Retinal nerve fiber layer thickness predicts CSF amyloid/tau before cognitive decline
Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) in...
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| Published in | PloS one Vol. 15; no. 5; p. e0232785 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
29.05.2020
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0232785 |
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| Abstract | Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß42 (A) and tau (T) ratio.
As part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression.
Mean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) μm; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity.
Our retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. |
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| AbstractList | Background Alzheimer’s disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß42 (A) and tau (T) ratio. Methods As part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression. Results Mean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) μm; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity. Conclusions Our retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß42 (A) and tau (T) ratio.BACKGROUNDAlzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß42 (A) and tau (T) ratio.As part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression.METHODSAs part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression.Mean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) μm; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity.RESULTSMean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) μm; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity.Our retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology.CONCLUSIONSOur retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. Background Alzheimer’s disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß42 (A) and tau (T) ratio. Methods As part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression. Results Mean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) μm; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity. Conclusions Our retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. BackgroundAlzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß42 (A) and tau (T) ratio.MethodsAs part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression.ResultsMean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) μm; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity.ConclusionsOur retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß42 (A) and tau (T) ratio. As part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression. Mean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) μm; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity. Our retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß.sub.42 (A) and tau (T) ratio. As part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression. Mean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) [mu]m; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity. Our retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. Background Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal nerve fiber layer (RNFL) is depleted in established AD, we tested whether its thickness can predict whether cognitively healthy (CH) individuals have a normal or pathological cerebrospinal fluid (CSF) Aß.sub.42 (A) and tau (T) ratio. Methods As part of an ongoing longitudinal study, we enrolled CH individuals, excluding those with cognitive impairment and significant ocular pathology. We classified the CH group into two sub-groups, normal (CH-NAT, n = 16) or pathological (CH-PAT, n = 27), using a logistic regression model from the CSF AT ratio that identified >85% of patients with a clinically probable AD diagnosis. Spectral-domain optical coherence tomography (OCT) was acquired for RNFL, ganglion cell-inner plexiform layer (GC-IPL), and macular thickness. Group differences were tested using mixed model repeated measures and a classification model derived using multiple logistic regression. Results Mean age (± standard deviation) in the CH-PAT group (n = 27; 75.2 ± 8.4 years) was similar (p = 0.50) to the CH-NAT group (n = 16; 74.1 ± 7.9 years). Mean RNFL (standard error) was thinner in the CH-PAT group by 9.8 (2.7) [mu]m; p < 0.001. RNFL thickness classified CH-NAT vs. CH-PAT with 87% sensitivity and 56.3% specificity. Conclusions Our retinal data predict which individuals have CSF biomarkers of AD pathology before cognitive deficits are detectable with 87% sensitivity. Such results from easy-to-acquire, objective and non-invasive measurements of the RNFL merit further study of OCT technology to monitor or screen for early AD pathology. |
| Audience | Academic |
| Author | Felix, Christian M. Pogoda, Janice M. Asanad, Samuel Karanjia, Rustum Sagare, Abhay P. Sultan, William Fantini, Michele Harrington, Michael G. Sadun A. A., Alfredo A. Arakaki, Xianghong Fonteh, Alfred N. Ross-Cisneros, Fred N. Chui, Helena C. Nassisi, Marco Wu, Jessica Zlokovic, Berislav V. |
| AuthorAffiliation | 8 Department of Neurology, University of Southern California, Los Angeles, CA, United States of America 4 Department of Clinical Sciences and Community Health, Ophthalmological Unit, IRCCS-Cà Granda Foundation—Ospedale Maggiore Policlinico, University of Milan, Milan, Italy 1 Doheny Eye Institute, Los Angeles, CA, United States of America 9 Cipher Biostatistics & Reporting, Reno, NV, United States of America 10 Huntington Medical Research Institutes, Pasadena, CA, United States of America 2 Department of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States of America 3 Department of Medicine, Ophthalmology, University of Udine, Udine, Italy 5 Department of Ophthalmology, University of Ottawa, Ottawa, Ontario, Canada 6 Ottawa Hospital Research Institute, Ottawa, Ontario, Canada 7 Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America University |
| AuthorAffiliation_xml | – name: 8 Department of Neurology, University of Southern California, Los Angeles, CA, United States of America – name: 10 Huntington Medical Research Institutes, Pasadena, CA, United States of America – name: 1 Doheny Eye Institute, Los Angeles, CA, United States of America – name: 7 Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America – name: 4 Department of Clinical Sciences and Community Health, Ophthalmological Unit, IRCCS-Cà Granda Foundation—Ospedale Maggiore Policlinico, University of Milan, Milan, Italy – name: 5 Department of Ophthalmology, University of Ottawa, Ottawa, Ontario, Canada – name: 2 Department of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States of America – name: University of Florida, UNITED STATES – name: 3 Department of Medicine, Ophthalmology, University of Udine, Udine, Italy – name: 9 Cipher Biostatistics & Reporting, Reno, NV, United States of America – name: 6 Ottawa Hospital Research Institute, Ottawa, Ontario, Canada |
| Author_xml | – sequence: 1 givenname: Samuel surname: Asanad fullname: Asanad, Samuel – sequence: 2 givenname: Michele surname: Fantini fullname: Fantini, Michele – sequence: 3 givenname: William surname: Sultan fullname: Sultan, William – sequence: 4 givenname: Marco surname: Nassisi fullname: Nassisi, Marco – sequence: 5 givenname: Christian M. surname: Felix fullname: Felix, Christian M. – sequence: 6 givenname: Jessica surname: Wu fullname: Wu, Jessica – sequence: 7 givenname: Rustum surname: Karanjia fullname: Karanjia, Rustum – sequence: 8 givenname: Fred N. surname: Ross-Cisneros fullname: Ross-Cisneros, Fred N. – sequence: 9 givenname: Abhay P. surname: Sagare fullname: Sagare, Abhay P. – sequence: 10 givenname: Berislav V. surname: Zlokovic fullname: Zlokovic, Berislav V. – sequence: 11 givenname: Helena C. surname: Chui fullname: Chui, Helena C. – sequence: 12 givenname: Janice M. surname: Pogoda fullname: Pogoda, Janice M. – sequence: 13 givenname: Xianghong surname: Arakaki fullname: Arakaki, Xianghong – sequence: 14 givenname: Alfred N. surname: Fonteh fullname: Fonteh, Alfred N. – sequence: 15 givenname: Alfredo A. surname: Sadun A. A. fullname: Sadun A. A., Alfredo A. – sequence: 16 givenname: Michael G. orcidid: 0000-0002-7923-8032 surname: Harrington fullname: Harrington, Michael G. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32469871$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2020 Public Library of Science 2020 Asanad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Asanad et al 2020 Asanad et al |
| Copyright_xml | – notice: COPYRIGHT 2020 Public Library of Science – notice: 2020 Asanad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2020 Asanad et al 2020 Asanad et al |
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| DOI | 10.1371/journal.pone.0232785 |
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| Snippet | Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since the retinal... Background Alzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since... Background Alzheimer’s disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since... BackgroundAlzheimer's disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since... Background Alzheimer’s disease (AD) pathology precedes symptoms and its detection can identify at-risk individuals who may benefit from early treatment. Since... |
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| SubjectTerms | Aged Aged, 80 and over Algorithms Alzheimer Disease - cerebrospinal fluid Alzheimer Disease - diagnostic imaging Alzheimer Disease - genetics Alzheimer Disease - pathology Alzheimer's disease Amyloid beta-Peptides - cerebrospinal fluid Amyloid beta-Peptides - genetics Amyloid beta-protein Amyloidosis - cerebrospinal fluid Amyloidosis - diagnostic imaging Amyloidosis - genetics Amyloidosis - pathology Biology and Life Sciences Biomarkers Biomarkers - cerebrospinal fluid Care and treatment Cerebrospinal fluid Cognitive ability Cognitive Dysfunction - cerebrospinal fluid Cognitive Dysfunction - diagnostic imaging Cognitive Dysfunction - genetics Cognitive Dysfunction - pathology Correlation analysis Dementia Development and progression Disease Female Geffen, David Genetic aspects Health aspects Humans Laboratories Longitudinal studies Male Medical research Medicine Medicine and Health Sciences Middle Aged Nerve Fibers - metabolism Nerve Fibers - pathology Neurodegenerative diseases Neurosciences Ophthalmology Optic Disk - diagnostic imaging Optic Disk - metabolism Optic Disk - pathology Optical Coherence Tomography Pathology Physiology Regression models Research and Analysis Methods Retina Retina - diagnostic imaging Retina - metabolism Retina - pathology Retinal Ganglion Cells - metabolism Retinal Ganglion Cells - pathology Sensitivity Signs and symptoms Standard error Tau protein Tau proteins tau Proteins - cerebrospinal fluid tau Proteins - genetics Thickness Tomography Tomography, Optical Coherence |
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| Title | Retinal nerve fiber layer thickness predicts CSF amyloid/tau before cognitive decline |
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