Skip to main content
Log in

Metabolomic characterization of vigor to frailty among community-dwelling older Black and White men and women

  • Original Article
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Older women and Black individuals are more likely to experience frailty. A metabolomic characterization of frailty may help inform more effective interventions aimed at improving health, reducing disparities, and preventing frailty with aging. We sought to identify metabolites and pathways associated with vigor to frailty and determine whether associations differed by sex and/or race among n = 2189 older Black and White men and women from the Health, Aging, and Body Composition (Health ABC) study. Fasting plasma metabolites were measured using liquid chromatography-mass spectrometry. Vigor to frailty was based on weight change, physical activity, gait speed, grip strength, and usual energy. We used linear regression of a single metabolite on vigor to frailty, adjusting for age, sex, race, study site, and multiple comparisons using a Bonferroni correction. Among 500 metabolites, 113 were associated with vigor to frailty (p < 0.0001). Associations between metabolites and vigor to frailty did not differ significantly by race and/or sex. Lower amino acids, glycerophospholipids, sphingolipids, and dehydroepiandrosterone sulfate and higher acylcarnitines, fatty acids, amino acid derivatives, organic acids, carbohydrates, citric acid cycle metabolites, and trimethylamine oxide were associated with frailer scores. Pathway analyses identified the citric acid cycle as containing more frailty-associated metabolites than expected by chance (p = 0.00005). Calories and protein intake did not differ by vigor to frailty. Frailer Health ABC participants may have lower utilization of energy pathways, potentially as a result of less demand and less efficient utilization of similar amounts of nutrients when compared to more vigorous participants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Fried LP, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–57.

    Article  CAS  PubMed  Google Scholar 

  2. Fried LP. Interventions for human frailty: physical activity as a model. Cold Spring Harb Perspect Med. 2016;6(6):a025916.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hirsch C, et al. The association of race with frailty: the cardiovascular health study. Ann Epidemiol. 2006;16(7):545–53.

    Article  PubMed  Google Scholar 

  4. Okoro CA, et al. Prevalence of disabilities and health care access by disability status and type among adults—United States, 2016. Morb Mortal Wkly Rep. 2018;67(32):882.

    Article  Google Scholar 

  5. Control, C.f.D., Prevention, and N.C.f.H. Statistics. Underlying Cause of Death 1999-2019 on CDC WONDER Online Database, released in 2020. In: Data are from the Multiple Cause of Death Files, 1999-2019, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program, vol. 16; 2021. p. 2021.

    Google Scholar 

  6. Marron MM, et al. Metabolites associated with vigor to frailty among community-dwelling older black men. Metabolites. 2019;9(5):83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Marron MM, et al. A metabolite composite score attenuated a substantial portion of the higher mortality risk associated with frailty among community-dwelling older adults. J Gerontol. 2021;76(2):378–84.

    Article  Google Scholar 

  8. Townsend MK, et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem. 2013;59(11):1657–67.

    Article  CAS  PubMed  Google Scholar 

  9. Marron MM, et al. Metabolites associated with walking ability among the oldest old from the CHS All Stars study. J Gerontol. 2020;75(12):2371–8.

    Article  CAS  Google Scholar 

  10. Wei R, et al. Missing value imputation approach for mass spectrometry-based metabolomics data. Sci Rep. 2018;8(1):1–10.

    Google Scholar 

  11. Xia J, et al. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37(suppl_2):W652–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sanders JL, et al. Measurement of organ structure and function enhances understanding of the physiological basis of frailty: the cardiovascular health study. J Am Geriatr Soc. 2011;59(9):1581–8.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sanders JL, et al. Association between mortality and heritability of the scale of aging vigor in epidemiology. J Am Geriatr Soc. 2016;64(8):1679–83.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Houston DK, et al. Dietary protein intake is associated with lean mass change in older, community-dwelling adults: the health, aging, and body composition (Health ABC) study. Am J Clin Nutr. 2008;87(1):150–5.

    Article  CAS  PubMed  Google Scholar 

  15. Inker LA, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pang Z, et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49(W1):W388–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Xia J, Wishart DS. Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinform. 2016;55(1):14.10.1–14.10.91.

    Article  Google Scholar 

  18. Wishart DS, et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 2022;50(D1):D622–31.

    Article  CAS  PubMed  Google Scholar 

  19. Li S, Gao D, Jiang Y. Function, detection and alteration of acylcarnitine metabolism in hepatocellular carcinoma. Metabolites. 2019;9(2):36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dambrova M, et al. Acylcarnitines: nomenclature, biomarkers, therapeutic potential, drug targets, and clinical trials. Pharmacol Rev. 2022;74(3):506–51.

    Article  CAS  PubMed  Google Scholar 

  21. Sharma S, Black SM. Carnitine homeostasis, mitochondrial function and cardiovascular disease. Drug Discov Today. 2009;6(1-4):e31–9.

    Article  CAS  Google Scholar 

  22. Vallance H, et al. Marked elevation in plasma trimethylamine-N-oxide (TMAO) in patients with mitochondrial disorders treated with oral l-carnitine. Mol Genet Metab Rep. 2018;15:130–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Cheng S, et al. Potential impact and study considerations of metabolomics in cardiovascular health and disease: a scientific statement from the American Heart Association. Circulation: Cardiovascular. Genetics. 2017;10(2):e000032.

    Google Scholar 

  24. Montoliu I, et al. Serum profiling of healthy aging identifies phospho-and sphingolipid species as markers of human longevity. Aging. 2014;6(1):9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Goñi FM. Sphingomyelin: what is it good for? Biochem Biophys Res Commun. 2022;633:23–5.

    Article  PubMed  Google Scholar 

  26. Pan M, et al. Arginine transport in catabolic disease states. J Nutr. 2004;134(10):2826S–9S.

    Article  CAS  PubMed  Google Scholar 

  27. Mehraj V, Routy J-P. Tryptophan catabolism in chronic viral infections: handling uninvited guests. Int J Tryptophan Res. 2015;8:IJTR.S26862.

    Article  Google Scholar 

  28. White PJ, et al. Insulin action, type 2 diabetes, and branched-chain amino acids: a two-way street. Mol Metab. 2021;52:101261.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bauer J, et al. Evidence-based recommendations for optimal dietary protein intake in older people: a position paper from the PROT-AGE Study Group. J Am Med Dir Assoc. 2013;14(8):542–59.

    Article  PubMed  Google Scholar 

  30. Fried LP, et al. The physical frailty syndrome as a transition from homeostatic symphony to cacophony. Nat Aging. 2021;1(1):36–46.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Westbrook R, et al. Metabolomics-based identification of metabolic dysfunction in frailty. J Gerontol. 2022;77(12):2367–72.

    Article  CAS  Google Scholar 

  32. Pan Y, et al. Metabolomics-based frailty biomarkers in older Chinese adults. Front Med. 2022;8:830723.

    Article  Google Scholar 

  33. Santos JL, et al. Circulating citric acid cycle metabolites and risk of cardiovascular disease in the PREDIMED study. Nutr Metab Cardiovasc Dis. 2023;33(4):835–43.

    Article  CAS  PubMed  Google Scholar 

  34. Cheng S, et al. Distinct metabolomic signatures are associated with longevity in humans. Nat Commun. 2015;6(1):6791.

    Article  CAS  PubMed  Google Scholar 

  35. Sulkowski PL, et al. Oncometabolites suppress DNA repair by disrupting local chromatin signalling. Nature. 2020;582(7813):586–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Borkum JM. The tricarboxylic acid cycle as a central regulator of the rate of aging: implications for metabolic interventions. Adv Biol. 2023:2300095.

Download references

Funding

This work was supported by National Institute on Aging Contracts N01-AG-6–2101, N01-AG-6–2103, and N01-AG-6–2106; National Institute on Aging Grant R01-AG-028050; and National Institute of Nursing Research Grant R01-NR-012459. This work was also supported in part by the Intramural Research Program of the National Institute on Aging. Metabolomics in the Health ABC study were supported by National Institute on Aging Grant R01-AG059729. Dr. Marron is supported by National Institute on Aging Career Development Award K01-AG-075143. Drs. Murthy and Shah are supported in part by grants from the National Institute on Aging (R01 AG059729); National Heart, Lung and Blood Institute (R01 HL136685); and an American Heart Association Strategically Focused Research Network grant in Cardiometabolic Disease.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Megan M. Marron.

Ethics declarations

Competing interests

Dr. Murthy owns stock or stock options in General Electric, Cardinal Health, Ionetix, Boston Scientific, Merck, Eli Lilly, Johnson and Johnson, and Pfizer. He has received research grants and consulting fees from Siemens Medical Imaging. He has served on medical advisory boards for Ionetix. Dr. Shah serves as a consultant for Amgen. Dr. Shah is a co-inventor on a patent for ex-RNA signatures of cardiac remodeling. There are no disclosures from the other authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

ESM 1

(DOCX 233 KB)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marron, M.M., Yao, S., Shah, R.V. et al. Metabolomic characterization of vigor to frailty among community-dwelling older Black and White men and women. GeroScience 46, 2371–2389 (2024). https://doi.org/10.1007/s11357-023-01005-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-023-01005-y

Keywords

Navigation