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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. Genome Med 2024; 16:65. [PMID: 38685057 PMCID: PMC11057104 DOI: 10.1186/s13073-024-01338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
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2
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Tan L, Zhong MM, Zhao YQ, Zhao J, Dusenge MA, Feng Y, Ye Q, Hu J, Ou-Yang ZY, Chen NX, Su XL, Zhang Q, Liu Q, Yuan H, Wang MY, Feng YZ, Guo Y. Type 1 diabetes, glycemic traits, and risk of dental caries: a Mendelian randomization study. Front Genet 2023; 14:1230113. [PMID: 37881806 PMCID: PMC10597668 DOI: 10.3389/fgene.2023.1230113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
Background: Regarding past epidemiological studies, there has been disagreement over whether type 1 diabetes (T1DM) is one of the risk factors for dental caries. The purpose of this study was to determine the causative links between genetic susceptibility to T1DM, glycemic traits, and the risk of dental caries using Mendelian randomization (MR) approaches. Methods: Summary-level data were collected on genome-wide association studies (GWAS) of T1DM, fasting glucose (FG), glycated hemoglobin (HbA1c), fasting insulin (FI), and dental caries. MR was performed using the inverse-variance weighting (IVW) method, and sensitivity analyses were conducted using the MR-Egger method, weighted median, weighted mode, replication cohort, and multivariable MR conditioning on potential mediators. Results: The risk of dental caries increased as a result of genetic susceptibility to T1DM [odds ratio (OR) = 1.044; 95% confidence interval (CI) = 1.015-1.074; p = 0.003], with consistent findings in the replication cohort. The relationship between T1DM and dental caries was stable when adjusted for BMI, smoking, alcohol intake, and type 2 diabetes (T2DM) in multivariable MR. However, no significant correlations between the risk of dental caries and FG, HbA1c, or FI were found. Conclusion: These results indicate that T1DM has causal involvement in the genesis of dental caries. Therefore, periodic reinforcement of oral hygiene instructions must be added to the management and early multidisciplinary intervention of T1DM patients, especially among adolescents and teenagers, who are more susceptible to T1DM.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yun-Zhi Feng
- Department of Stomatology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yue Guo
- Department of Stomatology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
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3
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Roche EF, McKenna AM, O'Regan M, Ryder KJ, Fitzgerald HM, Hoey HMCV. The incidence of type 1 diabetes in children under 15 years of age is rising again-a nationwide study. Eur J Pediatr 2023; 182:4615-4623. [PMID: 37550598 PMCID: PMC10587220 DOI: 10.1007/s00431-023-05125-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/08/2023] [Accepted: 07/18/2023] [Indexed: 08/09/2023]
Abstract
International incidence rates (IRs) and trends of childhood type 1 diabetes (T1D) vary. Recent data from Ireland and other high incidence countries suggested a stabilisation in IRs of T1D in children aged under 15 years. Our primary objective was to report the IR of T1D in children in Ireland from 2019 to 2021 and evaluate if age, sex and season of diagnosis had changed. Incident cases of T1D in those aged under 15 years were identified prospectively by clinicians nationally and reported to the Irish Childhood Diabetes National Register (ICDNR). Following case verification, capture-recapture methodology was applied, and IRs calculated. Numbers of children including age, sex and season of diagnosis per year were evaluated. There were 1027 cases, 542 males (53%). The direct standardised incidence rates (SIRs) increased by 21% overall and were 31.1, 32.2 and 37.6/100,000/year, respectively, with no significant sex difference. The highest IRs were in the 10-14-year category until 2021, then changed to the 5-9-year category (40% of cases). Whilst autumn and winter remain dominant diagnostic seasons, seasonality differed in 2021 with a greater number presenting in spring. CONCLUSION The incidence of childhood T1D in Ireland is increasing, observed prior to the COVID-19 pandemic, and shifting to an earlier age at diagnosis for the first time. The pattern of seasonality also appears to have changed. This may reflect an increased severity of diabetes with important implications for healthcare providers. WHAT IS KNOWN • Ireland has a very high incidence of T1D in childhood, which had stabilised following a rapid rise, similar to other high incidence countries. • The incidence rate is consistently highest in older children (10-14 years). WHAT IS NEW • Irish IR is no longer stable and has increased again, with the highest incidence occurring in the younger 5-9 age category for the first time. • The seasonality of diagnosis has changed during the COVID-19 pandemic years of 2020-2021.
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Affiliation(s)
- Edna F Roche
- The Discipline of Paediatrics, School of Medicine, Trinity College Dublin, Dublin, Ireland.
- The Department of Paediatric Growth, Diabetes, and Endocrinology, Children's Health Ireland (CHI) at, Tallaght University Hospital, Dublin, Ireland.
| | - Amanda M McKenna
- The Discipline of Paediatrics, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Myra O'Regan
- The Discipline of Paediatrics, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Kerry J Ryder
- The Research and Evidence Office, Health Service Executive, Dublin, Ireland
| | - Helen M Fitzgerald
- The Department of Paediatric Growth, Diabetes, and Endocrinology, Children's Health Ireland (CHI) at, Tallaght University Hospital, Dublin, Ireland
| | - Hilary M C V Hoey
- The Discipline of Paediatrics, School of Medicine, Trinity College Dublin, Dublin, Ireland
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.27.559579. [PMID: 37808714 PMCID: PMC10557724 DOI: 10.1101/2023.09.27.559579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R. Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
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Lernmark Å, Akolkar B, Hagopian W, Krischer J, McIndoe R, Rewers M, Toppari J, Vehik K, Ziegler AG. Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes. J Intern Med 2023; 294:145-158. [PMID: 37143363 PMCID: PMC10524683 DOI: 10.1111/joim.13648] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.
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Affiliation(s)
- Åke Lernmark
- Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden
| | - Beena Akolkar
- National Institute of Diabetes & Digestive & Kidney Diseases, Bethesda, MD USA
| | | | - Jeffrey Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL USA
| | - Richard McIndoe
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA USA
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado USA
| | - Jorma Toppari
- Department of Pediatrics, Turku University Hospital, and Institute of Biomedicine, Research Centre for Integrated Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Kendra Vehik
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL USA
| | - Anette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, and Klinikum rechts der Isar, Technische Universität München, and Forschergruppe Diabetes e.V., Neuherberg, Germany
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6
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Mistry S, Riches NO, Gouripeddi R, Facelli JC. Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review. Artif Intell Med 2023; 135:102461. [PMID: 36628796 PMCID: PMC9834645 DOI: 10.1016/j.artmed.2022.102461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 10/06/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022]
Abstract
BACKGROUND Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed. OBJECTIVE We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction. METHODS We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes. RESULTS We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes. DISCUSSION The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.
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Affiliation(s)
- Sejal Mistry
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA
| | - Naomi O Riches
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA; Department of Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Ramkiran Gouripeddi
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, UT, USA
| | - Julio C Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, UT, USA.
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7
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Fan S, Wilson CM, Fridley BL, Li Q. Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis. Methods Mol Biol 2023; 2629:247-269. [PMID: 36929081 DOI: 10.1007/978-1-0716-2986-4_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
In this chapter, we review the cutting-edge statistical and machine learning methods for missing value imputation, normalization, and downstream analyses in mass spectrometry metabolomics studies, with illustration by example datasets. The missing peak recovery includes simple imputation by zero or limit of detection, regression-based or distribution-based imputation, and prediction by random forest. The batch effect can be removed by data-driven methods, internal standard-based, and quality control sample-based normalization. We also summarize different types of statistical analysis for metabolomics and clinical outcomes, such as inference on metabolic biomarkers, clustering of metabolomic profiles, metabolite module building, and integrative analysis with transcriptome.
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Affiliation(s)
- Sili Fan
- Graduate Group of Biostatistics, University of California, Davis, CA, USA
| | - Christopher M Wilson
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
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8
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Zhang J, Xiao Y, Hu J, Liu S, Zhou Z, Xie L. Lipid metabolism in type 1 diabetes mellitus: Pathogenetic and therapeutic implications. Front Immunol 2022; 13:999108. [PMID: 36275658 PMCID: PMC9583919 DOI: 10.3389/fimmu.2022.999108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease with insulin deficiency due to pancreatic β cell destruction. Multiple independent cohort studies revealed specific lipid spectrum alterations prior to islet autoimmunity in T1DM. Except for serving as building blocks for membrane biogenesis, accumulative evidence suggests lipids and their derivatives can also modulate different biological processes in the progression of T1DM, such as inflammation responses, immune attacks, and β cell vulnerability. However, the types of lipids are huge and majority of them have been largely unexplored in T1DM. In this review, based on the lipid classification system, we summarize the clinical evidence on dyslipidemia related to T1DM and elucidate the potential mechanisms by which they participate in regulating inflammation responses, modulating lymphocyte function and influencing β cell susceptibility to apoptosis and dysfunction. This review systematically recapitulates the role and mechanisms of various lipids in T1DM, providing new therapeutic approaches for T1DM from a nutritional perspective.
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9
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Franko A, Irmler M, Prehn C, Heinzmann SS, Schmitt-Kopplin P, Adamski J, Beckers J, von Kleist-Retzow JC, Wiesner R, Häring HU, Heni M, Birkenfeld AL, de Angelis MH. Bezafibrate Reduces Elevated Hepatic Fumarate in Insulin-Deficient Mice. Biomedicines 2022; 10:biomedicines10030616. [PMID: 35327418 PMCID: PMC8945094 DOI: 10.3390/biomedicines10030616] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 02/01/2023] Open
Abstract
Glucotoxic metabolites and pathways play a crucial role in diabetic complications, and new treatment options which improve glucotoxicity are highly warranted. In this study, we analyzed bezafibrate (BEZ) treated, streptozotocin (STZ) injected mice, which showed an improved glucose metabolism compared to untreated STZ animals. In order to identify key molecules and pathways which participate in the beneficial effects of BEZ, we studied plasma, skeletal muscle, white adipose tissue (WAT) and liver samples using non-targeted metabolomics (NMR spectroscopy), targeted metabolomics (mass spectrometry), microarrays and mitochondrial enzyme activity measurements, with a particular focus on the liver. The analysis of muscle and WAT demonstrated that STZ treatment elevated inflammatory pathways and reduced insulin signaling and lipid pathways, whereas BEZ decreased inflammatory pathways and increased insulin signaling and lipid pathways, which can partly explain the beneficial effects of BEZ on glucose metabolism. Furthermore, lysophosphatidylcholine levels were lower in the liver and skeletal muscle of STZ mice, which were reverted in BEZ-treated animals. BEZ also improved circulating and hepatic glucose levels as well as lipid profiles. In the liver, BEZ treatment reduced elevated fumarate levels in STZ mice, which was probably due to a decreased expression of urea cycle genes. Since fumarate has been shown to participate in glucotoxic pathways, our data suggests that BEZ treatment attenuates the urea cycle in the liver, decreases fumarate levels and, in turn, ameliorates glucotoxicity and reduces insulin resistance in STZ mice.
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Affiliation(s)
- Andras Franko
- Division of Diabetology, Endocrinology and Nephrology, Department of Internal Medicine IV, University Hospital Tübingen, 72076 Tuebingen, Germany; (A.F.); (H.-U.H.); (M.H.); (A.L.B.)
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich, University of Tübingen, 72076 Tuebingen, Germany
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany;
- Institute of Experimental Genetics, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany; (M.I.); (J.A.)
| | - Martin Irmler
- Institute of Experimental Genetics, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany; (M.I.); (J.A.)
| | - Cornelia Prehn
- Metabolomics and Proteomics Core (MPC), Helmholtz Zentrum München, 85764 Neuherberg, Germany;
| | - Silke S. Heinzmann
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (S.S.H.); (P.S.-K.)
| | - Philippe Schmitt-Kopplin
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (S.S.H.); (P.S.-K.)
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany; (M.I.); (J.A.)
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Johannes Beckers
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany;
- Institute of Experimental Genetics, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany; (M.I.); (J.A.)
- Chair of Experimental Genetics, Technical University of Munich, 85354 Freising, Germany
| | - Jürgen-Christoph von Kleist-Retzow
- Center for Physiology and Pathophysiology, Institute of Vegetative Physiology, University of Köln, 50931 Cologne, Germany; (J.-C.v.K.-R.); (R.W.)
- Department of Pediatrics, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Rudolf Wiesner
- Center for Physiology and Pathophysiology, Institute of Vegetative Physiology, University of Köln, 50931 Cologne, Germany; (J.-C.v.K.-R.); (R.W.)
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Köln, 50931 Cologne, Germany
- Center for Molecular Medicine Cologne, University of Köln, 50931 Cologne, Germany
| | - Hans-Ulrich Häring
- Division of Diabetology, Endocrinology and Nephrology, Department of Internal Medicine IV, University Hospital Tübingen, 72076 Tuebingen, Germany; (A.F.); (H.-U.H.); (M.H.); (A.L.B.)
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich, University of Tübingen, 72076 Tuebingen, Germany
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany;
| | - Martin Heni
- Division of Diabetology, Endocrinology and Nephrology, Department of Internal Medicine IV, University Hospital Tübingen, 72076 Tuebingen, Germany; (A.F.); (H.-U.H.); (M.H.); (A.L.B.)
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich, University of Tübingen, 72076 Tuebingen, Germany
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany;
| | - Andreas L. Birkenfeld
- Division of Diabetology, Endocrinology and Nephrology, Department of Internal Medicine IV, University Hospital Tübingen, 72076 Tuebingen, Germany; (A.F.); (H.-U.H.); (M.H.); (A.L.B.)
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich, University of Tübingen, 72076 Tuebingen, Germany
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany;
| | - Martin Hrabě de Angelis
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany;
- Institute of Experimental Genetics, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany; (M.I.); (J.A.)
- Chair of Experimental Genetics, Center of Life and Food Sciences, Weihenstephan, Technische Universität München, 85354 Freising, Germany
- Correspondence: ; Tel.: +49-89-3187-3302
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10
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Chai J, Sun Z, Xu J. A Contemporary Insight of Metabolomics Approach for Type 1 Diabetes: Potential for Novel Diagnostic Targets. Diabetes Metab Syndr Obes 2022; 15:1605-1625. [PMID: 35642181 PMCID: PMC9148614 DOI: 10.2147/dmso.s357007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/08/2022] [Indexed: 11/23/2022] Open
Abstract
High-throughput omics has been widely applied in metabolic disease, type 1 diabetes (T1D) was one of the most typical diseases. Effective prevention and early diagnosis are very important because of infancy and persistent characteristics of T1D. The occurrence and development of T1D is a chronic and continuous process, in which the production of autoantibodies (ie serum transformation) occupies the central position. Metabolomics can evaluate the metabolic characteristics of serum before seroconversion, the changes with age and T1D complications. And the addition of natural drug metabolomics is more conducive to the systematic and comprehensive diagnosis and treatment of T1D. This paper reviewed the metabolic changes and main pathogenesis from pre-diagnosis to treatment in T1D. The metabolic spectrum of significant abnormal energy and glucose-related metabolic pathway, down-regulation of lipid metabolism and up-regulation of some antioxidant pathways has appeared before seroconversion, indicating that the body has been in the dual state of disease progression and disease resistance before T1D onset. Some metabolites (such as methionine) are closely related to age, and the types of autoantibodies produced are age-specific. Some metabolites may jointly predict DN with eGFR, and metabolomics can further contribute to the pathogenesis based on the correlation between DN and DR. Many natural drug components have been proved to act on abnormal metabolic pathways of T1D and have a positive impact on some metabolic levels, which is very important for further finding therapeutic targets and developing new drugs with small side effects. Metabolomics can provide auxiliary value for the diagnosis of T1D and provide a new direction to reveal the pathogenesis of T1D and find new therapeutic targets. The development of T1D metabolomics shows that high-throughput research methods are expected to be introduced into clinical practice.
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Affiliation(s)
- Jiatong Chai
- Department of Laboratory Medicine, The First Hospital of Jilin University, Changchun, People’s Republic of China
| | - Zeyu Sun
- Department of Laboratory Medicine, The First Hospital of Jilin University, Changchun, People’s Republic of China
| | - Jiancheng Xu
- Department of Laboratory Medicine, The First Hospital of Jilin University, Changchun, People’s Republic of China
- Correspondence: Jiancheng Xu, Department of Laboratory Medicine, The First Bethune Hospital of Jilin University, 71 Xinmin Street, Changchun, 130021, People’s Republic of China, Tel +86-431-8878-2595, Fax +86-431-8878-6169, Email
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