动态血糖监测甘精胰岛素治疗老年2型糖尿病的研究

目的通过动态血糖监测（CGMS），评估甘精胰岛素治疗老年2型糖尿病（T2DM）的疗效和安全性。方法对39例口服药联合治疗空腹血糖控制不佳的老年T2DM患者，加用甘精胰岛素（IG）和中性鱼精蛋白锌胰岛素（NPH）睡前皮下注射，治疗12周。治疗前后测定空腹血糖（FPG）、餐后2h血糖（2hPG）、糖化血红蛋白（HbAlc）、空腹C肽及餐后2hC肽等，并进行比较。结果治疗后，2组血糖和HbAlc均较治疗前下降（P〈0．05或P〈0．01），IG组血糖下降更明显（P〈0．05），2组HbAlc无明显差异（P〉0．05），IG组治疗后餐后2hC肽水平提高（P〈0．05）。CGMS显示IG组24h血糖曲线平缓，血糖达标时间延长，夜间低血糖的发生率低（P〈0．01）．血糖波动幅度小。结论IG作为老年T2DM患者的基础胰岛素替代治疗，血糖控制达标率高，胰岛素剂量控制更方便、安全，优于NPH。     

Improved glycemic control with once-weekly dulaglutide in addition to insulin therapy in type 2 diabetes mellitus patients on hemodialysis evaluated by continuous glucose monitoring

Aims

To evaluate the efficacy and safety of adding once-weekly dulaglutide to insulin therapy in type 2 diabetes mellitus (T2DM) patients on hemodialysis.

动态监测新诊断2型糖尿病患者的血糖水平

目的　动态监测新诊断2型糖尿病(T2DM)患者血糖漂移的细节及波动趋势。　方法　采用动态血糖监测系统(CGMS)对40 例新诊断、未经干预治疗的T2DM患者进行连续71(43~90)小时的血糖监测。　结果　CGMS所测的血糖值与血浆血糖值及指端血糖值均呈显著正相关(r=0.92, r=0.93, P均<0.001)。患者一天中血糖较高的时间段为早餐后2 h及中、晚餐后3 h。6 am~< 11 am是血糖高峰最集中(52.5%)的时间段,而62.5%的血糖低谷值出现在1 am~ <6 am。血糖>7 8 及11.1 mmol/L所占的时间百分比分别为96(37~100)%和62(8~100)%。血糖>7.8 及11.1 mmol/L的时间百分比与HbA1c(9.8%±1.9%)均呈显著正相关(r=0.74, r=0.76,P均<0 001)。　结论　动态血糖监测能较详细地显示T2DM患者血糖水平波动的特征,对拟定更为合理的治疗方案提供临床依据。     

Discrepancies in glycemic metrics derived from different continuous glucose monitoring systems in adult patients with type 1 diabetes mellitus

BackgroundContinuous glucose monitoring systems have been widely used but discrepancies among various brands of devices are rarely discussed. This study aimed to explore differences in glycemic metrics between FreeStyle Libre (FSL) and iPro2 among adults with type 1 diabetes mellitus (T1DM).MethodsParticipants with T1DM and glycosylated hemoglobin of 7%–10% were included and wore FSL and iPro2 for 2 weeks simultaneously. Datasets collected on the insertion and detachment day, and those with insufficient quantity (<90%) were excluded. Agreements of measurement accuracy and glycemic metrics were evaluated.ResultsA total of 40 498 paired data were included. Compared with the values from FSL, significantly higher median value was observed in iPro2 (147.6 [106.2, 192.6] vs. 144.0 [100.8, 192.6] mg/dl, p < 0.001) and the largest discordance was observed in hypoglycemic range (median absolute relative difference with iPro2 as reference value: 25.8% [10.8%, 42.1%]). Furthermore, significant differences in glycemic metrics between iPro2 and FSL were also observed in time in range (TIR) 70–180 mg/dl (TIR, 62.8 ± 12.4% vs. 58.8 ± 12.3%, p = 0.004), time spent below 70 mg/dl (4.4 [1.8, 10.9]% vs. 7.2 [5.4, 13.3]%, p < 0.001), time spent below 54 mg/dl (0.9 [0.3, 4.0]% vs. 2.6 [1.3, 5.6]%, p = 0.011), and coefficient of variation (CV, 38.7 ± 8.5% vs. 40.9 ± 9.3%, p = 0.017).ConclusionsDuring 14 days of use, FSL and iPro2 provided different estimations on TIR, CV, and hypoglycemia‐related parameters, which needs to be considered when making clinical decisions and clinical trial designs.     

Benefits of blinded continuous glucose monitoring during a randomized clinical trial

Background:

Real-time, personal continuous glucose monitoring (CGM) is a validated technology that can help patients improve glycemic control. Blinded CGM is a promising technology for obtaining retrospective data in clinical research where the quantity and quality of blood glucose information is important. This study was designed to investigate the use of novel procedures to enhance data capture from blinded CGM.

Methods:

Following a 4-week run-in, 46 patients with type 1 diabetes were randomized to one of two prandial insulins for a 12-week treatment period, after which they were crossed over to the alternate treatment for 12 weeks. Continuous glucose monitoring was implemented at the end of run-in (practice only) and during the last 2 weeks of each treatment period. Eighty percent of 288 possible daily glucose values were required for at least three days. Continuous glucose monitoring was extended for an additional week if these criteria were not met, and patients were allowed to insert sensors at home when necessary. Continuous glucose monitoring results were compared to reference eight-point self-monitoring of blood glucose (SMBG).

Results:

Higher than expected sensor failure rate was approximately 25%. During run-in, 12 of 45 attempted profiles failed adequacy criteria. However, treatment periods had only 1 of 82 attempted profiles considered inadequate (6 cases required an additional week of CGM). Using SMBG as reference, 93.7% of 777 CGM values were in Clarke error grid zones A+B.

Conclusions:

With appropriate training, adequate practice, and opportunity to repeat blinded CGM as needed, nearly 100% of attempted profiles can be obtained successfully.     

Professional continuous glucose monitoring in patients with diabetes mellitus: A systematic review and meta-analysis

Aim

To evaluate the effect on glucose control of professional continuous glucose monitoring (p-CGM)-based care as compared with standard care in the management of patients with type 1 and type 2 diabetes.

Materials and methods

The PubMed database was searched comprehensively to identify prospective or retrospective studies evaluating p-CGM as a diagnostic tool for subsequent implementation of lifestyle and/or medication changes and reporting glycated haemoglobin (HbA1c) as an outcome measure.

Results

We found 872 articles, 22 of which were included in the meta-analysis. Overall, the use of p-CGM was associated with greater HbA1c reduction from baseline (−0.28%, 95% confidence interval [CI] −0.36% to −0.21%, I² = 0%, P < 0.00001) than usual care, irrespective of type of diabetes, length of follow-up, frequency of continuous glucose monitoring (CGM) use and duration of CGM recording. In the few studies describing CGM-derived glucose metrics, p-CGM showed a beneficial effect on change in time in range from baseline (5.59%, 95% CI 0.12 to 11.06, I² = 0%, P = 0.05) and a neutral effect on change in time below the target range from baseline (−0.11%, 95% CI −1.76% to 1.55%, I² = 33%, P = 0.90).

Conclusions

In patients with type 1 and type 2 diabetes, p-CGM-driven care is superior to usual care in improving glucose control without increasing hypoglycaemia.     

2型糖尿病患者血浆Apelin-12水平与血糖的关系

目的探讨2型糖尿病患者血浆脂肪细胞因子Apelin-12水平变化及其与血糖的关系。方法选取2012年1月至2015年6月在民航总医院内分泌科住院的41例2型糖尿病患者作为研究对象,并招募44例健康对照者作为对照组。根据有无合并冠心病、脑血管病及高血压病将2型糖尿病患者再分为2型糖尿病合并心脑血管疾病组(21例)和单纯糖尿病组(20例)。酶联免疫吸附法测定Apelin-12的水平,测定其糖化血红蛋白、总胆固醇、甘油三酯、低密度脂蛋白以及空腹和餐后2 h血糖、C肽的水平,并对以上指标进行相关性分析。结果与对照组相比,2型糖尿病患者血浆Apelin-12水平显著降低(t=2.70,P=0.01); 2型糖尿病合并心脑血管疾病组血浆Apelin-12的水平与单纯糖尿病组差异无统计学意义(t=-0.44,P=0.67)。多元逐步回归分析显示,在2型糖尿病患者中,空腹血糖与Apelin-12相关(B=-0.12,β=-0.42,t=-2.03,P=0.04)。结论 2型糖尿病患者血浆Apelin-12水平显著降低且与空腹血糖呈负相关。     

Efficacy of continuous glucose monitoring in improving glycemic control and reducing hypoglycemia: a systematic review and meta-analysis of randomized trials

Objective

We conducted a systematic review and meta-analysis to assess the efficacy of continuous glucose monitoring (CGM) in improving glycemic control and reducing hypoglycemia compared to self-monitored blood glucose (SMBG).

Methods

We searched MEDLINE, EMBASE, Cochrane Central, Web of Science, and Scopus for randomized trials of adults and children with type 1 or type 2 diabetes mellitus (T1DM or T2DM). Pairs of reviewers independently selected studies, assessed methodological quality, and extracted data. Meta-analytic estimates of treatment effects were generated using a random-effects model.

Results

Nineteen trials were eligible and provided data for meta-analysis. Overall, CGM was associated with a significant reduction in mean hemoglobin A1c [HbA1c; weighted mean difference (WMD) of -0.27% (95% confidence interval [CI] -0.44 to -0.10)]. This was true for adults with T1DM as well as T2DM [WMD -0.50% (95% CI -0.69 to -0.30) and -0.70 (95% CI, -1.14 to -0.27), respectively]. No significant effect was noted in children and adolescents. There was no significant difference in HbA1c reduction between studies of real-time versus non-realtime devices (WMD -0.22%, 95% CI, -0.59 to 0.15 versus -0.30%, 95% CI, -0.49 to -0.10; p for interaction 0.71). The quality of evidence was moderate due to imprecision, suggesting increased risk for bias. Data for the incidence of severe or nocturnal hypoglycemia were sparse and imprecise. In studies that reported patient satisfaction, users felt confident about the device and gave positive reviews.

Conclusion

Continuous glucose monitoring seems to help improve glycemic control in adults with T1DM and T2DM. The effect on hypoglycemia incidence is imprecise and unclear. Larger trials with longer follow-up are needed to assess the efficacy of CGM in reducing patient-important complications without significantly increasing the burden of care for patients with diabetes.     

Objective sleep characteristics and continuous glucose monitoring profiles of type 2 diabetes patients in real-life settings

Aim

To investigate the association between objective sleep parameters and glycaemic variability determined by continous glucose monitoring (CGM) among patients with type 2 diabetes, given the significant role of sleep in glycaemic control.

Methods

In this study, CGM was carried out in 28 patients with T2D (aged 62.3 ± 4.8 years, 57% women). Sleep characteristics were assessed by actigraphy within the CGM period. CGM-derived outcomes included glucose level, and percentages of time in range (TIR) and time above range (TAR) during the monitoring period. Associations between intraindividual night-to-night variations in sleep characteristics and overall CGM outcomes were analysed using linear regression. Associations between sleep characteristics during each night and time-matched CGM outcomes were analysed using linear mixed models.

Results

A total of 249 person-days of CGM, coupled with 221 nights of sleep characteristics, were documented. Greater standard deviation (SD) of objective sleep duration (minutes) between measurement nights was associated with higher glucose level (coefficient 0.018 mmol/L [95% confidence interval {CI} 0.004, 0.033], P = 0.017), smaller proportion of TIR (% in observation period; coefficient −0.20% [95% CI −0.36, −0.03], P = 0.023), and greater proportion of TAR (coefficient 0.22% [95% CI 0.06, 0.39], P = 0.011). Later sleep midpoint (minutes from midnight) was associated with greater SD of glucose during the same sleep period (coefficient 0.002 minutes [95% CI 0.0001, 0.003], P = 0.037), longer nocturnal sleep duration was associated with smaller coefficient of variation of glucose level in the upcoming day (−0.015% [95% CI −0.03, −0.001], P = 0.041).

Conclusion

Objectively determined sleep duration and sleep midpoint, as well as their daily variability, are associated with CGM-derived glucose profiles in T2D patients.     

2型糖尿病患者频繁静脉取血法动态血糖监测的准确性评估

目的应用频繁静脉取血法在2型糖尿病患者中对回顾性动态血糖监测系统（CGMS）的点准确度及趋势准确度进行评价。方法2010年8月至10月共纳入11例2型糖尿病患者,应用美敦力公司GOLD^TM（GMS）连续监测3d血糖,同时每天输入4次指尖血糖值进行校正。受试者在CGMS监测的3d内随机选择1d参加连续7h的频繁静脉取血（15min取1次）,并用美国YSI STAT Plus^TM葡萄糖乳酸分析仪（YSI值）进行血浆葡萄糖值的测定。应用多种统计方法和统计量来综合进行准确性评估,包括CGMS值和匹配的YSI值相比在20％和30％偏差范围内的一致率、误差栅格分析、绝对差值的相对数（ARD）、Bland—Ahman分析以及趋势分析等。两变量相关分析采用Pearson相关分析。结果11例患者共收集到319对YSI—CGMS配对数据值;与YSI值相比,88．4％（95％CI：0．84—0．92）的CGMS值在20％偏差范围内,96．9％的CGMS值在30％偏差范围内。Clarke误差栅格分析显示YSI—CGMS配对数据值进入A区和B区的比例分别为88％、12％,共识误差栅格分析显示进入A区和B区的比例分别为96．2％、3．8％。连续误差栅格分析显示YSI—CGMS配对数据值在准确数据区、良性错误区及错误数据区分别为94．4％、2．8％及2．8％。ARD的均值为10．5％,中位数为8．4％,Bland—Altman分析均值为0．47mmol/L（95％CI：-1．90-3．01）。趋势分析显示82．5％的两者变化率差异集中在0．06mmol·L^-1·min^-1的变化范围内,只有1．7％的数据对绝对差异超过0．17mmd·L^-1·min^-1。结论无论对点时血糖还是对血糖变化趋势的反映,CGMS均有较好的准确性;但对低血糖事件进行评判时,尚需结合临床实际情况具体分析。     

应用动态血糖监测评估2型糖尿病患者的夜间低血糖

动态血糖监测系统(CGMS)监测1 147例2型糖尿病患者血糖变化.结果 显示,夜间低血糖多发生在22:00～2:00,与平均血糖及晚餐后3 h血糖负相关,晚餐后3 h血糖4.7 mmoL/L时发生夜间低血糖几率达50%.     

血糖控制不佳2型糖尿病患者轻度认知障碍影响因素研究

目的探讨血糖控制不佳2型糖尿病患者(T2DM)轻度认知障碍(MCI)发生的影响因素,为疾病预防与控制提供依据。方法入选2014年10月至2015年5月安徽医科大学第一附属医院和第二附属医院内分泌科T2DM患者181例,其中男性105例,女性76例,年龄45～75(59.0±8.5)岁,采用长沙版蒙特利尔认知评估量表(MoCA)评定患者的总体认知,了解认知损害现状并根据得分分组分析影响因素。应用SPSS 16.0统计软件对数据进行分析。采用t检验或x2检验比较组间差异。单因素分析影响T2DM患者认知功能的因素,进一步采用logistic回归分析发生MCI的危险因素。结果血糖控制不佳T2DM患者的MCI患病率为52.5%(95/181)。单因素分析显示年龄、性别、文化程度、家庭人均月收入、饮酒、吸烟、脑梗死史、糖尿病周围神经病变、低血糖史和尿微量白蛋白/肌酐比值(A/C)比值与MCI相关(P0.05)。多因素logistic回归显示年龄与MCI正相关(OR=1.437,95%CI 1.017～2.029;P0.05),教育程度和低血糖史与MCI负相关(OR=0.326,95%CI0.197～0.539;OR=0.400,95%CI0.167～0.958;P0.05)。结论医务人员应关注血糖控制不佳T2DM患者的认知功能,及时采取措施延缓认知功能减退。     

Interindividual and intraindividual variations in postprandial glycemia peak time complicate precise recommendations for self-monitoring of glucose in persons with type 1 diabetes mellitus

Background

In glycemic control, postprandial glycemia may be important to monitor and optimize as it reveals glycemic control quality, and postprandial hyperglycemia partly predicts late diabetic complications. Self-monitoring of blood glucose (SMBG) may be an appropriate technology to use, but recommendations on measurement time are crucial.

Method

We retrospectively analyzed interindividual and intraindividual variations in postprandial glycemic peak time. Continuous glucose monitoring (CGM) and carbohydrate intake were collected in 22 patients with type 1 diabetes mellitus. Meals were identified from carbohydrate intake data. For each meal, peak time was identified as time from meal to CGM zenith within 40–150 min after meal start. Interindividual (one-way Anova) and intraindividual (intraclass correlation coefficient) variation was calculated.

Results

Nineteen patients were included with sufficient meal data quality. Mean peak time was 87 ± 29 min. Mean peak time differed significantly between patients (p = 0.02). Intraclass correlation coefficient was 0.29.

Conclusions

Significant interindividual and intraindividual variations exist in postprandial glycemia peak time, thus hindering simple and general advice regarding postprandial SMBG for detection of maximum values.     

实时动态血糖监测在老年糖尿病低血糖检出和干预中的作用研究

目的 以传统动态血糖监测(continuous glucose monitoring system,CGMS)为对照,评估实时动态血糖监测(real-time continuous glucose monitoring system,RT-CGMS)对老年2型糖尿病患者低血糖的检出和干预作用.方法 选取2010年1月-2013年12月住院的60岁及以上2型糖尿病患者166例,随机分入RT-CGMS组(试验组,n=84)和传统CGMS组(对照组,n=82)进行72 h持续血糖监测.设备安装当天为设备调试日,随后连续观察2d.试验组每日3次根据实时显示血糖曲线调整降糖策略,并设定高低血糖报警界限;对照组每日3次根据指末血糖调整降糖策略.比较两组间平均血糖、低血糖发生率、平均每人每天低血糖发生次数、平均每次低血糖持续时间、低血糖所占时间百分比以及时间分布规律.结果 (1)试验组和对照组平均血糖:(8.3±1.8) mmol/L vs (8.6士1.9) mmol/L,试验组较低,差异无统计学意义(t=1.286,P＞0.05);(2)试验组和对照组的低血糖发生率29.8％vs50.0％(x2=7.096,P＜0.05);平均每人每天低血糖发生次数、夜间低血糖发生次数分别为0 (0～0.5)次/人/dvs0.25 (0～0.5)次/人/d、0 (0～0)次/人/晚vs0(0～0.5)次/人/晚,差异均有统计学意义(Z=2.548、2.293,P均＜0.05);(3)试验组和对照组每次低血糖持续时间20 (15 ～35) min vs 40 (20～80) min,差异有统计学意义(Z=3.030,P＜0.05).血糖低于3.9 mmol/L所占时间百分比为(1.2±2.7)％vs(3.9±6.7)％(t=3.452,P＜0.05).结论 采用实时动态血糖监测在控制老年2型糖尿病低血糖发生方面优于指末血糖.     

老年2型糖尿病患者1,5-脱水葡萄糖醇与平均血糖及漂移幅度的相关性

目的研究老年2型糖尿病患者1,5-脱水葡萄糖醇（1,5-AG）与平均血糖（MBG）及漂移幅度的关系,探讨1,5-AG是否可作为糖尿病临床观察及治疗监控的指标之一。方法选取95例老年2型糖尿病住院患者,男性65例,女性30例,年龄70～88（80.1±4.3）岁,连续进行3d的动态血糖监测,统一进餐时间,期间记录每日参比血糖、饮食、服药及锻炼等活动事件。在第3天禁食8h以上,抽取空腹静脉血分别测定1,5-AG、糖化血红蛋白（HbA1c）、糖化血清蛋白（GSP）等数值。结果1,5-AG与空腹血糖、餐后2h血糖、HbAlc、GSP、3d MBG及平均血糖漂移幅度呈负相关（均P＜0.05）,将1,5-AG与日内不同时段的MBG进行Pearson相关分析,显示其与早餐前1h、早餐后2h、早餐后3h、晚餐后2h、晚餐后3h及2∶00～4∶00的MBG呈负相关（均P＜0.05）,与其他时段MBG相关性不明显。结论1,5-AG能较好地反映短时间内的MBG水平和血糖漂移,可作为糖尿病筛查和治疗监控的指标之一。     

糖尿病肾病和血糖控制的关系及相关危险因素分析

目的探讨糖尿病肾病(DKD)与血糖控制指标关系以及相关危险因素。方法回顾性分析2010年3月至2016年12月在西京医院老年病科住院且使用持续血糖监测系统(CGMS)的2型糖尿病(T2DM)患者142例,根据是否伴有DKD分为DKD组(n=54)和非DKD组(n=88)。收集患者一般临床资料、实验室指标及CGMS结果,分析DKD与血糖控制指标关系并对其危险因素进行综合分析。采用SPSS 19.0统计软件对数据进行分析,组间比较采用t检验、非参数检验或χ~2检验。Spearman秩相关分析两变量相关性,多因素分析采用逐步二元logistic回归分析。结果相比非DKD组,DKD组患者年龄偏大、病程长、高血压病史比例较高、高密度脂蛋白胆固醇(HDL-C)水平偏低、双胍类降糖药物使用率明显降低,糖化血红蛋白A1c(Hb A1c)水平、完整24 h高血糖时间波动百分比、高血糖曲线下面积(AUC)、餐后2 h血糖(2h-PBG)和24 h平均血糖水平(MBG)显著升高,差异有统计学意义(P0.05);胱抑素C(Cys C)、尿素氮(BUN)、血肌酐(SCr)水平显著高于非DKD组,估算肾小球滤过率(e GFR)显著低于非DKD组,差异有统计学意义(P0.001)。Spearman相关分析结果显示,DKD与年龄、病程、高血压史、Hb A1c、24 h高血糖时间波动百分比、高血糖AUC、24 h MBG、2h-PBG均呈正相关,与HDL-C呈负相关(r=-0.205,P=0.014)。逐步二元logistic回归分析结果显示年龄(OR=1.048,95%CI 1.022~1.074;P=0.000)和Hb A1c(OR=1.569,95%CI 1.212~2.031;P=0.001)与DKD呈正相关。结论 Hb A1c是T2DM患者发生DKD的主要危险因素,DKD的发生与血糖波动并无显著相关。