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Skeletal muscle function has been studied to determine its effect on glucose metabolism; however, its effect on glycemic variability (GV), which is a significant glycemic marker in patients with coronary artery disease, is unknown. The aim of the present study was to elucidate the association between skeletal muscle mass and GV. Two hundred and eight consecutive ST-segment elevation myocardial infarction (STEMI) patients who underwent continuous glucose monitoring to evaluate mean amplitude of glycemic excursion (MAGE) as GV and a dual-energy X-ray absorptiometry (DEXA) to evaluate skeletal muscle mass were enrolled. Skeletal muscle index (SMI) level was calculated as skeletal muscle mass divided by height squared (kg/m 2 ). SMI level in men had a weak inverse correlation with Log MAGE level by the linear regression model in diabetes mellitus (DM) patients (R 2  = 0.139, P = 0.004) and even in non-DM patients (R 2  = 0.068, P = 0.004). Multivariate linear regression analysis with a stepwise algorithm (age, male sex, body mass index [BMI], hemoglobin A1c [HbA1c], fasting glucose, HOMA-IR, and SMI; R 2  = 0.203, P < 0.001) demonstrated that HbA1c level (B = 0.077, P < 0.001) and SMI level (B = - 0.062, P < 0.001) were both independently associated with Log MAGE level. This association was also confirmed in limited non-DM patients with a subgroup analysis. SMI level was associated with Log MAGE level (B = - 0.055, P = 0.001) independent of BMI or HbA1c level. SMI level was inversely associated with MAGE level independent of glucose metabolism in STEMI patients, suggesting the significance of skeletal muscle mass as blood glucose storage for glucose homeostasis to reduce GV.

Background: 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., Methods: 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)., Result: 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)., Conclusion: 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.