Association of Preclinical Alzheimer Disease With Optical Coherence Tomographic Angiography Findings | Radiology | JAMA Ophthalmology | JAMA Network
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Figure 1.  Foveal Avascular Zone (FAZ) Measurements
Foveal Avascular Zone (FAZ) Measurements

Measurements were obtained using optical coherence tomography (OCT) angiography (Avanti OptoVue; OptoVue). Top images depict the angiogram with nonflow areas of 0.212 mm2 (A) and 0.311 mm2 (B); bottom images, OCT scans.

Figure 2.  Foveal Thickness and Foveal Avascular Zone (FAZ) Measurements
Foveal Thickness and Foveal Avascular Zone (FAZ) Measurements

Data are shown as box and whisker plots, where whiskers represent 1.5 times the interquartile range. A, Positron emission tomography (PET) imaging results are shown for fluorine 18–labeled florbetapir compound testing. Open circles indicate outliers. B, Cerebrospinal fluid (CSF) analysis results are shown for β-amyloid 42 and τ protein biomarkers. C and D, Participants with negative findings for all biomarkers (PET and/or CSF) were compared with those with positive findings for at least 1 test.

Figure 3.  Receiver Operating Characteristics Curve for Foveal Avascular Zone (FAZ)
Receiver Operating Characteristics Curve for Foveal Avascular Zone (FAZ)

The receiver operating characteristics curve shows sensitivities (true-positive rate) and specificities (false-positive rate) of the FAZ comparison between all participants with biomarker-positive and biomarker-negative findings. Area under the curve is 0.8007 (95% CI, 0.6647-0.9367). Lower CI limits were also calculated for the data point closest to the nondiscriminatory (diagonal) line, assuming a normal distribution and a binomial distribution of the data.

1.
Alzheimer’s Association.  2016 Alzheimer’s disease facts and figures.  Alzheimers Dement. 2016;12(4):459-509. doi:10.1016/j.jalz.2016.03.001PubMedGoogle ScholarCrossref
2.
Ballard  C, Gauthier  S, Corbett  A, Brayne  C, Aarsland  D, Jones  E.  Alzheimer’s disease.  Lancet. 2011;377(9770):1019-1031. doi:10.1016/S0140-6736(10)61349-9PubMedGoogle ScholarCrossref
3.
Reitz  C, Mayeux  R.  Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers.  Biochem Pharmacol. 2014;88(4):640-651. doi:10.1016/j.bcp.2013.12.024PubMedGoogle ScholarCrossref
4.
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. doi:10.1016/j.jalz.2011.03.003PubMedGoogle ScholarCrossref
5.
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
6.
Bacskai  BJ, Frosch  MP, Freeman  SH,  et al.  Molecular imaging with Pittsburgh Compound B confirmed at autopsy: a case report.  Arch Neurol. 2007;64(3):431-434. doi:10.1001/archneur.64.3.431PubMedGoogle ScholarCrossref
7.
Wong  DF, Rosenberg  PB, Zhou  Y,  et al.  In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18).  J Nucl Med. 2010;51(6):913-920. doi:10.2967/jnumed.109.069088PubMedGoogle ScholarCrossref
8.
Johnson  KA, Sperling  RA, Gidicsin  CM,  et al; AV45-A11 study group.  Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer’s disease dementia, mild cognitive impairment, and normal aging.  Alzheimers Dement. 2013;9(5)(suppl):S72-S83. doi:10.1016/j.jalz.2012.10.007PubMedGoogle ScholarCrossref
9.
Clark  CM, Schneider  JA, Bedell  BJ,  et al; AV45-A07 Study Group.  Use of florbetapir-PET for imaging beta-amyloid pathology [published correction appears in JAMA. 2011;305(11):1096].  JAMA. 2011;305(3):275-283. doi:10.1001/jama.2010.2008PubMedGoogle ScholarCrossref
10.
Blennow  K, Hampel  H, Weiner  M, Zetterberg  H.  Cerebrospinal fluid and plasma biomarkers in Alzheimer disease.  Nat Rev Neurol. 2010;6(3):131-144. doi:10.1038/nrneurol.2010.4PubMedGoogle ScholarCrossref
11.
Scheltens  P, Blennow  K, Breteler  MMB,  et al.  Alzheimer’s disease.  Lancet. 2016;388(10043):505-517. doi:10.1016/S0140-6736(15)01124-1PubMedGoogle ScholarCrossref
12.
Morris  E, Chalkidou  A, Hammers  A, Peacock  J, Summers  J, Keevil  S.  Diagnostic accuracy of (18)F amyloid PET tracers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis.  Eur J Nucl Med Mol Imaging. 2016;43(2):374-385. doi:10.1007/s00259-015-3228-xPubMedGoogle ScholarCrossref
13.
Blennow  K, Dubois  B, Fagan  AM, Lewczuk  P, de Leon  MJ, Hampel  H.  Clinical utility of cerebrospinal fluid biomarkers in the diagnosis of early Alzheimer’s disease.  Alzheimers Dement. 2015;11(1):58-69. doi:10.1016/j.jalz.2014.02.004PubMedGoogle ScholarCrossref
14.
Roe  CM, Fagan  AM, Grant  EA,  et al.  Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later.  Neurology. 2013;80(19):1784-1791. doi:10.1212/WNL.0b013e3182918ca6PubMedGoogle ScholarCrossref
15.
Di Marco  LY, Venneri  A, Farkas  E, Evans  PC, Marzo  A, Frangi  AF.  Vascular dysfunction in the pathogenesis of Alzheimer’s disease: a review of endothelium-mediated mechanisms and ensuing vicious circles.  Neurobiol Dis. 2015;82:593-606. doi:10.1016/j.nbd.2015.08.014PubMedGoogle ScholarCrossref
16.
Berisha  F, Feke  GT, Trempe  CL, McMeel  JW, Schepens  CL.  Retinal abnormalities in early Alzheimer’s disease.  Invest Ophthalmol Vis Sci. 2007;48(5):2285-2289. doi:10.1167/iovs.06-1029PubMedGoogle ScholarCrossref
17.
Cheung  CY-L, Ong  YT, Ikram  MK,  et al.  Microvascular network alterations in the retina of patients with Alzheimer’s disease.  Alzheimers Dement. 2014;10(2):135-142. doi:10.1016/j.jalz.2013.06.009PubMedGoogle ScholarCrossref
18.
Golzan  SM, Goozee  K, Georgevsky  D,  et al.  Retinal vascular and structural changes are associated with amyloid burden in the elderly: ophthalmic biomarkers of preclinical Alzheimer’s disease.  Alzheimers Res Ther. 2017;9(1):13. doi:10.1186/s13195-017-0239-9PubMedGoogle ScholarCrossref
19.
Feke  GT, Hyman  BT, Stern  RA, Pasquale  LR.  Retinal blood flow in mild cognitive impairment and Alzheimer’s disease.  Alzheimers Dement (Amst). 2015;1(2):144-151.PubMedGoogle Scholar
20.
Jiang  H, Wei  Y, Shi  Y,  et al.  Altered macular microvasculature in mild cognitive impairment and Alzheimer disease  [published online October 16, 2017].  J Neuroophthalmol. doi:10.1097/WNO.0000000000000580Google Scholar
21.
Arevalo-Rodriguez  I, Smailagic  N, Roqué I Figuls  M,  et al.  Mini-Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI).  Cochrane Database Syst Rev. 2015;(3):CD010783.PubMedGoogle Scholar
22.
Räihä  I, Isoaho  R, Ojanlatva  A, Viramo  P, Sulkava  R, Kivelä  SL.  Poor performance in the Mini-Mental State Examination due to causes other than dementia.  Scand J Prim Health Care. 2001;19(1):34-38. doi:10.1080/028134301300034620PubMedGoogle ScholarCrossref
23.
World Medical Association.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.  JAMA. 2013;310(20):2191-2194. doi:10.1001/jama.2013.281053Google ScholarCrossref
24.
Bressler  SB, Edwards  AR, Andreoli  CM,  et al; for the Diabetic Retinopathy Clinical Research Network/Writing Committee.  Reproducibility of Optovue RTVue optical coherence tomography retinal thickness measurements and conversion to equivalent Zeiss Stratus metrics in diabetic macular edema.  Transl Vis Sci Technol. 2015;4(1):5. doi:10.1167/tvst.4.1.5PubMedGoogle ScholarCrossref
25.
Hinton  DR, Sadun  AA, Blanks  JC, Miller  CA.  Optic-nerve degeneration in Alzheimer’s disease.  N Engl J Med. 1986;315(8):485-487. doi:10.1056/NEJM198608213150804PubMedGoogle ScholarCrossref
26.
Blanks  JC, Torigoe  Y, Hinton  DR, Blanks  RH.  Retinal pathology in Alzheimer’s disease, I: ganglion cell loss in foveal/parafoveal retina.  Neurobiol Aging. 1996;17(3):377-384. doi:10.1016/0197-4580(96)00010-3PubMedGoogle ScholarCrossref
27.
Blanks  JC, Schmidt  SY, Torigoe  Y, Porrello  KV, Hinton  DR, Blanks  RH.  Retinal pathology in Alzheimer’s disease, II: regional neuron loss and glial changes in GCL.  Neurobiol Aging. 1996;17(3):385-395. doi:10.1016/0197-4580(96)00009-7PubMedGoogle ScholarCrossref
28.
Park  KW, Yoon  HJ, Kang  D-Y, Kim  BC, Kim  S, Kim  JW.  Regional cerebral blood flow differences in patients with mild cognitive impairment between those who did and did not develop Alzheimer’s disease.  Psychiatry Res. 2012;203(2-3):201-206. doi:10.1016/j.pscychresns.2011.12.007PubMedGoogle ScholarCrossref
29.
Caroli  A, Testa  C, Geroldi  C,  et al.  Cerebral perfusion correlates of conversion to Alzheimer’s disease in amnestic mild cognitive impairment.  J Neurol. 2007;254(12):1698-1707. doi:10.1007/s00415-007-0631-7PubMedGoogle ScholarCrossref
30.
Nobili  F, Frisoni  GB, Portet  F,  et al.  Brain SPECT in subtypes of mild cognitive impairment: findings from the DESCRIPA multicenter study.  J Neurol. 2008;255(9):1344-1353. doi:10.1007/s00415-008-0897-4PubMedGoogle ScholarCrossref
31.
Kalaria  RN, Pax  AB.  Increased collagen content of cerebral microvessels in Alzheimer’s disease.  Brain Res. 1995;705(1-2):349-352. doi:10.1016/0006-8993(95)01250-8PubMedGoogle ScholarCrossref
32.
De Jong  GI, De Vos  RA, Steur  EN, Luiten  PG.  Cerebrovascular hypoperfusion: a risk factor for Alzheimer’s disease? animal model and postmortem human studies.  Ann N Y Acad Sci. 1997;826:56-74. doi:10.1111/j.1749-6632.1997.tb48461.xPubMedGoogle ScholarCrossref
33.
Christov  A, Ottman  J, Hamdheydari  L, Grammas  P.  Structural changes in Alzheimer’s disease brain microvessels.  Curr Alzheimer Res. 2008;5(4):392-395. doi:10.2174/156720508785132334PubMedGoogle ScholarCrossref
34.
Farkas  E, De Jong  GI, de Vos  RA, Jansen Steur  EN, Luiten  PG.  Pathological features of cerebral cortical capillaries are doubled in Alzheimer’s disease and Parkinson’s disease.  Acta Neuropathol. 2000;100(4):395-402. doi:10.1007/s004010000195PubMedGoogle ScholarCrossref
35.
Patton  N, Aslam  T, Macgillivray  T, Pattie  A, Deary  IJ, Dhillon  B.  Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures.  J Anat. 2005;206(4):319-348. doi:10.1111/j.1469-7580.2005.00395.xPubMedGoogle ScholarCrossref
36.
Koronyo-Hamaoui  M, Koronyo  Y, Ljubimov  AV,  et al.  Identification of amyloid plaques in retinas from Alzheimer’s patients and noninvasive in vivo optical imaging of retinal plaques in a mouse model.  NeuroImage. 2011;54S1:S204-S217. doi:10.1016/j.neuroimage.2010.06.020Google Scholar
37.
Koronyo  Y, Biggs  D, Barron  E,  et al.  Retinal amyloid pathology and proof-of-concept imaging trial in Alzheimer’s disease.  JCI Insight. 2017;2(16):93621. doi:10.1172/jci.insight.93621PubMedGoogle ScholarCrossref
38.
Tsai  Y, Lu  B, Ljubimov  AV,  et al.  Ocular changes in TgF344-AD rat model of Alzheimer’s disease.  Invest Ophthalmol Vis Sci. 2014;55(1):523-534. doi:10.1167/iovs.13-12888PubMedGoogle ScholarCrossref
39.
La Morgia  C, Ross-Cisneros  FN, Koronyo  Y,  et al.  Melanopsin retinal ganglion cell loss in Alzheimer disease.  Ann Neurol. 2016;79(1):90-109. doi:10.1002/ana.24548PubMedGoogle ScholarCrossref
40.
Ho  C-Y, Troncoso  JC, Knox  D, Stark  W, Eberhart  CG.  Beta-amyloid, phospho-tau and alpha-synuclein deposits similar to those in the brain are not identified in the eyes of Alzheimer’s and Parkinson’s disease patients.  Brain Pathol. 2014;24(1):25-32. doi:10.1111/bpa.12070PubMedGoogle ScholarCrossref
41.
Schön  C, Hoffmann  NA, Ochs  SM,  et al.  Long-term in vivo imaging of fibrillar tau in the retina of P301S transgenic mice.  PLoS One. 2012;7(12):e53547. doi:10.1371/journal.pone.0053547PubMedGoogle ScholarCrossref
42.
Chiasseu  M, Alarcon-Martinez  L, Belforte  N,  et al.  Tau accumulation in the retina promotes early neuronal dysfunction and precedes brain pathology in a mouse model of Alzheimer’s disease.  Mol Neurodegener. 2017;12(1):58. doi:10.1186/s13024-017-0199-3PubMedGoogle ScholarCrossref
43.
Jiang  J, Wang  H, Li  W, Cao  X, Li  C.  Amyloid plaques in retina for diagnosis in Alzheimer’s patients: a meta-analysis.  Front Aging Neurosci. 2016;8:267. doi:10.3389/fnagi.2016.00267PubMedGoogle Scholar
44.
Palmqvist  S, Zetterberg  H, Mattsson  N,  et al; Alzheimer’s Disease Neuroimaging Initiative; Swedish BioFINDER Study Group.  Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease.  Neurology. 2015;85(14):1240-1249. doi:10.1212/WNL.0000000000001991PubMedGoogle ScholarCrossref
45.
Vlassenko  AG, McCue  L, Jasielec  MS,  et al.  Imaging and cerebrospinal fluid biomarkers in early preclinical Alzheimer disease.  Ann Neurol. 2016;80(3):379-387. doi:10.1002/ana.24719PubMedGoogle ScholarCrossref
46.
Forlenza  OV, Radanovic  M, Talib  LL,  et al.  Cerebrospinal fluid biomarkers in Alzheimer’s disease: diagnostic accuracy and prediction of dementia.  Alzheimers Dement (Amst). 2015;1(4):455-463.PubMedGoogle Scholar
47.
Bloudek  LM, Spackman  DE, Blankenburg  M, Sullivan  SD.  Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer’s disease.  J Alzheimers Dis. 2011;26(4):627-645. doi:10.3233/JAD-2011-110458PubMedGoogle ScholarCrossref
Original Investigation
November 2018

Association of Preclinical Alzheimer Disease With Optical Coherence Tomographic Angiography Findings

Author Affiliations
  • 1Department of Ophthalmology and Vision Science, Washington University in St Louis, St Louis, Missouri
  • 2Department of Medicine, Washington University in St Louis, St Louis, Missouri
  • 3Department of Developmental Biology, Washington University in St Louis, St Louis, Missouri
  • 4Blue Sky Neurology, Denver, Colorado
  • 5Division of Biostatistics, Washington University in St Louis, St Louis, Missouri
  • 6Department of Neurology, Washington University in St Louis, St Louis, Missouri
JAMA Ophthalmol. 2018;136(11):1242-1248. doi:10.1001/jamaophthalmol.2018.3556
Key Points

Question  Do study participants with biomarker-positive findings for preclinical Alzheimer disease have retinal microvascular alterations detectable by optical coherence tomographic angiography compared with control individuals with biomarker-negative findings?

Findings  In this single-center, case-control study, the foveal avascular zone was larger in participants with preclinical Alzheimer disease determined by the presence of β-amyloid biomarkers (mean [SD], 0.364 [0.095] mm2) compared with those without preclinical Alzheimer disease (mean [SD], 0.275 [0.060] mm2).

Meaning  Foveal avascular zone enlargement may offer a noninvasive, cost-efficient, and rapid screen to identify preclinical Alzheimer disease.

Abstract

Importance  Biomarker testing for asymptomatic, preclinical Alzheimer disease (AD) is invasive and expensive. Optical coherence tomographic angiography (OCTA) is a noninvasive technique that allows analysis of retinal and microvascular anatomy, which is altered in early-stage AD.

Objective  To determine whether OCTA can detect early retinal alterations in cognitively normal study participants with preclinical AD diagnosed by criterion standard biomarker testing.

Design, Setting, and Participants  This case-control study included 32 participants recruited from the Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St Louis, St Louis, Missouri. Results of extensive neuropsychometric testing determined that all participants were cognitively normal. Participants underwent positron emission tomography and/or cerebral spinal fluid testing to determine biomarker status. Individuals with prior ophthalmic disease, media opacity, diabetes, or uncontrolled hypertension were excluded. Data were collected from July 1, 2016, through September 30, 2017, and analyzed from July 30, 2016, through December 31, 2017.

Main Outcomes and Measures  Automated measurements of retinal nerve fiber layer thickness, ganglion cell layer thickness, inner and outer foveal thickness, vascular density, macular volume, and foveal avascular zone were collected using an OCTA system from both eyes of all participants. Separate model III analyses of covariance were used to analyze individual data outcome.

Results  Fifty-eight eyes from 30 participants (53% female; mean [SD] age, 74.5 [5.6] years; age range, 62-92 years) were included in the analysis. One participant was African American and 29 were white. Fourteen participants had biomarkers positive for AD and thus a diagnosis of preclinical AD (mean [SD] age, 73.5 [4.7] years); 16 without biomarkers served as a control group (mean [SD] age, 75.4 [6.6] years). The foveal avascular zone was increased in the biomarker-positive group compared with controls (mean [SD], 0.364 [0.095] vs 0.275 [0.060] mm2; P = .002). Mean (SD) inner foveal thickness was decreased in the biomarker-positive group (66.0 [9.9] vs 75.4 [10.6] μm; P = .03).

Conclusions and Relevance  This study suggests that cognitively healthy individuals with preclinical AD have retinal microvascular abnormalities in addition to architectural alterations and that these changes occur at earlier stages of AD than has previously been demonstrated. Longitudinal studies in larger cohorts are needed to determine whether this finding has value in identifying preclinical AD.

Introduction

Alzheimer disease (AD) is the most common form of dementia, affecting an estimated 5.4 million US residents.1 The pathophysiologic changes of AD involve loss of neurons, brain atrophy, extracellular deposition of β-amyloid (Aβ) plaques, and intracellular accumulation of neurofibrillary tangles.2,3 Unfortunately, the classic clinical symptoms of AD, including progressive memory loss and behavioral changes, are only apparent after massive, irreversible neuronal loss has occurred. Preclinical AD is a recently recognized period in which the key pathophysiologic changes are under way within the brain, but symptoms have not yet become apparent.4

Preclinical AD can be diagnosed based on the presence of clinically validated biomarkers measuring amyloid burden within the central nervous system. Carbon 11–labeled Pittsburgh Compound B (PiB) (N-methyl-[11C]2-(4′-methylaminophenyl)-6-hydroxybenzothiazole; not commercially available)5 and fluorine 18–labeled florbetapir (18F-AV-45; Amyvid) compounds bind amyloid protein within central nervous system tissue and can estimate disease burden when viewed by positron emission tomography (PET).5-9 In addition, levels of Aβ42 and τ protein in the cerebrospinal fluid (CSF) can be quantified in samples acquired by lumbar puncture.2,10,11

Both tests for biomarker status have especially high negative predictive value in assessing the risk of developing clinically detectable AD.10,12,13 In addition, both tests have been validated in long-term longitudinal studies to estimate onset of clinical dementia,14 such that positive findings for either test is considered diagnostic of preclinical AD.11 Although these methods are useful in assessing individuals at risk for AD, they are expensive, time-consuming, invasive, and difficult to implement in routine clinical screening and care.

Recent data have suggested that AD is also marked by vascular dysfunction, although whether the dysfunction is secondary or contributes to the Aβ accumulation is unclear.15 In the retina specifically, venous narrowing and reduced blood flow have been established in individuals with AD16-18 and mild cognitive impairment (MCI).19 A small study using optical coherence tomographic angiography (OCTA) to compare patients with MCI and those with advanced AD20 suggested decreased density of the deep vascular plexus specifically. However, determination of disease status was based on results of neuropsychiatric testing (eg, Mini-Mental State Examination) rather than objective biomarker status, which has been shown to be inaccurate in estimating conversion from MCI to dementia,21 because it is influenced by other factors such as socioeconomic status, level of education, and presence of confounding neuropsychiatric disorders such as depression and stroke.22

Because clinical trials are under way to evaluate new drugs designed to prevent neuronal loss, it is imperative to be able to identify which individuals with preclinical AD would benefit from potential therapy. Currently accepted testing methods are expensive and invasive. In this study, we evaluated whether OCTA technology has the potential to characterize early retinal architecture and vascular changes in individuals with preclinical AD.

Methods
Study Participants

Cognitively normal study participants were recruited from the Charles F. and Joanne Knight Alzheimer Disease Research Center (ADRC) of Washington University in St Louis, St Louis, Missouri. Study participants were volunteers in the Memory and Aging Project of the ADRC. The study design was approved by the institutional review board of Washington University in St Louis at the Human Research Protection Office and adhered to the tenets of the Declaration of Helsinki.23 Risks and benefits were discussed with each individual, and written informed consent was obtained before beginning the ophthalmologic examination.

Data were collected from July 1, 2016, through September 30, 2017. Inclusion criteria required a Clinical Dementia Rating classification of 0 (no evidence of dementia). The Clinical Dementia Rating is a 5-point scale used to characterize 6 domains of cognitive function and performance to evaluate Alzheimer type dementia, including memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care, based on an extensive battery of neuropsychometric tests (eTable in the Supplement).

Additional inclusion criteria consisted of completion of PET imaging for PiB or 18F-AV-45 compound or CSF analysis of Aβ42 protein level within 1 year of recruitment; many participants underwent both tests. Biomarker status was kept by the ADRC during data collection stage so that testing and data gathering were performed in a masked manner. Additional data regarding age, sex, self-reported ethnicity, and medical history were collected from a review of the medical records. Information on family history or genetic testing (such as APOE4 allele status) was not collected in this study.

Exclusion criteria included previously diagnosed, clinically apparent AD. Additional exclusion criteria consisted of a known history of glaucoma or age-related macular degeneration; intraocular pressure of 22 mg Hg or higher; dense media opacity precluding measurement; history of ocular trauma or concomitant ocular diseases, including previous retinal disease; presence of significant refractive error (more than 5 diopters [D] of spherical equivalent refraction or 3 D of astigmatism); and previous retinal laser therapy. Additional medical exclusion criteria included diabetes and uncontrolled hypertension.

Study Procedures

All participants received a complete neuro-ophthalmic examination, including standard assessment of Snellen visual acuity, color perception using Ishihara color plates, ocular motility, intraocular pressure, refractive status, and examination of the anterior segment and dilated fundus. Optical coherence tomographic imaging of the optic disc and macula and OCTA were performed using the Avanti Optovue OCTA system (Optovue, Inc). Measurements were automated using the manufacturer’s software (Optovue RTVue) from 6 OCT images per eye and thus collected in an objective manner. Although the software reproducibility has been substantiated in measuring central subfield thickness in diabetic macular edema,24 each data point was reviewed by two of us (B.E.O. and N.K.) to evaluate for potential confounding pathologic findings (eg, optic nerve head drusen) and subjective appropriateness of the measurements. Data outcomes collected included total and temporal retinal nerve fiber layer thickness; ganglion cell layer thickness; macular volume; inner, outer, and total foveal thickness; total macular, foveal, and parafoveal vascular density; and foveal avascular zone (FAZ) (Figure 1).

Data Analysis

Data were analyzed from July 30, 2016, through December 31, 2017. Data outcomes measured on a ratio scale were analyzed using mixed-effects analysis of covariance, whereas data outcomes measured on a percentage scale were analyzed using mixed-effects generalized linear models (GLIMMIX in SAS software; SAS, Inc). Data points for each eye were treated as repeated measurements for the study participant. Separate analyses were run for CSF alone and PET alone and group analysis to compare all participants with at least 1 positive biomarker finding with participants without either biomarker. Age was included as a covariate in the models. Intraocular pressure was also included as a covariate in analyzing retinal nerve fiber layer and ganglion cell layer data. Two-sided P values were generated using SAS software (version 9.4), and these P values were not adjusted because comparisons were not made between the CSF group, PET group, or combined CSF-PET biomarker group.

Results
Descriptive Statistics

A total of 32 study participants were recruited through the Washington University in St Louis ADRC. One patient was excluded owing to suspected undiagnosed normal tension glaucoma based on an increased cup-disc ratio; another was excluded owing to the presence of bilateral optic nerve head drusen. One eye was excluded owing to a full-thickness macular hole and another for vitreomacular traction causing distortion of the retinal architecture. Four images were excluded owing to motion artifact or segmentation error; an additional 6 images were excluded owing to poor automated mapping that did not accurately represent the optic nerve disc or FAZ. Data were collected from 58 eyes of 30 participants (16 women [53%] and 14 men [47%]; mean [SD] age, 74.5 [5.6] years; age range, 62-92 years) for inclusion in the analysis.

Mean (SD) age of participants with biomarker-positive status was 73.5 (4.7) years; of participants with biomarker-negative status, 75.2 (6.6) years. One participant was African American; the remainder reported white race. Among the biomarker-negative group, 10 of 16 (62%) were women; among the biomarker-positive group, 6 of 14 (43%) were women. Common comorbidities included medically controlled hypertension, hyperlipidemia, and depression.

PET Scan Biomarkers

Twenty-seven individuals completed PET scanning for PiB or 18F-AV-45 binding. Of these, 7 individuals had positive findings for preclinical AD. The mean (SD) age of the PET-negative group was 73.2 (4.6) years; of the PET-positive group, 76.4 (7.6) years old. Mean (SD) FAZ was larger in participants with PET-positive status (0.398 [0.066] mm2) compared with PET-negative controls (0.288 [0.0915] mm2; P < .001) (Figure 2A).

CSF Biomarker

Twenty-eight individuals completed CSF sampling and analysis. Ten had Aβ42-positive findings and 18 had Aβ42-negative findings. Mean (SD) age of the Aβ42-positive group was 75.7 (7.2) years; of the Aβ42-negative group, 73.1 (4.4) years. Outer foveal measurements were thinner in the Aβ42-positive group (180.8 [8.8] μm) than the Aβ42-negative group (189.3 [10.0] μm; P = .03) (Figure 2B), as was total foveal thickness (245.9 [16.6] vs 263.0 [17.4] μm; P = .03) (Figure 2B).

All Biomarker-Positive Findings

Additional analysis was performed comparing individuals with results positive for the CSF or the PET biomarker compared with those with negative results. Inner foveal thickness was smaller in the biomarker-positive group (66.0 [9.9] μm) compared with the biomarker-negative group (75.4 [10.6] μm; P = .03) (Figure 2C). The FAZ was larger in participants with biomarker-positive findings (0.364 [0.095] mm2) compared with those with biomarker-negative findings (0.275 [0.060] mm2; P = .002) (Figure 2D).

A receiver operating characteristics (ROC) curve was generated for the FAZ in the all-biomarker analysis (Figure 3). The area under the curve was found to be 0.8007 (95% CI, 0.6647-0.9367). Given the limited sample size, single, lower 95% CI points were generated for the point along the ROC curve closest to the nondiscriminatory diagonal (50:50) line, assuming normal distribution and binomial distribution (0.26087 and 0.4166).

Discussion

Our data suggest that individuals with biomarker-positive, preclinical AD might have retinal vascular and architectural alterations that are apparent before the onset of clinically detectable cognitive symptoms. This finding may be interpreted to imply that the retina undergoes neuronal loss and vascular modifications far earlier in disease progression than previously thought. A similar phenomenon is seen with AD-associated cerebral neuronal loss, which begins far in advance of symptom onset. However, these findings could be owing to confounding factors unrelated to the FAZ enlargement, and longitudinal studies in larger cohorts are needed to determine whether this finding has value in identifying preclinical AD.

Our findings of inner foveal thinning in participants with biomarker-positive test results are consistent with those of prior studies using traditional OCT technology and early autopsy studies.25-27 Unfortunately, although the difference between groups for disease status is statistically significant, the considerable overlap in distribution makes these findings of little use clinically.

We also observed dropout of vasculature specifically within the fovea, leading to enlargement of the FAZ in the biomarker-positive group. Since 2007, studies8,15,28-30 have reported that vascular dysfunction in individuals with MCI and AD leads to cerebral hypoperfusion during AD development. Older in vivo and autopsy data31-34 demonstrated that AD is associated with deposition of amyloid and collagen within the cerebral capillaries, resulting in cellular apoptosis and vessel dropout. Because retinal and cerebral vasculature are anatomically and physiologically homologous,30-33,35 the retinal vasculature may similarly be affected in AD progression; however, our study is observational and does not investigate causative mechanisms.

Another potential explanation for FAZ enlargement in individuals with preclinical AD may be secondary to retinal degeneration from Aβ accumulation within the retina itself. Several studies have demonstrated accumulation of Aβ plaques in the inner retina of postmortem tissue from individuals with AD36-39; although a few sources37,39 suggested that the accumulation is limited to the superior retinal tissue, most studies did not comment on location of the deposits. However, other studies in human tissues did not identify retinal Aβ,40 and still others suggest that τ accumulation may be more significant.41,42 A meta-analysis of the current literature published on retinal amyloid plaques ultimately concluded that “the limited number of eligible studies and their methodological heterogeneity make it impossible to come to a conclusion whether pathological retinal Aβ detection is an effective diagnostic tool for AD.”43

The difference in FAZ distribution between individuals with biomarker-positive and biomarker-negative findings (Figure 2D) provides a potentially clinically useful screening tool, if further studies confirm a false-positive rate of less than 40% as suggested by the ROC curve (Figure 3). Despite a promising area under the curve in the ROC with a lower 95% CI of greater than 0.5, larger, longitudinal studies may not validate our findings. If the final outcome confirms the lower 95% CI at the data points closest to the diagonal line, FAZ would prove to be a poor discriminatory marker in screening for preclinical AD.

Although we excluded participants with diabetes and uncontrolled hypertension from this study, we acknowledge that multiple other potential causes for an enlarged FAZ exist and that further assessment of OCTA in the general population is necessary. Despite this possible limitation, our data suggest that OCTA has the potential for rapid, noninvasive, and cost-effective identification of individuals who are likely to have preclinical AD unless these findings are owing to confounding factors unrelated to the FAZ enlargement. As noted, longitudinal studies in larger cohorts are be needed to determine whether this finding has value in identifying preclinical AD.

Strengths and Limitations

Strengths of our study include the use of biomarkers to identify individuals with preclinical AD. Previously published studies rely on the use of neurocognitive testing, namely, the Mini-Mental State Examination, to identify individuals with early dementia; however, a 2015 Cochrane review21 concluded that the Mini-Mental State Examination alone, without supporting testing or repetitive testing, could not accurately estimate conversion from MCI to dementia.

The PET and CSF biomarkers have been clinically validated and correlated with postmortem autopsy study findings4,6 and have been validated in longitudinal studies as an early diagnostic marker of individuals who will develop clinically significant Alzheimer-type dementia.14 In a comparative study, both biomarkers were found to be equally accurate in identifying early-stage AD44 with a relatively high concordance of approximately 80%.45 In our study, 5 participants underwent PET testing and lumbar puncture with conflicting results; in 4, PET findings were negative but CSF findings were positive; in 1, PET findings were positive but CSF findings were negative. Overall, the discordance rate was 15.6%. Although the participants with biomarker-negative findings who had only 1 biomarker available may have been misclassified, more likely these discrepancies are merely associated with the specificity of the individual test and are in line with a low rate of discordance.45-47 As such, any individual with a positive marker was considered to have biomarker-positive findings in the collective analysis.

A major limitation of our study is the small sample size, including a limited number of nonwhite individuals. An additional limitation is exclusion of individuals with known vascular disease from our study; we are therefore unable to determine whether these results are translatable to individuals who may have retinal microvascular changes due to other causes. Also, inclusion only of those with preclinical, biomarker-positive disease limits comparison to those with cognitive changes or advanced AD. Recruitment is under way to evaluate individuals with biomarker-positive MCI and more advanced AD and to follow up individuals with biomarker-positive findings over time for longitudinal evaluation of changes in retinal vasculature.

Conclusions

At present, preclinical AD is diagnosable only by invasive, expensive, and time-consuming PET or CSF testing. Our data suggest that OCTA may enable quick, inexpensive, and noninvasive screening for individuals with preclinical AD based on FAZ enlargement. However, these findings could be owing to confounding factors unrelated to the FAZ enlargement. Longitudinal studies in larger cohorts would be needed to determine whether this finding has value in identifying preclinical AD, so that these individuals may receive appropriate care.

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Article Information

Accepted for Publication: June 24, 2018.

Corresponding Authors: Rajendra S. Apte, MD, PhD (apte@wustl.edu), and Gregory P. Van Stavern, MD (vantaverng@wustl.edu), Department of Ophthalmology and Vision Science, Washington University in St Louis, 660 S Euclid Ave, St Louis, MO 63110.

Published Online: August 23, 2018. doi:10.1001/jamaophthalmol.2018.3556

Author Contributions: Drs Apte and Van Stavern were co–principal investigators. Drs O’Bryhim and Coble had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Apte, Van Stavern.

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

Drafting of the manuscript: O’Bryhim.

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

Statistical analysis: O’Bryhim, Coble.

Obtained funding: Van Stavern.

Administrative, technical, or material support: O’Bryhim, Apte, Van Stavern.

Supervision: Apte, Van Stavern.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: This study was supported in part by an unrestricted grant from Research to Prevent Blindness, Inc (Department of Ophthalmology and Vision Sciences at Washington University in St Louis) and by an educational grant from Optovue, Inc.

Role of the Funder/Sponsor: The sponsors 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.

Meeting Presentation: This study was presented at the 4th Annual North American Neuro-Ophthalmology Society Meeting; March 6, 2018; Waikaloa Village, Hawaii; at the 41st Annual Macula Society Meeting; February 23, 2018; Beverly Hills, California; and the 70th Annual American Academy of Neurology meeting; April 23, 2017; Los Angeles, California.

Additional Information: Washington University in St Louis has filed intellectual property related to the discoveries described in this article.

References
1.
Alzheimer’s Association.  2016 Alzheimer’s disease facts and figures.  Alzheimers Dement. 2016;12(4):459-509. doi:10.1016/j.jalz.2016.03.001PubMedGoogle ScholarCrossref
2.
Ballard  C, Gauthier  S, Corbett  A, Brayne  C, Aarsland  D, Jones  E.  Alzheimer’s disease.  Lancet. 2011;377(9770):1019-1031. doi:10.1016/S0140-6736(10)61349-9PubMedGoogle ScholarCrossref
3.
Reitz  C, Mayeux  R.  Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers.  Biochem Pharmacol. 2014;88(4):640-651. doi:10.1016/j.bcp.2013.12.024PubMedGoogle ScholarCrossref
4.
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. doi:10.1016/j.jalz.2011.03.003PubMedGoogle ScholarCrossref
5.
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
6.
Bacskai  BJ, Frosch  MP, Freeman  SH,  et al.  Molecular imaging with Pittsburgh Compound B confirmed at autopsy: a case report.  Arch Neurol. 2007;64(3):431-434. doi:10.1001/archneur.64.3.431PubMedGoogle ScholarCrossref
7.
Wong  DF, Rosenberg  PB, Zhou  Y,  et al.  In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18).  J Nucl Med. 2010;51(6):913-920. doi:10.2967/jnumed.109.069088PubMedGoogle ScholarCrossref
8.
Johnson  KA, Sperling  RA, Gidicsin  CM,  et al; AV45-A11 study group.  Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer’s disease dementia, mild cognitive impairment, and normal aging.  Alzheimers Dement. 2013;9(5)(suppl):S72-S83. doi:10.1016/j.jalz.2012.10.007PubMedGoogle ScholarCrossref
9.
Clark  CM, Schneider  JA, Bedell  BJ,  et al; AV45-A07 Study Group.  Use of florbetapir-PET for imaging beta-amyloid pathology [published correction appears in JAMA. 2011;305(11):1096].  JAMA. 2011;305(3):275-283. doi:10.1001/jama.2010.2008PubMedGoogle ScholarCrossref
10.
Blennow  K, Hampel  H, Weiner  M, Zetterberg  H.  Cerebrospinal fluid and plasma biomarkers in Alzheimer disease.  Nat Rev Neurol. 2010;6(3):131-144. doi:10.1038/nrneurol.2010.4PubMedGoogle ScholarCrossref
11.
Scheltens  P, Blennow  K, Breteler  MMB,  et al.  Alzheimer’s disease.  Lancet. 2016;388(10043):505-517. doi:10.1016/S0140-6736(15)01124-1PubMedGoogle ScholarCrossref
12.
Morris  E, Chalkidou  A, Hammers  A, Peacock  J, Summers  J, Keevil  S.  Diagnostic accuracy of (18)F amyloid PET tracers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis.  Eur J Nucl Med Mol Imaging. 2016;43(2):374-385. doi:10.1007/s00259-015-3228-xPubMedGoogle ScholarCrossref
13.
Blennow  K, Dubois  B, Fagan  AM, Lewczuk  P, de Leon  MJ, Hampel  H.  Clinical utility of cerebrospinal fluid biomarkers in the diagnosis of early Alzheimer’s disease.  Alzheimers Dement. 2015;11(1):58-69. doi:10.1016/j.jalz.2014.02.004PubMedGoogle ScholarCrossref
14.
Roe  CM, Fagan  AM, Grant  EA,  et al.  Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later.  Neurology. 2013;80(19):1784-1791. doi:10.1212/WNL.0b013e3182918ca6PubMedGoogle ScholarCrossref
15.
Di Marco  LY, Venneri  A, Farkas  E, Evans  PC, Marzo  A, Frangi  AF.  Vascular dysfunction in the pathogenesis of Alzheimer’s disease: a review of endothelium-mediated mechanisms and ensuing vicious circles.  Neurobiol Dis. 2015;82:593-606. doi:10.1016/j.nbd.2015.08.014PubMedGoogle ScholarCrossref
16.
Berisha  F, Feke  GT, Trempe  CL, McMeel  JW, Schepens  CL.  Retinal abnormalities in early Alzheimer’s disease.  Invest Ophthalmol Vis Sci. 2007;48(5):2285-2289. doi:10.1167/iovs.06-1029PubMedGoogle ScholarCrossref
17.
Cheung  CY-L, Ong  YT, Ikram  MK,  et al.  Microvascular network alterations in the retina of patients with Alzheimer’s disease.  Alzheimers Dement. 2014;10(2):135-142. doi:10.1016/j.jalz.2013.06.009PubMedGoogle ScholarCrossref
18.
Golzan  SM, Goozee  K, Georgevsky  D,  et al.  Retinal vascular and structural changes are associated with amyloid burden in the elderly: ophthalmic biomarkers of preclinical Alzheimer’s disease.  Alzheimers Res Ther. 2017;9(1):13. doi:10.1186/s13195-017-0239-9PubMedGoogle ScholarCrossref
19.
Feke  GT, Hyman  BT, Stern  RA, Pasquale  LR.  Retinal blood flow in mild cognitive impairment and Alzheimer’s disease.  Alzheimers Dement (Amst). 2015;1(2):144-151.PubMedGoogle Scholar
20.
Jiang  H, Wei  Y, Shi  Y,  et al.  Altered macular microvasculature in mild cognitive impairment and Alzheimer disease  [published online October 16, 2017].  J Neuroophthalmol. doi:10.1097/WNO.0000000000000580Google Scholar
21.
Arevalo-Rodriguez  I, Smailagic  N, Roqué I Figuls  M,  et al.  Mini-Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI).  Cochrane Database Syst Rev. 2015;(3):CD010783.PubMedGoogle Scholar
22.
Räihä  I, Isoaho  R, Ojanlatva  A, Viramo  P, Sulkava  R, Kivelä  SL.  Poor performance in the Mini-Mental State Examination due to causes other than dementia.  Scand J Prim Health Care. 2001;19(1):34-38. doi:10.1080/028134301300034620PubMedGoogle ScholarCrossref
23.
World Medical Association.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.  JAMA. 2013;310(20):2191-2194. doi:10.1001/jama.2013.281053Google ScholarCrossref
24.
Bressler  SB, Edwards  AR, Andreoli  CM,  et al; for the Diabetic Retinopathy Clinical Research Network/Writing Committee.  Reproducibility of Optovue RTVue optical coherence tomography retinal thickness measurements and conversion to equivalent Zeiss Stratus metrics in diabetic macular edema.  Transl Vis Sci Technol. 2015;4(1):5. doi:10.1167/tvst.4.1.5PubMedGoogle ScholarCrossref
25.
Hinton  DR, Sadun  AA, Blanks  JC, Miller  CA.  Optic-nerve degeneration in Alzheimer’s disease.  N Engl J Med. 1986;315(8):485-487. doi:10.1056/NEJM198608213150804PubMedGoogle ScholarCrossref
26.
Blanks  JC, Torigoe  Y, Hinton  DR, Blanks  RH.  Retinal pathology in Alzheimer’s disease, I: ganglion cell loss in foveal/parafoveal retina.  Neurobiol Aging. 1996;17(3):377-384. doi:10.1016/0197-4580(96)00010-3PubMedGoogle ScholarCrossref
27.
Blanks  JC, Schmidt  SY, Torigoe  Y, Porrello  KV, Hinton  DR, Blanks  RH.  Retinal pathology in Alzheimer’s disease, II: regional neuron loss and glial changes in GCL.  Neurobiol Aging. 1996;17(3):385-395. doi:10.1016/0197-4580(96)00009-7PubMedGoogle ScholarCrossref
28.
Park  KW, Yoon  HJ, Kang  D-Y, Kim  BC, Kim  S, Kim  JW.  Regional cerebral blood flow differences in patients with mild cognitive impairment between those who did and did not develop Alzheimer’s disease.  Psychiatry Res. 2012;203(2-3):201-206. doi:10.1016/j.pscychresns.2011.12.007PubMedGoogle ScholarCrossref
29.
Caroli  A, Testa  C, Geroldi  C,  et al.  Cerebral perfusion correlates of conversion to Alzheimer’s disease in amnestic mild cognitive impairment.  J Neurol. 2007;254(12):1698-1707. doi:10.1007/s00415-007-0631-7PubMedGoogle ScholarCrossref
30.
Nobili  F, Frisoni  GB, Portet  F,  et al.  Brain SPECT in subtypes of mild cognitive impairment: findings from the DESCRIPA multicenter study.  J Neurol. 2008;255(9):1344-1353. doi:10.1007/s00415-008-0897-4PubMedGoogle ScholarCrossref
31.
Kalaria  RN, Pax  AB.  Increased collagen content of cerebral microvessels in Alzheimer’s disease.  Brain Res. 1995;705(1-2):349-352. doi:10.1016/0006-8993(95)01250-8PubMedGoogle ScholarCrossref
32.
De Jong  GI, De Vos  RA, Steur  EN, Luiten  PG.  Cerebrovascular hypoperfusion: a risk factor for Alzheimer’s disease? animal model and postmortem human studies.  Ann N Y Acad Sci. 1997;826:56-74. doi:10.1111/j.1749-6632.1997.tb48461.xPubMedGoogle ScholarCrossref
33.
Christov  A, Ottman  J, Hamdheydari  L, Grammas  P.  Structural changes in Alzheimer’s disease brain microvessels.  Curr Alzheimer Res. 2008;5(4):392-395. doi:10.2174/156720508785132334PubMedGoogle ScholarCrossref
34.
Farkas  E, De Jong  GI, de Vos  RA, Jansen Steur  EN, Luiten  PG.  Pathological features of cerebral cortical capillaries are doubled in Alzheimer’s disease and Parkinson’s disease.  Acta Neuropathol. 2000;100(4):395-402. doi:10.1007/s004010000195PubMedGoogle ScholarCrossref
35.
Patton  N, Aslam  T, Macgillivray  T, Pattie  A, Deary  IJ, Dhillon  B.  Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures.  J Anat. 2005;206(4):319-348. doi:10.1111/j.1469-7580.2005.00395.xPubMedGoogle ScholarCrossref
36.
Koronyo-Hamaoui  M, Koronyo  Y, Ljubimov  AV,  et al.  Identification of amyloid plaques in retinas from Alzheimer’s patients and noninvasive in vivo optical imaging of retinal plaques in a mouse model.  NeuroImage. 2011;54S1:S204-S217. doi:10.1016/j.neuroimage.2010.06.020Google Scholar
37.
Koronyo  Y, Biggs  D, Barron  E,  et al.  Retinal amyloid pathology and proof-of-concept imaging trial in Alzheimer’s disease.  JCI Insight. 2017;2(16):93621. doi:10.1172/jci.insight.93621PubMedGoogle ScholarCrossref
38.
Tsai  Y, Lu  B, Ljubimov  AV,  et al.  Ocular changes in TgF344-AD rat model of Alzheimer’s disease.  Invest Ophthalmol Vis Sci. 2014;55(1):523-534. doi:10.1167/iovs.13-12888PubMedGoogle ScholarCrossref
39.
La Morgia  C, Ross-Cisneros  FN, Koronyo  Y,  et al.  Melanopsin retinal ganglion cell loss in Alzheimer disease.  Ann Neurol. 2016;79(1):90-109. doi:10.1002/ana.24548PubMedGoogle ScholarCrossref
40.
Ho  C-Y, Troncoso  JC, Knox  D, Stark  W, Eberhart  CG.  Beta-amyloid, phospho-tau and alpha-synuclein deposits similar to those in the brain are not identified in the eyes of Alzheimer’s and Parkinson’s disease patients.  Brain Pathol. 2014;24(1):25-32. doi:10.1111/bpa.12070PubMedGoogle ScholarCrossref
41.
Schön  C, Hoffmann  NA, Ochs  SM,  et al.  Long-term in vivo imaging of fibrillar tau in the retina of P301S transgenic mice.  PLoS One. 2012;7(12):e53547. doi:10.1371/journal.pone.0053547PubMedGoogle ScholarCrossref
42.
Chiasseu  M, Alarcon-Martinez  L, Belforte  N,  et al.  Tau accumulation in the retina promotes early neuronal dysfunction and precedes brain pathology in a mouse model of Alzheimer’s disease.  Mol Neurodegener. 2017;12(1):58. doi:10.1186/s13024-017-0199-3PubMedGoogle ScholarCrossref
43.
Jiang  J, Wang  H, Li  W, Cao  X, Li  C.  Amyloid plaques in retina for diagnosis in Alzheimer’s patients: a meta-analysis.  Front Aging Neurosci. 2016;8:267. doi:10.3389/fnagi.2016.00267PubMedGoogle Scholar
44.
Palmqvist  S, Zetterberg  H, Mattsson  N,  et al; Alzheimer’s Disease Neuroimaging Initiative; Swedish BioFINDER Study Group.  Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease.  Neurology. 2015;85(14):1240-1249. doi:10.1212/WNL.0000000000001991PubMedGoogle ScholarCrossref
45.
Vlassenko  AG, McCue  L, Jasielec  MS,  et al.  Imaging and cerebrospinal fluid biomarkers in early preclinical Alzheimer disease.  Ann Neurol. 2016;80(3):379-387. doi:10.1002/ana.24719PubMedGoogle ScholarCrossref
46.
Forlenza  OV, Radanovic  M, Talib  LL,  et al.  Cerebrospinal fluid biomarkers in Alzheimer’s disease: diagnostic accuracy and prediction of dementia.  Alzheimers Dement (Amst). 2015;1(4):455-463.PubMedGoogle Scholar
47.
Bloudek  LM, Spackman  DE, Blankenburg  M, Sullivan  SD.  Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer’s disease.  J Alzheimers Dis. 2011;26(4):627-645. doi:10.3233/JAD-2011-110458PubMedGoogle ScholarCrossref
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