Literature DB >> 29493238

Obesity-Related Metabolomic Profiles and Discrimination of Metabolically Unhealthy Obesity.

Minoo Bagheri1, Farshad Farzadfar2, Lu Qi3, Mir Saeed Yekaninejad4, Maryam Chamari1, Oana A Zeleznik5, Zahra Kalantar6, Zarin Ebrahimi7, Ali Sheidaie2, Berthold Koletzko8, Olaf Uhl8, Abolghasem Djazayery1.   

Abstract

A particular subgroup of obese adults, considered as metabolically healthy obese (MHO), has a reduced risk of metabolic complications. However, the molecular basis contributing to this healthy phenotype remains unclear. The objective of this work was to identify obesity-related metabolite patterns differed between MHO and metabolically unhealthy obese (MUHO) groups and examine whether these patterns are associated with the development of cardiometabolic disorders in a sample of Iranian adult population aged 18-50 years. Valid metabolites were defined as metabolites that passed the quality control analysis of the study. In this case-control study, 104 valid metabolites of 107 MHO and 100 MUHO patients were separately compared to those of 78 normal-weight metabolically healthy (NWMH) adults. Multivariable linear regression was used to investigate all potential relations in the study. A targeted metabolomic approach using liquid chromatography coupled to triple quadrupole mass spectrometry was employed to profile plasma metabolites. The study revealed that, after Bonferroni correction, branched-chain amino-acids, tyrosine, glutamic acid, diacyl-phosphatidylcholines C32:1 and C38:3 were directly and acyl-carnitine C18:2, acyl-lysophosphatidylcholines C18:1 and C18:2, and alkyl-lysophosphatidylcholines C18.0 were inversely associated with MHO phenotype. The same patterns were observed in MUHO patients except for the acyl-carnitine and lysophosphatidylcholine profiles where acyl-carnitine C3:0 and acyl-lysophosphatidylcholine C16:1 were higher and acyl-lysophosphatidylcholines C18:1, C18:2 were lower in this phenotype. Furthermore, proline, and diacyl-phosphatidylcholines C32:2 and C34:2 were directly and serine, asparagines, and acyl-alkyl-phosphatidylcholine C34:3 were negatively linked to MUHO group. Factors composed of amino acids were directly and those containing lysophosphatidylcholines were inversely related to cardiometabolic biomarkers in both phenotypes. Interestingly, the diacyl-phosphatidylcholines-containing factor was directly associated with cardiometabolic disorders in the MUHO group. A particular pattern of amino acids and choline-containing phospholipids may aid in the identification of metabolic health among obese patients.

Entities:  

Keywords:  branched-chain amino acids; metabolomics; obesity; phospholipids; untargeted

Mesh:

Substances:

Year:  2018        PMID: 29493238     DOI: 10.1021/acs.jproteome.7b00802

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  17 in total

1.  Altered anabolic signalling and reduced stimulation of myofibrillar protein synthesis after feeding and resistance exercise in people with obesity.

Authors:  Joseph W Beals; Sarah K Skinner; Colleen F McKenna; Elizabeth G Poozhikunnel; Samee A Farooqi; Stephan van Vliet; Isabel G Martinez; Alexander V Ulanov; Zhong Li; Scott A Paluska; Nicholas A Burd
Journal:  J Physiol       Date:  2018-09-30       Impact factor: 5.182

Review 2.  Obesity Genomics and Metabolomics: a Nexus of Cardiometabolic Risk.

Authors:  Jessica A Regan; Svati H Shah
Journal:  Curr Cardiol Rep       Date:  2020-10-10       Impact factor: 2.931

3.  Genome-wide association studies of 74 plasma metabolites of German shepherd dogs reveal two metabolites associated with genes encoding their enzymes.

Authors:  Pamela Xing Yi Soh; Juliana Maria Marin Cely; Sally-Anne Mortlock; Christopher James Jara; Rachel Booth; Siria Natera; Ute Roessner; Ben Crossett; Stuart Cordwell; Mehar Singh Khatkar; Peter Williamson
Journal:  Metabolomics       Date:  2019-09-06       Impact factor: 4.290

4.  Metabolomic Profiles of Overweight/Obesity Phenotypes During Adolescence: A Cross-Sectional Study in Project Viva.

Authors:  Wei Perng; Sheryl L Rifas-Shiman; Joanne Sordillo; Marie-France Hivert; Emily Oken
Journal:  Obesity (Silver Spring)       Date:  2019-12-26       Impact factor: 5.002

5.  Flavonoids in Decorticated Sorghum Grains Exert Antioxidant, Antidiabetic and Antiobesity Activities.

Authors:  Fred Kwame Ofosu; Fazle Elahi; Eric Banan-Mwine Daliri; Su-Jung Yeon; Hun Ju Ham; Joong-Hark Kim; Sang-Ik Han; Deog-Hwan Oh
Journal:  Molecules       Date:  2020-06-20       Impact factor: 4.411

6.  A lipidome-wide association study of the lipoprotein insulin resistance index.

Authors:  Minoo Bagheri; Hemant K Tiwari; Anarina L Murillo; Rafet Al-Tobasei; Donna K Arnett; Tobias Kind; Dinesh Kumar Barupal; Sili Fan; Oliver Fiehn; Jeff O'connell; May Montasser; Stella Aslibekyan; Marguerite R Irvin
Journal:  Lipids Health Dis       Date:  2020-06-25       Impact factor: 3.876

7.  Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies.

Authors:  Oscar Daniel Rangel-Huerta; Belén Pastor-Villaescusa; Angel Gil
Journal:  Metabolomics       Date:  2019-06-13       Impact factor: 4.290

8.  Lipidomic Profile Revealed the Association of Plasma Lysophosphatidylcholines with Adolescent Obesity.

Authors:  Yang Wang; Chang-Tao Jiang; Jie-Yun Song; Qi-Ying Song; Jun Ma; Hai-Jun Wang
Journal:  Biomed Res Int       Date:  2019-12-13       Impact factor: 3.411

Review 9.  Metabolomics-A Promising Approach to Pituitary Adenomas.

Authors:  Oana Pînzariu; Bogdan Georgescu; Carmen E Georgescu
Journal:  Front Endocrinol (Lausanne)       Date:  2019-01-17       Impact factor: 5.555

10.  A Cross-Sectional Study of Obesity Effects on the Metabolomic Profile of a Leptin-Resistant Swine Model.

Authors:  M Victoria Sanz-Fernandez; Laura Torres-Rovira; Jose L Pesantez-Pacheco; Marta Vazquez-Gomez; Consolacion Garcia-Contreras; Susana Astiz; Antonio Gonzalez-Bulnes
Journal:  Metabolites       Date:  2020-03-05
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