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Last updated Jan 15, 2019
2016 |
Strouthidis Shweikh Yang Reisman Muthy Chakravarthy Lotery Keane Tufail Grossi Patel P J N G Y Q C A Z A U A J P A A C M P J Ko F. Foster Associations with Retinal Pigment Epithelium Thickness Measures in a Large Cohort: Results from the UK Biobank Journal Article In: Ophthalmology, 2016. Abstract | Links | BibTeX | Tags: 6507, epithelium, imaging, pigment, Retinal @article{KoF2016, title = {Associations with Retinal Pigment Epithelium Thickness Measures in a Large Cohort: Results from the UK Biobank}, author = {Strouthidis Shweikh Yang Reisman Muthy Chakravarthy Lotery Keane Tufail Grossi Patel P J N G Y Q C A Z A U A J P A A C M P J Ko F. Foster}, url = {https://www.ncbi.nlm.nih.gov/pubmed/27720551}, year = {2016}, date = {2016-10-11}, journal = {Ophthalmology}, abstract = {PURPOSE: To describe associations of ocular and systemic factors with retinal pigment epithelium (RPE)-Bruch's membrane (BM) complex thickness as measured by spectral-domain (SD) optical coherence tomography (OCT). DESIGN: Multisite community-based study. This research has been conducted using the UK Biobank Resource. PARTICIPANTS: Sixty-seven thousand three hundred eighteen people 40 to 69 years old received questionnaires, physical examination, and eye examination, including macular SD OCT. Systematic selection process identified 34 652 eyes with high-quality SD OCT images from normal individuals for analysis. METHODS: We included people with no self-reported ocular disease, diabetes, or neurologic disorders; visual acuity of >/=20/25 or better; refraction between -6 diopters (D) to 6 D, and IOP of 6 to 21 mmHg. Only high-quality, well-centered SD OCT images with central, stable fixation were included. Descriptive statistics, t tests, and regression analyses were performed. Multivariate regression modeling was used to adjust for covariates and to identify relationships between RPE-BM thickness and ocular and systemic features. MAIN OUTCOME MEASURES: Retinal pigment epithelium-BM thickness, as measured by SD OCT segmentation using Topcon Advanced Boundary Segmentation at 9 Early Treatment of Diabetic Retinopathy Study subfields. RESULTS: Mean RPE-BM thickness was 26.3 mum (standard deviation, 4.8 mum) at central subfield. Multivariate regression with age stratification showed that RPE thinning became apparent after age 45 years. Among those aged =45, RPE-BM was significantly thicker among those of black or mixed/other race (+3.61 mum and +1.77 mum vs. white, respectively; P < 0.001) and higher hyperopia (+0.4 mum/D; P < 0.001), but not for other variables considered. Among those age >45, RPE-BM was significantly thinner with older age (-0.10 mum/year; P < 0.001), Asian ethnicity (-0.45 mum vs. white; P = 0.02), taller height (-0.02 mum/cm; P < 0.001), higher IOP (-0.03 mum/mmHg; P < 0.001), and regular smoking (-0.27 mum vs. nonsmokers; P = 0.02). In contrast, RPE-BM was significantly thicker among black or mixed/other race (+3.29 mum and +0.81 mum vs. white, respectively; P < 0.001) and higher hyperopia (+0.28 mum/D; P < 0.001). There was no significant association with sex or Chinese ethnicity. CONCLUSIONS: We describe novel findings of RPE-BM thickness in normal individuals, a structure that varies with age, ethnicity, refraction, IOP, and smoking. The significant association with IOP is especially interesting and may have relevance for the etiology of glaucoma, while the association between age and smoking may have relevance for the etiology of age-related macular degeneration.}, keywords = {6507, epithelium, imaging, pigment, Retinal}, pubstate = {published}, tppubtype = {article} } PURPOSE: To describe associations of ocular and systemic factors with retinal pigment epithelium (RPE)-Bruch's membrane (BM) complex thickness as measured by spectral-domain (SD) optical coherence tomography (OCT). DESIGN: Multisite community-based study. This research has been conducted using the UK Biobank Resource. PARTICIPANTS: Sixty-seven thousand three hundred eighteen people 40 to 69 years old received questionnaires, physical examination, and eye examination, including macular SD OCT. Systematic selection process identified 34 652 eyes with high-quality SD OCT images from normal individuals for analysis. METHODS: We included people with no self-reported ocular disease, diabetes, or neurologic disorders; visual acuity of >/=20/25 or better; refraction between -6 diopters (D) to 6 D, and IOP of 6 to 21 mmHg. Only high-quality, well-centered SD OCT images with central, stable fixation were included. Descriptive statistics, t tests, and regression analyses were performed. Multivariate regression modeling was used to adjust for covariates and to identify relationships between RPE-BM thickness and ocular and systemic features. MAIN OUTCOME MEASURES: Retinal pigment epithelium-BM thickness, as measured by SD OCT segmentation using Topcon Advanced Boundary Segmentation at 9 Early Treatment of Diabetic Retinopathy Study subfields. RESULTS: Mean RPE-BM thickness was 26.3 mum (standard deviation, 4.8 mum) at central subfield. Multivariate regression with age stratification showed that RPE thinning became apparent after age 45 years. Among those aged </=45, RPE-BM was significantly thicker among those of black or mixed/other race (+3.61 mum and +1.77 mum vs. white, respectively; P < 0.001) and higher hyperopia (+0.4 mum/D; P < 0.001), but not for other variables considered. Among those age >45, RPE-BM was significantly thinner with older age (-0.10 mum/year; P < 0.001), Asian ethnicity (-0.45 mum vs. white; P = 0.02), taller height (-0.02 mum/cm; P < 0.001), higher IOP (-0.03 mum/mmHg; P < 0.001), and regular smoking (-0.27 mum vs. nonsmokers; P = 0.02). In contrast, RPE-BM was significantly thicker among black or mixed/other race (+3.29 mum and +0.81 mum vs. white, respectively; P < 0.001) and higher hyperopia (+0.28 mum/D; P < 0.001). There was no significant association with sex or Chinese ethnicity. CONCLUSIONS: We describe novel findings of RPE-BM thickness in normal individuals, a structure that varies with age, ethnicity, refraction, IOP, and smoking. The significant association with IOP is especially interesting and may have relevance for the etiology of glaucoma, while the association between age and smoking may have relevance for the etiology of age-related macular degeneration. |
Keane A P; CM Grossi; Foster J P; Q Yang; Reisman A C; K Chan; T Peto; D Thomas; Patel J P Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies Journal Article In: PLoS One, 2016. Abstract | Links | BibTeX | Tags: 6507, imaging, methodology, Optical Coherence Tomography @article{KeanePA2016, title = {Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies}, author = {Keane A P and CM Grossi and Foster J P and Q Yang and Reisman A C and K Chan and T Peto and D Thomas and Patel J P}, url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164095}, year = {2016}, date = {2016-10-08}, journal = {PLoS One}, abstract = {PURPOSE: To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. METHODS: In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. RESULTS: 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. CONCLUSIONS: We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging.}, keywords = {6507, imaging, methodology, Optical Coherence Tomography}, pubstate = {published}, tppubtype = {article} } PURPOSE: To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. METHODS: In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. RESULTS: 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. CONCLUSIONS: We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging. |