Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis

Introducción: La enfermedad renal diabética (ERD) representa la principal causa de enfermedad renal crónica en pacientes con diabetes mellitus tipo 2 (DM2). La detección temprana basada en factores de riesgo y modelos predictivos clínicamente útiles es esencial para la detección e intervención tempr...

Full description

Autores:
Rodríguez Camargo, Ricardo David
Rodríguez Carrascal, Fabio David
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2025
Institución:
Universidad Libre
Repositorio:
RIU - Repositorio Institucional UniLibre
Idioma:
spa
OAI Identifier:
oai:repository.unilibre.edu.co:10901/31524
Acceso en línea:
https://hdl.handle.net/10901/31524
Palabra clave:
Diabetes - Diagnóstico
Diabetes - Complicaciones
Neuropatías diabéticas
Type 2 Diabetes Mellitus
Diabetic Kidney Disease
Meta-analysis
Risk Factors
Predictive Models
Diabetic nephropathy
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
id RULIBRE2_20382f6d23cd2ee305fd4b7b27beb617
oai_identifier_str oai:repository.unilibre.edu.co:10901/31524
network_acronym_str RULIBRE2
network_name_str RIU - Repositorio Institucional UniLibre
repository_id_str
dc.title.spa.fl_str_mv Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
dc.title.alternative.spa.fl_str_mv Risk factors and diagnostic performance of predictive models for diabetic kidney disease in type 2 diabetes mellitus: a systematic review and meta-analysis
title Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
spellingShingle Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
Diabetes - Diagnóstico
Diabetes - Complicaciones
Neuropatías diabéticas
Type 2 Diabetes Mellitus
Diabetic Kidney Disease
Meta-analysis
Risk Factors
Predictive Models
Diabetic nephropathy
title_short Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
title_full Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
title_fullStr Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
title_full_unstemmed Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
title_sort Factores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisis
dc.creator.fl_str_mv Rodríguez Camargo, Ricardo David
Rodríguez Carrascal, Fabio David
dc.contributor.advisor.none.fl_str_mv Mendoza Torres, Evelin
Cano Peñaloza, Raquel Amira
Viñas, Alvaro
dc.contributor.author.none.fl_str_mv Rodríguez Camargo, Ricardo David
Rodríguez Carrascal, Fabio David
dc.subject.spa.fl_str_mv Diabetes - Diagnóstico
Diabetes - Complicaciones
Neuropatías diabéticas
topic Diabetes - Diagnóstico
Diabetes - Complicaciones
Neuropatías diabéticas
Type 2 Diabetes Mellitus
Diabetic Kidney Disease
Meta-analysis
Risk Factors
Predictive Models
Diabetic nephropathy
dc.subject.subjectenglish.spa.fl_str_mv Type 2 Diabetes Mellitus
Diabetic Kidney Disease
Meta-analysis
Risk Factors
Predictive Models
Diabetic nephropathy
description Introducción: La enfermedad renal diabética (ERD) representa la principal causa de enfermedad renal crónica en pacientes con diabetes mellitus tipo 2 (DM2). La detección temprana basada en factores de riesgo y modelos predictivos clínicamente útiles es esencial para la detección e intervención temprana que modifiquen el curso de la enfermedad. Objetivo: Identificar los factores de riesgo clínicos y bioquímicos asociados al desarrollo de ERD en DM2 y evaluar el desempeño diagnóstico de modelos predictivos mediante revisión sistemática y metaanálisis. Metodología: Se realizó una búsqueda exhaustiva en PubMed, Embase, Scopus, Web of Science y Cochrane Library. Se incluyeron estudios observacionales con datos cuantificables, evaluados mediante la escala Newcastle-Ottawa. Se ejecutaron dos metaanálisis independientes utilizando el modelo de efectos aleatorios de DerSimonian y Laird. Se reportaron odds ratios (OR) e intervalos de confianza del 95 %, sensibilidad, especificidad y área bajo la curva (AUC). Resultados: Se incluyeron 8 estudios primarios para factores de riesgo y 6 para modelos predictivos. Los principales factores asociados significativamente a ERD fueron: edad > 60 años (OR 3.00), antecedente familiar de nefropatía (OR 2.80), sexo masculino (OR 2.32), hipertensión arterial, HbA1c ≥ 8%, dislipidemia (OR 1.11), leucocitosis (OR 1.19) y T3 libre baja (OR 0.71). Entre los modelos predictivos, los mejores presentaron AUC entre 0.75 y 0.87. La mayoría no contaba con validación externa. Se propuso una tabla de riesgo clínico ilustrativa para estratificación primaria, con aplicación en escenarios de baja complejidad. Conclusión: La ERD en DM2 está fuertemente asociada con factores modificables y no modificables identificables clínicamente. Los modelos predictivos existentes muestran buen desempeño discriminativo, pero requieren validación externa. Esta revisión ofrece una base importante para el desarrollo futuro de herramientas locales de predicción clínica en poblaciones colombianas y/o latinoamericanas.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-07-17T19:55:57Z
dc.date.available.none.fl_str_mv 2025-07-17T19:55:57Z
dc.date.created.none.fl_str_mv 2025-06-28
dc.type.local.spa.fl_str_mv Tesis de Especialización
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/bachelorThesis
format http://purl.org/coar/resource_type/c_7a1f
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10901/31524
url https://hdl.handle.net/10901/31524
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv American Diabetes Association Professional Practice Committee, ElSayed NA, McCoy RG, Aleppo G, Bajaj M, Balapattabi K, et al. Introduction and Methodology: Standards of Care in Diabetes—2025. Diabetes Care 2025;48:S1–5. https://doi.org/10.2337/dc25-SINT.
ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. Introduction and Methodology: Standards of Care in Diabetes—2023. Diabetes Care 2023;46:S1–4. https://doi.org/10.2337/dc23-Sint.
Wang L, Li X, Wang Z, Bancks MP, Carnethon MR, Greenland P, et al. Trends in Prevalence of Diabetes and Control of Risk Factors in Diabetes Among US Adults, 1999-2018. JAMA 2021;326:704. https://doi.org/10.1001/jama.2021.9883
Fontalvo JR, Daza Arnedo R, Aguilar Salcedo N, Alfaro M, Navas Torrejano D, Cardona Blanco M, et al. Nueva evidencia en el tratamiento de la enfermedad renal diabética: ¿qué aporta la finerenona? Rev Colomb Nefrol 2022;9. https://doi.org/10.22265/acnef.9.3.603.
Rico-Fontalvo J, Yama-Mosquera E, Robayo-García A, Aroca-Martínez G, Arango-Álvarez JJ, Barros-Camargo L, et al. Situación de la enfermedad renal crónica en Colombia. NEFRO 2022;19:9891. https://doi.org/10.24875/NEFRO.22000030
Rico Fontalvo JE. Guía de práctica clínica para la enfermedad renal diabética. Rev Colomb Nefrol 2021;8. https://doi.org/10.22265/acnef.8.2.561
Acosta Ruiza LX, Angarita Merchán M, Orjuela Vargas L. Diabetes mellitus tipo 2: Latinoamérica y Colombia, análisis del último quinquenio. Rev Med 2024;31:35–46. https://doi.org/10.18359/rmed.6067
Situación de la enfermedad renal crónica, la hipertensión arterial y la diabetes mellitus en Colombia 2022 - Cuenta de Alto Costo 2023. https://cuentadealtocosto.org/publicaciones/situacion-de-la-enfermedad-renal-cronica-la-hipertension-arterial-y-la-diabetes-mellitus-en-colombia-2022/
Actualización del consenso basado en la evidencia – HTA, DM y ERC - Cuenta de Alto Costo 2025. https://cuentadealtocosto.org/publicaciones/actualizacion-del-consenso-basado-en-la-evidencia-hta-dm-y-erc/
Obrador GT, Álvarez-Estévez G, Bellorin-Font E, Bonanno-Hidalgo C, Clavero R, Correa-Rotter R, et al. Documento de consenso sobre nuevas terapias para retrasar la progresión de la enfermedad renal crónica con énfasis en los iSGLT-2: implicaciones para Latinoamérica. NEFRO 2024;21:14493
Rico Fontalvo JE. Enfermedad renal diabética: de cara a la prevención, diagnóstico e intervención temprana. Rev Colomb Nefrol 2020;7. https://doi.org/10.22265/acnef.7.2.506.
Mercado Marchena RC, Mantilla Morales YS. Características epidemiológicas y su relación con el control glucémico en pacientes con Diabetes mellitus 2 que han desarrollado enfermedad renal crónica por diabetes que asisten a consulta externa en una clínica de cuarto nivel de Barranquilla. Universidad Libre, 2023
Saji S, Suresh S, Mc D, Krishnaswamy SK, Asirvatham AJ, Kumar M, et al. A retrospective case - control study for assessing the risk factors for development of Diabetic Kidney Disease among people with Type 2 Diabetes in Tamil Nadu and Puducherry 2022. https://doi.org/10.1101/2022.12.02.22283018.
Adler AI, Stevens RJ, Manley SE, Bilous RW, Cull CA, Holman RR, et al. Development and progression of nephropathy in type 2 diabetes: The United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney International 2003;63:225–32. https://doi.org/10.1046/j.1523-1755.2003.00712.x
RODRIGUEZ RAMOS JF, Herrera Miranda GL. Factores de riesgo relacionados con enfermedad renal crónica. Policlínico Luis A. Turcios Lima, Pinar del Río, 2019. MediSur 2022;20:59–66
Lopera Medina MM. La enfermedad renal crónica en Colombia: necesidades en salud y respuesta del Sistema General de Seguridad Social en Salud. RGYPS 2016;15. https://doi.org/10.11144/Javeriana.rgyps15-30.ercc
Martinez J, Perez-Rondon A, Zea J, Ilano I, Castano-Villegas N, Nguyen Cong L, et al. Validation of Machine Learning-Based Screening Tools for Early Detection of CKD in Patients with Type 2 Diabetes (T2D): FR-PO1098. Journal of the American Society of Nephrology 2024;35:10.1681/ASN.20244zmahyvr. https://doi.org/10.1681/ASN.20244zmahyvr.
DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers 2015;1:15019. https://doi.org/10.1038/nrdp.2015.19
Valero K, Marante D, Torres R M, Ramírez G, Cortéz R, Carlini R. Complicaciones microvasculares de la diabetes. Rev Venez Endocrinol Metab 2012;10:111–37. http://ve.scielo.org/scielo.php?script=sci_arttext&pid=S1690-31102012000400014&lng=es&tlng=es.
Mogensen CE, Christensen CK, Vittinghus E. The Stages in Diabetic Renal Disease: With Emphasis on the Stage of Incipient Diabetic Nephropathy. Diabetes 1983;32:64–78. https://doi.org/10.2337/diab.32.2.S64
Stevens PE, Ahmed SB, Carrero JJ, Foster B, Francis A, Hall RK, et al. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney International 2024;105:S117–314. https://doi.org/10.1016/j.kint.2023.10.018
Villena Pacheco A. Factores de riesgo de nefropatía diabética. Acta Med Peru 2022;38. https://doi.org/10.35663/amp.2021.384.2256
Tuttle KR. Back to the Future: Glomerular Hyperfiltration and the Diabetic Kidney. Diabetes 2017;66:14–6. https://doi.org/10.2337/dbi16-0056.
Natesan V, Kim S-J. Diabetic Nephropathy - a Review of Risk Factors, Progression, Mechanism, and Dietary Management. Biomol Ther (Seoul) 2021;29:365–72. https://doi.org/10.4062/biomolther.2020.204
Ghelichi-Ghojogh M, Fararouei M, Seif M, Pakfetrat M. Chronic kidney disease and its health-related factors: a case-control study. BMC Nephrol 2022;23:24. https://doi.org/10.1186/s12882-021-02655-w
Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet 1998;352:837–53. https://doi.org/10.1016/S0140-6736(98)07019-6
Retnakaran R, Cull CA, Thorne KI, Adler AI, Holman RR, for the UKPDS Study Group. Risk Factors for Renal Dysfunction in Type 2 Diabetes. Diabetes 2006;55:1832–9. https://doi.org/10.2337/db05-1620
Unnikrishnan R, Rema M, Pradeepa R, Deepa M, Shanthirani CS, Deepa R, et al. Prevalence and Risk Factors of Diabetic Nephropathy in an Urban South Indian Population. Diabetes Care 2007;30:2019–24. https://doi.org/10.2337/dc06-2554
Alrawahi AH, Rizvi SGA, Al-Riyami D, Al-Anqoodi Z. Prevalence and Risk Factors of Diabetic Nephropathy in Omani Type 2 Diabetics in Al-Dakhiliyah Region. Oman Med J 2012;27:212–6. https://doi.org/10.5001/omj.2012.48
Al-Rubeaan K, Youssef AM, Subhani SN, Ahmad NA, Al-Sharqawi AH, Al-Mutlaq HM, et al. Diabetic Nephropathy and Its Risk Factors in a Society with a Type 2 Diabetes Epidemic: A Saudi National Diabetes Registry-Based Study. PLoS ONE 2014;9:e88956. https://doi.org/10.1371/journal.pone.0088956
Roy S, Schweiker-Kahn O, Jafry B, Masel-Miller R, Raju RS, O’Neill LMO, et al. Risk Factors and Comorbidities Associated with Diabetic Kidney Disease. J Prim Care Community Health 2021;12:21501327211048556. https://doi.org/10.1177/21501327211048556.
Siddiqui K, George TP, Joy SS, Alfadda AA. Risk factors of chronic kidney disease among type 2 diabetic patients with longer duration of diabetes. Front Endocrinol 2022;13:1079725. https://doi.org/10.3389/fendo.2022.1079725
Joshi R, Subedi P, Yadav GK, Khadka S, Rijal T, Amgain K, et al. Prevalence and risk factors of chronic kidney disease among patients with type 2 diabetes mellitus at a tertiary care hospital in Nepal: a cross-sectional study. BMJ Open 2023;13:e067238. https://doi.org/10.1136/bmjopen-2022-067238.
Liu W, Du J, Ge X, Jiang X, Peng W, Zhao N, et al. The analysis of risk factors for diabetic kidney disease progression: a single-centre and cross-sectional experiment in Shanghai. BMJ Open 2022;12:e060238. https://doi.org/10.1136/bmjopen-2021-060238
Wu M, Lu J, Zhang L, Liu F, Chen S, Han Y, et al. A non-laboratory-based risk score for predicting diabetic kidney disease in Chinese patients with type 2 diabetes. Oncotarget 2017;8:102550–8. https://doi.org/10.18632/oncotarget.21684
Mongkolsomlit S, Rawdaree P, Komoltri C, Tawichasri C, Patumanond J. The development and validation of a risk score for predicting microalbuminuria in type 2 diabetic patients. JDM 2012;02:227–33. https://doi.org/10.4236/jdm.2012.22036
Wang G, Wang B, Qiao G, Lou H, Xu F, Chen Z, et al. Screening Tools Based on Nomogram for Diabetic Kidney Diseases in Chinese Type 2 Diabetes Mellitus Patients. Diabetes Metab J 2021;45:708–18. https://doi.org/10.4093/dmj.2020.0117
Wang X, Liu X, Zhao J, Chen M, Wang L. Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM. DMSO 2024;Volume 17:215–25. https://doi.org/10.2147/DMSO.S442925
Hui D, Zhang F, Lu Y, Hao H, Tian S, Fan X, et al. A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus. DMSO 2023;Volume 16:385–95. https://doi.org/10.2147/DMSO.S391781
Liao L-N, Li T-C, Li C-I, Liu C-S, Lin W-Y, Lin C-H, et al. Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients. Sci Rep 2019;9:19897. https://doi.org/10.1038/s41598-019-56400-3.
Helou N, Dwyer A, Shaha M, Zanchi A. Multidisciplinary management of diabetic kidney disease: a systematic review and meta-analysis. JBI Database of Systematic Reviews and Implementation Reports 2016;14:169–207.
Fenta ET, Eshetu HB, Kebede N, Bogale EK, Zewdie A, Kassie TD, et al. Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol Metab Syndr 2023;15:245. https://doi.org/10.1186/s13098-023-01202-x
Kanakamani J, Ammini AC, Gupta N, Dwivedi SN. Prevalence of Microalbuminuria Among Patients with Type 2 Diabetes Mellitus—A Hospital-Based Study from North India. Diabetes Technology & Therapeutics 2010;12:161–6. https://doi.org/10.1089/dia.2009.0133.
Tangri N. A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure. JAMA 2011;305:1553. https://doi.org/10.1001/jama.2011.451.
Echouffo-Tcheugui JB, Kengne AP. Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review. PLoS Med 2012;9:e1001344. https://doi.org/10.1371/journal.pmed.1001344
Sandholm N, Dahlström EH, Groop P-H. Genetic and epigenetic background of diabetic kidney disease. Front Endocrinol 2023;14:1163001. https://doi.org/10.3389/fendo.2023.1163001
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rights.license.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 2.5 Colombia
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/co/
Atribución-NoComercial-SinDerivadas 2.5 Colombia
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.spa.fl_str_mv PDF
dc.coverage.spatial.spa.fl_str_mv Barranquilla
institution Universidad Libre
bitstream.url.fl_str_mv http://repository.unilibre.edu.co/bitstream/10901/31524/4/RODRIGUEZ%20AUTORIZACION.pdf.jpg
http://repository.unilibre.edu.co/bitstream/10901/31524/5/RODRIGUEZ.pdf.jpg
http://repository.unilibre.edu.co/bitstream/10901/31524/3/license.txt
http://repository.unilibre.edu.co/bitstream/10901/31524/1/RODRIGUEZ.pdf
http://repository.unilibre.edu.co/bitstream/10901/31524/2/RODRIGUEZ%20AUTORIZACION.pdf
bitstream.checksum.fl_str_mv f2beca732b6cccf5412332fa48ba7217
00ebe974859a42ec3a97f88ecdb78502
8a4605be74aa9ea9d79846c1fba20a33
5256f3b12ffa2633ecd3ff6f0c752392
8f61355ea0c82270c67266c084559a41
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Unilibre
repository.mail.fl_str_mv repositorio@unilibrebog.edu.co
_version_ 1851053893717327872
spelling Mendoza Torres, EvelinCano Peñaloza, Raquel AmiraViñas, AlvaroRodríguez Camargo, Ricardo DavidRodríguez Carrascal, Fabio DavidBarranquilla2025-07-17T19:55:57Z2025-07-17T19:55:57Z2025-06-28https://hdl.handle.net/10901/31524Introducción: La enfermedad renal diabética (ERD) representa la principal causa de enfermedad renal crónica en pacientes con diabetes mellitus tipo 2 (DM2). La detección temprana basada en factores de riesgo y modelos predictivos clínicamente útiles es esencial para la detección e intervención temprana que modifiquen el curso de la enfermedad. Objetivo: Identificar los factores de riesgo clínicos y bioquímicos asociados al desarrollo de ERD en DM2 y evaluar el desempeño diagnóstico de modelos predictivos mediante revisión sistemática y metaanálisis. Metodología: Se realizó una búsqueda exhaustiva en PubMed, Embase, Scopus, Web of Science y Cochrane Library. Se incluyeron estudios observacionales con datos cuantificables, evaluados mediante la escala Newcastle-Ottawa. Se ejecutaron dos metaanálisis independientes utilizando el modelo de efectos aleatorios de DerSimonian y Laird. Se reportaron odds ratios (OR) e intervalos de confianza del 95 %, sensibilidad, especificidad y área bajo la curva (AUC). Resultados: Se incluyeron 8 estudios primarios para factores de riesgo y 6 para modelos predictivos. Los principales factores asociados significativamente a ERD fueron: edad > 60 años (OR 3.00), antecedente familiar de nefropatía (OR 2.80), sexo masculino (OR 2.32), hipertensión arterial, HbA1c ≥ 8%, dislipidemia (OR 1.11), leucocitosis (OR 1.19) y T3 libre baja (OR 0.71). Entre los modelos predictivos, los mejores presentaron AUC entre 0.75 y 0.87. La mayoría no contaba con validación externa. Se propuso una tabla de riesgo clínico ilustrativa para estratificación primaria, con aplicación en escenarios de baja complejidad. Conclusión: La ERD en DM2 está fuertemente asociada con factores modificables y no modificables identificables clínicamente. Los modelos predictivos existentes muestran buen desempeño discriminativo, pero requieren validación externa. Esta revisión ofrece una base importante para el desarrollo futuro de herramientas locales de predicción clínica en poblaciones colombianas y/o latinoamericanas.Universidad Libre Seccional Barranquilla -- Facultad de Ciencias de la Salud y Exactas y Naturales -- Especialización en Medicina InternaIntroduction: Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease among individuals with type 2 diabetes mellitus (T2DM). Early identification based on well-established clinical and biochemical risk factors, along with reliable predictive models, is essential to detect renal involvement in time and initiate interventions that can modify the course of the disease. Objective: To identify the clinical and biochemical risk factors associated with DKD in T2DM and evaluate the diagnostic performance of predictive models through a systematic review and meta-analysis. Methods: A comprehensive search was conducted in PubMed, Embase, Scopus, Web of Science, and the Cochrane Library. Observational studies with quantifiable data were included and assessed using the Newcastle-Ottawa Scale. Two independent meta-analyses were performed using the DerSimonian and Laird random-effects model. Pooled odds ratios (ORs) with 95% confidence intervals (CIs), sensitivity, specificity, and area under the curve (AUC) values were reported. Results: Eight primary studies about risk factors and six about predictive models were included. The most significant risk factors associated with DKD were age >60 years (OR 3.00), family history of nephropathy (OR 2.80), male sex (OR 2.32), hypertension, HbA1c ≥8%, dyslipidemia (OR 1.11), leukocytosis (OR 1.19), and low free T3 levels (OR 0.71). Among predictive models, the best-performing ones showed AUCs between 0.75 and 0.87, although most lacked external validation. Based on these findings, a practical clinical risk table was proposed for use in primary care and low-resource settings. Conclusion: DKD in T2DM is strongly associated with both modifiable and non-modifiable risk factors that are clinically accessible. While existing predictive models show good discriminatory performance, external validation remains necessary. The findings of this review support the future development and validation of locally tailored clinical prediction models, with relevance to Colombian and broader Latin American populations.PDFspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadas 2.5 Colombiainfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Diabetes - DiagnósticoDiabetes - ComplicacionesNeuropatías diabéticasType 2 Diabetes MellitusDiabetic Kidney DiseaseMeta-analysisRisk FactorsPredictive ModelsDiabetic nephropathyFactores de riesgo y desempeño diagnóstico de modelos predictivos para enfermedad renal diabética en diabetes mellitus tipo 2: Revisión Sistemática y Meta-análisisRisk factors and diagnostic performance of predictive models for diabetic kidney disease in type 2 diabetes mellitus: a systematic review and meta-analysisTesis de Especializacióninfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisAmerican Diabetes Association Professional Practice Committee, ElSayed NA, McCoy RG, Aleppo G, Bajaj M, Balapattabi K, et al. Introduction and Methodology: Standards of Care in Diabetes—2025. Diabetes Care 2025;48:S1–5. https://doi.org/10.2337/dc25-SINT.ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. Introduction and Methodology: Standards of Care in Diabetes—2023. Diabetes Care 2023;46:S1–4. https://doi.org/10.2337/dc23-Sint.Wang L, Li X, Wang Z, Bancks MP, Carnethon MR, Greenland P, et al. Trends in Prevalence of Diabetes and Control of Risk Factors in Diabetes Among US Adults, 1999-2018. JAMA 2021;326:704. https://doi.org/10.1001/jama.2021.9883Fontalvo JR, Daza Arnedo R, Aguilar Salcedo N, Alfaro M, Navas Torrejano D, Cardona Blanco M, et al. Nueva evidencia en el tratamiento de la enfermedad renal diabética: ¿qué aporta la finerenona? Rev Colomb Nefrol 2022;9. https://doi.org/10.22265/acnef.9.3.603.Rico-Fontalvo J, Yama-Mosquera E, Robayo-García A, Aroca-Martínez G, Arango-Álvarez JJ, Barros-Camargo L, et al. Situación de la enfermedad renal crónica en Colombia. NEFRO 2022;19:9891. https://doi.org/10.24875/NEFRO.22000030Rico Fontalvo JE. Guía de práctica clínica para la enfermedad renal diabética. Rev Colomb Nefrol 2021;8. https://doi.org/10.22265/acnef.8.2.561Acosta Ruiza LX, Angarita Merchán M, Orjuela Vargas L. Diabetes mellitus tipo 2: Latinoamérica y Colombia, análisis del último quinquenio. Rev Med 2024;31:35–46. https://doi.org/10.18359/rmed.6067Situación de la enfermedad renal crónica, la hipertensión arterial y la diabetes mellitus en Colombia 2022 - Cuenta de Alto Costo 2023. https://cuentadealtocosto.org/publicaciones/situacion-de-la-enfermedad-renal-cronica-la-hipertension-arterial-y-la-diabetes-mellitus-en-colombia-2022/Actualización del consenso basado en la evidencia – HTA, DM y ERC - Cuenta de Alto Costo 2025. https://cuentadealtocosto.org/publicaciones/actualizacion-del-consenso-basado-en-la-evidencia-hta-dm-y-erc/Obrador GT, Álvarez-Estévez G, Bellorin-Font E, Bonanno-Hidalgo C, Clavero R, Correa-Rotter R, et al. Documento de consenso sobre nuevas terapias para retrasar la progresión de la enfermedad renal crónica con énfasis en los iSGLT-2: implicaciones para Latinoamérica. NEFRO 2024;21:14493Rico Fontalvo JE. Enfermedad renal diabética: de cara a la prevención, diagnóstico e intervención temprana. Rev Colomb Nefrol 2020;7. https://doi.org/10.22265/acnef.7.2.506.Mercado Marchena RC, Mantilla Morales YS. Características epidemiológicas y su relación con el control glucémico en pacientes con Diabetes mellitus 2 que han desarrollado enfermedad renal crónica por diabetes que asisten a consulta externa en una clínica de cuarto nivel de Barranquilla. Universidad Libre, 2023Saji S, Suresh S, Mc D, Krishnaswamy SK, Asirvatham AJ, Kumar M, et al. A retrospective case - control study for assessing the risk factors for development of Diabetic Kidney Disease among people with Type 2 Diabetes in Tamil Nadu and Puducherry 2022. https://doi.org/10.1101/2022.12.02.22283018.Adler AI, Stevens RJ, Manley SE, Bilous RW, Cull CA, Holman RR, et al. Development and progression of nephropathy in type 2 diabetes: The United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney International 2003;63:225–32. https://doi.org/10.1046/j.1523-1755.2003.00712.xRODRIGUEZ RAMOS JF, Herrera Miranda GL. Factores de riesgo relacionados con enfermedad renal crónica. Policlínico Luis A. Turcios Lima, Pinar del Río, 2019. MediSur 2022;20:59–66Lopera Medina MM. La enfermedad renal crónica en Colombia: necesidades en salud y respuesta del Sistema General de Seguridad Social en Salud. RGYPS 2016;15. https://doi.org/10.11144/Javeriana.rgyps15-30.erccMartinez J, Perez-Rondon A, Zea J, Ilano I, Castano-Villegas N, Nguyen Cong L, et al. Validation of Machine Learning-Based Screening Tools for Early Detection of CKD in Patients with Type 2 Diabetes (T2D): FR-PO1098. Journal of the American Society of Nephrology 2024;35:10.1681/ASN.20244zmahyvr. https://doi.org/10.1681/ASN.20244zmahyvr.DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers 2015;1:15019. https://doi.org/10.1038/nrdp.2015.19Valero K, Marante D, Torres R M, Ramírez G, Cortéz R, Carlini R. Complicaciones microvasculares de la diabetes. Rev Venez Endocrinol Metab 2012;10:111–37. http://ve.scielo.org/scielo.php?script=sci_arttext&pid=S1690-31102012000400014&lng=es&tlng=es.Mogensen CE, Christensen CK, Vittinghus E. The Stages in Diabetic Renal Disease: With Emphasis on the Stage of Incipient Diabetic Nephropathy. Diabetes 1983;32:64–78. https://doi.org/10.2337/diab.32.2.S64Stevens PE, Ahmed SB, Carrero JJ, Foster B, Francis A, Hall RK, et al. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney International 2024;105:S117–314. https://doi.org/10.1016/j.kint.2023.10.018Villena Pacheco A. Factores de riesgo de nefropatía diabética. Acta Med Peru 2022;38. https://doi.org/10.35663/amp.2021.384.2256Tuttle KR. Back to the Future: Glomerular Hyperfiltration and the Diabetic Kidney. Diabetes 2017;66:14–6. https://doi.org/10.2337/dbi16-0056.Natesan V, Kim S-J. Diabetic Nephropathy - a Review of Risk Factors, Progression, Mechanism, and Dietary Management. Biomol Ther (Seoul) 2021;29:365–72. https://doi.org/10.4062/biomolther.2020.204Ghelichi-Ghojogh M, Fararouei M, Seif M, Pakfetrat M. Chronic kidney disease and its health-related factors: a case-control study. BMC Nephrol 2022;23:24. https://doi.org/10.1186/s12882-021-02655-wIntensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet 1998;352:837–53. https://doi.org/10.1016/S0140-6736(98)07019-6Retnakaran R, Cull CA, Thorne KI, Adler AI, Holman RR, for the UKPDS Study Group. Risk Factors for Renal Dysfunction in Type 2 Diabetes. Diabetes 2006;55:1832–9. https://doi.org/10.2337/db05-1620Unnikrishnan R, Rema M, Pradeepa R, Deepa M, Shanthirani CS, Deepa R, et al. Prevalence and Risk Factors of Diabetic Nephropathy in an Urban South Indian Population. Diabetes Care 2007;30:2019–24. https://doi.org/10.2337/dc06-2554Alrawahi AH, Rizvi SGA, Al-Riyami D, Al-Anqoodi Z. Prevalence and Risk Factors of Diabetic Nephropathy in Omani Type 2 Diabetics in Al-Dakhiliyah Region. Oman Med J 2012;27:212–6. https://doi.org/10.5001/omj.2012.48Al-Rubeaan K, Youssef AM, Subhani SN, Ahmad NA, Al-Sharqawi AH, Al-Mutlaq HM, et al. Diabetic Nephropathy and Its Risk Factors in a Society with a Type 2 Diabetes Epidemic: A Saudi National Diabetes Registry-Based Study. PLoS ONE 2014;9:e88956. https://doi.org/10.1371/journal.pone.0088956Roy S, Schweiker-Kahn O, Jafry B, Masel-Miller R, Raju RS, O’Neill LMO, et al. Risk Factors and Comorbidities Associated with Diabetic Kidney Disease. J Prim Care Community Health 2021;12:21501327211048556. https://doi.org/10.1177/21501327211048556.Siddiqui K, George TP, Joy SS, Alfadda AA. Risk factors of chronic kidney disease among type 2 diabetic patients with longer duration of diabetes. Front Endocrinol 2022;13:1079725. https://doi.org/10.3389/fendo.2022.1079725Joshi R, Subedi P, Yadav GK, Khadka S, Rijal T, Amgain K, et al. Prevalence and risk factors of chronic kidney disease among patients with type 2 diabetes mellitus at a tertiary care hospital in Nepal: a cross-sectional study. BMJ Open 2023;13:e067238. https://doi.org/10.1136/bmjopen-2022-067238.Liu W, Du J, Ge X, Jiang X, Peng W, Zhao N, et al. The analysis of risk factors for diabetic kidney disease progression: a single-centre and cross-sectional experiment in Shanghai. BMJ Open 2022;12:e060238. https://doi.org/10.1136/bmjopen-2021-060238Wu M, Lu J, Zhang L, Liu F, Chen S, Han Y, et al. A non-laboratory-based risk score for predicting diabetic kidney disease in Chinese patients with type 2 diabetes. Oncotarget 2017;8:102550–8. https://doi.org/10.18632/oncotarget.21684Mongkolsomlit S, Rawdaree P, Komoltri C, Tawichasri C, Patumanond J. The development and validation of a risk score for predicting microalbuminuria in type 2 diabetic patients. JDM 2012;02:227–33. https://doi.org/10.4236/jdm.2012.22036Wang G, Wang B, Qiao G, Lou H, Xu F, Chen Z, et al. Screening Tools Based on Nomogram for Diabetic Kidney Diseases in Chinese Type 2 Diabetes Mellitus Patients. Diabetes Metab J 2021;45:708–18. https://doi.org/10.4093/dmj.2020.0117Wang X, Liu X, Zhao J, Chen M, Wang L. Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM. DMSO 2024;Volume 17:215–25. https://doi.org/10.2147/DMSO.S442925Hui D, Zhang F, Lu Y, Hao H, Tian S, Fan X, et al. A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus. DMSO 2023;Volume 16:385–95. https://doi.org/10.2147/DMSO.S391781Liao L-N, Li T-C, Li C-I, Liu C-S, Lin W-Y, Lin C-H, et al. Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients. Sci Rep 2019;9:19897. https://doi.org/10.1038/s41598-019-56400-3.Helou N, Dwyer A, Shaha M, Zanchi A. Multidisciplinary management of diabetic kidney disease: a systematic review and meta-analysis. JBI Database of Systematic Reviews and Implementation Reports 2016;14:169–207.Fenta ET, Eshetu HB, Kebede N, Bogale EK, Zewdie A, Kassie TD, et al. Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol Metab Syndr 2023;15:245. https://doi.org/10.1186/s13098-023-01202-xKanakamani J, Ammini AC, Gupta N, Dwivedi SN. Prevalence of Microalbuminuria Among Patients with Type 2 Diabetes Mellitus—A Hospital-Based Study from North India. Diabetes Technology & Therapeutics 2010;12:161–6. https://doi.org/10.1089/dia.2009.0133.Tangri N. A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure. JAMA 2011;305:1553. https://doi.org/10.1001/jama.2011.451.Echouffo-Tcheugui JB, Kengne AP. Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review. PLoS Med 2012;9:e1001344. https://doi.org/10.1371/journal.pmed.1001344Sandholm N, Dahlström EH, Groop P-H. Genetic and epigenetic background of diabetic kidney disease. Front Endocrinol 2023;14:1163001. https://doi.org/10.3389/fendo.2023.1163001THUMBNAILRODRIGUEZ AUTORIZACION.pdf.jpgRODRIGUEZ AUTORIZACION.pdf.jpgIM Thumbnailimage/jpeg28663http://repository.unilibre.edu.co/bitstream/10901/31524/4/RODRIGUEZ%20AUTORIZACION.pdf.jpgf2beca732b6cccf5412332fa48ba7217MD54RODRIGUEZ.pdf.jpgRODRIGUEZ.pdf.jpgIM Thumbnailimage/jpeg10583http://repository.unilibre.edu.co/bitstream/10901/31524/5/RODRIGUEZ.pdf.jpg00ebe974859a42ec3a97f88ecdb78502MD55LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repository.unilibre.edu.co/bitstream/10901/31524/3/license.txt8a4605be74aa9ea9d79846c1fba20a33MD53ORIGINALRODRIGUEZ.pdfRODRIGUEZ.pdfArchivo del trabajo de gradoapplication/pdf1406894http://repository.unilibre.edu.co/bitstream/10901/31524/1/RODRIGUEZ.pdf5256f3b12ffa2633ecd3ff6f0c752392MD51RODRIGUEZ AUTORIZACION.pdfRODRIGUEZ AUTORIZACION.pdfAutorización para la publicaciónapplication/pdf15311701http://repository.unilibre.edu.co/bitstream/10901/31524/2/RODRIGUEZ%20AUTORIZACION.pdf8f61355ea0c82270c67266c084559a41MD5210901/31524oai:repository.unilibre.edu.co:10901/315242025-11-06 12:28:04.433Repositorio Institucional Unilibrerepositorio@unilibrebog.edu.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

Publicaciones similares