Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Biomechanical response of the CNS is associated with frailty in NPH-suspected patients

Abstract

Frailty is known to predict dementia. However, its link with neurodegenerative alterations of the central nervous system (CNS) is not well understood at present. We investigated the association between the biomechanical response of the CNS and frailty in older adults suspected of normal pressure hydrocephalus (NPH) presenting with markers of multiple co-existing pathologies. The biomechanical response of the CNS was characterized by the CNS elastance coefficient inferred from phase contrast magnetic resonance imaging and intracranial pressure monitoring during a lumbar infusion test. Frailty was assessed with an index of health deficit accumulation. We found a significant association between the CNS elastance coefficient and frailty, with an effect size comparable to that between frailty and age, the latter being the strongest known risk factor for frailty. Results were independent of CSF dynamics, showing that they are not specific to the NPH neuropathological condition. The CNS biomechanical characterization may help to understand how frailty is related to neurodegeneration and detect the shift from normal to pathological brain ageing.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Jack CR, Knopman DS, Jagust WJ et al (2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9:119–128. https://doi.org/10.1016/S1474-4422(09)70299-6

  2. 2.

    Sperling RA, Karlawish J, Johnson KA (2013) Preclinical Alzheimer disease-the challenges ahead. Nat Rev Neurol 9:54–58. https://doi.org/10.1038/nrneurol.2012.241

  3. 3.

    Cesari M, Vellas B, Gambassi G (2013) The stress of aging. Exp Gerontol 48:451–456. https://doi.org/10.1016/j.exger.2012.10.004

  4. 4.

    Kojima G, Taniguchi Y, Iliffe S, Walters K (2016) Frailty as a predictor of alzheimer disease, vascular dementia, and all dementia among community-dwelling older people: a systematic review and meta-analysis. J Am Med Dir Assoc 17:881–888. https://doi.org/10.1016/j.jamda.2016.05.013

  5. 5.

    Song X, Mitnitski A, Rockwood K (2014) Age-related deficit accumulation and the risk of late-life dementia. Alzheimers Res Ther 6:54. https://doi.org/10.1186/s13195-014-0054-5

  6. 6.

    Song X, Mitnitski A, Rockwood K (2011) Nontraditional risk factors combine to predict Alzheimer disease and dementia. Neurology 77:227–234. https://doi.org/10.1212/WNL.0b013e318225c6bc

  7. 7.

    Oppenheim H, Paolillo EW, Moore RC et al (2018) Neurocognitive functioning predicts frailty index in HIV. Neurology 91:e162–e170. https://doi.org/10.1212/WNL.0000000000005761

  8. 8.

    Robertson DA, Savva GM, Kenny RA (2013) Frailty and cognitive impairment–a review of the evidence and causal mechanisms. Ageing Res Rev 12:840–851. https://doi.org/10.1016/j.arr.2013.06.004

  9. 9.

    Rolfson DB, Wilcock G, Mitnitski A et al (2013) An assessment of neurocognitive speed in relation to frailty. Age Ageing 42:191–196. https://doi.org/10.1093/ageing/afs185

  10. 10.

    Kant IMJ, de Bresser J, van Montfort SJT et al (2018) The association between brain volume, cortical brain infarcts, and physical frailty. Neurobiol Aging 70:247–253. https://doi.org/10.1016/j.neurobiolaging.2018.06.032

  11. 11.

    Gallucci M, Piovesan C, Di Battista ME (2018) Associations between the Frailty Index and Brain Atrophy: the Treviso Dementia (TREDEM) Registry. J Alzheimers Dis 62:1623–1634. https://doi.org/10.3233/JAD-170938

  12. 12.

    Buchman AS, Yu L, Wilson RS et al (2013) Association of brain pathology with the progression of frailty in older adults. Neurology 80:2055–2061. https://doi.org/10.1212/WNL.0b013e318294b462

  13. 13.

    Goriely A, Geers MGD, Holzapfel GA et al (2015) Mechanics of the brain: perspectives, challenges, and opportunities. Biomech Model Mechanobiol 14:931–965. https://doi.org/10.1007/s10237-015-0662-4

  14. 14.

    Holter KE, Kehlet B, Devor A et al (2017) Interstitial solute transport in 3D reconstructed neuropil occurs by diffusion rather than bulk flow. Proc Natl Acad Sci 114:9894–9899. https://doi.org/10.1073/pnas.1706942114

  15. 15.

    Hernández JCC, Bracko O, Kersbergen CJ et al (2019) Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in Alzheimer’s disease mouse models. Nat Neurosci 22:413. https://doi.org/10.1038/s41593-018-0329-4

  16. 16.

    Klassen BT, Ahlskog JE (2011) Normal pressure hydrocephalus. Neurology 77:1119. https://doi.org/10.1212/WNL.0b013e31822f02f5

  17. 17.

    Malm J, Graff-Radford NR, Ishikawa M, et al. (2013) Influence of comorbidities in idiopathic normal pressure hydrocephalus — research and clinical care. A report of the ISHCSF task force on comorbidities in INPH. Fluids Barriers CNS 10:22. https://doi.org/10.1186/2045-8118-10-22**

  18. 18.

    Marmarou A, Young HF, Aygok GA et al (2005) Diagnosis and management of idiopathic normal-pressure hydrocephalus: a prospective study in 151 patients. J Neurosurg 102:987–997. https://doi.org/10.3171/jns.2005.102.6.0987

  19. 19.

    Relkin N, Marmarou A, Klinge P et al (2005) Diagnosing idiopathic normal-pressure hydrocephalus. Neurosurgery. https://doi.org/10.1227/01.neu.0000168185.29659.c5

  20. 20.

    Vellas B, Guigoz Y, Garry PJ et al (1999) The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition 15:116–122. https://doi.org/10.1016/S0899-9007(98)00171-3

  21. 21.

    Feck E, Zulfiqar AA (2018) Screening of frailty in family practice by the modified SEGA grid. Rev Med Liege 73:513–518

  22. 22.

    Araki I, Kuno S (2000) Assessment of voiding dysfunction in Parkinson’s disease by the international prostate symptom score. J Neurol Neurosurg Psychiatry 68:429–433. https://doi.org/10.1136/jnnp.68.4.429

  23. 23.

    Mo Y, Stromswold J, Wilson K et al (2017) A multinational study distinguishing Alzheimer’s and healthy patients using cerebrospinal fluid tau/Aβ42 cutoff with concordance to amyloid positron emission tomography imaging. Alzheimers Dement Diagn Assess Dis Monit 6:201–209. https://doi.org/10.1016/j.dadm.2017.02.004

  24. 24.

    Fazekas F, Chawluk JB, Alavi A et al (1987) MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 149:351–356. https://doi.org/10.2214/ajr.149.2.351

  25. 25.

    Balédent O, Henry-Feugeas M-C, Idy-Peretti I (2001) Cerebrospinal fluid dynamics and relation with blood flow: a magnetic resonance study with semiautomated cerebrospinal fluid segmentation. Invest Radiol 36:368

  26. 26.

    Lenfeldt N, Koskinen L-OD, Bergenheim AT et al (2007) CSF pressure assessed by lumbar puncture agrees with intracranial pressure. Neurology 68:155–158. https://doi.org/10.1212/01.wnl.0000250270.54587.71

  27. 27.

    Szewczykowski J, liwka S, Kunicki A et al (1977) A fast method of estimating the elastance of the intracranial system. J Neurosurg 47:19–26. https://doi.org/10.3171/jns.1977.47.1.0019

  28. 28.

    Marmarou A, Shulman K, Rosende RM (1978) A nonlinear analysis of the cerebrospinal fluid system and intracranial pressure dynamics. J Neurosurg 48:332–344. https://doi.org/10.3171/jns.1978.48.3.0332

  29. 29.

    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395. https://doi.org/10.1145/358669.358692

  30. 30.

    Searle SD, Mitnitski A, Gahbauer EA et al (2008) A standard procedure for creating a frailty index. BMC Geriatr 8:24. https://doi.org/10.1186/1471-2318-8-24

  31. 31.

    Rockwood K, Mitnitski A (2011) Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clin Geriatr Med 27:17–26. https://doi.org/10.1016/j.cger.2010.08.008

  32. 32.

    Vásárhelyi B, Debreczeni LA (2017) Lab test findings in the elderly. EJIFCC 28:328–332

  33. 33.

    Irwin DJ, Grossman M, Weintraub D et al (2017) Neuropathological and genetic correlates of survival and dementia onset in synucleinopathies: a retrospective analysis. Lancet Neurol 16:55–65. https://doi.org/10.1016/S1474-4422(16)30291-5

  34. 34.

    Iturria-Medina Y, Sotero RC, Toussaint PJ et al (2016) Early role of vascular dysregulation on late-onset Alzheimer/’s disease based on multifactorial data-driven analysis. Nat Commun 7:11934. https://doi.org/10.1038/ncomms11934

  35. 35.

    Wirth B, Sobey I (2009) Analytic solution during an infusion test of the linear unsteady poroelastic equations in a spherically symmetric model of the brain. Math Med Biol J IMA 26:25–61. https://doi.org/10.1093/imammb/dqn021

  36. 36.

    Pini L, Pievani M, Bocchetta M et al (2016) Brain atrophy in Alzheimer’s Disease and aging. Ageing Res Rev 30:25–48. https://doi.org/10.1016/j.arr.2016.01.002

  37. 37.

    Murphy MC, Huston J, Jack CR et al (2011) Decreased brain stiffness in Alzheimer’s disease determined by magnetic resonance elastography. J Magn Reson Imaging JMRI 34:494–498. https://doi.org/10.1002/jmri.22707

  38. 38.

    Sack I, Streitberger K-J, Krefting D et al (2011) The influence of physiological aging and atrophy on brain viscoelastic properties in humans. PLOS One 6:e23451. https://doi.org/10.1371/journal.pone.0023451

  39. 39.

    Streitberger K-J, Wiener E, Hoffmann J et al (2011) In vivo viscoelastic properties of the brain in normal pressure hydrocephalus. NMR Biomed 24:385–392. https://doi.org/10.1002/nbm.1602

  40. 40.

    Leinonen V, Koivisto AM, Alafuzoff I et al (2012) cortical brain biopsy in long-term prognostication of 468 patients with possible normal pressure hydrocephalus. Neurodegener Dis 10:166–169. https://doi.org/10.1159/000335155

  41. 41.

    Jaraj D, Agerskov S, Rabiei K et al (2016) Vascular factors in suspected normal pressure hydrocephalus. Neurology 86:592. https://doi.org/10.1212/WNL.0000000000002369

  42. 42.

    Clegg A, Young J, Iliffe S et al (2013) Frailty in elderly people. Lancet 381:752–762. https://doi.org/10.1016/S0140-6736(12)62167-9

  43. 43.

    Stone J, Johnstone DM, Mitrofanis J, O’Rourke M (2015) The mechanical cause of age-related dementia (Alzheimer’s disease): the brain is destroyed by the pulse. J Alzheimers Dis 44:355–373. https://doi.org/10.3233/JAD-141884

  44. 44.

    Rahimi J, Kovacs GG (2014) Prevalence of mixed pathologies in the aging brain. Alzheimer’s Res Ther 6:82. https://doi.org/10.1186/s13195-014-0082-1

  45. 45.

    Olde Rikkert MGM, Melis RJF (2019) Rerouting geriatric medicine by complementing static frailty measures with dynamic resilience indicators of recovery potential. Front Physiol. https://doi.org/10.3389/fphys.2019.00723

  46. 46.

    Khan TK (2018) An algorithm for preclinical diagnosis of alzheimer’s disease. Front Neurosci. https://doi.org/10.3389/fnins.2018.00275

  47. 47.

    Payton NM, Kalpouzos G, Rizzuto D et al (2018) Combining cognitive, genetic, and structural neuroimaging markers to identify individuals with increased dementia risk. J Alzheimers Dis JAD 64:533–542. https://doi.org/10.3233/JAD-180199

  48. 48.

    Spilt A, Box FMA, van der Geest RJ et al (2002) Reproducibility of total cerebral blood flow measurements using phase contrast magnetic resonance imaging. J Magn Reson Imaging JMRI 16:1–5. https://doi.org/10.1002/jmri.10133

  49. 49.

    Khan MN, Shallwani H, Khan MU, Shamim MS (2017) Noninvasive monitoring intracranial pressure: a review of available modalities. Surg Neurol Int. https://doi.org/10.4103/sni.sni_403_16

Download references

Acknowledgements

This study was supported by a grant from the French Ministry of Health with the participation of the Groupement Interrégional de Recherche Clinique et d’Innovation Sud-Ouest Outre-Mer Hospitalier (PHRCInterRégional 2010), by research funding from the Occitania Region (RPBIO 2015 no 14054344), and by the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC grant agreement no 615102 (https://erc.europa.eu/).

Author information

AV designed and conceptualized the study, processed, analysed and interpreted the data, performed the statistical analysis, and drafted and revised the manuscript. NDC and EH designed and conceptualized the study on the frailty part, interpreted the data, and drafted and revised the manuscript. AL segmented and analysed PCMRI data. OB provided tools and methods for PCMRI acquisition and data processing and analysed PCMRI data. ZC interpreted ICP measurements and revised the manuscript. LB and PP played a major role in the acquisition of clinical data and MRI data, respectively. PS designed and conceptualized the biomechanical part of the study, interpreted the data, and drafted and revised the manuscript. SL and ES designed and conceptualized the study, interpreted the data, drafted and revised the manuscript, supervised the study and obtained funding.

Correspondence to A. Vallet or E. Schmidt.

Ethics declarations

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vallet, A., Del Campo, N., Hoogendijk, E.O. et al. Biomechanical response of the CNS is associated with frailty in NPH-suspected patients. J Neurol (2020). https://doi.org/10.1007/s00415-019-09689-z

Download citation

Keywords

  • Biomechanics
  • Frailty
  • Normal pressure hydrocephalus
  • Lumbar infusion test
  • Phase contrast magnetic resonance imaging
  • Neurodegenerative CNS changes