Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes

Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yang Yang [verfasserIn] Li-hua Zhao [verfasserIn] Dan-dan Li [verfasserIn] Feng Xu [verfasserIn] Xiao-hua Wang [verfasserIn] Chun-feng Lu [verfasserIn] Chun-hua Wang [verfasserIn] Chao Yu [verfasserIn] Xiu-lin Zhang [verfasserIn] Li-yan Ning [verfasserIn] Xue-qin Wang [verfasserIn] Jian-bin Su [verfasserIn] Li-hua Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Glycemic variability Sleep quality Type 2 diabetes

Übergeordnetes Werk:	In: Diabetology & Metabolic Syndrome - BMC, 2010, 13(2021), 1, Seite 10
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:1 ; pages:10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13098-021-00720-w

Katalog-ID:	DOAJ004351584

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245	1	0	\|a Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes
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520			\|a Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.
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650		4	\|a Sleep quality
650		4	\|a Type 2 diabetes
653		0	\|a Nutritional diseases. Deficiency diseases
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allfields	10.1186/s13098-021-00720-w doi (DE-627)DOAJ004351584 (DE-599)DOAJdbd6e9ba172f4da7b5b86a26fb171305 DE-627 ger DE-627 rakwb eng RC620-627 Yang Yang verfasserin aut Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D. Glycemic variability Sleep quality Type 2 diabetes Nutritional diseases. Deficiency diseases Li-hua Zhao verfasserin aut Dan-dan Li verfasserin aut Feng Xu verfasserin aut Xiao-hua Wang verfasserin aut Chun-feng Lu verfasserin aut Chun-hua Wang verfasserin aut Chao Yu verfasserin aut Xiu-lin Zhang verfasserin aut Li-yan Ning verfasserin aut Xue-qin Wang verfasserin aut Jian-bin Su verfasserin aut Li-hua Wang verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 13(2021), 1, Seite 10 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:13 year:2021 number:1 pages:10 https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/article/dbd6e9ba172f4da7b5b86a26fb171305 kostenfrei https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 10
spelling	10.1186/s13098-021-00720-w doi (DE-627)DOAJ004351584 (DE-599)DOAJdbd6e9ba172f4da7b5b86a26fb171305 DE-627 ger DE-627 rakwb eng RC620-627 Yang Yang verfasserin aut Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D. Glycemic variability Sleep quality Type 2 diabetes Nutritional diseases. Deficiency diseases Li-hua Zhao verfasserin aut Dan-dan Li verfasserin aut Feng Xu verfasserin aut Xiao-hua Wang verfasserin aut Chun-feng Lu verfasserin aut Chun-hua Wang verfasserin aut Chao Yu verfasserin aut Xiu-lin Zhang verfasserin aut Li-yan Ning verfasserin aut Xue-qin Wang verfasserin aut Jian-bin Su verfasserin aut Li-hua Wang verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 13(2021), 1, Seite 10 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:13 year:2021 number:1 pages:10 https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/article/dbd6e9ba172f4da7b5b86a26fb171305 kostenfrei https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 10
allfields_unstemmed	10.1186/s13098-021-00720-w doi (DE-627)DOAJ004351584 (DE-599)DOAJdbd6e9ba172f4da7b5b86a26fb171305 DE-627 ger DE-627 rakwb eng RC620-627 Yang Yang verfasserin aut Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D. Glycemic variability Sleep quality Type 2 diabetes Nutritional diseases. Deficiency diseases Li-hua Zhao verfasserin aut Dan-dan Li verfasserin aut Feng Xu verfasserin aut Xiao-hua Wang verfasserin aut Chun-feng Lu verfasserin aut Chun-hua Wang verfasserin aut Chao Yu verfasserin aut Xiu-lin Zhang verfasserin aut Li-yan Ning verfasserin aut Xue-qin Wang verfasserin aut Jian-bin Su verfasserin aut Li-hua Wang verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 13(2021), 1, Seite 10 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:13 year:2021 number:1 pages:10 https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/article/dbd6e9ba172f4da7b5b86a26fb171305 kostenfrei https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 10
allfieldsGer	10.1186/s13098-021-00720-w doi (DE-627)DOAJ004351584 (DE-599)DOAJdbd6e9ba172f4da7b5b86a26fb171305 DE-627 ger DE-627 rakwb eng RC620-627 Yang Yang verfasserin aut Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D. Glycemic variability Sleep quality Type 2 diabetes Nutritional diseases. Deficiency diseases Li-hua Zhao verfasserin aut Dan-dan Li verfasserin aut Feng Xu verfasserin aut Xiao-hua Wang verfasserin aut Chun-feng Lu verfasserin aut Chun-hua Wang verfasserin aut Chao Yu verfasserin aut Xiu-lin Zhang verfasserin aut Li-yan Ning verfasserin aut Xue-qin Wang verfasserin aut Jian-bin Su verfasserin aut Li-hua Wang verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 13(2021), 1, Seite 10 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:13 year:2021 number:1 pages:10 https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/article/dbd6e9ba172f4da7b5b86a26fb171305 kostenfrei https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 10
allfieldsSound	10.1186/s13098-021-00720-w doi (DE-627)DOAJ004351584 (DE-599)DOAJdbd6e9ba172f4da7b5b86a26fb171305 DE-627 ger DE-627 rakwb eng RC620-627 Yang Yang verfasserin aut Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D. Glycemic variability Sleep quality Type 2 diabetes Nutritional diseases. Deficiency diseases Li-hua Zhao verfasserin aut Dan-dan Li verfasserin aut Feng Xu verfasserin aut Xiao-hua Wang verfasserin aut Chun-feng Lu verfasserin aut Chun-hua Wang verfasserin aut Chao Yu verfasserin aut Xiu-lin Zhang verfasserin aut Li-yan Ning verfasserin aut Xue-qin Wang verfasserin aut Jian-bin Su verfasserin aut Li-hua Wang verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 13(2021), 1, Seite 10 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:13 year:2021 number:1 pages:10 https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/article/dbd6e9ba172f4da7b5b86a26fb171305 kostenfrei https://doi.org/10.1186/s13098-021-00720-w kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 10
language	English
source	In Diabetology & Metabolic Syndrome 13(2021), 1, Seite 10 volume:13 year:2021 number:1 pages:10
sourceStr	In Diabetology & Metabolic Syndrome 13(2021), 1, Seite 10 volume:13 year:2021 number:1 pages:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Glycemic variability Sleep quality Type 2 diabetes Nutritional diseases. Deficiency diseases
isfreeaccess_bool	true
container_title	Diabetology & Metabolic Syndrome
authorswithroles_txt_mv	Yang Yang @@aut@@ Li-hua Zhao @@aut@@ Dan-dan Li @@aut@@ Feng Xu @@aut@@ Xiao-hua Wang @@aut@@ Chun-feng Lu @@aut@@ Chun-hua Wang @@aut@@ Chao Yu @@aut@@ Xiu-lin Zhang @@aut@@ Li-yan Ning @@aut@@ Xue-qin Wang @@aut@@ Jian-bin Su @@aut@@ Li-hua Wang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	610606689
id	DOAJ004351584
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ004351584</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503111646.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13098-021-00720-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ004351584</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJdbd6e9ba172f4da7b5b86a26fb171305</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC620-627</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yang Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Glycemic variability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sleep quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Type 2 diabetes</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Nutritional diseases. Deficiency diseases</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Li-hua Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Dan-dan Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Feng Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiao-hua Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chun-feng Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chun-hua Wang</subfield><subfield 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callnumber-first	R - Medicine
author	Yang Yang
spellingShingle	Yang Yang misc RC620-627 misc Glycemic variability misc Sleep quality misc Type 2 diabetes misc Nutritional diseases. Deficiency diseases Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes
authorStr	Yang Yang
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topic_title	RC620-627 Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes Glycemic variability Sleep quality Type 2 diabetes
topic	misc RC620-627 misc Glycemic variability misc Sleep quality misc Type 2 diabetes misc Nutritional diseases. Deficiency diseases
topic_unstemmed	misc RC620-627 misc Glycemic variability misc Sleep quality misc Type 2 diabetes misc Nutritional diseases. Deficiency diseases
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title	Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes
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title_full	Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes
author_sort	Yang Yang
journal	Diabetology & Metabolic Syndrome
journalStr	Diabetology & Metabolic Syndrome
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author_browse	Yang Yang Li-hua Zhao Dan-dan Li Feng Xu Xiao-hua Wang Chun-feng Lu Chun-hua Wang Chao Yu Xiu-lin Zhang Li-yan Ning Xue-qin Wang Jian-bin Su Li-hua Wang
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doi_str_mv	10.1186/s13098-021-00720-w
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title_sort	association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes
callnumber	RC620-627
title_auth	Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes
abstract	Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.
abstractGer	Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.
abstract_unstemmed	Abstract Background Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D). Methods We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9–10 mmol/L (TIR3.9–10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose. Results HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9–10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9–10 (r = − 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9–10 (β = − 0.401, t = − 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177). Conclusions Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.
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title_short	Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes
url	https://doi.org/10.1186/s13098-021-00720-w https://doaj.org/article/dbd6e9ba172f4da7b5b86a26fb171305 https://doaj.org/toc/1758-5996
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author2	Li-hua Zhao Dan-dan Li Feng Xu Xiao-hua Wang Chun-feng Lu Chun-hua Wang Chao Yu Xiu-lin Zhang Li-yan Ning Xue-qin Wang Jian-bin Su Li-hua Wang
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up_date	2024-07-03T23:18:56.718Z
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Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes

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