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

Date Published: April 3, 2017

Publisher: Public Library of Science

Author(s): Gemma Llauradó, Albert Cano, Cristina Hernández, Montserrat González-Sastre, Ato-Antonio Rodríguez, Jordi Puntí, Eugenio Berlanga, Lara Albert, Rafael Simó, Joan Vendrell, José-Miguel González Clemente, Doan TM Ngo.

http://doi.org/10.1371/journal.pone.0174640

Abstract

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.

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.

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).

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.

Partial Text

Cardiovascular disease (CVD) is the main cause of death in patients with type 1 diabetes mellitus (T1DM)[1], representing around 40–47% of deaths in certain cohorts [2,3]. Coronary artery disease (CAD) is its principal clinical manifestation [4]. The relative risk of death by CAD in T1DM can be as much as ten times greater than in the non-diabetic population, especially in women, and it is even greater than the relative risk in type 2 diabetes (T2DM)[1,5]. It causes a life-expectancy loss of about four years, which represents one-third of these subjects’ total life-expectancy loss [6]. Additionally, CAD produces important disabilities (e.g., heart failure, angina), which cause quality of life to deteriorate and involve considerable economic costs.

SMI was diagnosed in 10 out of 84 (11.9%) patients with T1DM (7 with mild, 2 with moderate and 1 with severe ischemia). The main clinical and analytical characteristics of the study population are shown in Table 1. Patients with T1DM and SMI, as compared with those without SMI, were more hypertensive (70.0% vs. 36.5%; p = 0.044), had more insulin resistance (5.5 (4.8–6.7) mg·kg-1·min-1 vs. 8.1 (5.9–9.5) mg·kg-1·min-1; p = 0.010) and had a tendency toward a worse glycaemic control (HbA1c: 8.3 (7.9–9.4)% vs. 7.7 (7.1–8.6)%; p = 0.053) although it did not reach statistical significance. There were no significant differences between groups regarding other traditional cardiovascular risk factors (such as age, gender, smoking habit, dyslipidaemia or family history of premature CVD) or the prevalence of metabolic syndrome. There were no significant differences for aPWV between groups (p = 0.885). In the univariate analyses, SMI was associated with SBP (OR = 1.062, p = 0.049), HbA1c (OR = 1.936, p = 0.050) and eGDR (OR = 0.671, p = 0.016) (Table 2). In addition, there was an inverse relationship between the degree of SMI and eGDR values (OR = -0.435; p = 0.013).

The present study provides, for the first time, a good, sensitive risk-estimation model for predicting SMI in T1DM. Furthermore, it also shows that SMI (detected by stress MPI-SPECT tests) is relatively common in patients with T1DM of at least ten-year duration and no previous clinical cardiovascular disease and that it is associated with active smoking and insulin-resistance in this population. These results have the potential to lead to improvements in CAD care in T1DM through a strategy focused on accurate, cost-effective detection of SMI.

 

Source:

http://doi.org/10.1371/journal.pone.0174640

 

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