IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-58819-x.html
   My bibliography  Save this article

Health octo tool matches personalized health with rate of aging

Author

Listed:
  • Sh Salimi

    (University of Washington)

  • A. Vehtari

    (Aalto University)

  • M. Salive

    (National Institute on Aging)

  • M. Kaeberlein

    (Optispan Inc)

  • D. Raftery

    (Northwest Metabolomics Research Center)

  • L. Ferrucci

    (National Institute on Aging)

Abstract

Medical practice mainly addresses single diseases, neglecting multimorbidity as a heterogeneous health decline across organ systems. Aging is a multidimensional process and cannot be captured by a single metric. Therefore, we assessed global health in longitudinal studies, BLSA (n = 907), InCHIANTI (n = 986), and NHANES (n = 40,790), by examining disease severities in 13 bodily systems, generating the Body Organ Disease Number (BODN), reflecting progressive system morbidities. We used Bayesian ordinal models, regressing BODN over organ specific and all organs disease severities to obtain Body System-Specific Clocks and the Body Clock, respectively. The Body Clock is BODN weighted by the posterior coefficient of diseases for each individual. It supersedes the frailty index, predicting disability, geriatric syndrome, SPPB, and mortality with ≥90% accuracy. The Health Octo Tool, derived from Bodily System-Specific Clocks, the Body Clock and Clocks that incorporate walking speed and disability and their aging rates, captures multidimensional aging heterogeneity across organs and individuals.

Suggested Citation

  • Sh Salimi & A. Vehtari & M. Salive & M. Kaeberlein & D. Raftery & L. Ferrucci, 2025. "Health octo tool matches personalized health with rate of aging," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58819-x
    DOI: 10.1038/s41467-025-58819-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-58819-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-58819-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. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Christopher J. M. Whitty & Fiona M. Watt, 2020. "Map clusters of diseases to tackle multimorbidity," Nature, Nature, vol. 579(7800), pages 494-496, March.
    3. Michael B. Schultz & Alice E. Kane & Sarah J. Mitchell & Michael R. MacArthur & Elisa Warner & David S. Vogel & James R. Mitchell & Susan E. Howlett & Michael S. Bonkowski & David A. Sinclair, 2020. "Publisher Correction: Age and life expectancy clocks based on machine learning analysis of mouse frailty," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    4. Michael B. Schultz & Alice E. Kane & Sarah J. Mitchell & Michael R. MacArthur & Elisa Warner & David S. Vogel & James R. Mitchell & Susan E. Howlett & Michael S. Bonkowski & David A. Sinclair, 2020. "Age and life expectancy clocks based on machine learning analysis of mouse frailty," Nature Communications, Nature, vol. 11(1), pages 1-12, 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. Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Cristal M. Hill & Diana C. Albarado & Lucia G. Coco & Redin A. Spann & Md Shahjalal Khan & Emily Qualls-Creekmore & David H. Burk & Susan J. Burke & J. Jason Collier & Sangho Yu & David H. McDougal & , 2022. "FGF21 is required for protein restriction to extend lifespan and improve metabolic health in male mice," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Carolin Thomas & Reto Erni & Jia Yee Wu & Fabian Fischer & Greta Lamers & Giovanna Grigolon & Sarah J. Mitchell & Kim Zarse & Erick M. Carreira & Michael Ristow, 2023. "A naturally occurring polyacetylene isolated from carrots promotes health and delays signatures of aging," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    4. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    5. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    6. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," Papers 2012.14503, arXiv.org.
    7. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    8. Spilker Finn & Ötting Marius, 2024. "No cheering in the background? Individual performance in professional darts during COVID-19," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 20(3), pages 219-234.
    9. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    10. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    11. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    12. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    13. Alina Ferecatu & Arnaud Bruyn & Prithwiraj Mukherjee, 2024. "Silently killing your panelists one email at a time: The true cost of email solicitations," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 1216-1239, July.
    14. Burbano, Vanessa & Padilla, Nicolas & Meier, Stephan, 2020. "Gender Differences in Preferences for Meaning at Work," IZA Discussion Papers 13053, Institute of Labor Economics (IZA).
    15. Robert Kubinec & Haillie Na‐Kyung Lee & Andrey Tomashevskiy, 2021. "Politically connected companies are less likely to shutdown due to COVID‐19 restrictions," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2155-2169, September.
    16. Barrington-Leigh, C.P., 2024. "The econometrics of happiness: Are we underestimating the returns to education and income?," Journal of Public Economics, Elsevier, vol. 230(C).
    17. Salvatore Nunnari & Massimiliano Pozzi, 2022. "Meta-Analysis of Inequality Aversion Estimates," CESifo Working Paper Series 9851, CESifo.
    18. Andreas Kryger Jensen & Claus Thorn Ekstrøm, 2021. "Quantifying the trendiness of trends," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 98-121, January.
    19. Lauderdale, Benjamin E. & Bailey, Delia & Blumenau, Jack & Rivers, Douglas, 2020. "Model-based pre-election polling for national and sub-national outcomes in the US and UK," International Journal of Forecasting, Elsevier, vol. 36(2), pages 399-413.
    20. Tamara Broderick & Ryan Giordano & Rachael Meager, 2020. "An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?," Papers 2011.14999, arXiv.org, revised Jul 2023.

    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:16:y:2025:i:1:d:10.1038_s41467-025-58819-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.