Skip to main content
SpringerLink
Log in

A metabolomic signature of the APOE2 allele

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

Abstract 

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR < 10%. The group includes 10 glycerolipids and 4 glycerophospholipids that were all higher in E2 carriers compared to E3, with fold change ranging from 1.08 to 1.25. The organic acid 6-hydroxyindole sulfate, previously linked to changes in gut microbiome that were reflective of healthy aging and longevity, was also higher in E2 carriers compared to E3 carriers. Three sterol lipids and one sphingolipid species were significantly lower in carriers of the E2 genotype group. For some of these metabolites, the effect of the E2 genotype opposed the age effect. No metabolites reached a statistically significant association with the E4 group. This work confirms and expands previous results connecting the APOE gene to lipid regulation and suggests new links between the e2 allele, lipid metabolism, aging, and the gut-brain axis.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Ji Y, et al. Apolipoprotein Epsilon epsilon4 frequency is increased among Chinese patients with frontotemporal dementia and Alzheimer’s disease. Dement Geriatr Cogn Disord. 2013;36(3–4):163–70.

    Article  CAS  Google Scholar 

  2. Farrer LA, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis APOE and Alzheimer Disease Meta Analysis Consortium. Jama. 1997;278(16):1349–56.

    Article  CAS  Google Scholar 

  3. Corbo RM, R Scacchi, Apolipoprotein E (APOE) allele distribution in the world. Is APOE*4 a ʻthriftyʼ allele? Ann Hum Genet, 1999; 63(Pt 4) 301–10

  4. Sebastiani P, et al. APOE alleles and extreme human longevity. J Gerontol A Biol Sci Med Sci. 2019;74(1):44–51.

    Article  CAS  Google Scholar 

  5. Sweigart B, et al. APOE E2/E2 is associated with slower rate of cognitive decline with age. J Alzheimers Dis, 2021.

  6. Kim YJ, et al. Protective effects of APOE e2 against disease progression in subcortical vascular mild cognitive impairment patients: a three-year longitudinal study. Sci Rep. 2017;7(1):1910.

    Article  Google Scholar 

  7. Reiman EM, et al. Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5,000-person neuropathological study. Nat Commun. 2020;11(1):667.

    Article  CAS  Google Scholar 

  8. Huang Y, Mahley RW. Apolipoprotein E: structure and function in lipid metabolism, neurobiology, and Alzheimer’s diseases. Neurobiology of Disease. 2014;72:3–12.

    Article  CAS  Google Scholar 

  9. Wang T, et al. APOE epsilon2 resilience for Alzheimer’s disease is mediated by plasma lipid species: analysis of three independent cohort studies. Alzheimers Dement, 2022.

  10. Liu Y, et al. Plasma lipidome is dysregulated in Alzheimer’s disease and is associated with disease risk genes. Transl Psychiatry. 2021;11(1):344.

    Article  Google Scholar 

  11. Hysi PG, et al. Metabolome genome-wide association study identifies 74 novel genomic regions influencing plasma metabolites levels. Metabolites. 2022;12(1):61.

    Article  CAS  Google Scholar 

  12. Sebastiani P, Perls TT. The genetics of extreme longevity: lessons from the New England Centenarian Study. Front Genet. 2012;3:277.

    Article  Google Scholar 

  13. Ferrucci L. The Baltimore Longitudinal Study of Aging (BLSA): a 50-year-long journey and plans for the future. J Gerontol A Biol Sci Med Sci. 2008;63(12):1416–9.

    Article  Google Scholar 

  14. Earls JC, et al. Multi-omic biological age estimation and its correlation with wellness and disease phenotypes: a longitudinal study of 3,558 individuals. J Gerontol A Biol Sci Med Sci. 2019;74(Suppl_1):S52-s60.

    Article  Google Scholar 

  15. Barzilai N, et al. Unique lipoprotein phenotype and genotype associated with exceptional longevity. JAMA. 2003;290(15):2030–40.

    Article  CAS  Google Scholar 

  16. Gubbi S, et al. Effect of exceptional parental longevity and lifestyle factors on prevalence of cardiovascular disease in offspring. Am J Cardiol. 2017;120(12):2170–5.

    Article  Google Scholar 

  17. Wojczynski MK, et al. NIA Long Life Family Study: objectives, design, and heritability of cross sectional and longitudinal phenotypes. J Gerontol A Biol Sci Med Sci, 2021.

  18. Wilmanski T, et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat Metab. 2021;3(2):274–86.

    Article  Google Scholar 

  19. Sebastiani P, et al. A serum protein signature of APOE genotypes in centenarians. Aging Cell, 2019; e13023.

  20. Fahy E, Subramaniam S. RefMet: a reference nomenclature for metabolomics. Nat Methods. 2020;17(12):1173–4.

    Article  CAS  Google Scholar 

  21. Wishart DS, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 2021;50(D1):D622–31.

    Article  Google Scholar 

  22. Tuck MK, et al. Standard operating procedures for serum and plasma collection: Early Detection Research Network Consensus Statement Standard Operating Procedure Integration Working Group. J Proteome Res. 2009;8(1):113–7.

    Article  CAS  Google Scholar 

  23. Hixson JE, Vernier DT. Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J Lipid Res. 1990;31(3):545–8.

    Article  CAS  Google Scholar 

  24. Koch W et al. TaqMan systems for genotyping of disease-related polymorphisms present in the gene encoding apolipoprotein E. 2002;40(11):1123-1131.

  25. Yamaguchi Y, et al. Plasma metabolites associated with chronic kidney disease and renal function in adults from the Baltimore Longitudinal Study of Aging. Metabolomics. 2021;17(1):9.

    Article  CAS  Google Scholar 

  26. Mahieu NG, et al. Defining and detecting complex peak relationships in mass spectral data: the Mz unity Algorithm. Anal Chem. 2016;88(18):9037–46.

    Article  CAS  Google Scholar 

  27. Cho K, et al. Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics. Anal Chim Acta. 2021;1149:338210.

    Article  CAS  Google Scholar 

  28. Stancliffe E, et al. DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution. Nat Methods. 2021;18(7):779–87.

    Article  CAS  Google Scholar 

  29. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006;8(1):118–27.

    Article  Google Scholar 

  30. Sumner LW, et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2007;3(3):211–21.

    Article  CAS  Google Scholar 

  31. Little, R.J.A. and D.B. Rubin, Statistical analysis with missing data. 1987: Wiley.

  32. Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Statist Soc Series B. 1995;57(1):289–300.

    Google Scholar 

  33. Marron MM et al. Using lipid profiling to better characterize metabolic differences in apolipoprotein E (APOE) genotype among community-dwelling older Black men. Geroscience, 2021.

  34. Proitsi P, et al. Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer’s disease. Transl Psychiatry. 2015;5(1):e494–e494.

    Article  CAS  Google Scholar 

  35. Mundra PA et al. Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention. JCI Insight; 2018. 3(17).

  36. Baloni P et al. Multi-omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer’s disease. medRxiv, 2021 2021.07.16.21260601.

  37. Wood PL, et al. Targeted lipidomics distinguishes patient subgroups in mild cognitive impairment (MCI) and late onset Alzheimer’s disease (LOAD). BBA clinical. 2015;5:25–8.

    Article  Google Scholar 

  38. Wood PL, et al. Targeted lipidomics of fontal cortex and plasma diacylglycerols (DAG) in mild cognitive impairment and Alzheimer’s disease: validation of DAG accumulation early in the pathophysiology of Alzheimer’s disease. Journal of Alzheimer’s disease : JAD. 2015;48(2):537–46.

    Article  CAS  Google Scholar 

  39. Wood PL, Cebak JE, Woltjer RL. Diacylglycerols as biomarkers of sustained immune activation in proteinopathies associated with dementia. Clinica Chimica Acta. 2018;476:107–10.

    Article  CAS  Google Scholar 

  40. Callender JA, Newton AC. Conventional protein kinase C in the brain: 40 years later. Neuronal signaling. 2017;1(2):NS0160005-NS20160005.

    Article  Google Scholar 

  41. Soto-Avellaneda A, Morrison BE. Signaling and other functions of lipids in autophagy: a review. Lipids Health Dis. 2020;19(1):214.

    Article  Google Scholar 

  42. Herrera JJ et al. Acarbose has sex-dependent and -independent effects on age-related physical function, cardiac health, and lipid biology. JCI Insight, 2020; 5(21).

  43. Sonowal R, et al. Indoles from commensal bacteria extend healthspan. Proc Natl Acad Sci. 2017;114(36):E7506–15.

    Article  CAS  Google Scholar 

  44. Masuo Y, et al. 6-Hydroxyindole is an endogenous long-lasting OATP1B1 inhibitor elevated in renal failure patients. Drug Metab Pharmacokinet. 2020;35(6):555–62.

    Article  CAS  Google Scholar 

  45. Dekkers KF et al. An online atlas of human plasma metabolite signatures of gut microbiome composition. medRxiv, 2021 2021.12.23.21268179.

  46. Badal VD, et al. The gut microbiome, aging, and longevity: a systematic review. Nutrients. 2020;12(12):3759.

    Article  Google Scholar 

  47. Zajac DJ, et al. APOE genetics influence murine gut microbiome. Sci Rep. 2022;12(1):1906.

    Article  CAS  Google Scholar 

  48. Crumeyrolle-Arias M, et al. Inhibition of brain mitochondrial monoamine oxidases by the endogenous compound 5-hydroxyoxindole. Biochem Pharmacol. 2004;67(5):977–9.

    Article  CAS  Google Scholar 

  49. Behl T et al. Role of monoamine oxidase activity in Alzheimer’s disease: an insight into the therapeutic potential of inhibitors. Molecules, 2021; 26(12).

  50. Bu G. Apolipoprotein E and its receptors in Alzheimer’s disease: pathways, pathogenesis and therapy. Nat Rev Neurosci. 2009;10(5):333–44.

    Article  CAS  Google Scholar 

  51. Wong MWK, et al. APOE genotype differentially modulates plasma lipids in healthy older individuals, with relevance to brain health. J Alzheimers Dis. 2019;72(3):703–16.

    Article  CAS  Google Scholar 

  52. Miranda AM, et al. Effects of APOE4 allelic dosage on lipidomic signatures in the entorhinal cortex of aged mice. Transl Psychiatry. 2022;12(1):129.

    Article  CAS  Google Scholar 

  53. Gurinovich A et al. Effect of longevity genetic variants on the molecular aging rate. Geroscience, 2021.

Download references

Acknowledgements

BLSA was supported by the Intramural Research Program of the National Institute on Aging. We acknowledge Mr. Brendan A. Mitchell for statistical assistance.

Funding

NIA R01AG061844 (PS, TTP), NIA U19-AG023122 (to NR, TTP, PS); USDA 58–1950-4–003 (MSL), NHLBI T32-HL083825 (MMM). NIA R01AG057909 (NB, SM), NIA R01AG061155 (SM, NB). NIA U19AG063893 (GJP, TTP), NIA UH2AG064704 (TTP, PS, SM, NB). This project was supported in part by the USDA Agricultural Research Service Cooperative Agreement 58-8050-9-004. The content is the sole responsibility of the authors and does not necessarily represent the official views of the USDA.

The authors declare no competing interests. All study participants provided informed consent and the studies were approved by the Institutions’ IRB as described in the “Methods.”

Author information

Authors and Affiliations

  1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Boston, MA, 02111, USA

    Paola Sebastiani & Anastasia Gurinovich

  2. Department of Biostatistics, Boston University, Boston, MA, USA

    Zeyuan Song & Qingyan Xiang

  3. Institute for Systems Biology, Seattle, WA, USA

    Dylan Ellis, Cory C. Funk, Priyanka Baloni & Noa Rappaport

  4. Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, Baltimore, MD, USA

    Qu Tian & Luigi Ferrucci

  5. Department of Chemistry, Department of Medicine, Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, USA

    Michaela Schwaiger-Haber, Ethan Stancliffe & Gary J. Patti

  6. Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center On Aging at Tufts University, Boston, MA, USA

    Michael S. Lustgarten

  7. Department of Cell Biology at Harvard Medical School, Boston, MA, USA

    Cong-Hui Yao, Shakchhi Joshi & Marcia Haigis

  8. Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

    Megan M. Marron

  9. Bioinformatics Program, Boston University, Boston, MA, USA

    Mengze Li, Anastasia Leshchyk & Stefano Monti

  10. Department of Medicine, Boston University School of Medicine, Boston, MA, USA

    Stacy L. Andersen & Thomas T. Perls

  11. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MI, USA

    Mary F. Feitosa

  12. Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark

    Mette Soerensen

  13. West Coast Metabolomics Center, University of California, Davis, CA, USA

    Oliver Fiehn

  14. Nutrition and Genomics Team, Jean Mayer USDA Human Nutrition Research Center On Aging and Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MB, USA

    Jose M. Ordovas

  15. Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA

    Stefano Monti

  16. Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA

    Nir Barzilai & Sofiya Milman

  17. Biodemography of Aging Research Unit, Social Science Research, Duke University, Durham, NC, USA

    Svetlana Ukraintseva

Authors
  1. Paola Sebastiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeyuan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dylan Ellis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qu Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michaela Schwaiger-Haber
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ethan Stancliffe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael S. Lustgarten
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Cory C. Funk
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Priyanka Baloni
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Cong-Hui Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shakchhi Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Megan M. Marron
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Anastasia Gurinovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mengze Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Anastasia Leshchyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Qingyan Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Stacy L. Andersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Mary F. Feitosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Svetlana Ukraintseva
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Mette Soerensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Oliver Fiehn
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Jose M. Ordovas
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Marcia Haigis
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Stefano Monti
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Nir Barzilai
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Sofiya Milman
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Luigi Ferrucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Noa Rappaport
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Gary J. Patti
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Thomas T. Perls
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paola Sebastiani.

Additional information

Publisher's note

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

Supplementary Information

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sebastiani, P., Song, Z., Ellis, D. et al. A metabolomic signature of the APOE2 allele. GeroScience 45, 415–426 (2023). https://doi.org/10.1007/s11357-022-00646-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-022-00646-9

Keywords

Search

Navigation