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1.
Albert  MS, DeKosky  ST, Dickson  D,  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(3):270-279. doi:10.1016/j.jalz.2011.03.008PubMedGoogle ScholarCrossref
2.
McKhann  GM, Knopman  DS, Chertkow  H,  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(3):263-269. doi:10.1016/j.jalz.2011.03.005PubMedGoogle ScholarCrossref
3.
Mattsson  N, Carrillo  MC, Dean  RA,  et al.  Revolutionizing Alzheimer’s disease and clinical trials through biomarkers.  Alzheimers Dement (Amst). 2015;1(4):412-419.PubMedGoogle Scholar
4.
Blennow  K, Mattsson  N, Schöll  M, Hansson  O, Zetterberg  H.  Amyloid biomarkers in Alzheimer’s disease.  Trends Pharmacol Sci. 2015;36(5):297-309. doi:10.1016/j.tips.2015.03.002PubMedGoogle ScholarCrossref
5.
Alzheimer’s Association.  2017 Alzheimer’s disease facts and figures.  Alzheimers Dement. 2017;13 (4):325-273. doi:10.1016/j.jalz.2017.02.001Google ScholarCrossref
6.
Brookmeyer  R, Johnson  E, Ziegler-Graham  K, Arrighi  HM.  Forecasting the global burden of Alzheimer’s disease.  Alzheimers Dement. 2007;3(3):186-191. doi:10.1016/j.jalz.2007.04.381PubMedGoogle ScholarCrossref
7.
Keshavan  A, Heslegrave  A, Zetterberg  H, Schott  JM.  Blood biomarkers for Alzheimer’s disease: much promise, cautious progress.  Mol Diagn Ther. 2017;21(1):13-22. doi:10.1007/s40291-016-0241-0PubMedGoogle ScholarCrossref
8.
Voyle  N, Baker  D, Burnham  SC,  et al; AIBL research group.  Blood protein markers of neocortical amyloid-β burden: a candidate study using SOMAscan technology.  J Alzheimers Dis. 2015;46(4):947-961. doi:10.3233/JAD-150020PubMedGoogle ScholarCrossref
9.
Chatterjee  P, Goozee  K, Sohrabi  HR,  et al.  Association of plasma neurofilament light chain with neocortical amyloid-β load and cognitive performance in cognitively normal elderly participants.  J Alzheimers Dis. 2018;63(2):479-487. doi:10.3233/JAD-180025PubMedGoogle ScholarCrossref
10.
Dage  JL, Wennberg  AMV, Airey  DC,  et al.  Levels of tau protein in plasma are associated with neurodegeneration and cognitive function in a population-based elderly cohort.  Alzheimers Dement. 2016;12(12):1226-1234. doi:10.1016/j.jalz.2016.06.001PubMedGoogle ScholarCrossref
11.
Deters  KD, Risacher  SL, Kim  S,  et al; Alzheimer Disease Neuroimaging Initiative.  Plasma tau association with brain atrophy in mild cognitive impairment and Alzheimer’s disease.  J Alzheimers Dis. 2017;58(4):1245-1254. doi:10.3233/JAD-161114PubMedGoogle ScholarCrossref
12.
Fandos  N, Pérez-Grijalba  V, Pesini  P,  et al; AIBL Research Group.  Plasma amyloid β 42/40 ratios as biomarkers for amyloid β cerebral deposition in cognitively normal individuals.  Alzheimers Dement (Amst). 2017;8:179-187.PubMedGoogle Scholar
13.
Janelidze  S, Stomrud  E, Palmqvist  S,  et al.  Plasma β-amyloid in Alzheimer’s disease and vascular disease.  Sci Rep. 2016;6:26801. doi:10.1038/srep26801PubMedGoogle ScholarCrossref
14.
Lue  LF, Sabbagh  MN, Chiu  MJ,  et al.  Plasma levels of Aβ42 and tau identified probable Alzheimer’s dementia: findings in two cohorts.  Front Aging Neurosci. 2017;9:226. doi:10.3389/fnagi.2017.00226PubMedGoogle ScholarCrossref
15.
Mattsson  N, Andreasson  U, Zetterberg  H, Blennow  K; Alzheimer’s Disease Neuroimaging Initiative.  Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease.  JAMA Neurol. 2017;74(5):557-566. doi:10.1001/jamaneurol.2016.6117PubMedGoogle ScholarCrossref
16.
Mattsson  N, Zetterberg  H, Janelidze  S,  et al; ADNI Investigators.  Plasma tau in Alzheimer disease.  Neurology. 2016;87(17):1827-1835. doi:10.1212/WNL.0000000000003246PubMedGoogle ScholarCrossref
17.
Zhou  W, Zhang  J, Ye  F,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Plasma neurofilament light chain levels in Alzheimer’s disease.  Neurosci Lett. 2017;650:60-64. doi:10.1016/j.neulet.2017.04.027PubMedGoogle ScholarCrossref
18.
Olsson  B, Lautner  R, Andreasson  U,  et al.  CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis.  Lancet Neurol. 2016;15(7):673-684. doi:10.1016/S1474-4422(16)00070-3PubMedGoogle ScholarCrossref
19.
Nakamura  A, Kaneko  N, Villemagne  VL,  et al.  High performance plasma amyloid-β biomarkers for Alzheimer’s disease.  Nature. 2018;554(7691):249-254. doi:10.1038/nature25456PubMedGoogle ScholarCrossref
20.
Ovod  V, Ramsey  KN, Mawuenyega  KG,  et al.  Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis.  Alzheimers Dement. 2017;13(8):841-849. doi:10.1016/j.jalz.2017.06.2266PubMedGoogle ScholarCrossref
21.
Mattsson  N, Andreasson  U, Persson  S,  et al; Alzheimer’s Association QC Program Work Group.  CSF biomarker variability in the Alzheimer’s Association quality control program  [published correction appears in Alzheimers Dement. 2015;11(2):237].  Alzheimers Dement. 2013;9(3):251-261. doi:10.1016/j.jalz.2013.01.010PubMedGoogle ScholarCrossref
22.
Vos  SJ, Visser  PJ, Verhey  F,  et al.  Variability of CSF Alzheimer’s disease biomarkers: implications for clinical practice.  PLoS One. 2014;9(6):e100784. doi:10.1371/journal.pone.0100784PubMedGoogle ScholarCrossref
23.
Bittner  T, Zetterberg  H, Teunissen  CE,  et al.  Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of β-amyloid (1-42) in human cerebrospinal fluid.  Alzheimers Dement. 2016;12(5):517-526. doi:10.1016/j.jalz.2015.09.009PubMedGoogle ScholarCrossref
24.
Hansson  O, Seibyl  J, Stomrud  E,  et al; Swedish BioFINDER study group; Alzheimer’s Disease Neuroimaging Initiative.  CSF biomarkers of Alzheimer’s disease concord with amyloid-β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts.  Alzheimers Dement. 2018;14(11):1470-1481. doi:10.1016/j.jalz.2018.01.010PubMedGoogle ScholarCrossref
25.
Janelidze  S, Pannee  J, Mikulskis  A,  et al.  Concordance between different amyloid immunoassays and visual amyloid positron emission tomographic assessment.  JAMA Neurol. 2017;74(12):1492-1501. doi:10.1001/jamaneurol.2017.2814PubMedGoogle ScholarCrossref
26.
Jack  CR  Jr, Bennett  DA, Blennow  K,  et al; Contributors.  NIA-AA research framework: toward a biological definition of Alzheimer’s disease.  Alzheimers Dement. 2018;14(4):535-562. doi:10.1016/j.jalz.2018.02.018PubMedGoogle ScholarCrossref
27.
Mattsson  N, Insel  PS, Palmqvist  S,  et al.  Increased amyloidogenic APP processing in APOE ɛ4-negative individuals with cerebral β-amyloidosis.  Nat Commun. 2016;7:10918. doi:10.1038/ncomms10918PubMedGoogle ScholarCrossref
28.
Petersen  RC.  Mild cognitive impairment: current research and clinical implications.  Semin Neurol. 2007;27(1):22-31. doi:10.1055/s-2006-956752PubMedGoogle ScholarCrossref
29.
The Swedish BIOFINDER Study. http://biofinder.se/. Accessed May 24, 2019.
30.
Rózga  M, Bittner  T, Batrla  R, Karl  J.  Preanalytical sample handling recommendations for Alzheimer’s disease plasma biomarkers.  Alzheimers Dement (Amst). 2019;11:291-300. .PubMedGoogle Scholar
31.
Palmqvist  S, Zetterberg  H, Blennow  K,  et al.  Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid β-amyloid 42: a cross-validation study against amyloid positron emission tomography.  JAMA Neurol. 2014;71(10):1282-1289. doi:10.1001/jamaneurol.2014.1358PubMedGoogle ScholarCrossref
32.
Janelidze  S, Zetterberg  H, Mattsson  N,  et al; Swedish BioFINDER study group.  CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios: better diagnostic markers of Alzheimer disease.  Ann Clin Transl Neurol. 2016;3(3):154-165. doi:10.1002/acn3.274PubMedGoogle ScholarCrossref
33.
Leuzy  A, Chiotis  K, Hasselbalch  SG,  et al.  Pittsburgh compound B imaging and cerebrospinal fluid amyloid-β in a multicentre European memory clinic study.  Brain. 2016;139(Pt 9):2540-2553. doi:10.1093/brain/aww160PubMedGoogle ScholarCrossref
34.
Lewczuk  P, Matzen  A, Blennow  K,  et al.  Cerebrospinal fluid Aβ42/40 corresponds better than Aβ42 to amyloid PET in Alzheimer’s disease.  J Alzheimers Dis. 2017;55(2):813-822. doi:10.3233/JAD-160722PubMedGoogle ScholarCrossref
35.
Bertens  D, Tijms  BM, Scheltens  P, Teunissen  CE, Visser  PJ.  Unbiased estimates of cerebrospinal fluid β-amyloid 1-42 cutoffs in a large memory clinic population.  Alzheimers Res Ther. 2017;9(1):8. doi:10.1186/s13195-016-0233-7PubMedGoogle ScholarCrossref
36.
Palmqvist  S, Schöll  M, Strandberg  O,  et al.  Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity.  Nat Commun. 2017;8(1):1214. doi:10.1038/s41467-017-01150-xPubMedGoogle ScholarCrossref
37.
Petersen  RC, Smith  GE, Waring  SC, Ivnik  RJ, Tangalos  EG, Kokmen  E.  Mild cognitive impairment: clinical characterization and outcome.  Arch Neurol. 1999;56(3):303-308. doi:10.1001/archneur.56.3.303PubMedGoogle ScholarCrossref
38.
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(7):939-944. doi:10.1212/WNL.34.7.939PubMedGoogle ScholarCrossref
39.
Jansen  WJ, Ossenkoppele  R, Knol  DL,  et al; Amyloid Biomarker Study Group.  Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis.  JAMA. 2015;313(19):1924-1938. doi:10.1001/jama.2015.4668PubMedGoogle ScholarCrossref
40.
Palmqvist  S, Insel  PS, Zetterberg  H,  et al; Alzheimer’s Disease Neuroimaging Initiative; Swedish BioFINDER study.  Accurate risk estimation of beta-amyloid positivity to identify prodromal Alzheimer’s disease: cross-validation study of practical algorithms.  Alzheimers Dement. 2018.PubMedGoogle Scholar
41.
Olofsen  E, Dahan  A.  Using Akaike’s information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study.  F1000Res. 2013;2:71. doi:10.12688/f1000research.2-71.v1PubMedGoogle ScholarCrossref
42.
DeLong  ER, DeLong  DM, Clarke-Pearson  DL.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.  Biometrics. 1988;44(3):837-845. doi:10.2307/2531595PubMedGoogle ScholarCrossref
43.
Insel  PS, Palmqvist  S, Mackin  RS,  et al.  Assessing risk for preclinical β-amyloid pathology with APOE, cognitive, and demographic information.  Alzheimers Dement (Amst). 2016;4:76-84.PubMedGoogle Scholar
44.
Kim  HJ, Park  KW, Kim  TE,  et al.  Elevation of the plasma Aβ40/Aβ42 ratio as a diagnostic marker of sporadic early-onset Alzheimer’s disease.  J Alzheimers Dis. 2015;48(4):1043-1050. doi:10.3233/JAD-143018PubMedGoogle ScholarCrossref
45.
Mayeux  R, Honig  LS, Tang  MX,  et al.  Plasma A[beta]40 and A[beta]42 and Alzheimer’s disease: relation to age, mortality, and risk.  Neurology. 2003;61(9):1185-1190. doi:10.1212/01.WNL.0000091890.32140.8FPubMedGoogle ScholarCrossref
46.
Mielke  MM, Hagen  CE, Xu  J,  et al.  Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography.  Alzheimers Dement. 2018;14(8):989-997. doi:10.1016/j.jalz.2018.02.013PubMedGoogle ScholarCrossref
47.
Hansson  O, Janelidze  S, Hall  S,  et al; Swedish BioFINDER study.  Blood-based NfL: a biomarker for differential diagnosis of parkinsonian disorder.  Neurology. 2017;88(10):930-937. doi:10.1212/WNL.0000000000003680PubMedGoogle ScholarCrossref
48.
Palmqvist  S, Insel  PS, Zetterberg  H,  et al; Alzheimer’s Disease Neuroimaging Initiative; Swedish BioFINDER study.  Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer’s disease: cross-validation study of practical algorithms.  Alzheimers Dement. 2019;15(2):194-204. doi:10.1016/j.jalz.2018.08.014PubMedGoogle ScholarCrossref
Original Investigation
June 24, 2019

Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease–Related β-Amyloid Status

Author Affiliations
  • 1Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
  • 2Department of Neurology, Skåne University Hospital, Malmö, Sweden
  • 3Memory Clinic, Skåne University Hospital, Malmö, Sweden
  • 4Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
  • 5Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
  • 6Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom
  • 7UK Dementia Research Institute at UCL, London, United Kingdom
  • 8Roche Diagnostics GmbH, Penzberg, Germany
  • 9Genentech, a Member of the Roche Group, Basel, Switzerland
JAMA Neurol. 2019;76(9):1060-1069. doi:10.1001/jamaneurol.2019.1632
Key Points

Question  Do plasma levels of β-amyloid 42, β-amyloid 40, and tau detect cerebral β-amyloid status when measured using fully automated immunoassays?

Findings  In 2 cross-sectional studies, plasma β-amyloid 42 to β-amyloid 40 ratio, measured using immunoassay, accurately predicted cerebral β-amyloid status in all stages of Alzheimer disease in the BioFINDER cohort (n = 842) and in an independent validation cohort (n = 237). The diagnostic accuracy was further increased by analyzing APOE genotype.

Meaning  Blood-based β-amyloid 42 and β-amyloid 40 ratio together with APOE genotype may be used as prescreening tests in primary care and in clinical Alzheimer disease trials to lower the costs and number of positron emission tomography scans and lumbar punctures.

Abstract

Importance  Accurate blood-based biomarkers for Alzheimer disease (AD) might improve the diagnostic accuracy in primary care, referrals to memory clinics, and screenings for AD trials.

Objective  To examine the accuracy of plasma β-amyloid (Aβ) and tau measured using fully automated assays together with other blood-based biomarkers to detect cerebral Aβ.

Design, Setting, and Participants  Two prospective, cross-sectional, multicenter studies. Study participants were consecutively enrolled between July 6, 2009, and February 11, 2015 (cohort 1), and between January 29, 2000, and October 11, 2006 (cohort 2). Data were analyzed in 2018. The first cohort comprised 842 participants (513 cognitively unimpaired [CU], 265 with mild cognitive impairment [MCI], and 64 with AD dementia) from the Swedish BioFINDER study. The validation cohort comprised 237 participants (34 CU, 109 MCI, and 94 AD dementia) from a German biomarker study.

Main Outcome and Measures  The cerebrospinal fluid (CSF) Aβ42/Aβ40 ratio was used as the reference standard for brain Aβ status. Plasma Aβ42, Aβ40 and tau were measured using Elecsys immunoassays (Roche Diagnostics) and examined as predictors of Aβ status in logistic regression models in cohort 1 and replicated in cohort 2. Plasma neurofilament light chain (NFL) and heavy chain (NFH) and APOE genotype were also examined in cohort 1.

Results  The mean (SD) age of the 842 participants in cohort 1 was 72 (5.6) years, with a range of 59 to 88 years, and 446 (52.5%) were female. For the 237 in cohort 2, mean (SD) age was 66 (10) years with a range of 23 to 85 years, and 120 (50.6%) were female. In cohort 1, plasma Aβ42 and Aβ40 predicted Aβ status with an area under the receiver operating characteristic curve (AUC) of 0.80 (95% CI, 0.77-0.83). When adding APOE, the AUC increased significantly to 0.85 (95% CI, 0.82-0.88). Slight improvements were seen when adding plasma tau (AUC, 0.86; 95% CI, 0.83-0.88) or tau and NFL (AUC, 0.87; 95% CI, 0.84-0.89) to Aβ42, Aβ40 and APOE. The results were similar in CU and cognitively impaired participants, and in younger and older participants. Applying the plasma Aβ42 and Aβ40 model from cohort 1 in cohort 2 resulted in slightly higher AUC (0.86; 95% CI, 0.81-0.91), but plasma tau did not contribute. Using plasma Aβ42, Aβ40, and APOE in an AD trial screening scenario reduced positron emission tomography costs up to 30% to 50% depending on cutoff.

Conclusions and Relevance  Plasma Aβ42 and Aβ40 measured using Elecsys immunoassays predict Aβ status in all stages of AD with similar accuracy in a validation cohort. Their accuracy can be further increased by analyzing APOE genotype. Potential future applications of these blood tests include prescreening of Aβ positivity in clinical AD trials to lower the costs and number of positron emission tomography scans or lumbar punctures.

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