Llauradó Gemma, Cano Albert, Hernández Cristina, González-Sastre Montserrat, Rodríguez Ato-Antonio, Puntí Jordi, Berlanga Eugenio, Albert Lara, Simó Rafael, Vendrell Joan, González Clemente José-Miguel
Department of Endocrinology and Nutrition, Hospital del Mar, Barcelona, Spain.
Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.
PLoS One. 2017 Apr 3;12(4):e0174640. doi: 10.1371/journal.pone.0174640. eCollection 2017.
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.
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.
本研究旨在开发一种新型风险评估模型，用于预测1型糖尿病（T1DM）且无临床心血管疾病患者的无症状心肌缺血（SMI），评估胰岛素抵抗在此类模型中的潜在作用。此外，将该模型的准确性与目前用于预测一般人群和糖尿病患者临床冠状动脉疾病（CAD）的现有模型进行比较。
研究、设计与方法：对连续入选的T1DM患者（年龄35 - 65岁，病程>10年）且无临床心血管疾病进行如下评估：1）临床和人体测量数据（包括经典心血管危险因素）；2）胰岛素敏感性（葡萄糖处置率估计值（eGDR））；3）通过负荷心肌灌注门控单光子发射计算机断层扫描（SPECT）诊断的SMI。
共评估了84例T1DM患者[年龄50.1±9.3岁，男性占50%，36.9%为当前吸烟者，T1DM病程：19.0（15.9 - 27.5）年，eGDR为7.8（5.5 - 9.4）mg·kg-1·min-1]。其中，10例被诊断为SMI（11.9%）。多因素逻辑回归模型显示，仅eGDR（比值比（OR）=-0.593，p = 0.005）和当前吸烟（OR = 7.964，p = 0.018）与SMI独立相关。该风险评估模型预测SMI的受试者工作特征曲线（ROC）曲线下面积（AUC）为0.833（95%置信区间：0.692 - 0.974），高于使用目前用于预测临床CAD的现有模型所获得的值（弗明汉风险方程：0.833对0.688，p = 0.122；英国前瞻性糖尿病研究（UKPDS）风险引擎：0.833对0.559，p = 0.001；欧洲糖尿病心血管疾病风险评估（EDC）方程：0.833对0.558，p = 0.027）。
本研究提供了首个报道的用于预测T1DM患者SMI的风险评估模型。该模型仅将胰岛素抵抗和当前吸烟作为SMI的主要预测因素。
