Use of Flutemetamol F 18–Labeled Positron Emission Tomography and Other Biomarkers to Assess Risk of Clinical Progression in Patients With Amnestic Mild Cognitive Impairment | Dementia and Cognitive Impairment | JAMA Neurology | JAMA Network
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1.
Petersen  RC.  Mild cognitive impairment as a diagnostic entity.  J Intern Med. 2004;256(3):183-194.PubMedGoogle ScholarCrossref
2.
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.PubMedGoogle ScholarCrossref
3.
Vandenberghe  R, Van Laere  K, Ivanoiu  A,  et al.  18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial.  Ann Neurol. 2010;68(3):319-329.PubMedGoogle ScholarCrossref
4.
Kapasi  A, DeCarli  C, Schneider  JA.  Impact of multiple pathologies on the threshold for clinically overt dementia.  Acta Neuropathol. 2017;134(2):171-186.PubMedGoogle ScholarCrossref
5.
Petersen  RC, Morris  JC.  Mild cognitive impairment as a clinical entity and treatment target.  Arch Neurol. 2005;62(7):1160-1163.PubMedGoogle ScholarCrossref
6.
Wechsler  D.  WMS-R Wechsler Memory Scale—Revised Manual. New York, NY: The Psychological Corporation, Harcourt Brace Jovanovich, Inc; 1987.
7.
Aisen  PS, Petersen  RC, Donohue  M, Weiner  MW; Alzheimer’s Disease Neuroimaging Initiative.  Alzheimer’s Disease Neuroimaging Initiative 2 Clinical Core: progress and plans.  Alzheimers Dement. 2015;11(7):734-739.PubMedGoogle ScholarCrossref
8.
Morris  JC.  The Clinical Dementia Rating (CDR): current version and scoring rules.  Neurology. 1993;43(11):2412-2414.PubMedGoogle ScholarCrossref
9.
Hachinski  VC, Iliff  LD, Zilhka  E,  et al.  Cerebral blood flow in dementia.  Arch Neurol. 1975;32(9):632-637.PubMedGoogle ScholarCrossref
10.
Folstein  MF, Folstein  SE, McHugh  PR.  ‘Mini-mental state’: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198.Google ScholarCrossref
11.
Hamilton  M.  A rating scale for depression.  J Neurol Neurosurg Psychiatry. 1960;23:56-62.PubMedGoogle ScholarCrossref
12.
World Medical Association.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.  JAMA. 2013;310(20):2191-2194.PubMedGoogle ScholarCrossref
13.
Buckley  CJ, Sherwin  PF, Smith  AP, Wolber  J, Weick  SM, Brooks  DJ.  Validation of an electronic image reader training programme for interpretation of [18F]flutemetamol β-amyloid PET brain images.  Nucl Med Commun. 2017;38(3):234-241.PubMedGoogle ScholarCrossref
14.
Coupé  P, Manjón  JV, Fonov  V, Pruessner  J, Robles  M, Collins  DL.  Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.  Neuroimage. 2011;54(2):940-954.PubMedGoogle ScholarCrossref
15.
Ghafoorian  M, Karssemeijer  N, van Uden  IW,  et al.  Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease.  Med Phys. 2016;43(12):6246.PubMedGoogle ScholarCrossref
16.
Galasko  D, Bennett  D, Sano  M,  et al.  An inventory to assess activities of daily living for clinical trials in Alzheimer’s disease: the Alzheimer’s Disease Cooperative Study.  Alzheimer Dis Assoc Disord. 1997;11(suppl 2):S33-S39.PubMedGoogle ScholarCrossref
17.
Rosen  WG, Mohs  RC, Davis  KL.  A new rating scale for Alzheimer’s disease.  Am J Psychiatry. 1984;141(11):1356-1364.PubMedGoogle ScholarCrossref
18.
Wechsler  DA.  Wechsler Adult Intelligence Scale-Revised. New York, NY: Psychological Corporation; 1987.
19.
Butters  N, Granholm  E, Salmon  DP, Grant  I, Wolfe  J.  Episodic and semantic memory: a comparison of amnesic and demented patients.  J Clin Exp Neuropsychol. 1987;9(5):479-497.PubMedGoogle ScholarCrossref
20.
Reitan  RM.  Validity of the Trail Making Test as an indicator of organic brain disease.  Percept Mot Skills. 1958;8(3):271-276.Google ScholarCrossref
21.
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.PubMedGoogle ScholarCrossref
22.
Spruance  SL, Reid  JE, Grace  M, Samore  M.  Hazard ratio in clinical trials.  Antimicrob Agents Chemother. 2004;48(8):2787-2792.PubMedGoogle ScholarCrossref
23.
Wisse  LEM, Butala  N, Das  SR,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Suspected non-AD pathology in mild cognitive impairment.  Neurobiol Aging. 2015;36(12):3152-3162.PubMedGoogle ScholarCrossref
24.
Sperling  RA, Aisen  PS, Beckett  LA,  et al.  Toward defining the preclinical stages of 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):280-292.PubMedGoogle ScholarCrossref
25.
Dubois  B, Feldman  HH, Jacova  C,  et al.  Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria.  Lancet Neurol. 2014;13(6):614-629.PubMedGoogle ScholarCrossref
26.
Ward  A, Tardiff  S, Dye  C, Arrighi  HM.  Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: a systematic review of the literature.  Dement Geriatr Cogn Dis Extra. 2013;3(1):320-332.PubMedGoogle ScholarCrossref
27.
Okello  A, Koivunen  J, Edison  P,  et al.  Conversion of amyloid positive and negative MCI to AD over 3 years: an 11C-PIB PET study.  Neurology. 2009;73(10):754-760.PubMedGoogle ScholarCrossref
28.
Forsberg  A, Engler  H, Almkvist  O,  et al.  PET imaging of amyloid deposition in patients with mild cognitive impairment.  Neurobiol Aging. 2008;29(10):1456-1465.PubMedGoogle ScholarCrossref
29.
Wolk  DA, Price  JC, Saxton  JA,  et al.  Amyloid imaging in mild cognitive impairment subtypes  [published correction appears in Ann Neurol. 2009;66(1):123].  Ann Neurol. 2009;65(5):557-568.PubMedGoogle ScholarCrossref
30.
Ong  KT, Villemagne  VL, Bahar-Fuchs  A,  et al.  Aβ imaging with 18F-florbetaben in prodromal Alzheimer’s disease: a prospective outcome study.  J Neurol Neurosurg Psychiatry. 2015;86(4):431-436.PubMedGoogle ScholarCrossref
31.
Jack  CR  Jr, Wiste  HJ, Vemuri  P,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer’s disease.  Brain. 2010;133(11):3336-3348.PubMedGoogle ScholarCrossref
32.
Doraiswamy  PM, Sperling  RA, Johnson  K,  et al; AV45-A11 Study Group; AV45-A11 Study Group.  Florbetapir F 18 amyloid PET and 36-month cognitive decline: a prospective multicenter study.  Mol Psychiatry. 2014;19(9):1044-1051.PubMedGoogle ScholarCrossref
33.
Rowe  CC, Bourgeat  P, Ellis  KA,  et al.  Predicting Alzheimer disease with β-amyloid imaging: results from the Australian imaging, biomarkers, and lifestyle study of ageing.  Ann Neurol. 2013;74(6):905-913.PubMedGoogle ScholarCrossref
34.
Koivunen  J, Scheinin  N, Virta  JR,  et al.  Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study.  Neurology. 2011;76(12):1085-1090.PubMedGoogle ScholarCrossref
35.
Varon  D, Barker  W, Loewenstein  D,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Visual rating and volumetric measurement of medial temporal atrophy in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort: baseline diagnosis and the prediction of MCI outcome.  Int J Geriatr Psychiatry. 2015;30(2):192-200.PubMedGoogle ScholarCrossref
36.
Ewers  M, Walsh  C, Trojanowski  JQ,  et al; North American Alzheimer’s Disease Neuroimaging Initiative (ADNI).  Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance.  Neurobiol Aging. 2012;33(7):1203-1214.PubMedGoogle ScholarCrossref
37.
Gomar  JJ, Bobes-Bascaran  MT, Conejero-Goldberg  C, Davies  P, Goldberg  TE; Alzheimer’s Disease Neuroimaging Initiative.  Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer’s Disease Neuroimaging Initiative.  Arch Gen Psychiatry. 2011;68(9):961-969.PubMedGoogle ScholarCrossref
38.
Lim  A, Tsuang  D, Kukull  W,  et al.  Clinico-neuropathological correlation of Alzheimer’s disease in a community-based case series.  J Am Geriatr Soc. 1999;47(5):564-569.PubMedGoogle ScholarCrossref
39.
Jobst  KA, Barnetson  LP, Shepstone  BJ.  Accurate prediction of histologically confirmed Alzheimer’s disease and the differential diagnosis of dementia: the use of NINCDS-ADRDA and DSM-III-R criteria, SPECT, X-ray CT, and Apo E4 in medial temporal lobe dementias: Oxford Project to Investigate Memory and Aging.  Int Psychogeriatr. 1998;10(3):271-302.PubMedGoogle ScholarCrossref
40.
Kazee  AM, Eskin  TA, Lapham  LW, Gabriel  KR, McDaniel  KD, Hamill  RW.  Clinicopathologic correlates in Alzheimer disease: assessment of clinical and pathologic diagnostic criteria.  Alzheimer Dis Assoc Disord. 1993;7(3):152-164.PubMedGoogle ScholarCrossref
41.
Lopez  OL, Litvan  I, Catt  KE,  et al.  Accuracy of four clinical diagnostic criteria for the diagnosis of neurodegenerative dementias.  Neurology. 1999;53(6):1292-1299.PubMedGoogle ScholarCrossref
42.
Blacker  D, Albert  MS, Bassett  SS, Go  RC, Harrell  LE, Folstein  MF; The National Institute of Mental Health Genetics Initiative.  Reliability and validity of NINCDS-ADRDA criteria for Alzheimer’s disease.  Arch Neurol. 1994;51(12):1198-1204.PubMedGoogle ScholarCrossref
43.
Nagy  Z, Esiri  MM, Hindley  NJ,  et al.  Accuracy of clinical operational diagnostic criteria for Alzheimer’s disease in relation to different pathological diagnostic protocols.  Dement Geriatr Cogn Disord. 1998;9(4):219-226.PubMedGoogle ScholarCrossref
44.
Jack  CR  Jr, Bennett  DA, Blennow  K,  et al.  A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers.  Neurology. 2016;87(5):539-547.PubMedGoogle ScholarCrossref
45.
Dubois  B, Hampel  H, Feldman  HH,  et al; Proceedings of the Meeting of the International Working Group (IWG) and the American Alzheimer’s Association on “The Preclinical State of AD”; July 23, 2015; Washington DC, USA.  Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria.  Alzheimers Dement. 2016;12(3):292-323.PubMedGoogle ScholarCrossref
46.
Jack  CR  Jr, Knopman  DS, Weigand  SD,  et al.  An operational approach to National Institute on Aging–Alzheimer’s Association criteria for preclinical Alzheimer disease.  Ann Neurol. 2012;71(6):765-775.PubMedGoogle ScholarCrossref
47.
Petersen  RC, Aisen  P, Boeve  BF,  et al.  Mild cognitive impairment due to Alzheimer disease in the community.  Ann Neurol. 2013;74(2):199-208.PubMedGoogle Scholar
48.
Prestia  A, Caroli  A, van der Flier  WM,  et al.  Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease.  Neurology. 2013;80(11):1048-1056.PubMedGoogle ScholarCrossref
49.
Knopman  DS, Jack  CR  Jr, Wiste  HJ,  et al.  Brain injury biomarkers are not dependent on β-amyloid in normal elderly.  Ann Neurol. 2013;73(4):472-480.PubMedGoogle ScholarCrossref
50.
Raz  N, Lindenberger  U, Rodrigue  KM,  et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers.  Cereb Cortex. 2005;15(11):1676-1689.PubMedGoogle ScholarCrossref
51.
Shing  YL, Rodrigue  KM, Kennedy  KM,  et al.  Hippocampal subfield volumes: age, vascular risk, and correlation with associative memory.  Front Aging Neurosci. 2011;3:2.PubMedGoogle ScholarCrossref
52.
Erten-Lyons  D, Woltjer  R, Kaye  J,  et al.  Neuropathologic basis of white matter hyperintensity accumulation with advanced age.  Neurology. 2013;81(11):977-983.PubMedGoogle ScholarCrossref
Original Investigation
September 2018

Use of Flutemetamol F 18–Labeled Positron Emission Tomography and Other Biomarkers to Assess Risk of Clinical Progression in Patients With Amnestic Mild Cognitive Impairment

Author Affiliations
  • 1Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia
  • 2Division of Neurology, Nova Southeastern University, Fort Lauderdale, Florida
  • 3Division of Neurology, MD Clinical, Hallandale Beach, Florida
  • 4Turku PET Centre, University of Turku, Turku, Finland
  • 5Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
  • 6Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida
  • 7Imperial College Healthcare National Health Service Trust Charing Cross Hospital, London, United Kingdom
  • 8Mental Health and Clinical Research, Miami Jewish Health Systems, Miami, Florida
  • 9Galiz Research, Miami Springs, Florida
  • 10Barrows Neurological Institute, St Joseph’s Hospital and Medical Center, Phoenix, Arizona
  • 11Department of Neurology, Cliniques Universitaires St Luc, Brussels, Belgium
  • 12Memory Clinic, Department of Clinical Sciences, Lund University, Malmö, Sweden
  • 13Division of Psychiatry, University College London, London, United Kingdom
  • 14Specialist Dementia and Frailty Service, Essex Partnership University Foundation Trust, Essex, United Kingdom
  • 15Danish Dementia Research Centre, Rigshospitalet, Copenhagen University, Copenhagen, Denmark
  • 16Memory Assessment and Research Centre, Moorgreen Hospital, Southampton, United Kingdom
  • 17Clinical and Experimental Sciences, University of Southampton, Southampton, United Kingdom
  • 18Banner Sun Health Research Institute, Sun City, Arizona
  • 19Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 20Banner Alzheimer’s Institute, Phoenix, Arizona
  • 21Now with Eli Lilly and Company, Indianapolis, Indiana
  • 22The Princess Margaret Hospital, Windsor, United Kingdom
  • 23Neurologie Tervuursevest, Leuven, Belgium
  • 24Department of Nuclear Medicine and Molecular Imaging, University of Michigan Health System, Ann Arbor
  • 25Department of Neurology, Regional Dementia Research Centre, Copenhagen University Hospital, Roskilde, Denmark
  • 26Department of Neurology, Michigan State University, East Lansing
  • 27Kingshill Research Centre, Swindon, United Kingdom
  • 28Cyclotron Research Centre, University of Liège, Liège, Belgium
  • 29GE Healthcare Life Sciences, Amersham, Buckinghamshire, United Kingdom
  • 30GE Healthcare Life Sciences, Marlborough, Massachusetts
  • 31Institute of Molecular Bioimaging and Physiology, Rome, Italy
  • 32Glasgow Memory Clinic, Glasgow, United Kingdom
JAMA Neurol. 2018;75(9):1114-1123. doi:10.1001/jamaneurol.2018.0894
Key Points

Question  How can biomarkers be used to supplement clinical assessments in the workup of patients with amnestic mild cognitive impairment?

Findings  In this multicenter cohort study assessing progression from amnestic mild cognitive impairment to probable Alzheimer disease after flutemetamol F 18–labeled positron emission tomography, patients with β-amyloid–positive scans had approximately 2.5 times the risk of progressing to probable Alzheimer disease within 3 years compared with those with negative scan results. Adding the biomarkers of hippocampal volume and cognitive status to the model increased the risk of progression to 8.5:1 during the same observation period.

Meaning  Biomarker combinations may have more utility than single diagnostic tests to assist physicians in assessing the risk of future cognitive decline.

Abstract

Importance  Patients with amnestic mild cognitive impairment (aMCI) may progress to clinical Alzheimer disease (AD), remain stable, or revert to normal. Earlier progression to AD among patients who were β-amyloid positive vs those who were β-amyloid negative has been previously observed. Current research now accepts that a combination of biomarkers could provide greater refinement in the assessment of risk for clinical progression.

Objective  To evaluate the ability of flutemetamol F 18 and other biomarkers to assess the risk of progression from aMCI to probable AD.

Design, Setting, and Participants  In this multicenter cohort study, from November 11, 2009, to January 16, 2014, patients with aMCI underwent positron emission tomography (PET) at baseline followed by local clinical assessments every 6 months for up to 3 years. Patients with aMCI (365 screened; 232 were eligible) were recruited from 28 clinical centers in Europe and the United States. Physicians remained strictly blinded to the results of PET, and the standard of truth was an independent clinical adjudication committee that confirmed or refuted local assessments. Flutemetamol F 18–labeled PET scans were read centrally as either negative or positive by 5 blinded readers with no knowledge of clinical status. Statistical analysis was conducted from February 19, 2014, to January 26, 2018.

Interventions  Flutemetamol F 18–labeled PET at baseline followed by up to 6 clinical visits every 6 months, as well as magnetic resonance imaging and multiple cognitive measures.

Main Outcomes and Measures  Time from PET to probable AD or last follow-up was plotted as a Kaplan-Meier survival curve; PET scan results, age, hippocampal volume, and aMCI stage were entered into Cox proportional hazards logistic regression analyses to identify variables associated with progression to probable AD.

Results  Of 232 patients with aMCI (118 women and 114 men; mean [SD] age, 71.1 [8.6] years), 98 (42.2%) had positive results detected on PET scan. By 36 months, the rates of progression to probable AD were 36.2% overall (81 of 224 patients), 53.6% (52 of 97) for patients with positive results detected on PET scan, and 22.8% (29 of 127) for patients with negative results detected on PET scan. Hazard ratios for association with progression were 2.51 (95% CI, 1.57-3.99; P < .001) for a positive β-amyloid scan alone (primary outcome measure), 5.60 (95% CI, 3.14-9.98; P < .001) with additional low hippocampal volume, and 8.45 (95% CI, 4.40-16.24; P < .001) when poorer cognitive status was added to the model.

Conclusions and Relevance  A combination of positive results of flutemetamol F 18–labeled PET, low hippocampal volume, and cognitive status corresponded with a high probability of risk of progression from aMCI to probable AD within 36 months.

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