Literature DB >> 34222079

Mass spectrometry with derivatization method for concurrent measurement of amino acids and acylcarnitines in plasma of diabetic type 2 patients with diabetic nephropathy.

Parsa Esmati1,2, Niloufar Najjar2, Solaleh Emamgholipour3, Shaghayegh Hosseinkhani3, Babak Arjmand2,4, Amin Soleimani5, Ardeshir Kakaii6, Farideh Razi7.   

Abstract

BACKGROUND: Amino acids (AAs) and acylcarnitines play a key role in metabolic disease and can be used as biomarkers of various diseases such as malignancies, type 2 diabetes (T2D), insulin resistance, and cardiovascular diseases, therefore, designing an accurate and simple laboratory method that simultaneously measure both groups of substances, could improve the process of analytes quantification. In this research, a flow injection tandem mass spectrometry (FI-MS/MS) method for simultaneous measurement of AAs and acylcarnitines in addition to results of validation is explained.
METHODS: Samples were mixed with internal standards and after derivatization (with butanolic-HCL), AAs, and acylcarnitines were quantified by tandem mass spectrometry (SCIEX API 3200). Analytical performance studies were designed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines including precision, accuracy, linearity, and limit of detection-quantification (LOD-LOQ) experiments. Samples from patients with T2D in different stages of kidney disease were also analyzed to ensure the clinical usage of the method.
RESULTS: Performance evaluation of the method demonstrated adequate results. The mean of estimated inter-assay precision (reported as a coefficient variation) for AAs and acylcarnitines were less than 8.7% and 12.3%, the estimated mean bias was below 8.8% and 10.2% respectively. LOD of analytes ranged between 0.6-10 μmol per liter (μmol/L) for AAs and 0.02-1 μmol/L for acylcarnitines. LOQ analytes showed a range of 2-25 μmol/L and 0.05-5 μmol/L for AAs and carnitine/acylcarnitines respectively. In diabetic patients sample analysis, a significant increase in acylcarnitines (C2, C4, C5DC, C6, C8, C10, C14) and citrulline with a significant decrease in valine were seen in patients with severely increased albuminuria.
CONCLUSION: FI-MS/MS method with pre-injection derivatization with butanolic-HCL can be used for concurrent measurement of AAs and carnitine/acylcarnitines in a short time and it satisfies the analytical performance requirements. This method is applied for AAs and carnitine/acylcarnitines measurement in patient with T2DM and results show some of the acylcarnitines and AAs can be involved in diabetic nephropathy development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40200-021-00786-3. © Springer Nature Switzerland AG 2021.

Entities:  

Keywords:  Acylcarnitine; Amino acid; Carnitine; Diabetes; Nephropathy; Tandem mass spectrometry

Year:  2021        PMID: 34222079      PMCID: PMC8212236          DOI: 10.1007/s40200-021-00786-3

Source DB:  PubMed          Journal:  J Diabetes Metab Disord        ISSN: 2251-6581


  38 in total

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Review 2.  Separation of carnitine and acylcarnitines in biological samples: a review.

Authors:  Fotouh R Mansour; Wenjun Wei; Neil D Danielson
Journal:  Biomed Chromatogr       Date:  2013-08-23       Impact factor: 1.902

3.  Arginine, citrulline, and nitric oxide metabolism in end-stage renal disease patients.

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4.  Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes.

Authors:  Ahmed T Ahmed; Siamak MahmoudianDehkordi; Sudeepa Bhattacharyya; Matthias Arnold; Duan Liu; Drew Neavin; M Arthur Moseley; J Will Thompson; Lisa St John Williams; Gregory Louie; Michelle K Skime; Liewei Wang; Patricio Riva-Posse; William M McDonald; William V Bobo; W Edward Craighead; Ranga Krishnan; Richard M Weinshilboum; Boadie W Dunlop; David S Millington; A John Rush; Mark A Frye; Rima Kaddurah-Daouk
Journal:  J Affect Disord       Date:  2019-11-30       Impact factor: 4.839

5.  Serum metabolite concentrations and decreased GFR in the general population.

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Journal:  Am J Kidney Dis       Date:  2012-03-30       Impact factor: 8.860

6.  Comparison of amino acid derivatization reagents for LC-ESI-MS analysis. Introducing a novel phosphazene-based derivatization reagent.

Authors:  Riin Rebane; Maarja-Liisa Oldekop; Koit Herodes
Journal:  J Chromatogr B Analyt Technol Biomed Life Sci       Date:  2012-07-31       Impact factor: 3.205

Review 7.  Acylcarnitines: role in brain.

Authors:  Lauren L Jones; David A McDonald; Peggy R Borum
Journal:  Prog Lipid Res       Date:  2009-08-29       Impact factor: 16.195

8.  Electrospray ionization mass spectrometry: a technique to access the information beyond the molecular weight of the analyte.

Authors:  Shibdas Banerjee; Shyamalava Mazumdar
Journal:  Int J Anal Chem       Date:  2011-12-15       Impact factor: 1.885

9.  Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions.

Authors:  Boris Sarvin; Shoval Lagziel; Nikita Sarvin; Dzmitry Mukha; Praveen Kumar; Elina Aizenshtein; Tomer Shlomi
Journal:  Nat Commun       Date:  2020-06-24       Impact factor: 14.919

10.  A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients.

Authors:  Sahir Kalim; Clary B Clish; Julia Wenger; Sammy Elmariah; Robert W Yeh; Joseph J Deferio; Kerry Pierce; Amy Deik; Robert E Gerszten; Ravi Thadhani; Eugene P Rhee
Journal:  J Am Heart Assoc       Date:  2013-12-05       Impact factor: 5.501

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  5 in total

1.  Metabolomic Profiling of Amino Acids in Human Plasma Distinguishes Diabetic Kidney Disease From Type 2 Diabetes Mellitus.

Authors:  Chunyu Zhou; Qing Zhang; Liqian Lu; Jiao Wang; Dongwei Liu; Zhangsuo Liu
Journal:  Front Med (Lausanne)       Date:  2021-11-29

Review 2.  Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer.

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3.  Circulating amino acids and acylcarnitines correlated with different CAC score ranges in diabetic postmenopausal women using LC-MS/MS based metabolomics approach.

Authors:  Shaghayegh Hosseinkhani; Pooneh Salari; Fatemeh Bandarian; Mojgan Asadi; Shapour Shirani; Niloufar Najjar; Hojat Dehghanbanadaki; Parvin Pasalar; Farideh Razi
Journal:  BMC Endocr Disord       Date:  2022-07-21       Impact factor: 3.263

Review 4.  Application of Metabolomics in Various Types of Diabetes.

Authors:  Fangqin Wu; Pengfei Liang
Journal:  Diabetes Metab Syndr Obes       Date:  2022-07-13       Impact factor: 3.249

5.  Metabolic signatures of insulin resistance in non-diabetic individuals.

Authors:  Babak Arjmand; Saeed Ebrahimi Fana; Erfan Ghasemi; Ameneh Kazemi; Robabeh Ghodssi-Ghassemabadi; Hojat Dehghanbanadaki; Niloufar Najjar; Ardeshir Kakaii; Katayoon Forouzanfar; Ensieh Nasli-Esfahani; Farshad Farzadfar; Bagher Larijani; Farideh Razi
Journal:  BMC Endocr Disord       Date:  2022-08-24       Impact factor: 3.263

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