Multimodal Retinal Imaging for Systemic Microvascular Phenotyping: Chronic Kidney Disease as a Translational Reference Model
5 affiliations
- 1Department of Ophthalmology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu 610041, China
- 2Department of Eye and Vision Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool L7 8TX, UK
- 3Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology and Medical Imaging, West China Hospital, Sichuan University, Chengdu 610041, China
- 4Department of Primary Care and Mental Health, University of Liverpool, Liverpool L7 8TX, UK
- 5Institute of Life Course & Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK
Abstract
Multimodal retinal imaging enables repeated, noninvasive assessment of vascular, neuroretinal, choroidal, perfusion, oxygenation, and flow-related phenotypes, positioning the retina as a platform for systemic microvascular phenotyping. Chronic kidney disease (CKD) provides an informative translational reference model because estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) offer standardized clinical anchors across disease severity and progression. This critical narrative review, informed by structured searches of PubMed/MEDLINE, Embase, and Web of Science, evaluates retinal phenotypes from biological association and longitudinal prediction to incremental clinical value, external validation, and implementation. Fundus photography, ultra-widefield imaging, optical coherence tomography (OCT), OCT angiography (OCTA), and functional retinal imaging provide complementary information, but retinal signals are not organ-specific. Artificial intelligence (AI)-enabled models may support automated phenotyping and opportunistic risk enrichment, but credible translation requires appropriate comparators, external validation, calibration, subgroup assessment, decision-analytic utility, and workflow evaluation. Retinal imaging should therefore be viewed as an adjunctive platform rather than a stand-alone CKD diagnostic test, with its near-term role being to identify individuals warranting confirmatory kidney assessment when retinal images are already available and recent eGFR/UACR data are absent.
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BibTeX
@article{gu2026multimodal,
title = {{Multimodal Retinal Imaging for Systemic Microvascular Phenotyping: Chronic Kidney Disease as a Translational Reference Model}},
author = {Qiumei Gu and Zhengju Chen and Jiacen Wan and Zhuangzhi Gao and Hongyi Qin and Ziyan He and Yalin Zheng and Fang Lu},
journal = {Journal of Clinical Medicine},
year = {2026},
month = nov,
doi = {10.3390/jcm15218657},
url = {https://www.mdpi.com/2077-0383/15/21/8657},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multimodal Retinal Imaging for Systemic Microvascular Phenotyping: Chronic Kidney Disease as a Translational Reference Model
AU - Qiumei Gu
AU - Zhengju Chen
AU - Jiacen Wan
AU - Zhuangzhi Gao
AU - Hongyi Qin
AU - Ziyan He
AU - Yalin Zheng
AU - Fang Lu
PY - 2026
DA - 2026/11/06
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218657
UR - https://www.mdpi.com/2077-0383/15/21/8657
AB - Multimodal retinal imaging enables repeated, noninvasive assessment of vascular, neuroretinal, choroidal, perfusion, oxygenation, and flow-related phenotypes, positioning the retina as a platform for systemic microvascular phenotyping. Chronic kidney disease (CKD) provides an informative translational reference model because estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) offer standardized clinical anchors across disease severity and progression. This critical narrative review, informed by structured searches of PubMed/MEDLINE, Embase, and Web of Science, evaluates retinal phenotypes from biological association and longitudinal prediction to incremental clinical value, external validation, and implementation. Fundus photography, ultra-widefield imaging, optical coherence tomography (OCT), OCT angiography (OCTA), and functional retinal imaging provide complementary information, but retinal signals are not organ-specific. Artificial intelligence (AI)-enabled models may support automated phenotyping and opportunistic risk enrichment, but credible translation requires appropriate comparators, external validation, calibration, subgroup assessment, decision-analytic utility, and workflow evaluation. Retinal imaging should therefore be viewed as an adjunctive platform rather than a stand-alone CKD diagnostic test, with its near-term role being to identify individuals warranting confirmatory kidney assessment when retinal images are already available and recent eGFR/UACR data are absent.
SN - 2077-0383
ER -