Literature DB >> 33815888

Metabolomics Signatures of Aging: Recent Advances.

Sunil S Adav1, Yulan Wang1.   

Abstract

Metabolomics is the latest state-of-the-art omics technology that provides a comprehensive quantitative profile of metabolites. The metabolites are the cellular end products of metabolic reactions that explain the ultimate response to genomic, transcriptomic, proteomic, or environmental changes. Aging is a natural inevitable process characterized by a time-dependent decline of various physiological and metabolic functions and are dominated collectively by genetics, proteomics, metabolomics, environmental factors, diet, and lifestyle. The precise mechanism of the aging process is unclear, but the metabolomics has the potential to add significant insight by providing a detailed metabolite profile and altered metabolomic functions with age. Although the application of metabolomics to aging research is still relatively new, extensive attempts have been made to understand the biology of aging through a quantitative metabolite profile. This review summarises recent developments and up-to-date information on metabolomics studies in aging research with a major emphasis on aging biomarkers in less invasive biofluids. The importance of an integrative approach that combines multi-omics data to understand the complex aging process is discussed. Despite various innovations in metabolomics and metabolite associated with redox homeostasis, central energy pathways, lipid metabolism, and amino acid, a major challenge remains to provide conclusive aging biomarkers. copyright:
© 2021 Adav et al.

Entities:  

Keywords:  aging; amino acids; lipids; mass spectrometry; metabolites; metabolomics

Year:  2021        PMID: 33815888      PMCID: PMC7990359          DOI: 10.14336/AD.2020.0909

Source DB:  PubMed          Journal:  Aging Dis        ISSN: 2152-5250            Impact factor:   6.745


  135 in total

1.  Oxidative stress, redox imbalance, and the aging process.

Authors:  Tory M Hagen
Journal:  Antioxid Redox Signal       Date:  2003-10       Impact factor: 8.401

Review 2.  Current metabolomics: practical applications.

Authors:  Sastia P Putri; Yasumune Nakayama; Fumio Matsuda; Takato Uchikata; Shizu Kobayashi; Atsuki Matsubara; Eiichiro Fukusaki
Journal:  J Biosci Bioeng       Date:  2013-01-29       Impact factor: 2.894

3.  Individual variability in human blood metabolites identifies age-related differences.

Authors:  Romanas Chaleckis; Itsuo Murakami; Junko Takada; Hiroshi Kondoh; Mitsuhiro Yanagida
Journal:  Proc Natl Acad Sci U S A       Date:  2016-03-28       Impact factor: 11.205

4.  NAD+ supplementation normalizes key Alzheimer's features and DNA damage responses in a new AD mouse model with introduced DNA repair deficiency.

Authors:  Yujun Hou; Sofie Lautrup; Stephanie Cordonnier; Yue Wang; Deborah L Croteau; Eduardo Zavala; Yongqing Zhang; Kanako Moritoh; Jennifer F O'Connell; Beverly A Baptiste; Tinna V Stevnsner; Mark P Mattson; Vilhelm A Bohr
Journal:  Proc Natl Acad Sci U S A       Date:  2018-02-05       Impact factor: 11.205

5.  Assessment of reference ranges for blood Cu, Mn, Se and Zn in a selected Italian population.

Authors:  Beatrice Bocca; Roberto Madeddu; Yolande Asara; Paola Tolu; Juan A Marchal; Giovanni Forte
Journal:  J Trace Elem Med Biol       Date:  2011-01-15       Impact factor: 3.849

Review 6.  Old Proteins in Man: A Field in its Infancy.

Authors:  Roger J W Truscott; Kevin L Schey; Michael G Friedrich
Journal:  Trends Biochem Sci       Date:  2016-07-11       Impact factor: 13.807

7.  Biomarker signatures of aging.

Authors:  Paola Sebastiani; Bharat Thyagarajan; Fangui Sun; Nicole Schupf; Anne B Newman; Monty Montano; Thomas T Perls
Journal:  Aging Cell       Date:  2017-01-06       Impact factor: 9.304

8.  Human serum metabolic profiles are age dependent.

Authors:  Zhonghao Yu; Guangju Zhai; Paula Singmann; Ying He; Tao Xu; Cornelia Prehn; Werner Römisch-Margl; Eva Lattka; Christian Gieger; Nicole Soranzo; Joachim Heinrich; Marie Standl; Elisabeth Thiering; Kirstin Mittelstraß; Heinz-Erich Wichmann; Annette Peters; Karsten Suhre; Yixue Li; Jerzy Adamski; Tim D Spector; Thomas Illig; Rui Wang-Sattler
Journal:  Aging Cell       Date:  2012-08-27       Impact factor: 9.304

Review 9.  Implication of Trimethylamine N-Oxide (TMAO) in Disease: Potential Biomarker or New Therapeutic Target.

Authors:  Manuel H Janeiro; María J Ramírez; Fermin I Milagro; J Alfredo Martínez; Maite Solas
Journal:  Nutrients       Date:  2018-10-01       Impact factor: 5.717

10.  Urinary Metabolomic Markers of Protein Glycation, Oxidation, and Nitration in Early-Stage Decline in Metabolic, Vascular, and Renal Health.

Authors:  Jinit Masania; Gernot Faustmann; Attia Anwar; Hildegard Hafner-Giessauf; Nasir Rajpoot; Johanna Grabher; Kashif Rajpoot; Beate Tiran; Barbara Obermayer-Pietsch; Brigitte M Winklhofer-Roob; Johannes M Roob; Naila Rabbani; Paul J Thornalley
Journal:  Oxid Med Cell Longev       Date:  2019-11-19       Impact factor: 6.543

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Authors:  Kwaku Amoah; Xiao-Hui Dong; Bei-Ping Tan; Shuang Zhang; Shu-Yan Chi; Qi-Hui Yang; Hong-Yu Liu; Xiao-Bo Yan; Yuan-Zhi Yang; Haitao Zhang
Journal:  Front Nutr       Date:  2022-06-16

2.  Metabolic Signature of Leukocyte Telomere Length in Elite Male Soccer Players.

Authors:  Shamma Al-Muraikhy; Maha Sellami; Alexander S Domling; Najeha Rizwana; Abdelali Agouni; Fatima Al-Khelaifi; Francesco Donati; Francesco Botre; Ilhame Diboun; Mohamed A Elrayess
Journal:  Front Mol Biosci       Date:  2021-12-16

Review 3.  Harnessing Metabolomics to Advance Epilepsy Research.

Authors:  Tore Eid
Journal:  Epilepsy Curr       Date:  2022-02-17       Impact factor: 7.500

4.  Independent and Interactive Effects of Genetic Background and Sex on Tissue Metabolomes of Adipose, Skeletal Muscle, and Liver in Mice.

Authors:  Ann E Wells; William T Barrington; Stephen Dearth; Nikhil Milind; Gregory W Carter; David W Threadgill; Shawn R Campagna; Brynn H Voy
Journal:  Metabolites       Date:  2022-04-08

5.  Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach.

Authors:  Parul Puri; Shri Kant Singh; Sanghamitra Pati
Journal:  BMJ Open       Date:  2022-07-12       Impact factor: 3.006

6.  Phosphatidylethanolamine N-Methyltransferase Knockout Modulates Metabolic Changes in Aging Mice.

Authors:  Qishun Zhou; Fangrong Zhang; Jakob Kerbl-Knapp; Melanie Korbelius; Katharina Barbara Kuentzel; Nemanja Vujić; Alena Akhmetshina; Gerd Hörl; Margret Paar; Ernst Steyrer; Dagmar Kratky; Tobias Madl
Journal:  Biomolecules       Date:  2022-09-09
  6 in total

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