Literature DB >> 26883715

Metabolomics in diabetes, a review.

Rigoberto Pallares-Méndez1, Carlos A Aguilar-Salinas1, Ivette Cruz-Bautista1, Laura Del Bosque-Plata2.   

Abstract

Metabolomics is a promising approach for the identification of chemical compounds that serve for early detection, diagnosis, prediction of therapeutic response and prognosis of disease. Moreover, metabolomics has shown to increase the diagnostic threshold and prediction of type 2 diabetes. Evidence suggests that branched-chain amino acids, acylcarnitines and aromatic amino acids may play an early role on insulin resistance, exposing defects on amino acid metabolism, β-oxidation, and tricarboxylic acid cycle. This review aims to provide a panoramic view of the metabolic shifts that antecede or follow type 2 diabetes. Key messages BCAAs, AAAs and acylcarnitines are strongly associated with early insulin resistance. Diabetes risk prediction has been improved when adding metabolomic markers of dysglycemia to standard clinical and biochemical factors.

Entities:  

Keywords:  Acylcarnitines; aromatic amino acids; branched-chain amino acids; diabetes; dysglycemia; insulin resistance; mass spectrometry; metabolomics; nuclear magnetic resonance spectroscopy; obesity

Mesh:

Substances:

Year:  2016        PMID: 26883715     DOI: 10.3109/07853890.2015.1137630

Source DB:  PubMed          Journal:  Ann Med        ISSN: 0785-3890            Impact factor:   4.709


  32 in total

Review 1.  The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges.

Authors:  Yuan Lin; Jennifer Wessel
Journal:  Curr Diab Rep       Date:  2019-02-26       Impact factor: 4.810

2.  Targeted metabolomics to understand the association between arsenic metabolism and diabetes-related outcomes: Preliminary evidence from the Strong Heart Family Study.

Authors:  Miranda J Spratlen; Maria Grau-Perez; Jason G Umans; Joseph Yracheta; Lyle G Best; Kevin Francesconi; Walter Goessler; Teodoro Bottiglieri; Mary V Gamble; Shelley A Cole; Jinying Zhao; Ana Navas-Acien
Journal:  Environ Res       Date:  2018-09-27       Impact factor: 6.498

3.  Development of Multimarker Diagnostic Models from Metabolomics Analysis for Gestational Diabetes Mellitus (GDM).

Authors:  Wolin Hou; Xiyan Meng; Aihua Zhao; Weijing Zhao; Jiemin Pan; Junling Tang; Yajuan Huang; Huaping Li; Wei Jia; Fang Liu; Weiping Jia
Journal:  Mol Cell Proteomics       Date:  2017-12-27       Impact factor: 5.911

Review 4.  Metabolomics based biomarker identification of anti-diabetes and anti-obesity properties of Malaysian herbs.

Authors:  Khaled Benchoula; Muhammad Sufyan Vohra; Ishwar S Parhar; Wong Eng Hwa
Journal:  Metabolomics       Date:  2022-01-29       Impact factor: 4.290

5.  Serum metabolomic profile of incident diabetes.

Authors:  Casey M Rebholz; Bing Yu; Zihe Zheng; Patrick Chang; Adrienne Tin; Anna Köttgen; Lynne E Wagenknecht; Josef Coresh; Eric Boerwinkle; Elizabeth Selvin
Journal:  Diabetologia       Date:  2018-03-20       Impact factor: 10.122

6.  Association of maternal prepregnancy BMI with metabolomic profile across gestation.

Authors:  C Hellmuth; K L Lindsay; O Uhl; C Buss; P D Wadhwa; B Koletzko; S Entringer
Journal:  Int J Obes (Lond)       Date:  2016-08-29       Impact factor: 5.095

Review 7.  Interrelationship between diabetes mellitus and heart failure: the role of peroxisome proliferator-activated receptors in left ventricle performance.

Authors:  Evangelos Oikonomou; Konstantinos Mourouzis; Petros Fountoulakis; Georgios Angelos Papamikroulis; Gerasimos Siasos; Alexis Antonopoulos; Georgia Vogiatzi; Sotiris Tsalamadris; Manolis Vavuranakis; Dimitris Tousoulis
Journal:  Heart Fail Rev       Date:  2018-05       Impact factor: 4.214

8.  Metabolic profiling of tissue-specific insulin resistance in human obesity: results from the Diogenes study and the Maastricht Study.

Authors:  Ellen E Blaak; Ilja C W Arts; Nicole Vogelzangs; Carla J H van der Kallen; Marleen M J van Greevenbroek; Birgitta W van der Kolk; Johan W E Jocken; Gijs H Goossens; Nicolaas C Schaper; Ronald M A Henry; Simone J P M Eussen; Armand Valsesia; Thomas Hankemeier; Arne Astrup; Wim H M Saris; Coen D A Stehouwer
Journal:  Int J Obes (Lond)       Date:  2020-03-17       Impact factor: 5.095

9.  A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits.

Authors:  Cheng Peng; Jun Wang; Isaac Asante; Stan Louie; Ran Jin; Lida Chatzi; Graham Casey; Duncan C Thomas; David V Conti
Journal:  Bioinformatics       Date:  2020-02-01       Impact factor: 6.937

10.  Untargeted Metabolomic Approach Shows No Differences in Subcutaneous Adipose Tissue of Diabetic and Non-Diabetic Subjects Undergoing Bariatric Surgery: An Exploratory Study.

Authors:  Carlotta Vizioli; Rosario B Jaime-Lara; Alexis T Franks; Rodrigo Ortiz; Paule V Joseph
Journal:  Biol Res Nurs       Date:  2020-08-07       Impact factor: 2.522

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