IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-32893-x.html
   My bibliography  Save this article

Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults

Author

Listed:
  • Xiangwei Li

    (German Cancer Research Center (DKFZ)
    University of Heidelberg)

  • Thomas Delerue

    (German Research Center for Environmental Health)

  • Ben Schöttker

    (German Cancer Research Center (DKFZ)
    University of Heidelberg)

  • Bernd Holleczek

    (Krebsregister Saarland)

  • Eva Grill

    (Ludwig-Maximilians-Universität München
    Klinikum der Universität München)

  • Annette Peters

    (German Research Center for Environmental Health
    Ludwig-Maximilians-Universität München
    Partner Site Munich Heart Alliance)

  • Melanie Waldenberger

    (German Research Center for Environmental Health
    German Research Center for Environmental Health)

  • Barbara Thorand

    (Ludwig-Maximilians-Universität München)

  • Hermann Brenner

    (German Cancer Research Center (DKFZ)
    German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT)
    German Cancer Research Center (DKFZ))

Abstract

DNA methylation (DNAm) patterns in peripheral blood have been shown to be associated with aging related health outcomes. We perform an epigenome-wide screening to identify CpGs related to frailty, defined by a frailty index (FI), in a large population-based cohort of older adults from Germany, the ESTHER study. Sixty-five CpGs are identified as frailty related methylation loci. Using LASSO regression, 20 CpGs are selected to derive a DNAm based algorithm for predicting frailty, the epigenetic frailty risk score (eFRS). The eFRS exhibits strong associations with frailty at baseline and after up to five-years of follow-up independently of established frailty risk factors. These associations are confirmed in another independent population-based cohort study, the KORA-Age study, conducted in older adults. In conclusion, we identify 65 CpGs as frailty-related loci, of which 20 CpGs are used to calculate the eFRS with predictive performance for frailty over long-term follow-up.

Suggested Citation

  • Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32893-x
    DOI: 10.1038/s41467-022-32893-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-32893-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-32893-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yan Zhang & Rory Wilson & Jonathan Heiss & Lutz P. Breitling & Kai-Uwe Saum & Ben Schöttker & Bernd Holleczek & Melanie Waldenberger & Annette Peters & Hermann Brenner, 2017. "DNA methylation signatures in peripheral blood strongly predict all-cause mortality," Nature Communications, Nature, vol. 8(1), pages 1-11, April.
    2. Ferreira José A., 2007. "The Benjamini-Hochberg Method in the Case of Discrete Test Statistics," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-18, July.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Yingyao Zhou & Bin Zhou & Lars Pache & Max Chang & Alireza Hadj Khodabakhshi & Olga Tanaseichuk & Christopher Benner & Sumit K. Chanda, 2019. "Metascape provides a biologist-oriented resource for the analysis of systems-level datasets," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marta Campo & Lisa Vermunt & Carel F. W. Peeters & Anne Sieben & Yanaika S. Hok-A-Hin & Alberto Lleó & Daniel Alcolea & Mirrelijn Nee & Sebastiaan Engelborghs & Juliette L. Alphen & Sanaz Arezoumandan, 2023. "CSF proteome profiling reveals biomarkers to discriminate dementia with Lewy bodies from Alzheimer´s disease," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    3. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    4. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    5. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    6. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    7. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    8. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    9. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
    10. Zhu Wang, 2022. "MM for penalized estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 54-75, March.
    11. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    12. Gustavo A. Alonso-Silverio & Víctor Francisco-García & Iris P. Guzmán-Guzmán & Elías Ventura-Molina & Antonio Alarcón-Paredes, 2021. "Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    13. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    14. Naimoli, Antonio, 2022. "Modelling the persistence of Covid-19 positivity rate in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    15. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 91-114, March.
    16. Ahmed Ismaïl & Hartikainen Anna-Liisa & Järvelin Marjo-Riitta & Richardson Sylvia, 2011. "False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-20, November.
    17. Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2022. "One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas," Working Papers 22-39, Federal Reserve Bank of Philadelphia.
    18. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    19. Wang, Wenjia & Zhou, Yi-Hui, 2021. "Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    20. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32893-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.