Abstract
Introduction
Although sarcopenia greatly affects health and quality of life in older people, its pathophysiological causes are not fully elucidated. To face this challenge, omics technologies can be used. The metabolome gives a vision of the interaction between the genome and the environment through metabolic networks, thus contributing in clarifying the pathophysiology of the sarcopenic phenotype.
Objectives
The main goal of this study was to compare the plasma metabolome of sarcopenic and non-sarcopenic older people.
Methods
Cross-sectional study of 20 sarcopenic and 21 non-sarcopenic older subjects with available frozen plasma samples. Non-targeted metabolomic study by ultra-high-performance liquid chromatography–electrospray ionization tandem mass spectrometry (UHPLC-ESI–MS/MS) analysis with later bioinformatics data analysis. Once the significantly different metabolites were identified, the KEGG database was used on them to establish which were the metabolic pathways mainly involved.
Results
From 657 features identified, 210 showed significant differences between the study groups, and 30 had a FoldChangeLog2 > 2. The most interesting metabolic pathways found with the KEGG database were the biosynthesis of amino acids, arginine and proline metabolism, the biosynthesis of alkaloids derived from ornithine, linoleic acid metabolism, and the biosynthesis of unsaturated fatty acids.
Conclusions
The study results allowed us to confirm that the concept of “sarcopenic phenotype” is also witnessed at the plasma metabolite levels. The non-targeted metabolomics study can open a wide view of the sarcopenic features changes at the plasma level, which would be linked to the sarcopenic physiopathological alterations.
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Data availability
The study raw data was deposited in MetaboLights repository ULR: www.ebi.ac.uk/metabolights/MTBLS2679
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Acknowledgements
We thank Dr. Fabricio Alves Barbosa da Silva, researcher of scientific computing program, Fundação Oswaldo Cruz, for his assistance in metabolomic pathways bioinformatic analysis, “network vision”. We acknowledge the Chilean National Fund for Scientific and Technological Development for the partial funding of this research (Fondecyt Grant 1130947 and Fondecyt Iniciación Grant 11190532).
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CA and RO conceptualized the study; GM, GRC, BA, CM, RO performed the experiments at different steps; LL, RO and GM performed the data analysis. CA, GM, BA, GRC, RO wrote the original draft. All authors reviewed and edited the manuscript.
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Rafael Opazo, Bárbara Angel, Carlos Márquez, Lydia Lera, Gustavo R. Cardoso Dos Santos, Gustavo Monnerat, and Cecilia Albala declare that they have no conflict of interest.
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11306_2021_1832_MOESM1_ESM.tiff
Supplementary file1—Fig. SF1 Score plot of principal component analysis (PCA) performed by Compound Discoverer 2.1 to evaluated the sample replicates reproducibility in the UHPLC-ESI-MS/MS analysis. PCA Dimension 1 versus Dimension 2 scores, with a 16.5% and 9.7% of the explained variances respectively. The blue spots are the total number of injections analyzed for the Sarcopenic group (three replicates of each sample); the orange spots are the total number of injections analyzed for the non-sarcopenic group (three replicates of each sample); the magenta spots are the QC-Pool samples in three replicates, and the green spots are the blank samples in three replicates. As it is possible to observe, the colored points are clearly grouped in differentiated clouds associated with the replicas of each experimental group, which allows us to propose that the analysis presented adequate reproducibility (TIFF 36719 kb)
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Supplementary file2—Fig. SF2 Score plot of principal component analysis (PCA) using 657 features identified by the metabolomic study between the study groups. PCA Dimension 1 versus Dimension 2 scores. The red points correspond to sarcopenic subjects, and the black points correspond to non-sarcopenic subjects (TIFF 1648 kb)
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Supplementary file3—Fig. SF3 Proportion bar chart of the sources of origin class for the 30 most differentiated plasma metabolites between the study groups. Only the metabolites with FoldchageLog2 equal to or greater than 2 (n = 28) were included in the analysis. The classification groups in descending order were endogenous/food, strictly food, drugs, herbicides, and non-determined (TIFF 1128 kb)
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Supplementary file4—Fig. SF4 Box and whisker plots of the unsaturated fatty acids with significant (padj ≤ 0.12) plasma level differences between non-sarcopenic and sarcopenic subjects. A) Omega 6 series, B) Omega 3 series, and C) Omega 9 and 7 series. The box shows the interquartile range (25th to 75th percentile), the blackline shows the median (50th percentile), and the whiskers show the maximum and minimum (TIFF 1758 kb)
11306_2021_1832_MOESM5_ESM.tiff
Supplementary file5—Fig. SF5 Box and whisker plot of the saturated fatty acids with significant (padj ≤ 0.12) plasma level differences between non-sarcopenic and sarcopenic subjects. The box shows the interquartile range (25th to 75th percentile), the blackline shows the median (50th percentile), and the whiskers show the maximum and minimum (TIFF 1289 kb)
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Supplementary file6—Fig. SF6 Box and whisker plots of A) glycolipid with significant (padj ≤ 0.12) plasma level differences between non-sarcopenic and sarcopenic subjects, and B) and phospholipid with significant (padj ≤ 0.12) plasma level differences between non-sarcopenic and sarcopenic subjects. The box shows the interquartile range (25th to 75th percentile), the blackline shows the median (50th percentile), and the whiskers show the maximum and minimum (TIFF 1758 kb)
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Opazo, R., Angel, B., Márquez, C. et al. Sarcopenic metabolomic profile reflected a sarcopenic phenotype associated with amino acid and essential fatty acid changes. Metabolomics 17, 83 (2021). https://doi.org/10.1007/s11306-021-01832-0
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DOI: https://doi.org/10.1007/s11306-021-01832-0