PURPOSE: To examine the influence of a range of cardiovascular risk factors and ocular conditions on retinal vascular fractal dimension in the Singapore Malay Eye Study. DESIGN: Population-based cross-sectional study. METHODS: Fractal analysis of the retinal vessels is a method to quantify the global geometric complexity of the retinal vasculature. Retinal vascular fractal dimension (D(f)) and caliber were measured from retinal photographs using a computer-assisted program. D(f) and arteriolar caliber were combined to form a retinal vascular optimality score (ranging from 0 to 3). Data on cardiovascular and ocular factors were collected from all participants based on a standardized protocol. RESULTS: Two thousand nine hundred thirteen (88.8% of 3280 participants) persons had retinal photographs of sufficient quality for the measurement. The mean D(f) was 1.405 (standard deviation, 0.046; interquartile range, 1.243 to 1.542). In the multiple linear regression analysis, after controlling for gender, serum glucose, intraocular pressure, anterior chamber depth, and retinal vascular caliber, smaller D(f) was associated independently with older age (standardized regression coefficient [sβ] = -0.311; P < .001), higher mean arterial blood pressure (sβ = -0.085; P < .001), a more myopic spherical equivalent (sβ = 0.152; P < .001), and presence of cataract (sβ = -0.107; P < .001). Retinal vascular optimality score was associated significantly with higher mean arterial blood pressure (P > .001 for trend). CONCLUSIONS: Age, blood pressure, refractive error, and lens opacity had significant influence on retinal vascular fractal measurements. A new score of retinal vascular optimality combining fractals and caliber showed strong association with blood pressure. Quantitative analysis of retinal vasculature therefore may provide additional information on microvascular architecture and optimality.
PURPOSE: To examine the influence of a range of cardiovascular risk factors and ocular conditions on retinal vascular fractal dimension in the Singapore Malay Eye Study. DESIGN: Population-based cross-sectional study. METHODS: Fractal analysis of the retinal vessels is a method to quantify the global geometric complexity of the retinal vasculature. Retinal vascular fractal dimension (D(f)) and caliber were measured from retinal photographs using a computer-assisted program. D(f) and arteriolar caliber were combined to form a retinal vascular optimality score (ranging from 0 to 3). Data on cardiovascular and ocular factors were collected from all participants based on a standardized protocol. RESULTS: Two thousand nine hundred thirteen (88.8% of 3280 participants) persons had retinal photographs of sufficient quality for the measurement. The mean D(f) was 1.405 (standard deviation, 0.046; interquartile range, 1.243 to 1.542). In the multiple linear regression analysis, after controlling for gender, serum glucose, intraocular pressure, anterior chamber depth, and retinal vascular caliber, smaller D(f) was associated independently with older age (standardized regression coefficient [sβ] = -0.311; P < .001), higher mean arterial blood pressure (sβ = -0.085; P < .001), a more myopic spherical equivalent (sβ = 0.152; P < .001), and presence of cataract (sβ = -0.107; P < .001). Retinal vascular optimality score was associated significantly with higher mean arterial blood pressure (P > .001 for trend). CONCLUSIONS: Age, blood pressure, refractive error, and lens opacity had significant influence on retinal vascular fractal measurements. A new score of retinal vascular optimality combining fractals and caliber showed strong association with blood pressure. Quantitative analysis of retinal vasculature therefore may provide additional information on microvascular architecture and optimality.
Authors: Lihua Huang; See Ling Loy; Wei-Qing Chen; Johan G Eriksson; Yap Seng Chong; Zhongwei Huang; Jerry Kok Yen Chan; Tien Yin Wong; Michael Kramer; Cuilin Zhang; Ling-Jun Li Journal: Hum Reprod Date: 2021-10-18 Impact factor: 6.918
Authors: Victor T T Chan; Tiffany H K Tso; Fangyao Tang; Clement Tham; Vincent Mok; Christopher Chen; Tien Y Wong; Carol Y Cheung Journal: J Vis Exp Date: 2017-11-06 Impact factor: 1.355
Authors: Ronald Klein; Kristine E Lee; Lorraine Danforth; Michael Y Tsai; Ronald E Gangnon; Stacy E Meuer; Tien Y Wong; Carol Y Cheung; Barbara E K Klein Journal: Ophthalmology Date: 2018-05-18 Impact factor: 12.079
Authors: Ling-Jun Li; Izzuddin Aris; Lin Lin Su; Mya Thway Tint; Carol Yim-Lui Cheung; M Kamran Ikram; Peter Gluckman; Keith M Godfrey; Kok Hian Tan; George Yeo; Fabian Yap; Kenneth Kwek; Seang-Mei Saw; Yap-Seng Chong; Tien-Yin Wong; Yung Seng Lee Journal: PLoS One Date: 2015-04-24 Impact factor: 3.240