Top

European Journal of Medical Research

Published in:

Open Access 01-12-2023 | Biomarkers | Research

Lysophospholipids and branched chain amino acids are associated with aging: a metabolomics-based study of Chinese adults

Authors: Yiming Pan, Pan Liu, Shijie Li, Bowen Li, Yun Li, Lina Ma

Published in: European Journal of Medical Research | Issue 1/2023

Abstract

Background

Aging is an inevitable process associated with impairments in multiple organ systems, which increases the risk of comorbidity and disability, and reduces the health-span. Metabolomics is a powerful tool in aging research, which can reflect the characteristics of aging at the level of terminal metabolism, and may contribute to the exploration of aging mechanisms and the formulation of anti-aging strategies.

Methods

To identify possible biomarkers and pathways associated with aging using untargeted metabolomics methods, we performed liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics profiling on serum samples from 32 older adults and 32 sex-matched young controls.

Results

Metabolite profiling could distinguish the two groups. Among the 349 metabolites identified, 80—including lysophospholipids whose levels gradually decline—are possible candidate aging biomarkers. Valine, leucine and isoleucine degradation and biosynthesis were important pathways in aging, with reduced levels of l-isoleucine (r = − 0.30, p = 0.017) and l-leucine (r = − 0.32, p = 0.010) observed in older adults.

Conclusions

We preliminarily revealed the metabolite changes associated with aging in Chinese adults. Decreases in mitochondrial membrane-related lysophospholipids and dysfunction of branched-chain amino acid metabolism were determined to be the characteristics and promising research targets for aging.

Available only for authorised users

Lopez-Otin C, Blasco MA, Partridge L, et al. The hallmarks of aging. Cell. 2013;153(6):1194–217. https://doi.org/10.1016/j.cell.2013.05.039.CrossRef

Aunan JR, Cho WC, Soreide K. The biology of aging and cancer: a brief overview of shared and divergent molecular hallmarks. Aging Dis. 2017;8(5):628–42. https://doi.org/10.14336/AD.2017.0103.CrossRef

Gioran A, Chondrogianni N. Mitochondria (cross)talk with proteostatic mechanisms: focusing on ageing and neurodegenerative diseases. Mech Ageing Dev. 2020;190:111324. https://doi.org/10.1016/j.mad.2020.111324.CrossRef

Li H, Hastings MH, Rhee J, et al. Targeting age-related pathways in heart failure. Circ Res. 2020;126(4):533–51. https://doi.org/10.1161/CIRCRESAHA.119.315889.CrossRef

Kontis V, Bennett JE, Mathers CD, et al. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 2017;389(10076):1323–35. https://doi.org/10.1016/S0140-6736(16)32381-9.CrossRef

Beard JR, Officer A, de Carvalho IA, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet. 2016;387(10033):2145–54. https://doi.org/10.1016/S0140-6736(15)00516-4.CrossRef

Ferrucci L, Levine ME, Kuo PL, et al. Time and the metrics of aging. Circ Res. 2018;123(7):740–4. https://doi.org/10.1161/CIRCRESAHA.118.312816.CrossRef

Maldonado-Lasuncion I, Atienza M, Sanchez-Espinosa MP, et al. Aging-related changes in cognition and cortical integrity are associated with serum expression of candidate MicroRNAs for Alzheimer disease. Cereb Cortex. 2019;29(10):4426–37. https://doi.org/10.1093/cercor/bhy323.CrossRef

Roh JD, Hobson R, Chaudhari V, et al. Activin type II receptor signaling in cardiac aging and heart failure. Sci Transl Med. 2019;11(482):eaau8680. https://doi.org/10.1126/scitranslmed.aau8680.CrossRef

10.

Sosulski ML, Gongora R, Danchuk S, et al. Deregulation of selective autophagy during aging and pulmonary fibrosis: the role of TGFbeta1. Aging Cell. 2015;14(5):774–83. https://doi.org/10.1111/acel.12357.CrossRef

11.

Sebastiani P, Federico A, Morris M, et al. Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans. Aging Cell. 2021;20(2):e13290. https://doi.org/10.1111/acel.13290.CrossRef

12.

Tedone E, Huang E, O’Hara R, et al. Telomere length and telomerase activity in T cells are biomarkers of high-performing centenarians. Aging Cell. 2019;18(1):e12859. https://doi.org/10.1111/acel.12859.CrossRef

13.

Tomas-Loba A, Bernardes de Jesus B, Mato JM, et al. A metabolic signature predicts biological age in mice. Aging Cell. 2013;12(1):93–101. https://doi.org/10.1111/acel.12025.CrossRef

14.

Son HG, Altintas O, Kim EJE, et al. Age-dependent changes and biomarkers of aging in Caenorhabditis elegans. Aging Cell. 2019;18(2):e12853. https://doi.org/10.1111/acel.12853.CrossRef

15.

Lee TT, Chen PL, Su MP, et al. Loss of Fis1 impairs proteostasis during skeletal muscle aging in Drosophila. Aging Cell. 2021;20(6):e13379. https://doi.org/10.1111/acel.13379.CrossRef

16.

Song JW, Lam SM, Fan X, et al. Omics-driven systems interrogation of metabolic dysregulation in COVID-19 pathogenesis. Cell Metab. 2020;32(2):188-202.e185. https://doi.org/10.1016/j.cmet.2020.06.016.CrossRef

17.

Tian H, Ni Z, Lam SM, et al. Precise metabolomics reveals a diversity of aging-associated metabolic features. Small Methods. 2022;6(7):e2200130. https://doi.org/10.1002/smtd.202200130.CrossRef

18.

Xia J, Wishart DS. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 2010;38(Web service issue):W71-77. https://doi.org/10.1093/nar/gkq329.CrossRef

19.

Xia J, Wishart DS. MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics. 2010;26(18):2342–4. https://doi.org/10.1093/bioinformatics/btq418.CrossRef

20.

Blazenovic I, Kind T, Ji J, et al. Software tools and approaches for compound identification of LC–MS/MS data in metabolomics. Metabolites. 2018;8(2):31. https://doi.org/10.3390/metabo8020031.CrossRef

21.

Kameoka S, Adachi Y, Okamoto K, et al. Phosphatidic acid and cardiolipin coordinate mitochondrial dynamics. Trends Cell Biol. 2018;28(1):67–76. https://doi.org/10.1016/j.tcb.2017.08.011.CrossRef

22.

Horvath SE, Daum G. Lipids of mitochondria. Prog Lipid Res. 2013;52(4):590–614. https://doi.org/10.1016/j.plipres.2013.07.002.CrossRef

23.

Tatsuta T, Scharwey M, Langer T. Mitochondrial lipid trafficking. Trends Cell Biol. 2014;24(1):44–52. https://doi.org/10.1016/j.tcb.2013.07.011.CrossRef

24.

Ubaida-Mohien C, Lyashkov A, Gonzalez-Freire M, et al. Discovery proteomics in aging human skeletal muscle finds change in spliceosome, immunity, proteostasis and mitochondria. Elife. 2019;8:e49874. https://doi.org/10.7554/eLife.49874.CrossRef

25.

Gonzalez-Freire M, Moaddel R, Sun K, et al. Targeted metabolomics shows low plasma lysophosphatidylcholine 18:2 predicts greater decline of gait speed in older adults: the baltimore longitudinal study of aging. J Gerontol A Biol Sci Med Sci. 2019;74(1):62–7. https://doi.org/10.1093/gerona/gly100.CrossRef

26.

Stegemann C, Pechlaner R, Willeit P, et al. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. Circulation. 2014;129(18):1821–31. https://doi.org/10.1161/CIRCULATIONAHA.113.002500.CrossRef

27.

Polonis K, Wawrzyniak R, Daghir-Wojtkowiak E, et al. Metabolomic signature of early vascular aging (EVA) in hypertension. Front Mol Biosci. 2020;7:12. https://doi.org/10.3389/fmolb.2020.00012.CrossRef

28.

Llano DA, Devanarayan V. Alzheimer’s Disease Neuroimaging I. Serum phosphatidylethanolamine and lysophosphatidylethanolamine levels differentiate Alzheimer’s disease from controls and predict progression from mild cognitive impairment. J Alzheimers Dis. 2021;80(1):311–9. https://doi.org/10.3233/JAD-201420.CrossRef

29.

Klavins K, Koal T, Dallmann G, et al. The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement (Amst). 2015;1(3):295–302. https://doi.org/10.1016/j.dadm.2015.05.003.CrossRef

30.

Lee GB, Lee JC, Moon MH. Plasma lipid profile comparison of five different cancers by nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry. Anal Chim Acta. 2019;1063:117–26. https://doi.org/10.1016/j.aca.2019.02.021.CrossRef

31.

Papsdorf K, Brunet A. Linking lipid metabolism to chromatin regulation in aging. Trends Cell Biol. 2019;29(2):97–116. https://doi.org/10.1016/j.tcb.2018.09.004.CrossRef

32.

Eum JY, Lee JC, Yi SS, et al. Aging-related lipidomic changes in mouse serum, kidney, and heart by nanoflow ultrahigh-performance liquid chromatography–tandem mass spectrometry. J Chromatogr A. 2020;1618:460849. https://doi.org/10.1016/j.chroma.2020.460849.CrossRef

33.

MohammadzadehHonarvar N, Zarezadeh M, Molsberry SA, et al. Changes in plasma phospholipids and sphingomyelins with aging in men and women: a comprehensive systematic review of longitudinal cohort studies. Ageing Res Rev. 2021;68:101340. https://doi.org/10.1016/j.arr.2021.101340.CrossRef

34.

Saxton RA, Sabatini DM. mTOR signaling in growth, metabolism, and disease. Cell. 2017;168(6):960–76. https://doi.org/10.1016/j.cell.2017.02.004.CrossRef

35.

MacIntosh C, Morley JE, Chapman IM. The anorexia of aging. Nutrition. 2000;16(10):983–95. https://doi.org/10.1016/s0899-9007(00)00405-6.CrossRef

36.

Solon-Biet SM, Cogger VC, Pulpitel T, et al. Branched chain amino acids impact health and lifespan indirectly via amino acid balance and appetite control. Nat Metab. 2019;1(5):532–45. https://doi.org/10.1038/s42255-019-0059-2.CrossRef

37.

Ribeiro RV, Solon-Biet SM, Pulpitel T, et al. Of older mice and men: branched-chain amino acids and body composition. Nutrients. 2019;11(8):1882. https://doi.org/10.3390/nu11081882.CrossRef

38.

Coelho-Junior HJ, Calvani R, Picca A, et al. Protein-related dietary parameters and frailty status in older community-dwellers across different frailty instruments. Nutrients. 2020;12(2):508. https://doi.org/10.3390/nu12020508.CrossRef

39.

Ko CH, Wu SJ, Wang ST, et al. Effects of enriched branched-chain amino acid supplementation on sarcopenia. Aging (Albany NY). 2020;12(14):15091–103. https://doi.org/10.18632/aging.103576.CrossRef

40.

Wolfe RR. Branched-chain amino acids and muscle protein synthesis in humans: myth or reality? J Int Soc Sports Nutr. 2017;14:30. https://doi.org/10.1186/s12970-017-0184-9.CrossRef

41.

Le Couteur DG, Solon-Biet SM, Cogger VC, et al. Branched chain amino acids, aging and age-related health. Ageing Res Rev. 2020;64:101198. https://doi.org/10.1016/j.arr.2020.101198.CrossRef

Title: Lysophospholipids and branched chain amino acids are associated with aging: a metabolomics-based study of Chinese adults
Authors: Yiming Pan
Pan Liu
Shijie Li
Bowen Li
Yun Li
Lina Ma
Publication date: 01-12-2023
Publisher: BioMed Central
Keyword: Biomarkers
Published in: European Journal of Medical Research / Issue 1/2023
Electronic ISSN: 2047-783X
DOI: https://doi.org/10.1186/s40001-023-01021-w

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Lysophospholipids and branched chain amino acids are associated with aging: a metabolomics-based study of Chinese adults

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2023

Speech deficits in multiple sclerosis: a narrative review of the existing literature

Research progress of metabolomics in cervical cancer

Clinicopathological characteristics and prognosis of medullary thyroid microcarcinoma: a tumor with a similar prognosis to macrocarcinoma

Hexarelin alleviates apoptosis on ischemic acute kidney injury via MDM2/p53 pathway

Higher plasma aldosterone concentrations in patients with aortic diseases and hypertension: a retrospective observational study

Evaluation of the safety and efficacy of extracorporeal carbon dioxide removal in the critically ill using the PrismaLung+ device