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|Title:||A multi-regression framework to improve diagnostic ability of optical coherence tomography retinal biomarkers to discriminate mild cognitive impairment and Alzheimer's disease||Authors:||Chua, Jacqueline
Ho, Lucius Kang Hua
Sng, Chelvin C. A.
Cheung, Carol Y.
Wong, Tien Yin
Chen, Christopher Li-Hsian
|Keywords:||Engineering::Bioengineering||Issue Date:||2022||Source:||Chua, J., Li, C., Ho, L. K. H., Wong, D., Tan, B., Yao, X., Gan, A., Schwarzhans, F., Garhöfer, G., Sng, C. C. A., Hilal, S., Venketasubramanian, N., Cheung, C. Y., Fischer, G., Vass, C., Wong, T. Y., Chen, C. L. & Schmetterer, L. (2022). A multi-regression framework to improve diagnostic ability of optical coherence tomography retinal biomarkers to discriminate mild cognitive impairment and Alzheimer's disease. Alzheimer's Research and Therapy, 14(1), 41-. https://dx.doi.org/10.1186/s13195-022-00982-0||Project:||CG/C010A/2017
|Journal:||Alzheimer's Research and Therapy||Abstract:||Background: Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer’s disease (AD) and mild cognitive impairment (MCI) remains limited. We assessed whether compensating the circumpapillary retinal nerve fiber layer (cpRNFL) thickness for multiple demographic and anatomical factors as well as the combination of macular layers improves the detection of MCI and AD. Methods: This cross-sectional study of 62 AD (n = 92 eyes), 108 MCI (n = 158 eyes), and 55 cognitively normal control (n = 86 eyes) participants. Macular ganglion cell complex (mGCC) thickness was extracted. Circumpapillary retinal nerve fiber layer (cpRNFL) measurement was compensated for several ocular factors. Thickness measurements and their corresponding areas under the receiver operating characteristic curves (AUCs) were compared between the groups. The main outcome measure was OCT thickness measurements. Results: Participants with MCI/AD showed significantly thinner measured and compensated cpRNFL, mGCC, and altered retinal vessel density (p < 0.05). Compensated RNFL outperformed measured RNFL for discrimination of MCI/AD (AUC = 0.74 vs 0.69; p = 0.026). Combining macular and compensated cpRNFL parameters provided the best detection of MCI/AD (AUC = 0.80 vs 0.69; p < 0.001). Conclusions and relevance: Accounting for interindividual variations of ocular anatomical features in cpRNFL measurements and incorporating macular information may improve the identification of high-risk individuals with early cognitive impairment.||URI:||https://hdl.handle.net/10356/163081||ISSN:||1758-9193||DOI:||10.1186/s13195-022-00982-0||Rights:||© The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCBE Journal Articles|
Updated on Dec 3, 2022
Updated on Dec 3, 2022
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