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

  • Dades identificatives

    Identificador: imarina:6656563
    Autors:
    Llauradó GCano AHernández CGonzález-Sastre MRodríguez APuntí JBerlanga EAlbert LSimó RVendrell JClemente J
    Resum:
    © 2017 Llauradó et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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: 0.8
  • Altres:

    Autor segons l'article: Llauradó G; Cano A; Hernández C; González-Sastre M; Rodríguez A; Puntí J; Berlanga E; Albert L; Simó R; Vendrell J; Clemente J
    Departament: Medicina i Cirurgia
    Autor/s de la URV: Vendrell Ortega, Juan José
    Resum: © 2017 Llauradó et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.
    Àrees temàtiques: Zootecnia / recursos pesqueiros Sociology Sociología Serviço social Saúde coletiva Química Psychology Psicología Planejamento urbano e regional / demografia Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Letras / linguística Interdisciplinary research in the social sciences Interdisciplinar Human geography and urban studies History & philosophy of science Historia Geografía Geociências General medicine General biochemistry,genetics and molecular biology General agricultural and biological sciences Farmacia Environmental studies Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação física Educação Economia Direito Demography Comunicação e informação Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência política e relações internacionais Ciência de alimentos Ciência da computação Biotecnología Biology Biodiversidade Biochemistry, genetics and molecular biology (miscellaneous) Astronomia / física Arquitetura, urbanismo e design Archaeology Antropologia / arqueologia Anthropology Agricultural and biological sciences (miscellaneous) Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 19326203
    Adreça de correu electrònic de l'autor: juanjose.vendrell@urv.cat
    Identificador de l'autor: 0000-0002-6994-6115
    Data d'alta del registre: 2024-09-07
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Plos One. 12 (4): e0174640-
    Referència de l'ítem segons les normes APA: Llauradó G; Cano A; Hernández C; González-Sastre M; Rodríguez A; Puntí J; Berlanga E; Albert L; Simó R; Vendrell J; Clemente J (2017). Type 1 diabetes: Developing the first riskestimation model for predicting silent myocardial ischemia. The potential role of insulin resistance. Plos One, 12(4), e0174640-. DOI: 10.1371/journal.pone.0174640
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2017
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Agricultural and Biological Sciences (Miscellaneous),Biochemistry, Genetics and Molecular Biology (Miscellaneous),Biology,Medicine (Miscellaneous),Multidisciplinary,Multidisciplinary Sciences
    Zootecnia / recursos pesqueiros
    Sociology
    Sociología
    Serviço social
    Saúde coletiva
    Química
    Psychology
    Psicología
    Planejamento urbano e regional / demografia
    Odontología
    Nutrição
    Multidisciplinary sciences
    Multidisciplinary
    Medicine (miscellaneous)
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Linguística e literatura
    Letras / linguística
    Interdisciplinary research in the social sciences
    Interdisciplinar
    Human geography and urban studies
    History & philosophy of science
    Historia
    Geografía
    Geociências
    General medicine
    General biochemistry,genetics and molecular biology
    General agricultural and biological sciences
    Farmacia
    Environmental studies
    Ensino
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Enfermagem
    Educação física
    Educação
    Economia
    Direito
    Demography
    Comunicação e informação
    Ciências sociais aplicadas i
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência política e relações internacionais
    Ciência de alimentos
    Ciência da computação
    Biotecnología
    Biology
    Biodiversidade
    Biochemistry, genetics and molecular biology (miscellaneous)
    Astronomia / física
    Arquitetura, urbanismo e design
    Archaeology
    Antropologia / arqueologia
    Anthropology
    Agricultural and biological sciences (miscellaneous)
    Administração, ciências contábeis e turismo
    Administração pública e de empresas, ciências contábeis e turismo
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