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Skeletal muscle mass is associated with glycemic variability in patients with ST-segment elevation myocardial infarction

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Abstract

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/m2). SMI level in men had a weak inverse correlation with Log MAGE level by the linear regression model in diabetes mellitus (DM) patients (R2 = 0.139, P = 0.004) and even in non-DM patients (R2 = 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; R2 = 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.

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References

  1. The DECODE study group on behalf of the Europe an Diabetes Epidemiology Group (1999) Glucose tolerance and mortality: Comparison of who and american diabetes association diagnostic criteria. The decode study group. European diabetes epidemiology group. Diabetes epidemiology: Collaborative analysis of diagnostic criteria in Europe. Lancet 354:617–621

  2. Forstermann U, Xia N, Li H (2017) Roles of vascular oxidative stress and nitric oxide in the pathogenesis of atherosclerosis. Circ Res 120:713–735

    Article  Google Scholar 

  3. D’Archangelo MJ (2008) New guideline supports the development and evaluation of continuous interstitial glucose monitoring devices. J Diabetes Sci Technol 2:332–334

    Article  Google Scholar 

  4. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF (1970) Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes 19:644–655

    Article  CAS  Google Scholar 

  5. Benalia M, Zeller M, Mouhat B, Guenancia C, Yameogo V, Greco C, Yao H, Maza M, Vergès B, Cottin Y (2019) Glycaemic variability is associated with severity of coronary artery disease in patients with poorly controlled type 2 diabetes and acute myocardial infarction. Diabetes Metab 45:446–452

    Article  CAS  Google Scholar 

  6. Gohbara M, Hibi K, Mitsuhashi T, Maejima N, Iwahashi N, Kataoka S, Akiyama E, Tsukahara K, Kosuge M, Ebina T, Umemura S, Kimura K (2016) Glycemic variability on continuous glucose monitoring system correlates with non-culprit vessel coronary plaque vulnerability in patients with first-episode acute coronary syndrome—optical coherence tomography study. Circ J 80:202–210

    Article  CAS  Google Scholar 

  7. Kataoka S, Gohbara M, Iwahashi N, Sakamaki K, Nakachi T, Akiyama E, Maejima N, Tsukahara K, Hibi K, Kosuge M, Ebina T, Umemura S, Kimura K (2015) Glycemic variability on continuous glucose monitoring system predicts rapid progression of non-culprit lesions in patients with acute coronary syndrome. Circ J 79:2246–2254

    Article  CAS  Google Scholar 

  8. Takahashi H, Iwahashi N, Kirigaya J, Kataoka S, Minamimoto Y, Gohbara M, Abe T, Okada K, Matsuzawa Y, Konishi M, Maejima N, Hibi K, Kosuge M, Ebina T, Tamura K, Kimura K (2018) Glycemic variability determined with a continuous glucose monitoring system can predict prognosis after acute coronary syndrome. Cardiovasc Diabetol 17:116

    Article  CAS  Google Scholar 

  9. Huang S, Czech MP (2007) The glut4 glucose transporter. Cell Metab 5:237–252

    Article  CAS  Google Scholar 

  10. Hayashi T, Hirshman MF, Kurth EJ, Winder WW, Goodyear LJ (1998) Evidence for 5’ amp-activated protein kinase mediation of the effect of muscle contraction on glucose transport. Diabetes 47:1369–1373

    CAS  PubMed  Google Scholar 

  11. Ogama N, Sakurai T, Kawashima S, Tanikawa T, Tokuda H, Satake S, Miura H, Shimizu A, Kokubo M, Niida S, Toba K, Umegaki H, Kuzuya M (2019) Association of glucose fluctuations with sarcopenia in older adults with type 2 diabetes mellitus. J Clin Med 8:319

    Article  CAS  Google Scholar 

  12. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C (2006) Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295:1681–1687

    Article  CAS  Google Scholar 

  13. Meng SJ, Yu LJ (2010) Oxidative stress, molecular inflammation and sarcopenia. Int J Mol Sci 11:1509–1526

    Article  CAS  Google Scholar 

  14. Gohbara M, Iwahashi N, Nakahashi H, Kataoka S, Takahashi H, Kirigaya J, Minamimoto Y, Akiyama E, Okada K, Matsuzawa Y, Konishi M, Maejima N, Hibi K, Kosuge M, Ebina T, Sugano T, Ishikawa T, Tamura K, Kimura K (2020) Clinical impact of admission urinary 8-hydroxydeoxyguanosine level for predicting cardiovascular mortality in patients with acute coronary syndrome. Heart Vessels. https://doi.org/10.1007/s00380-020-01663-4

    Article  PubMed  Google Scholar 

  15. American Diabetes Association (2018) 2. Classification and diagnosis of diabetes: Standards of medical care in diabetes-2018. Diabetes Care 41:S13-S27

  16. Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, Chou MY, Chen LY, Hsu PS, Krairit O, Lee JS, Lee WJ, Lee Y, Liang CK, Limpawattana P, Lin CS, Peng LN, Satake S, Suzuki T, Won CW, Wu CH, Wu SN, Zhang T, Zeng P, Akishita M, Arai H (2014) Sarcopenia in asia: Consensus report of the asian working group for sarcopenia. J Am Med Dir Assoc 15:95–101

    Article  Google Scholar 

  17. Saito S, Yamauchi H, Hasui Y, Kurashige J, Ochi H, Yoshida K (2000) Quantitative determination of urinary 8-hydroxydeoxyguanosine (8-oh-dg) by using elisa. Res Commun Mol Pathol Pharmacol 107:39–44

    CAS  PubMed  Google Scholar 

  18. Friedrichsen M, Mortensen B, Pehmøller C, Birk JB, Wojtaszewski JF (2013) Exercise-induced ampk activity in skeletal muscle: Role in glucose uptake and insulin sensitivity. Mol Cell Endocrinol 366:204–214

    Article  CAS  Google Scholar 

  19. Sugimoto K, Tabara Y, Ikegami H, Takata Y, Kamide K, Ikezoe T, Kiyoshige E, Makutani Y, Onuma H, Gondo Y, Ikebe K, Ichihashi N, Tsuboyama T, Matsuda F, Kohara K, Kabayama M, Fukuda M, Katsuya T, Osawa H, Hiromine Y, Rakugi H (2019) Hyperglycemia in non-obese patients with type 2 diabetes is associated with low muscle mass: The multicenter study for clarifying evidence for sarcopenia in patients with diabetes mellitus. J Diabetes Investig 10:1471–1479

    Article  CAS  Google Scholar 

  20. Uchida S, Kamiya K, Hamazaki N, Matsuzawa R, Nozaki K, Ichikawa T, Suzuki Y, Nakamura T, Yamashita M, Kariya H, Maekawa E, Yamaoka-Tojo M, Matsunaga A, Ako J (2020) Association between sarcopenia and atherosclerosis in elderly patients with ischemic heart disease. Heart Vessels 35:769–775

    Article  Google Scholar 

  21. Nagayoshi Y, Kawano H, Hokamaki J, Miyamoto S, Kojima S, Shimomura H, Tsujita K, Sakamoto T, Yoshimura M, Ogawa H (2005) Urinary 8-hydroxy-2’-deoxyguanosine levels increase after reperfusion in acute myocardial infarction and may predict subsequent cardiac events. Am J Cardiol 95:514–517

    Article  CAS  Google Scholar 

  22. Sato R, Akiyama E, Konishi M, Matsuzawa Y, Suzuki H, Kawashima C, Kimura Y, Okada K, Maejima N, Iwahashi N, Hibi K, Kosuge M, Ebina T, von Haehling S, Anker SD, Tamura K, Kimura K (2020) Decreased appendicular skeletal muscle mass is associated with poor outcomes after st-segment elevation myocardial infarction. J Atheroscler Thromb. https://doi.org/10.5551/jat.52282

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was funded by Astellas, Astra Zeneca, Boehringer Ingelheim, Daiichi Sankyo, Eisai, Kowa, Kyowa Hakko Kirin, Merck, Mitsubishi Tanabe, Ono, Otsuka, Pfizer, Sanofi Aventis, Shionogi, Sumitomo Dainippon, Takeda, and Terumo (Grant number: Research grant support).

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Correspondence to Masaomi Gohbara.

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Dr. Kimura reports receiving research grant support from Astellas, Astra Zeneca, Boehringer Ingelheim, Daiichi Sankyo, Eisai, Kowa, Kyowa Hakko Kirin, Merck, Mitsubishi Tanabe, Ono, Otsuka, Pfizer, Sanofi Aventis, Shionogi, Sumitomo Dainippon, Takeda, and Terumo.

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Gohbara, M., Iwahashi, N., Sato, R. et al. Skeletal muscle mass is associated with glycemic variability in patients with ST-segment elevation myocardial infarction. Heart Vessels 36, 945–954 (2021). https://doi.org/10.1007/s00380-021-01781-7

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