Kuroda Masaru, Shinke Toshiro, Otake Hiromasa, Sugiyama Daisuke, Takaya Tomofumi, Takahashi Hachidai, Terashita Daisuke, Uzu Kenzo, Tahara Natsuko, Kashiwagi Daiji, Kuroda Koji, Shinkura Yuto, Nagasawa Yoshinori, Sakaguchi Kazuhiko, Hirota Yushi, Ogawa Wataru, Hirata Ken-Ichi
Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan.
Department of Preventive Medicine and Public Health, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Cardiovasc Diabetol. 2016 May 21;15:79. doi: 10.1186/s12933-016-0395-4.
Several studies have revealed that glucose fluctuations provoke oxidative stress that leads to endothelial cell dysfunction, progression of coronary atherosclerosis, and plaque vulnerability. However, little is known regarding their effect on neointimal growth after stenting in patients with coronary artery disease (CAD). We aimed to investigate the effects of glucose fluctuations on neointimal growth after everolimus-eluting stent (EES) implantation.
This study examined 50 patients who underwent a 9-month follow-up using optical coherence tomography (OCT) after EES implantation. Glucose fluctuation was expressed as the mean amplitude of glycemic excursion (MAGE), and was determined via continuous glucose monitoring before stenting. At the OCT follow-up, we evaluated the percentage of uncovered struts and three-dimensional uniformity of neointimal distribution by calculating the mean neointimal thickness (NIT) within 360 equally-spaced radial sectors for every 1-mm cross-sectional OCT analysis, and assessed the incidence of major adverse cardiovascular events (MACE).
We evaluated 60 lesions in 50 patients. Linear mixed effect models were used to explore the influence of different variables on variability in NIT and the percentage of uncovered struts and to adjust for covariates. Univariate analysis showed that MAGE was most strongly correlated with the previously mentioned OCT measurements (coefficient β ± standard error = 0.267 ± 0.073 and 0.016 ± 0.003, t = 3.668 and 6.092, both P < 0.001, respectively). In multivariate analysis, MAGE had the strongest effect on variability in NIT (coefficient β ± standard error = 0.239 ± 0.093, P = 0.014) and the percentage of uncovered struts (coefficient β ± standard error = 0.019 ± 0.004, P < 0.001). Five lesions in four patients required target lesion revascularization (10.0 %) at a mean duration of 9 months after EES implantation. Compared to non-MACE cases, cases of MACE exhibited a significantly higher MAGE (99 vs. 68; P = 0.004), maximum NIT (580 vs. 330 µm; P = 0.002), and variability in NIT (100 vs. 65; P = 0.007), although there was no significant difference in these groups' HbA1c levels.
Glucose fluctuation may affect vessel healing after EES implantation in patients with CAD who are receiving lipid-lowering therapy. Therefore, glucose fluctuations may be an important target for secondary prevention after coronary stenting, which is independent of dyslipidemia control.
多项研究表明,血糖波动会引发氧化应激,导致内皮细胞功能障碍、冠状动脉粥样硬化进展和斑块易损性。然而,关于其对冠心病(CAD)患者支架置入术后新生内膜生长的影响知之甚少。我们旨在研究血糖波动对依维莫司洗脱支架(EES)植入术后新生内膜生长的影响。
本研究纳入了50例在EES植入术后接受9个月光学相干断层扫描(OCT)随访的患者。血糖波动以血糖波动幅度平均值(MAGE)表示,在支架置入前通过连续血糖监测确定。在OCT随访时,我们通过计算每1mm横截面OCT分析中360个等间距径向扇区内的平均新生内膜厚度(NIT)来评估未覆盖支架的百分比和新生内膜分布的三维均匀性,并评估主要不良心血管事件(MACE)的发生率。
我们评估了50例患者的60个病变。采用线性混合效应模型探讨不同变量对NIT变异性和未覆盖支架百分比的影响,并对协变量进行调整。单因素分析显示,MAGE与上述OCT测量值相关性最强(系数β±标准误 = 0.267±0.073和0.016±0.003,t = 3.668和6.092,P均<0.001)。多因素分析中,MAGE对NIT变异性(系数β±标准误 =