Age-related changes of the retinal microvasculature
Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure....
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| Published in: | PloS one Vol. 14; no. 5; p. e0215916 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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02.05.2019
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure.
We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature.
These findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status. |
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| AbstractList | Purpose Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure. Results We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature. Conclusions These findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status. Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure. We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature. These findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status. Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure. We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature. These findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status. Purpose Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure. Results We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")—comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers—with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20–28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59–84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature. Conclusions These findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status. Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure.PURPOSEBlood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure.We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature.RESULTSWe used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature.These findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status.CONCLUSIONSThese findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status. PurposeBlood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure.ResultsWe used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature.ConclusionsThese findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status. |
| Audience | Academic |
| Author | Delitala, Alessandro Ding, Jun Marongiu, Michele Orlov, Nikita V. Chew, Emily Wong, Wai Lakatta, Edward Schlessinger, David Swaroop, Anand Coletta, Cristopher Goldberg, Ilya G. van Asten, Freekje AlGhatrif, Majd Qian, Yong Fiorillo, Edoardo |
| AuthorAffiliation | 6 Department of Clinical and Experimental Medicine, Azienda Ospedaliero Universitaria di Sassari, Sassari, Italy 4 Laboratory of Cardiovascular Science, National Institute on Aging/National Institutes of Health, Baltimore, Maryland, United States of America 5 Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy 1 Laboratory of Genetics & Genomics, National Institute on Aging/National Institutes of Health, Baltimore, Maryland, United States of America 2 Division of Epidemiology and Clinical Applications, National Eye Institute/National Institutes of Health, Baltimore, Maryland, United States of America 3 Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute/National Institutes of Health, Baltimore, Maryland, United States of America University of Florida, UNITED STATES |
| AuthorAffiliation_xml | – name: 2 Division of Epidemiology and Clinical Applications, National Eye Institute/National Institutes of Health, Baltimore, Maryland, United States of America – name: 3 Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute/National Institutes of Health, Baltimore, Maryland, United States of America – name: 6 Department of Clinical and Experimental Medicine, Azienda Ospedaliero Universitaria di Sassari, Sassari, Italy – name: 5 Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy – name: 1 Laboratory of Genetics & Genomics, National Institute on Aging/National Institutes of Health, Baltimore, Maryland, United States of America – name: University of Florida, UNITED STATES – name: 4 Laboratory of Cardiovascular Science, National Institute on Aging/National Institutes of Health, Baltimore, Maryland, United States of America |
| Author_xml | – sequence: 1 givenname: Nikita V. orcidid: 0000-0002-3716-5146 surname: Orlov fullname: Orlov, Nikita V. – sequence: 2 givenname: Cristopher surname: Coletta fullname: Coletta, Cristopher – sequence: 3 givenname: Freekje surname: van Asten fullname: van Asten, Freekje – sequence: 4 givenname: Yong surname: Qian fullname: Qian, Yong – sequence: 5 givenname: Jun surname: Ding fullname: Ding, Jun – sequence: 6 givenname: Majd surname: AlGhatrif fullname: AlGhatrif, Majd – sequence: 7 givenname: Edward surname: Lakatta fullname: Lakatta, Edward – sequence: 8 givenname: Emily surname: Chew fullname: Chew, Emily – sequence: 9 givenname: Wai surname: Wong fullname: Wong, Wai – sequence: 10 givenname: Anand surname: Swaroop fullname: Swaroop, Anand – sequence: 11 givenname: Edoardo surname: Fiorillo fullname: Fiorillo, Edoardo – sequence: 12 givenname: Alessandro surname: Delitala fullname: Delitala, Alessandro – sequence: 13 givenname: Michele orcidid: 0000-0002-7289-9815 surname: Marongiu fullname: Marongiu, Michele – sequence: 14 givenname: Ilya G. orcidid: 0000-0001-8514-6110 surname: Goldberg fullname: Goldberg, Ilya G. – sequence: 15 givenname: David surname: Schlessinger fullname: Schlessinger, David |
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| DOI | 10.1371/journal.pone.0215916 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Current address: Mindshare Medical, Inc., Seattle, Washington, United States of America Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Ilya G. Goldberg is employed by Mindshare Medical, Inc. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials. Current address: Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands |
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| SubjectTerms | Adult Age Aged Aged, 80 and over Aging Aging - physiology Algorithms Artificial intelligence Atherosclerosis Bifurcations Biology and Life Sciences Blood Pressure Blood vessels Cardiovascular disease Cardiovascular diseases Care and treatment Cohort Studies Correlation Diabetic retinopathy Echocardiography Epidemiology Female Fractals Fundus Oculi Genomics Health Humans Image analysis Image processing Laboratories Macular degeneration Male Medical personnel Medicine and Health Sciences Microvasculature Microvessels - anatomy & histology Microvessels - physiology Middle Aged Neurobiology Neurodegeneration Neurosciences Parameterization Patient outcomes Pattern recognition Photography Physical Sciences Population studies Research and Analysis Methods Retina Retinal Vessels - anatomy & histology Retinal Vessels - physiology Self-similarity Studies Tortuosity Young Adult |
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| Title | Age-related changes of the retinal microvasculature |
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