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糖尿病患者血糖波动评价指标研究进展

师瑞 冯磊 唐灵通 张春婷 骆贝贝 毕千叶 曹慧颖 章艳碧

师瑞, 冯磊, 唐灵通, 张春婷, 骆贝贝, 毕千叶, 曹慧颖, 章艳碧. 糖尿病患者血糖波动评价指标研究进展[J]. 中华全科医学, 2022, 20(12): 2105-2109. doi: 10.16766/j.cnki.issn.1674-4152.002780
引用本文: 师瑞, 冯磊, 唐灵通, 张春婷, 骆贝贝, 毕千叶, 曹慧颖, 章艳碧. 糖尿病患者血糖波动评价指标研究进展[J]. 中华全科医学, 2022, 20(12): 2105-2109. doi: 10.16766/j.cnki.issn.1674-4152.002780
SHI Rui, FENG Lei, TANG Ling-tong, ZHANG Chun-ting, LUO Bei-bei, BI Qian-ye, CAO Hui-ying, ZHANG Yan-bi. Research progress on evaluation indicators of blood glucose fluctuation in patients with diabetes[J]. Chinese Journal of General Practice, 2022, 20(12): 2105-2109. doi: 10.16766/j.cnki.issn.1674-4152.002780
Citation: SHI Rui, FENG Lei, TANG Ling-tong, ZHANG Chun-ting, LUO Bei-bei, BI Qian-ye, CAO Hui-ying, ZHANG Yan-bi. Research progress on evaluation indicators of blood glucose fluctuation in patients with diabetes[J]. Chinese Journal of General Practice, 2022, 20(12): 2105-2109. doi: 10.16766/j.cnki.issn.1674-4152.002780

糖尿病患者血糖波动评价指标研究进展

doi: 10.16766/j.cnki.issn.1674-4152.002780
基金项目: 

国家自然科学基金项目 82160402

云南省医学领军人才培养专项经费资助项目 L-2019022

云南省教育厅科学研究基金项目 2019J1309

详细信息
    通讯作者:

    冯磊,E-mail:fngj2004@163.com

  • 中图分类号: R587.1

Research progress on evaluation indicators of blood glucose fluctuation in patients with diabetes

  • 摘要: 自2001年有国外学者提出血糖的过度波动可加重糖尿病患者血管内皮损伤以来,血糖波动逐渐受到重视,大量的基础及临床试验证实血糖波动可促进糖尿病并发症的发生发展。国际上提出“精细降糖,平稳达标”这一新的糖尿病治疗理念,既要严格控制糖化血红蛋白、空腹血糖、餐后血糖,又要尽量减少血糖波动的幅度及频率,监测评估血糖波动也就成为实现血糖精细化管理的重要环节。目前存在诸多的血糖波动评价指标,国际、国内也相继颁布指南推荐糖尿病患者血糖波动的核心评价指标,但各核心指标对于不同糖尿病并发症的临床评估效能仍在探索中,同时针对各核心评价指标的局限性,新的血糖波动评价指标随之走进大众视野,新指标种类繁多,各有其优缺点,新指标对于不同糖尿病并发症的评估预测效能也成为目前的研究热点。现阶段由于监测方法过于繁复、价格高昂等原因,导致其并不利于糖尿病患者的门诊随诊,因此探索一个具有良好临床适用性的血糖波动筛查指标已成为目前解决糖尿病患者门诊初步评估血糖波动情况亟须解决的问题。本文简要概述糖尿病患者血糖波动的各评价指标及其与糖尿病并发症关系的研究进展,以期为下一步完善糖尿病患者血糖波动评价体系及探索血糖波动筛查指标提供新的思路。

     

  • 表  1  短期血糖波动核心评价指标

    Table  1.   Core evaluation indicators of short-term glycemic variability

    血糖波动指标 动态血糖监测 自我血糖监测 特点及临床意义 参考区间 推荐指南/专家共识
    SD 监测期间所有血糖数值的标准差,评价总体偏离平均血糖值的程度 SD < 1.4 mmol/L(CGM); SD < 2.0 mmol/L(SMBG) 指南1;共识1;共识2
    CV 所监测血糖值的SD除以所监测血糖值的平均值,评价总体偏离平均血糖值的程度,国际共识认为CV是一个相对于平均值的SD百分比,CV对于血糖波动的程度更为直观 < 33%(国内); < 36%(国际) 指南2;共识1
    平均血糖波动幅度 去除所有幅度未超过一定阈值(一般为1SD)的血糖波动后,根据第一个有效波动的方向计算血糖波动幅度而得到的平均值,去除细小波动 < 3.9 mmol/L 指南1;共识2
    最大血糖波动幅度 监测期内最大和最小血糖值之差,评价最大血糖波动的幅度 < 4.4 mmol/L 共识2
    日间血糖平均绝对差 连续完整48 h内相对应测定值间相减所得差的绝对值的平均值,评估日间血糖的波动程度,体现日间血糖的重复性 < 0.83 mmol/L 共识2
    餐后血糖波动幅度 三餐后2 h的血糖与其相应餐前血糖差值绝对值的平均值 < 2.2 mmol/L 共识2
      注:指南1为《中国持续葡萄糖监测临床应用指南(2017年版)》;指南2为《中国血糖监测临床应用指南(2021年版)》;共识1为《Ternational consensus on use of continuous glucose monitoring(2017)》;共识2为《糖尿病患者血糖波动管理专家共识》。
    下载: 导出CSV

    表  2  血糖波动评估新指标

    Table  2.   New evaluation indicator of glycemic variability

    血糖波动指标 计算方法及临床意义 优缺点
    GVP ${\mathrm{GVP}}=\frac{L}{L_0-1} \times 100 \%$
    L:监测时间内血糖波动的真实轨迹长度
    L0:监测持续时间的轨迹长度
    可指示血糖波动的幅度、频率;对高低血糖风险的评估作用较差,计算非常复杂,完全依赖CGM仪。
    GDI GDI=g·MGI'+(1-g)·SDGI
    MGI':加权计算后的平均葡萄糖指数(减少高血糖对低血糖的“中和”作用)
    $g=\frac{{\mathrm{MGI}}^{\prime}}{{\mathrm{MGI}}^{\prime}+{\mathrm{SDGI}}}$
    SDGI:葡萄糖指数标准差
    GDI随血糖SD的增加而增加,且对低血糖风险有一定的评估作用;未考虑时间因素,对血糖波动频率的评估作用较差,计算复杂。
    CONGAn 血糖值与n小时前血糖值差值的标准差${\rm { CONGA }}n=\sqrt{\frac{\sum_{t=1}^{{\mathrm{t}}_{k *}}\left(D_{\mathrm{t}}-\bar{D}\right)^2}{k *-1}}$
    Dtt时间点血糖值与之前n小时血糖值的差值
    D:血糖值差值的平均值
    k*:观察的次数
    指示血糖波动幅度,可根据临床需要灵活调整观察频率及间隔周期,是能用于临床决策的指标;无血糖波动频率及高低血糖风险指示作用。
    持续葡萄糖监测的四分位间距 连续几天内给定时间点的葡萄糖数据分布的变化 指示在相同时间点血糖值的离散程度,用于评估日间血糖波动,数据可直接由CGM仪提供;无血糖波动频率及高低血糖风险指示作用。
    ADRR 每日低血糖和高血糖风险总和的平均值${\mathrm{ADRR}}=\frac{1}{n} \sum_{i=1}^n(LRi+HRi)$
    LRi:每天血糖最小值对应的危险值
    HRi:每天血糖最大值对应的危险值
    预测低血糖和高血糖方面表现出卓越的敏感性;血糖波动评估作用不显著。
    MAG 单位时间内绝对血糖的增加或减少${\mathrm{MAG}}=\frac{\sum_{i=1}^n|\Delta x|}{\Delta h}$
    x:邻近两次血糖差值
    h:相邻两次血糖监测的时间
    指示血糖波动的幅度,较直观地反映葡萄糖值的增减量变化及血糖值的变化速度;受细小血糖波动的影响,无高低血糖风险评估作用,计算复杂。
    ARV 连续数值之间差值绝对值之和的平均值${\mathrm{ARV}}=\frac{1}{n-1} \sum_{i=1}^{n-1}\left|\chi_{{\mathrm{i}}+1}-\chi_{\mathrm{i}}\right|$ 不仅可反映短期血糖波动情况,也可用于计算HbA1c变异性等长期血糖波动指标,计算相对简单,易于临床解释;只能粗略反映血糖波动情况,未考虑时间序列上的变化。
    GLI ${\mathrm{GLI}}=\sum\left(\frac{\Delta \chi^2}{\Delta h}\right) / d$
    x:邻近两次血糖差值
    h:邻近两次血糖测量时间间隔
    d:血糖测量总天数
    指示血糖变化的幅度、速度,且考虑了血糖的时间间隔和时间宽度,有一定的血糖波动频率指示作用;受细小血糖波动影响,无高低血糖风险评估作用,计算复杂。
    TIR 24 h血糖值在目标范围内(糖尿病患者为3.9~10.0 mmol/L,非糖尿病患者为3.9~7.8 mmol/L)所占时间百分比 指南推荐为评价血糖整体控制水平指标,而非血糖波动评价指标,但有研究证实较大的血糖波动可显著影响TIR[10],在一定程度上也可反映血糖波动。
      注:GVP为血糖变异百分比(glycemic variation percentage);GDI为血糖偏差指数(glycemic deviation index);CONGAn为连续净重叠血糖作用(continuous overlapping net glycemic action);ADRR为日均风险(average daily risk range);MAG为平均绝对血糖改变值(mean absolute glucose variation);ARV为平均真实变异度;GLI为血糖不稳定指数(glycemic lability index);TIR为葡萄糖在目标范围内时间(time in range)。
    下载: 导出CSV
  • [1] VALENTE T, ARBEX A K. Glycemic variability, oxidative stress, and impact on complications related to type 2 diabetes mellitus[J]. Curr Diabetes Rev, 2021, 17(7): e071620183816. DOI: 10.2174/1573399816666200716201550.
    [2] 高霜, 张艳丽, 吴姗姗, 等. 2型糖尿病患者的糖尿病痛苦现状调查及影响因素分析[J]. 中华全科医学, 2020, 18(12): 2136-2139. doi: 10.16766/j.cnki.issn.1674-4152.001707

    GAO S, ZHANG Y L, WU S S, et al. Investigation on the current situation of diabetes pain in patients with type 2 diabetes and analysis of its influencing factors[J]. Chinese Journal of General Practice, 2020, 18(12): 2136-2139. doi: 10.16766/j.cnki.issn.1674-4152.001707
    [3] 中华医学会内分泌学分会. 糖尿病患者血糖波动管理专家共识[J]. 中华内分泌代谢杂志, 2017, 33(8): 633-636. doi: 10.3760/cma.j.issn.1000-6699.2017.08.002

    Chinese Society of Endocrinology. Expert consensus on management of blood glucose fluctuation in patients with diabetes[J]. Chinese Journal of Endocrinology and Metabolism, 2017, 33(8): 633-636. doi: 10.3760/cma.j.issn.1000-6699.2017.08.002
    [4] ZHANG Z Y, MIAO L F, QIAN L L, et al. Molecular mechanisms of glucose fluctuations on diabetic complications[J]. Front Endocrinol(Lausanne), 2019, 10: 640. doi: 10.3389/fendo.2019.00640
    [5] 周建华, 李晓华, 贝鹏剑, 等. 早期T2DM肾病患者血糖波动与氧化应激, 炎症及单核细胞自噬的相关性[J]. 中国老年学杂志, 2021, 41(1): 32-36. doi: 10.3969/j.issn.1005-9202.2021.01.009

    ZHOU J H, LI X H, BEI P J, et al. The relationship between blood glucose fluctuation and oxidative stress, inflammation and monocyte autophagy in patients with early T2DM nephropathy[J]. Chinese Journal of Gerontology, 2021, 41(1): 32-36. doi: 10.3969/j.issn.1005-9202.2021.01.009
    [6] 中华医学会糖尿病学分会. 中国血糖监测临床应用指南(2021年版)[J]. 中华糖尿病杂志, 2021, 13(10): 936-948. doi: 10.3760/cma.j.cn115791-20210810-00436

    Chinese Diabetes Society. Guidelines for clinical application of blood glucose monitoring in China(2021)[J]. Chinese Gournal of Diabetes Mellitus, 2021, 13(10): 936-948. doi: 10.3760/cma.j.cn115791-20210810-00436
    [7] SHEN Y, DAI D J, LU J Y, et al. Visit-to-visit variability of glycated albumin was associated with incidence or progression of lower extremity atherosclerotic disease[J]. Cardiovasc Diabetol, 2020, 19(1): 211. doi: 10.1186/s12933-020-01187-1
    [8] FLEISCHER J, CICHOSZ S L, HANSEN T K. Comment on Lachin et al. Association of glycemic variability in type 1 diabetes with progression of microvascular outcomes in the diabetes control and complications trial[J]. Diabetes Care, 2017, 40(11): e164. DOI: 10.2337/dc17-1339.
    [9] BABAYA N, NOSO S, HIROMINE Y, et al. Flash glucose monitoring in type 1 diabetes: A comparison with self-monitoring blood glucose[J]. J Diabetes Investig, 2020, 11(5): 1222-1229. doi: 10.1111/jdi.13229
    [10] LU J Y, MA X J, ZHANG L, et al. Glycemic variability modifies the relationship between time in range and hemoglobin A1c estimated from continuous glucose monitoring: A preliminary study[J]. Diabetes Res Clin Pract, 2020, 161: 108032. DOI: 10.1016/j.diabres.2020.108032.
    [11] TSENG J Y, CHEN H H, HUANG K C, et al. Effect of mean HbA1c on the association of HbA1c variability and all-cause mortality in patients with type 2 diabetes[J]. Diabetes Obes Metab, 2020, 22(4): 680-687. doi: 10.1111/dom.13951
    [12] CERIELLO A, LUCISANO G, PRATTICHIZZO F, et al. HbA1c variability predicts cardiovascular complications in type 2 diabetes regardless of being at glycemic target[J]. Cardiovasc Diabetol, 2022, 21(1): 13. doi: 10.1186/s12933-022-01445-4
    [13] ZINMAN B, MARSO S P, POULTER N R, et al. Day-to-day fasting glycaemic variability in DEVOTE: Associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2)[J]. Diabetologia, 2018, 61(1): 48-57. doi: 10.1007/s00125-017-4423-z
    [14] BI J N, SONG L L, WANG L L, et al. Visit-to-visit fasting blood glucose variability and lifetime risk of cardiovascular disease: A prospective study[J]. Cardiovasc Diabetol, 2021, 20(1): 207. doi: 10.1186/s12933-021-01397-1
    [15] ECHOUFFO-TCHEUGUI J B, ZHAO S, BROCK G, et al. Visit-to-visit glycemic variability and risks of cardiovascular events and all-cause mortality: The ALLHAT study[J]. Diabetes Care, 2019, 42(3): 486-493. doi: 10.2337/dc18-1430
    [16] SU J B, ZHAO L H, ZHANG X L, et al. HbA1c variability and diabetic peripheral neuropathy in type 2 diabetic patients[J]. Cardiovasc Diabetol, 2018, 17(1): 47. doi: 10.1186/s12933-018-0693-0
    [17] LEE C L, CHEN C H, WU M J, et al. The variability of glycated hemoglobin is associated with renal function decline in patients with type 2 diabetes[J]. Ther Adv Chronic Dis, 2020, 11: 2040622319898370. DOI: 10.1177/2040622319898370.
    [18] HU J Q, HSU H C, YUAN X D, et al. HbA1c variability as an independent predictor of diabetes retinopathy in patients with type 2 diabetes[J]. J Endocrinol Invest, 2021, 44(6): 1229-1236. doi: 10.1007/s40618-020-01410-6
    [19] HSIEH Y T, HSIEH M C. Fasting plasma glucose variability is an independent risk factor for diabetic retinopathy and diabetic macular oedema in type 2 diabetes: An 8-year prospective cohort study[J]. Clin Exp Ophthalmol, 2020, 48(4): 470-476. doi: 10.1111/ceo.13728
    [20] BENALIA M, ZELLER M, MOUHAT B, et al. Glycaemic variability is associated with severity of coronary artery disease in patients with poorly controlled type 2 diabetes and acute myocardial infarction[J]. Diabetes Metab, 2019, 45(5): 446-452. doi: 10.1016/j.diabet.2019.01.012
    [21] GERBAUD E, DARIER R, MONTAUDON M, et al. Glycemic variability is a powerful independent predictive factor of midterm major adverse cardiac events in patients with diabetes with acute coronary syndrome[J]. Diabetes Care, 2019, 42(4): 674-681. doi: 10.2337/dc18-2047
    [22] LACHIN J M, BEBU I, BERGENSTAL R M, et al. Association of glycemic variability in type 1 diabetes with progression of microvascular outcomes in the diabetes control and complications trial[J]. Diabetes Care, 2017, 40(6): 777-783. doi: 10.2337/dc16-2426
    [23] LU J Y, MA X J, ZHANG L, et al. Glycemic variability assessed by continuous glucose monitoring and the risk of diabetic retinopathy in latent autoimmune diabetes of the adult and type 2 diabetes[J]. J Diabetes Investig, 2019, 10(3): 753-759. doi: 10.1111/jdi.12957
    [24] JUN J E, LEE S E, LEE Y B, et al. Glycated albumin and its variability as an indicator of cardiovascular autonomic neuropathy development in type 2 diabetic patients[J]. Cardiovasc Diabetol, 2017, 16(1): 127. doi: 10.1186/s12933-017-0619-2
    [25] PAN J M, YAN X F, LI F W, et al. Association of glycemic variability assessed by continuous glucose monitoring with subclinical diabetic polyneuropathy in type 2 diabetes patients[J]. J Diabetes Investig, 2022, 13(2): 328-335. doi: 10.1111/jdi.13652
    [26] CHAN C L, PYLE L, KELSEY M M, et al. Alternate glycemic markers reflect glycemic variability in continuous glucose monitoring in youth with prediabetes and type 2 diabetes[J]. Pediatr Diabetes, 2017, 18(7): 629-636. doi: 10.1111/pedi.12475
    [27] WANG B R, YAO J T, ZHENG H, et al. Association of glycated albumin/glycosylated hemoglobin ratio with blood glucose fluctuation and long-term blood glucose control in patients with Type 2 diabetes mellitus[J]. Diabetes Metab Syndr Obes, 2021, 14: 1809-1815. doi: 10.2147/DMSO.S297730
    [28] FENG L, NIAN S Y, ZHAO Y, et al. Higher HbA1c and/or glucose levels alter the association patterns between glycated hemoglobin and fasting glucose levels[J]. Diabetes Res Clin Pract, 2018, 142: 353-362. doi: 10.1016/j.diabres.2018.06.011
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  • 收稿日期:  2022-06-17
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