data, which undeniably becomes an important issue for the quality control of metabolomics. This study proposed a novel data normalization and integration method, Norm ISWSVR. It is a two-step approach via combining the best-performance internal standard correction with support vector regression normalization, comprehensively removing the systematic and random errors and matrix effects. This method was investigated in three untargeted lipidomics or metabolomics datasets, and the performance was further evaluated systematically in comparison with that of 11 other normalization methods. As a result, Norm ISWSVR decreased the data's median cross-validated relative standard deviation (cvRSD), increased the correlation between QCs, improved the classification accuracy of biomarkers, and was well-compatible with quantitative data. More importantly, Norm ISWSVR also allows a low frequency of QCs, which could significantly decrease the burden of a large-scale experiment. Correspondingly, Norm ISWSVR favorably improves the data quality of large-scale metabolomics data.
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Affiliation(s)
- Xian Ding
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050 Beijing, China
| | - Fen Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Center of Drug Clinical Trial, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Yanhua Chen
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics, Minzu University of China, State Ethnic Affairs Commission, 100081 Beijing, China.,Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, 100081 Beijing, China.,Key Laboratory of Ethnomedicine of Ministry of Education, School of Pharmacy, Minzu University of China, Beijing 100081, China
| | - Jing Xu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050 Beijing, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050 Beijing, China
| | - Ruiping Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050 Beijing, China
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050 Beijing, China.,Key Laboratory of Mass Spectrometry Imaging and Metabolomics, Minzu University of China, State Ethnic Affairs Commission, 100081 Beijing, China.,Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, 100081 Beijing, China.,Key Laboratory of Ethnomedicine of Ministry of Education, School of Pharmacy, Minzu University of China, Beijing 100081, China
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8
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Cracowski JL, Hulot JS, Laporte S, Charvériat M, Roustit M, Deplanque D, Girodet PO. Clinical pharmacology: Current innovations and future challenges. Fundam Clin Pharmacol 2021; 36:456-467. [PMID: 34954839 DOI: 10.1111/fcp.12747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/08/2021] [Accepted: 12/18/2021] [Indexed: 11/28/2022]
Abstract
Clinical pharmacology is the study of drugs in humans, from first-in-human studies to randomized controlled trials (RCTs) and benefit-risk ratio assessment in large populations. The objective of this review is to present the recent innovations that may revolutionize the development of drugs in the future. On behalf of the French Society of Pharmacology and Therapeutics, we provide recommendations to address those future challenges in clinical pharmacology. Whatever the future will be, robust preliminary data on drug mechanism of action and rigorous study design will remain crucial prior to the start of pharmacological studies in human. At the present time, RCTs remains the gold standard to evaluate the efficacy of human drugs, although alternative designs (pragmatic trials, platform trials, etc.) are emerging. Innovations in healthy volunteers' studies and the contribution of new technologies such as artificial intelligence, machine learning and internet-based trials have the potential to improve drug development. In the field of precision medicine, new disease phenotypes and endotypes will probably help to identify new pharmacological targets, responders to therapies and patients at risk for drug adverse events. In such a moving landscape, the development of translational research through academic and private partnership, transparent sharing of clinical trial data and enhanced interactions between drug experts, patients and the general public are priority areas for action.
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Affiliation(s)
- Jean-Luc Cracowski
- Univ. Grenoble Alpes, U1042, INSERM, Grenoble, France.,CHU de Grenoble, Service de Pharmacologie - Pharmacosurveillance, CIC1406, Centre Régional de Pharmacovigilance, Grenoble, France
| | - Jean-Sébastien Hulot
- Université de Paris, INSERM, PARCC, Paris, France.,CIC1418 and DMU CARTE, AP-HP, Hôpital Européen Georges-Pompidou, Paris, France
| | - Silvy Laporte
- Univ. Jean-Monnet, Saint-Etienne, UMR1059, Saint-Etienne, France.,CHU de Saint-Etienne, Unité de recherche clinique, Innovation et pharmacologie, Saint-Etienne, France
| | | | - Matthieu Roustit
- Univ. Grenoble Alpes, U1042, INSERM, Grenoble, France.,CHU de Grenoble, Service de Pharmacologie - Pharmacosurveillance, CIC1406, Centre Régional de Pharmacovigilance, Grenoble, France
| | - Dominique Deplanque
- Univ. Lille, Inserm, CHU Lille, U1172 - Degenerative & vascular cognitive disorders, Lille, France.,Univ. Lille, Inserm, CHU Lille, CIC 1403 - Clinical Investigation Center, Lille, France
| | - Pierre-Olivier Girodet
- Univ. Bordeaux, CIC1401, U1045, INSERM, Bordeaux, France.,CHU de Bordeaux, CIC1401, Service de Pharmacologie Médicale, Bordeaux, France
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Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage. Cancers (Basel) 2021; 13:cancers13246253. [PMID: 34944874 PMCID: PMC8699523 DOI: 10.3390/cancers13246253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 11/17/2022] Open
Abstract
Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.
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Khan SR, Al Rijjal D, Piro A, Wheeler MB. Integration of AI and traditional medicine in drug discovery. Drug Discov Today 2021; 26:982-992. [PMID: 33476566 DOI: 10.1016/j.drudis.2021.01.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/01/2020] [Accepted: 01/11/2021] [Indexed: 11/24/2022]
Abstract
AI integration in plant-based traditional medicine could be used to overcome drug discovery challenges.
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Affiliation(s)
- Saifur R Khan
- Endocrine and Diabetes Platform, Department of Physiology, University of Toronto, Medical Sciences Building, Room 3352, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Advanced Diagnostics, Metabolism, Toronto General Hospital Research Institute, Toronto, ON, Canada.
| | - Dana Al Rijjal
- Endocrine and Diabetes Platform, Department of Physiology, University of Toronto, Medical Sciences Building, Room 3352, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Advanced Diagnostics, Metabolism, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Anthony Piro
- Endocrine and Diabetes Platform, Department of Physiology, University of Toronto, Medical Sciences Building, Room 3352, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Advanced Diagnostics, Metabolism, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Michael B Wheeler
- Endocrine and Diabetes Platform, Department of Physiology, University of Toronto, Medical Sciences Building, Room 3352, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Advanced Diagnostics, Metabolism, Toronto General Hospital Research Institute, Toronto, ON, Canada
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La Joie R, Visani AV, Lesman-Segev OH, Baker SL, Edwards L, Iaccarino L, Soleimani-Meigooni DN, Mellinger T, Janabi M, Miller ZA, Perry DC, Pham J, Strom A, Gorno-Tempini ML, Rosen HJ, Miller BL, Jagust WJ, Rabinovici GD. Association of APOE4 and Clinical Variability in Alzheimer Disease With the Pattern of Tau- and Amyloid-PET. Neurology 2020; 96:e650-e661. [PMID: 33262228 DOI: 10.1212/wnl.0000000000011270] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 09/11/2020] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE To assess whether Alzheimer disease (AD) clinical presentation and APOE4 relate to the burden and topography of β-amyloid (Aβ) and tau pathologies using in vivo PET imaging. METHODS We studied 119 Aβ-positive symptomatic patients aged 48-95 years, including 29 patients with logopenic variant primary progressive aphasia (lvPPA) and 21 with posterior cortical atrophy (PCA). Pittsburgh compound B (PiB)-Aβ and flortaucipir (tau)-PET standardized uptake value ratio (SUVR) images were created. General linear models assessed relationships between demographic/clinical variables (phenotype, age), APOE4, and PET (including global cortical and voxelwise SUVR values) while controlling for disease severity using the Clinical Dementia Rating Sum of Boxes. RESULTS PiB-PET binding showed a widespread cortical distribution with subtle differences across phenotypes and was unrelated to demographic/clinical variables or APOE4. Flortaucipir-PET was commonly elevated in temporoparietal regions, but showed marked phenotype-associated differences, with higher binding observed in occipito-parietal areas for PCA, in left temporal and inferior frontal for lvPPA, and in medial temporal areas for other AD. Cortical flortaucipir-PET binding was higher in younger patients across phenotypes (r = -0.63, 95% confidence interval [CI] -0.72, -0.50), especially in parietal and dorsal prefrontal cortices. The presence of APOE4 was associated with a focal medial temporal flortaucipir-SUVR increase, controlling for all other variables (entorhinal: + 0.310 SUVR, 95% CI 0.091, 0.530). CONCLUSIONS Clinical phenotypes are associated with differential patterns of tau but not amyloid pathology. Older age and APOE4 are not only risk factors for AD but also seem to affect disease expression by promoting a more medial temporal lobe-predominant pattern of tau pathology.
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Affiliation(s)
- Renaud La Joie
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley.
| | - Adrienne V Visani
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Orit H Lesman-Segev
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Suzanne L Baker
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Lauren Edwards
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Leonardo Iaccarino
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - David N Soleimani-Meigooni
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Taylor Mellinger
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Mustafa Janabi
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Zachary A Miller
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - David C Perry
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Julie Pham
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Amelia Strom
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Maria Luisa Gorno-Tempini
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Howard J Rosen
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Bruce L Miller
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - William J Jagust
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
| | - Gil D Rabinovici
- From the Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences (R.L.J., A.V.V., O.H.L.-V., L.E., L.I., D.N.S.-M., T.M., Z.A.M., D.C.P., J.P., A.S., M.L.G.-T., H.J.R., B.L.M., G.D.R.), and Department of Radiology and Biomedical Imaging (G.D.R.), University of California, San Francisco; Department of Diagnostic Imaging (O.H.L.-V.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel; Molecular Biophysics and Integrated Bioimaging Division (S.L.B., M.J., W.J.J., G.D.R.), Lawrence Berkeley National Laboratory; and Helen Wills Neuroscience Institute (W.J.J., G.D.R.), University of California Berkeley
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Diray-Arce J, Conti MG, Petrova B, Kanarek N, Angelidou A, Levy O. Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections. Metabolites 2020; 10:E492. [PMID: 33266347 PMCID: PMC7760881 DOI: 10.3390/metabo10120492] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/24/2020] [Accepted: 11/30/2020] [Indexed: 12/16/2022] Open
Abstract
Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.
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Affiliation(s)
- Joann Diray-Arce
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
| | - Maria Giulia Conti
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Maternal and Child Health, Sapienza University of Rome, 5, 00185 Rome, Italy
| | - Boryana Petrova
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Department of Pathology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Naama Kanarek
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Department of Pathology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Asimenia Angelidou
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Ofer Levy
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Broad Institute of MIT & Harvard, Cambridge, MA 02142, USA
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Khan SR, Manialawy Y, Obersterescu A, Cox BJ, Gunderson EP, Wheeler MB. Diminished Sphingolipid Metabolism, a Hallmark of Future Type 2 Diabetes Pathogenesis, Is Linked to Pancreatic β Cell Dysfunction. iScience 2020; 23:101566. [PMID: 33103069 PMCID: PMC7578680 DOI: 10.1016/j.isci.2020.101566] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/20/2020] [Accepted: 09/11/2020] [Indexed: 12/12/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is the top risk factor for future type 2 diabetes (T2D) development. Ethnicity profoundly influences who will transition from GDM to T2D, with high risk observed in Hispanic women. To better understand this risk, a nested 1:1 pair-matched, Hispanic-specific, case-control design was applied to a prospective cohort with GDM history. Women who were non-diabetic 6-9 weeks postpartum (baseline) were monitored for the development of T2D. Metabolomics were performed on baseline plasma to identify metabolic pathways associated with T2D risk. Notably, diminished sphingolipid metabolism was highly associated with future T2D. Defects in sphingolipid metabolism were further implicated by integrating metabolomics and genome-wide association data, which identified two significantly enriched T2D-linked genes, CERS2 and CERS4. Follow-up experiments in mice and cells demonstrated that inhibiting sphingolipid metabolism impaired pancreatic β cell function. These data suggest early postpartum alterations in sphingolipid biosynthesis contribute to β cell dysfunction and T2D risk.
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Affiliation(s)
- Saifur R. Khan
- Department of Physiology, University of Toronto, ON, Canada
- Advanced Diagnostics, Metabolism, Toronto General Research Institute, ON, Canada
| | - Yousef Manialawy
- Department of Physiology, University of Toronto, ON, Canada
- Advanced Diagnostics, Metabolism, Toronto General Research Institute, ON, Canada
| | | | - Brian J. Cox
- Department of Physiology, University of Toronto, ON, Canada
- Department of Obstetrics and Gynaecology, University of Toronto, ON, Canada
| | - Erica P. Gunderson
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, USA
| | - Michael B. Wheeler
- Department of Physiology, University of Toronto, ON, Canada
- Advanced Diagnostics, Metabolism, Toronto General Research Institute, ON, Canada
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Abstract
The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.
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Tolstikov V, Moser AJ, Sarangarajan R, Narain NR, Kiebish MA. Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics. Metabolites 2020; 10:metabo10060224. [PMID: 32485899 PMCID: PMC7345110 DOI: 10.3390/metabo10060224] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/21/2020] [Accepted: 05/27/2020] [Indexed: 12/16/2022] Open
Abstract
Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.
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Affiliation(s)
- Vladimir Tolstikov
- BERG, Precision Medicine Division, Framingham, MA 01701, USA; (V.T.); (R.S.); (N.R.N.)
| | - A. James Moser
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA;
| | | | - Niven R. Narain
- BERG, Precision Medicine Division, Framingham, MA 01701, USA; (V.T.); (R.S.); (N.R.N.)
| | - Michael A. Kiebish
- BERG, Precision Medicine Division, Framingham, MA 01701, USA; (V.T.); (R.S.); (N.R.N.)
- Correspondence: ; Tel.: +1-617-588-2245
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