Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Communications
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. nature communications
  3. articles
  4. article
Biomarker-integrated prognostic stagings for Alzheimer’s Disease
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 02 February 2026

Biomarker-integrated prognostic stagings for Alzheimer’s Disease

  • Daeun Shin1 na1,
  • Sungjoo Lee  ORCID: orcid.org/0000-0003-3564-42202 na1,
  • Jun Pyo Kim  ORCID: orcid.org/0000-0003-4376-31071,
  • Hyemin Jang3,
  • Jihwan Yun4,
  • Min Young Chun5,6,
  • Jehyun Ahn1,
  • Seongmi Kim1,
  • Kyoungmin Kim  ORCID: orcid.org/0009-0000-4161-35731,
  • Soyeon Yoon1,
  • Hee Jin Kim1,7,8,9,
  • Heekyoung Kang1,
  • Sohyun Yim  ORCID: orcid.org/0000-0002-9357-768X1,
  • Hee Kyung Park1,
  • Seonghyeon Kim1,
  • Duk L. Na  ORCID: orcid.org/0000-0002-0098-75921,
  • Henrik Zetterberg  ORCID: orcid.org/0000-0003-3930-435410,11,12,13,14,15,
  • Kaj Blennow  ORCID: orcid.org/0000-0002-1890-419310,11,16,17,18,
  • Fernando Gonzalez-Ortiz  ORCID: orcid.org/0000-0001-7897-945610,11,
  • Nicholas J. Ashton  ORCID: orcid.org/0000-0002-3579-880410,19,20,21,
  • Michael W. Weiner22,
  • Sang Won Seo  ORCID: orcid.org/0000-0002-8747-01221,7,8,9 na2 &
  • …
  • Kyunga Kim  ORCID: orcid.org/0000-0002-0865-22362,23,24 na2 

Nature Communications , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Alzheimer's disease
  • Prognostic markers

Abstract

Accurately predicting disease progression remains a major challenge in Alzheimer’s disease (AD). Here we show that a biomarker-integrated prognostic staging system can stratify progression risk across the disease course by jointly incorporating cognitive status, established risk factors, plasma biomarkers, and neuroimaging measures. In the K-ROAD cohort (N = 1,263), the dominant prognostic contributors varied by clinical context—GFAP in cognitively unimpaired individuals, hippocampal volume in mild cognitive impairment, and age in dementia—while plasma phosphorylated tau-217 provided consistent secondary prognostic information across stages. Outcome-specific staging captured clinically meaningful gradients of progression risk and informed construction of a unified six-stage framework (Stage 0–IVB) with distinct inflection points of accelerated decline. External validation in the ADNI cohort (N = 290) demonstrated consistent patterns of worsening prognosis, particularly in early and intermediate stages. This system provides a clinically interpretable approach to risk stratification and may serve as an exploratory framework for biomarker-integrated prognostic stratification in AD.

Data availability

The data supporting the findings of this study include plasma biomarker data, brain MRI, and PET imaging data from the K-ROAD cohort, as well as imaging and clinical data from the ADNI cohort. These datasets contain sensitive human participant information, which carry a potential risk of participant re-identification. Due to ethical restrictions imposed by the institutional review boards and the informed consent provided by study participants, the K-ROAD data cannot be made publicly available without restriction. De-identified K-ROAD data are available from the corresponding authors upon reasonable request and subject to approval by the relevant institutional review boards and data use agreements. Requests for data access will be reviewed and responded to within 2–4 weeks of receipt. Inquiries regarding K-ROAD data access should be directed to the corresponding authors (S.W. Seo; sangwonseo@empas.com). ADNI data are publicly available through the ADNI data sharing platform (https://adni.loni.usc.edu) upon registration and approval, in accordance with ADNI data use policies. Source data underlying the figures and tables presented in this study are provided with this paper. Source data are provided with this paper.

References

  1. Riedel, B. C., Thompson, P. M. & Brinton, R. D. Age, APOE and sex: triad of risk of Alzheimer’s disease. J. Steroid Biochem. Mol. Biol. 160, 134–147 (2016).

    Google Scholar 

  2. Belloy, M. E. et al. APOE genotype and Alzheimer disease risk across age, sex, and population ancestry. JAMA Neurol. 80, 1284–1294 (2023).

    Google Scholar 

  3. Schneider, L. S., Kennedy, R. E., Wang, G. & Cutter, G. R. Differences in Alzheimer disease clinical trial outcomes based on age of the participants. Neurology 84, 1121–1127 (2015).

    Google Scholar 

  4. Fortea, J. et al. APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease. Nat. Med. 30, 1284–1291 (2024).

    Google Scholar 

  5. Petersen, R. C. Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256, 183–194 (2004).

    Google Scholar 

  6. Wilson, R. S. et al. The natural history of cognitive decline in Alzheimer’s disease. Psychol. Aging 27, 1008–1017 (2012).

    Google Scholar 

  7. Kim, Y. J. et al. Classification and prediction of cognitive trajectories of cognitively unimpaired individuals. Front. Aging Neurosci. 15, 1122927 (2023).

    Google Scholar 

  8. Kim, Y. J. et al. Data-driven prognostic features of cognitive trajectories in patients with amnestic mild cognitive impairments. Alzheimers Res. Ther. 11, 10 (2019).

    Google Scholar 

  9. Lee, J. S. et al. Prediction models of cognitive trajectories in patients with nonamnestic mild cognitive impairment. Sci. Rep. 8, 10468 (2018).

    Google Scholar 

  10. Wang, X., Ye, T., Zhou, W. & Zhang, J. Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach. Alzheimers Res. Ther. 15, 57 (2023).

    Google Scholar 

  11. Wang, X., Ye, T., Jiang, D., Zhou, W. & Zhang, J. Characterizing the clinical heterogeneity of early symptomatic Alzheimer’s disease: a data-driven machine learning approach. Front Aging Neurosci. 16, 1410544 (2024).

    Google Scholar 

  12. Kim, S. J. et al. Development of prediction models for distinguishable cognitive trajectories in patients with amyloid positive mild cognitive impairment. Neurobiol. Aging 114, 84–93 (2022).

    Google Scholar 

  13. DeTure, M. A. & Dickson, D. W. The neuropathological diagnosis of Alzheimer’s disease. Mol. Neurodegener. 14, 32 (2019).

    Google Scholar 

  14. Jack, C. R. Jr. et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimers Dement. 20, 5143–5169 (2024).

    Google Scholar 

  15. Teunissen, C. E. et al. Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation. Lancet Neurol. 21, 66–77 (2022).

    Google Scholar 

  16. Park, S. A., Jang, Y. J., Kim, M. K., Lee, S. M. & Moon, S. Y. Promising blood biomarkers for clinical use in Alzheimer’s disease: a focused update. J. Clin. Neurol. 18, 401–409 (2022).

    Google Scholar 

  17. Hansson, O., Blennow, K., Zetterberg, H. & Dage, J. Blood biomarkers for Alzheimer’s disease in clinical practice and trials. Nat. Aging 3, 506–519 (2023).

    Google Scholar 

  18. Jang, H. et al. Differential roles of Alzheimer’s disease plasma biomarkers in stepwise biomarker-guided diagnostics. Alzheimers Dement. 21, e14526 (2025).

    Google Scholar 

  19. Cullen, N. C. et al. Plasma biomarkers of Alzheimer’s disease improve prediction of cognitive decline in cognitively unimpaired elderly populations. Nat. Commun. 12, 3555 (2021).

    Google Scholar 

  20. Cullen, N. C. et al. Individualized prognosis of cognitive decline and dementia in mild cognitive impairment based on plasma biomarker combinations. Nat. Aging 1, 114–123 (2021).

    Google Scholar 

  21. Palmqvist, S. et al. Blood biomarkers to detect Alzheimer disease in primary care and secondary care. Jama 332, 1245–1257 (2024).

    Google Scholar 

  22. Schindler, S. E. et al. Head-to-head comparison of leading blood tests for Alzheimer’s disease pathology. Alzheimers Dement. 20, 8074–8096 (2024).

    Google Scholar 

  23. Rafii, M. S. et al. The AHEAD 3-45 Study: design of a prevention trial for Alzheimer’s disease. Alzheimers Dement. 19, 1227–1233 (2023).

    Google Scholar 

  24. Jack, C. R. Jr. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).

    Google Scholar 

  25. Aisen, P. S. et al. On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimers Res. Ther. 9, 60 (2017).

    Google Scholar 

  26. Gill, T. M. The central role of prognosis in clinical decision making. JAMA 307, 199–200 (2012).

    Google Scholar 

  27. Pereira, J. B. et al. Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer’s disease. Brain 144, 3505–3516 (2021).

    Google Scholar 

  28. Bellaver, B. et al. Astrocyte reactivity influences amyloid-β effects on tau pathology in preclinical Alzheimer’s disease. Nat. Med. 29, 1775–1781 (2023).

    Google Scholar 

  29. Jack, C. R. Jr. et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52, 1397–1403 (1999).

    Google Scholar 

  30. Apostolova, L. G. et al. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch. Neurol. 63, 693–699 (2006).

    Google Scholar 

  31. Ossenkoppele, R. et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA 313, 1939–1949 (2015).

    Google Scholar 

  32. Baek, M. S. et al. Longitudinal changes in A/T/N imaging biomarkers in early-onset and late-onset Alzheimer’s disease. Alzheimer’s. Dement. 16, e046718 (2020).

    Google Scholar 

  33. Seo, S. W. et al. Effects of demographic factors on cortical thickness in Alzheimer’s disease. Neurobiol. Aging 32, 200–209 (2011).

    Google Scholar 

  34. Cho, H. et al. Amyloid and tau-PET in early-onset AD: baseline data from the longitudinal early-onset Alzheimer’s Disease Study (LEADS). Alzheimers Dement. 19, S98–s114 (2023).

    Google Scholar 

  35. Sirkis, D. W., Bonham, L. W., Johnson, T. P., La Joie, R. & Yokoyama, J. S. Dissecting the clinical heterogeneity of early-onset Alzheimer’s disease. Mol. Psychiatry 27, 2674–2688 (2022).

    Google Scholar 

  36. Sperling, R. A. et al. Amyloid and tau prediction of cognitive and functional decline in unimpaired older individuals: longitudinal data from the A4 and LEARN Studies. J. Prev. Alzheimers Dis. 11, 802–813 (2024).

    Google Scholar 

  37. Ossenkoppele, R. et al. Plasma p-tau217 and tau-PET predict future cognitive decline among cognitively unimpaired individuals: implications for clinical trials. Nat. Aging 5, 883–896 (2025).

    Google Scholar 

  38. Salvadó, G. et al. Specific associations between plasma biomarkers and postmortem amyloid plaque and tau tangle loads. EMBO Mol. Med. 15, e17123 (2023).

    Google Scholar 

  39. Shin, D. et al. Potential utility of plasma pTau217 for assessing amyloid and tau biomarker profiles. Eur. J. Nucl. Med. Mol. Imaging https://doi.org/10.1007/s00259-025-07457-y (2025).

  40. Ahn, J. et al. Tailoring thresholds for interpreting plasma p-tau217 levels. J. Neurol. Neurosurg. Psychiatry 96, 722–727 (2025).

    Google Scholar 

  41. Kang, S. H. et al. Cerebral amyloid angiopathy and downstream Alzheimer disease plasma biomarkers. JAMA Netw. Open 8, e258842 (2025).

    Google Scholar 

  42. Kim, Y. J. et al. Difference in trajectories according to early amyloid accumulation in cognitively unimpaired elderly. Eur. J. Neurol. 31, e16482 (2024).

    Google Scholar 

  43. van Dyck, C. H. et al. Lecanemab in early Alzheimer’s disease. N. Engl. J. Med. 388, 9–21 (2023).

    Google Scholar 

  44. Livingston, G. et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 404, 572–628 (2024).

    Google Scholar 

  45. Bader, I., Groot, C., Van Der Flier, W. M., Pijnenburg, Y. A. L. & Ossenkoppele, R. Survival differences between individuals with typical and atypical phenotypes of Alzheimer disease. Neurology 104, e213603 (2025).

    Google Scholar 

  46. Jang, H. et al. Korea-registries to overcome dementia and accelerate dementia research (K-ROAD): a cohort for dementia research and ethnic-specific insights. Dement. Neurocogn. Disord. 23, 212–223 (2024).

    Google Scholar 

  47. Ahn, H. J. et al. Seoul neuropsychological screening battery-dementia version (SNSB-D): a useful tool for assessing and monitoring cognitive impairments in dementia patients. J. Korean Med. Sci. 25, 1071–1076 (2010).

    Google Scholar 

  48. Kim, J., Jahng, S., Kim, S. & Kang, Y. A comparison of item characteristics and test information between the K-MMSE~2:SV and K-MMSE. Dement. Neurocogn. Disord. 23, 117–126 (2024).

    Google Scholar 

  49. Albert, M. S. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 270–279 (2011).

    Google Scholar 

  50. McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–269 (2011).

    Google Scholar 

  51. Kwak, K. et al. Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening. Magn. Reson. Imaging 31, 1190–1196 (2013).

    Google Scholar 

  52. Cho, S. H. et al. A new Centiloid method for (18)F-florbetaben and (18)F-flutemetamol PET without conversion to PiB. Eur. J. Nucl. Med Mol. Imaging 47, 1938–1948 (2020).

    Google Scholar 

  53. Morris, J. C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412–2414 (1993).

    Google Scholar 

  54. Galasko, D. et al. ADCS Prevention Instrument Project: assessment of instrumental activities of daily living for community-dwelling elderly individuals in dementia prevention clinical trials. Alzheimer Dis. Assoc. Disord. 20, S152–S169 (2006).

    Google Scholar 

  55. O’Bryant, S. E. et al. Staging dementia using Clinical Dementia Rating Scale Sum of Boxes scores: a Texas Alzheimer’s research consortium study. Arch. Neurol. 65, 1091–1095 (2008).

    Google Scholar 

  56. Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841–860 (2008).

    Google Scholar 

  57. Segal, M. R. Regression trees for censored data. Biometrics 44, 35–47 (1988).

    Google Scholar 

  58. LeBlanc, M. & Crowley, J. Survival trees by goodness of split. J. Am. Stat. Assoc. 88, 457–467 (1993).

    Google Scholar 

  59. Brier, G. W. Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78, 1–3 (1950).

    Google Scholar 

  60. Schemper, M. & Smith, T. L. A note on quantifying follow-up in studies of failure time. Control Clin. Trials 17, 343–346 (1996).

    Google Scholar 

  61. Agresti, A. Categorical Data Analysis Vol. 3rd (John Wiley & Sons, 2013).

  62. Ettore Marubini, M. G. V. Analysing Survival Data from Clinical Trials and Observational Studies (John Wiley & Sons, 2004).

  63. Nieto, F. J. & Coresh, J. Adjusting survival curves for confounders: a review and a new method. Am. J. Epidemiol. 143, 1059–1068 (1996).

    Google Scholar 

  64. Therneau, T. M., Crowson, C. S. & Atkinson, E. J. Adjusted Survival Curves https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.695.4138 (2015).

  65. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

    Google Scholar 

  66. Firth, D. Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38 (1993).

    Google Scholar 

  67. Goodnight, J. H. & Harvey, W. R. Least Squares Means in the Fixed Effects General Model (1997).

  68. Inc, S. I. in SAS/STAT 9.3 User’s Guide (SAS Institute Inc., Cary, NC, 2012).

Download references

Acknowledgements

This study utilized BeuBrain Amylo’s image processing technology to quantify amyloid uptakes using PET-CT. This research was supported by a grant of the Korea Dementia Research Project through the Korea Dementia Research Center (KDRC), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (grant number: RS-2020-KH106434); the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2025-02223212); the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2019-NR040057); Future Medicine 20×30 Project of the Samsung Medical Center (#SMX1250081); the “Korea National Institute of Health” research project(2024-ER1003-02); and the Technology development Program (RS-2025-25466827) funded by the Ministry of SMEs and Startups (MSS, Korea). H.Z. is a Wallenberg scholar and a distinguished professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018, and #2019-02397), European Union’s Horizon Europe research and innovation program under grant agreement No 101053962, Swedish State Support for Clinical Research (#ALFGBG-71320), Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), AD Strategic Fund and the Alzheimer’s Association (#ADSF-21-831376-C, #ADSF-21-831381-C, #ADSF-21-831377-C, and #ADSF-24-1284328-C), Bluefield Project, Cure Alzheimer’s Fund, Olav Thon Foundation, Erling-Persson Family Foundation, Familjen Rönströms Stiftelse, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2022-0270), European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), European Union Joint Programme – Neurodegenerative Disease Research (JPND2021-00694), National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI-1003). K.B. is supported by the Swedish Research Council (#2017-00915 and #2022-00732), Swedish Alzheimer Foundation (#AF-930351, #AF-939721, #AF-968270, and #AF-994551), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), Swedish state under the agreement between the Swedish government and the County Councils, ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495), Alzheimer’s Association 2022–2025 Grant (SG-23-1038904 QC), La Fondation Recherche Alzheimer (FRA), Paris, France, Kirsten and Freddy Johansen Foundation, Copenhagen, Denmark, and Familjen Rönströms Stiftelse, Stockholm, Sweden.

Author information

Author notes

  1. These authors contributed equally: Daeun Shin, Sungjoo Lee.

  2. These authors jointly supervised this work: Sang Won Seo, Kyunga Kim.

Authors and Affiliations

  1. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Daeun Shin, Jun Pyo Kim, Jehyun Ahn, Seongmi Kim, Kyoungmin Kim, Soyeon Yoon, Hee Jin Kim, Heekyoung Kang, Sohyun Yim, Hee Kyung Park, Seonghyeon Kim, Duk L. Na & Sang Won Seo

  2. Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea

    Sungjoo Lee & Kyunga Kim

  3. Department of Neurology, Asan Medical Center, Ulsan University School of Medicine, Seoul, Republic of Korea

    Hyemin Jang

  4. Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea

    Jihwan Yun

  5. Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Min Young Chun

  6. Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin-si, Gyeonggi-do, Republic of Korea

    Min Young Chun

  7. Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea

    Hee Jin Kim & Sang Won Seo

  8. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

    Hee Jin Kim & Sang Won Seo

  9. Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea

    Hee Jin Kim & Sang Won Seo

  10. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

    Henrik Zetterberg, Kaj Blennow, Fernando Gonzalez-Ortiz & Nicholas J. Ashton

  11. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Göteborg, Sweden

    Henrik Zetterberg, Kaj Blennow & Fernando Gonzalez-Ortiz

  12. Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK

    Henrik Zetterberg

  13. UK Dementia Research Institute at UCL, London, UK

    Henrik Zetterberg

  14. Hong Kong Center for Neurodegenerative Diseases, Hong Kong, PR China

    Henrik Zetterberg

  15. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

    Henrik Zetterberg

  16. Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France

    Kaj Blennow

  17. Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, PR China

    Kaj Blennow

  18. Department of Neurology, Institute on Aging and Brain Disorders, First Affiliated Hospital of USTC, Hefei, PR China

    Kaj Blennow

  19. Banner Alzheimer’s Institute and University of Arizona, Phoenix, AZ, USA

    Nicholas J. Ashton

  20. Banner Sun Health Research Institute, Sun City, AZ, USA

    Nicholas J. Ashton

  21. Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway

    Nicholas J. Ashton

  22. Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

    Michael W. Weiner

  23. Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Kyunga Kim

  24. Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

    Kyunga Kim

Authors
  1. Daeun Shin
    View author publications

    Search author on:PubMed Google Scholar

  2. Sungjoo Lee
    View author publications

    Search author on:PubMed Google Scholar

  3. Jun Pyo Kim
    View author publications

    Search author on:PubMed Google Scholar

  4. Hyemin Jang
    View author publications

    Search author on:PubMed Google Scholar

  5. Jihwan Yun
    View author publications

    Search author on:PubMed Google Scholar

  6. Min Young Chun
    View author publications

    Search author on:PubMed Google Scholar

  7. Jehyun Ahn
    View author publications

    Search author on:PubMed Google Scholar

  8. Seongmi Kim
    View author publications

    Search author on:PubMed Google Scholar

  9. Kyoungmin Kim
    View author publications

    Search author on:PubMed Google Scholar

  10. Soyeon Yoon
    View author publications

    Search author on:PubMed Google Scholar

  11. Hee Jin Kim
    View author publications

    Search author on:PubMed Google Scholar

  12. Heekyoung Kang
    View author publications

    Search author on:PubMed Google Scholar

  13. Sohyun Yim
    View author publications

    Search author on:PubMed Google Scholar

  14. Hee Kyung Park
    View author publications

    Search author on:PubMed Google Scholar

  15. Seonghyeon Kim
    View author publications

    Search author on:PubMed Google Scholar

  16. Duk L. Na
    View author publications

    Search author on:PubMed Google Scholar

  17. Henrik Zetterberg
    View author publications

    Search author on:PubMed Google Scholar

  18. Kaj Blennow
    View author publications

    Search author on:PubMed Google Scholar

  19. Fernando Gonzalez-Ortiz
    View author publications

    Search author on:PubMed Google Scholar

  20. Nicholas J. Ashton
    View author publications

    Search author on:PubMed Google Scholar

  21. Michael W. Weiner
    View author publications

    Search author on:PubMed Google Scholar

  22. Sang Won Seo
    View author publications

    Search author on:PubMed Google Scholar

  23. Kyunga Kim
    View author publications

    Search author on:PubMed Google Scholar

Contributions

D.E. Shin, S.J. Lee, S.W. Seo, and K.A. Kim conceived and designed the study. S.J. Lee and K.A. Kim performed the statistical analyses. D.E. Shin, S.J. Lee, S.W. Seo, and K.A. Kim wrote the manuscript. D.E. Shin, S.J. Lee, J.P. Kim, H.M. Jang, J.H. Yun, M.Y. Chun, J.H. Ahn, S.M. Kim, K.M. Kim, S.Y. Yoon, H.J. Kim, H.K. Kang, S.H. Yim, H.K. Park, S.H. Kim, D.L. Na, H. Zetterberg, K. Blennow, F. Gonzalez-Ortiz, and N.J. Ashton, M.W. Weiner, S.W. Seo, and K.A. Kim conducted data preprocessing, interpretation and critically revised the manuscript for intellectual content. S.W. Seo and K.A. Kim supervised the study. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Sang Won Seo or Kyunga Kim.

Ethics declarations

Competing interests

H.Z. has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has delivered lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Kaj Blennow has served as a consultant and on advisory boards for Abbvie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served on data monitoring committees for Julius Clinical and Novartis; has delivered lectures, produced educational materials, and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

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

Supplementary information

Supplementary Information

Reporting Summary

Transparent Peer Review file

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shin, D., Lee, S., Kim, J.P. et al. Biomarker-integrated prognostic stagings for Alzheimer’s Disease. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68732-6

Download citation

  • Received: 06 September 2025

  • Accepted: 14 January 2026

  • Published: 02 February 2026

  • DOI: https://doi.org/10.1038/s41467-026-68732-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Videos
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Editors
  • Journal Information
  • Open Access Fees and Funding
  • Calls for Papers
  • Editorial Values Statement
  • Journal Metrics
  • Editors' Highlights
  • Contact
  • Editorial policies
  • Top Articles

Publish with us

  • For authors
  • For Reviewers
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Nature Communications (Nat Commun)

ISSN 2041-1723 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing