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

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 accurac...

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Autores: Llauradó Cabot, Gemma, Cano, Albert, Hernández Rodríguez, Cristina, González-Sastre, Montserrat, Rodríguez, Ato-Antonio, Puntí, Jordi, Berlanga, Eugenio, Albert, Lara, Simó, Rafael, Vendrell, Joan, González Clemente, José Miguel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/34008
Acceso en línea:http://hdl.handle.net/10230/34008
http://dx.doi.org/10.1371/journal.pone.0174640
Access Level:acceso abierto
Palabra clave:Diabetis -- Complicacions
Isquèmia
Malalties coronàries
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spelling Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistanceLlauradó Cabot, GemmaCano, AlbertHernández Rodríguez, CristinaGonzález-Sastre, MontserratRodríguez, Ato-AntonioPuntí, JordiBerlanga, EugenioAlbert, LaraSimó, RafaelVendrell, JoanGonzález Clemente, José MiguelDiabetis -- ComplicacionsIsquèmiaMalalties coronàriesOBJECTIVES: 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.Public Library of Science (PLoS)201820182017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/34008http://dx.doi.org/10.1371/journal.pone.0174640reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésPLoS One. 2017 Apr 3;12(4):e0174640© 2017 Llaurado et al. This is an open access article distributed under the terms of the https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.info:eu-repo/semantics/openAccessoai:recercat.cat:10230/340082026-05-29T05:05:01Z
dc.title.none.fl_str_mv Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
title Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
spellingShingle Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
Llauradó Cabot, Gemma
Diabetis -- Complicacions
Isquèmia
Malalties coronàries
title_short Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
title_full Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
title_fullStr Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
title_full_unstemmed Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
title_sort Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance
dc.creator.none.fl_str_mv Llauradó Cabot, Gemma
Cano, Albert
Hernández Rodríguez, Cristina
González-Sastre, Montserrat
Rodríguez, Ato-Antonio
Puntí, Jordi
Berlanga, Eugenio
Albert, Lara
Simó, Rafael
Vendrell, Joan
González Clemente, José Miguel
author Llauradó Cabot, Gemma
author_facet Llauradó Cabot, Gemma
Cano, Albert
Hernández Rodríguez, Cristina
González-Sastre, Montserrat
Rodríguez, Ato-Antonio
Puntí, Jordi
Berlanga, Eugenio
Albert, Lara
Simó, Rafael
Vendrell, Joan
González Clemente, José Miguel
author_role author
author2 Cano, Albert
Hernández Rodríguez, Cristina
González-Sastre, Montserrat
Rodríguez, Ato-Antonio
Puntí, Jordi
Berlanga, Eugenio
Albert, Lara
Simó, Rafael
Vendrell, Joan
González Clemente, José Miguel
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Diabetis -- Complicacions
Isquèmia
Malalties coronàries
topic Diabetis -- Complicacions
Isquèmia
Malalties coronàries
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/34008
http://dx.doi.org/10.1371/journal.pone.0174640
url http://hdl.handle.net/10230/34008
http://dx.doi.org/10.1371/journal.pone.0174640
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PLoS One. 2017 Apr 3;12(4):e0174640
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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