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Zhang Z, Cao B, Wu Q. Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico. Metab Syndr Relat Disord 2024. [PMID: 38742978 DOI: 10.1089/met.2024.0030] [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: 05/16/2024] Open
Abstract
Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (β = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.
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Affiliation(s)
- Zekai Zhang
- Second College of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Beibei Cao
- Academy of Paediatrics, Nanjing Medical University, Nanjing, China
| | - Qiutong Wu
- Second College of Clinical Medicine, Nanjing Medical University, Nanjing, China
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Moorthi RN, Moe SM, O'Connell T, Dickinson S, Kalim S, Thadhani R, Clish CB, Shafi T, Rhee EP, Avin KG. Plasma metabolites and physical function in patients undergoing hemodialysis. Sci Rep 2024; 14:8427. [PMID: 38600145 PMCID: PMC11006868 DOI: 10.1038/s41598-024-58522-9] [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: 08/23/2023] [Accepted: 03/31/2024] [Indexed: 04/12/2024] Open
Abstract
Impaired physical function contributes to falls, fractures, and mortality among patients undergoing dialysis. Using a metabolomic approach, we identified metabolite alterations and effect size-based composite scores for constructs of impaired gait speed and grip strength. 108 participants incident to dialysis had targeted plasma metabolomics via liquid chromatography-mass spectrometry and physical function assessed (i.e., 4 m walk, handgrip strength). Physical function measures were categorized as above/ below median, with grip utilizing sex-based medians. To develop composite scores, metabolites were identified via Wilcoxon uncorrected p < 0.05 and effect size > 0.40. Receiver operating characteristic analyses tested whether scores differentiated between above/below function groups. Participants were 54% male, 77% Black and 53 ± 14 y with dialysis vintage of 101 ± 50 days. Median (IQR) grip strength was 35.5 (11.1) kg (males) and 20 (8.4) kg (females); median gait speed was 0.82 (0.34) m/s. Of 246 measured metabolites, composite scores were composed of 22 and 12 metabolites for grip strength and gait speed, respectively. Area under the curve for metabolite composite was 0.88 (gait) and 0.911 (grip). Composite scores of physical function performed better than clinical parameters alone in patients on dialysis. These results provide potential pathways for interventions and needed validation in an independent cohort.
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Affiliation(s)
| | - Sharon M Moe
- Indiana University School of Medicine, Indianapolis, IN, USA
| | | | | | - Sahir Kalim
- Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Ravi Thadhani
- Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Tariq Shafi
- Department of Medicine, University of Mississippi Medical Center, Jackson, MI, 39216, USA
| | - Eugene P Rhee
- Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Keith G Avin
- Indiana University School of Medicine, Indianapolis, IN, USA.
- School of Health and Human Sciences, IUPUI, Indianapolis, IN, USA.
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3
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Development of a metabolite-based deep learning algorithm for clinical precise diagnosis of the progression of diabetic kidney disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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4
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Peng Y, Li Y, Zhang W, ShangGuan Y, Xie T, Wang K, Qiu J, Pu W, Hu B, Zhang X, Yin L, Tang D, Dai Y. The characteristics of extrachromosomal circular DNA in patients with end-stage renal disease. Eur J Med Res 2023; 28:134. [PMID: 36967395 PMCID: PMC10041755 DOI: 10.1186/s40001-023-01064-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/15/2023] [Indexed: 03/28/2023] Open
Abstract
BACKGROUND End-stage renal disease (ESRD) is the final stage of chronic kidney disease (CKD). In addition to the structurally intact chromosome genomic DNA, there is a double-stranded circular DNA called extrachromosomal circular DNA (eccDNA), which is thought to be involved in the epigenetic regulation of human disease. However, the features of eccDNA in ESRD patients are barely known. In this study, we identified eccDNA from ESRD patients and healthy people, as well as revealed the characteristics of eccDNA in patients with ESRD. METHODS Using the high-throughput Circle-Sequencing technique, we examined the eccDNA in peripheral blood mononuclear cells (PBMCs) from healthy people (NC) (n = 12) and ESRD patients (n = 16). We analyzed the length distribution, genome elements, and motifs feature of eccDNA in ESRD patients. Then, after identifying the specific eccDNA in ESRD patients, we explored the potential functions of the target genes of the specific eccDNA. Finally, we investigated the probable hub eccDNA using algorithms. RESULTS In total, 14,431 and 11,324 eccDNAs were found in the ESRD and NC groups, respectively, with sizes ranging from 0.01 kb to 60 kb at most. Additionally, the ESRD group had a greater distribution of eccDNA on chromosomes 4, 11, 13, and 20. In two groups, we also discovered several motifs of specific eccDNAs. Furthermore, we identified 13,715 specific eccDNAs in the ESRD group and 10,585 specific eccDNAs in the NC group, both of which were largely annotated as mRNA catalog. Pathway studies using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that the specific eccDNA in ESRD was markedly enriched in cell junction and communication pathways. Furthermore, we identified potentially 20 hub eccDNA-targeting genes from all ESRD-specific eccDNA-targeting genes. Also, we found that 39 eccDNA-targeting genes were associated with ESRD, and some of these eccDNAs may be related to the pathogenesis of ESRD. CONCLUSIONS Our findings revealed the characteristics of eccDNA in ESRD patients and discovered potentially hub and ESRD-relevant eccDNA-targeting genes, suggesting a novel probable mechanism of ESRD.
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Affiliation(s)
- Yue Peng
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China
- Institute of Nephrology and Blood Purification, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Yixi Li
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China
- Institute of Nephrology and Blood Purification, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Wei Zhang
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China
| | - Yu ShangGuan
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China
| | - Ting Xie
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China
- Institute of Nephrology and Blood Purification, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Kang Wang
- Key Renal Laboratory of Shenzhen, Department of Nephrology, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, 518020, Guangdong, China
| | - Jing Qiu
- Key Renal Laboratory of Shenzhen, Department of Nephrology, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, 518020, Guangdong, China
| | - Wenjun Pu
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China
| | - Biying Hu
- Institute of Nephrology and Blood Purification, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Xinzhou Zhang
- Key Renal Laboratory of Shenzhen, Department of Nephrology, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, 518020, Guangdong, China
| | - Lianghong Yin
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China.
- Guangzhou Enttxs Medical Products Co., Ltd. P.R. Guangzhou, Guangzhou, China.
| | - Donge Tang
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China.
| | - Yong Dai
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen, China.
- Department of Pathology, The 924th Hospital of the Chinese People's Liberation Army Joint Logistic Support Force, Guangxi Key Laboratory of Metabolic Diseases Research, Guilin, 541002, Guangxi, China.
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Kshirsagar AV, Zeitler EM, Weaver A, Franceschini N, Engel LS. Environmental Exposures and Kidney Disease. KIDNEY360 2022; 3:2174-2182. [PMID: 36591345 PMCID: PMC9802544 DOI: 10.34067/kid.0007962021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/04/2022] [Indexed: 12/31/2022]
Abstract
Accumulating evidence underscores the large role played by the environment in the health of communities and individuals. We review the currently known contribution of environmental exposures and pollutants on kidney disease and its associated morbidity. We review air pollutants, such as particulate matter; water pollutants, such as trace elements, per- and polyfluoroalkyl substances, and pesticides; and extreme weather events and natural disasters. We also discuss gaps in the evidence that presently relies heavily on observational studies and animal models, and propose using recently developed analytic methods to help bridge the gaps. With the expected increase in the intensity and frequency of many environmental exposures in the decades to come, an improved understanding of their potential effect on kidney disease is crucial to mitigate potential morbidity and mortality.
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Affiliation(s)
- Abhijit V. Kshirsagar
- UNC Kidney Center and Division of Nephrology and Hypertension, University of North Carolina, Chapel Hill, North Carolina
| | - Evan M. Zeitler
- UNC Kidney Center and Division of Nephrology and Hypertension, University of North Carolina, Chapel Hill, North Carolina
| | - Anne Weaver
- Center for Public Health and Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Chapel Hill, North Carolina
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - Lawrence S. Engel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
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An Untargeted Metabolomics Approach on Carfilzomib-Induced Nephrotoxicity. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27227929. [PMID: 36432029 PMCID: PMC9697636 DOI: 10.3390/molecules27227929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Carfilzomib (Cfz) is an anti-cancer drug related to cardiorenal adverse events, with cardiovascular and renal complications limiting its clinical use. Despite the important progress concerning the discovery of the underlying causes of Cfz-induced nephrotoxicity, the molecular/biochemical background is still not well clarified. Furthermore, the number of metabolomics-based studies concerning Cfz-induced nephrotoxicity is limited. METHODS A metabolomics UPLC-HRMS-DIA methodology was applied to three bio-sample types i.e., plasma, kidney, and urine, obtained from two groups of mice, namely (i) Cfz (8 mg Cfz/ kg) and (ii) Control (0.9% NaCl) (n = 6 per group). Statistical analysis, involving univariate and multivariate tools, was applied for biomarker detection. Furthermore, a sub-study was developed, aiming to estimate metabolites' correlation among bio-samples, and to enlighten potential mechanisms. RESULTS Cfz mostly affects the kidneys and urine metabolome. Fifty-four statistically important metabolites were discovered, and some of them have already been related to renal diseases. Furthermore, the correlations between bio-samples revealed patterns of metabolome alterations due to Cfz. CONCLUSIONS Cfz causes metabolite retention in kidney and dysregulates (up and down) several metabolites associated with the occurrence of inflammation and oxidative stress.
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Duangkumpha K, Jariyasopit N, Wanichthanarak K, Dhakal E, Wisanpitayakorn P, Thotsiri S, Sirivatanauksorn Y, Kitiyakara C, Sathirapongsasuti N, Khoomrung S. GC × GC-TOFMS metabolomics analysis identifies elevated levels of plasma sugars and sugar alcohols in diabetic mellitus patients with kidney failure. J Biol Chem 2022; 298:102445. [PMID: 36055403 PMCID: PMC9531178 DOI: 10.1016/j.jbc.2022.102445] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 11/12/2022] Open
Abstract
Two dimensional GC (GC × GC)-time-of-flight mass spectrometry (TOFMS) has been used to improve accurate metabolite identification in the chemical industry, but this method has not been applied as readily in biomedical research. Here, we evaluated and validated the performance of high resolution GC × GC-TOFMS against that of GC-TOFMS for metabolomics analysis of two different plasma matrices, from healthy controls (CON) and diabetes mellitus (DM) patients with kidney failure (DM with KF). We found GC × GC-TOFMS outperformed traditional GC-TOFMS in terms of separation performance and metabolite coverage. Several metabolites from both the CON and DM with KF matrices, such as carbohydrates and carbohydrate-conjugate metabolites, were exclusively detected using GC × GC-TOFMS. Additionally, we applied this method to characterize significant metabolites in the DM with KF group, with focused analysis of four metabolite groups: sugars, sugar alcohols, amino acids, and free fatty acids. Our plasma metabolomics results revealed 35 significant metabolites (12 unique and 23 concentration-dependent metabolites) in the DM with KF group, as compared with those in the CON and DM groups (N = 20 for each group). Interestingly, we determined 17 of the 35 (14/17 verified with reference standards) significant metabolites identified from both the analyses were metabolites from the sugar and sugar alcohol groups, with significantly higher concentrations in the DM with KF group than in the CON and DM groups. Enrichment analysis of these 14 metabolites also revealed that alterations in galactose metabolism and the polyol pathway are related to DM with KF. Overall, our application of GC × GC-TOFMS identified key metabolites in complex plasma matrices.
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Affiliation(s)
- Kassaporn Duangkumpha
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Narumol Jariyasopit
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Kwanjeera Wanichthanarak
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Esha Dhakal
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Pattipong Wisanpitayakorn
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Sansanee Thotsiri
- Somdech Phra Debaratana Medical Center, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Yongyut Sirivatanauksorn
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Chagriya Kitiyakara
- Department of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; Research Network of NANOTEC - MU Ramathibodi on Nanomedicine, Bangkok, Thailand
| | - Nuankanya Sathirapongsasuti
- Research Network of NANOTEC - MU Ramathibodi on Nanomedicine, Bangkok, Thailand; Section of Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Center of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok, Thailand.
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Bernard L, Zhou L, Surapaneni A, Chen J, Rebholz CM, Coresh J, Yu B, Boerwinkle E, Schlosser P, Grams ME. Serum Metabolites and Kidney Outcomes: The Atherosclerosis Risk in Communities Study. Kidney Med 2022; 4:100522. [PMID: 36046612 PMCID: PMC9420957 DOI: 10.1016/j.xkme.2022.100522] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Rationale & Objective Novel metabolite biomarkers of kidney failure with replacement therapy (KFRT) may help identify people at high risk for adverse kidney outcomes and implicated pathways may aid in developing targeted therapeutics. Study Design Prospective cohort. Setting & Participants The cohort included 3,799 Atherosclerosis Risk in Communities study participants with serum samples available for measurement at visit 1 (1987-1989). Exposure Baseline serum levels of 318 metabolites. Outcomes Incident KFRT, kidney failure (KFRT, estimated glomerular filtration rate <15 mL/min/1.73 m2, or death from kidney disease). Analytical Approach Because metabolites are often intercorrelated and represent shared pathways, we used a high dimension reduction technique called Netboost to cluster metabolites. Longitudinal associations between clusters of metabolites and KFRT and kidney failure were estimated using a Cox proportional hazards model. Results Mean age of study participants was 53 years, 61% were African American, and 13% had diabetes. There were 160 KFRT cases and 357 kidney failure cases over a mean of 23 years. The 314 metabolites were grouped in 43 clusters. Four clusters were significantly associated with risk of KFRT and 6 were associated with kidney failure (including 3 shared clusters). The 3 shared clusters suggested potential pathways perturbed early in kidney disease: cluster 5 (15 metabolites involved in alanine, aspartate, and glutamate metabolism as well as 5-oxoproline and several gamma-glutamyl amino acids), cluster 26 (6 metabolites involved in sugar and inositol phosphate metabolism), and cluster 34 (21 metabolites involved in glycerophospholipid metabolism). Several individual metabolites were also significantly associated with both KFRT and kidney failure, including glucose and mannose, which were associated with higher risk of both outcomes, and 5-oxoproline, gamma-glutamyl amino acids, linoleoylglycerophosphocholine, 1,5-anhydroglucitol, which were associated with lower risk of both outcomes. Limitations Inability to determine if the metabolites cause or are a consequence of changes in kidney function. Conclusions We identified several clusters of metabolites reproducibly associated with development of KFRT. Future experimental studies are needed to validate our findings as well as continue unraveling metabolic pathways involved in kidney function decline.
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Chasapi SA, Karagkouni E, Kalavrizioti D, Vamvakas S, Zompra A, Takis PG, Goumenos DS, Spyroulias GA. NMR-Based Metabolomics in Differential Diagnosis of Chronic Kidney Disease (CKD) Subtypes. Metabolites 2022; 12:metabo12060490. [PMID: 35736423 PMCID: PMC9230636 DOI: 10.3390/metabo12060490] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 02/01/2023] Open
Abstract
Chronic Kidney Disease (CKD) is considered as a major public health problem as it can lead to end-stage kidney failure, which requires replacement therapy. A prompt and accurate diagnosis, along with the appropriate treatment, can delay CKD’s progression, significantly. Herein, we sought to determine whether CKD etiology can be reflected in urine metabolomics during its early stage. This is achieved through the analysis of the urine metabolic fingerprint from 108 CKD patients by means of Nuclear Magnetic Resonance (NMR) spectroscopy metabolomic analysis. We report the first NMR—metabolomics data regarding the three most common etiologies of CKD: Chronic Glomerulonephritis (IgA and Membranous Nephropathy), Diabetic Nephropathy (DN) and Hypertensive Nephrosclerosis (HN). Analysis aided a moderate glomerulonephritis clustering, providing characterization of the metabolic fluctuations between the CKD subtypes and control disease. The urine metabolome of IgA Nephropathy reveals a specific metabolism, reflecting its different etiology or origin and is useful for determining the origin of the disease. In contrast, urine metabolomes from DN and HN patients did not reveal any indicative metabolic pattern, which is consistent with their fused clinical phenotype. These findings may contribute to improving diagnostics and prognostic approaches for CKD, as well as improving our understanding of its pathology.
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Affiliation(s)
- Styliani A. Chasapi
- Department of Pharmacy, University of Patras, 26504 Patras, Greece; (S.A.C.); (E.K.); (A.Z.)
| | - Evdokia Karagkouni
- Department of Pharmacy, University of Patras, 26504 Patras, Greece; (S.A.C.); (E.K.); (A.Z.)
| | - Dimitra Kalavrizioti
- Department of Nephrology and Renal Transplantation, University Hospital of Patras, 26504 Patras, Greece; (D.K.); (S.V.)
| | - Sotirios Vamvakas
- Department of Nephrology and Renal Transplantation, University Hospital of Patras, 26504 Patras, Greece; (D.K.); (S.V.)
| | - Aikaterini Zompra
- Department of Pharmacy, University of Patras, 26504 Patras, Greece; (S.A.C.); (E.K.); (A.Z.)
| | - Panteleimon G. Takis
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, UK;
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London W120NN, UK
| | - Dimitrios S. Goumenos
- Department of Nephrology and Renal Transplantation, University Hospital of Patras, 26504 Patras, Greece; (D.K.); (S.V.)
- Correspondence: (D.S.G.); (G.A.S.)
| | - Georgios A. Spyroulias
- Department of Pharmacy, University of Patras, 26504 Patras, Greece; (S.A.C.); (E.K.); (A.Z.)
- Correspondence: (D.S.G.); (G.A.S.)
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BOZACI İ, TATAR E. OBEZ HASTALARDA VE OBEZ KRONİK BÖBREK HASTALARINDA ENFLAMASYON BELİRTECİ OLARAK HEMOGRAM PARAMETRELERİNİN DEĞERLENDİRİLMESİ. ACTA MEDICA ALANYA 2021. [DOI: 10.30565/medalanya.943299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Costenbader KH, DiIorio M, Chu SH, Cui J, Sparks JA, Lu B, Moss L, Kelmenson L, Feser M, Edison J, Clish C, Lasky-Su J, Deane KD, Karlson EW. Circulating blood metabolite trajectories and risk of rheumatoid arthritis among military personnel in the Department of Defense Biorepository. Ann Rheum Dis 2021; 80:989-996. [PMID: 33753325 PMCID: PMC8455711 DOI: 10.1136/annrheumdis-2020-219682] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 01/14/2023]
Abstract
OBJECTIVES We sought to identify metabolic changes potentially related to rheumatoid arthritis (RA) pathogenesis occurring in the blood prior to its diagnosis. METHODS In a US military biorepository, serum samples collected at two timepoints prior to a diagnosis of RA were identified. These were matched to controls who did not develop RA by subject age, race and time between sample collections and RA diagnosis time to stored serum samples. Relative abundances of 380 metabolites were measured using liquid chromatography-tandem mass spectrometry. We determined whether pre-RA case versus control status predicted metabolite concentration differences and differences over time (trajectories) using linear mixed models, assessing for interactions between time, pre-RA status and metabolite concentrations. We separately examined pre-RA and pre-seropositive RA cases versus matched controls and adjusted for smoking. Multiple comparison adjustment set the false discovery rate to 0.05. RESULTS 291 pre-RA cases (80.8% pre seropositive RA) were matched to 292 controls, all with two serum samples (2.7±1.6 years; 1.0±0.9 years before RA/matched date). 52.0% were women; 52.8% were White, 26.8% Black and 20.4% other race. Mean age was 31.2 (±8.1) years at earliest blood draw. Fourteen metabolites had statistically significant trajectory differences among pre-RA subjects versus controls, including sex steroids, amino acid/lipid metabolism and xenobiotics. Results were similar when limited to pre seropositive RA and after adjusting for smoking. CONCLUSIONS In this military case-control study, metabolite concentration trajectory differences in pre-RA cases versus controls implicated steroidogenesis, lipid/amino acid metabolism and xenobiotics in RA pathogenesis. Metabolites may have potential as biomarkers and/or therapeutic targets preceding RA diagnosis.
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Affiliation(s)
- Karen H Costenbader
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA .,Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael DiIorio
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Su H Chu
- Channing Department of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jing Cui
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jeffrey A Sparks
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Bing Lu
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | | | | | - Marie Feser
- University of Colorado, Aurora, Colorado, USA
| | - Jess Edison
- Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Clary Clish
- Metabolomics Group, Broad Institute, Cambridge, Massachusetts, USA
| | - Jessica Lasky-Su
- Channing Department of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Kevin D Deane
- Division of Rheumatology, Department of Medicine, University of Colorado, Aurora, Colorado, USA
| | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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12
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10-hydroxy-2E-decenoic acid (10HDA) does not promote caste differentiation in Melipona scutellaris stingless bees. Sci Rep 2021; 11:9882. [PMID: 33972627 PMCID: PMC8110752 DOI: 10.1038/s41598-021-89212-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/16/2021] [Indexed: 02/03/2023] Open
Abstract
In bees from genus Melipona, differential feeding is not enough to fully explain female polyphenism. In these bees, there is a hypothesis that in addition to the environmental component (food), a genetic component is also involved in caste differentiation. This mechanism has not yet been fully elucidated and may involve epigenetic and metabolic regulation. Here, we verified that the genes encoding histone deacetylases HDAC1 and HDAC4 and histone acetyltransferase KAT2A were expressed at all stages of Melipona scutellaris, with fluctuations between developmental stages and castes. In larvae, the HDAC genes showed the same profile of Juvenile Hormone titers-previous reported-whereas the HAT gene exhibited the opposite profile. We also investigated the larvae and larval food metabolomes, but we did not identify the putative queen-fate inducing compounds, geraniol and 10-hydroxy-2E-decenoic acid (10HDA). Finally, we demonstrated that the histone deacetylase inhibitor 10HDA-the major lipid component of royal jelly and hence a putative regulator of honeybee caste differentiation-was unable to promote differentiation in queens in Melipona scutellaris. Our results suggest that epigenetic and hormonal regulations may act synergistically to drive caste differentiation in Melipona and that 10HDA is not a caste-differentiation factor in Melipona scutellaris.
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13
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Kalantari S, Chashmniam S, Nafar M, Samavat S, Rezaie D, Dalili N. A Noninvasive Urine Metabolome Panel as Potential Biomarkers for Diagnosis of T Cell-Mediated Renal Transplant Rejection. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 24:140-147. [PMID: 32176594 DOI: 10.1089/omi.2019.0158] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Acute T cell-mediated rejection (TCMR) is a major complication after renal transplantation. TCMR diagnosis is very challenging and currently depends on invasive renal biopsy and nonspecific markers such as serum creatinine. A noninvasive metabolomics panel could allow early diagnosis and improved accuracy and specificity. We report, in this study, on urine metabolome changes in renal transplant recipients diagnosed with TCMR, with a view to future metabolomics-based diagnostics in transplant medicine. We performed urine metabolomic analyses in three study groups: (1) 7 kidney transplant recipients with acute TCMR, (2) 15 kidney transplant recipients without rejection but with impaired kidney function, and (3) 6 kidney transplant recipients with stable renal function, using 1H-nuclear magnetic resonance. Multivariate modeling of metabolites suggested a diagnostic panel where the diagnostic accuracy of each metabolite was calculated by receiver operating characteristic curve analysis. The impaired metabolic pathways associated with TCMR were identified by pathway analysis. In all, a panel of nine differential metabolites encompassing nicotinamide adenine dinucleotide, 1-methylnicotinamide, cholesterol sulfate, gamma-aminobutyric acid (GABA), nicotinic acid, nicotinamide adenine dinucleotide phosphate, proline, spermidine, and alpha-hydroxyhippuric acid were identified as novel potential metabolite biomarkers of TCMR. Proline, spermidine, and GABA had the highest area under the curve (>0.7) and were overrepresented in the TCMR group. Nicotinate and nicotinamide metabolism was the most important pathway in TCMR. These findings call for clinical validation in larger study samples and suggest that urinary metabolomics warrants future consideration as a noninvasive research tool for TCMR diagnostic innovation.
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Affiliation(s)
- Shiva Kalantari
- Department of Nephrology, Chronic Kidney Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saeed Chashmniam
- Department of Chemistry, Sharif University of Technology, Tehran, Iran
| | - Mohsen Nafar
- Department of Nephrology, Chronic Kidney Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shiva Samavat
- Department of Nephrology, Urology-Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Danial Rezaie
- Department of Nephrology, Shahid Labbafinejad Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nooshin Dalili
- Department of Nephrology, Shahid Labbafinejad Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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14
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Lousa I, Reis F, Beirão I, Alves R, Belo L, Santos-Silva A. New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature. Int J Mol Sci 2020; 22:E43. [PMID: 33375198 PMCID: PMC7793089 DOI: 10.3390/ijms22010043] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/14/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
The prevalence of chronic kidney disease (CKD) is increasing worldwide, and the mortality rate continues to be unacceptably high. The biomarkers currently used in clinical practice are considered relevant when there is already significant renal impairment compromising the early use of potentially successful therapeutic interventions. More sensitive and specific biomarkers to detect CKD earlier on and improve patients' prognoses are an important unmet medical need. The aim of this review is to summarize the recent literature on new promising early CKD biomarkers of renal function, tubular lesions, endothelial dysfunction and inflammation, and on the auspicious findings from metabolomic studies in this field. Most of the studied biomarkers require further validation in large studies and in a broad range of populations in order to be implemented into routine CKD management. A panel of biomarkers, including earlier biomarkers of renal damage, seems to be a reasonable approach to be applied in clinical practice to allow earlier diagnosis and better disease characterization based on the underlying etiologic process.
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Affiliation(s)
- Irina Lousa
- UCIBIO\REQUIMTE, Laboratory of Biochemistry, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; (I.L.); (L.B.)
| | - Flávio Reis
- Institute of Pharmacology & Experimental Therapeutics, & Coimbra Institute for Clinical and Biomedical Research (iCBR), Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal;
- Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, 3004-504 Coimbra, Portugal
- Clinical Academic Center of Coimbra (CACC), 3000-075 Coimbra, Portugal
| | - Idalina Beirão
- Universitary Hospital Centre of Porto (CHUP), 4099-001 Porto, Portugal;
- Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, 4050-313 Porto, Portugal
| | - Rui Alves
- Nephrology Department, Coimbra University Hospital Center, 3004-561 Coimbra, Portugal;
- University Clinic of Nephrology, Faculty of Medicine, University of Coimbra, 3000-075 Coimbra, Portugal
| | - Luís Belo
- UCIBIO\REQUIMTE, Laboratory of Biochemistry, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; (I.L.); (L.B.)
| | - Alice Santos-Silva
- UCIBIO\REQUIMTE, Laboratory of Biochemistry, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; (I.L.); (L.B.)
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15
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Qamar W, Alqahtani S, Ahamad SR, Ali N, Altamimi MA. Untargeted GC-MS investigation of serum metabolomics of coronary artery disease patients. Saudi J Biol Sci 2020; 27:3727-3734. [PMID: 33304184 PMCID: PMC7715060 DOI: 10.1016/j.sjbs.2020.08.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/24/2020] [Accepted: 08/11/2020] [Indexed: 01/03/2023] Open
Abstract
Recent advances in metabolomics provide tools to investigate human metabolome in order to establish new parameters to study different approaches towards diagnostics, diseases and their treatment. The present study focused on the untargeted identification of metabolites in serum of patients with coronary artery disease who were under treatment at the time of sample collection. AUCs (Area Under the Curves) from different peaks were considered for the analysis and comparison purposes. The metabolome was studied using GC–MS (Gas Chromatography Mass Spectrometry) and the metabolites were identified with NIST (The National Institute of Standards and Technology) and Wiley library matches. A total of 17 metabolites were identified and focused on to compare with the metabolome of healthy individuals. T test analysis found significant differences in alanine, malonic acid, ribitol, D-glucose, mannose (P < 0.001), acetohydroxamic acid, N-carboxyglycine, and aminobutyrate (P < 0.05). Principal Component Analysis of serum metabolites data found three components out of 17 metabolites; RC1 (Acetohydroxamic acid, alanine, D-glucose, malonic acid, mannose, N-carboxy glycine and ribitol), RC2 (Heptadecanoic acid, hexadecanoic acid, octadecanoic acid and Trans-9-octadecanoic acid), RC3 (Aminobutyrate, D-sorbit, gamma lactone, valine, benzene propanoic acid and lactic acid). No correlation was found among the components.
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Affiliation(s)
- Wajhul Qamar
- Central Laboratory, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Kingdom of Saudi Arabia
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, Kingdom of Saudi Arabia
| | - Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Syed Rizwan Ahamad
- Central Laboratory, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Kingdom of Saudi Arabia
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Nemat Ali
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, Kingdom of Saudi Arabia
| | - Mohammad A. Altamimi
- Central Laboratory, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Kingdom of Saudi Arabia
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia
- Corresponding author at: Central Laboratory, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Kingdom of Saudi Arabia, Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia.
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16
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Cordero-Pérez P, Sánchez-Martínez C, García-Hernández PA, Saucedo AL. Metabolomics of the diabetic nephropathy: behind the fingerprint of development and progression indicators. Nefrologia 2020; 40:585-596. [PMID: 33036786 DOI: 10.1016/j.nefro.2020.07.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 06/24/2020] [Accepted: 07/05/2020] [Indexed: 01/01/2023] Open
Abstract
Current diagnostic methods are not very sensitive to detect the initial stages diabetic nephropathy of type 2. In this work, a review of metabolomic approximation studies for the identification of biomarkers of this disease with potential to differentiate between early stages, evaluate and direct treatment and help slow kidney damage. Using public (Pubmed and Google Scholar) and private (Scopus and Web of Knowledge) databases, a systematic search of the information published related to metabolomics of diabetic nephropathy in different biospecimens (urine, serum, plasma and blood) was made. Later, the MetaboAnalyst 4.0 software was used to identify the metabolic pathways associated with these metabolites. Groups of potential metabolites were identified for monitoring diabetic nephropathy with the available literature data. In the urine, oxide-3-hydroxyisovalerate, TMAO, aconite and citrate and hydroxypropionate derivatives are highlighted; meanwhile, in the serum: citrate, creatinine, arginine and its derivatives; and in the plasma: amino acids such as histidine, methionine and arginine has a potential contribution. Using MetaboAnalyst 4.0 the metabolic pathways related to these metabolites were related. The search for biomarkers to measure the progression of diabetic nephropathy, together with analytical strategies for their detection and quantification, are the starting point for designing new methods of clinical chemistry analysis. The association between the metabolic pathway dysfunction could be useful for the overall assessment of the treatment and clinical follow-up of this disease.
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Affiliation(s)
- Paula Cordero-Pérez
- Unidad de Hígado, Hospital Universitario "Dr. José Eleuterio González", Universidad Autónoma de Nuevo León, Monterrey, NL, México
| | - Concepción Sánchez-Martínez
- Centro Regional de Enfermedades Renales, Hospital Universitario "Dr. José Eleuterio González", Universidad Autónoma de Nuevo León, Monterrey, NL, México
| | - Pedro Alberto García-Hernández
- Servicio de Endocrinología, Hospital Universitario "Dr. José Eleuterio González", Universidad Autónoma de Nuevo León, Monterrey, NL, México
| | - Alma L Saucedo
- Departamento de Química Analítica, Facultad de Medicina, Universidad Autónoma de Nuevo León, Monterrey, NL, México; Consejo Nacional de Ciencia y Tecnología, Cátedras CONACYT, Ciudad de México, México.
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17
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Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation. Sci Rep 2020; 10:17223. [PMID: 33057167 PMCID: PMC7560840 DOI: 10.1038/s41598-020-74245-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 09/23/2020] [Indexed: 02/06/2023] Open
Abstract
Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the 1HNMR based metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the multiple analyses of variance (ANOVA) methods such as ANOVA simultaneous component analysis and ANOVA-target projection. We also propose here the combined use of ANOVA and classification and regression trees (ANOVA-CART) under the assumption that a small set of metabolites the binary splits on which may better describe the graft accommodation processes over time. This assumption is very important for developing a medical protocol for evaluating a patient's health state. The results showed that besides creatinine, which is routinely used to monitor renal activity, the changes in levels of hippurate, mannitol and alanine may be associated with the changes in renal function during the post-transplantation recovery period. Specifically, the level of hippurate (or histidine) is more sensitive to any short-term changes in renal activity than creatinine.
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18
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Mass spectrometry-based metabolomics for an in-depth questioning of human health. Adv Clin Chem 2020; 99:147-191. [PMID: 32951636 DOI: 10.1016/bs.acc.2020.02.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Today, metabolomics is becoming an indispensable tool to get a more comprehensive analysis of complex living systems, providing insights on multiple aspects of physiology. Although its application in large scale population-based studies is very challenging due to the processing of large sample sets as well as the complexity of data information, its potential to characterize human health is well recognized. Technological advances in metabolomics pave the way for the efficient biomarker discovery of disease etiology, diagnosis and prognosis. Here, different steps of the metabolomics workflow, particularly mass spectrometry-based approaches, are discussed to demonstrate the potential of metabolomics to address biological questioning in human health. First an overview of metabolomics is provided with its interest in human health studies. Analytical development and advances in mass spectrometry instrumentation and computational tools are discussed regarding their application limits. Advancing metabolomics for applicability in human health and large-scale studies is presented and discussed in conclusion.
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