Association of Longitudinal β-Amyloid Accumulation Determined by Positron Emission Tomography With Clinical and Cognitive Decline in Adults With Probable Lewy Body Dementia | Dementia and Cognitive Impairment | JAMA Network Open | JAMA Network
[Skip to Navigation]
Sign In
Figure 1.  Trajectories of Change in Carbon-11 Labeled Pittsburgh Compound B Standardized Uptake Value Ratio (PiB SUVR) and Baseline PiB SUVR
Trajectories of Change in Carbon-11 Labeled Pittsburgh Compound B Standardized Uptake Value Ratio (PiB SUVR) and Baseline PiB SUVR

A, Regardless of clinical group, change in PiB SUVR increases, peaks at a baseline PiB SUVR of approximately 1.8, and then decreases, forming an inverted U–shaped curve. Change in PiB SUVR did not differ between the probable dementia with Lewy bodies (DLB) and cognitively unimpaired (CU) groups in the shape or vertical shift between the trajectories; confidence bands, indicated by shaded areas, largely overlap. The widening of the confidence bands on the right side of the panel reflects the lower number of participants (n = 11) with higher baseline PiB SUVR values (ie, >1.7). B, The inverted U–shaped curves were integrated into the sigmoid-shaped trajectory of cumulative PiB SUVR as a function of time in years.

Figure 2.  Rate of Change in Clinical and Cognitive Measures by Baseline Carbon-11 Labeled Pittsburgh Compound B Standardized Uptake Value Ratios (PiB SUVR) and Change in PiB SUVR Among Patients with Probable Dementia with Lewy Bodies
Rate of Change in Clinical and Cognitive Measures by Baseline Carbon-11 Labeled Pittsburgh Compound B Standardized Uptake Value Ratios (PiB SUVR) and Change in PiB SUVR Among Patients with Probable Dementia with Lewy Bodies

A, Scatterplots show significant associations of the baseline cross-sectional PiB SUVR with the annualized rates of change in measures of clinical and cognitive decline in patients with probable DLB. B, Scatterplots show associations of change in PiB SUVR with changes in measures of clinical and cognitive decline; associations with change in Clinical Dementia Rating, sum of boxes (CDR-SOB) score and Auditory Verbal Learning Test (AVLT), delayed recall are significant. The estimates for these associations are from simple linear regression models (Table 2). BNT indicates Boston Naming Test; DRS, Dementia Rating Scale; and TMT-A, Trail Making Test, part A.

Table 1.  Participants’ Baseline Characteristics
Participants’ Baseline Characteristics
Table 2.  Associations of Baseline PiB SUVR and Change in PiB SUVR With Clinical and Cognitive Decline in Probable Dementia with Lewy Bodies
Associations of Baseline PiB SUVR and Change in PiB SUVR With Clinical and Cognitive Decline in Probable Dementia with Lewy Bodies
Table 3.  Sample Size Estimates for Hypothetical Clinical Trial in Dementia with Lewy Bodiesa
Sample Size Estimates for Hypothetical Clinical Trial in Dementia with Lewy Bodiesa
1.
McKeith  IG, Dickson  DW, Lowe  J,  et al; Consortium on DLB.  Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium.  Neurology. 2005;65(12):1863-1872. doi:10.1212/01.wnl.0000187889.17253.b1PubMedGoogle ScholarCrossref
2.
Schneider  JA, Arvanitakis  Z, Bang  W, Bennett  DA.  Mixed brain pathologies account for most dementia cases in community-dwelling older persons.  Neurology. 2007;69(24):2197-2204. doi:10.1212/01.wnl.0000271090.28148.24PubMedGoogle ScholarCrossref
3.
Ferman  TJ, Aoki  N, Crook  JE,  et al.  The limbic and neocortical contribution of α-synuclein, tau, and amyloid β to disease duration in dementia with Lewy bodies.  Alzheimers Dement. 2018;14(3):330-339. doi:10.1016/j.jalz.2017.09.014PubMedGoogle ScholarCrossref
4.
Irwin  DJ, Grossman  M, Weintraub  D,  et al.  Neuropathological and genetic correlates of survival and dementia onset in synucleinopathies: a retrospective analysis.  Lancet Neurol. 2017;16(1):55-65. doi:10.1016/S1474-4422(16)30291-5PubMedGoogle ScholarCrossref
5.
Wakisaka  Y, Furuta  A, Tanizaki  Y, Kiyohara  Y, Iida  M, Iwaki  T.  Age-associated prevalence and risk factors of Lewy body pathology in a general population: the Hisayama study.  Acta Neuropathol. 2003;106(4):374-382. doi:10.1007/s00401-003-0750-xPubMedGoogle ScholarCrossref
6.
Mueller  C, Soysal  P, Rongve  A,  et al.  Survival time and differences between dementia with Lewy bodies and Alzheimer’s disease following diagnosis: a meta-analysis of longitudinal studies.  Ageing Res Rev. 2019;50:72-80. doi:10.1016/j.arr.2019.01.005PubMedGoogle ScholarCrossref
7.
Graff-Radford  J, Aakre  J, Savica  R,  et al.  Duration and pathologic correlates of Lewy body disease.  JAMA Neurol. 2017;74(3):310-315. doi:10.1001/jamaneurol.2016.4926PubMedGoogle ScholarCrossref
8.
Klunk  WE, Engler  H, Nordberg  A,  et al.  Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-B.  Ann Neurol. 2004;55(3):306-319. doi:10.1002/ana.20009PubMedGoogle ScholarCrossref
9.
Fodero-Tavoletti  MT, Smith  DP, McLean  CA,  et al.  In vitro characterization of Pittsburgh compound-B binding to Lewy bodies.  J Neurosci. 2007;27(39):10365-10371. doi:10.1523/JNEUROSCI.0630-07.2007PubMedGoogle ScholarCrossref
10.
Kantarci  K, Yang  C, Schneider  JA,  et al.  Antemortem amyloid imaging and β-amyloid pathology in a case with dementia with Lewy bodies.  Neurobiol Aging. 2012;33(5):878-885. doi:10.1016/j.neurobiolaging.2010.08.007PubMedGoogle ScholarCrossref
11.
Petrou  M, Dwamena  BA, Foerster  BR,  et al.  Amyloid deposition in Parkinson’s disease and cognitive impairment: a systematic review.  Mov Disord. 2015;30(7):928-935. doi:10.1002/mds.26191PubMedGoogle ScholarCrossref
12.
Donaghy  P, Thomas  AJ, O’Brien  JT.  Amyloid PET imaging in Lewy body disorders.  Am J Geriatr Psychiatry. 2015;23(1):23-37. doi:10.1016/j.jagp.2013.03.001PubMedGoogle ScholarCrossref
13.
Villemagne  VL, Burnham  S, Bourgeat  P,  et al; Australian Imaging Biomarkers and Lifestyle (AIBL) Research Group.  Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study.  Lancet Neurol. 2013;12(4):357-367. doi:10.1016/S1474-4422(13)70044-9PubMedGoogle ScholarCrossref
14.
Villain  N, Chételat  G, Grassiot  B,  et al; AIBL Research Group.  Regional dynamics of amyloid-β deposition in healthy elderly, mild cognitive impairment and Alzheimer’s disease: a voxelwise PiB-PET longitudinal study.  Brain. 2012;135(Pt 7):2126-2139. doi:10.1093/brain/aws125PubMedGoogle ScholarCrossref
15.
Jack  CR  Jr, Wiste  HJ, Lesnick  TG,  et al.  Brain β-amyloid load approaches a plateau.  Neurology. 2013;80(10):890-896. doi:10.1212/WNL.0b013e3182840bbePubMedGoogle ScholarCrossref
16.
McKeith  IG, Boeve  BF, Dickson  DW,  et al.  Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB Consortium.  Neurology. 2017;89(1):88-100. doi:10.1212/WNL.0000000000004058PubMedGoogle ScholarCrossref
17.
Roberts  RO, Geda  YE, Knopman  DS,  et al.  The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics.  Neuroepidemiology. 2008;30(1):58-69. doi:10.1159/000115751PubMedGoogle ScholarCrossref
18.
Boeve  BF, Molano  JR, Ferman  TJ,  et al.  Validation of the Mayo Sleep Questionnaire to screen for REM sleep behavior disorder in an aging and dementia cohort.  Sleep Med. 2011;12(5):445-453. doi:10.1016/j.sleep.2010.12.009PubMedGoogle ScholarCrossref
19.
Ferman  TJ, Smith  GE, Boeve  BF,  et al.  DLB fluctuations: specific features that reliably differentiate DLB from AD and normal aging.  Neurology. 2004;62(2):181-187. doi:10.1212/WNL.62.2.181PubMedGoogle ScholarCrossref
20.
Schwarz  CG, Senjem  ML, Gunter  JL,  et al.  Optimizing PiB-PET SUVR change-over-time measurement by a large-scale analysis of longitudinal reliability, plausibility, separability, and correlation with MMSE.  Neuroimage. 2017;144(Pt A):113-127.PubMedGoogle ScholarCrossref
21.
Kantarci  K, Lowe  VJ, Boeve  BF,  et al.  Multimodality imaging characteristics of dementia with Lewy bodies.  Neurobiol Aging. 2012;33(9):2091-2105. doi:10.1016/j.neurobiolaging.2011.09.024PubMedGoogle ScholarCrossref
22.
Ashburner  J, Friston  KJ.  Unified segmentation.  Neuroimage. 2005;26(3):839-851. doi:10.1016/j.neuroimage.2005.02.018PubMedGoogle ScholarCrossref
23.
Meltzer  CC, Leal  JP, Mayberg  HS, Wagner  HN  Jr, Frost  JJ.  Correction of PET data for partial volume effects in human cerebral cortex by MR imaging.  J Comput Assist Tomogr. 1990;14(4):561-570. doi:10.1097/00004728-199007000-00011PubMedGoogle ScholarCrossref
24.
Avants  BB, Epstein  CL, Grossman  M, Gee  JC.  Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.  Med Image Anal. 2008;12(1):26-41. doi:10.1016/j.media.2007.06.004PubMedGoogle ScholarCrossref
25.
Schwarz  CG, Gunter  JL, Ward  CP,  et al. The Mayo Clinic Adult Lifespan Template (MCALT): better quantification across the lifespan. Paper presented at: Alzheimer’s Association International Conference; July 15, 2017; London, UK.
26.
Neuroimaging Tool and Resources Collaboratory. Mayo Clinic Adult Lifespan Template and atlases. https://www.nitrc.org/projects/mcalt/. Accessed October 21, 2019.
27.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Defining imaging biomarker cut points for brain aging and Alzheimer’s disease.  Alzheimers Dement. 2017;13(3):205-216. doi:10.1016/j.jalz.2016.06.077PubMedGoogle ScholarCrossref
28.
Clinton  LK, Blurton-Jones  M, Myczek  K, Trojanowski  JQ, LaFerla  FM.  Synergistic interactions between abeta, tau, and alpha-synuclein: acceleration of neuropathology and cognitive decline.  J Neurosci. 2010;30(21):7281-7289. doi:10.1523/JNEUROSCI.0490-10.2010PubMedGoogle ScholarCrossref
29.
Halliday  GM, Song  YJ, Harding  AJ.  Striatal β-amyloid in dementia with Lewy bodies but not Parkinson’s disease.  J Neural Transm (Vienna). 2011;118(5):713-719. doi:10.1007/s00702-011-0641-6PubMedGoogle ScholarCrossref
30.
Gomperts  SN, Locascio  JJ, Marquie  M,  et al.  Brain amyloid and cognition in Lewy body diseases.  Mov Disord. 2012;27(8):965-973. doi:10.1002/mds.25048PubMedGoogle ScholarCrossref
31.
Lee  SH, Cho  H, Choi  JY,  et al.  Distinct patterns of amyloid-dependent tau accumulation in Lewy body diseases.  Mov Disord. 2018;33(2):262-272. doi:10.1002/mds.27252PubMedGoogle ScholarCrossref
32.
Foster  ER, Campbell  MC, Burack  MA,  et al.  Amyloid imaging of Lewy body-associated disorders.  Mov Disord. 2010;25(15):2516-2523. doi:10.1002/mds.23393PubMedGoogle ScholarCrossref
33.
Gomperts  SN, Rentz  DM, Moran  E,  et al.  Imaging amyloid deposition in Lewy body diseases.  Neurology. 2008;71(12):903-910. doi:10.1212/01.wnl.0000326146.60732.d6PubMedGoogle ScholarCrossref
34.
Sarro  L, Senjem  ML, Lundt  ES,  et al.  Amyloid-β deposition and regional grey matter atrophy rates in dementia with Lewy bodies.  Brain. 2016;139(Pt 10):2740-2750. doi:10.1093/brain/aww193PubMedGoogle ScholarCrossref
35.
Villemagne  VL, Pike  KE, Chételat  G,  et al.  Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease.  Ann Neurol. 2011;69(1):181-192. doi:10.1002/ana.22248PubMedGoogle ScholarCrossref
36.
Rowe  CC, Ellis  KA, Rimajova  M,  et al.  Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging.  Neurobiol Aging. 2010;31(8):1275-1283. doi:10.1016/j.neurobiolaging.2010.04.007PubMedGoogle ScholarCrossref
37.
Xiong  C, Jasielec  MS, Weng  H,  et al.  Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study.  Neurology. 2016;86(16):1499-1506. doi:10.1212/WNL.0000000000002593PubMedGoogle ScholarCrossref
38.
Donohue  MC, Sperling  RA, Petersen  R, Sun  CK, Weiner  MW, Aisen  PS; Alzheimer’s Disease Neuroimaging Initiative.  Association between elevated brain amyloid and subsequent cognitive decline among cognitively normal persons.  JAMA. 2017;317(22):2305-2316. doi:10.1001/jama.2017.6669PubMedGoogle ScholarCrossref
39.
Kantarci  K, Lowe  VJ, Boeve  BF,  et al.  AV-1451 tau and β-amyloid positron emission tomography imaging in dementia with Lewy bodies.  Ann Neurol. 2017;81(1):58-67. doi:10.1002/ana.24825PubMedGoogle ScholarCrossref
40.
Smits  LL, van Harten  AC, Pijnenburg  YA,  et al.  Trajectories of cognitive decline in different types of dementia.  Psychol Med. 2015;45(5):1051-1059. doi:10.1017/S0033291714002153PubMedGoogle ScholarCrossref
41.
Kramberger  MG, Auestad  B, Garcia-Ptacek  S,  et al; E-DLB.  Long-term cognitive decline in dementia with Lewy bodies in a large multicenter, international cohort.  J Alzheimers Dis. 2017;57(3):787-795. doi:10.3233/JAD-161109PubMedGoogle ScholarCrossref
42.
Nedelska  Z, Przybelski  SA, Lesnick  TG,  et al.  1H-MRS metabolites and rate of β-amyloid accumulation on serial PET in clinically normal adults.  Neurology. 2017;89(13):1391-1399. doi:10.1212/WNL.0000000000004421PubMedGoogle ScholarCrossref
43.
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
44.
Rahimi  J, Kovacs  GG.  Prevalence of mixed pathologies in the aging brain.  Alzheimers Res Ther. 2014;6(9):82. doi:10.1186/s13195-014-0082-1PubMedGoogle ScholarCrossref
45.
Graff-Radford  J, Boeve  BF, Pedraza  O,  et al.  Imaging and acetylcholinesterase inhibitor response in dementia with Lewy bodies.  Brain. 2012;135(Pt 8):2470-2477. doi:10.1093/brain/aws173PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    1 Comment for this article
    EXPAND ALL
    β-amyloid accumulation and cognitive decline in adults with probable Lewy body dementia
    Tomoyuki Kawada, MD | Nippon Medical School
    Nedelska et al. determined the trajectory of β-amyloid accumulation in patients with probable dementia with Lewy bodies (DLB) and investigated the associations of β-amyloid accumulation with measures of clinical and cognitive decline over time in DLB (1). Carbon-11 labeled Pittsburgh compound B (PiB) positron emission tomography was used for the analysis. The rate of change in PiB standardized uptake value ratio (SUVR) among participants with probable DLB increased, peaked, and then decreased, which was similar to controls and Alzheimer disease patients. Interestingly, higher baseline PiB SUVR and change in PiB SUVR were associated with more rapid clinical and cognitive decline over time. I want to add some information regarding PiB positron emission tomography.

    First, Kantarci et al. investigated the pathologic correlates of PiB uptake on PET in cases with antemortem diagnosis of probable DLB or Lewy body disease (LBD) at autopsy (2). Lower global cortical PiB SUVR distinguished cases with LBD from cases with AD or mixed pathology with an accuracy of 93%. In addition, the severity of diffuse Aβ pathology was the primary contributor to elevated PiB uptake in LBD. I understand that PiB SUVR is useful for classifying different clinical manifestations, and changing patterns in PiB SUVR should be specified by further study.

    Second, Sarro et al. investigated the associations between baseline PiB positron emission tomography and the longitudinal rates of grey matter atrophy in patients with DLB (3). They recognized that higher amyloid-β deposition at baseline was predictive of faster neurodegeneration in the cortex and also in the striatum, which is suggestive of possible interactions among amyloid-β, tau and α-synuclein aggregates. They recognized that higher amyloid-β deposition at baseline predicted a faster clinical decline over time in patients with probable DLB, which was in agreement with data by Nedelska et al. I have a great interest regarding the relationship between PiB information and disease-specific aggregation of biochemical substances.


    References
    1. Nedelska Z, Schwarz CG, Lesnick TG, et al. Association of longitudinal β-amyloid accumulation determined by positron emission tomography with clinical and cognitive decline in adults with probable Lewy body dementia. JAMA Netw Open. 2019;2(12):e1916439. doi:10.1001/jamanetworkopen.2019.16439
    2. Kantarci K, Lowe VJ, Chen Q, et al. β-Amyloid PET and neuropathology in dementia with Lewy bodies. Neurology. 2020;94(3):e282-e291. doi:10.1212/WNL.0000000000008818
    3. Sarro L, Senjem ML, Lundt ES, et al. Amyloid-β deposition and regional grey matter atrophy rates in dementia with Lewy bodies. Brain. 2016;139(Pt 10):2740-2750. doi
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Original Investigation
    Neurology
    December 2, 2019

    Association of Longitudinal β-Amyloid Accumulation Determined by Positron Emission Tomography With Clinical and Cognitive Decline in Adults With Probable Lewy Body Dementia

    Author Affiliations
    • 1Department of Radiology, Mayo Clinic, Rochester, Minnesota
    • 2Department of Health Sciences, Mayo Clinic, Rochester, Minnesota
    • 3Department of Neurology, Mayo Clinic, Rochester, Minnesota
    • 4Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida
    • 5Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
    JAMA Netw Open. 2019;2(12):e1916439. doi:10.1001/jamanetworkopen.2019.16439
    Key Points español 中文 (chinese)

    Question  What is the trajectory of β-amyloid accumulation over time, and how is it associated with clinical and cognitive decline among patients with probable dementia with Lewy bodies?

    Findings  This cohort study of 175 participants found that the cumulative density of β-amyloid accumulation by time in years followed a sigmoid-shaped form among patients with probable dementia with Lewy bodies as well as among cognitively unimpaired participants who were matched by age, sex, and apolipoprotein e4 status. In dementia with Lewy bodies, both baseline and longitudinal β-amyloid load accumulation were associated with measures of clinical and cognitive decline over time.

    Meaning  The results of this study suggest that longitudinal β-amyloid accumulation among patients with dementia with Lewy bodies could be used to track the clinical progression of dementia with Lewy bodies and potentially to design clinical trials targeting β-amyloid in dementia with Lewy bodies.

    Abstract

    Importance  In patients with probable dementia with Lewy bodies (DLB), overlapping Alzheimer disease pathology is frequent and is associated with faster decline and shorter survival. More than half of patients with DLB have elevated β-amyloid levels on carbon-11 labeled Pittsburgh compound B (PiB) positron emission tomography, but the trajectory of longitudinal β-amyloid accumulation and its associations with clinical and cognitive decline in DLB are not known.

    Objectives  To determine the trajectory of β-amyloid accumulation in patients with probable DLB and to investigate the associations of β-amyloid accumulation with measures of clinical and cognitive decline over time in DLB.

    Design, Setting, and Participants  This cohort study included 35 consecutive patients with probable DLB from the Mayo Clinic Alzheimer Disease Research Center and matched them by age, sex, and apolipoprotein e4 status with 140 cognitively unimpaired participants from the population-based Mayo Clinic Study of Aging. Participants were observed from April 2010 to September 2017. Data analysis was conducted from January 2018 to January 2019.

    Exposure  Baseline and follow-up PiB positron emission tomography and comprehensive clinical evaluations.

    Main Outcomes and Measures  Rate of change in PiB standardized uptake value ratios (SUVRs) by PiB SUVR and time in years; the associations between baseline PiB SUVR, change in PiB SUVR, and change in several measures of clinical and cognitive decline.

    Results  A total of 175 participants were evaluated (35 [20.0%] with probable DLB; mean [SD] age, 69.6 [7.3] years; 16 [45.7%] apolipoprotein e4 carriers; 31 [88.6%] men; and 140 [80.0%] cognitively unimpaired adults; mean [SD] age, 69.7 [7.2] years; 64 [45.7%] apolipoprotein e4 carriers; 124 [88.6%] men). In both groups, the rates of change in PiB SUVR showed an initial acceleration at lower baseline PiB SUVR followed by a deceleration at higher baseline PiB SUVR, thus forming an inverted-U shape. The trajectories of the rates of change in PiB SUVR did not differ between participants with probable DLB and cognitively unimpaired participants in terms of shape (P = .59) or vertical shift (coefficient [SE] 0.007 [0.006]; P = .22). The integral association of cumulative PiB SUVR with time in years showed a sigmoid-shaped functional form in both groups. In participants with probable DLB, higher baseline PiB SUVR and change in PiB SUVR were associated with more rapid clinical decline, as measured by the Clinical Dementia Rating, sum of boxes (baseline PiB SUVR: regression coefficient [SE], 1.90 [0.63]; P = .005; R2 = 0.215; change in PiB SUVR, regression coefficient [SE], 16.17 [7.47]; P = .04; R2 = 0.124) and the Auditory Verbal Learning Test, delayed recall (baseline PiB SUVR, regression coefficient [SE], −2.09 [0.95]; P = .04; R2 = 0.182; change in PiB SUVR, regression coefficient [SE], −25.05 [10.04]; P = .02; R2 = 0.221).

    Conclusions and Relevance  In this study, the rate of change in PiB SUVR among participants with probable DLB increased, peaked, and then decreased, which was similar to the trajectory in cognitively unimpaired participants and the Alzheimer disease dementia continuum. Higher baseline PiB SUVR and change in PiB SUVR were associated with more rapid clinical and cognitive decline over time. Measuring the change in PiB SUVR has implications for designing anti–β-amyloid randomized clinical trials for individuals with probable DLB.

    Introduction

    Dementia with Lewy bodies (DLB) is a common neurodegenerative dementia associated with Lewy body disease pathology. Patients with probable DLB frequently have varying levels of Alzheimer disease (AD) pathology, β-amyloid, and neurofibrillary tangles (NFT), in addition to Lewy body disease pathology.1,2 In DLB, concomitant AD pathology has been associated with a faster clinical progression and a shorter survival in autopsy-confirmed cohorts.3-7

    Positron emission tomography (PET) imaging with carbon-11 labeled Pittsburgh compound B (PiB) is a well-established biomarker of β-amyloid in vivo.8-10 Approximately two-thirds of patients with DLB have elevated PiB uptake on PET.11 However, the association of a higher PiB uptake with greater clinical or cognitive impairment has been equivocal in DLB cross-sectionally.12 Longitudinal studies in DLB are needed to understand the trajectory of PiB uptake over time and to determine its association with clinical progression. Monitoring these aspects will be important for identifying the most eligible candidates for emerging targeted treatments and for assessing the response to such treatments.

    Using serial PiB PET, prospective studies13-15 in cognitively unimpaired (CU) and in cognitively impaired individuals within the AD continuum with a range of baseline PiB standardized uptake value ratios (SUVRs) demonstrated that the rate of change in PiB SUVR is not linear. At lower baseline PiB SUVR, the rate of change in PiB SUVR accelerates and then decelerates at a higher baseline PiB SUVR,13-15 thus forming an inverted-U shaped curve as a function of baseline PiB SUVR.13,15 Consequentially, cumulative PiB SUVR as a function of time follows a sigmoid-shaped trajectory,13,15 reaching a plateau at high baseline PiB SUVR within the AD continuum,13,15 with implications for the timing of treatment strategies.

    In DLB, the trajectory of the change in PiB SUVR is not known. Nor is it known whether accelerated rates of change in PiB SUVR are associated with faster clinical declines in DLB. In this longitudinal PiB PET cohort study, our objective was to determine the change in PiB SUVR and the cumulative PiB SUVR over time in patients with probable DLB compared with CU adults with similar demographic characteristics. Our second objective was to evaluate the associations of baseline PiB SUVR and change in PiB SUVR with measures of longitudinal clinical and cognitive decline in probable DLB. A final objective was to calculate sample size estimates for a hypothetical randomized clinical trial targeting β-amyloid in DLB.

    Methods
    Data Source, Study Design, and Population

    The probable DLB group included 35 consecutive patients observed through the Mayo Clinic Alzheimer Disease Research Center between April 2010 and September 2017, of whom 32 met clinical criteria for probable DLB at baseline16 and 3 had mild cognitive impairment (MCI) at baseline and developed probable DLB by the first follow up. To compare the trajectory of change in PiB SUVR, we included 140 CU participants observed through the Mayo Clinic Study of Aging, a longitudinal, population-based cohort study.17 Cognitively unimpaired individuals were matched 4:1 with patients with probable DLB on age, sex, and apolipoprotein (APOE) e4 status; they remained CU throughout the study duration.

    Baseline and Follow-up Visits

    All participants were required to have a baseline PiB PET coupled with a comprehensive clinical evaluation and an identical follow-up within 12 to 15 months for the probable DLB group and within 15 to 30 months for the CU group. Baseline and follow-up visits incorporated a medical history review, informant interview, neurologic examination, neuropsychological assessment, and a series of informant questionnaires.3,17-19 After each visit, a consensus panel, composed of the study nurse, neurologist (B.F.B, J.G.-R., D.S.K., or R.C.P.), and neuropsychologist (T.J.F. or J.A.F.) who evaluated the participant, established the clinical diagnosis after accounting for visual or hearing deficits, education, and prior level of functioning.

    Clinical and Cognitive Measures

    Clinical severity and progression were determined using global cognitive assessments (ie, Mini-Mental State Examination [MMSE] and Dementia Rating Scale [DRS]) and noncognitive functional assessments (Clinical Dementia Rating scale, sum of boxes [CDR-SOB] and motor impairment by Unified Parkinson Disease Rating Scale part III [UPDRS-III]). Neuropsychological evaluations included the Auditory Verbal Learning Test (AVLT) for memory, the Boston Naming Test (BNT) for object naming, the Trail Making Test, part A (TMT-A) for divided attention, and the Rey Complex Figure (RCF) test for visual-perceptual processing.

    The study was approved by the Mayo Clinic institutional review board, and informed consent on participation was obtained from every participant or an appropriate surrogate. The study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    Imaging Study

    Baseline and follow-up PiB PET imaging was performed on PET-computed tomography systems operating in a 3-dimensional mode (GE Medical Systems). Scans consisted of four 5-minute dynamic frames acquired from 40 to 60 minutes after injection of PiB; detailed descriptions have been published elsewhere.15,20 For anatomic segmentation and labeling of PiB PET images, 3-dimensional, high-resolution, magnetization-prepared rapid gradient echo T1-weighted magnetic resonance imaging (MRI) scans, performed during the same visit cycle as the PiB PET, were acquired with a 3-T MRI scanner with 1 mm3 resolution (GE Medical Systems).21 Baseline and follow-up MRI images were automatically segmented and bias corrected using unified segmentation22 in statistical parametric mapping 12. We rigidly aligned PET images to MRI images, using statistical parametric mapping 12 (baseline-to-baseline and follow-up–to–follow-up), and MRI segmentations were used to perform 2-class partial volume correction.23 For consistency, we also performed analyses with no partial volume correction of PiB SUVR. Regions were automatically located using advanced normalization tools24 with the Mayo Clinic Adult Lifespan Template.25,26 For each PiB image, PiB uptake was calculated as the SUVR in a standard composite region consisting of voxels in the parietal, posterior cingulate, precuneus, prefrontal, orbitofrontal, temporal, and anterior cingulate cortices.15 To maximize the reliability and plausibility of measurements, we used 2 reference regions: 1 for baseline PiB SUVR and 1 for longitudinal change in PiB SUVR. For the baseline PiB SUVR measurement, we used a standard cerebellar crus reference region.27 To measure the change in PiB SUVR, we used a composite reference region of eroded supratentorial white matter, whole cerebellum, and pons; this technique was developed by our group, has been extensively tested and compared with multiple alternative approaches, and has been shown to improve reliability and plausibility for serial measurements compared with cross-sectional approaches.20

    Statistical Analysis

    Demographic, clinical, and cognitive characteristics of participants with probable DLB and CU participants at baseline were summarized using means with SDs or proportions. A log transformation or a square root transformation was performed to normalize the distribution of baseline PiB SUVR, MMSE score, and CDR-SOB score. Continuous variables were compared between probable DLB and CU groups using analysis of variance with a random block design with an added predictor to account for matching. The change in PiB SUVR for probable DLB and CU groups was constructed from partial volume-corrected serial PiB SUVR. Changes in PiB SUVR and in clinical and cognitive measures were annualized. We chose generalized additive models (GAMs) with 95% CIs to model the change in PiB SUVR as a function of baseline PiB SUVR. We used 4-df penalized splines in GAMs as our primary analysis to estimate the shapes of change in PiB SUVR vs baseline PiB SUVR for probable DLB and CU groups separately. Subsequently, we tested for a type of interaction between group (probable DLB or CU) and change in PiB SUVR by fitting fixed 4-df regression splines (to control the smooths and produce nested models) within each group and then by fitting a 4-df regression spline without differentiating the groups. We used an approximate F test from the analysis of deviance table comparing the models to test the interaction. We used GAMs to estimate the cumulative PiB SUVR as a function of time in years in the probable DLB and CU groups; GAMs accounted for matching between the groups. We used linear regression models to determine the association of baseline PiB SUVR and rate of change in PiB SUVR with rate of change in measures of clinical and cognitive decline. We reported results of models without adjustment for any covariates. We investigated regression models, adjusting for combinations of age, sex, education, and APOE e4 carrier status but found that no covariates were statistically significant nor did inclusion of the covariates produce qualitatively different results for PiB SUVR or change in PiB SUVR. Finally, in the probable DLB group, we estimated sample size for a hypothetical anti–β-amyloid clinical trial in patients with probable DLB. Mixed-effect models and the jackknife-based resampling method were used to estimate the sample sizes expressed as mean values with asymptotic confidence intervals. Change in PiB SUVR, CDR-SOB score, DRS score, and MMSE score were used for these calculations, assuming 1-sided tests, 80% power, α = 0.05, and readings at 12, 18, and 24 months of follow-up. Analyses were performed using SAS statistical software version 9.4 (SAS Institute) and R statistical software version 3.1.1 (R Foundation for Statistical Computing) with P < .05 considered statistically significant. All tests were 2-tailed, except for tests for sample size estimates, which were were 1-tailed.

    Results
    Baseline Cohort Characteristics

    Baseline characteristics of participants in the probable DLB and CU groups, matched on age, sex, and APOE e4 status, are listed in Table 1. In total, 175 participants were evaluated. Of these, 35 (20.0%) had probable DLB, with mean (SD) age of 69.6 (7.3) years; 16 (45.7%) were APOE e4 carriers; and 31 (88.6%) were men. A total of 140 CU participants (80.0%) were matched on age (mean [SD] age 69.7 [7.2] years), APOE e4 status (64 [45.7%] carriers), and sex (124 [88.6%] men) to patients with probable DLB. Dementia severity of participants with probable DLB was mild based on MMSE, DRS, and CDR-SOB scores. Mean (SD) baseline PiB SUVR, reported with partial volume correction, was higher among participants with probable DLB than among CU participants (1.58 [0.41] vs 1.36 [0.22]; P < .001; range, 1.17-2.57 vs 1.11-2.36). We obtained similar results on baseline PiB SUVR and findings in this study when we analyzed PiB SUVR data with no partial volume correction (mean [SD] baseline PiB SUVR 1.44 [0.36] vs 1.26 [0.20]; P < .001; range, 1.05-2.23 vs 1.01-2.21). The interval between baseline and follow-up visit was shorter among the probable DLB group than the CU group because of recruitment from 2 sources; therefore, change in PiB SUVR and changes in clinical and cognitive measures were annualized. Compared with patients with probable DLB who did not carry APOE e4, APOE e4 carriers had higher mean (SD) baseline PiB SUVR (1.40 [0.27] vs 1.79 [0.46]; P = .005) and lower mean (SD) UPDRS-III motor score (11.1 [5.5] vs 6.5 [5.7]; P = .02). In clinical and cognitive measures and frequencies of probable DLB, APOE e4 carriers vs noncarriers did not differ (ie, all P > .05). We did not examine differences in the change in PiB SUVR between participants with probable DLB who were APOE e4 carriers vs noncarriers because of relatively small subgroups.

    Trajectories of Change in PiB SUVR

    Change in PiB SUVR by baseline PiB SUVR did not differ between the probable DLB and CU groups (Figure 1A); the regression-based smooth curves for rate of change in PiB SUVR did not differ between DLB and CU (P = .59). Moreover, we observed no difference in the shape (vertical shift) of trajectories between the probable DLB and CU groups (regression spline model, approximate P = .07; penalized spline model, coefficient [SE] 0.007 [0.006]; P = .22) (Figure 1A). The association between change in PiB SUVR and baseline PiB SUVR was nonlinear (test of linearity, P < .001) in both PDLB and CU groups. In both probable DLB and CU groups, change in PiB SUVR accelerated at lower baseline PiB SUVR, peaked at a PiB SUVR of approximately 1.8, and then decelerated at higher baseline PiB SUVR, forming an inverted U–shaped curve as a function of baseline PiB SUVR. Subsequently, the associations of change in PiB SUVR as a function of baseline PiB SUVR were integrated into PiB SUVR as a function of time associations in probable DLB and CU groups (ie, the cumulative density function) (Figure 1B). The integral association of PiB SUVR by time rendered sigmoid-shaped trajectories for both probable DLB and CU groups (Figure 1B).

    Association of Baseline and Change in PiB SUVR With Clinical and Cognitive Decline in Patients With Probable DLB

    In patients with probable DLB, the associations of baseline PiB SUVR and change in PiB SUVR with measures of clinical progression are summarized in Table 2 and Figure 2. Higher baseline PiB SUVR was associated with a greater longitudinal decline, as measured by the DRS (regression coefficient [SE], −22.40 [6.53]; P = .002; R2 = 0.312), the CDR-SOB (regression coefficient [SE], 1.90 [0.63]; P = .005; R2 = 0.215), the AVLT, delayed recall (regression coefficient [SE], −2.09 [0.95]; P = .04; R2 = 0.182), the BNT (regression coefficient [SE], −2.39 [0.84]; P = .009; R2 = 0.245), and the TMT-A (regression coefficient [SE], 43.43 [12.96]; P = .002; R2 = 0.286). Similarly, greater change in PiB SUVR was associated with greater decline as measured by the CDR-SOB (regression coefficient [SE], 16.17 [7.47]; P = .04; R2 = 0.124) and the AVLT, delayed recall (regression coefficient [SE], −25.05 [10.04]; P = .02; R2 = 0.221). Baseline PiB SUVR and change in PiB SUVR were not associated with changes in MMSE score, UPDRS-III score, or visual-perceptual processing (Table 2).

    The nature of the selection of the CU participants resulted in a restricted range of change in cognition and clinical scales. For example, only 8 CU participants (5.7%) had nonzero values for change in CDR-SOB score. Thus, the findings from only 8 influential participants would have to be interpreted with extreme caution. In addition, since we selected CU participants to match patients with probable DLB on age, sex, and APOE e4 status, we could only make inferences about this CU sample, which does not fully represent the CU population.

    Sample Size Estimates for Hypothetical Clinical Trial in DLB

    The sample size estimates for a hypothetical clinical trial in patients with DLB showed that using the change in PiB SUVR to measure therapeutic effect would require the smallest sample size. Change in PiB SUVR was followed by change in CDR-SOB score, whereas using the measurements of changes in DRS and MMSE scores would require larger samples (Table 3).

    Discussion

    In this longitudinal cohort PiB PET study, we determined the trajectories of change in PiB SUVR in patients with mild probable DLB compared with CU participants, matched on demographic variables and APOE e4 status. The trajectories of change in PiB SUVR did not differ between probable DLB and CU groups. In both groups, the trajectories were nonlinear, with an initial acceleration at lower baseline PiB SUVR followed by a deceleration at higher baseline PiB SUVR. The integral association between cumulative PiB SUVR and time showed a sigmoid-shaped functional form in both probable DLB and CU groups, very similar to the trajectories reported in AD continuum cohorts, which included CU participants with a range of baseline PiB SUVRs.13-15 Furthermore, the rate of clinical progression in probable DLB was associated with both baseline PiB SUVR and change in PiB SUVR. We showed that measuring change in PiB SUVR and change in CDR-SOB score would require a smaller sample size in a hypothetical clinical trial among patients with probable DLB. Altogether, our findings suggest that measuring change in PiB SUVR is a valid biomarker of longitudinal β-amyloid accumulation in individuals with probable DLB and that progression of β-amyloid pathology in probable DLB is associated with functional and cognitive decline.

    We compared change in PiB SUVR between participants with probable DLB and CU participants who were matched by age, sex, and APOE e4 status. We hypothesized that such matching could allow for an indirect evaluation of the effect of α-synuclein on the change in PiB SUVR in participants with probable DLB. Interestingly, we found that change in PiB SUVR in the probable DLB group did not diverge from the CU group. However, the trajectories of change in PiB SUVR seen in our study closely resembled the trajectories of change in PiB SUVR in previous longitudinal studies on change in PiB SUVR among CU patients, patients with MCI, and patients with AD.13-15 These similarities across large cohorts and studies would suggest a relatively uniform progression of β-amyloid pathology with respect to baseline β-amyloid load in various neurodegenerative syndromes (ie, AD and DLB) and individuals with no cognitive impairment.

    We note that the primary underlying pathology contributing to cognitive impairment in probable DLB patients is α-synuclein, with additional β-amyloid, NFT-tau, and possibly other pathologies, such as vascular disease or TAR DNA-binding protein-43. There is growing evidence of complex interactions between α-synuclein, β-amyloid, and NFT-tau,4,28,29 such that individuals with higher α-synuclein levels also tend to have higher β-amyloid and NFT-tau burdens. However, our findings suggest that the likely presence of α-synuclein in patients with mild probable DLB does not significantly alter the trajectory of β-amyloid accumulation as measured by PET.

    The associations of baseline PiB SUVR with clinical and cognitive impairment have been ambiguous in probable DLB,12 which may be because of discrepancies in study design, small sample sizes of generally cross-sectional cohorts, and discrepancies in the interpretation of findings because observing an association is not equal to finding a causal association. Many studies combined patients with probable DLB, Parkinson disease dementia, or even MCI with Parkinson disease in 1 group. Some reported an association of higher PiB SUVR with lower MMSE scores,30 worse semantic memory,30,31 or lower CDR scores,32 whereas others did not find an association with MMSE33 or CDR scores.21 A study performed by our group34 observed an association of higher baseline PiB SUVR with worsening in CDR-SOB score over time. In the current study, we showed associations of baseline PiB SUVR with measures of longitudinal clinical and cognitive decline in patients with DLB. We found that a higher baseline PiB SUVR was associated with a more rapid decline as measured by DRS, CDR-SOB, AVLT, BNT, and TMT-A. Moreover, longitudinally, a greater change in PiB SUVR was associated with greater changes in CDR-SOB and AVLT scores. Thus, these 2 measures may be more sensitive and optimal for monitoring the cooccurrence of β-amyloid progression and clinical progression in probable DLB. The association of memory decline with PiB SUVR in probable DLB is interesting because, early in the AD continuum, many studies did not confirm associations of baseline PiB SUVR or change in PiB SUVR with memory decline.35,36 This could be owing to floor effect in AD and MCI studies, in which baseline memory performance is already moderately to severely impaired, but in DLB, baseline memory scores are less impaired. Aside from methodological issues, a potential biological explanation has been that β-amyloid alone is insufficient to influence cognitive impairment directly and rather constitutes an early event causing a chain of downstream pathologic changes leading to cognitive decline.35,37,38 We have shown that a higher PiB SUVR in patients with probable DLB was associated with higher fluoride-18 flortaucipir (AV-1451) uptake.39 It remains to be seen whether the associations of baseline PiB SUVR and change in PiB SUVR with clinical and cognitive decline in probable DLB are direct effects of the progression of β-amyloid accumulation or whether it is the progression of α-synuclein or NFT-tau that influences cognitive decline, thus making the association of β-amyloid progression with clinical and cognitive decline indirect.

    There was no association of PiB SUVR or change in PiB SUVR with changes in MMSE score, UPDRS-III score, or RCF-measured visual-perceptual performance. A potential explanation is lower statistical power or relatively narrow range of values in a probable DLB group of this size. Additionally, cognitive fluctuations may contribute to both short-term and long-term variability in clinical and cognitive evaluations. Moreover, the MMSE might not be an optimal measure of global cognitive decline in probable DLB,40 although some studies have suggested otherwise.41 Most importantly, these clinical and cognitive measures may be influenced by other pathologies, such as NFT-tau or α-synuclein, or by other neurologic and functional factors, such as mood or daytime sleepiness.

    Sample size calculations for a hypothetical clinical trial in patients with probable DLB showed that measuring change in PiB SUVR followed by change in CDR-SOB score required the smallest sample size compared with the most often–used global cognitive and functional measures. Favorable sample size estimates using change in CDR-SOB score may again suggest that global functional measures may be more optimal for tracking overall impairment in probable DLB and may track better with complex symptoms, such as cognitive, motor, sleep-related, affective, and psychiatric symptoms. Conversely, a large sample size by change in MMSE score indicated that the MMSE may not be an optimal measure for global cognitive decline in probable DLB in a clinical trial setting.

    Limitations

    Our study has some limitations. Although this longitudinal study sample was larger than most cross-sectional β-amyloid PET studies among individuals with probable DLB, it may still not have the sufficient power to detect subtle associations or conduct subgroup analyses, such as change in PiB SURV by APOE e4 status or by sex. The differences in change in PiB SUVR between CU APOE e4 carriers vs noncarriers were previously investigated42 but need to be investigated among individuals with probable DLB. A recent meta-analysis did not show greater prevalence of β-amyloid pathology by PET in women vs men within the AD continuum,43 but the sex effects need to be investigated further in probable DLB. Furthermore, CU participants may have various subthreshold pathologies owing to their population-based origin.44 Approximately 30% of CU participants have elevated baseline PiB SUVR.43 Some of them develop cognitive impairment and dementia,35 whereas others remain without cognitive impairment. It is likely that some of the CU participants in this study will later develop cognitive impairment. To mitigate this, CU participants had to remain clinically unimpaired during the follow-up period.

    Conclusions

    In this cohort study, the sigmoid trajectory of cumulative PiB SUVR by time observed in patients with probable DLB was consistent with the trajectories in the AD continuum, including the CU participants with lower baseline PiB SUVR. This finding suggests that, at sufficiently high baseline PiB SUVR, PiB uptake would reach equilibrium. This has potential implications for the timing of potential anti–β-amyloid strategies in probable DLB. Whereas the consequences of anti–β-amyloid approaches among patients with probable DLB are unknown at this time, associations of PiB SUVR and change in PiB SUVR with clinical and cognitive decline suggest that anti–β-amyloid strategies may have a place in clinical trials involving patients with probable DLB. However, how an anti–β-amyloid treatment would affect the progression of α-synuclein and NFT-tau in probable DLB remains to be seen. Because of the interactions among β-amyloid, α-synuclein, and NFT-tau,3,4 it is possible that targeting β-amyloid alone might contribute to overall pathologic progression and functional improvement in probable DLB patients. However, owing to the heterogeneity and complexity of underlying proteinopathies and clinical symptoms in probable DLB, individualized combination therapies with acetyl-cholinesterase inhibitors,45 lifestyle interventions, treatment of age-related comorbidities, and anti-tau treatments will need to be considered.

    Back to top
    Article Information

    Accepted for Publication: October 4, 2019.

    Published: December 2, 2019. doi:10.1001/jamanetworkopen.2019.16439

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Nedelska Z et al. JAMA Network Open.

    Corresponding Author: Kejal Kantarci, MD, MS, Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (kantarci.kejal@mayo.edu).

    Author Contributions: Dr Kantarci had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Nedelska, Kremers, Kantarci.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Nedelska, Lesnick, Kantarci.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Nedelska, Schwarz, Lesnick, Przybelski, Kremers.

    Obtained funding: Lowe, Kantarci.

    Administrative, technical, or material support: Boeve, Lowe, Senjem, Graff-Radford, Jack, Jr, Kantarci.

    Supervision: Nedelska, Lowe, Kantarci.

    Conflict of Interest Disclosures: Dr Schwarz reporting receiving grants from the National Institutes of Health outside the submitted work. Dr Boeve reported receiving grants from the National Institutes of Health, the Little Family Foundation, and the Turner Foundation during the conduct of the study; and receiving grants from Alector and Biogen; serving as an investigator for clinical trials sponsored by Axovant Gene Therapies and Biogen; receiving personal fees from the Tau Consortium for serving on its advisory board; and receiving royalties from Cambridge Medicine for Behavioral Neurology of Dementia outside the submitted work. Dr Lowe reported receiving grants from GE Healthcare, Siemens Healthcare, Avid Radiopharmaceuticals, the Minnesota Partnership for Biotechnology and Medical Genomics, and the Leukemia and Lymphoma Society; receiving research support from the National Institutes of Health, and serving as a consultant for Bayer Pharmaceuticals and Piramal Inc outside the submitted work. Dr Kremers reported receiving grants from the National Institutes of Health during the conduct of the study and grants from AstraZeneca, Biogen, and Roche outside the submitted work. Mr Senjem reported receiving grants from the National Institutes of Health during the conduct of the study; having stock options in Align Technology, CRISPR Therapeutics, Gilead Sciences, Globus Medical Inc, Inovio Pharmaceuticals, Ionis Pharmaceuticals, Johnson and Johnson, LHC Group Inc, Medtronic, Mesa Labs, Natus Medical Incorporated, Parexel International, and Varex Imaging outside the submitted work. Dr Graff-Radford reported receiving research support from the National Institutes of Health outside of the submitted work. Dr Fields reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Knopman reported serving as deputy editor of Neurology; serving on the data and safety monitoring boards for Eli Lilly and Co, Lundbeck Pharmaceuticals, and the Dominantly Inherited Alzheimer Disease Network Trials Unit; being an investigator in clinical trials supported by Baxter, Elan Pharmaceuticals, and the Tau Consortium; and receiving grants from the National Institutes of Health outside the submitted work. Dr Petersen reported receiving grants from the National Institutes of Health during the conduct of the study; and serving as a consultant for Roche Holding, Merck, Biogen, Eli Lilly and Company, Pfizer, Elan Pharmaceuticals, Wyeth Pharmaceuticals, GE Healthcare, and Eisai; receiving royalties from Oxford University Press for Mild Cognitive Impairment; serving on the data and safety monitoring board for Genentech; and presenting at GE Healthcare outside the submitted work. Dr Jack reported serving as a consultant for Eli Lilly and Co, serving on an independent data monitoring board for Roche Holding, and speaking for Eisai, but he received no personal compensation from any commercial entity; and receiving research support from the National Institutes of Health and the Alexander Family Alzheimer Disease Research Professorship of the Mayo Clinic outside the submitted work. Dr Kantarci reported serving on the data and safety monitoring board for Takeda Pharmaceutical Company; receiving research support from Avid Radiopharmaceuticals and Eli Lilly and Co; and receiving funding from the National Institutes of Health, the Bluefield Project to Cure Frontotemporal Dementia, the National Center for Advancing Translational Sciences, and the Alzheimer’s Drug Discovery Foundation outside the submitted work. No other disclosures were reported.

    Funding/Support: This study was supported by grants U01-NS100620, P50-AG016574, U01-AG006786, R01-AG011378, R01-AG041851, R01-AG040042, C06-RR018898, and R01-NS080820 from the National Institutes of Health, by the Fondation Dr Corinne Schuler, the Mangurian Foundation for Lewy Body Research, the Elsie and Marvin Dekelboum Family Foundation, and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer Disease Research Program. Dr Nedelska was supported by Clinical and Translational Science Awards grant UL1 TR002377 from the National Center for Advancing Translational Sciences.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The National Center for Advancing Translational Sciences is a component of the National Institutes of Health; the article’s contents are solely the responsibility of the authors and do not necessarily represent the official view of the National Institutes of Health.

    References
    1.
    McKeith  IG, Dickson  DW, Lowe  J,  et al; Consortium on DLB.  Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium.  Neurology. 2005;65(12):1863-1872. doi:10.1212/01.wnl.0000187889.17253.b1PubMedGoogle ScholarCrossref
    2.
    Schneider  JA, Arvanitakis  Z, Bang  W, Bennett  DA.  Mixed brain pathologies account for most dementia cases in community-dwelling older persons.  Neurology. 2007;69(24):2197-2204. doi:10.1212/01.wnl.0000271090.28148.24PubMedGoogle ScholarCrossref
    3.
    Ferman  TJ, Aoki  N, Crook  JE,  et al.  The limbic and neocortical contribution of α-synuclein, tau, and amyloid β to disease duration in dementia with Lewy bodies.  Alzheimers Dement. 2018;14(3):330-339. doi:10.1016/j.jalz.2017.09.014PubMedGoogle ScholarCrossref
    4.
    Irwin  DJ, Grossman  M, Weintraub  D,  et al.  Neuropathological and genetic correlates of survival and dementia onset in synucleinopathies: a retrospective analysis.  Lancet Neurol. 2017;16(1):55-65. doi:10.1016/S1474-4422(16)30291-5PubMedGoogle ScholarCrossref
    5.
    Wakisaka  Y, Furuta  A, Tanizaki  Y, Kiyohara  Y, Iida  M, Iwaki  T.  Age-associated prevalence and risk factors of Lewy body pathology in a general population: the Hisayama study.  Acta Neuropathol. 2003;106(4):374-382. doi:10.1007/s00401-003-0750-xPubMedGoogle ScholarCrossref
    6.
    Mueller  C, Soysal  P, Rongve  A,  et al.  Survival time and differences between dementia with Lewy bodies and Alzheimer’s disease following diagnosis: a meta-analysis of longitudinal studies.  Ageing Res Rev. 2019;50:72-80. doi:10.1016/j.arr.2019.01.005PubMedGoogle ScholarCrossref
    7.
    Graff-Radford  J, Aakre  J, Savica  R,  et al.  Duration and pathologic correlates of Lewy body disease.  JAMA Neurol. 2017;74(3):310-315. doi:10.1001/jamaneurol.2016.4926PubMedGoogle ScholarCrossref
    8.
    Klunk  WE, Engler  H, Nordberg  A,  et al.  Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-B.  Ann Neurol. 2004;55(3):306-319. doi:10.1002/ana.20009PubMedGoogle ScholarCrossref
    9.
    Fodero-Tavoletti  MT, Smith  DP, McLean  CA,  et al.  In vitro characterization of Pittsburgh compound-B binding to Lewy bodies.  J Neurosci. 2007;27(39):10365-10371. doi:10.1523/JNEUROSCI.0630-07.2007PubMedGoogle ScholarCrossref
    10.
    Kantarci  K, Yang  C, Schneider  JA,  et al.  Antemortem amyloid imaging and β-amyloid pathology in a case with dementia with Lewy bodies.  Neurobiol Aging. 2012;33(5):878-885. doi:10.1016/j.neurobiolaging.2010.08.007PubMedGoogle ScholarCrossref
    11.
    Petrou  M, Dwamena  BA, Foerster  BR,  et al.  Amyloid deposition in Parkinson’s disease and cognitive impairment: a systematic review.  Mov Disord. 2015;30(7):928-935. doi:10.1002/mds.26191PubMedGoogle ScholarCrossref
    12.
    Donaghy  P, Thomas  AJ, O’Brien  JT.  Amyloid PET imaging in Lewy body disorders.  Am J Geriatr Psychiatry. 2015;23(1):23-37. doi:10.1016/j.jagp.2013.03.001PubMedGoogle ScholarCrossref
    13.
    Villemagne  VL, Burnham  S, Bourgeat  P,  et al; Australian Imaging Biomarkers and Lifestyle (AIBL) Research Group.  Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study.  Lancet Neurol. 2013;12(4):357-367. doi:10.1016/S1474-4422(13)70044-9PubMedGoogle ScholarCrossref
    14.
    Villain  N, Chételat  G, Grassiot  B,  et al; AIBL Research Group.  Regional dynamics of amyloid-β deposition in healthy elderly, mild cognitive impairment and Alzheimer’s disease: a voxelwise PiB-PET longitudinal study.  Brain. 2012;135(Pt 7):2126-2139. doi:10.1093/brain/aws125PubMedGoogle ScholarCrossref
    15.
    Jack  CR  Jr, Wiste  HJ, Lesnick  TG,  et al.  Brain β-amyloid load approaches a plateau.  Neurology. 2013;80(10):890-896. doi:10.1212/WNL.0b013e3182840bbePubMedGoogle ScholarCrossref
    16.
    McKeith  IG, Boeve  BF, Dickson  DW,  et al.  Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB Consortium.  Neurology. 2017;89(1):88-100. doi:10.1212/WNL.0000000000004058PubMedGoogle ScholarCrossref
    17.
    Roberts  RO, Geda  YE, Knopman  DS,  et al.  The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics.  Neuroepidemiology. 2008;30(1):58-69. doi:10.1159/000115751PubMedGoogle ScholarCrossref
    18.
    Boeve  BF, Molano  JR, Ferman  TJ,  et al.  Validation of the Mayo Sleep Questionnaire to screen for REM sleep behavior disorder in an aging and dementia cohort.  Sleep Med. 2011;12(5):445-453. doi:10.1016/j.sleep.2010.12.009PubMedGoogle ScholarCrossref
    19.
    Ferman  TJ, Smith  GE, Boeve  BF,  et al.  DLB fluctuations: specific features that reliably differentiate DLB from AD and normal aging.  Neurology. 2004;62(2):181-187. doi:10.1212/WNL.62.2.181PubMedGoogle ScholarCrossref
    20.
    Schwarz  CG, Senjem  ML, Gunter  JL,  et al.  Optimizing PiB-PET SUVR change-over-time measurement by a large-scale analysis of longitudinal reliability, plausibility, separability, and correlation with MMSE.  Neuroimage. 2017;144(Pt A):113-127.PubMedGoogle ScholarCrossref
    21.
    Kantarci  K, Lowe  VJ, Boeve  BF,  et al.  Multimodality imaging characteristics of dementia with Lewy bodies.  Neurobiol Aging. 2012;33(9):2091-2105. doi:10.1016/j.neurobiolaging.2011.09.024PubMedGoogle ScholarCrossref
    22.
    Ashburner  J, Friston  KJ.  Unified segmentation.  Neuroimage. 2005;26(3):839-851. doi:10.1016/j.neuroimage.2005.02.018PubMedGoogle ScholarCrossref
    23.
    Meltzer  CC, Leal  JP, Mayberg  HS, Wagner  HN  Jr, Frost  JJ.  Correction of PET data for partial volume effects in human cerebral cortex by MR imaging.  J Comput Assist Tomogr. 1990;14(4):561-570. doi:10.1097/00004728-199007000-00011PubMedGoogle ScholarCrossref
    24.
    Avants  BB, Epstein  CL, Grossman  M, Gee  JC.  Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.  Med Image Anal. 2008;12(1):26-41. doi:10.1016/j.media.2007.06.004PubMedGoogle ScholarCrossref
    25.
    Schwarz  CG, Gunter  JL, Ward  CP,  et al. The Mayo Clinic Adult Lifespan Template (MCALT): better quantification across the lifespan. Paper presented at: Alzheimer’s Association International Conference; July 15, 2017; London, UK.
    26.
    Neuroimaging Tool and Resources Collaboratory. Mayo Clinic Adult Lifespan Template and atlases. https://www.nitrc.org/projects/mcalt/. Accessed October 21, 2019.
    27.
    Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Defining imaging biomarker cut points for brain aging and Alzheimer’s disease.  Alzheimers Dement. 2017;13(3):205-216. doi:10.1016/j.jalz.2016.06.077PubMedGoogle ScholarCrossref
    28.
    Clinton  LK, Blurton-Jones  M, Myczek  K, Trojanowski  JQ, LaFerla  FM.  Synergistic interactions between abeta, tau, and alpha-synuclein: acceleration of neuropathology and cognitive decline.  J Neurosci. 2010;30(21):7281-7289. doi:10.1523/JNEUROSCI.0490-10.2010PubMedGoogle ScholarCrossref
    29.
    Halliday  GM, Song  YJ, Harding  AJ.  Striatal β-amyloid in dementia with Lewy bodies but not Parkinson’s disease.  J Neural Transm (Vienna). 2011;118(5):713-719. doi:10.1007/s00702-011-0641-6PubMedGoogle ScholarCrossref
    30.
    Gomperts  SN, Locascio  JJ, Marquie  M,  et al.  Brain amyloid and cognition in Lewy body diseases.  Mov Disord. 2012;27(8):965-973. doi:10.1002/mds.25048PubMedGoogle ScholarCrossref
    31.
    Lee  SH, Cho  H, Choi  JY,  et al.  Distinct patterns of amyloid-dependent tau accumulation in Lewy body diseases.  Mov Disord. 2018;33(2):262-272. doi:10.1002/mds.27252PubMedGoogle ScholarCrossref
    32.
    Foster  ER, Campbell  MC, Burack  MA,  et al.  Amyloid imaging of Lewy body-associated disorders.  Mov Disord. 2010;25(15):2516-2523. doi:10.1002/mds.23393PubMedGoogle ScholarCrossref
    33.
    Gomperts  SN, Rentz  DM, Moran  E,  et al.  Imaging amyloid deposition in Lewy body diseases.  Neurology. 2008;71(12):903-910. doi:10.1212/01.wnl.0000326146.60732.d6PubMedGoogle ScholarCrossref
    34.
    Sarro  L, Senjem  ML, Lundt  ES,  et al.  Amyloid-β deposition and regional grey matter atrophy rates in dementia with Lewy bodies.  Brain. 2016;139(Pt 10):2740-2750. doi:10.1093/brain/aww193PubMedGoogle ScholarCrossref
    35.
    Villemagne  VL, Pike  KE, Chételat  G,  et al.  Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease.  Ann Neurol. 2011;69(1):181-192. doi:10.1002/ana.22248PubMedGoogle ScholarCrossref
    36.
    Rowe  CC, Ellis  KA, Rimajova  M,  et al.  Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging.  Neurobiol Aging. 2010;31(8):1275-1283. doi:10.1016/j.neurobiolaging.2010.04.007PubMedGoogle ScholarCrossref
    37.
    Xiong  C, Jasielec  MS, Weng  H,  et al.  Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study.  Neurology. 2016;86(16):1499-1506. doi:10.1212/WNL.0000000000002593PubMedGoogle ScholarCrossref
    38.
    Donohue  MC, Sperling  RA, Petersen  R, Sun  CK, Weiner  MW, Aisen  PS; Alzheimer’s Disease Neuroimaging Initiative.  Association between elevated brain amyloid and subsequent cognitive decline among cognitively normal persons.  JAMA. 2017;317(22):2305-2316. doi:10.1001/jama.2017.6669PubMedGoogle ScholarCrossref
    39.
    Kantarci  K, Lowe  VJ, Boeve  BF,  et al.  AV-1451 tau and β-amyloid positron emission tomography imaging in dementia with Lewy bodies.  Ann Neurol. 2017;81(1):58-67. doi:10.1002/ana.24825PubMedGoogle ScholarCrossref
    40.
    Smits  LL, van Harten  AC, Pijnenburg  YA,  et al.  Trajectories of cognitive decline in different types of dementia.  Psychol Med. 2015;45(5):1051-1059. doi:10.1017/S0033291714002153PubMedGoogle ScholarCrossref
    41.
    Kramberger  MG, Auestad  B, Garcia-Ptacek  S,  et al; E-DLB.  Long-term cognitive decline in dementia with Lewy bodies in a large multicenter, international cohort.  J Alzheimers Dis. 2017;57(3):787-795. doi:10.3233/JAD-161109PubMedGoogle ScholarCrossref
    42.
    Nedelska  Z, Przybelski  SA, Lesnick  TG,  et al.  1H-MRS metabolites and rate of β-amyloid accumulation on serial PET in clinically normal adults.  Neurology. 2017;89(13):1391-1399. doi:10.1212/WNL.0000000000004421PubMedGoogle ScholarCrossref
    43.
    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
    44.
    Rahimi  J, Kovacs  GG.  Prevalence of mixed pathologies in the aging brain.  Alzheimers Res Ther. 2014;6(9):82. doi:10.1186/s13195-014-0082-1PubMedGoogle ScholarCrossref
    45.
    Graff-Radford  J, Boeve  BF, Pedraza  O,  et al.  Imaging and acetylcholinesterase inhibitor response in dementia with Lewy bodies.  Brain. 2012;135(Pt 8):2470-2477. doi:10.1093/brain/aws173PubMedGoogle ScholarCrossref
    ×