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Patterson EJ, Bounds AD, Wagner SK, Kadri-Langford R, Taylor R, Daly D. Oculomics: A Crusade Against the Four Horsemen of Chronic Disease. Ophthalmol Ther 2024; 13:1427-1451. [PMID: 38630354 PMCID: PMC11109082 DOI: 10.1007/s40123-024-00942-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 05/22/2024] Open
Abstract
Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.
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Affiliation(s)
| | | | - Siegfried K Wagner
- Moorfields Eye Hospital NHS Trust, 162 City Road, London, EC1V 2PD, UK
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London, EC1V 9EL, UK
| | | | - Robin Taylor
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
| | - Dan Daly
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
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Li M, Huang K, Xu Q, Yang J, Zhang Y, Ji Z, Xie K, Yuan S, Liu Q, Chen Q. OCTA-500: A retinal dataset for optical coherence tomography angiography study. Med Image Anal 2024; 93:103092. [PMID: 38325155 DOI: 10.1016/j.media.2024.103092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 11/10/2023] [Accepted: 01/22/2024] [Indexed: 02/09/2024]
Abstract
Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age/gender/eye/disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an about 10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.
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Affiliation(s)
- Mingchao Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Kun Huang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Qiuzhuo Xu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Jiadong Yang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Yuhan Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Zexuan Ji
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Keren Xie
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Qinghuai Liu
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
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Joseph S, Robbins CB, Haystead A, Hemesath A, Allen A, Kundu A, Ma JP, Scott BL, Moore KPL, Agrawal R, Gunasan V, Stinnett SS, Grewal DS, Fekrat S. Characterizing differences in retinal and choroidal microvasculature and structure in individuals with Huntington's Disease compared to healthy controls: A cross-sectional prospective study. PLoS One 2024; 19:e0296742. [PMID: 38289919 PMCID: PMC10826956 DOI: 10.1371/journal.pone.0296742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 12/18/2023] [Indexed: 02/01/2024] Open
Abstract
OBJECTIVE To characterize retinal and choroidal microvascular and structural changes in patients who are gene positive for mutant huntingtin protein (mHtt) with symptoms of Huntington's Disease (HD). METHODS This study is a cross-sectional comparison of patients who are gene positive for mHtt and exhibit symptoms of HD, either motor manifest or prodromal (HD group), and cognitively normal individuals without a family history of HD (control group). HD patients were diagnosed by Duke movement disorder neurologists based on the Unified Huntington's Disease Rating Scale (UHDRS). Fovea and optic nerve centered OCT and OCTA images were captured using Zeiss Cirrus HD-5000 with AngioPlex. Outcome metrics included central subfield thickness (CST), peripapillary retinal nerve fiber layer (pRNFL) thickness, ganglion cell-inner plexiform layer (GCIPL) thickness, and choroidal vascularity index (CVI) on OCT, and foveal avascular zone (FAZ) area, vessel density (VD), perfusion density (PD), capillary perfusion density (CPD), and capillary flux index (CFI) on OCTA. Generalized estimating equation (GEE) models were used to account for inter-eye correlation. RESULTS Forty-four eyes of 23 patients in the HD group and 77 eyes of 39 patients in the control group were analyzed. Average GCIPL thickness and FAZ area were decreased in the HD group compared to controls (p = 0.001, p < 0.001). No other imaging metrics were significantly different between groups. CONCLUSIONS Patients in the HD group had decreased GCIPL thickness and smaller FAZ area, highlighting the potential use of retinal biomarkers in detecting neurodegenerative changes in HD.
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Affiliation(s)
- Suzanna Joseph
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
| | - Cason B. Robbins
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
| | - Alice Haystead
- iMIND Research Group, Durham, NC, United States of America
| | - Angela Hemesath
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
| | - Ariana Allen
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
| | - Anita Kundu
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
| | - Justin P. Ma
- iMIND Research Group, Durham, NC, United States of America
| | - Burton L. Scott
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States of America
| | - Kathryn P. L. Moore
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States of America
| | - Rupesh Agrawal
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Vithiya Gunasan
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Sandra S. Stinnett
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
| | - Dilraj S. Grewal
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
| | - Sharon Fekrat
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, United States of America
- iMIND Research Group, Durham, NC, United States of America
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States of America
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4
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Shen Z, Zhang S, Yu W, Yue M, Hong C. Optical Coherence Tomography Angiography: Revolutionizing Clinical Diagnostics and Treatment in Central Nervous System Disease. Aging Dis 2024:AD.2024.0112. [PMID: 38300645 DOI: 10.14336/ad.2024.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/12/2024] [Indexed: 02/02/2024] Open
Abstract
Optical coherence tomography angiography (OCTA), as a new generation of non-invasive and efficient fundus imaging technology, can provide non-invasive assessment of vascular lesions in the retina and choroid. In terms of anatomy and development, the retina is referred to as an extension of the central nervous system (CNS). CNS diseases are closely related to changes in fundus structure and blood vessels, and direct visualization of fundus structure and blood vessels provides an effective "window" for CNS research. This has important practical significance for identifying the characteristic changes of various CNS diseases on OCTA in the future, and plays a key role in promoting early screening, diagnosis, and monitoring of disease progression in CNS diseases. This article reviews relevant fundus studies by comparing and summarizing the unique advantages and existing limitations of OCTA in various CNS disease patients, in order to demonstrate the clinical significance of OCTA in the diagnosis and treatment of CNS diseases.
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Affiliation(s)
- Zeqi Shen
- Postgraduate training base Alliance of Wenzhou Medical University (Affiliated People's Hospital), Hangzhou, Zhejiang, China
| | - Sheng Zhang
- Center for Rehabilitation Medicine, Department of Neurology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Weitao Yu
- The Second School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Mengmeng Yue
- Postgraduate training base Alliance of Wenzhou Medical University (Affiliated People's Hospital), Hangzhou, Zhejiang, China
| | - Chaoyang Hong
- Center for Rehabilitation Medicine, Department of Ophthalmology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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Suh A, Ong J, Kamran SA, Waisberg E, Paladugu P, Zaman N, Sarker P, Tavakkoli A, Lee AG. Retina Oculomics in Neurodegenerative Disease. Ann Biomed Eng 2023; 51:2708-2721. [PMID: 37855949 DOI: 10.1007/s10439-023-03365-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/05/2023] [Indexed: 10/20/2023]
Abstract
Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging technologies including electroretinography, optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy, fluorescence lifetime imaging ophthalmoscopy, and OCT angiography have revolutionized the ability to understand systemic diseases and even detect them earlier than clinical manifestations for earlier intervention. With the advent of increasingly large ophthalmic imaging datasets, machine learning models can be integrated into these ocular imaging biomarkers to provide further insights and prognostic predictions of neurodegenerative disease. In this manuscript, we review the use of ophthalmic imaging to provide insights into neurodegenerative diseases including Alzheimer Disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, and Huntington Disease. We discuss recent advances in ophthalmic technology including eye-tracking technology and integration of artificial intelligence techniques to further provide insights into these neurodegenerative diseases. Ultimately, oculomics opens the opportunity to detect and monitor systemic diseases at a higher acuity. Thus, earlier detection of systemic diseases may allow for timely intervention for improving the quality of life in patients with neurodegenerative disease.
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Affiliation(s)
- Alex Suh
- Tulane University School of Medicine, New Orleans, LA, USA.
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Ethan Waisberg
- University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Phani Paladugu
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Prithul Sarker
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, 6560 Fannin St #450, Houston, TX, 77030, USA
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA
- Departments of Ophthalmology, Neurology and Neurosurgery, Weill Cornell Medicine, New York, NY, USA
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Texas A&M College of Medicine, Bryan, TX, USA
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA
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6
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Is retina affected in Huntington's disease? Is optical coherence tomography a good biomarker? PLoS One 2023; 18:e0282175. [PMID: 36827300 PMCID: PMC9955964 DOI: 10.1371/journal.pone.0282175] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 02/08/2023] [Indexed: 02/25/2023] Open
Abstract
AIM OF THE STUDY Comparative cross-sectional study of retinal parameters in Huntington's disease and their evaluation as marker of disease progression. CLINICAL RATIONALE FOR THE STUDY Huntington's disease (HD) is a neurodegenerative disorder with dominant motor and neuropsychiatric symptoms. Involvement of sensory functions in HD has been investigated, however studies of retinal pathology are incongruent. Effect sizes of previous findings were not published. OCT data of the subjects in previous studies have not been published. Additional examination of structural and functional parameters of retina in larger sample of patients with HD is warranted. MATERIALS AND METHODS This is a prospective cross-sectional study that included: peripapillary retinal nerve fiber layer thickness (RNFL) and total macular volume (TMV) measured by spectral domain optical coherence tomography (OCT) of retina, Pelli-Robson Contrast Sensitivity test, Farnsworth 15 Hue Color discrimination test, ophthalmology examination and Unified Huntington's disease Rating Scale (UHDRS). Ninety-four eyes of 41 HD patients examined in total 47 visits and 82 eyes of 41 healthy controls (HC) examined in total 41 visits were included. Analyses were performed by repeated measures linear mixed effects model with age and gender as covariates. False discovery rate was corrected by Benjamini-Hochberg procedure. RESULTS HD group included 21 males and 20 females (age 50.6±12.0 years [mean ± standard deviation], disease duration 7.1±3.6 years, CAG triplet repeats 44.1±2.4). UHDRS Total Motor Score (TMS) was 30.0±12.3 and Total Functional Capacity 8.2±3.2. Control group (HC) included 19 males and 22 females with age 48.2±10.3 years. There was no statistically significant difference between HD and HC in age. The effect of the disease was not significant in temporal segment RNFL thickness. It was significant in the mean RNFL thickness and TMV, however not passing false discovery rate adjustment and with small effect size. In the HD group, the effect of disease duration and TMS was not significant. The Contrast Sensitivity test in HD was within normal limits and the 15-hue-test in HD did not reveal any specific pathology. CONCLUSIONS The results of our study support possible diffuse retinal changes in global RNFL layer and in macula in Huntington's disease, however, these changes are small and not suitable as a biomarker for disease progression. We found no other structural or functional changes in retina of Huntington's disease patients using RNFL layer and macular volume spectral domain OCT and Contrast Sensitivity Test and 15-hue-test. CLINICAL IMPLICATIONS Current retinal parameters are not appropriate for monitoring HD disease progression.
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7
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Huntington’s disease and neurovascular structure of retina. Neurol Sci 2022; 43:5933-5941. [DOI: 10.1007/s10072-022-06232-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/17/2022] [Indexed: 10/17/2022]
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8
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Christinaki E, Kulenovic H, Hadoux X, Baldassini N, Van Eijgen J, De Groef L, Stalmans I, van Wijngaarden P. Retinal imaging biomarkers of neurodegenerative diseases. Clin Exp Optom 2022; 105:194-204. [PMID: 34751086 DOI: 10.1080/08164622.2021.1984179] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The timely detection of neurodegenerative diseases is central to improving clinical care as well as enabling the development and deployment of disease-modifying therapies. Retinal imaging is emerging as a method to detect features of a number of neurodegenerative diseases, given the anatomical and functional similarities between the retina and the brain. This review provides an overview of the current status of retinal imaging biomarkers of neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, Lewy body dementia, frontotemporal dementia, Huntington's disease and multiple sclerosis. Whilst research findings are promising, efforts to harmonise study designs and imaging methods will be important in translating these findings into clinical care. Doing so may mean that eye care providers will play important roles in the detection of a variety of neurodegenerative diseases in future.
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Affiliation(s)
- Eirini Christinaki
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Hana Kulenovic
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Xavier Hadoux
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Nicole Baldassini
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Jan Van Eijgen
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Department of Ophthalmology, University Hospitals Leuven, Leuven, Belgium
| | - Lies De Groef
- Neural Circuit Development and Regeneration Research Group, Department of Biology, University of Leuven (KU Leuven), Leuven, Belgium.,Leuven Brain Institute, Leuven, Belgium
| | - Ingeborg Stalmans
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Department of Ophthalmology, University Hospitals Leuven, Leuven, Belgium.,Neural Circuit Development and Regeneration Research Group, Department of Biology, University of Leuven (KU Leuven), Leuven, Belgium
| | - Peter van Wijngaarden
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Parkville, Australia
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Wang X, Wei Q, Wu X, Cao S, Chen C, Zhang J, Yan Y, Geng Z, Tian Y, Wang K. The vessel density of the superficial retinal capillary plexus as a new biomarker in cerebral small vessel disease: an optical coherence tomography angiography study. Neurol Sci 2021; 42:3615-3624. [PMID: 33432462 DOI: 10.1007/s10072-021-05038-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 01/01/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Optical coherence tomography angiography (OCTA) is a novel and noninvasive technique for the quantitative assessment of retinal microvascular perfusion. Since the retinal and cerebral small vessels share similar embryological origins, anatomical features, and physiological properties, altered retinal microvasculature might provide a new perspective on the mechanisms of cerebral small vessel disease (CSVD). OBJECTIVE We aimed to evaluate retinal vessel density (VD) in patients with CSVD using OCTA and identify associations with cerebral magnetic resonance imaging (MRI) markers and cognitive function. METHODS We prospectively recruited 47 CSVD patients and 30 healthy controls (HCs) to participate in the study. All participants underwent OCTA to evaluate retinal microvascular perfusion. The VDs of the macular region in the superficial retinal capillary plexus (SRCP), deep retinal capillary plexus (DRCP), and foveal avascular zone (FAZ) were determined, along with the VD of the optic nerve head (ONH) in the radial peripapillary capillary (RPC) network. Additionally, cerebral MRI and cognitive function tests were performed. RESULTS In the macula area, the VD of the CSVD patients was significantly lower than HCs in the temporal quadrant of SRCP. In the ONH area, CSVD patients had lower VD than HCs in the peripapillary RPC network. According to multiple linear regression analysis, decreased VD of the macular SRCP was associated with white matter hyperintensity scores after adjustment for age, hypertension, diabetes, and hyperlipidemia. Furthermore, the VD of the macular SRCP was significantly correlated with CSVD patients' cognitive function, especially global cognition, memory function, attention function, information processing, and executive function. CONCLUSION OCTA revealed a significant decrease in retinal microvascular perfusion in CSVD patients, and retinal hypoperfusion was related to MRI markers and cognitive function, suggesting that these parameters could have potential utility as early disease biomarkers.
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Affiliation(s)
- Xiaojing Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
| | - Qiang Wei
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230032, China
| | - Xingqi Wu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
| | - Shanshan Cao
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
| | - Chen Chen
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
| | - Jun Zhang
- Department of Neurology, the Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China
| | - Yibing Yan
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
| | - Zhi Geng
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230032, China
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230032, China.
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