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Sharma V, Khokhar M, Panigrahi P, Gadwal A, Setia P, Purohit P. Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy. Clin Chim Acta 2024; 561:119842. [PMID: 38969086 DOI: 10.1016/j.cca.2024.119842] [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] [Received: 03/30/2024] [Revised: 06/25/2024] [Accepted: 06/29/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies. METHODS We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN. RESULTS Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care. CONCLUSION Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.
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Affiliation(s)
- V Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - M Khokhar
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Panigrahi
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - A Gadwal
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Setia
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Purohit
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India.
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Liu Y, Liu JE, He H, Qin M, Lei H, Meng J, Liu C, Chen X, Luo W, Zhong S. Characterizing the metabolic divide: distinctive metabolites differentiating CAD-T2DM from CAD patients. Cardiovasc Diabetol 2024; 23:14. [PMID: 38184583 PMCID: PMC10771670 DOI: 10.1186/s12933-023-02102-0] [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: 11/09/2023] [Accepted: 12/25/2023] [Indexed: 01/08/2024] Open
Abstract
OBJECTIVE To delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk. METHOD Plasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites. RESULTS Metabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR < 0.05) and validation (P < 0.05) cohorts, respectively, that were associated with T2DM risk in univariate logistic regression. Further multivariate regression analyses identified 22 characteristic metabolites consistently associated with T2DM risk in both cohorts. Notably, pipecolinic acid and L-pipecolic acid, lysine derivatives, exhibited negative association with CAD-T2DM and influenced cellular glucose metabolism in hepatocytes. Transcriptomic insights shed light on potential metabolic action sites of these metabolites. CONCLUSIONS This research underscores the metabolic disparities between CAD and CAD-T2DM patients, spotlighting the protective attributes of pipecolinic acid and L-pipecolic acid. The comprehensive metabolomic and transcriptomic findings provide novel insights into the mechanism research, prophylaxis and treatment of comorbidity of CAD and T2DM.
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Affiliation(s)
- Yingjian Liu
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Ju-E Liu
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Huafeng He
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Min Qin
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Heping Lei
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jinxiu Meng
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Chen Liu
- Department of Cardiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
| | - Wenwei Luo
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
| | - Shilong Zhong
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
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Metwaly A, Reitmeier S, Haller D. Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders. Nat Rev Gastroenterol Hepatol 2022; 19:383-397. [PMID: 35190727 DOI: 10.1038/s41575-022-00581-2] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/10/2022] [Indexed: 12/12/2022]
Abstract
The intestine harbours a complex array of microorganisms collectively known as the gut microbiota. The past two decades have witnessed increasing interest in studying the gut microbiota in health and disease, largely driven by rapid innovation in high-throughput multi-omics technologies. As a result, microbial dysbiosis has been linked to many human pathologies, including type 2 diabetes mellitus and inflammatory bowel disease. Integrated analyses of multi-omics data, including metagenomics and metabolomics along with measurements of host response and cataloguing of bacterial isolates, have identified many bacteria and bacterial products that are correlated with disease. Nevertheless, insight into the mechanisms through which microbes affect intestinal health requires going beyond correlation to causation. Current understanding of the contribution of the gut microbiota to disease causality remains limited, largely owing to the heterogeneity of microbial community structures, interindividual differences in disease evolution and incomplete understanding of the mechanisms that integrate microbiota-derived signals into host signalling pathways. In this Review, we provide a broad insight into the microbiome signatures linked to inflammatory and metabolic disorders, discuss outstanding challenges in this field and propose applications of multi-omics technologies that could lead to an improved mechanistic understanding of microorganism-host interactions.
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Affiliation(s)
- Amira Metwaly
- Chair of Nutrition and Immunology, Technical University of Munich, Freising, Germany
| | - Sandra Reitmeier
- ZIEL Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Dirk Haller
- Chair of Nutrition and Immunology, Technical University of Munich, Freising, Germany. .,ZIEL Institute for Food & Health, Technical University of Munich, Freising, Germany.
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4
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Yousri NA, Suhre K, Yassin E, Al-Shakaki A, Robay A, Elshafei M, Chidiac O, Hunt SC, Crystal RG, Fakhro KA. Metabolic and Metabo-Clinical Signatures of Type 2 Diabetes, Obesity, Retinopathy, and Dyslipidemia. Diabetes 2022; 71:184-205. [PMID: 34732537 PMCID: PMC8914294 DOI: 10.2337/db21-0490] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022]
Abstract
Macro- and microvascular complications of type 2 diabetes (T2D), obesity, and dyslipidemia share common metabolic pathways. In this study, using a total of 1,300 metabolites from 996 Qatari adults (57% with T2D) and 1,159 metabolites from an independent cohort of 2,618 individuals from the Qatar BioBank (11% with T2D), we identified 373 metabolites associated with T2D, obesity, retinopathy, dyslipidemia, and lipoprotein levels, 161 of which were novel. Novel metabolites included phospholipids, sphingolipids, lysolipids, fatty acids, dipeptides, and metabolites of the urea cycle and xanthine, steroid, and glutathione metabolism. The identified metabolites enrich pathways of oxidative stress, lipotoxicity, glucotoxicity, and proteolysis. Second, we identified 15 patterns we defined as "metabo-clinical signatures." These are clusters of patients with T2D who group together based on metabolite levels and reveal the same clustering in two or more clinical variables (obesity, LDL, HDL, triglycerides, and retinopathy). These signatures revealed metabolic pathways associated with different clinical patterns and identified patients with extreme (very high/low) clinical variables associated with extreme metabolite levels in specific pathways. Among our novel findings are the role of N-acetylmethionine in retinopathy in conjunction with dyslipidemia and the possible roles of N-acetylvaline and pyroglutamine in association with high cholesterol levels and kidney function.
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Affiliation(s)
- Noha A. Yousri
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
- Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
- Corresponding author: Noha A. Yousri,
| | - Karsten Suhre
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Esraa Yassin
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Amal Robay
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Omar Chidiac
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Steven C. Hunt
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Khalid A. Fakhro
- Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
- Translational Research, Sidra Medical and Research Center, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
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5
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Metabolomics Signatures and Subsequent Maternal Health among Mothers with a Congenital Heart Defect-Affected Pregnancy. Metabolites 2022; 12:metabo12020100. [PMID: 35208175 PMCID: PMC8877777 DOI: 10.3390/metabo12020100] [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: 11/20/2021] [Revised: 01/13/2022] [Accepted: 01/15/2022] [Indexed: 12/24/2022] Open
Abstract
Congenital heart defects (CHDs) are the most prevalent and serious of all birth defects in the United States. However, little is known about the impact of CHD-affected pregnancies on subsequent maternal health. Thus, there is a need to characterize the metabolic alterations associated with CHD-affected pregnancies. Fifty-six plasma samples were identified from post-partum women who participated in the National Birth Defects Prevention Study between 1997 and 2011 and had (1) unaffected control offspring (n = 18), (2) offspring with tetralogy of Fallot (ToF, n = 22), or (3) hypoplastic left heart syndrome (HLHS, n = 16) in this pilot study. Absolute concentrations of 408 metabolites using the AbsoluteIDQ® p400 HR Kit (Biocrates) were evaluated among case and control mothers. Twenty-six samples were randomly selected from above as technical repeats. Analysis of covariance (ANCOVA) and logistic regression models were used to identify significant metabolites after controlling for the maternal age at delivery and body mass index. The receiver operating characteristic (ROC) curve and area-under-the-curve (AUC) are reported to evaluate the performance of significant metabolites. Overall, there were nine significant metabolites (p < 0.05) identified in HLHS case mothers and 30 significant metabolites in ToF case mothers. Statistically significant metabolites were further evaluated using ROC curve analyses with PC (34:1), two sphingolipids SM (31:1), SM (42:2), and PC-O (40:4) elevated in HLHS cases; while LPC (18:2), two triglycerides: TG (44:1), TG (46:2), and LPC (20:3) decreased in ToF; and cholesterol esters CE (22:6) were elevated among ToF case mothers. The metabolites identified in the study may have profound structural and functional implications involved in cellular signaling and suggest the need for postpartum dietary supplementation among women who gave birth to CHD offspring.
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Luís C, Baylina P, Soares R, Fernandes R. Metabolic Dysfunction Biomarkers as Predictors of Early Diabetes. Biomolecules 2021; 11:1589. [PMID: 34827587 PMCID: PMC8615896 DOI: 10.3390/biom11111589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 10/26/2021] [Accepted: 10/26/2021] [Indexed: 12/23/2022] Open
Abstract
During the pathophysiological course of type 2 diabetes (T2D), several metabolic imbalances occur. There is increasing evidence that metabolic dysfunction far precedes clinical manifestations. Thus, knowing and understanding metabolic imbalances is crucial to unraveling new strategies and molecules (biomarkers) for the early-stage prediction of the disease's non-clinical phase. Lifestyle interventions must be made with considerable involvement of clinicians, and it should be considered that not all patients will respond in the same manner. Individuals with a high risk of diabetic progression will present compensatory metabolic mechanisms, translated into metabolic biomarkers that will therefore show potential predictive value to differentiate between progressors/non-progressors in T2D. Specific novel biomarkers are being proposed to entrap prediabetes and target progressors to achieve better outcomes. This study provides a review of the latest relevant biomarkers in prediabetes. A search for articles published between 2011 and 2021 was conducted; duplicates were removed, and inclusion criteria were applied. From the 29 studies considered, a survey of the most cited (relevant) biomarkers was conducted and further discussed in the two main identified fields: metabolomics, and miRNA studies.
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Affiliation(s)
- Carla Luís
- FMUP–Departamento de Biomedicina, Faculdade de Medicina da Universidade do Porto, 4200-319 Porto, Portugal;
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal;
- LABMI-PORTIC, Laboratory of Medical & Industrial Biotechnology, Porto Research, Technology and Innovation Center, Porto Polytechnic, 4200-375 Porto, Portugal;
| | - Pilar Baylina
- LABMI-PORTIC, Laboratory of Medical & Industrial Biotechnology, Porto Research, Technology and Innovation Center, Porto Polytechnic, 4200-375 Porto, Portugal;
- IPP–Escola Superior de Saúde, Instituto Politécnico do Porto, 4200-072 Porto, Portugal
| | - Raquel Soares
- FMUP–Departamento de Biomedicina, Faculdade de Medicina da Universidade do Porto, 4200-319 Porto, Portugal;
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal;
- Biochemistry Unit, Department of Biochemistry, FMUP, Faculty of Medicine, University of Porto, Al Prof Hernani Monteiro, 4200-319 Porto, Portugal
| | - Rúben Fernandes
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal;
- LABMI-PORTIC, Laboratory of Medical & Industrial Biotechnology, Porto Research, Technology and Innovation Center, Porto Polytechnic, 4200-375 Porto, Portugal;
- IPP–Escola Superior de Saúde, Instituto Politécnico do Porto, 4200-072 Porto, Portugal
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Yuan X, Nie C, Liu H, Ma Q, Peng B, Zhang M, Chen Z, Li J. Comparison of metabolic fate, target organs, and microbiota interactions of free and bound dietary advanced glycation end products. Crit Rev Food Sci Nutr 2021:1-22. [PMID: 34698575 DOI: 10.1080/10408398.2021.1991265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Increased intake of Western diets and ultra-processed foods is accompanied by increased intake of advanced glycation end products (AGEs). AGEs can be generated exogenously in the thermal processing of food and endogenously in the human body, which associated with various chronic diseases. In food, AGEs can be divided into free and bound forms, which differ in their bioavailability, digestion, absorption, gut microbial interactions and untargeted metabolites. We summarized the measurements and contents of free and bound AGE in foods. Moreover, the ingestion, digestion, absorption, excretion, gut microbiota interactions, and metabolites and metabolic pathways between free and bound AGEs based on animal and human studies were compared. Bound AGEs were predominant in most of the selected foods, while beer and soy sauce were rich in free AGEs. Only 10%-30% of AGEs were absorbed into the systemic circulation when orally administered. The excretion of ingested free and bound AGEs was approximately 90% and 60%, respectively. Dietary free CML has a detrimental effect on gut microbiota composition, while bound AGEs have both detrimental and beneficial impacts. Free and bound dietary AGEs changed amino acid metabolism, energy metabolism and carbohydrate metabolism. And besides, bound dietary AGEs altered vitamin metabolism, and glycerolipid metabolism.
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Affiliation(s)
- Xiaojin Yuan
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Chenxi Nie
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Huicui Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Qingyu Ma
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Bo Peng
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Min Zhang
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Zhifei Chen
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Juxiu Li
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
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Alvarez-Silva C, Kashani A, Hansen TH, Pinna NK, Anjana RM, Dutta A, Saxena S, Støy J, Kampmann U, Nielsen T, Jørgensen T, Gnanaprakash V, Gnanavadivel R, Sukumaran A, Rani CSS, Færch K, Radha V, Balasubramanyam M, Nair GB, Das B, Vestergaard H, Hansen T, Mande SS, Mohan V, Arumugam M, Pedersen O. Trans-ethnic gut microbiota signatures of type 2 diabetes in Denmark and India. Genome Med 2021; 13:37. [PMID: 33658058 PMCID: PMC7931542 DOI: 10.1186/s13073-021-00856-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 02/12/2021] [Indexed: 12/15/2022] Open
Abstract
Background Type 2 diabetes (T2D), a multifactorial disease influenced by host genetics and environmental factors, is the most common endocrine disease. Several studies have shown that the gut microbiota as a close-up environmental mediator influences host physiology including metabolism. The aim of the present study is to examine the compositional and functional potential of the gut microbiota across individuals from Denmark and South India with a focus on T2D. Many earlier studies have investigated the microbiome aspects of T2D, and it has also been anticipated that such microbial associations would be dependent on diet and ethnic origin. However, there has been no large scale trans-ethnic microbiome study earlier in this direction aimed at evaluating any “universal” microbiome signature of T2D. Methods 16S ribosomal RNA gene amplicon sequencing was performed on stool samples from 279 Danish and 294 Indian study participants. Any differences between the gut microbiota of both populations were explored using diversity measures and negative binomial Wald tests. Study samples were stratified to discover global and country-specific microbial signatures for T2D and treatment with the anti-hyperglycemic drug, metformin. To identify taxonomical and functional signatures of the gut microbiota for T2D and metformin treatment, we used alpha and beta diversity measures and differential abundances analysis, comparing metformin-naive T2D patients, metformin-treated T2D patients, and normoglycemic individuals. Results Overall, the gut microbial communities of Danes and Indians are compositionally very different. By analyzing the combined study materials, we identify microbial taxonomic and functional signatures for T2D and metformin treatment. T2D patients have an increased relative abundance of two operational taxonomic units (OTUs) from the Lachnospiraceae family, and a decreased abundance of Subdoligranulum and Butyricicoccus. Studying each population per se, we identified T2D-related microbial changes at the taxonomic level within the Danish population only. Alpha diversity indices show that there is no significant difference between normoglycemic individuals and metformin-naive T2D patients, whereas microbial richness is significantly decreased in metformin-treated T2D patients compared to metformin-naive T2D patients and normoglycemic individuals. Enrichment of two OTUs from Bacteroides and depletion of Faecalibacterium constitute a trans-ethnic signature of metformin treatment. Conclusions We demonstrate major compositional differences of the gut microbiota between Danish and South Indian individuals, some of which may relate to differences in ethnicity, lifestyle, and demography. By comparing metformin-naive T2D patients and normoglycemic individuals, we identify T2D-related microbiota changes in the Danish and Indian study samples. In the present trans-ethnic study, we confirm that metformin changes the taxonomic profile and functional potential of the gut microbiota. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00856-4.
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Affiliation(s)
- Camila Alvarez-Silva
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Alireza Kashani
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark.,Danish Academy of Diabetes, Odense University Hospital, DK-5000 Odense C, Kløvervænget 6, Odense, Denmark
| | - Tue Haldor Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark.,Department of Cardiology and Endocrinology, Slagelse Hospital, Slagelse, Denmark
| | - Nishal Kumar Pinna
- TCS Research, Tata Consultancy Services Limited, 54B Hadapsar Industrial Estate, Pune, 411013, India
| | - Ranjit Mohan Anjana
- Madras Diabetes Research Foundation, No. 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Anirban Dutta
- TCS Research, Tata Consultancy Services Limited, 54B Hadapsar Industrial Estate, Pune, 411013, India
| | - Shruti Saxena
- Molecular Genetics Laboratory, Infections and Immunology, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Julie Støy
- Steno Diabetes Center Aarhus, Aarhus Universitetshospital, Hedeager 3, 2. sal, Aarhus, 8200, Denmark
| | - Ulla Kampmann
- Steno Diabetes Center Aarhus, Aarhus Universitetshospital, Hedeager 3, 2. sal, Aarhus, 8200, Denmark
| | - Trine Nielsen
- Danish Academy of Diabetes, Odense University Hospital, DK-5000 Odense C, Kløvervænget 6, Odense, Denmark
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospitals, University of Copenhagen, Copenhagen, Denmark
| | - Visvanathan Gnanaprakash
- Madras Diabetes Research Foundation, No. 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Rameshkumar Gnanavadivel
- Madras Diabetes Research Foundation, No. 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | - Aswath Sukumaran
- Madras Diabetes Research Foundation, No. 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | | | - Kristine Færch
- Steno Diabetes Center Aarhus, Aarhus Universitetshospital, Hedeager 3, 2. sal, Aarhus, 8200, Denmark
| | - Venkatesan Radha
- Madras Diabetes Research Foundation, No. 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India
| | | | - Gopinath Balakrish Nair
- Molecular Genetics Laboratory, Infections and Immunology, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Bhabatosh Das
- Molecular Genetics Laboratory, Infections and Immunology, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Sharmila Shekhar Mande
- TCS Research, Tata Consultancy Services Limited, 54B Hadapsar Industrial Estate, Pune, 411013, India.
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, No. 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India.
| | - Manimozhiyan Arumugam
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark. .,Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark.
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Quan W, Jiao Y, Li Y, Xue C, Liu G, Wang Z, Qin F, He Z, Zeng M, Chen J. Metabolic changes from exposure to harmful Maillard reaction products and high-fat diet on Sprague-Dawley rats. Food Res Int 2021; 141:110129. [DOI: 10.1016/j.foodres.2021.110129] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 12/17/2020] [Accepted: 01/06/2021] [Indexed: 12/11/2022]
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10
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Michálková L, Horník Š, Sýkora J, Habartová L, Setnička V, Bunganič B. Early Detection of Pancreatic Cancer in Type 2 Diabetes Mellitus Patients Based on 1H NMR Metabolomics. J Proteome Res 2021; 20:1744-1753. [PMID: 33617266 DOI: 10.1021/acs.jproteome.0c00990] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The association of pancreatic cancer with type 2 diabetes mellitus was investigated by 1H NMR metabolomic analysis of blood plasma. Concentration data of 58 metabolites enabled discrimination of pancreatic cancer (PC) patients from healthy controls (HC) and long-term type 2 diabetes mellitus (T2DM) patients. A panel of eight metabolites was proposed and successfully tested for group discrimination. Furthermore, a prediction model for the identification of at-risk individuals for future development of pancreatic cancer was built and tested on recent-onset diabetes mellitus (RODM) patients. Six of 59 RODM samples were assessed as PC with an accuracy of more than 80%. The health condition of these individuals was re-examined, and in four cases, a correlation to the prediction was found. The current health condition can be retrospectively attributed to misdiagnosed pancreatogenic diabetes or to early-stage pancreatic cancer.
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Affiliation(s)
- Lenka Michálková
- Department of Analytical Chemistry, Institute of Chemical Process Fundamentals of the CAS, Prague 6 16502, Czech Republic.,Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Štěpán Horník
- Department of Analytical Chemistry, Institute of Chemical Process Fundamentals of the CAS, Prague 6 16502, Czech Republic.,Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Jan Sýkora
- Department of Analytical Chemistry, Institute of Chemical Process Fundamentals of the CAS, Prague 6 16502, Czech Republic
| | - Lucie Habartová
- Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Vladimír Setnička
- Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Bohuš Bunganič
- Department of Internal Medicine, 1st Faculty of Medicine of Charles University and Military University Hospital, Prague 6 16902, Czech Republic
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11
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Han D, Shi L, Pan H. Plasma metabolomics analysis of the effects of drinking soda water on hyperglycemia mice using ultrahigh-pressure liquid chromatography-quadrupole-time of flight-mass spectrometry. Biomed Chromatogr 2020; 35:e5032. [PMID: 33220100 DOI: 10.1002/bmc.5032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 11/12/2020] [Accepted: 11/17/2020] [Indexed: 11/09/2022]
Abstract
The aim of this study was to evaluate the effects of a natural soda water [Shi Han Quan (SHQ)] on hyperglycemia and plasma metabolic profiling and explore the mechanism using metabolomics techniques. Kun-Ming mice weighing 26 ± 2 g were used for the hyperglycemia animal model with alloxan and divided into control, hyperglycemia (HG), and HG + SHQ soda water (SHQ) groups. The experiment lasted for 30 days. The plasma metabolomic profiling of mice was determined using ultrahigh-pressure liquid chromatography-quadrupole-time of flight-mass spectrometry. After the mice drank SHQ soda water, the levels of insulin and blood glucose were significantly lower in the SHQ group compared with the control group, and the level of insulin sensitivity [insulin sensitivity index (ISI)] was significantly higher in the SHQ group compared with the HG group. The mice in the different groups after SHQ intervention could be separated into distinct clusters, and nine major plasma metabolites with significant differences between groups were found closely associated with blood glucose and ISI. The metabolic pathway analysis of these metabolites involved abnormal fatty acid oxidation and phospholipid, acylcarnitine, and corticoid metabolism. The results suggested the metabolic changes and possible mechanism of SHQ improving the alloxan-induced HG, and the findings provided insights into the prevention and control of HG and diabetes.
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Affiliation(s)
- Dan Han
- College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Litian Shi
- Harbin Greenstone Water Research Institute, Harbin, China
| | - Hongzhi Pan
- Collaborative Scientific Research Center, Shanghai University of Medicine & Health Sciences, Shanghai, China
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12
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Shah AM, Wondisford FE. Tracking the carbons supplying gluconeogenesis. J Biol Chem 2020; 295:14419-14429. [PMID: 32817317 PMCID: PMC7573258 DOI: 10.1074/jbc.rev120.012758] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 08/12/2020] [Indexed: 11/06/2022] Open
Abstract
As the burden of type 2 diabetes mellitus (T2DM) grows in the 21st century, the need to understand glucose metabolism heightens. Increased gluconeogenesis is a major contributor to the hyperglycemia seen in T2DM. Isotope tracer experiments in humans and animals over several decades have offered insights into gluconeogenesis under euglycemic and diabetic conditions. This review focuses on the current understanding of carbon flux in gluconeogenesis, including substrate contribution of various gluconeogenic precursors to glucose production. Alterations of gluconeogenic metabolites and fluxes in T2DM are discussed. We also highlight ongoing knowledge gaps in the literature that require further investigation. A comprehensive analysis of gluconeogenesis may enable a better understanding of T2DM pathophysiology and identification of novel targets for treating hyperglycemia.
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Affiliation(s)
- Ankit M Shah
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
| | - Fredric E Wondisford
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
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13
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Lee YF, Sim XY, Teh YH, Ismail MN, Greimel P, Murugaiyah V, Ibrahim B, Gam LH. The effects of high-fat diet and metformin on urinary metabolites in diabetes and prediabetes rat models. Biotechnol Appl Biochem 2020; 68:1014-1026. [PMID: 32931602 DOI: 10.1002/bab.2021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022]
Abstract
High-fat diet (HFD) interferes with the dietary plan of patients with type 2 diabetes mellitus (T2DM). However, many diabetes patients consume food with higher fat content for a better taste bud experience. In this study, we examined the effect of HFD on rats at the early onset of diabetes and prediabetes by supplementing their feed with palm olein oil to provide a fat content representing 39% of total calorie intake. Urinary profile generated from liquid chromatography-mass spectrometry analysis was used to construct the orthogonal partial least squares discriminant analysis (OPLS-DA) score plots. The data provide insights into the physiological state of an organism. Healthy rats fed with normal chow (NC) and HFD cannot be distinguished by their urinary metabolite profiles, whereas diabetic and prediabetic rats showed a clear separation in OPLS-DA profile between the two diets, indicating a change in their physiological state. Metformin treatment altered the metabolomics profiles of diabetic rats and lowered their blood sugar levels. For prediabetic rats, metformin treatment on both NC- and HFD-fed rats not only reduced their blood sugar levels to normal but also altered the urinary metabolite profile to be more like healthy rats. The use of metformin is therefore beneficial at the prediabetes stage.
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Affiliation(s)
- Yan-Fen Lee
- USM-RIKEN International Centre of Aging Science, USM, Minden, Penang, Malaysia.,School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Xuan-Yi Sim
- USM-RIKEN International Centre of Aging Science, USM, Minden, Penang, Malaysia.,School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Ying-Hui Teh
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Mohd Nazri Ismail
- Analytical Biochemistry Research Centre (ABrC), USM, Minden, Penang, Malaysia
| | - Peter Greimel
- Laboratory for Cell Function Dynamics, RIKEN Centre for Brain Sciences, Wako, Saitama, Japan
| | | | - Baharudin Ibrahim
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Lay-Harn Gam
- USM-RIKEN International Centre of Aging Science, USM, Minden, Penang, Malaysia.,School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia
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14
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Chen K, Wei X, Zhang J, Pariyani R, Jokioja J, Kortesniemi M, Linderborg KM, Heinonen J, Sainio T, Zhang Y, Yang B. Effects of Anthocyanin Extracts from Bilberry ( Vaccinium myrtillus L.) and Purple Potato ( Solanum tuberosum L. var. 'Synkeä Sakari') on the Plasma Metabolomic Profile of Zucker Diabetic Fatty Rats. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2020; 68:9436-9450. [PMID: 32786839 PMCID: PMC7586333 DOI: 10.1021/acs.jafc.0c04125] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
This study compared the effects of the nonacylated and acylated anthocyanin-rich extracts on plasma metabolic profiles of Zucker diabetic fatty rats. The rats were fed with the nonacylated anthocyanin extract from bilberries (NAAB) or the acylated anthocyanin extract from purple potatoes (AAPP) at daily doses of 25 and 50 mg/kg body weight for 8 weeks. 1H NMR metabolomics was used to study the changes in plasma metabolites. A reduced fasting plasma glucose level was seen in all anthocyanin-fed groups, especially in the groups fed with NAAB. Both NAAB and AAPP decreased the levels of branched-chain amino acids and improved lipid profiles. AAPP increased the glutamine/glutamate ratio and decreased the levels of glycerol and metabolites involved in glycolysis, suggesting improved insulin sensitivity, gluconeogenesis, and glycolysis. AAPP decreased the hepatic TBC1D1 and G6PC messenger RNA level, suggesting regulation of gluconeogenesis and lipogenesis. This study indicated that AAPP and NAAB affected the plasma metabolic profile of diabetic rats differently.
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Affiliation(s)
- Kang Chen
- Food
Chemistry and Food Development, Department of Biochemistry, University of Turku, Turun yliopisto, Turku FI-20014, Finland
| | - Xuetao Wei
- Beijing
Key Laboratory of Toxicological Research and Risk Assessment for Food
Safety, School of Public Health, Peking
University, Beijing 100191, China
| | - Jian Zhang
- Department
of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing 100191, China
| | - Raghunath Pariyani
- Food
Chemistry and Food Development, Department of Biochemistry, University of Turku, Turun yliopisto, Turku FI-20014, Finland
| | - Johanna Jokioja
- Food
Chemistry and Food Development, Department of Biochemistry, University of Turku, Turun yliopisto, Turku FI-20014, Finland
| | - Maaria Kortesniemi
- Food
Chemistry and Food Development, Department of Biochemistry, University of Turku, Turun yliopisto, Turku FI-20014, Finland
| | - Kaisa M. Linderborg
- Food
Chemistry and Food Development, Department of Biochemistry, University of Turku, Turun yliopisto, Turku FI-20014, Finland
| | - Jari Heinonen
- School
of Engineering Science, Lappeenranta University
of Technology, Lappeenranta FI-53850, Finland
| | - Tuomo Sainio
- School
of Engineering Science, Lappeenranta University
of Technology, Lappeenranta FI-53850, Finland
| | - Yumei Zhang
- Department
of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing 100191, China
- . Phone: +8613426134251
| | - Baoru Yang
- Food
Chemistry and Food Development, Department of Biochemistry, University of Turku, Turun yliopisto, Turku FI-20014, Finland
- . Phone: +358 452737988
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15
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Urpi-Sarda M, Almanza-Aguilera E, Llorach R, Vázquez-Fresno R, Estruch R, Corella D, Sorli JV, Carmona F, Sanchez-Pla A, Salas-Salvadó J, Andres-Lacueva C. Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants. DIABETES & METABOLISM 2018; 45:167-174. [PMID: 29555466 DOI: 10.1016/j.diabet.2018.02.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 01/24/2018] [Accepted: 02/13/2018] [Indexed: 01/20/2023]
Abstract
AIM To characterize the urinary metabolomic fingerprint and multi-metabolite signature associated with type 2 diabetes (T2D), and to classify the population into metabotypes related to T2D. METHODS A metabolomics analysis using the 1H-NMR-based, non-targeted metabolomic approach was conducted to determine the urinary metabolomic fingerprint of T2D compared with non-T2D participants in the PREDIMED trial. The discriminant metabolite fingerprint was subjected to logistic regression analysis and ROC analyses to establish and to assess the multi-metabolite signature of T2D prevalence, respectively. Metabotypes associated with T2D were identified using the k-means algorithm. RESULTS A total of 33 metabolites were significantly different (P<0.05) between T2D and non-T2D participants. The multi-metabolite signature of T2D comprised high levels of methylsuccinate, alanine, dimethylglycine and guanidoacetate, and reduced levels of glutamine, methylguanidine, 3-hydroxymandelate and hippurate, and had a 96.4% AUC, which was higher than the metabolites on their own and glucose. Amino-acid and carbohydrate metabolism were the main metabolic alterations in T2D, and various metabotypes were identified in the studied population. Among T2D participants, those with a metabotype of higher levels of phenylalanine, phenylacetylglutamine, p-cresol and acetoacetate had significantly higher levels of plasma glucose. CONCLUSION The multi-metabolite signature of T2D highlights the altered metabolic fingerprint associated mainly with amino-acid, carbohydrate and microbiota metabolism. Metabotypes identified in this patient population could be related to higher risk of long-term cardiovascular events and therefore require further studies. Metabolomics is a useful tool for elucidating the metabolic complexity and interindividual variation in T2D towards the development of stratified precision nutrition and medicine. Trial registration at www.controlled-trials.com: ISRCTN35739639.
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Affiliation(s)
- M Urpi-Sarda
- Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA-UB, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain; CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain.
| | - E Almanza-Aguilera
- Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA-UB, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain; CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - R Llorach
- Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA-UB, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain; CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - R Vázquez-Fresno
- Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA-UB, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain; Department of Computing Science and Biological Sciences, University of Alberta, Edmonton, Canada
| | - R Estruch
- Department of Internal Medicine, Hospital Clinic, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain; CIBER Pathophysiology of Obesity and Nutrition (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - D Corella
- CIBER Pathophysiology of Obesity and Nutrition (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
| | - J V Sorli
- CIBER Pathophysiology of Obesity and Nutrition (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
| | - F Carmona
- Statistics Department, Biology Faculty, University of Barcelona, Barcelona, Spain
| | - A Sanchez-Pla
- Statistics Department, Biology Faculty, University of Barcelona, Barcelona, Spain
| | - J Salas-Salvadó
- CIBER Pathophysiology of Obesity and Nutrition (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain; Human Nutrition Unit, Biochemistry and Biotechnology Department. Hospital Universitari de Sant Joan de Reus, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
| | - C Andres-Lacueva
- Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA-UB, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain; CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain.
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16
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Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations. NPJ Syst Biol Appl 2017; 3:28. [PMID: 28948040 PMCID: PMC5608949 DOI: 10.1038/s41540-017-0029-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 08/18/2017] [Accepted: 08/25/2017] [Indexed: 12/27/2022] Open
Abstract
The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered. Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field.
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17
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Kellogg JJ, Wallace ED, Graf TN, Oberlies NH, Cech NB. Conventional and accelerated-solvent extractions of green tea (camellia sinensis) for metabolomics-based chemometrics. J Pharm Biomed Anal 2017; 145:604-610. [PMID: 28787673 DOI: 10.1016/j.jpba.2017.07.027] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 07/21/2017] [Accepted: 07/23/2017] [Indexed: 01/05/2023]
Abstract
Metabolomics has emerged as an important analytical technique for multiple applications. The value of information obtained from metabolomics analysis depends on the degree to which the entire metabolome is present and the reliability of sample treatment to ensure reproducibility across the study. The purpose of this study was to compare methods of preparing complex botanical extract samples prior to metabolomics profiling. Two extraction methodologies, accelerated solvent extraction and a conventional solvent maceration, were compared using commercial green tea [Camellia sinensis (L.) Kuntze (Theaceae)] products as a test case. The accelerated solvent protocol was first evaluated to ascertain critical factors influencing extraction using a D-optimal experimental design study. The accelerated solvent and conventional extraction methods yielded similar metabolite profiles for the green tea samples studied. The accelerated solvent extraction yielded higher total amounts of extracted catechins, was more reproducible, and required less active bench time to prepare the samples. This study demonstrates the effectiveness of accelerated solvent as an efficient methodology for metabolomics studies.
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Affiliation(s)
- Joshua J Kellogg
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States
| | - Emily D Wallace
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States
| | - Tyler N Graf
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States
| | - Nicholas H Oberlies
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States
| | - Nadja B Cech
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States.
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18
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Tam ZY, Ng SP, Tan LQ, Lin CH, Rothenbacher D, Klenk J, Boehm BO. Metabolite profiling in identifying metabolic biomarkers in older people with late-onset type 2 diabetes mellitus. Sci Rep 2017; 7:4392. [PMID: 28663594 PMCID: PMC5491522 DOI: 10.1038/s41598-017-01735-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 03/30/2017] [Indexed: 12/31/2022] Open
Abstract
Regulation of blood glucose requires precise coordination between different endocrine systems and multiple organs. Type 2 diabetes mellitus (T2D) arises from a dysregulated response to elevated glucose levels in the circulation. Globally, the prevalence of T2D has increased dramatically in all age groups. T2D in older adults is associated with higher mortality and reduced functional status, leading to higher rate of institutionalization. Despite the potential healthcare challenges associated with the presence of T2D in the elderly, the pathogenesis and phenotype of late-onset T2D is not well studied. Here we applied untargeted metabolite profiling of urine samples from people with and without late-onset T2D using ultra-performance liquid-chromatography mass-spectrometry (UPLC-MS) to identify urinary biomarkers for late-onset T2D in the elderly. Statistical modeling of measurements and thorough validation of structural assignment using liquid chromatography tandem mass-spectrometry (LC-MS/MS) have led to the identification of metabolite biomarkers associated with late-onset T2D. Lower levels of phenylalanine, acetylhistidine, and cyclic adenosine monophosphate (cAMP) were found in urine samples of T2D subjects validated with commercial standards. Elevated levels of 5′-methylthioadenosine (MTA), which previously has only been implicated in animal model of diabetes, was found in urine of older people with T2D.
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Affiliation(s)
- Zhi Yang Tam
- Singapore Phenome Center, Experimental Medicine Building, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
| | - Sean Pin Ng
- Singapore Phenome Center, Experimental Medicine Building, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
| | - Ling Qiao Tan
- Singapore Phenome Center, Experimental Medicine Building, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
| | - Chih-Hsien Lin
- Singapore Phenome Center, Experimental Medicine Building, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
| | - Dietrich Rothenbacher
- Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstrasse 22, 89081, Ulm, Germany
| | - Jochen Klenk
- Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstrasse 22, 89081, Ulm, Germany.,Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstrasse 110, 70376, Stuttgart, Germany
| | - Bernhard Otto Boehm
- Singapore Phenome Center, Experimental Medicine Building, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore. .,Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore. .,Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK. .,Department of Internal Medicine I, Ulm University Medical Centre, Ulm University, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
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19
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Almanza-Aguilera E, Urpi-Sarda M, Llorach R, Vázquez-Fresno R, Garcia-Aloy M, Carmona F, Sanchez A, Madrid-Gambin F, Estruch R, Corella D, Andres-Lacueva C. Microbial metabolites are associated with a high adherence to a Mediterranean dietary pattern using a 1H-NMR-based untargeted metabolomics approach. J Nutr Biochem 2017; 48:36-43. [PMID: 28692847 DOI: 10.1016/j.jnutbio.2017.06.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 05/09/2017] [Accepted: 06/01/2017] [Indexed: 11/18/2022]
Abstract
The study of biomarkers of dietary patterns including the Mediterranean diet (MedDiet) is scarce and could improve the assessment of these patterns. Moreover, it could provide a better understanding of health benefits of dietary patterns in nutritional epidemiology. We aimed to determine a robust and accurate biomarker associated with a high adherence to a MedDiet pattern that included dietary assessment and its biological effect. In this cross-sectional study, we included 56 and 63 individuals with high (H-MDA) and low (L-MDA) MedDiet adherence categories, respectively, all from the Prevención con Dieta Mediterránea trial. A 1H-NMR-based untargeted metabolomics approach was applied to urine samples. Multivariate statistical analyses were conducted to determine the metabolite differences between groups. A stepwise logistic regression and receiver operating characteristic curves were used to build and evaluate the prediction model for H-MDA. Thirty-four metabolites were identified as discriminant between H-MDA and L-MDA. The fingerprint associated with H-MDA included higher excretion of proline betaine and phenylacetylglutamine, among others, and decreased amounts of metabolites related to glucose metabolism. Three microbial metabolites - phenylacetylglutamine, p-cresol and 4-hydroxyphenylacetate - were included in the prediction model of H-MDA (95% specificity, 95% sensitivity and 97% area under the curve). The model composed of microbial metabolites was the biomarker that defined high adherence to a Mediterranean dietary pattern. The overall metabolite profiling identified reflects the metabolic modulation produced by H-MDA. The proposed biomarker may be a better tool for assessing and aiding nutritional epidemiology in future associations between H-MDA and the prevention or amelioration of chronic diseases.
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Affiliation(s)
- Enrique Almanza-Aguilera
- Biomarkers and Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
| | - Mireia Urpi-Sarda
- Biomarkers and Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid 28028, Spain.
| | - Rafael Llorach
- Biomarkers and Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid 28028, Spain
| | - Rosa Vázquez-Fresno
- Biomarkers and Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
| | - Mar Garcia-Aloy
- Biomarkers and Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid 28028, Spain
| | - Francesc Carmona
- Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Alex Sanchez
- Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain; Statistics and Bioinformatics Unit. Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Francisco Madrid-Gambin
- Biomarkers and Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid 28028, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid 28028, Spain; Department of Internal Medicine, Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, 08036, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid 28028, Spain; Department of Preventive Medicine, University of Valencia, Valencia 46010, Spain
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid 28028, Spain.
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20
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Madrid-Gambin F, Llorach R, Vázquez-Fresno R, Urpi-Sarda M, Almanza-Aguilera E, Garcia-Aloy M, Estruch R, Corella D, Andres-Lacueva C. Urinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study. J Proteome Res 2017; 16:1483-1491. [DOI: 10.1021/acs.jproteome.6b00860] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Francisco Madrid-Gambin
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
| | - Rafael Llorach
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
- CIBER Fragilidad y Envejecimiento Saludable
(CIBERFES), Instituto de Salud Carlos III, Barcelona 08028, Spain
| | - Rosa Vázquez-Fresno
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
- Department of Biological Sciences and Department
of Computing Science, University of Alberta, Edmonton AB T6G 2E9, Canada
| | - Mireia Urpi-Sarda
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
- CIBER Fragilidad y Envejecimiento Saludable
(CIBERFES), Instituto de Salud Carlos III, Barcelona 08028, Spain
| | - Enrique Almanza-Aguilera
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
| | - Mar Garcia-Aloy
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
- CIBER Fragilidad y Envejecimiento Saludable
(CIBERFES), Instituto de Salud Carlos III, Barcelona 08028, Spain
| | - Ramon Estruch
- Department of Internal Medicine, Hospital
Clinic, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona 08036, Spain
- CIBER OBN, The Spanish Biomedical Research Centre in
Physiopathology of Obesity and Nutrition, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Dolores Corella
- CIBER OBN, The Spanish Biomedical Research Centre in
Physiopathology of Obesity and Nutrition, Instituto de Salud Carlos III, Madrid 28029, Spain
- Department
of Preventive Medicine and Public Health, University of Valencia, Valencia 46010, Spain
| | - Cristina Andres-Lacueva
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona, Barcelona 08028, Spain
- CIBER Fragilidad y Envejecimiento Saludable
(CIBERFES), Instituto de Salud Carlos III, Barcelona 08028, Spain
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21
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Gao X, Zhang W, Wang Y, Pedram P, Cahill F, Zhai G, Randell E, Gulliver W, Sun G. Serum metabolic biomarkers distinguish metabolically healthy peripherally obese from unhealthy centrally obese individuals. Nutr Metab (Lond) 2016; 13:33. [PMID: 27175209 PMCID: PMC4865032 DOI: 10.1186/s12986-016-0095-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 05/03/2016] [Indexed: 12/11/2022] Open
Abstract
Background Metabolic abnormalities are more associated with central obesity than peripheral obesity, but the underlying mechanisms are largely unknown. The present study was to identify serum metabolic biomarkers which distinguish metabolically unhealthy centrally obese (MUCO) from metabolically healthy peripherally obese (MHPO) individuals. Methods A two-stage case–control study design was employed. In the discovery stage, 20 individuals (10 MHPO and 10 MUCO) were included and in the following validation stage, 79 individuals (20 normal weight (NW), 30 MHPO, 29 MUCO) were utilized. Study groups were matched for age, sex, physical activity and total dietary calorie intake with MHPO and MUCO additionally matched for BMI. Metabolic abnormality was defined as: 1) HOMA-IR > 4.27 (90th percentile), 2) high-density lipoprotein cholesterol < 1.03 mmol/L in men and < 1.30 mmol/L in women, 3) fasting blood glucose ≥ 5.6 mmol/L, and 4) waist circumference > 102 cm in men and > 88 cm in women. MUCO individuals had all of these abnormalities whereas MHPO and NW individuals had none of them. A targeted metabolomics approach was performed on fasting serum samples, which can simultaneously identify and quantify 186 metabolites. Results In the discovery stage, serum leucine, isoleucine, tyrosine, valine, phenylalanine, alpha-aminoadipic acid, methioninesulfoxide and propionylcarnitine were found to be significantly higher in MUCO, compared with MHPO group after multiple testing adjustment. Significant changes of five metabolites (leucine, isoleucine, valine, alpha-aminoadipic acid, propionylcarnitine) were confirmed in the validation stage. Conclusions Significantly higher levels of serum leucine, isoleucine, valine, alpha-aminoadipic acid, propionylcarnitine are characteristic of metabolically unhealthy centrally obese patients. The finding provides novel insights into the pathogenesis of metabolic abnormalities in obesity. Electronic supplementary material The online version of this article (doi:10.1186/s12986-016-0095-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiang Gao
- College of Food Science and Engineering, Ocean University of China, No.5, Yu Shan Road, Qingdao, Shandong Province China ; Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
| | - Weidong Zhang
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
| | - Yongbo Wang
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada ; Department of Endocrinology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000 Liaoning China
| | - Pardis Pedram
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
| | - Farrell Cahill
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
| | - Guangju Zhai
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
| | - Edward Randell
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
| | - Wayne Gulliver
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
| | - Guang Sun
- Faculty of Medicine, Memorial University, 300 Prince Philip Drive, St. John's, NL Canada
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