BIOMARCADORES RETINIANOS Y RADIOLÓGICOS DE ENFERMEDAD RENAL CRÓNICA EN DIABETES
Palabras clave:
Retina, Nefropatías diabéticas, Insuficiencia renal crónica, Biomarcadores, Microcirculación, Diabetes mellitusResumen
DOI: https://doi.org/10.46296/yc.v10i18.0900
Resumen
Introducción: La imagen retiniana permite observar la microcirculación humana de forma no invasiva y ha ganado interés como biomarcador de enfermedad renal crónica y de daño microvascular sistémico en personas con diabetes. Objetivo: Sintetizar críticamente la evidencia clínica y traslacional sobre fotografía de fondo de ojo, tomografía de coherencia óptica, angiografía por tomografía de coherencia óptica e inteligencia artificial aplicadas a la predicción, detección o fenotipificación de enfermedad renal en diabetes, y discutir su relación con hallazgos sistémicos en radiología. Metodología de búsqueda: Se realizó una revisión crítica estructurada de literatura publicada entre 2008 y agosto de 2025 en bases biomédicas internacionales, priorizando revisiones sistemáticas, metaanálisis, cohortes longitudinales y estudios con validación externa. Resultados: La asociación más robusta corresponde a la severidad de la retinopatía diabética, que se relaciona con albuminuria, descenso de la filtración glomerular y progresión renal, aunque con precisión diagnóstica insuficiente para sustituir los estándares nefrológicos. Los biomarcadores cuantitativos vasculares aportan señal adicional, pero siguen siendo heterogéneos. La tomografía de coherencia óptica y su angiografía muestran potencial para detectar alteraciones más tempranas, y los modelos de inteligencia artificial son prometedores para tamizaje de enfermedad renal crónica, aunque persisten dudas sobre validez externa y utilidad clínica real. Discusión: La retina funciona mejor como biomarcador complementario de riesgo y fenotipo que como sustituto diagnóstico renal. Conclusiones: La integración de imagen retiniana con laboratorio renal y radiología sistémica representa una vía plausible para medicina de precisión, pero requiere estandarización, validación multicéntrica y estudios de impacto.
Palabras claves: Retina, Nefropatías diabéticas, Insuficiencia renal crónica, Biomarcadores, Microcirculación, Diabetes mellitus.
Abstract
Introduction: Retinal imaging enables noninvasive visualization of the human microcirculation and has emerged as a potential biomarker of chronic kidney disease and systemic microvascular injury in people with diabetes. Objective: To critically synthesize the clinical and translational evidence on fundus photography, optical coherence tomography, optical coherence tomography angiography, and artificial intelligence for the prediction, detection, or phenotyping of kidney disease in diabetes, and to discuss their relationship with systemic imaging findings in radiology. Search methodology: A structured critical review of literature published from 2008 to August 2025 was performed in major biomedical databases, prioritizing systematic reviews, meta-analyses, longitudinal cohorts, and externally validated studies. Results: The strongest signal comes from diabetic retinopathy severity, which is associated with albuminuria, reduced glomerular filtration, and kidney disease progression, but has insufficient diagnostic accuracy to replace nephrology standards. Quantitative retinal vascular biomarkers add biological information, although reproducibility remains heterogeneous. Optical coherence tomography and optical coherence tomography angiography are promising for earlier detection, while artificial intelligence improves scalability but still requires stronger external validation. Discussion: The retina currently performs best as a complementary risk and phenotyping biomarker rather than as a stand-alone renal surrogate. Conclusions: Integrating retinal imaging with renal laboratory markers and systemic radiology may support precision medicine in diabetes, but standardization, multicenter validation, and impact studies are still required.
Keywords: Retina, Diabetic Nephropathies, Renal Insufficiency Chronic, Biomarkers, Microcirculation, Diabetes Mellitus.
Información del manuscrito:
Fecha de recepción: 11 de marzo de 2026.
Fecha de aceptación: 20 de mayo de 2026.
Fecha de publicación: 18 de junio de 2026.
Descargas
Citas
Kidney Disease: Improving Global Outcomes Diabetes Work Group. KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 2022;102(5 Suppl):S1-S127.
de Boer IH, Khunti K, Sadusky T, Tuttle KR, Neumiller JJ, Rhee CM, et al. Diabetes management in chronic kidney disease: a consensus report by the American Diabetes Association and Kidney Disease: Improving Global Outcomes. Diabetes Care. 2022;45(12):3075-90.
Hamzah NAA, Abdul Rani NS, Abdul Halim WH, et al. Retinal imaging and oculomics biomarkers for diabetes mellitus. PeerJ. 2024;12:e17786.
Cheung N, Wong TY. Diabetic retinopathy and systemic vascular complications. Prog Retin Eye Res. 2008;27(2):161-76.
Wong TY, Cheung CMG, Larsen M, Sharma S, Simó R. Diabetic retinopathy. Nat Rev Dis Primers. 2016;2:16012.
Thomas MC, Brownlee M, Susztak K, Sharma K, Jandeleit-Dahm KAM, Zoungas S, et al. Diabetic kidney disease. Nat Rev Dis Primers. 2015;1:15018.
Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes. 2005;54(6):1615-25.
Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93(1):137-88.
Tuttle KR, Bakris GL, Bilous RW, Chiang JL, de Boer IH, Goldstein-Fuchs J, et al. Diabetic kidney disease: a report from an American Diabetes Association Consensus Conference. Diabetes Care. 2014;37(10):2864-83.
Fiorentino M, Bolignano D, Tesar V, Pisano A, Biesen WV, Tripepi G, et al. Renal biopsy in patients with diabetes: a pooled meta-analysis of 48 studies. Nephrol Dial Transplant. 2017;32(1):97-110.
He F, Xia X, Wu XF, Yu XQ, Huang FX. Diabetic retinopathy in predicting diabetic nephropathy in patients with type 2 diabetes and renal disease: a meta-analysis. Diabetologia. 2013;56(3):457-66.
Liang S, Zhang XG, Cai GY, Zhu HY, Zhou JH, Wu J, et al. Identifying parameters to distinguish non-diabetic renal diseases from diabetic nephropathy in patients with type 2 diabetes mellitus: a meta-analysis. PLoS One. 2013;8(5):e64184.
Aronov M, Nasser S, Sergeyeva O, Amit U, Aharon A, Yagil Y, et al. Retinal vascular signs and chronic kidney disease: a systematic review and meta-analysis. J Pers Med. 2021;11(7):665.
Ooi QL, Tow FK, Deva R, Alias MA, Kawasaki R, Wong TY, et al. The microvasculature in chronic kidney disease. Clin J Am Soc Nephrol. 2011;6(8):1872-8.
Sng CCA, Sabanayagam C, Lamoureux EL, Liu E, Lim SC, Tai ES, et al. Fractal analysis of the retinal vasculature and chronic kidney disease. Nephrol Dial Transplant. 2010;25(7):2252-8.
Liew G, Wong TY, Mitchell P, Wang JJ. Retinal microvascular signs are associated with chronic kidney disease in persons with and without diabetes. Kidney Blood Press Res. 2012;35(6):589-94.
Yip W, Sabanayagam C, Teo BW, Tay WT, Ikram MK, Wang JJ, et al. Retinal microvascular abnormalities and risk of renal failure in Asian populations. Sci Rep. 2017;7:9374.
Nusinovici S, Sabanayagam C, Teo BW, Tan GSW, Tai ES, Lim SC, et al. Retinal microvascular signs and risk of diabetic kidney disease in Asian and White populations. Sci Rep. 2021;11:4898.
Benitez-Aguirre P, Craig ME, Sasongko MB, Jenkins AJ, McKay R, Wong TY, et al. Retinal vascular geometry predicts incident renal dysfunction in young people with type 1 diabetes. Diabetes Care. 2012;35(11):2275-80.
Gupta M, Venkataraman K, Gella L, Ravindran RD, Mohan V, Sharma T, et al. Diabetic retinopathy is a predictor of progression of diabetic kidney disease: a systematic review and meta-analysis. Int J Hypertens. 2022;2022:3922398.
Zhuang X, Cao D, Zeng Y, Yang D, Hu Y, Yang C, et al. Association of retinal vessel density and thickness with impaired renal function in type 2 diabetic patients with retinopathy. Diabetes Res Clin Pract. 2020;168:108373.
Alé-Chilet A, Cejka C, Hernández T, Hernández C, Simó-Servat O, Ortega E, et al. Optical coherence tomography angiography biomarkers in diabetic kidney disease in type 1 diabetes. J Clin Med. 2022;11(1):197.
Surawatsatien N, Mahawong W, Ruamviboonsuk P, Srisawat N, Kantachuvesiri S, Tham YC, et al. Relationships between retinal vascular characteristics and diabetic kidney disease in patients with type 2 diabetes mellitus: the Diabetic Eye and Kidney Disease Study. Transl Vis Sci Technol. 2023;12(4):19.
Tom E, Kesler A, Goldstein M, Dotan G, Habot-Wilner Z. Retinal capillary nonperfusion on optical coherence tomography angiography and estimated glomerular filtration rate in diabetes with chronic kidney disease: a proof-of-concept study. Retina. 2020;40(8):1564-72.
da Silva MO, Almeida FK, Bernardi L, Bortolotto PB, Lavinsky D. Retinal neurovascular and choroidal structural changes in patients with type 1 diabetes with early renal dysfunction. Int J Retina Vitreous. 2021;7:73.
da Silva MO, Bernardi L, Bortolotto PB, Lavinsky D. Longitudinal retinal neurovascular and choroidal changes associated with early diabetic kidney disease in type 1 diabetes. BMC Ophthalmol. 2024;24:85.
Vadalà M, Castellino N, La Blasca T, Guarrasi G, Terrasi M, Marrone G, et al. Retinal and choroidal vasculature changes associated with chronic kidney disease. Graefes Arch Clin Exp Ophthalmol. 2019;257(8):1687-98.
Spaide RF, Fujimoto JG, Waheed NK, Sadda SR, Staurenghi G. Optical coherence tomography angiography. Prog Retin Eye Res. 2018;64:1-55.
Kashani AH, Chen CL, Gahm JK, Zheng F, Richter GM, Rosenfeld PJ, et al. Optical coherence tomography angiography: a comprehensive review of current methods and clinical applications. Prog Retin Eye Res. 2017;60:66-100.
Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health. 2020;2(6):e295-e302.
Betzler BK, Chen J, Lin H, Sabanayagam C, Xu D, Hu Y, et al. Detecting diabetic kidney disease from retinal photographs in patients with diabetes using deep learning. J Am Med Inform Assoc. 2023;30(12):1904-16.
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158-64.
Zhang K, Liu X, Xu J, Yuan J, Cai W, Chen T, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomed Eng. 2021;5(6):533-45.
Zhao X, Wu Z, Zhao Y, Zhang H, Peng Y, Wang X, et al. Development and validation of an ultrawide-field fundus photography-based deep learning system for chronic kidney disease screening. npj Digit Med. 2024;7:275.
Wu Q, Zhou Y, Wang X, Li H, Zhao Y, Chen F, et al. Retinal image foundation model-enabled noninvasive kidney screening and pathology prediction. Nat Commun. 2025;16:6962.
Wagner SK, Fu DJ, Faes L, Liu X, Huemer J, Khalid H, et al. Insights into systemic disease through retinal imaging-based oculomics. Transl Vis Sci Technol. 2020;9(2):6.
Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-75.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.
Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate disease from imaging. Nat Mach Intell. 2021;3:199-217.
Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, et al. Developing trustworthy artificial intelligence for health care. Nat Med. 2021;27(8):1321-9.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
Biffi E, Stanzione A, D’Anna L, Cicala G, Rella E, Cirillo M, et al. Retinal biomarkers of cerebral small vessel disease: a systematic review and meta-analysis. PLoS One. 2022;17(4):e0266974.
Simó R, Hernández C, Porta M, Bandello F, Grauslund J, Harding SP. Diabetic retinopathy as an independent predictor of subclinical cardiovascular disease in type 2 diabetes: the PRECISED study. BMJ Open Diabetes Res Care. 2019;7:e000845.
McGeechan K, Liew G, Macaskill P, Irwig L, Klein R, Klein BEK, et al. Meta-analysis: retinal vessel caliber and risk for coronary heart disease. Ann Intern Med. 2009;151(6):404-13.
Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol. 2017;12(12):2032-45.
Publicado
Cómo citar
Licencia
Derechos de autor 2026 REVISTA CIENTÍFICA MULTIDISCIPLINARIA ARBITRADA YACHASUN - ISSN: 2697-3456
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
