Acute glycemic variability on admission predicts the prognosis in hospitalized patients with coronary artery disease: a meta-analysis.

Abstract

PURPOSE:

Increased glycemic variability has been related with poor prognosis in patients with coronary artery disease (CAD). However, whether diabetic status or subtype of CAD could affect the association remains unknown. We performed a meta-analysis to systematically evaluate the association between the mean amplitude of glycemic excursions (MAGE) on continuous glucose monitoring and the incidence of major adverse cardiovascular events (MACEs) in CAD patients.

METHODS:

Relevant prospective cohort studies were identified through search of PubMed, Embase, WanFang, and CNKI databases. A random-effect model was used to pool the results. Subgroup analyses were performed to evaluate the influences of the predefined study characteristics on the outcome.

RESULTS:

Eleven cohort studies with 2666 hospitalized patients with acute coronary syndrome (ACS) or stable CAD for percutaneous coronary intervention were included. Pooled results showed that higher MAGE at admission was associated with higher incidence of MACEs during follow-up (adjusted relative risk [RR]: 1.84, p < 0.001; I2 = 12%). Stratified analyses showed that the association between higher MAGE and higher risk of MACEs in CAD patients were consistent in patients with or without diabetes, and in those with ACS or stable CAD (p for subgroup difference both >0.05). Significant publication bias was detected (p = 0.041). Trim-and-fill analysis retrieved three studies to generate symmetrical funnel plots. Meta-analysis that incorporated these studies showed similar results (RR: 1.80, p < 0.001).

CONCLUSIONS:

Increased glycemic variability may be associated with poor prognosis in CAD patients regardless of the diabetic status and the subtype of CAD.

• Authors: Zhaokun Pu, Lihong Lai, Xishan Yang, Yanyu Wang, Pingshuan Dong, Dan Wang, Yingli Xie, Zesen Han
• Journal: Endocrine (Endocrine) Vol. 67 Issue 3 Pg. 526-534 (03 2020) ISSN: 1559-0100 [Electronic] United States
• PMID: 31828526 (Publication Type: Journal Article, Meta-Analysis)

Chemical References

• Biomarkers
• Blood Glucose

Topics

• Biomarkers
• Blood Glucose
• Blood Glucose Self-Monitoring
• Coronary Artery Disease (epidemiology)
• Humans
• Prognosis
• Prospective Studies
• Risk Factors