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A neuronal blood marker is associated with mortality in old age

Abstract

Neurofilament light chain (NfL) has emerged as a promising blood biomarker for the progression of various neurological diseases. NfL is a structural protein of nerve cells, and elevated NfL levels in blood are thought to mirror damage to the nervous system. We find that plasma NfL levels increase in humans with age (n = 122; 21–107 years of age) and correlate with changes in other plasma proteins linked to neural pathways. In centenarians (n = 135), plasma NfL levels are associated with mortality equally or better than previously described multi-item scales of cognitive or physical functioning, and this observation was replicated in an independent cohort of nonagenarians (n = 180). Plasma NfL levels also increase in aging mice (n = 114; 2–30 months of age), and dietary restriction, a paradigm that extends lifespan in mice, attenuates the age-related increase in plasma NfL levels. These observations suggest a contribution of nervous system functional deterioration to late-life mortality.

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Fig. 1: Linkage of the age-related changes in plasma NfL levels to the plasma proteome of neural pathways.
Fig. 2: Plasma NfL levels are associated with survival in centenarians and nonagenarians.
Fig. 3: Age-related increase in plasma NfL levels in mice is attenuated through DR.

Data availability

The SOMAscan proteomic data are available in Supplementary Table 16 of a previous study20. According to Danish legislation, transfer and sharing of individual-level data requires prior approval from the Danish Data Protection Agency, which requires that data sharing requests be dealt with on a case-by-case basis. For this reason, the data cannot be deposited in a public database, and data presentation at an individual level is avoided. However, we welcome any enquiries regarding collaboration and individual requests for data sharing.

Code availability

All codes can be made available upon request from the corresponding author.

References

  1. 1.

    Guarente, L. & Kenyon, C. Genetic pathways that regulate ageing in model organisms. Nature 408, 255–262 (2000).

    CAS  PubMed  Google Scholar 

  2. 2.

    Satoh, A., Imai, S. I. & Guarente, L. The brain, sirtuins, and ageing. Nat. Rev. Neurosci. 18, 362–374 (2017).

    CAS  PubMed  Google Scholar 

  3. 3.

    Zhang, G. et al. Hypothalamic programming of systemic ageing involving IKK-β, NF-κB and GnRH. Nature 497, 211–216 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Zullo, J. M. et al. Regulation of lifespan by neural excitation and REST. Nature 574, 359–364 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Formiga, F. et al. Predictors of long-term survival in nonagenarians: the NonaSantfeliu study. Age Ageing 40, 111–116 (2011).

    PubMed  Google Scholar 

  6. 6.

    Taekema, D. G., Gussekloo, J., Westendorp, R. G., de Craen, A. J. & Maier, A. B. Predicting survival in oldest old people. Am. J. Med. 125, 1188–1194 (2012).

    PubMed  Google Scholar 

  7. 7.

    Thinggaard, M. et al. Survival prognosis in very old adults. J. Am. Geriatr. Soc. 64, 81–88 (2016).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Justice, J. N. et al. A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: report from the TAME Biomarkers Workgroup. Geroscience 40, 419–436 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Khalil, M. et al. Neurofilaments as biomarkers in neurological disorders. Nat. Rev. Neurol. 14, 577–589 (2018).

    CAS  PubMed  Google Scholar 

  10. 10.

    Gafson, A. R. et al. Neurofilaments: neurobiological foundations for biomarker applications. Brain 143, 1975–1998 (2020).

    PubMed  Google Scholar 

  11. 11.

    Kern, S. et al. Association of cerebrospinal fluid neurofilament light protein with risk of mild cognitive impairment among individuals without cognitive impairment. JAMA Neurol. 76, 187–193 (2019).

    PubMed  Google Scholar 

  12. 12.

    Osborn, K. E. et al. Cerebrospinal fluid and plasma neurofilament light relate to abnormal cognition. Alzheimers Dement. 11, 700–709 (2019).

    Google Scholar 

  13. 13.

    Bacioglu, M. et al. Neurofilament light chain in blood and CSF as marker of disease progression in mouse models and in neurodegenerative diseases. Neuron 91, 56–66 (2016).

    CAS  PubMed  Google Scholar 

  14. 14.

    Barro, C. et al. Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis. Brain 141, 2382–2391 (2018).

    PubMed  Google Scholar 

  15. 15.

    Kuhle, J. et al. Serum neurofilament light chain in early relapsing remitting MS is increased and correlates with CSF levels and with MRI measures of disease severity. Mult. Scler. 22, 1550–1559 (2016).

    CAS  PubMed  Google Scholar 

  16. 16.

    Constantinescu, R., Rosengren, L., Eriksson, B., Blennow, K. & Axelsson, M. Cerebrospinal fluid neurofilament light and τ protein as mortality biomarkers in parkinsonism. Acta Neurol. Scand. 140, 147–156 (2019).

    CAS  PubMed  Google Scholar 

  17. 17.

    Gendron, T. F. et al. Plasma neurofilament light predicts mortality in patients with stroke. Sci. Transl. Med. 12, eaay1913 (2020).

    CAS  PubMed  Google Scholar 

  18. 18.

    Skillback, T., Mattsson, N., Blennow, K. & Zetterberg, H. Cerebrospinal fluid neurofilament light concentration in motor neuron disease and frontotemporal dementia predicts survival. Amyotroph. Lateral Scler. Frontotemporal Degener. 18, 397–403 (2017).

    PubMed  Google Scholar 

  19. 19.

    Khalil, M. et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat. Commun. 11, 812 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Takemoto, M. et al. Laminar and areal expression of Unc5d and its role in cortical cell survival. Cereb. Cortex 21, 1925–1934 (2011).

    PubMed  Google Scholar 

  22. 22.

    Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Rathore, N. et al. Paired immunoglobulin-like type 2 receptor α G78R variant alters ligand binding and confers protection to Alzheimer’s disease. PLoS Genet. 14, e1007427 (2018).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Elazar, N. et al. Axoglial adhesion by Cadm4 regulates CNS myelination. Neuron 101, 224–231 (2019).

    CAS  PubMed  Google Scholar 

  25. 25.

    Sedger, L. M. & McDermott, M. F. TNF and TNF-receptors: from mediators of cell death and inflammation to therapeutic giants—past, present and future. Cytokine Growth Factor Rev. 25, 453–472 (2014).

    CAS  PubMed  Google Scholar 

  26. 26.

    Smith, L. K. et al. β2-microglobulin is a systemic pro-aging factor that impairs cognitive function and neurogenesis. Nat. Med. 21, 932–937 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Hruska, M. & Dalva, M. B. Ephrin regulation of synapse formation, function and plasticity. Mol. Cell. Neurosci. 50, 35–44 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Engberg, H., Oksuzyan, A., Jeune, B., Vaupel, J. W. & Christensen, K. Centenarians—a useful model for healthy aging? A 29-year follow-up of hospitalizations among 40,000 Danes born in 1905. Aging Cell 8, 270–276 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Newman, A. B. & Murabito, J. M. The epidemiology of longevity and exceptional survival. Epidemiol. Rev. 35, 181–197 (2013).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Jucker, M. & Ingram, D. K. Murine models of brain aging and age-related neurodegenerative diseases. Behav. Brain Res. 85, 1–26 (1997).

    CAS  PubMed  Google Scholar 

  31. 31.

    Mattson, M. P. & Arumugam, T. V. Hallmarks of brain aging: adaptive and pathological modification by metabolic states. Cell Metab. 27, 1176–1199 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Walker, L. C. & Jucker, M. The exceptional vulnerability of humans to Alzheimer’s disease. Trends Mol. Med. 23, 534–545 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Fontana, L. & Partridge, L. Promoting health and longevity through diet: from model organisms to humans. Cell 161, 106–118 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Weindruch, R., Walford, R. L., Fligiel, S. & Guthrie, D. The retardation of aging in mice by dietary restriction: longevity, cancer, immunity and lifetime energy intake. J. Nutr. 116, 641–654 (1986).

    CAS  PubMed  Google Scholar 

  35. 35.

    Preische, O. et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277–283 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    van der Ende, E. L. et al. Serum neurofilament light chain in genetic frontotemporal dementia: a longitudinal, multicentre cohort study. Lancet Neurol. 18, 1103–1111 (2019).

    PubMed  Google Scholar 

  37. 37.

    Ganz, A. B. et al. Neuropathology and cognitive performance in self-reported cognitively healthy centenarians. Acta Neuropathol. Commun. 6, 64 (2018).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Neltner, J. H. et al. Brain pathologies in extreme old age. Neurobiol. Aging 37, 1–11 (2016).

    PubMed  Google Scholar 

  39. 39.

    Yuan, A. et al. Neurofilament subunits are integral components of synapses and modulate neurotransmission and behavior in vivo. Mol. Psychiatry 20, 986–994 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Yuan, A. et al. Neurofilament light interaction with GluN1 modulates neurotransmission and schizophrenia-associated behaviors. Transl. Psychiatry 8, 167 (2018).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Bruunsgaard, H., Andersen-Ranberg, K., Hjelmborg, J., Pedersen, B. K. & Jeune, B. Elevated levels of tumor necrosis factor α and mortality in centenarians. Am. J. Med. 115, 278–283 (2003).

    CAS  PubMed  Google Scholar 

  42. 42.

    Goldman, N., Glei, D. A. & Weinstein, M. The best predictors of survival: do they vary by age, sex, and race? Popul. Dev. Rev. 43, 541–560 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Barron, E., Lara, J., White, M. & Mathers, J. C. Blood-borne biomarkers of mortality risk: systematic review of cohort studies. PLoS ONE 10, e0127550 (2015).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Deelen, J. et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat. Commun. 10, 3346 (2019).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Orwoll, E. S. et al. Proteomic assessment of serum biomarkers of longevity in older men. Aging Cell 19, e13253 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Sathyan, S. et al. Plasma proteomic profile of age, health span, and all-cause mortality in older adults. Aging Cell 19, e13250 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Sebastiani, P. et al. Biomarker signatures of aging. Aging Cell 16, 329–338 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Tanaka, T. et al. Plasma proteomic biomarker signature of age predicts health and life span. eLife 9, e61073 (2020).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Folstein, M. F., Folstein, S. E. & McHugh, P. R. ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Rasmussen, S. H. et al. Cohort profile: the 1895, 1905, 1910 and 1915 Danish birth cohort studies—secular trends in the health and functioning of the very old. Int. J. Epidemiol. 46, 1746–1746j (2017).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Katz, S., Downs, T. D., Cash, H. R. & Grotz, R. C. Progress in development of the index of ADL. Gerontologist 10, 20–30 (1970).

    CAS  PubMed  Google Scholar 

  52. 52.

    Pedersen, C. B., Gotzsche, H., Moller, J. O. & Mortensen, P. B. The Danish Civil Registration System. A cohort of eight million persons. Dan. Med. Bull. 53, 441–449 (2006).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Nygaard, M. et al. Birth cohort differences in the prevalence of longevity-associated variants in APOE and FOXO3A in Danish long-lived individuals. Exp. Gerontol. 57, 41–46 (2014).

    CAS  PubMed  Google Scholar 

  54. 54.

    Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Pisco, A. O. et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    Google Scholar 

  57. 57.

    Fox, J., Weisberg, S. & Fox, J. An R Companion to Applied Regression (SAGE Publications, 2011).

  58. 58.

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

    Google Scholar 

  59. 59.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).

    CAS  PubMed  Google Scholar 

  61. 61.

    Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank G. Eschweiler, B. Wegenast-Braun, O. Preische, T. Gasser (Tübingen, Germany), L. Walker (Atlanta, GA), O. Hahn (Stanford) and all other members of our laboratories for helpful comments. This work was made possible by a generous grant from Cure Alzheimer’s Fund (S.A.K. and M.J.), the National Institute on Aging (DP1-AG053015 to T.W.-C.) and the NOMIS Foundation (T.W.-C.). The Danish Aging Research Center is supported by a grant from the VELUX Foundation and funded the centenarian study. We also thank the participants of this study for their time and personal contribution.

Author information

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Authors

Contributions

T.W.-C., J.M.-F. and M.J. designed the overall study. S.G. and L.P. designed and supervised the study of the effects of DR. S.A.K., B.L., M.T., A.A., L.M.H. and C.B. performed the experimental work and the statistical analyses. D.B., B.J. and K.C. were involved in sample and data collection. T.W.-C., J.M.-F. and M.J. wrote the manuscript with the help of all co-authors. This work was partly done while M.J. was a guest professor in Stanford.

Corresponding authors

Correspondence to Tony Wyss-Coray or Jonas Mengel-From or Mathias Jucker.

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The authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks Henne Holstege, Thomas Perls and P. Eline Slagboom for their contribution to the peer review of this work.

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 NfL trajectory with age in humans and mice.

a-c, Plasma NfL concentration of the human age cohort presented in Fig. 1b. Scatter plots for (a) log NfL concentration, (b) log NfL concentration for females versus males, and (c) NfL variance in plasma with aging (n = 122). NfL variance was calculated using a 10-year sliding window. Local polynomial regression fitting using LOESS was done. Thick lines represent LOESS fitted values and shaded areas representing the standard errors around the model estimates. Although the cohort appears too small for a reliable description of the age-related changes, the data are consistent with previous work of a slow and more linear increase until approximately 70 years of age, followed by a much more rapid increase (Khalil, M., et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat Commun 11, 812, 2020). Interestingly, the present data may suggest that this rapid increase reaches a plateau in nonagenarians and centenarians. It is possible that at these ages individuals with very high NfL levels die out and thus reduce the further increase on a population level (Christensen, K., McGue, M., Petersen, I., Jeune, B. & Vaupel, J.W. Exceptional longevity does not result in excessive levels of disability. Proc Natl Acad Sci U S A 105, 13274, 2008). d-f, Plasma NfL concentration of the aging C57BL/6 J mouse cohort presented in Figure 3a-c. Scatter plots for (d) log NfL concentration; (e) log NfL concentration for females versus males; and (f) NfL variance in plasma with aging (n = 114). NfL variance was calculated using a 5-month sliding window. Local polynomial regression fitting using LOESS was done. Thick lines represent LOESS fitted values and shaded areas represent the standard errors around the model estimates. Similar to the human data, a slow and more linear increase until approximately 15–18 months of age is followed by a much more rapid increase with an increased variance towards the end of the lifespan.

Extended Data Fig. 2 NfL gene expression in tissues and cells.

a, NfL gene expression in humans from the GTEx consortium (see Online Methods). NEFL is highly expressed in all brain regions. In all other tissues the expression is lower. b, Tissue-specific NfL gene expression in C57BL/6JN mice (see Methods). Again, Nefl is highly expressed in brain and lowly expressed in other tissues. Data represent mice at 3 months of age (top) and 18 months of age (bottom). Box plots are showing center line as the median, the box as the first and third quartile and the whiskers as the adjacent values which are the largest observation that is less or equal than the third quartile + 1.5 x interquartile range and the lowest observation that is greater or equal than the first quartile - 1.5 x interquartile range. c, NfL gene expression in non-myeloid brain cells from the Tabula Muris Senis consortium (see Online Methods). The FACS data is visualized using a UMAP. Murine brain cells are color-coded according cell ontology class (left). Cells expressing Nefl (log cpm threshold of 1.954) are visualized on the right where 22 % (66/303) of the cells correspond to medium spiny neurons and 61% (185/303) to neurons.

Extended Data Fig. 3 Physical activity and cognitive ability in centenarians as predictors of survival and their association with plasma NfL.

a, b, Kaplan-Meier survival curve for Activity of Daily Living (ADL: no (n = 38), moderate (n = 53), severe disability(n = 44)) and Mini-Mental State Estimation (MMSE: 0–17 (n = 37), 18–23 (n = 45), 24–30 (n = 46)). For Cox regression analysis see Table 2. c, d, Plasma NfL concentrations (Box plots showing center line as the median, the box as the first and third quartile and the whiskers as the adjacent values which are the largest observation that is less or equal than the third quartile + 1.5 x interquartile range and the lowest observation that is greater or equal than the first quartile - 1.5 x interquartile range) for the different ADL and MMSE categories. The association between NfL and ADL across all three groups was not significant with an estimated median increase of NfL of 3.7 pg/ml with increasing disability (95% CI: (−4.2; 11.7), p = 0.354). In contrast, the association between NfL and MMSE was significant across all three groups with an estimated median decrease of NfL of 8.5pg/ml with increasing MMSE (95% CI: (2.3; 14.7), p = 0.008).

Extended Data Fig. 4 Survival prediction for NfL, MMSE, and ADL disability.

The size of the prediction was estimated as area under the curve (AUC) from the prediction of survival from blood sample until a certain age (the x-axis of time-dependent AUC). a, Survival prediction for the centenarians up to 103 years of age. The average AUC over the follow-up period were for the centenarians 0.65, 95% CI: (0.56: 0.75), 0.62, 95% CI: (0.52: 0.72) and 0.64, 95% CI: (0.54: 0.73) for NfL, MMSE and ADL, respectively. b, Survival prediction for the 93-year-old nonagenarians up to 100 years of age. The average AUC over the follow-up period were for the nonagenarians 0.68, 95% CI: (0.59: 0.76), 0.55, 95% CI: (0.46: 0.64) and 0.63, 95% CI: (0.54: 0.71) for NfL, MMSE and ADL, respectively. These observations indicate that blood NfL predicts survival better compared to MMSE and ADL.

Supplementary information

Supplementary Information

Supplementary Table 3.

Reporting Summary

Supplementary Table 1

Nomenclature of 1,305 proteins analyzed in the Stanford aging cohort using the SOMAscan assay. Corresponding external identifiers were provided by SomaLogic.

Supplementary Table 2

Associations between plasma NfL levels and 1,305 protein levels measured by the SOMAscan assay. A linear model for each of the 1,305 proteins was fitted and adjusted for age, sex and subcohort (n = 122). Type II SS were calculated and tested using a two-tailed F-test. Q values were estimated using the BH approach.

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Kaeser, S.A., Lehallier, B., Thinggaard, M. et al. A neuronal blood marker is associated with mortality in old age. Nat Aging 1, 218–225 (2021). https://doi.org/10.1038/s43587-021-00028-4

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