Background: The effect of postload glucose spikes (PGS), the difference between 2 hour post-load plasma glucose (2hPLPG) and fasting plasma glucose (FPG), on post-myocardial infarction (post-MI) prognosis in nondiabetic patients is unexplored.
Methods: This is a retrospective cohort analysis of 847 nondiabetic post-MI survivors who underwent a predischarge oral glucose tolerance test (median PGS: 2.4 mmol/L). Patients were divided into the unmatched groups 1 and 2 (PGS ≤ and > 2.4 mmol/L) and the propensity score-matched groups 1M and 2M (355 pairs assembled from the overall cohort), and these groups were compared. Major adverse cardiac events (MACE: death and nonfatal reinfarction) were recorded during follow-up (median: 3.4 years). Event-free survival was compared by the Kaplan-Meier method. Multivariate Cox proportional hazards regression determined the predictors of MACE. C-statistics (change in area under the curve, δAUC), continuous net reclassification improvement (NRI>0 ), and integrated discrimination improvement (IDI) were used to compare models.
Results: The number of MACE was higher in groups 2 (27.3% vs 14.2%, P < .001) and 2M (24.5% vs 15.5%, P < .001). Event-free survival was worse in groups 2 (hazard ratio [HR] 2.01; 95% CI, 1.49-2.71; P < .001) and 2M (HR 1.63; 95% CI, 1.17-2.27; P = .004). PGS independently predicted MACE-free survival in the whole (HR 1.16; 95% CI, 1.06-1.26; P = .002) and matched cohort (HR 1.12; 95% CI, 1.02-1.24; P = .021). PGS, but not FPG or 2hPPG, improved the predictive performance of the base model (δAUC 0.013, P = .046), with greater improvement seen when PGS was added and compared to 2hPPG (δAUC 0.005, P = .034; NRI>0 0.2107, P = .013; IDI 0.0042, P = .046).
Conclusion: PGS is a better predictor of post-MI prognosis than 2hPPG in nondiabetic patients.
背景: 负荷后血糖峰值(PGS)，即负荷后2h血糖(2hPLPG)与空腹血糖(FPG)之差，对非糖尿病患者心肌梗死后(Post-MI)预后的影响尚不清楚。 方法: 对847名接受出院前口服葡萄糖耐量试验(中位数PGS:2.4 mmol/L)的非糖尿病心肌梗死后幸存者进行回顾性队列分析。将患者分为PGS≤和>2.4mmoL/L的组1和组2，以及倾向得分匹配组1M和2M(从总队列中收集355对)，并对这些组进行比较。随访期间记录主要不良心脏事件(MACE:死亡和非致命性再梗死)(中位随访时间:3.4年)。无事件生存率比较采用Kaplan-Meier法。多因素Cox比例风险回归确定MACE的预测因素。采用C-统计量(曲线下面积变化，δAUC)、连续性重分类改善指标(NRI>0 )和综合辨别改善值(IDI)对模型进行比较。 结果: 组2(27.3%比14.2%，P<0.001)和2M组(24.5%比15.5%，P<0.001) MACE发生率较高。无事件生存率在组2(危险比[HR]2.01；95%CI，1.49~2.71；P<0.001)和2M组(HR 1.63；95%CI，1.17~2.27；P=0.004)中也更差。PGS能独立预测整体(HR 1.16；95%CI，1.06-1.26；P=0.002)和匹配队列(HR 1.12；95%CI，1.02-1.24；P=0.021)的无MACE生存率。PGS而不是空腹血糖FPG或2hPLPG可改善基础模型的预测性能(δAUC 0.013，P=0.046)，当加入PGS指标后，相对2hPLPG来说预测模型有更大的改善(δAUC 0.005，P=0.034；NRI>0 0.2107，P=0.013；IDI 0.0042，P=0.046)。 结论: 在非糖尿病患者中，PGS比2hPLPG更能预测MI后的预后.
Keywords: acute coronary syndrome; diabetes mellitus; glucose excursion; glucose spike; myocardial infarction; oral glucose tolerance test; 口服葡萄糖耐量试验; 心肌梗死; 急性冠脉综合征; 糖尿病; 血糖漂移; 血糖高峰.
© 2020 Ruijin Hospital, Shanghai JiaoTong University School of Medicine and John Wiley & Sons Australia, Ltd.