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Impact of Spectral Component of LED Lighting System on Glucose and Lipid Metabolism |
FAN Xiao-jing1,2, CHEN De-fu3, ZENG Jing2, LIANG Xin-yue1,2, XU Yi-xuan1,2, QIU Hai-xia2*, GU Ying2,3,4* |
1. Medical School of Chinese PLA, Beijing 100853, China
2. Department of Laser Medicine, the First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
3. Institute of Engineering Medicine, Beijing Institute of Technology, Beijing 100081, China
4. Precision Laser Medical Diagnosis and Treatment Innovation Unit, Chinese Academy of Medical Sciences, Beijing 100730, China |
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Abstract The mammalian circadian system has a different sensitivity to various spectral components. The chronically alternating light-dark cyde (“jetlag”) has been shown to cause circadian disturbances and increase the risk of metabolic diseases. However, it remains unknown whether the spectral component affects the metabolic effects under “jetlag” light cycles. In this study, broadband white light-emitting diode (LED) and narrow-band LEDs [blue light (BL) and red light (RL) with significantly different sensitivity to circadian system] were used to analyze the effect of the spectral component on the metabolism under normal and aberrant light cycles in C57BL/6J mice. All the light intensities is 120 μW·cm-2. The results showed that jetlag white light (WL) mice exhibited the most body weight gain. Jetlag RL mice suffered from significant lipid metabolism disorders and impaired liver function. Jetlag WL significantly reduced glucose tolerance and insulin sensitivity, while RL and BL prevented jetlag mice from an increase in fasting serum glucose. This study shows that modulating the spectral component may improve the adverse effects of the “jetlag” light pattern on glucose and lipid metabolism.
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Received: 2021-03-08
Accepted: 2021-03-31
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Corresponding Authors:
QIU Hai-xia, GU Ying
E-mail: guyinglaser301@163.com; qiuhxref@126.com
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[1] Dauchy R T, Blask D E, Hoffman A E. Comparative Medicine, 2019, 69: 350.
[2] Provencio I, Rodriguez IR, Jiang G. J. Neurosci., 2000, 20: 600.
[3] Hattar S, Liao H W, Takao M. Science, 2002, 295: 1065.
[4] Hattar S, Lucas R J, Mrosovsky N. Nature, 2003, 424: 76.
[5] Peirson S N, Brown L A, Pothecary C A. J. Neurosci. Methods, 2018, 300: 26.
[6] Panda S. Science, 2016, 354: 1008.
[7] Poggiogalle E, Jamshed H, Peterson C M. Metabolism, 2017, 84: 11.
[8] McFadden E, Jones M E, Schoemaker M J. Am. J. Epidemiol., 2014, 180: 245.
[9] Shan Z, Li Y, Zong G. BMJ, 2018, 363: k4641.
[10] Kotilainen T, Aphalo P J, Brelsford C C. Agric. for Meteorol., 2020, 291: 108041.
[11] Walmsley L, Hanna L, Mouland J. PLoS Biol., 2015, 13: e1002127.
[12] Opperhuizen A-L, Stenvers DJ, Jansen RD. Diabetologia, 2017, 60: 1333.
[13] Zhang S, Zhang Y, Zhang W. Ecotoxicol. Environ. Saf., 2019, 178: 94.
[14] Masis-Vargas A, Ritsema W, Mendoza J. Obesity (Silver Spring), 2020, 28(Suppl. 1): S114.
[15] Cheung I N, Zee P C, Shalman D. PLoS One, 2016, 11: e0155601.
[16] Nayak G, Zhang K X, Vemaraju S. Cell. Rep., 2020, 30: 672.
[17] Ondrusova K, Fatehi M, Barr A. Sci. Rep., 2017, 7: 16332.
[18] Mohawk J A, Pargament J M, Lee T M. Physiol. Behav., 2007, 92: 800.
[19] Figueiro M G, Rea M S. Int. J. Endocrinol., 2010: 829351.
[20] Petrowski K, Buehrer S, Niedling M. Stress, 2021, 24: 29.
[21] Arble D M, Bass J, Laposky A D. Obesity (Silver Spring), 2009, 17: 2100.
[22] Chaix A, Zarrinpar A, Miu P. Cell. Metab., 2014, 20: 991.
[23] Mukherji A, Kobiita A, Damara M. Proc. Natl. Acad. Sci. USA, 2015, 112: E6691.
[24] Schilperoort M, van den Berg R, Dolle M E T. Sci. Rep., 2019, 9: 7874.
[25] Bourgin P, Hubbard J. PLoS Biol., 2016, 14: e2000111.
[26] Motamedzadeh M, Golmohammadi R, Kazemi R. Physiol. Behav., 2017, 177: 208.
[27] Pilorz V, Tam S K, Hughes S. PLoS Biol., 2016, 14: e1002482.
[28] Harvey A G. Annu. Rev. Clin. Psychol., 2011, 7: 297.
[29] Mansur R B, Brietzke E, McIntyre R S. Neurosci. Biobehav. Rev., 2015, 52: 89.
[30] Bedrosian T A, Nelson R J. Transl. Psychiatry, 2017, 7: e1017.
[31] Dauchy R T, Wren-Dail M A, Hoffman A E. Comp. Med., 2016, 66: 373.
[32] Figueiro M G, Bullough J D, Parsons R H. Neuroreport, 2004, 15: 313. |
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