Articles producció científica> Medicina i Cirurgia

Type 1 diabetes: Developing the first riskestimation model for predicting silent myocardial ischemia. The potential role of insulin resistance

  • Datos identificativos

    Identificador: PC:2629
    Autores:
    Vendrell, J.Llauradó, G.Cano, A.Hernández, C.González-Sastre, M.Rodríguez, A.-A.Puntí, J.Berlanga, E.Albert, L.Simó, R.Clemente, J.-M.G.
    Resumen:
    Objectives: The aim of the study was to develop a novel risk estimation model for predicting silent myocardial ischemia (SMI) in patients with type 1 diabetes (T1DM) and no clinical cardiovascular disease, evaluating the potential role of insulin resistance in such a model. Additionally, the accuracy of this model was compared with currently available models for predicting clinical coronary artery disease (CAD) in general and diabetic populations. Research, design and methods: Patients with T1DM (35-65years, >10-year duration) and no clinical cardiovascular disease were consecutively evaluated for: 1) clinical and anthropometric data (including classical cardiovascular risk factors), 2) insulin sensitivity (estimate of glucose disposal rate (eGDR)), and 3) SMI diagnosed by stress myocardial perfusion gated SPECTs. Results: Eighty-four T1DM patients were evaluated [50.1±9.3 years, 50% men, 36.9% active smokers, T1DM duration: 19.0(15.9-27.5) years and eGDR 7.8(5.5-9.4)mg·kg-1·min-1]. Of these, ten were diagnosed with SMI (11.9%). Multivariate logistic regression models showed that only eGDR (OR = -0.593, p = 0.005) and active smoking (OR = 7.964, p = 0.018) were independently associated with SMI. The AUC of the ROC curve of this risk estimation model for predicting SMI was 0.833 (95%CI:0.692-0.974), higher than those obtained with the use of currently available models for predicting clinical CAD (Framingham Risk Equation: 0.833 vs. 0.688, p = 0.122; UKPDS Risk Engine (0.833 vs. 0.559; p = 0.001) and EDC equation: 0.833 vs. 0.558, p = 0.027). Conclusion: This study provides the first ever reported risk-estimation model for predicting SMI in T1DM. The model only includes insulin resistance and active smoking as main predictors of SMI.
  • Otros:

    Autor según el artículo: Vendrell, J.; Llauradó, G.; Cano, A.; Hernández, C.; González-Sastre, M.; Rodríguez, A.-A.; Puntí, J.; Berlanga, E.; Albert, L.; Simó, R.; Clemente, J.-M.G.
    Departamento: Medicina i Cirurgia
    Autor/es de la URV: VENDRELL ORTEGA, JUAN JOSÉ; Llauradó, G.; Cano, A.; Hernández, C.; González-Sastre, M.; Rodríguez, A.-A.; Puntí, J.; Berlanga, E.; Albert, L.; Simó, R.; Clemente, J.-M.G.
    Palabras clave: Està en blanc
    Resumen: Objectives: The aim of the study was to develop a novel risk estimation model for predicting silent myocardial ischemia (SMI) in patients with type 1 diabetes (T1DM) and no clinical cardiovascular disease, evaluating the potential role of insulin resistance in such a model. Additionally, the accuracy of this model was compared with currently available models for predicting clinical coronary artery disease (CAD) in general and diabetic populations. Research, design and methods: Patients with T1DM (35-65years, >10-year duration) and no clinical cardiovascular disease were consecutively evaluated for: 1) clinical and anthropometric data (including classical cardiovascular risk factors), 2) insulin sensitivity (estimate of glucose disposal rate (eGDR)), and 3) SMI diagnosed by stress myocardial perfusion gated SPECTs. Results: Eighty-four T1DM patients were evaluated [50.1±9.3 years, 50% men, 36.9% active smokers, T1DM duration: 19.0(15.9-27.5) years and eGDR 7.8(5.5-9.4)mg·kg-1·min-1]. Of these, ten were diagnosed with SMI (11.9%). Multivariate logistic regression models showed that only eGDR (OR = -0.593, p = 0.005) and active smoking (OR = 7.964, p = 0.018) were independently associated with SMI. The AUC of the ROC curve of this risk estimation model for predicting SMI was 0.833 (95%CI:0.692-0.974), higher than those obtained with the use of currently available models for predicting clinical CAD (Framingham Risk Equation: 0.833 vs. 0.688, p = 0.122; UKPDS Risk Engine (0.833 vs. 0.559; p = 0.001) and EDC equation: 0.833 vs. 0.558, p = 0.027). Conclusion: This study provides the first ever reported risk-estimation model for predicting SMI in T1DM. The model only includes insulin resistance and active smoking as main predictors of SMI.
    Grupo de investigación: Grup de Recerca Biomèdica HJ23
    Áreas temáticas: Health sciences Ciencias de la salud Ciències de la salut
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1932-6203
    Identificador del autor: N/D; N/D; N/D; N/D; N/D; N/D; N/D; N/D; N/D; N/D; N/D
    Fecha de alta del registro: 2017-04-21
    Volumen de revista: 12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174640
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1371/journal.pone.0174640
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2017
    Tipo de publicación: Article Artículo Article
  • Palabras clave:

    Malalties coronàries -- Prevenció
    Insulinoresistència
    Diabetis
    Està en blanc
    Health sciences
    Ciencias de la salud
    Ciències de la salut
    1932-6203
  • Documentos:

  • Cerca a google

    Search to google scholar