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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
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    References listed on IDEAS

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    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.
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