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.

  • Article
  • Published:

Performance of plasma phosphorylated tau 181 and 217 in the community

Nature Medicine volume 28, pages 1398–1405 (2022)Cite this article

Subjects

An Author Correction to this article was published on 10 October 2022

This article has been updated

Abstract

Plasma phosphorylated tau 181 (P-tau181) and 217 (P-tau217) are indicators of both amyloid and tau pathology in clinical settings, but their performance in heterogeneous community-based populations is unclear. We examined P-tau181 and P-tau217 (n = 1,329, aged 30–98 years), in the population-based Mayo Clinic Study of Aging. Continuous, unadjusted plasma P-tau181 and P-tau217 predicted abnormal amyloid positron-emission tomography (PET) (area under the receiver operating characteristic curve (AUROC) = 0.81–0.86) and tau PET entorhinal cortex (AUROC > 0.80), but was less predictive of a tau PET temporal region of interest (AUROC < 0.70). Multiple comorbidities were associated with higher plasma P-tau181 and P-tau217 levels; the difference between participants with and without chronic kidney disease (CKD) was similar to the difference between participants with and without elevated brain amyloid. The exclusion of participants with CKD and other comorbidities affected the establishment of a normal reference range and cutpoints. Understanding the effect of comorbidities on P-tau181 and P-tau217 levels is important for their future interpretation in the context of clinical screening, diagnosis or prognosis at the population level.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Plasma P-tau181 and P-tau217 levels by age.
Fig. 2: Forest plots of associations between multiple factors and plasma P-tau181 and P-tau217 using linear regression among all participants (n = 1,329 unique biological participants).

Similar content being viewed by others

Data availability

Raw and analyzed de-identified data from the MCSA can be requested at https://ras-rdrs.mayo.edu/Request/IndexRequest. The request will be reviewed by the MCSA investigators and Mayo Clinic to verify whether the request is subject to any intellectual property or confidentiality obligations. A data sharing agreement must be obtained prior to release.

Code availability

No custom code or mathematical algorithm that was central to the conclusions was used in this study.

Change history

References

  1. Mielke, M. M. et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 14, 989–997 (2018).

    Article  Google Scholar 

  2. Janelidze, S. et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat. Med. 26, 379–386 (2020).

    Article  CAS  Google Scholar 

  3. Karikari, T. K. et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. 19, 422–433 (2020).

    Article  CAS  Google Scholar 

  4. Thijssen, E. H. et al. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat. Med. 26, 387–397 (2020).

    Article  CAS  Google Scholar 

  5. Palmqvist, S. et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. JAMA 324, 772–781 (2020).

    Article  CAS  Google Scholar 

  6. Mattsson-Carlgren, N. et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease. Brain 143, 3234–3241 (2020).

    Article  Google Scholar 

  7. Moscoso, A. et al. Time course of phosphorylated-tau181 in blood across the Alzheimer’s disease spectrum. Brain 144, 325–339 (2021).

    Article  Google Scholar 

  8. Barthélemy, N. R., Horie, K., Sato, C. & Bateman, R. J. Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer’s disease. J. Exp. Med. 217, e20200861 (2020).

    Article  Google Scholar 

  9. Benussi, A. et al. Diagnostic and prognostic value of serum NfL and p-Tau181 in frontotemporal lobar degeneration. J. Neurol. Neurosurg. Psychiatry 91, 960–967 (2020).

    Article  Google Scholar 

  10. Simrén, J. et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer’s disease. Alzheimers Dement. 17, 1145–1156 (2021).

    Article  Google Scholar 

  11. Mielke, M. M. et al. Comparison of plasma phosphorylated tau species with amyloid and tau positron emission tomography, neurodegeneration, vascular pathology, and cognitive outcomes. JAMA Neurol. 78, 1108–1117 (2021).

    Article  Google Scholar 

  12. O’Bryant, S. E. et al. Blood-based biomarkers in Alzheimer disease: current state of the science and a novel collaborative paradigm for advancing from discovery to clinic. Alzheimers Dement. 13, 45–58 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Mielke, M. M. Consideration of sex differences in the measurement and interpretation of Alzheimer disease-related biofluid-based biomarkers. J. Appl. Lab. Med. 5, 158–169 (2020).

    Article  Google Scholar 

  15. Syrjanen, J. A. et al. Associations of amyloid and neurodegeneration plasma biomarkers with comorbidities. Alzheimers Dement. https://doi.org/10.1002/alz.12466 (online ahead of print).

  16. Jack, C. R.Jr. et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimers Dement. 13, 205–216 (2017).

    Article  Google Scholar 

  17. Lowe, V. J. et al. Tau-positron emission tomography correlates with neuropathology findings. Alzheimers Dement. 16, 561–571 (2020).

    Article  Google Scholar 

  18. Ossenkoppele, R. et al. Tau PET correlates with different Alzheimer’s disease-related features compared to CSF and plasma p-tau biomarkers. EMBO Mol. Med. 13, e14398 (2021).

    Article  CAS  Google Scholar 

  19. Brickman, A. M. et al. Plasma p-tau181, p-tau217, and other blood-based Alzheimer’s disease biomarkers in a multi-ethnic, community study. Alzheimers Dement. 17, 1353–1364 (2021).

    Article  CAS  Google Scholar 

  20. Ozarda, Y. Reference intervals: current status, recent developments and future considerations. Biochem. Med. (Zagreb) 26, 5–16 (2016).

    Article  Google Scholar 

  21. Manrai, A. K., Patel, C. J. & Ioannidis, J. P. A. In the era of precision medicine and big data, who is normal? JAMA 319, 1981–1982 (2018).

    Article  Google Scholar 

  22. Yoshihara, T. et al. Cerebrospinal fluid protein concentration in healthy older Japanese volunteers. Int. J. Environ. Res. Public Health 18, 8683 (2021).

    Article  CAS  Google Scholar 

  23. Thijssen, E. H. et al. Plasma phosphorylated tau 217 and phosphorylated tau 181 as biomarkers in Alzheimer’s disease and frontotemporal lobar degeneration: a retrospective diagnostic performance study. Lancet Neurol. 20, 739–752 (2021).

    Article  CAS  Google Scholar 

  24. Roberts, R. O. et al. The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology 30, 58–69 (2008).

    Article  Google Scholar 

  25. St Sauver, J. L. et al. Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system. Int. J. Epidemiol. 41, 1614–1624 (2012).

    Article  Google Scholar 

  26. Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40, 373–383 (1987).

    Article  CAS  Google Scholar 

  27. Beck, A. T., Steer, R. A. & Brown, G. K. Manual for the Beck Depression Inventory-II (Psychological Corporation, 1996).

  28. Beck, A. T. & Steer, R. A. Beck Anxiety Inventory Manual (Psychological Corporation, 1993).

  29. Ivnik, R. J. et al. Mayo’s Older Americans Normative Studies: updated AVLT norms for ages 56 to 97. Clin. Neuropsychol. 6, 83–104 (1992).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  31. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) (American Psychiatric Association, 1994).

  32. Lowe, V. J. et al. Comparison of 18F-FDG and PiB PET in cognitive impairment. J. Nucl. Med. 50, 878–886 (2009).

    Article  Google Scholar 

  33. Price, J. C. et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J. Cereb. Blood Flow. Metab. 25, 1528–1547 (2005).

    Article  CAS  Google Scholar 

  34. McNamee, R. L. et al. Consideration of optimal time window for Pittsburgh compound B PET summed uptake measurements. J. Nucl. Med. 50, 348–355 (2009).

    Article  CAS  Google Scholar 

  35. Jack, C. R.Jr. et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain 131, 665–680 (2008).

    Article  Google Scholar 

  36. Therneau, T. M. A package for survival analysis in R. R package version 3.2-7, https://CRAN.R-project.org/package=survival (2020).

Download references

Acknowledgements

Funding for this study was provided by grants from the National Institutes of Health (NIH) (U01 AG006786 to M.M.M., C.R.J. and R.C.P.; R37 AG011378 to C.R.J.; R01 NS097495 to P.V.; P30 AG062677 to R.C.P.; RF1 AG069052 to M.M.M., J.G-R. and P.V.; and R01 AG041851 to C.R.J. and D.S.K.) and the GHR Foundation (to R.C.P.). This study was made possible using the resources of the REP, which is supported by the NIH National Institute on Aging under award no. R01 AG034676. Meso Scale Discovery P-tau181 and P-tau217 assays were performed at Eli Lilly and Company. The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author had full access to all data in the study, and all authors had final responsibility for the decision to submit for publication.

Author information

Authors and Affiliations

  1. Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

    Michelle M. Mielke & Ronald C. Petersen

  2. Department of Neurology, Mayo Clinic, Rochester, MN, USA

    Michelle M. Mielke, David S. Knopman, Jonathan Graff-Radford & Ronald C. Petersen

  3. Department of Epidemiology and Prevention, School of Medicine, Wake Forest University, Winston-Salem, NC, USA

    Michelle M. Mielke

  4. Stark Neurosciences Research Institute, School of Medicine, Indiana University, Indianapolis, IN, USA

    Jeffrey L. Dage

  5. Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

    Ryan D. Frank

  6. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

    Alicia Algeciras-Schimnich

  7. Department of Radiology, Mayo Clinic, Rochester, MN, USA

    Val J. Lowe, Prashanthi Vemuri & Clifford R. Jack Jr

  8. Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA

    Guojun Bu

Authors
  1. Michelle M. Mielke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeffrey L. Dage
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryan D. Frank
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alicia Algeciras-Schimnich
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David S. Knopman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Val J. Lowe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guojun Bu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Prashanthi Vemuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jonathan Graff-Radford
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Clifford R. Jack Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ronald C. Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.M.M. and J.L.D. designed and planned this study. M.M.M. and R.D.F. designed the specific analysis. M.M.M. drafted the manuscript. All authors contributed to interpretation of the study results and revised and approved the manuscript for intellectual content. M.M.M. is the guarantor and accepts full responsibility for the work and conduct of the study, had access to the data and controlled the decision to publish. The corresponding author (M.M.M.) attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Corresponding author

Correspondence to Michelle M. Mielke.

Ethics declarations

Competing interests

M.M.M. has served as a consultant for Biogen, Brain Protection Company and Labcorp. J.L.D. was previously an employee and is still a minor shareholder of Eli Lilly and Company. J.L.D. is an inventor on patents or patent applications of Eli Lilly and Company relating to the assays, methods, reagents and/or compositions of matter used in this work. J.L.D. has also served as a consultant for Karuna Therapeutics and received research support from ADx Neurosciences, Roche Diagnostics and Eli Lilly and Company. R.D.F. and A.A.-S. report no conflicts. D.S.K. serves on a data safety monitoring board for the Dominantly Inherited Alzheimer Network (DIAN) study. D.S.K. also serves on a data safety monitoring board for Biogen but receives no personal compensation. D.S.K. is an investigator in clinical trials sponsored by Biogen, Lilly Pharmaceuticals and the University of Southern California. D.S.K. serves as a consultant for Roche, Samus Therapeutics, Third Rock and Alzeca Biosciences but receives no personal compensation. V.J.L. received consulting fees from Bayer Schering Pharma, Piramal Life Sciences and Merck Research, and grants from GE Healthcare, Siemens Molecular Imaging, Avid Radiopharmaceuticals and the NIH. G.B. serves as a consultant for AbbVie, E-Scape and SciNeuro. P.V. received speaking fees from Miller Medical Communications. J.G.-R. receives NIH funding and serves as an assistant editor for Neurology. C.R.J. serves on an independent data monitoring board for Roche, has served as a speaker for Eisai and consulted for Biogen but receives no personal compensation from any commercial entity. C.R.J. receives research support from the NIH and the Alexander Family Alzheimer’s Disease Research Professorship of Mayo Clinic. R.C.P. received consulting fees from Hoffman-La Roche, Merck, Genentech, Biogen, GE Healthcare and Eisai.

Peer review

Peer review information

Nature Medicine thanks Julien Dumurgier, Suzanne Schindler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

Additional information

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

Extended data

Extended Data Fig. 1 Plasma P-tau181 and P-tau217 levels by clinical diagnosis and amyloid PET status.

Abnormal amyloid PET (A + ) was defined as standard uptake value ratio (SUVR) > 1.48 using PiB-PET. P-values are from two-sided Wilcoxon rank-sum tests without adjustment for multiple comparisons. Box plots display the median, 25th percentile, 75th percentile (middle, bottom, and top bars of the box), and the whiskers go out to 1.5 times the interquartile range (75th percentile – 25th percentile) from the 25th and 75th percentile. CU, cognitively unimpaired; MCI, mild cognitive impairment; DEM, dementia.

Extended Data Fig. 2 Plasma P-tau181 and P-tau217 levels by clinical diagnosis and amyloid and tau PET status.

Abnormal amyloid PET (A + ) was defined as standard uptake value ratio (SUVR) ≥ 1.48 using PiB-PET. Abnormal tau PET (T + ) meta region of interest (ROI) was defined as standard uptake value ratio (SUVR) ≥ 1.29 using AV1451, and included the amygdala, entorhinal cortex, fusiform, parahippocampal, and inferior temporal and middle temporal gyri. P-values are from two-sided Kruskal-Wallis tests. Box plots display the median, 25th percentile, 75th percentile (middle, bottom, and top bars of the box), and the whiskers go out to 1.5 times the interquartile range (75th percentile – 25th percentile) from the 25th and 75th percentile. CU, cognitively unimpaired; MCI, mild cognitive impairment.

Extended Data Fig. 3 Plasma P-tau181 and P-tau217 levels by presence of an APOE ɛ4 allele.

P-values are from two-sided Wilcoxon rank-sum tests without adjustment for multiple comparisons. Box plots display the median, 25th percentile, 75th percentile (middle, bottom, and top bars of the box), and the whiskers go out to 1.5 times the interquartile range (75th percentile – 25th percentile) from the 25th and 75th percentile. CU, cognitively unimpaired; MCI, mild cognitive impairment; DEM, dementia.

Extended Data Fig. 4 Plasma P-tau181 and P-tau217 levels by sex.

P-values are from two-sided Wilcoxon rank-sum tests without adjustment for multiple comparisons. Box plots display the median, 25th percentile, 75th percentile (middle, bottom, and top bars of the box), and the whiskers go out to 1.5 times the interquartile range (75th percentile – 25th percentile) from the 25th and 75th percentile. CU, cognitively unimpaired; MCI, mild cognitive impairment; DEM, dementia.

Extended Data Fig. 5 Forest plots of associations between multiple factors and plasma P-tau181 and P-tau217 levels using linear regression after excluding all participants with chronic kidney disease (n = 1231).

Black lines indicate univariable associations and red lines indicate associations after adjustment for age and sex. Means and 95% confidence intervals are provided. A + , Elevated amyloid PET; AFib, atrial fibrillation; BAI total, Beck Anxiety Inventory; BDI dep, Beck Depression Inventory; BMI, body mass index; Chemo, chemotherapy for those with a cancer diagnosis; DM, diabetes mellitus; HTN, hypertension; MI, myocardial infarction.

Supplementary information

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mielke, M.M., Dage, J.L., Frank, R.D. et al. Performance of plasma phosphorylated tau 181 and 217 in the community. Nat Med 28, 1398–1405 (2022). https://doi.org/10.1038/s41591-022-01822-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-022-01822-2

This article is cited by

Search

Advanced search

Quick links

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