13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Visceral Adipose Tissue Phospholipid Signature of Insulin Sensitivity and Obesity

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Alterations in visceral adipose tissue (VAT) are closely linked to cardiometabolic abnormalities. The aim of this work is to define a metabolic signature in VAT of insulin resistance (IR) dependent on, and independent of, obesity. An untargeted UPLC-Q-Exactive metabolomic approach was carried out on the VAT of obese insulin-sensitive (IS) and insulin-resistant subjects ( N = 11 and N = 25, respectively) and nonobese IS and IR subjects ( N = 25 and N = 10, respectively). The VAT metabolome in obesity was defined among other things by changes in the metabolism of lipids, nucleotides, carbohydrates, and amino acids, whereas when combined with high IR, it affected the metabolism of 18 carbon fatty acyl-containing phospholipid species. A multimetabolite model created by glycerophosphatidylinositol (18:0); glycerophosphatidylethanolamine (18:2); glycerophosphatidylserine (18:0); and glycerophosphatidylcholine (18:0/18:1), (18:2/18:2), and (18:2/18:3) exhibited a highly predictive performance to identify the metabotype of “insulin-sensitive obesity” among obese individuals [area under the curve (AUC) 96.7% (91.9–100)] and within the entire study population [AUC 87.6% (79.0–96.2)]. We demonstrated that IR has a unique and shared metabolic signature dependent on, and independent of, obesity. For it to be used in clinical practice, these findings need to be validated in a more accessible sample, such as blood.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Visceral Adiposity Index

          OBJECTIVE To individuate a novel sex-specific index, based on waist circumference, BMI, triglycerides, and HDL cholesterol, indirectly expressing visceral fat function. RESEARCH DESIGN AND METHODS Visceral adiposity index (VAI) was first modeled on 315 nonobese healthy subjects. Using two multiple logistic regression models, VAI was retrospectively validated in 1,498 primary care patients in comparison to classical cardio- and cerebrovascular risk factors. RESULTS All components of metabolic syndrome increased significantly across VAI quintiles. VAI was independently associated with both cardiovascular (odd ratio [OR] 2.45; 95% CI 1.52–3.95; P < 0.001) and cerebrovascular (1.63; 1.06–2.50; P = 0.025) events. VAI also showed significant inverse correlation with insulin sensitivity during euglycemic-hyperinsulinemic clamp in a subgroup of patients (R s = −0.721; P < 0.001). By contrast, no correlations were found for waist circumference and BMI. CONCLUSIONS Our study suggests VAI is a valuable indicator of “visceral adipose function” and insulin sensitivity, and its increase is strongly associated with cardiometabolic risk.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Organization of GC/MS and LC/MS metabolomics data into chemical libraries

            Background Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Qu antify I ndividual C omponents in a S ample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. Results LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. Conclusion The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance.

              Previous studies have suggested that insulin resistance develops secondary to diminished fat oxidation and resultant accumulation of cytosolic lipid molecules that impair insulin signaling. Contrary to this model, the present study used targeted metabolomics to find that obesity-related insulin resistance in skeletal muscle is characterized by excessive beta-oxidation, impaired switching to carbohydrate substrate during the fasted-to-fed transition, and coincident depletion of organic acid intermediates of the tricarboxylic acid cycle. In cultured myotubes, lipid-induced insulin resistance was prevented by manipulations that restrict fatty acid uptake into mitochondria. These results were recapitulated in mice lacking malonyl-CoA decarboxylase (MCD), an enzyme that promotes mitochondrial beta-oxidation by relieving malonyl-CoA-mediated inhibition of carnitine palmitoyltransferase 1. Thus, mcd(-/-) mice exhibit reduced rates of fat catabolism and resist diet-induced glucose intolerance despite high intramuscular levels of long-chain acyl-CoAs. These findings reveal a strong connection between skeletal muscle insulin resistance and lipid-induced mitochondrial stress.
                Bookmark

                Author and article information

                Journal
                J Proteome Res
                J Proteome Res
                pr
                jprobs
                Journal of Proteome Research
                American Chemical Society
                1535-3893
                1535-3907
                24 March 2021
                07 May 2021
                : 20
                : 5
                : 2410-2419
                Affiliations
                []Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XIA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona , Barcelona 08028, Spain
                []CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III , Madrid 28029, Spain
                [§ ]Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Malaga (IBIMA), Virgen de la Victoria University Hospital,, Málaga University , Malaga 29010, Spain
                []Genetics, Microbiology and Statistics Department, Biology Faculty, University of Barcelona , Barcelona 08028, Spain
                []CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III , Madrid 28029, Spain
                Author notes
                [* ]Email: fjtinahones@ 123456uma.es . Phone: +34 951932734. Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Malaga (IBIMA), Virgen de la Victoria University Hospital, Málaga University.
                [* ]Email: candres@ 123456ub.edu . Phone: +34 934034840. Fax: +34 93403593. Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Faculty of Pharmacy and Food Sciences, University of Barcelona, Avenida Joan XXIII, 27-31, 08028 Barcelona, Spain.
                Author information
                http://orcid.org/0000-0001-7042-5651
                http://orcid.org/0000-0002-8494-4978
                Article
                10.1021/acs.jproteome.0c00918
                8631729
                33760621
                b0bb4844-34e6-4362-9141-a29f0129b267
                © 2021 American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 16 November 2020
                Funding
                Funded by: Instituto de Salud Carlos III, doi 10.13039/501100004587;
                Award ID: CP17/00133
                Funded by: Agència de Gestió d''Ajuts Universitaris i de Recerca, doi NA;
                Award ID: 2017 SGR 1546
                Funded by: Instituto de Salud Carlos III, doi 10.13039/501100004587;
                Award ID: PI13/01172
                Funded by: Instituto de Salud Carlos III, doi 10.13039/501100004587;
                Award ID: PI-0557-2013
                Categories
                Article
                Custom metadata
                pr0c00918
                pr0c00918

                Molecular biology
                discordant phenotypes,insulin resistance,lipid remodeling,metabotype,metabolomics,phospholipids,obesity,diabetes,biomarker

                Comments

                Comment on this article