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Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease

Nature Medicine volume 25, pages 277–283 (2019)Cite this article

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Abstract

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer’s disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini–Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer’s disease, which supports its potential utility as a clinically useful biomarker.

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Fig. 1: CSF and serum NfL levels are highly correlated and divert between mutation carriers and non-carriers already in the presymptomatic phase.
Fig. 2: Longitudinal serum NfL distinguishes mutation carriers from non-carriers very early in the presymptomatic disease process, with the NfL rate of change peaking in individuals converting from the presymptomatic to the symptomatic phase.
Fig. 3: Rate of change per year in serum NfL in mutation carriers mirrors rate of change in cortical thinning.
Fig. 4: Prediction of changes in cortical thinning and cognition by baseline serum NfL (retrospective prediction) and serum NfL rate of change (prospective prediction).

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Data availability

Data that support the findings of this study are available from DIAN at https://dian.wustl.edu/our-research/observational-study/dian-observational-study-investigator-resources/.

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Acknowledgements

We would like to thank M. Staufenbiel and M. Eichner for support and helpful comments and C. Haass and M. Suarez (Munich) for experimental and logistic support. Data collection and sharing for this project was supported by DIAN (grant no. UF1AG032438) funded by the National Institute on Aging and the German Center for Neurodegenerative Diseases (DZNE). Additional support came from the National Institutes of Health-funded NINDS Center Core for Brain Imaging (grant no. P30NS098577), the National Science Foundation (grant no. DGE-1745038), National Institutes of Health (grant no. UL1TR001873 to J.M.N.), the Swiss National Science Foundation (grant no. 320030-160221 to J.K.), the National Institute for Health Research University College London Hospitals Biomedical Research Centre, and the MRC Dementias Platform UK (grant nos. MR/L023784/1 and MR/009076/1). We acknowledge the altruism of the participants and their families and input of the DIAN research and support staff at each of the participating sites for their contributions to this study.

Author information

Author notes

  1. These authors contributed equally: Oliver Preische, Stephanie A. Schultz, Anja Apel.

Authors and Affiliations

  1. German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany

    Oliver Preische, Anja Apel, Stephan A. Kaeser, Susanne Gräber, Elke Kuder-Buletta, Christian LaFougere, Christoph Laske & Mathias Jucker

  2. Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany

    Oliver Preische, Anja Apel, Stephan A. Kaeser, Christoph Laske & Mathias Jucker

  3. Department of Neurology, Department of Radiology, and Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA

    Stephanie A. Schultz, Tammie L. S. Benzinger, John C. Morris, Randall J. Bateman, Guoqiao Wang, Anne M. Fagan, Eric M. McDade & Brian A. Gordon

  4. Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland

    Jens Kuhle & Christian Barro

  5. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

    Jonathan Vöglein & Johannes Levin

  6. Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany

    Jonathan Vöglein & Johannes Levin

  7. Neurodegeneration Division, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia

    Colin L. Masters

  8. School of Medical Health and Sciences, Edith Cowan University, Joondalup, Western Australia, Australia

    Ralph Martins

  9. Department of Biomedical Sciences, Macquarie University, Sydney, New South Wales, Australia

    Ralph Martins

  10. Neuroscience Research Australia, Randwick, New South Wales, Australia

    Peter R. Schofield

  11. School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia

    Peter R. Schofield

  12. Dementia Research Centre, Department of Neurodegeneration, Queen Square Institute of Neurology, University College London, London, UK

    Martin N. Rossor

  13. Department of Neurology, Mayo Clinic Jacksonville, Jacksonville, FL, USA

    Neill R. Graff-Radford

  14. Warren Alpert Medical School of Brown University, Providence, RI, USA

    Stephen Salloway

  15. Indiana Alzheimer Disease Center and Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

    Bernardino Ghetti

  16. Department of Neurology, Keck School of Medicine at USC, Los Angeles, CA, USA

    John M. Ringman

  17. Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University Medical Center, New York, NY, USA

    James M. Noble

  18. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Jasmeer Chhatwal

  19. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alison M. Goate

  20. FLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia), Buenos Aries, Argentina

    Ricardo Allegri, Patricio Chrem & Noelia Edigo

  21. Washington University School of Medicine, St. Louis, MO, USA

    Fatima Amtashar, Randall Bateman, Tammie Benzinger, Susan Brandon, Virginia Buckles, Carlos Cruchaga, Tamara Donahue, Anne Fagan, Angela Farrar, Gigi Flynn, Erin Franklin, Cortaiga Gant, Brian Gordon, Julia Gray, Jenny Gurney, Jason Hassenstab, David Holtzman, Russ Hornbeck, Gina Jerome, Celeste Karch, Daniel Marcus, Denise Maue-Dreyfus, Eric McDade, John Morris, Joanne Norton, Richard Perrin, Marc Raichle, Wendy Sigurdson, Laura Swisher, Peter Wang, Mary Wolfsberger, Chengjie Xiong & Xiong Xu

  22. University of Pittsburgh, Pittsburgh, PA, USA

    Sarah Berman, Snezana Ikonomovic & William Klunk

  23. Brown University-Butler Hospital, Providence, RI, USA

    Courtney Bodge & Stephen Salloway

  24. Neuroscience Research Australia, Sydney, New South Wales, Australia

    William Brooks, Mirelle D’Mello & Peter Schofield

  25. Indiana University, Bloomington, IN, USA

    Jill Buck, Marty Farlow & Bernardino Ghetti

  26. Mayo Clinic Jacksonville, Jacksonville, FL, USA

    Sochenda Chea, Jack Clifford, Nilufer Erekin-Taner & Neill Graff-Radford

  27. Brigham and Women’s Hospital–Massachusetts General Hospital, Boston, MA, USA

    Jasmeer Chhatwal & Paige Sparks

  28. University of Southern California, Los Angeles, CA, USA

    Helena Chui, Lucy Montoya & John Ringman

  29. University College London, London, UK

    Jake Cinco, Jane Douglas & Nick Fox

  30. University of California San Diego, San Diego, CA, USA

    Howard Feldman

  31. Osaka City University, Osaka, Japan

    Hisako Fujii, Hiroshi Mori & Hiroyuki Shimada

  32. Edith Cowan University, Perth, Western Australia, Australia

    Samantha Gardener, Ralph Martins, Hamid Sohrabi & Kevin Taddei

  33. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alison Goate & Jen Wang

  34. Columbia University, New York, NY, USA

    Jill Goldman, Katie Neimeyer & James Noble

  35. Hirosaki University, Aomori, Japan

    Mie Hirohara, Takeshi Kawarabayashi, Tomoyo Shiroto & Mikio Shoji

  36. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

    Siri Houeland DiBari & Johannes Levin

  37. Niigata University, Niigata, Japan

    Takeshi Ikeuchi & Kensaku Kasuga

  38. German Center for Neurodegnerative Diseases (DZNE), Tubingen, Germany

    Mathias Jucker, Elke Kuder-Buletta & Christoph Laske

  39. University of Michigan, Ann Arbor, MI, USA

    Robert Koeppe

  40. Asan Medical Center, Seoul, South Korea

    Jae-Hong Lee & Jee Hoon Roh

  41. University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Neal Scott Mason

  42. University of Melbourne, Melbourne, Victoria, Australia

    Colin Masters

  43. Tokyo University, Tokyo, Japan

    Akem Nagamatsu & Kazushi Suzuki

  44. University of California San Francisco, San Francisco, CA, USA

    Mike Weiner

Authors
  1. Oliver Preische
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  2. Stephanie A. Schultz
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  3. Anja Apel
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Consortia

Dominantly Inherited Alzheimer Network

  • Ricardo Allegri
  • , Fatima Amtashar
  • , Randall Bateman
  • , Tammie Benzinger
  • , Sarah Berman
  • , Courtney Bodge
  • , Susan Brandon
  • , William Brooks
  • , Jill Buck
  • , Virginia Buckles
  • , Sochenda Chea
  • , Jasmeer Chhatwal
  • , Patricio Chrem
  • , Helena Chui
  • , Jake Cinco
  • , Jack Clifford
  • , Carlos Cruchaga
  • , Mirelle D’Mello
  • , Tamara Donahue
  • , Jane Douglas
  • , Noelia Edigo
  • , Nilufer Erekin-Taner
  • , Anne Fagan
  • , Marty Farlow
  • , Angela Farrar
  • , Howard Feldman
  • , Gigi Flynn
  • , Nick Fox
  • , Erin Franklin
  • , Hisako Fujii
  • , Cortaiga Gant
  • , Samantha Gardener
  • , Bernardino Ghetti
  • , Alison Goate
  • , Jill Goldman
  • , Brian Gordon
  • , Neill Graff-Radford
  • , Julia Gray
  • , Jenny Gurney
  • , Jason Hassenstab
  • , Mie Hirohara
  • , David Holtzman
  • , Russ Hornbeck
  • , Siri Houeland DiBari
  • , Takeshi Ikeuchi
  • , Snezana Ikonomovic
  • , Gina Jerome
  • , Mathias Jucker
  • , Celeste Karch
  • , Kensaku Kasuga
  • , Takeshi Kawarabayashi
  • , William Klunk
  • , Robert Koeppe
  • , Elke Kuder-Buletta
  • , Christoph Laske
  • , Jae-Hong Lee
  • , Johannes Levin
  • , Daniel Marcus
  • , Ralph Martins
  • , Neal Scott Mason
  • , Colin Masters
  • , Denise Maue-Dreyfus
  • , Eric McDade
  • , Lucy Montoya
  • , Hiroshi Mori
  • , John Morris
  • , Akem Nagamatsu
  • , Katie Neimeyer
  • , James Noble
  • , Joanne Norton
  • , Richard Perrin
  • , Marc Raichle
  • , John Ringman
  • , Jee Hoon Roh
  • , Stephen Salloway
  • , Peter Schofield
  • , Hiroyuki Shimada
  • , Tomoyo Shiroto
  • , Mikio Shoji
  • , Wendy Sigurdson
  • , Hamid Sohrabi
  • , Paige Sparks
  • , Kazushi Suzuki
  • , Laura Swisher
  • , Kevin Taddei
  • , Jen Wang
  • , Peter Wang
  • , Mike Weiner
  • , Mary Wolfsberger
  • , Chengjie Xiong
  •  & Xiong Xu

Contributions

All authors were involved in sample and data collection. J.K., O.P., A.A., S.A.K., and C.B. performed the immunoassay work. S.A.S., A.A., G.W., and B.A.G. performed the statistical analyses. M.J., O.P., S.A.S., A.A., and B.A.G. designed the study and wrote the manuscript with the help of the coauthors J.K., S.A.K., C.B., S.G., E.K.-B., C. LaFougere, C. Laske, J.V., J.L., C.L.M., R.M., P.R.S., M.N.R., N.R.G.-R., S.S., B.G., J.M.R., J.M.N., J.C., A.M.G., T.L.S.B., J.C.M., R.J.B., G.W., A.M.F., and E.M.M.

Corresponding author

Correspondence to Mathias Jucker.

Ethics declarations

Competing interests

A.M.G. has consulted for Cognition Therapeutics, Biogen, GlaxoSmithKline, Illumina, Eisai, AbbVie, and Pfizer and served on the Scientific Advisory Board for Denali Therapeutics. A.M.F. is a member of the Scientific Advisory Boards for AbbVie, Genentech and Roche Diagnostics and provides consultation for Araclon/Grifols and DiamiR.

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 Difference distribution curve for baseline (cross-sectional) CSF and serum NfL levels in mutation carriers and non-carriers.

a,b, Difference of posterior distribution for baseline CSF NfL (n = 187) (a) and baseline serum NfL (n = 405) (b) as a function of EYO. The solid red lines depict the median of the difference distribution; the shaded area represents the 99% equal-tailed credible intervals. EYO was considered statistically significant if the 99% equal-tailed credible intervals of the posterior distribution did not overlap 0 (6.8 years before EYO for both baseline CSF and serum NfL). For the absolute values of baseline CSF and serum NfL, see Fig. 1a,b.

Extended Data Fig. 2 No difference in baseline CSF and serum NfL levels among APP, PSEN1, and PSEN2 mutation carriers.

a, Two-tailed pairwise Student’s t-test comparisons of CSF NfL levels of carriers of a mutation in APP (n = 14), PSEN1 (n = 82), or PSEN2 (n = 11). b, Same analysis, using a two-tailed pairwise Student’s t-test for the serum NfL of carriers of a mutation in APP (n = 39), PSEN1 (n = 185), or PSEN2 (n = 19). No differences in log(CSF NfL) or log(serum NfL) were found between the groups (F(2, 104) = 1.8108, P = 0.1686 and F(2, 240) = 1.9205, P = 0.1488, respectively). Similarly, no differences were found by two-tailed pairwise Student’s t-test when age and disease status (presymptomatic, symptomatic) were treated as covariates. The boxes map to the median, 25th and 75th quintiles, and the whiskers extend to the 1.5 × IQR.

Extended Data Fig. 3 Longitudinal serum NfL and bifurcation of mutation carriers from non-carriers.

a, Spaghetti plot showing longitudinal serum NfL for non-carriers (NC, n = 63, blue) and mutation carriers (MC, n = 133, red) as a function of EYO. These are the same data as in Fig. 2a but with a logarithmic scale on the y axis to better appreciate the changes during the presymptomatic stage (for details, see Fig. 2a). b, Difference of posterior distribution for serum NfL rate of change between mutation carriers and non-carriers, as a function of EYO (n = 196). The solid red line depicts the median of the difference distribution, and the shaded area represents the 99% equal-tailed credible intervals. EYO was considered statistically significant if the 99% equal-tailed credible intervals of the posterior distribution did not overlap 0 (16.2 years before EYO). c, Individual estimated rate of change in serum NfL (same data as in Fig. 2b, n = 63 for non-carriers and n = 133 for mutation carriers). A regression analysis was performed with two breaks of slope (see Methods for calculation). With this model the first bifurcation point was found at −18.6 years before EYO, the second at −5.8 years before EYO.

Extended Data Fig. 4 Rate of change per year of serum NfL is a better parameter to distinguish presymptomatic and symptomatic mutation carriers from non-carriers compared to single cross-sectional serum NfL.

Receiver operating characteristic analysis for non-carriers (NC) versus presymptomatic mutation carriers (MC) and non-carriers versus symptomatic mutation carriers with cross-sectional (baseline serum NfL) and longitudinal (serum NfL rate of change per year) data. The true positive fraction (sensitivity) is on the y axis and the false positive fraction (1-specificity) on the x axis. The area under the curve (AUC, accuracy), as well as the cutoff value and χ2 P value from the logistic regression are shown. The chance level of the area under the curve is 0.50. Converters (for rate of change, see Fig. 2c) were considered presymptomatic mutation carriers.

Extended Data Fig. 5 No difference in serum NfL rate of change among APP, PSEN1, and PSEN2 mutation carriers and no association with estimated age of onset.

a, Using two-tailed pairwise Student’s t-tests, no differences in the rate of change of log(serum NfL) (year−1) levels among APP (n = 24), PSEN1 (n = 104), and PSEN2 (n = 5) mutation carriers (F(2, 130) = 0.4678, P = 0.6274) was found. Similarly, no differences were found when age and disease status (presymptomatic, symptomatic) were treated as covariates in a two-tailed pairwise Student’s t-test. b, No difference between an individual’s deviation from the EYO-adjusted median rate of change in NfL and their expected age of symptom onset using LMEMs. Individuals were grouped in 4 categories with expected symptom onset at 20–39 (n = 17), 40–49 (n = 54), 50–59 (n = 56), and over 60 years of age (n = 6); group comparisons, P > 0.146. See Methods for the calculations. The boxes map to the median, 25th and 75th quintiles, and the whiskers extend to the 1.5 × IQR.

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Preische, O., Schultz, S.A., Apel, A. et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med 25, 277–283 (2019). https://doi.org/10.1038/s41591-018-0304-3

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