Praveen J Patel1, Paul J Foster2, Carlota M Grossi2, Pearse A Keane2, Fang Ko2, Andrew Lotery3, Tunde Peto2, Charles A Reisman4, Nicholas G Strouthidis5, Qi Yang4. 1. NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom. Electronic address: praveen.patel@moorfields.nhs.uk. 2. NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom. 3. Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, Southampton, United Kingdom. 4. Topcon Advanced Biomedical Imaging Laboratory, Oakland, New Jersey. 5. NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom; Singapore Eye Research Institute, Singapore; Discipline of Clinical Ophthalmology and Eye Health, University of Sydney, Sydney, New South Wales, Australia.
Abstract
PURPOSE: To derive macular thickness measures and their associations by performing rapid, automated segmentation of spectral-domain optical coherence tomography (SD OCT) images collected and stored as part of the UK Biobank (UKBB) study. DESIGN: Large, multisite cohort study in the United Kingdom. Analysis of cross-sectional data. PARTICIPANTS: Adults from the United Kingdom aged 40 to 69 years. METHODS: Participants had nonmydriatic SD OCT (Topcon 3D OCT-1000 Mark II; Topcon GB, Newberry, Berkshire, UK) performed as part of the ocular assessment module. Rapid, remote, automated segmentation of the images was performed using custom optical coherence tomography (OCT) image analysis software (Topcon Advanced Boundary Segmentation [TABS]; Topcon GB) to generate macular thickness values. We excluded people with a history of ocular or systemic disease (diabetes or neurodegenerative diseases) and eyes with reduced vision (<0.1 logarithm of the minimum angle of resolution) or with low SD OCT signal-to-noise ratio and low segmentation success certainty. MAIN OUTCOME MEASURES: Macular thickness values across 9 Early Treatment of Diabetic Retinopathy Study (ETDRS) subfields. RESULTS: The SD OCT scans of 67 321 subjects were available for analysis, with 32 062 people with at least 1 eye meeting the inclusion criteria. There were 17 274 women and 14 788 men, with a mean (standard deviation [SD]) age of 55.2 (8.2) years. The mean (SD) logarithm of the minimum angle of resolution visual acuity was -0.075 (0.087), and the refractive error was -0.071 (+1.91) diopters (D). The mean (SD) central macular thickness (CMT) in the central 1-mm ETDRS subfield was 264.5 (22.9) μm, with 95% confidence limits of 220.8 and 311.5 μm. After adjusting for covariates, CMT was positively correlated with older age, female gender, greater myopia, smoking, body mass index (BMI), and white ethnicity (all P < 0.001). Of note, macular thickness in other subfields was negatively correlated with older age and greater myopia. CONCLUSIONS: We report macular thickness data derived from SD OCT images collected as part of the UKBB study and found novel associations among older age, ethnicity, BMI, smoking, and macular thickness.
PURPOSE: To derive macular thickness measures and their associations by performing rapid, automated segmentation of spectral-domain optical coherence tomography (SD OCT) images collected and stored as part of the UK Biobank (UKBB) study. DESIGN: Large, multisite cohort study in the United Kingdom. Analysis of cross-sectional data. PARTICIPANTS: Adults from the United Kingdom aged 40 to 69 years. METHODS: Participants had nonmydriatic SD OCT (Topcon 3D OCT-1000 Mark II; Topcon GB, Newberry, Berkshire, UK) performed as part of the ocular assessment module. Rapid, remote, automated segmentation of the images was performed using custom optical coherence tomography (OCT) image analysis software (Topcon Advanced Boundary Segmentation [TABS]; Topcon GB) to generate macular thickness values. We excluded people with a history of ocular or systemic disease (diabetes or neurodegenerative diseases) and eyes with reduced vision (<0.1 logarithm of the minimum angle of resolution) or with low SD OCT signal-to-noise ratio and low segmentation success certainty. MAIN OUTCOME MEASURES: Macular thickness values across 9 Early Treatment of Diabetic Retinopathy Study (ETDRS) subfields. RESULTS: The SD OCT scans of 67 321 subjects were available for analysis, with 32 062 people with at least 1 eye meeting the inclusion criteria. There were 17 274 women and 14 788 men, with a mean (standard deviation [SD]) age of 55.2 (8.2) years. The mean (SD) logarithm of the minimum angle of resolution visual acuity was -0.075 (0.087), and the refractive error was -0.071 (+1.91) diopters (D). The mean (SD) central macular thickness (CMT) in the central 1-mm ETDRS subfield was 264.5 (22.9) μm, with 95% confidence limits of 220.8 and 311.5 μm. After adjusting for covariates, CMT was positively correlated with older age, female gender, greater myopia, smoking, body mass index (BMI), and white ethnicity (all P < 0.001). Of note, macular thickness in other subfields was negatively correlated with older age and greater myopia. CONCLUSIONS: We report macular thickness data derived from SD OCT images collected as part of the UKBB study and found novel associations among older age, ethnicity, BMI, smoking, and macular thickness.
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