Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114021
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dc.contributorSchool of Optometryen_US
dc.contributorResearch Centre for SHARP Visionen_US
dc.creatorYusufu, Men_US
dc.creatorWeinreb, RNen_US
dc.creatorKang, Men_US
dc.creatorVingrys, AJen_US
dc.creatorShang, Xen_US
dc.creatorZhang, Len_US
dc.creatorShi, Den_US
dc.creatorHe, Men_US
dc.date.accessioned2025-07-10T01:31:42Z-
dc.date.available2025-07-10T01:31:42Z-
dc.identifier.issn0026-2862en_US
dc.identifier.urihttp://hdl.handle.net/10397/114021-
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.rights© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).en_US
dc.rightsThe following publication Yusufu, M., Weinreb, R. N., Kang, M., Vingrys, A. J., Shang, X., Zhang, L., ... & He, M. (2025). Structural and causal links between retinal vascular geometry and neural layer thickness. Microvascular Research, 161, 104834 is available at https://doi.org/10.1016/j.mvr.2025.104834.en_US
dc.subjectRetinal vascular geometryen_US
dc.subjectRetina-based microvascular health assessment systemen_US
dc.subjectOptical coherence tomographyen_US
dc.subjectRetinal layer thicknessen_US
dc.subjectColor fundus photographyen_US
dc.titleStructural and causal links between retinal vascular geometry and neural layer thicknessen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume161en_US
dc.identifier.doi10.1016/j.mvr.2025.104834en_US
dcterms.abstractPurpose: To investigate structural relationships between retinal vasculometry from color fundus photography (CFP) and neural layers obtained from Optical Coherence Tomography (OCT) scans.en_US
dcterms.abstractMethods: This cross-sectional study used the Retina-based Microvascular Health Assessment System (RMHAS) to extract retinal vascular measurements in the 6*6 mm area centered on the macular region and analyzed their associations with OCT parameters. We investigated both pairwise correlations between individual retinal layers and vascular parameters and associations between sets of variables. Mendelian randomization was employed to investigate potential causality.en_US
dcterms.abstractResults: Data from 67,918 eyes of 43,029 participants were included. Among neural layers, Ganglion Cell-Inner Plexiform Layer (GC-IPL) showed the most notable correlations with vascular Density and Complexity (r = 0.199 for arterial Vessel Area Density and r = 0.175 for Number of Segments). Inner Nuclear Layer (INL) thickness correlated with Width (r = 0.122) and arterial Vessel Area Density (r = 0.127). Mendelian randomization indicated a bidirectional causal relationship. Genetically predicted higher Vessel Density was associated with increased thickness across various retinal layers, with standardized effect size of 1.50 on Inner Segment/Outer Segment + Photoreceptor Segment thickness. Genetically predicted increases in retinal layer thicknesses, particularly the Outer Plexiform Layer, were linked to higher Vessel Density (standardized effect size 0.45) and Fractal Dimension (standardized effect size 0.48).en_US
dcterms.abstractConclusions: GC-IPL and INL were positively associated with vascular Density and Caliber. Multidimensional relationships indicate a complementary nature between retinal vascular and neural parameters, highlighting their value as a composite biomarker. Mendelian Randomization uncovered a bidirectional causal relationship, providing insights into novel therapeutic approaches targeting vascular and neuronal components.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationMicrovascular research, Sept. 2025, v. 161, 104834en_US
dcterms.isPartOfMicrovascular researchen_US
dcterms.issued2025-09-
dc.identifier.eissn1095-9319en_US
dc.identifier.artn104834en_US
dc.description.validate202507 bcwhen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera3874-
dc.identifier.SubFormID51488-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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