Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer's disease in individuals with subjective cognitive decline.


Journal

Alzheimer's research & therapy
ISSN: 1758-9193
Titre abrégé: Alzheimers Res Ther
Pays: England
ID NLM: 101511643

Informations de publication

Date de publication:
24 12 2020
Historique:
received: 06 08 2020
accepted: 02 12 2020
entrez: 28 12 2020
pubmed: 29 12 2020
medline: 25 6 2021
Statut: epublish

Résumé

We evaluated Aβ misfolding in combination with Aβ Baseline plasma samples (n = 203) from SCD subjects in the SCIENCe project and Amsterdam Dementia Cohort (age 61 ± 9 years; 57% male, mean follow-up time 2.7 years) were analyzed using immuno-infrared-sensor technology. Within 6 years of follow-up, 22 (11%) individuals progressed to MCI or dementia due to AD. Sensor readout values > 1646 cm All 22 patients who converted to MCI or AD-dementia within 6 years exhibited Aβ misfolding at baseline. Cox analyses revealed a hazard ratio (HR) of 19 (95% confidence interval [CI] 2.2-157.8) for future conversion of SCD subjects with high misfolding and of 11 (95% CI 1.0-110.1) for those with low misfolding. T-ROC curve analyses yielded an area under the curve (AUC) of 0.94 (95% CI 0.86-1.00; 6-year follow-up) for Aβ misfolding in an age, sex, and APOEε4 model. A similar model with plasma Aβ A panel of structure- and concentration-based plasma amyloid biomarkers may predict conversion to clinical MCI and dementia due to AD in cognitively unimpaired subjects. These plasma biomarkers provide a noninvasive and cost-effective alternative for screening early AD pathological changes. Follow-up studies and external validation in larger cohorts are in progress for further validation of our findings.

Sections du résumé

BACKGROUND
We evaluated Aβ misfolding in combination with Aβ
METHODS
Baseline plasma samples (n = 203) from SCD subjects in the SCIENCe project and Amsterdam Dementia Cohort (age 61 ± 9 years; 57% male, mean follow-up time 2.7 years) were analyzed using immuno-infrared-sensor technology. Within 6 years of follow-up, 22 (11%) individuals progressed to MCI or dementia due to AD. Sensor readout values > 1646 cm
RESULTS
All 22 patients who converted to MCI or AD-dementia within 6 years exhibited Aβ misfolding at baseline. Cox analyses revealed a hazard ratio (HR) of 19 (95% confidence interval [CI] 2.2-157.8) for future conversion of SCD subjects with high misfolding and of 11 (95% CI 1.0-110.1) for those with low misfolding. T-ROC curve analyses yielded an area under the curve (AUC) of 0.94 (95% CI 0.86-1.00; 6-year follow-up) for Aβ misfolding in an age, sex, and APOEε4 model. A similar model with plasma Aβ
CONCLUSIONS
A panel of structure- and concentration-based plasma amyloid biomarkers may predict conversion to clinical MCI and dementia due to AD in cognitively unimpaired subjects. These plasma biomarkers provide a noninvasive and cost-effective alternative for screening early AD pathological changes. Follow-up studies and external validation in larger cohorts are in progress for further validation of our findings.

Identifiants

pubmed: 33357241
doi: 10.1186/s13195-020-00738-8
pii: 10.1186/s13195-020-00738-8
pmc: PMC7761044
doi:

Substances chimiques

Amyloid beta-Peptides 0
Biomarkers 0
Peptide Fragments 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

169

Commentaires et corrections

Type : ErratumIn

Références

Bateman RJ, Blennow K, Doody R, Hendrix S, Lovestone S, Salloway S, et al. Plasma biomarkers of AD emerging as essential tools for drug development: an EU/US CTAD task force report. J Prev Alzheimers Dis. 2019;6:169–73.
pubmed: 31062827 pmcid: 31062827
Witte MM, Foster NL, Fleisher AS, Williams MM, Quaid K, Wasserman M, et al. Clinical use of amyloid-positron emission tomography neuroimaging: Practical and bioethical considerations. Alzheimers Dement Diagn Assess Dis Monit. 2015;1:358–67. Elsevier Inc.
O’Brien JT, Herholz K. Amyloid imaging for dementia in clinical practice. BMC Med. 2015;13:163.
Rowe CC, Bourgeat P, Ellis KA, Brown B, Lim YY, Mulligan R, et al. Predicting Alzheimer disease with β-amyloid imaging: results from the Australian imaging, biomarkers, and lifestyle study of ageing. Ann Neurol. 2013;74:905–13.
pubmed: 24448836 pmcid: 24448836
Blennow K, Mattsson N, Schöll M, Hansson O, Zetterberg H. Amyloid biomarkers in Alzheimer’s disease. Trends Pharmacol Sci. 2015;36:297–309.
pubmed: 25840462 pmcid: 25840462
Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55:306–19.
pubmed: 14991808 pmcid: 14991808
Blennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol. 2010;6:131–44. Nature Publishing Group.
pubmed: 20157306 pmcid: 20157306
Janelidze S, Pannee J, Mikulskis A, Chiao P, Zetterberg H, Blennow K, et al. Concordance between different amyloid immunoassays and visual amyloid positron emission tomographic assessment. JAMA Neurol. 2017;74:1492–501.
pubmed: 29114726 pmcid: 29114726
Pannee J, Portelius E, Minthon L, Gobom J, Andreasson U, Zetterberg H, et al. Reference measurement procedure for CSF amyloid beta (Aβ)1–42 and the CSF Aβ1–42/Aβ1–40 ratio – a cross-validation study against amyloid PET. J Neurochem. 2016;139:651–8.
pubmed: 27579672 pmcid: 27579672
Janelidze S, Zetterberg H, Mattsson N, Palmqvist S, Vanderstichele H, Lindberg O, et al. CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol. 2016;3:154–65.
pubmed: 27042676 pmcid: 27042676
Hansson O, Zetterberg H, Buchhave P, Andreasson U, Londos E, Minthon L, et al. Prediction of Alzheimer’s disease using the CSF Aβ42/Aβ40 ratio in patients with mild cognitive impairment. Dement Geriatr Cogn Disord. 2007;23:316–20.
pubmed: 17374949 pmcid: 17374949
Bateman RJ, Wen G, Morris JC, Holtzman DM. Fluctuations of CSF amyloid-ß levels: implications for a diagnostic and therapeutic biomarker. Neurology. 2007;69:1063–5.
Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13:614–29.
pubmed: 24849862 pmcid: 24849862
Lövheim H, Elgh F, Johansson A, Zetterberg H, Blennow K, Hallmans G, et al. Plasma concentrations of free amyloid β cannot predict the development of Alzheimer’s disease. Alzheimers Dement. 2017;13:778–82.
pubmed: 28073031 pmcid: 28073031
Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93:e1647–59.
pubmed: 31371569 pmcid: 31371569
Verberk IMW, Slot RE, Verfaillie SCJ, Heijst H, Prins ND, van Berckel BNM, et al. Plasma amyloid as Prescreener for the earliest Alzheimer pathological changes. Ann Neurol. 2018;84:648–58.
pubmed: 30196548 pmcid: 30196548
Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V, et al. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature. 2018;554:249–54.
Verberk IMW, Hendriksen HMA, van Harten AC, Wesselman LMP, Verfaillie SCJ, van den Bosch KA, et al. Plasma amyloid is associated with the rate of cognitive decline in cognitively normal elderly: the SCIENCe project. Neurobiol Aging. 2020;89:99–107. Elsevier Inc.
de Wolf F, Ghanbari M, Licher S, McRae-McKee K, Gras L, Weverling GJ, et al. Plasma tau, neurofilament light chain and amyloid-β levels and risk of dementia; a population-based cohort study. Brain. 2020;143:1220–32.
pubmed: 32206776 pmcid: 32206776
Albani D, Marizzoni M, Ferrari C, Fusco F, Boeri L, Raimondi I, et al. Plasma Aβ42 as a biomarker of prodromal Alzheimer’s disease progression in patients with amnestic mild cognitive impairment: evidence from the PharmaCog/E-ADNI study. Perry G, editor. J Alzheimers Dis. 2019;69:37–48.
pubmed: 30149449 pmcid: 30149449
Vergallo A, Mégret L, Lista S, Cavedo E, Zetterberg H, Blennow K, et al. Plasma amyloid β 40/42 ratio predicts cerebral amyloidosis in cognitively normal individuals at risk for Alzheimer’s disease. Alzheimers Dement. 2019;15:764–75.
pubmed: 31113759 pmcid: 31113759
Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13:841–9. Elsevier Inc.
pubmed: 28734653 pmcid: 28734653
Nabers A, Ollesch J, Schartner J, Kötting C, Genius J, Haußmann U, et al. An infrared sensor analysing label-free the secondary structure of the Abeta peptide in presence of complex fluids. J Biophotonics. 2016;9:224–34.
pubmed: 25808829 pmcid: 25808829
Nabers A, Ollesch J, Schartner J, Kötting C, Genius J, Hafermann H, et al. Amyloid-β-secondary structure distribution in cerebrospinal fluid and blood measured by an immuno-infrared-sensor: a biomarker candidate for Alzheimer’s disease. Anal Chem. 2016;88:2755–62.
pubmed: 26828829 pmcid: 26828829
Nabers A, Perna L, Lange J, Mons U, Schartner J, Güldenhaupt J, et al. Amyloid blood biomarker detects Alzheimer’s disease. EMBO Mol Med. 2018;10:e8763.
pubmed: 29626112 pmcid: 29626112
Nabers A, Hafermann H, Wiltfang J, Gerwert K. Aβ and tau structure-based biomarkers for a blood- and CSF-based two-step recruitment strategy to identify patients with dementia due to Alzheimer’s disease. Alzheimers Dement Diagn Assess Dis Monit. 2019;11:257–63.
Bateman RJ, Xiong C, Benzinger TLS, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367:795–804.
pubmed: 22784036 pmcid: 22784036
Sarroukh R, Cerf E, Derclaye S, Dufrêne YF, Goormaghtigh E, Ruysschaert J-M, et al. Transformation of amyloid β (1–40) oligomers into fibrils is characterized by a major change in secondary structure. Cell Mol Life Sci. 2011;68:1429–38.
pubmed: 20853129 pmcid: 20853129
Serpell LC. Alzheimer’s amyloid fibrils: structure and assembly. Biochim Biophys Acta Mol Basis Dis. 2000;1502:16–30.
Milanesi L, Sheynis T, Xue WF, Orlova EV, Hellewell AL, Jelinek R, et al. Direct three-dimensional visualization of membrane disruption by amyloid fibrils. Proc Natl Acad Sci U S A. 2012;109:20455–60.
pubmed: 23184970 pmcid: 23184970
Zawisza I, Rózga M, Bal W. Affinity of copper and zinc ions to proteins and peptides related to neurodegenerative conditions (Aβ, APP, α-synuclein, PrP). Coord Chem Rev. 2012;256:2297–307. Elsevier B.V.
Benilova I, Karran E, De Strooper B. The toxic Aβ oligomer and Alzheimer’s disease: an emperor in need of clothes. Nat Neurosci. 2012;15:349–57. Nature Publishing Group.
pubmed: 22286176 pmcid: 22286176
Villemagne VL, Burnham S, Bourgeat P, Brown B, Ellis KA, Salvado O, et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol. 2013;12:357–67. Elsevier Ltd.
pubmed: 23477989 pmcid: 23477989
Schartner J, Nabers A, Budde B, Lange J, Hoeck N, Wiltfang J, et al. An ATR–FTIR sensor unraveling the drug intervention of Methylene Blue, Congo Red, and Berberine on Human Tau and Aβ. ACS Med Chem Lett. 2017;8:710–4.
pubmed: 28740603 pmcid: 28740603
Stocker H, Nabers A, Perna L, Möllers T, Rujescu D, Hartmann A, et al. Prediction of Alzheimer’s disease diagnosis within 14 years through Aβ misfolding in blood plasma compared to APOE4 status, and other risk factors. Alzheimers Dement. 2020;16:283–91.
pubmed: 31611055 pmcid: 31611055
van der Flier WM, Scheltens P. Amsterdam dementia cohort: performing research to optimize care. J Alzheimers Dis. 2018;62:1091–111. Perry G, Avila J, Tabaton M, Zhu X, editors.
pubmed: 29562540 pmcid: 29562540
Slot RER, Verfaillie SCJ, Overbeek JM, Timmers T, Wesselman LMP, Teunissen CE, et al. Subjective Cognitive Impairment Cohort (SCIENCe): study design and first results. Alzheimers Res Ther. 2018;10:76.
pubmed: 30081935 pmcid: 30081935
van der Flier WM, Pijnenburg YAL, Prins N, Lemstra AW, Bouwman FH, Teunissen CE, et al. Optimizing patient care and research: the Amsterdam dementia cohort. J Alzheimers Dis. 2014;41:313–27.
pubmed: 24614907 pmcid: 24614907
Stewart R. Subjective cognitive impairment. Curr Opin Psychiatry. 2012;25:445–50.
pubmed: 23037961 pmcid: 23037961
Tijms BM, Willemse EAJ, Zwan MD, Mulder SD, Visser PJ, van Berckel BNM, et al. Unbiased approach to counteract upward drift in cerebrospinal fluid amyloid-β 1–42 analysis results. Clin Chem. 2018;64:576–85.
pubmed: 29208658 pmcid: 29208658
Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chételat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement. 2014;10:844–52.
pubmed: 24798886 pmcid: 24798886
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment. Arch Neurol. 1999;56:303–9.
pubmed: 10190820 pmcid: 10190820
Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:270–9.
pubmed: 3312027 pmcid: 3312027
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer ’ s disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology. 1984;34:939.
pubmed: 6610841 pmcid: 6610841
McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:263–9.
Budde B, Schartner J, Tönges L, Kötting C, Nabers A, Gerwert K. Reversible Immuno-infrared sensor for the detection of Alzheimer’s disease related biomarkers. ACS Sensors. 2019;4:1851–6.
pubmed: 31241315 pmcid: 31241315
Uno H, Cai T, Tian L, Wei LJ. Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc. 2007;102:527–37.
Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62.
pubmed: 29653606 pmcid: 29653606

Auteurs

Julia Stockmann (J)

Competence Center for Biospectroscopy, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany.
Department of Biophysics, Ruhr University Bochum, Faculty of Biology and Biotechnology, Bochum, Germany.

Inge M W Verberk (IMW)

Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.

Nina Timmesfeld (N)

Ruhr University Bochum, Department of Medical Informatics, Biometry and Epidemiology, Bochum, Germany.

Robin Denz (R)

Ruhr University Bochum, Department of Medical Informatics, Biometry and Epidemiology, Bochum, Germany.

Brian Budde (B)

Competence Center for Biospectroscopy, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany.
Department of Biophysics, Ruhr University Bochum, Faculty of Biology and Biotechnology, Bochum, Germany.

Julia Lange-Leifhelm (J)

Competence Center for Biospectroscopy, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany.
Department of Biophysics, Ruhr University Bochum, Faculty of Biology and Biotechnology, Bochum, Germany.

Philip Scheltens (P)

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.

Wiesje M van der Flier (WM)

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.

Andreas Nabers (A)

Competence Center for Biospectroscopy, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany.
Department of Biophysics, Ruhr University Bochum, Faculty of Biology and Biotechnology, Bochum, Germany.

Charlotte E Teunissen (CE)

Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.

Klaus Gerwert (K)

Competence Center for Biospectroscopy, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany. klaus.gerwert@rub.de.
Department of Biophysics, Ruhr University Bochum, Faculty of Biology and Biotechnology, Bochum, Germany. klaus.gerwert@rub.de.

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