Skip to main page content
U.S. flag

An official website of the United States government

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Feb;25(2):277-283.
doi: 10.1038/s41591-018-0304-3. Epub 2019 Jan 21.

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

Collaborators, Affiliations

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

Oliver Preische et al. Nat Med. 2019 Feb.

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.

PubMed Disclaimer

Conflict of interest statement

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.

Figures

Extended Data Fig. 1 |
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 |
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 |
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 |
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 |
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.
Fig. 1 |
Fig. 1 |. CSF and serum NfL levels are highly correlated and divert between mutation carriers and non-carriers already in the presymptomatic phase.
a, CSF NfL values of non-carriers (blue, n = 80) and mutation carriers (red, n = 107) as a function of EYO. Shown is −27.5 until +15 years before or after EYO, respectively. b, Serum NfL for non-carriers (n = 162) and mutation carriers (n = 243) as a function of EYO. For a and b, the shaded areas represent the 99% credible intervals around the model estimates. The curves and credible intervals are drawn from the actual distributions of model fits derived by the Hamiltonian Markov chain Monte Carlo analyses (see Methods). The first EYO where non-carriers and mutation carriers differed was determined to be the first point where the 99% credible intervals around the difference distribution between non-carriers and mutation carriers did not overlap 0 (−6.8 years before EYO for both CSF and serum, see Extended Data Fig. 1). Our analysis is influenced by the available number of participants. Thus, results do not represent absolute measures, but rather relative EYO points where we could detect effects given the limitations of sample size. c,d, Significant associations from LMEMs between CSF NfL and serum NfL in non-carriers (n = 80; B(s.e.m.) = 0.350(0.14), P = 0.014) and mutation carriers (n = 107; B(s.e.m.) = 0.612(0.05), P < 2.0 × 10−16) were found.
Fig. 2 |
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.
a, Spaghetti plot showing longitudinal serum NfL for non-carriers (blue, n = 63) and mutation carriers (red, n = 133) as a function of EYO. The displayed x axis range is limited to −27.5 until +12.5 years before or after EYO, respectively, to maintain blinding of some individuals contributing to this dataset. In addition, again to maintain blinding, the EYO of two participants (one mutation carrier and one non-carrier) was set to the mean of both EYO values. A logarithmic version of the spaghetti plot is shown in Extended Data Fig. 3a to better appreciate that changes between mutation carriers and non-carriers already occur at presymptomatic levels. b, Estimated rate of change per year in serum NfL (see Methods for calculation) plotted against baseline EYO for mutation carriers and non-carriers (shown is −27.5 until +12.5 years). Individual random effect slope estimates are plotted as colored symbols. The shaded areas represent the 99% credible intervals around the model estimates. The curves and credible intervals are drawn from the actual distributions of model fits derived with the Hamiltonian Markov chain Monte Carlo analyses. The first EYO where groups (non-carriers and mutation carriers) differed was determined to be the first point where the 99% credible intervals around the difference distribution between non-carriers and mutation carriers did not overlap 0 (−16.2 years before EYO; see Extended Data Fig. 3b). An even earlier deviation of the two curves was calculated when linear regression analyses were performed (Extended Data Fig. 3c). c, Rate of change per year in serum NfL across four groups differing by mutation and cognitive status: non-carriers (blue, n = 63); presymptomatic (Presym) mutation carriers (yellow, n = 65) are individuals who scored as CDR= 0 across all visits; converters (orange, n = 13) are mutation carriers who scored as CDR= 0 at baseline and CDR> 0 at subsequent visits; symptomatic (Sym) mutation carriers (red, n = 55) are individuals who scored as CDR> 0 across all visits. The boxes map to the median, 25th and 75th quintiles, and the whiskers extend to 1.5 × interquartile range (IQR). Comparisons were done with LMEMs. Presymptomatic mutation carriers had a significantly higher annual rate of change compared to non-carriers (B(s.e.m.) = 0.009(0.003), P = 6.71 × 10−4). Converters had significantly higher rate of change compared to both non-carriers (B(s.e.m.) = 0.024(0.004), P = 3.05 × 10−7) and presymptomatic mutation carriers (B(s.e.m.) = 0.015(0.005), P = 1.19 × 10−3). Symptomatic mutation carriers had significantly higher rates of change compared to both non-carriers (B(s.e.m.) = 0.020(0.003), P = 8.78 × 10−12) and presymptomatic mutation carriers (B(s.e.m.) = 0.011(0.003), P = 1.51 × 10−4). There was no difference between converters and symptomatic mutation carriers (B(s.e.m.) = −0.004(0.005), P = 0.445).
Fig. 3 |
Fig. 3 |. Rate of change per year in serum NfL in mutation carriers mirrors rate of change in cortical thinning.
a, Relationship between estimated annual rate of change in serum NfL and estimated annual rate of change in precuneus cortical thickness for non-carriers, presymptomatic (Presym) mutation carriers, and symptomatic (Sym) mutation carriers (including converters to the symptomatic phase, see Fig. 2c). Results from LMEMs revealed a significant association in symptomatic mutation carriers (n = 60; B(s.e.m.) = −0.914(0.367), P = 0.018) and a close to significant association in presymptomatic mutation carriers (n = 65; B(s.e.m.) = −0.325(0.166), P = 0.054) but not in non-carriers (n = 59; B(s.e.m.) = −0.210(0.149), P = 0.886). Between-group comparison indicated that the rate of change in serum NfL was slightly more associated in symptomatic than in asymptomatic mutation carriers (B(s.e.m.) = −0.573(0.305), P = 0.063). b, Relationship between rate of change in serum NfL and rate of change in precuneus 18F-FDG PET. Using LMEMs, a positive association was only found in symptomatic mutation carriers (n = 55; B(s.e.m.) = −1.149(0.501), P = 0.031) but not in presymptomatic mutation carriers (n = 64; B(s.e.m.) = −0.227(0.456), P = 0.620) or non-carriers (n = 55; B(s.e.m.) = 0.161(0.347), P = 0.465). c, Relationship between rate of change in serum NfL and rate of change in precuneus amyloid-β-PET. Using LMEMs, no significant association in any of the three groups was found (non-carriers: n = 57; B(s.e.m.) = −0.468(0.547), P = 0.403; presymptomatic mutation carriers: n = 64; B(s.e.m.) = 1.248(1.000), P = 0.216; symptomatic mutation carriers: n = 51; B(s.e.m.) = 1.805(1.556), P = 0.266). The shaded area around each linear fit line represents one s.e.m. Note that not all participants with longitudinal NfL measurements had imaging parameters available, thus sample sizes (n) are slightly lower compared to those in Fig. 2c.
Fig. 4 |
Fig. 4 |. Prediction of changes in cortical thinning and cognition by baseline serum NfL (retrospective prediction) and serum NfL rate of change (prospective prediction).
a–c, Higher baseline serum NfL levels were significantly associated with an increased rate of change in cortical thickness (n = 125; B(s.e.m.) = −0.105(0.013), P = 4.47 × 10−13) (a), MMSE (n = 132; B(s.e.m.) = −3.980(0.537), P = 2.38 × 10−11) (b), and Logical Memory test (immediate recall, n = 133; B(s.e.m.) = −1.478(0.502), P = 0.004) (c). A similar significance (P = 0.015) was obtained for the Logical Memory test delayed recall. LMEMs (see Methods) were run with all mutation carriers together (n = 125) because of the high degree of overlap in cognitive and biomarker levels between presymptomatic (Presym) and symptomatic (Sym) mutation carriers. However, at least for cortical thickness, separate analyses for presymptomatic and symptomatic mutation carriers were also significant (n = 65, presymptomatic mutation carriers (yellow): B(s.e.m.) = −0.03(0.01), P = 0.047; n = 60, symptomatic mutation carriers (red): B(s.e.m.) = −0.10(0.03), P = 0.002). df, In a true prospective design, mutation carriers returning for follow-up visits after the last serum collection were included in the analysis. Individuals’ rates of change in serum NfL levels predicted subsequent cortical thinning (d; n = 30; B(s.e.m.) = −1.867(0.769), P = 0.024). The same predictive associations were also significant for the MMSE (e; n = 37; B(s.e.m.) = −52.23(20.19), P = 0.015) and Logical Memory test scores (f; immediate recall, n = 37; B(s.e.m.) = −75.91(18.07), P = 0.0002). For descriptive purposes, presymptomatic and symptomatic mutation carriers are plotted in yellow and red, respectively. Note that not all participants with baseline NfL measurements had longitudinal MRI imaging and longitudinal cognitive parameters available; thus, sample sizes (n) in ac are slightly lower than those in Supplementary Table 2. This was also true for the mutation carriers returning for follow-up visits after the last serum collection (df). The shaded area around each linear fit line represents one s.e.m. from the LMEMs.

Comment in

Similar articles

Cited by

  • Fluid biomarkers of chronic traumatic brain injury.
    Friberg S, Lindblad C, Zeiler FA, Zetterberg H, Granberg T, Svenningsson P, Piehl F, Thelin EP. Friberg S, et al. Nat Rev Neurol. 2024 Nov;20(11):671-684. doi: 10.1038/s41582-024-01024-z. Epub 2024 Oct 3. Nat Rev Neurol. 2024. PMID: 39363129 Review.
  • Blood-based biomarkers in Alzheimer's disease: a mini-review.
    Padala SP, Newhouse PA. Padala SP, et al. Metab Brain Dis. 2023 Jan;38(1):185-193. doi: 10.1007/s11011-022-01114-1. Epub 2022 Nov 7. Metab Brain Dis. 2023. PMID: 36342582 Free PMC article. Review.
  • Cell-free RNA signatures predict Alzheimer's disease.
    Cisterna-García A, Beric A, Ali M, Pardo JA, Chen HH, Fernandez MV, Norton J, Gentsch J, Bergmann K, Budde J, Perlmutter JS, Morris JC, Cruchaga C, Botia JA, Ibanez L. Cisterna-García A, et al. iScience. 2023 Nov 23;26(12):108534. doi: 10.1016/j.isci.2023.108534. eCollection 2023 Dec 15. iScience. 2023. PMID: 38089583 Free PMC article.
  • Real-world applicability of glial fibrillary acidic protein and neurofilament light chain in Alzheimer's disease.
    Parvizi T, König T, Wurm R, Silvaieh S, Altmann P, Klotz S, Rommer PS, Furtner J, Regelsberger G, Lehrner J, Traub-Weidinger T, Gelpi E, Stögmann E. Parvizi T, et al. Front Aging Neurosci. 2022 Aug 22;14:887498. doi: 10.3389/fnagi.2022.887498. eCollection 2022. Front Aging Neurosci. 2022. PMID: 36072480 Free PMC article.
  • The pathway to secondary prevention of Alzheimer's disease.
    McDade E, Bednar MM, Brashear HR, Miller DS, Maruff P, Randolph C, Ismail Z, Carrillo MC, Weber CJ, Bain LJ, Hake AM. McDade E, et al. Alzheimers Dement (N Y). 2020 Aug 27;6(1):e12069. doi: 10.1002/trc2.12069. eCollection 2020. Alzheimers Dement (N Y). 2020. PMID: 32885024 Free PMC article.

References

    1. Bateman RJ et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med 367, 795–804 (2012). - PMC - PubMed
    1. Gordon BA et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol 17, 241–250 (2018). - PMC - PubMed
    1. Jack CR Jr. et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14, 535–562 (2018). - PMC - PubMed
    1. Sperling RA, Karlawish J & Johnson KA Preclinical Alzheimer disease: the challenges ahead. Nat. Rev. Neurol 9, 54–58 (2013). - PMC - PubMed
    1. Fandos N et al. Plasma amyloid β 42/40 ratios as biomarkers for amyloid β cerebral deposition in cognitively normal individuals. Alzheimers Dement. (Amst) 8, 179–187 (2017). - PMC - PubMed

Publication types

MeSH terms

Substances