Title |
Glycemic variability determined with a continuous glucose monitoring system can predict prognosis after acute coronary syndrome
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Published in |
Cardiovascular Diabetology, August 2018
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DOI | 10.1186/s12933-018-0761-5 |
Pubmed ID | |
Authors |
Hironori Takahashi, Noriaki Iwahashi, Jin Kirigaya, Shunsuke Kataoka, Yugo Minamimoto, Masaomi Gohbara, Takeru Abe, Kozo Okada, Yasushi Matsuzawa, Masaaki Konishi, Nobuhiko Maejima, Kiyoshi Hibi, Masami Kosuge, Toshiaki Ebina, Kouichi Tamura, Kazuo Kimura |
Abstract |
Impaired glucose metabolism is an established risk factor for coronary artery disease. Previous studies revealed that glycemic variability (GV) is also important for glucose metabolism in patients with acute coronary syndrome (ACS). We explored the association between GV and prognosis in patients with ACS. A total of 417 patients with ACS who received reperfusion wore a continuous glucose monitoring system (CGMS) in a stable phase after admission and were monitored for at least 24 consecutive h. The mean amplitude of glycemic excursion (MAGE) was calculated as a marker of GV. We divided into two groups based on the highest tertile levels of MAGE (MAGE = 52 mg/dl). The groups were followed up for a median of 39 months [IQR 24-50 months]. The primary endpoint was the incidence of major adverse cardiovascular and cerebrovascular events (MACCE). During follow-up, 66 patients experienced MACCE (5 patients had cardiovascular death, 14 had recurrence of ACS, 27 had angina requiring revascularization, 8 had acute decompensated heart failure, and 16 had a stroke). MACCE was more frequently observed in the high MAGE group (23.5% vs. 11.6%, p = 0.002). In multivariate analysis, high MAGE was an independent predictive factor of poor prognosis for MACCE (odds ratio, 1.84; 95% confidence interval, 1.01-3.36; p = 0.045). Glycemic variability determined with a CGMS is a predictor of prognosis in patients with ACS without severe DM. Trial registration UMIN 000010620. Registered April 1st 2012. |
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Spain | 10 | 43% |
Mexico | 2 | 9% |
Australia | 1 | 4% |
Côte d'Ivoire | 1 | 4% |
United Kingdom | 1 | 4% |
Unknown | 8 | 35% |
Demographic breakdown
Type | Count | As % |
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Practitioners (doctors, other healthcare professionals) | 3 | 13% |
Scientists | 1 | 4% |
Mendeley readers
Geographical breakdown
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Unknown | 71 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Bachelor | 11 | 15% |
Student > Master | 8 | 11% |
Researcher | 7 | 10% |
Student > Ph. D. Student | 5 | 7% |
Student > Postgraduate | 4 | 6% |
Other | 9 | 13% |
Unknown | 27 | 38% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 21 | 30% |
Nursing and Health Professions | 6 | 8% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
Agricultural and Biological Sciences | 2 | 3% |
Materials Science | 2 | 3% |
Other | 4 | 6% |
Unknown | 34 | 48% |