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评估 1 型糖尿病成人的心血管风险:2019 ESC 风险分类与 Steno Type 1 Risk Engine 预测的 10 年心血管风险之间的一致性较差。

Evaluation of cardiovascular risk in adults with type 1 diabetes: poor concordance between the 2019 ESC risk classification and 10-year cardiovascular risk prediction according to the Steno Type 1 Risk Engine.

机构信息

Department of Clinical Medicine and Surgery, Federico II University, Via S. Pansini 5, 80131, Naples, Italy.

Department of Molecular Medicine and Medical Biotechnology, Federico II University, Naples, Italy.

出版信息

Cardiovasc Diabetol. 2020 Oct 3;19(1):166. doi: 10.1186/s12933-020-01137-x.

Abstract

BACKGROUND

Patients with type 1 diabetes (T1D) have higher mortality risk compared to the general population; this is largely due to increased rates of cardiovascular disease (CVD). As accurate CVD risk stratification is essential for an appropriate preventive strategy, we aimed to evaluate the concordance between 2019 European Society of Cardiology (ESC) CVD risk classification and the 10-year CVD risk prediction according to the Steno Type 1 Risk Engine (ST1RE) in adults with T1D.

METHODS

A cohort of 575 adults with T1D (272F/303M, mean age 36 ± 12 years) were studied. Patients were stratified in different CVD risk categories according to ESC criteria and the 10-year CVD risk prediction was estimated with ST1RE within each category.

RESULTS

Men had higher BMI, WC, SBP than women, while no difference was found in HbA1c levels between genders. According to the ESC classification, 92.5% of patients aged < 35 years and 100% of patients ≥ 35 years were at very high/high risk. Conversely, using ST1RE to predict the 10-year CVD risk within each ESC category, among patients at very high risk according to ESC, almost all (99%) had a moderate CVD risk according to ST1RE if age < 35 years; among patients aged ≥35 years, the majority (59.1%) was at moderate risk and only 12% had a predicted very high risk by ST1RE. The presence of target organ damage or three o more CV risk factors, or early onset T1D of long duration (> 20 years) alone identified few patients (< 30%) among those aged ≥35 years, who were at very high risk according to ESC, in whom this condition was confirmed by ST1RE; conversely, the coexistence of two or more of these criteria identified about half of the patients at high/very high risk also according to this predicting algorithm. When only patients aged ≥ 50 years were considered, there was greater concordance between ESC classification and ST1RE prediction, since as many as 78% of those at high/very high risk according to ESC were confirmed as such also by ST1RE.

CONCLUSIONS

Using ESC criteria, a large proportion (45%) of T1D patients without CVD are classified at very high CVD risk; however, among them, none of those < 35 years and only 12% of those ≥ 35 years could be confirmed at very high CVD risk by the ST1RE predicting algorithm. More studies are needed to characterize the clinical and metabolic features of T1D patients that identify those at very high CVD risk, in whom a very aggressive cardioprotective treatment would be justified.

摘要

背景

与一般人群相比,1 型糖尿病(T1D)患者的死亡率风险更高;这主要是由于心血管疾病(CVD)发病率的增加。由于准确的 CVD 风险分层对于适当的预防策略至关重要,因此我们旨在评估 2019 年欧洲心脏病学会(ESC)CVD 风险分类与 Steno Type 1 Risk Engine(ST1RE)在 T1D 成人中预测的 10 年 CVD 风险之间的一致性。

方法

研究了 575 名 T1D 成年患者(272F/303M,平均年龄 36±12 岁)。根据 ESC 标准将患者分层为不同的 CVD 风险类别,并在每个类别中使用 ST1RE 估计 10 年 CVD 风险。

结果

男性的 BMI、WC、SBP 高于女性,而性别之间的 HbA1c 水平没有差异。根据 ESC 分类,<35 岁的患者中 92.5%,≥35 岁的患者中 100%处于极高/高风险。相反,使用 ST1RE 在每个 ESC 类别内预测 10 年 CVD 风险,如果年龄<35 岁,根据 ESC 处于极高风险的患者中几乎所有人(99%)根据 ST1RE 预测具有中度 CVD 风险;在≥35 岁的患者中,大多数(59.1%)处于中度风险,只有 12%根据 ST1RE 预测具有极高风险。如果存在靶器官损伤或三个或更多 CV 危险因素,或 1 型糖尿病发病早且持续时间长(>20 年),单独识别出<35 岁的那些根据 ESC 处于极高风险的患者中很少(<30%),在这些情况下,根据 ST1RE 可以确认这一点;相反,这些标准中的两个或更多标准共同存在可以识别出大约一半的高/极高风险患者,根据该预测算法也是如此。当仅考虑≥50 岁的患者时,ESC 分类和 ST1RE 预测之间的一致性更高,因为根据 ESC 分类处于高/极高风险的患者中有 78%通过 ST1RE 预测得到确认。

结论

使用 ESC 标准,很大一部分(45%)没有 CVD 的 T1D 患者被归类为极高 CVD 风险;然而,在这些患者中,没有一个<35 岁的患者,只有 12%≥35 岁的患者可以根据 ST1RE 预测算法确认极高 CVD 风险。需要进一步研究以确定 T1D 患者的临床和代谢特征,以识别极高 CVD 风险的患者,这些患者需要进行非常积极的心脏保护治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0f6/7533035/80565370a7bc/12933_2020_1137_Fig1_HTML.jpg

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