- Email: [email protected]
Comparable efficacy of self-monitoring of quantitative urine glucose with self-monitoring of blood glucose on glycaemic control in non-insulin-treated type 2 diabetes
diabetes research and clinical practice 93 (2011) 179–186
Contents lists available at ScienceDirect
Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres
Comparable efficacy of self-monitoring of quantitative urine glucose with self-monitoring of blood glucose on glycaemic control in non-insulin-treated type 2 diabetes§,§§ J. Lu a,1, R.F. Bu b,1, Z.L. Sun a,1,*, Q.S. Lu a, H. Jin a, Y. Wang a, S.H. Wang a, L. Li a, Z.L. Xie a, B.Q. Yang a a b
Institute of Diabetes, Zhongda Hospital, Medical School, Southeast University, Nanjing, China Department of Endocrinology and Metabolism, Wuxi People’s Hospital, Wuxi, China
article info
abstract
Article history:
Aim: To assess whether self-monitoring of quantitative urine glucose or blood glucose is
Received 11 January 2011
effective, convenient and safe for glycaemic control in non-insulin treated type 2 diabetes.
Received in revised form
Methods: Adults with non-insulin treated type 2 diabetes were recruited and randomized
10 April 2011
into three groups: Group A, self-monitoring with a quantitative urine glucose meter (n = 38);
Accepted 12 April 2011
Group B, selfmonitoring with a blood glucose meter (n = 35); Group C, the control group
Published on line 12 May 2011
without selfmonitoring (n = 35). All patients were followed up for six months, during which
Keywords:
Results: There was a significant decrease in HbA1c within each group ( p < 0.05). At the study
Self-monitoring of quantitative
conclusion, mean changes in HbA1c from baseline were 1.9% for Group A, 1.5% for Group
identical diabetes care was provided.
urine glucose
B and 1.0% for Group C, and the proportion of patients achieving HbA1c6.5% were 38.9%,
Self-monitoring of blood glucose
35.3% and 20.0% respectively. However, no significant differences between the groups were
Type 2 diabetes
found. The average monitoring frequency was significantly higher in Group A than in Group
Glycaemic control
B. The incidence of hypoglycaemia and quality of life scores were similar between the groups. Conclusions: This study suggests that self-monitoring of urine glucose has comparable efficacy on glycaemic control, and facilitates better compliance than blood self monitoring, without influencing the quality of life or risk of hypoglycaemia. # 2011 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Diabetes mellitus is one of the most common non-communicable diseases globally, and its related complications result in increasing disability, reduced life expectancy and enormous health costs for virtually every society [1]. For effective
management of diabetes, self-care must play an active role [2,3]. Apart from lifestyle intervention and medication adherence, self-monitoring of glucose is also an essential and integral part of self-care in diabetes. Two primary techniques available for patients include self-monitoring of blood glucose and self-monitoring of urine glucose.
§
This study is part of the Key Program of Jiangsu Natural Science Foundation (BK2010087). Eighth International Diabetes Federation Western Pacific Region Congress 2010, Bexco, Busan, Korea. * Corresponding author at: Institute of Diabetes, Zhongda Hospital, Medical School, Southeast University, No. 87 DingJiaQiao, Nanjing, Jiangsu Province 210009, China. E-mail address: [email protected] (Z.L. Sun).
§§
1 These authors contributed equally to this work. 0168-8227/$ – see front matter # 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2011.04.012
180
diabetes research and clinical practice 93 (2011) 179–186
Self-monitoring of blood glucose is generally accepted as a useful and successful tool for patients with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) requiring insulin [4,5]; however, evidence for its effectiveness in non-insulin treated T2DM patients is less clear-cut. A large cross-sectional study [6] with 23,153 patients, demonstrated a significant inverse relationship between blood self-monitoring frequency and HbA1c in all type 2 participants. Other observational trials [7– 9], with smaller samples but still more than 1000 participants, were unable to detect such a correlation. Randomized controlled trials (RCT), which provide a much higher level of evidence, also varied in results on the effectiveness of blood self monitoring [10–12]. Self-monitoring of urine glucose by urine dipsticks, was predominant in detecting episodes of hyperglycaemia before the invention of blood glucose meters. Early trials have shown conflicting results in the efficacy of urine self monitoring compared to blood self monitoring [13– 15], while in recent years, studies in this field have become much fewer, probably due to wider promotion of blood glucose monitoring, and to the limitations of urine testing including semiquantitative, retrospective and indirect interpretation. Nevertheless, considering its convenience, painlessness and affordability, urine glucose monitoring should not be completely given up, especially in low-income regions. Recently, a portable urine glucose meter has been developed which can quantitatively monitor with high sensitivity and rapid response urine glucose levels [16]. Since this device has overcome some disadvantages of traditional urine testing, its use may be helpful in non-insulin treated T2DM. Miyashita et al. [16] suggested its value in monitoring postprandial glucose. Yet there has been no study about the application of quantitative urine testing in routine diabetes care. The primary aim of this pilot study is to assess whether selfmonitoring of blood or urine glucose, compared to usual care without self monitoring, is effective in improving glycaemic control in non-insulin treated T2DM patients.
2.
Participants
Patients were considered eligible if they had T2DM, were aged between 18 and 75 years, were managed with diet and/or oral hypoglycaemic agents, had a HbA1c level 7.0% and postprandial glucose >11.0 mmol/L. The exclusion criteria included: use of insulin, rapidly progressing diabetic complications, severe concurrent illness that would limit life or require extensive treatment, abnormal renal threshold during screening, women in pregnancy or lactation, alcohol misuse and inability to follow trial procedures. An oral glucose tolerance test (OGTT) was used for the determination of renal threshold. At baseline, 30 min, 60 min, 120 min and 180 min of OGTT, blood samples were taken for measurements of plasma glucose; meanwhile urine samples at each time-point were collected immediately after blood collections and tested by a quantitative urine meter. When the value of urine testing turned from <50 mg/dl to 50 mg/dl, the corresponding plasma glucose level was referred to as one’s renal threshold. This was generally in a range of 8.9– 10.0 mmol/L [17,18]. An abnormal renal threshold was defined as a level higher or lower than this range.
3.
Materials and methods
3.1.
Design
This study was an open label, randomized, controlled trial conducted in two diabetes centers. Approval to conduct the trial was obtained from the Research and Ethics Committees of each study center, and all participants provided written informed consent. With a randomly generated allocation code from a computer program, eligible patients were randomized into three groups: Group A (urine glucose group): participants were provided with urine glucose meters (TANITA1 UG - 201) and were required to test their urine twice every day (fasting, 2 h after breakfast), with at least two extra tests each week (2 h after dinner); Group B (blood glucose group): participants were provided with blood glucose meters (LifeScan OneTouch1 Ultra EasyTM) and were required to monitor at the same frequency as Group A; Group C (control group): participants were asked not to perform any self-monitoring, but only provided with standardized usual care. All three groups received similar diary books in which they had to report changes about diet and exercises; besides that, Groups A and B were required to record the obtained values of urine or blood glucose. TANITA1 UG-201 contains a sensitive micro-planer biosensor, which permits a wide measurement range of 0– 2000 mg/dl and possesses good performances of repeatability, stability, and absence from interferential substances (ascorbic acid, acetaminophen) [16]. The handling of the meter is convenient and non-invasive, but requires regular calibrations once every 7–14 days.
3.2.
Study protocol
All patients had a one-week run-in period, during which structured diabetes self-management education was provided by nurse practitioners, dieticians, diabetes educators, and physicians. The educational sessions focused on: diabetes disease process, lifestyle behaviors, utilization of medications, as well as prevention and detection of complications. In addition, patients in Groups A and B were instructed in the technique of urine or blood glucose testing respectively, and were given advice on interpreting and applying the results to lifestyle modification. Group A was asked to aim for glucose levels of <50 mg/dl, while Group B aimed for glucose levels of 4.0–6.0 mmol/L before meals and 6.0–8.0 mmol/L two hours after meals. Whenever there was a problem about meters, patients were allowed to contact the research staff for a check or calibration. After the run-in, patients attended scheduled clinical visits, every four weeks for six months. At each visit, fasting blood samples were taken for measurements of blood glucose every month and of HbA1c, triglyceride and total cholesterol every two months. All the plasma examinations were performed in regional central laboratories. HbA1c was measured using an ion-exchange HPLC assay (Variant HbA1c program; Bio-Rad Laboratories, Hercules, CA, USA); other determinations were measured by enzymatic methods using automated techni-
diabetes research and clinical practice 93 (2011) 179–186
ques. In addition, the diabetes quality-of-life (DQOL) developed by DCCT group [19] was used to evaluate the life quality at baseline and 6 months; weight and waist circumference were reviewed every other month. At each visit, information was collected on medications, hypoglycaemic episodes and adverse reactions or complications from all patients, whereas compliance, measured by completeness of urine- or blood-test records was only assessed in Groups A and B. Self-reported hypoglycaemic episodes were categorized as: Grade 1: transitory symptoms not affecting normal activity; Grade 2: temporarily incapacitated but able to treat symptoms without help; Grade 3: incapacitated and required assistance to treat symptoms; Grade 4: required medical attention or glucagon injection. During the scheduled visits, every patient was informed of laboratory measurement results and had access to physicians. Based on patients’ symptoms, outcome measures, and lifestyle diaries with or without self-monitoring records, physicians provided individualized therapeutic regimens, including necessary adjustments for medications in line with the Chinese guidelines for type 2 diabetes [20]. Insulin therapy would be considered if HbA1c was continuously higher than 7.5% despite optimized oral agents and lifestyle interventions. These patients were subsequently withdrawn from the trial.
3.3.
End points
The primary end point was the change in HbA1c from baseline. Secondary end points included the proportion of patients achieving HbA1c targets of <7.0% or 6.5%, incidence of hypoglycaemia and changes in BMI, waist circumference, fasting plasma glucose, triglyceride, total cholesterol, compliance to monitoring modality and quality of life. Compliance
Fig. 1 – Study-flow diagram.
181
was estimated by the average frequency of self-monitoring each month in either Group A or B.
3.4.
Statistical analysis
The study was designed to detect a 1% difference in HbA1c (1.5 SD) between the groups at a two-sided significance level of 0.05 with a power of 80%. To achieve the specified statistical power, at least 105 patients were required. HbA1c actual levels and changes from baseline were compared between groups using repeated measures analysis of variance. For secondary outcomes, comparisons of normally distributed variables were performed by repeated measures analysis of variance, partially distributed and qualitative variables were tested by Pearson x2-test, Kruskal–Wallis test, or Fisher’s exact tests. Data were analyzed using the SPSS version 13.0.
4.
Results
4.1.
Patient disposition and baseline characteristics
Between February 2009 and September 2009, 108 patients with non-insulin treated T2DM from two participating centers were randomized to one of three groups (Fig. 1). Five patients were lost to follow-up and this was equally distributed across the groups. No significant differences between groups in baseline personal characteristics or laboratory measurements existed (Table 1). The mean (SD) age was 54.2 (11.2) years, mean (SD) level of HbA1c was 8.6 (1.4)%, and median (interquartile) duration of diabetes was 3.0 (0.0–7.0) years. Range of the duration was wide from 0 to 19 years, because no limitation of duration had been set for eligibility assessment.
182
diabetes research and clinical practice 93 (2011) 179–186
Table 1 – Characteristics of participants at baseline (n = 108). Urine glucose group (n = 38)
Blood glucose group (n = 35)
Control group (n = 35)
p-Value*
26/12 53.13 9.27 24.96 3.61 94.50 7.96
20/15 53.03 13.77 24.67 3.34 90.37 7.63
23/12 57.26 9.71 25.45 4.67 94.99 11.07
0.475 0.256 0.733 0.067
13.9 72.2 13.9
5.9 79.4 14.7
4.0 76.0 20.0
0.677
21.1 31.6 47.4
11.4 34.3 54.3
11.1 33.3 55.6
0.773
Duration of diabetesa Laboratory measurements HbA1c (%) Fasting plasma glucose (mmol/L) Postprandial glucose (mmol/L) Triglyceride (mmol/L)a Total cholesterol (mmol/L)
1.0 (0.0–6.0)
4.0 (0.5–8.0)
3.0 (1.0–8.0)
0.155
8.8 1.4 8.60 1.97 15.89 3.25 1.78 (1.17–2.56) 4.57 0.91
8.5 1.1 9.33 3.23 15.51 2.75 1.51(1.04–2.15) 4.54 0.99
8.6 1.7 9.24 2.65 14.98 3.84 1.46 (0.95–2.20) 4.73 0.80
0.619 0.450 0.545 0.487 0.688
Quality of life (scores)
170.50 28.23
174.03 18.62
168.96 28.07
0.917
Personal characteristics Gender (M/F) Age (years) BMI (kg/m2) Waist (cm) Education level (%) Primary school High school College Occupational categories (%) Manual work Mental work Retire
Data are mean standard deviation (SD) or n (%). a Partially distributed variables are expressed as median (interquartile range). * p-Value represents differences between groups by simple analysis of variance, Pearson x2-test or Kruskal–Wallis test.
4.2.
HbA1c
A significant decrease in HbA1c was achieved within each group (Table 2). During the study period, both Groups A and B showed a constant reduction in HbA1c, whereas in the control group, an insignificant upward trend appeared towards the end of the study period (Fig. 2). At the final visit, Group A showed the largest reduction with a mean difference of (1.9%), followed by Group B with 1.5% and the control group with 1.0%. Between the groups, however, no significant differences were found neither in HbA1c levels ( p = 0.577) nor in the decrease of HbA1c ( p = 0.234). The mean difference in HbA1c over 6 months between the control and Group A was 0.9% (95% CI 0.2% to 1.9%) and between the control and Group B was 0.4% (95% CI 0.6% to 1.5%). At the end-point, 46.3% of patients achieved HbA1c <7.0% (58.3% Group A vs. 41.2% Group B vs. 36% control group, p = 0.172) and 32.6% achieved HbA1c 6.5% (38.9% Group A vs. 35.3% Group B vs. 20% control group, p = 0.277). Even though numerically there was a greater proportion of patients in Group A who reached both HbA1c targets, no significant between-group differences were observed.
4.3. Anthropometric, other laboratory determinations and life quality A significant reduction in levels of fasting plasma glucose from baseline to 6 months was observed in each of the three groups ( p < 0.05), with no statistical difference between groups at any time point (Table 2). Fig. 3 presents the fluctuation of fasting plasma glucose during the follow-up period. All three groups experienced an initial sharp decrease, and then Groups A and B fluctuated around 7 mmol/L, whereas the control group
showed a continuous upward trend with a significant increase at the 5th month. The mean difference in the control group between the 5th and 3rd months was 0.53 (95% CI 0.01 to 1.05) mmol/L ( p = 0.046). Table 2 demonstrates a significant reduction in BMI and waist circumferences for both Groups A and B ( p < 0.05), whereas the control group showed no change. Waist circumferences of patients in Group B were significantly lower than those in the control group at the 2nd, 4th and 6th months ( p < 0.05). Levels of triglyceride and total cholesterol were not different, either within each group or between groups. Significant increases in the scores for quality of life were found only in Groups A and B, but there was no statistical difference in the improved quality of life between groups ( p = 0.140).
4.4.
Safety and compliance
The average monitoring frequency over six months was 74.5 times per month in Group A compared to 58.6 times ( p < 0.05) in Group B. A significant and persistent adherence in Group A can be seen from the 2nd month of follow-up. Few patients reported hypoglycaemic episodes during the trial, with 2 patients in Group A, 1 in Group B and 2 in the control group (exact x2-test, p = 0.782). None of the five patients experienced more than grade 3 of hypoglycaemia.
4.5.
Pharmacological treatment
During this trial, a total of six different classes of oral glucoselowering drugs were prescribed to patients, with high rates of utilization for metformin, a-glucosidase inhibitors and sulfonylureas. Each class of drug was evenly distributed between
183
diabetes research and clinical practice 93 (2011) 179–186
Table 2 – Changes in HbA1c levels, fasting plasma glucose, BMI and waist, total cholesterol, triglyceride and scores for quality of life between baseline and 6 months (n = 103).
HbA1c (%) Baseline 6 months Mean difference (95% CI)b p-Valuey Fasting plasma glucose (mmol/L) Baseline 6 months Mean difference (95% CI) p-Valuey BMI (kg/m2) Baseline 6 months Mean difference (95% CI)b p-Valuey Waist circumference (cm) Baseline 6 months Mean difference (95% CI)b p-Valuey Total cholesterol (mmol/L) Baseline 6 months Mean difference (95% CI)b p-Valuey Triglyceride (mmol/L)a Baseline 6 months p-Valuey Quality of life (score) Baseline 6 months Mean difference (95% CI)b p-Valuey
p-Value*
Urine glucose group
Blood glucose group
8.8 1.4 6.9 1.2 1.9 (2.5 to 1.3) 0.000
8.5 1.1 7.0 0.9 1.5 (1.9 to 1.1) 0.000
8.5 1.7 7.4 1.1 1.0 (1.9 to 0.2) 0.016
8.52 1.98 6.95 1.64 1.57 (2.40 to 0.74) 0.001
9.36 3.27 7.22 1.71 2.14 (3.45 to 0.83) 0.002
9.30 2.75 7.61 1.72 1.70 (3.18 to 0.22) 0.027
24.71 3.47 24.29 3.47 0.42 (0.73 to 0.10) 0.012
24.61 3.37 23.67 2.83 0.94 (1.44 to 0.44) 0.001
25.90 4.52 25.83 4.56 0.07 (1.23 to 1.09) 0.901
93.97 7.61 89.29 7.72 4.68 (6.21 to 3.16) 0.000
90.52 7.69 87.85 7.72 2.67 (5.02 to 0.32) 0.027
95.62 11.27 93.74 8.56 1.92 (4.62 to 0.77) 0.153
4.51 0.88 4.39 0.79 0.13 (0.43 to 0.16) 0.365
4.59 0.96 4.45 0.85 0.15 (0.43 to 0.13) 0.292
4.64 0.76 4.63 1.06 0.01 (0.41 to 0.40) 0.963
1.78 (1.20 to 2.37) 1.39 (1.06 to 1.95) 0.062
1.54 (1.01 to 2.19) 1.22 (1.01 to 1.74) 0.345
1.38 (0.93 to 2.47) 1.15 (0.88 to 2.26) 0.418
0.531 0.544
169.86 25.60 185.08 22.12 15.22 (4.92 to 25.53) 0.005
174.06 18.86 187.47 22.27 13.41 (6.11 to 20.72) 0.001
172.24 24.84 176.28 27.37 4.04 (2.49 to 10.57) 0.214
0.750 0.184 0.140
Control group
0.577
0.419 0.753
0.147 0.122
0.023 0.165
0.336 0.812
Data are mean standard deviation (SD) or n (%). Partially distributed variables are expressed as median (interquartile range). b 95% confidence interval (CI) is for the mean difference between baseline and 6 months. * p-Value represents differences between groups by simple or repeated measures analysis of variance, or Kruskal–Wallis test. y p-Value represents differences within each group by Least Significant Difference (LSD), Friedman test, or paired-samples T test. a
the groups, either at baseline or at the final visit. No statistical difference between groups was found in the proportions of patients receiving pharmacological adjustments during follow-up ( p = 0.184). However, after adjustments of hypoglycaemic drugs, there was a difference in the use of combination therapy between the groups. At the end of the trial, none of the control group was prescribed a combination of three kinds of drugs, compared with 30.6% in Group A and 14.7% in Group B (exact x2-test, p = 0.004). 70.6% of the three-drug combination therapy combined metformin, a-glucosidase inhibitors and sulfonylureas.
5.
Discussion
During this 6-month trial, all patients experienced significant reductions in HbA1c and fasting plasma glucose. Selfmonitoring of urine glucose and self-monitoring of blood glucose shared comparable efficacy in glycaemic control, which appeared better than usual care without self monitoring, although no statistical significance was identified. BMI
and waist circumferences decreased significantly only in patients undertaking self-monitoring, which suggests a positive impact of self-monitoring on general metabolic status, probably by reinforcing moderations of lifestyle. On the other hand, triglyceride and total cholesterol levels showed no significant improvement either within or between groups at the end of the trial. This may be due to the instability of these markers which are influenced by diet, smoking, season or depression. Patients were more compliant with urine self monitoring than blood self monitoring, without affecting their quality of life or risk of hypoglycaemia. Among previous RCTs in non-insulin treated T2DM patients, only one study [21] was found comparing efficacy between urine self monitoring, blood self monitoring and usual care, in which mean decreases in HbA1c over six months were 0.1%, 0.4% and 0.5% respectively with no difference between each other, and Fontbonne et al. [21] concluded no definite advantage of regular self-monitoring. However, that trial had a drop-out rate of more than 20%, and patients were given no strategy for behavioral changes which indicates selfmonitoring not being fully utilized. Two early trials [13,14]
184
diabetes research and clinical practice 93 (2011) 179–186
Fig. 2 – Change in HbA1c over the 6-month study period. Urine glucose group (black); Blood glucose group (diagonal shade); Control group (white). Error bars indicate standard error (SE). * p < 0.05 compared to baseline within each group; y p < 0.05 compared to the 2nd month within each group.
Fig. 3 – Change in fasting plasma glucose over time. Urine glucose group (circles); Blood glucose group (squares); Control group (triangles). Error bars indicate standard error (SE).
demonstrated similar improvement in glycaemic control comparing urine to blood self monitoring, but one study had a small sample size of 54 participants, and the other was a crossover trial without an entirely clear mode of allocation. In 2006, Jansen et al. [15] performed a meta-analysis on 13 RCTs
which included the above three studies and showed that blood self monitoring was 88% more likely to be effective than urine self monitoring. Since included trials about urine monitoring were quite few and varied in internal validity quality, that conclusion still need further demonstration. The IN CONTOLtrial [22], designed to explore the effect of urine or blood self monitoring on diabetes related distress, is ongoing in the Netherlands. Difference in HbA1c is also one of the outcome measures which might provide new evidence for utilization of urine monitoring in glycaemic control, although the urine glucose was still tested semiquantitatively. There are relatively more studies assessing blood self monitoring in diabetes self-care, but these are varied in design and have shown conflicting results. The ASIA trial [23] reported a significant decrease in HbA1c level of 0.3% in blood glucose group compared with the control group, but the high drop-out rate of over 30% limits its validity. By contrast, the DiGEM trial [10] found no benefit of blood monitoring in glycaemic control; however, the baseline mean HbA1c was 7.5%, close to the target of ADA guidelines [4], which possibly have reduced its sensitivity to identify any positive result. The King-Drew Medical Center Trial [12] and the ESMON trial [11] are also recent large RCTs which reported improved HbA1c in all groups independent of blood self monitoring. But as some experts counterpointed [24–26], there was a lack of algorithms for patients’ or physicians’ response to self-monitoring readings in those RCTs; only if blood glucose monitoring is effectively translated into actions, could it be helpful in glycaemic control. The strengths of this study include the randomized controlled design, low drop-out rate (4.6%), identical diabetes self-management education to every participant, follow-up by consistent diabetes teams providing clinical feedback with equal attention, application of self-monitoring-guided diabetes management which included setting blood or urine glucose goals, interpreting self-monitoring readings for modification of lifestyle, and adjusting anti-diabetic drugs by physicians. Furthermore, urine glucose testing in our trial was quantitative which differed from previous studies. Compared to traditional urine dipsticks, this quantitative measurement appears more sensitive, objective and straightforward. Nevertheless, urine self monitoring should never be an alternative tool for blood self monitoring in patients with abnormal renal thresholds. As a result, renal threshold evaluation is necessary. Previous associated studies rarely took this into account when determining eligibity criteria [14,22], or simply made an estimation according to levels of urea nitrogen, creatinine [13], or occurrences of glucosuria, diabetic complications [20]. During the screening period of this trial, we further applied OGTT to the determination of renal thresholds, for insurance that urine self monitoring would be both reliable and safe to perform by patients. It is interesting to observe a significant reduction of HbA1c in the control group, probably owing to the systematic diabetes therapy in our trial, which consisted of structured education, lifestyle interventions, medications and regular clinical monitoring of HbA1c and fasting plasma glucose or simply the effect of being enrolled into a study. Despite such systematic therapy, we were unable to identify the advantage of self monitoring in further improvement of glycaemic
diabetes research and clinical practice 93 (2011) 179–186
control. Although the self monitoring groups showed lower HbA1c levels and a higher proportion of patients reaching HbA1c targets than the control group, no statistical significances were obtained. The weakness in detecting significance probably lies in the underestimated sample size. We enrolled 110 patients in order to detect a 1% difference in HbA1c between the groups, however, as the result suggests, only about 0.5% difference could be achieved. To significantly identify a difference, approximately 400 patients would have been required. Unlike some of the previous studies, physicians in this trial made prescriptions based on information available from an individual patient, rather than on a rigorous treatment algorithm. The distribution of oral anti-diabetic drugs in each group was uniform both at the entry and the last visit, which suggests that our strategy of treatment did not result in a pharmacological bias, especially bias of insulin secretagogues. Though significantly more patients in urine glucose group were prescribed with a three-drug combination therapy, it may suggest that, compared to no self-monitoring, urine self monitoring could provide more information for clinical diagnosis and optimum therapy. In conclusion, this pilot study evaluated self-monitoring policies for non-insulin treated type 2 diabetes. It showed that both self-monitoring of urine and blood glucose were effective though not statistically significant in glycaemic control, which suggests the need for further studies. Considering a greater numerical reduction in HbA1c, a higher percentage of patients reaching HbA1c targets, significantly better adherence than blood self monitoring, and low incidence of hypoglycaemia, self-monitoring of urine glucose will probably prove to be a specifically useful and effective tool in diabetes self-management in further large-scale and long term trials and can be considered a very practical tool especially in developing countries where the costs of blood glucose monitoring may be prohibitive.
Conflicts of interest The authors have a competing interest to declare. Rui-fang Bu and Zi-lin Sun have received funds for research from TANITA Corporation, Itabashi-ku, Tokyo, Japan.
Acknowledgements This study was sponsored by TANITA Corporation. We owe our sincere thanks to the local research teams and colleagues, especially C.H. Fan and Y.P. Wang from Wuxi People’s Hospital and X.H. Fang from Nanjing Zhongda Hospital. They contributed to the study organization, data collection and quality control. We would also like to thank Dr. Yang Lu from Shanghai Rundo Biotech Japan Co. Ltd. for providing document preparation and technology consultation during this trial. J.L. was responsible for collection and analysis of data and writing of the manuscript. R.F.B. and Z.L.S. were responsible for design of the experiment, analysis of data and writing of the manuscript, H.J., Y.W., Q.S.L., S.H.W., L.L., Z.L.X. and B.Q.Y. were responsible for collection of data and provision of advice.
185
references
[1] International Diabetes Federation (IDF), Diabetes atlas, fourth edition; 2009. [2] Song MJ. Diabetes mellitus and the importance of self-care. Cardiovasc Nurs 2010;25:93–8. [3] Khattab M, Khader YS, Al-Khawaldeh A, Ajlouni K. Factors associated with poor glycemic control among patients with type 2 diabetes. J Diabet Complications 2010;24:84–9. [4] American Diabetes Association. Standards of Medical Care in Diabetes. Diabetes Care 2010;33:S11–61. [5] International Diabetes Federation. Global guideline for type 2 diabetes. Brussels: International Diabetes Federation; 2005. [6] Karter AJ, Ackerson LM, Darbinian JA, D’Agostino Jr RB, Ferrara A, Liu J, et al. Self-monitoring of blood glucose levels and glycemic control: The Northern California Kaiser Permanente Diabetes Registry. Am J Med 2001;111:1–9. [7] Harris MI, National Health. Nutrition examination survey (NHANES III) frequency of blood glucose monitoring in relation to glycemic control in patients with type 2 diabetes. Diabetes Care 2001;24:979–82. [8] Davis WA, Bruce DG, Davis TM. Is self-monitoring of blood glucose appropriate for all type 2 diabetic patients? Diabetes Care 2006;29:1764–70. [9] Tengblad A, Grodzinsky E, Lindstro¨m K, Mo¨lstad S, Borgquist L, Ostgren CJ. Self-monitoring of blood glucose and glycaemic control in type 2 diabetes. Scand J Prim Health Care 2007;25:140–6. [10] Farmer A, Wade A, Goyder E, Yudkin P, French D, Craven A, et al. Impact of self monitoring of blood glucose in the management of patients with non-insulin treated diabetes: open parallel group randomized trial. BMJ 2007;335:132. [11] O’Kane MJ, Bunting B, Copeland M, Coates VE. Efficacy of self monitoring of blood glucose in patients with newly diagnosed type 2 diabetes (ESMON study): randomised controlled trial. BMJ 2008;336:1174–7. [12] Davidson MB, Castellanos M, Kain D, Duran P. The effect of self monitoring of blood glucose concentrations on glycated hemoglobin levels in diabetic patients not taking insulin: a blinded, randomized trial. Am J Med 2005;118:422–5. [13] Allen BT, DeLong ER, Feussner JR. Impact of glucose selfmonitoring on non-insulin-treated patients with type II diabetes mellitus. Randomized controlled trial comparing blood and urine testing. Diabetes Care 1990;13:1044–50. [14] Miles P, Everett J, Murphy J, Kerr D. Comparison of blood or urine testing by patients with newly diagnosed non-insulin dependent diabetes: patient survey after randomised crossover trial. BMJ 1997;315:348–9. [15] Jansen JP. Self-monitoring of glucose on type 2 diabetes mellitus: a Bayesian meta-analysis of direct and indirect comparisons. Curr Med Res Opin 2006;22:671–81. [16] Miyashita M, Ito N, Ikeda S, Murayama T, Oguma K, Kimura J. Development of urine glucose meter based on microplaner amperometric biosensor and its clinical application for self-monitoring of urine glucose. Biosens Bioelectron 2009;24:1336–40. [17] Ling Z. Practice of internal medicine, vol. 633. Beijing: People’s Medical Publishing House; 1990 (in Chinese). [18] Wang H. Laboratory diagnosis, vol.106. Beijing: People’s Medical Publishing House; 2001 (in Chinese). [19] The DCCT Research Group. Reliability and validity of a diabetes quality-of life measure for the diabetes control and complications trial (DCCT). Diabetes Care 1988;11:725–32. [20] Chinese Diabetes Society. Global guideline for type 2 diabetes. Chin J Endocrinol Metab 2008;24:2a-1-22.
186
diabetes research and clinical practice 93 (2011) 179–186
[21] Fontbonne A, Billault B, Acosta M, Percheron C, Varenne P, Besse A, et al. Is glucose self-monitoring beneficial in noninsulin-treated diabetic patients? Results of a randomized comparative trial. Diabet Metab 1989;15: 255–60. [22] Malanda UL, Bot SD, Kostense PJ, Snoek FJ, Dekker JM, Nijpels G. Effects of self-monitoring of glucose in noninsulin treated patients with type 2 diabetes: design of the IN CONTROL-trial. BMC Fam Pract 2009;10:26. [23] Guerci B, Drouin P, Grange V, Bougneres P, Fontaine P, Kerlan V, et al. Self-monitoring of blood glucose significantly improves metabolic control in patients with
type 2 diabetes mellitus: the auto-surveillance intervention active (ASIA) study. Diabet Metab 2003;29:587–94. [24] Ipp E, Aquino RL, Christenson P. Point: self-monitoring of blood glucose in type 2 diabetic patients not receiving insulin: the sanguine approach. Diabetes Care 2005;28:1528–30. [25] Kempf K, Neukirchen W, Martin S, Kolb H. Self-monitoring of blood glucose in type 2 diabetes: a new look at published trials. Diabetologia 2008;51:686–8. [26] Kolb H, Kempf K, Martin S, Stumvoll M, Landgraf R. On what evidence-base do we recommend self-monitoring of blood glucose? Diabetes Res Clin Pract 2010;87:150–6.
Contents lists available at ScienceDirect
Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres
Comparable efficacy of self-monitoring of quantitative urine glucose with self-monitoring of blood glucose on glycaemic control in non-insulin-treated type 2 diabetes§,§§ J. Lu a,1, R.F. Bu b,1, Z.L. Sun a,1,*, Q.S. Lu a, H. Jin a, Y. Wang a, S.H. Wang a, L. Li a, Z.L. Xie a, B.Q. Yang a a b
Institute of Diabetes, Zhongda Hospital, Medical School, Southeast University, Nanjing, China Department of Endocrinology and Metabolism, Wuxi People’s Hospital, Wuxi, China
article info
abstract
Article history:
Aim: To assess whether self-monitoring of quantitative urine glucose or blood glucose is
Received 11 January 2011
effective, convenient and safe for glycaemic control in non-insulin treated type 2 diabetes.
Received in revised form
Methods: Adults with non-insulin treated type 2 diabetes were recruited and randomized
10 April 2011
into three groups: Group A, self-monitoring with a quantitative urine glucose meter (n = 38);
Accepted 12 April 2011
Group B, selfmonitoring with a blood glucose meter (n = 35); Group C, the control group
Published on line 12 May 2011
without selfmonitoring (n = 35). All patients were followed up for six months, during which
Keywords:
Results: There was a significant decrease in HbA1c within each group ( p < 0.05). At the study
Self-monitoring of quantitative
conclusion, mean changes in HbA1c from baseline were 1.9% for Group A, 1.5% for Group
identical diabetes care was provided.
urine glucose
B and 1.0% for Group C, and the proportion of patients achieving HbA1c6.5% were 38.9%,
Self-monitoring of blood glucose
35.3% and 20.0% respectively. However, no significant differences between the groups were
Type 2 diabetes
found. The average monitoring frequency was significantly higher in Group A than in Group
Glycaemic control
B. The incidence of hypoglycaemia and quality of life scores were similar between the groups. Conclusions: This study suggests that self-monitoring of urine glucose has comparable efficacy on glycaemic control, and facilitates better compliance than blood self monitoring, without influencing the quality of life or risk of hypoglycaemia. # 2011 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Diabetes mellitus is one of the most common non-communicable diseases globally, and its related complications result in increasing disability, reduced life expectancy and enormous health costs for virtually every society [1]. For effective
management of diabetes, self-care must play an active role [2,3]. Apart from lifestyle intervention and medication adherence, self-monitoring of glucose is also an essential and integral part of self-care in diabetes. Two primary techniques available for patients include self-monitoring of blood glucose and self-monitoring of urine glucose.
§
This study is part of the Key Program of Jiangsu Natural Science Foundation (BK2010087). Eighth International Diabetes Federation Western Pacific Region Congress 2010, Bexco, Busan, Korea. * Corresponding author at: Institute of Diabetes, Zhongda Hospital, Medical School, Southeast University, No. 87 DingJiaQiao, Nanjing, Jiangsu Province 210009, China. E-mail address: [email protected] (Z.L. Sun).
§§
1 These authors contributed equally to this work. 0168-8227/$ – see front matter # 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2011.04.012
180
diabetes research and clinical practice 93 (2011) 179–186
Self-monitoring of blood glucose is generally accepted as a useful and successful tool for patients with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) requiring insulin [4,5]; however, evidence for its effectiveness in non-insulin treated T2DM patients is less clear-cut. A large cross-sectional study [6] with 23,153 patients, demonstrated a significant inverse relationship between blood self-monitoring frequency and HbA1c in all type 2 participants. Other observational trials [7– 9], with smaller samples but still more than 1000 participants, were unable to detect such a correlation. Randomized controlled trials (RCT), which provide a much higher level of evidence, also varied in results on the effectiveness of blood self monitoring [10–12]. Self-monitoring of urine glucose by urine dipsticks, was predominant in detecting episodes of hyperglycaemia before the invention of blood glucose meters. Early trials have shown conflicting results in the efficacy of urine self monitoring compared to blood self monitoring [13– 15], while in recent years, studies in this field have become much fewer, probably due to wider promotion of blood glucose monitoring, and to the limitations of urine testing including semiquantitative, retrospective and indirect interpretation. Nevertheless, considering its convenience, painlessness and affordability, urine glucose monitoring should not be completely given up, especially in low-income regions. Recently, a portable urine glucose meter has been developed which can quantitatively monitor with high sensitivity and rapid response urine glucose levels [16]. Since this device has overcome some disadvantages of traditional urine testing, its use may be helpful in non-insulin treated T2DM. Miyashita et al. [16] suggested its value in monitoring postprandial glucose. Yet there has been no study about the application of quantitative urine testing in routine diabetes care. The primary aim of this pilot study is to assess whether selfmonitoring of blood or urine glucose, compared to usual care without self monitoring, is effective in improving glycaemic control in non-insulin treated T2DM patients.
2.
Participants
Patients were considered eligible if they had T2DM, were aged between 18 and 75 years, were managed with diet and/or oral hypoglycaemic agents, had a HbA1c level 7.0% and postprandial glucose >11.0 mmol/L. The exclusion criteria included: use of insulin, rapidly progressing diabetic complications, severe concurrent illness that would limit life or require extensive treatment, abnormal renal threshold during screening, women in pregnancy or lactation, alcohol misuse and inability to follow trial procedures. An oral glucose tolerance test (OGTT) was used for the determination of renal threshold. At baseline, 30 min, 60 min, 120 min and 180 min of OGTT, blood samples were taken for measurements of plasma glucose; meanwhile urine samples at each time-point were collected immediately after blood collections and tested by a quantitative urine meter. When the value of urine testing turned from <50 mg/dl to 50 mg/dl, the corresponding plasma glucose level was referred to as one’s renal threshold. This was generally in a range of 8.9– 10.0 mmol/L [17,18]. An abnormal renal threshold was defined as a level higher or lower than this range.
3.
Materials and methods
3.1.
Design
This study was an open label, randomized, controlled trial conducted in two diabetes centers. Approval to conduct the trial was obtained from the Research and Ethics Committees of each study center, and all participants provided written informed consent. With a randomly generated allocation code from a computer program, eligible patients were randomized into three groups: Group A (urine glucose group): participants were provided with urine glucose meters (TANITA1 UG - 201) and were required to test their urine twice every day (fasting, 2 h after breakfast), with at least two extra tests each week (2 h after dinner); Group B (blood glucose group): participants were provided with blood glucose meters (LifeScan OneTouch1 Ultra EasyTM) and were required to monitor at the same frequency as Group A; Group C (control group): participants were asked not to perform any self-monitoring, but only provided with standardized usual care. All three groups received similar diary books in which they had to report changes about diet and exercises; besides that, Groups A and B were required to record the obtained values of urine or blood glucose. TANITA1 UG-201 contains a sensitive micro-planer biosensor, which permits a wide measurement range of 0– 2000 mg/dl and possesses good performances of repeatability, stability, and absence from interferential substances (ascorbic acid, acetaminophen) [16]. The handling of the meter is convenient and non-invasive, but requires regular calibrations once every 7–14 days.
3.2.
Study protocol
All patients had a one-week run-in period, during which structured diabetes self-management education was provided by nurse practitioners, dieticians, diabetes educators, and physicians. The educational sessions focused on: diabetes disease process, lifestyle behaviors, utilization of medications, as well as prevention and detection of complications. In addition, patients in Groups A and B were instructed in the technique of urine or blood glucose testing respectively, and were given advice on interpreting and applying the results to lifestyle modification. Group A was asked to aim for glucose levels of <50 mg/dl, while Group B aimed for glucose levels of 4.0–6.0 mmol/L before meals and 6.0–8.0 mmol/L two hours after meals. Whenever there was a problem about meters, patients were allowed to contact the research staff for a check or calibration. After the run-in, patients attended scheduled clinical visits, every four weeks for six months. At each visit, fasting blood samples were taken for measurements of blood glucose every month and of HbA1c, triglyceride and total cholesterol every two months. All the plasma examinations were performed in regional central laboratories. HbA1c was measured using an ion-exchange HPLC assay (Variant HbA1c program; Bio-Rad Laboratories, Hercules, CA, USA); other determinations were measured by enzymatic methods using automated techni-
diabetes research and clinical practice 93 (2011) 179–186
ques. In addition, the diabetes quality-of-life (DQOL) developed by DCCT group [19] was used to evaluate the life quality at baseline and 6 months; weight and waist circumference were reviewed every other month. At each visit, information was collected on medications, hypoglycaemic episodes and adverse reactions or complications from all patients, whereas compliance, measured by completeness of urine- or blood-test records was only assessed in Groups A and B. Self-reported hypoglycaemic episodes were categorized as: Grade 1: transitory symptoms not affecting normal activity; Grade 2: temporarily incapacitated but able to treat symptoms without help; Grade 3: incapacitated and required assistance to treat symptoms; Grade 4: required medical attention or glucagon injection. During the scheduled visits, every patient was informed of laboratory measurement results and had access to physicians. Based on patients’ symptoms, outcome measures, and lifestyle diaries with or without self-monitoring records, physicians provided individualized therapeutic regimens, including necessary adjustments for medications in line with the Chinese guidelines for type 2 diabetes [20]. Insulin therapy would be considered if HbA1c was continuously higher than 7.5% despite optimized oral agents and lifestyle interventions. These patients were subsequently withdrawn from the trial.
3.3.
End points
The primary end point was the change in HbA1c from baseline. Secondary end points included the proportion of patients achieving HbA1c targets of <7.0% or 6.5%, incidence of hypoglycaemia and changes in BMI, waist circumference, fasting plasma glucose, triglyceride, total cholesterol, compliance to monitoring modality and quality of life. Compliance
Fig. 1 – Study-flow diagram.
181
was estimated by the average frequency of self-monitoring each month in either Group A or B.
3.4.
Statistical analysis
The study was designed to detect a 1% difference in HbA1c (1.5 SD) between the groups at a two-sided significance level of 0.05 with a power of 80%. To achieve the specified statistical power, at least 105 patients were required. HbA1c actual levels and changes from baseline were compared between groups using repeated measures analysis of variance. For secondary outcomes, comparisons of normally distributed variables were performed by repeated measures analysis of variance, partially distributed and qualitative variables were tested by Pearson x2-test, Kruskal–Wallis test, or Fisher’s exact tests. Data were analyzed using the SPSS version 13.0.
4.
Results
4.1.
Patient disposition and baseline characteristics
Between February 2009 and September 2009, 108 patients with non-insulin treated T2DM from two participating centers were randomized to one of three groups (Fig. 1). Five patients were lost to follow-up and this was equally distributed across the groups. No significant differences between groups in baseline personal characteristics or laboratory measurements existed (Table 1). The mean (SD) age was 54.2 (11.2) years, mean (SD) level of HbA1c was 8.6 (1.4)%, and median (interquartile) duration of diabetes was 3.0 (0.0–7.0) years. Range of the duration was wide from 0 to 19 years, because no limitation of duration had been set for eligibility assessment.
182
diabetes research and clinical practice 93 (2011) 179–186
Table 1 – Characteristics of participants at baseline (n = 108). Urine glucose group (n = 38)
Blood glucose group (n = 35)
Control group (n = 35)
p-Value*
26/12 53.13 9.27 24.96 3.61 94.50 7.96
20/15 53.03 13.77 24.67 3.34 90.37 7.63
23/12 57.26 9.71 25.45 4.67 94.99 11.07
0.475 0.256 0.733 0.067
13.9 72.2 13.9
5.9 79.4 14.7
4.0 76.0 20.0
0.677
21.1 31.6 47.4
11.4 34.3 54.3
11.1 33.3 55.6
0.773
Duration of diabetesa Laboratory measurements HbA1c (%) Fasting plasma glucose (mmol/L) Postprandial glucose (mmol/L) Triglyceride (mmol/L)a Total cholesterol (mmol/L)
1.0 (0.0–6.0)
4.0 (0.5–8.0)
3.0 (1.0–8.0)
0.155
8.8 1.4 8.60 1.97 15.89 3.25 1.78 (1.17–2.56) 4.57 0.91
8.5 1.1 9.33 3.23 15.51 2.75 1.51(1.04–2.15) 4.54 0.99
8.6 1.7 9.24 2.65 14.98 3.84 1.46 (0.95–2.20) 4.73 0.80
0.619 0.450 0.545 0.487 0.688
Quality of life (scores)
170.50 28.23
174.03 18.62
168.96 28.07
0.917
Personal characteristics Gender (M/F) Age (years) BMI (kg/m2) Waist (cm) Education level (%) Primary school High school College Occupational categories (%) Manual work Mental work Retire
Data are mean standard deviation (SD) or n (%). a Partially distributed variables are expressed as median (interquartile range). * p-Value represents differences between groups by simple analysis of variance, Pearson x2-test or Kruskal–Wallis test.
4.2.
HbA1c
A significant decrease in HbA1c was achieved within each group (Table 2). During the study period, both Groups A and B showed a constant reduction in HbA1c, whereas in the control group, an insignificant upward trend appeared towards the end of the study period (Fig. 2). At the final visit, Group A showed the largest reduction with a mean difference of (1.9%), followed by Group B with 1.5% and the control group with 1.0%. Between the groups, however, no significant differences were found neither in HbA1c levels ( p = 0.577) nor in the decrease of HbA1c ( p = 0.234). The mean difference in HbA1c over 6 months between the control and Group A was 0.9% (95% CI 0.2% to 1.9%) and between the control and Group B was 0.4% (95% CI 0.6% to 1.5%). At the end-point, 46.3% of patients achieved HbA1c <7.0% (58.3% Group A vs. 41.2% Group B vs. 36% control group, p = 0.172) and 32.6% achieved HbA1c 6.5% (38.9% Group A vs. 35.3% Group B vs. 20% control group, p = 0.277). Even though numerically there was a greater proportion of patients in Group A who reached both HbA1c targets, no significant between-group differences were observed.
4.3. Anthropometric, other laboratory determinations and life quality A significant reduction in levels of fasting plasma glucose from baseline to 6 months was observed in each of the three groups ( p < 0.05), with no statistical difference between groups at any time point (Table 2). Fig. 3 presents the fluctuation of fasting plasma glucose during the follow-up period. All three groups experienced an initial sharp decrease, and then Groups A and B fluctuated around 7 mmol/L, whereas the control group
showed a continuous upward trend with a significant increase at the 5th month. The mean difference in the control group between the 5th and 3rd months was 0.53 (95% CI 0.01 to 1.05) mmol/L ( p = 0.046). Table 2 demonstrates a significant reduction in BMI and waist circumferences for both Groups A and B ( p < 0.05), whereas the control group showed no change. Waist circumferences of patients in Group B were significantly lower than those in the control group at the 2nd, 4th and 6th months ( p < 0.05). Levels of triglyceride and total cholesterol were not different, either within each group or between groups. Significant increases in the scores for quality of life were found only in Groups A and B, but there was no statistical difference in the improved quality of life between groups ( p = 0.140).
4.4.
Safety and compliance
The average monitoring frequency over six months was 74.5 times per month in Group A compared to 58.6 times ( p < 0.05) in Group B. A significant and persistent adherence in Group A can be seen from the 2nd month of follow-up. Few patients reported hypoglycaemic episodes during the trial, with 2 patients in Group A, 1 in Group B and 2 in the control group (exact x2-test, p = 0.782). None of the five patients experienced more than grade 3 of hypoglycaemia.
4.5.
Pharmacological treatment
During this trial, a total of six different classes of oral glucoselowering drugs were prescribed to patients, with high rates of utilization for metformin, a-glucosidase inhibitors and sulfonylureas. Each class of drug was evenly distributed between
183
diabetes research and clinical practice 93 (2011) 179–186
Table 2 – Changes in HbA1c levels, fasting plasma glucose, BMI and waist, total cholesterol, triglyceride and scores for quality of life between baseline and 6 months (n = 103).
HbA1c (%) Baseline 6 months Mean difference (95% CI)b p-Valuey Fasting plasma glucose (mmol/L) Baseline 6 months Mean difference (95% CI) p-Valuey BMI (kg/m2) Baseline 6 months Mean difference (95% CI)b p-Valuey Waist circumference (cm) Baseline 6 months Mean difference (95% CI)b p-Valuey Total cholesterol (mmol/L) Baseline 6 months Mean difference (95% CI)b p-Valuey Triglyceride (mmol/L)a Baseline 6 months p-Valuey Quality of life (score) Baseline 6 months Mean difference (95% CI)b p-Valuey
p-Value*
Urine glucose group
Blood glucose group
8.8 1.4 6.9 1.2 1.9 (2.5 to 1.3) 0.000
8.5 1.1 7.0 0.9 1.5 (1.9 to 1.1) 0.000
8.5 1.7 7.4 1.1 1.0 (1.9 to 0.2) 0.016
8.52 1.98 6.95 1.64 1.57 (2.40 to 0.74) 0.001
9.36 3.27 7.22 1.71 2.14 (3.45 to 0.83) 0.002
9.30 2.75 7.61 1.72 1.70 (3.18 to 0.22) 0.027
24.71 3.47 24.29 3.47 0.42 (0.73 to 0.10) 0.012
24.61 3.37 23.67 2.83 0.94 (1.44 to 0.44) 0.001
25.90 4.52 25.83 4.56 0.07 (1.23 to 1.09) 0.901
93.97 7.61 89.29 7.72 4.68 (6.21 to 3.16) 0.000
90.52 7.69 87.85 7.72 2.67 (5.02 to 0.32) 0.027
95.62 11.27 93.74 8.56 1.92 (4.62 to 0.77) 0.153
4.51 0.88 4.39 0.79 0.13 (0.43 to 0.16) 0.365
4.59 0.96 4.45 0.85 0.15 (0.43 to 0.13) 0.292
4.64 0.76 4.63 1.06 0.01 (0.41 to 0.40) 0.963
1.78 (1.20 to 2.37) 1.39 (1.06 to 1.95) 0.062
1.54 (1.01 to 2.19) 1.22 (1.01 to 1.74) 0.345
1.38 (0.93 to 2.47) 1.15 (0.88 to 2.26) 0.418
0.531 0.544
169.86 25.60 185.08 22.12 15.22 (4.92 to 25.53) 0.005
174.06 18.86 187.47 22.27 13.41 (6.11 to 20.72) 0.001
172.24 24.84 176.28 27.37 4.04 (2.49 to 10.57) 0.214
0.750 0.184 0.140
Control group
0.577
0.419 0.753
0.147 0.122
0.023 0.165
0.336 0.812
Data are mean standard deviation (SD) or n (%). Partially distributed variables are expressed as median (interquartile range). b 95% confidence interval (CI) is for the mean difference between baseline and 6 months. * p-Value represents differences between groups by simple or repeated measures analysis of variance, or Kruskal–Wallis test. y p-Value represents differences within each group by Least Significant Difference (LSD), Friedman test, or paired-samples T test. a
the groups, either at baseline or at the final visit. No statistical difference between groups was found in the proportions of patients receiving pharmacological adjustments during follow-up ( p = 0.184). However, after adjustments of hypoglycaemic drugs, there was a difference in the use of combination therapy between the groups. At the end of the trial, none of the control group was prescribed a combination of three kinds of drugs, compared with 30.6% in Group A and 14.7% in Group B (exact x2-test, p = 0.004). 70.6% of the three-drug combination therapy combined metformin, a-glucosidase inhibitors and sulfonylureas.
5.
Discussion
During this 6-month trial, all patients experienced significant reductions in HbA1c and fasting plasma glucose. Selfmonitoring of urine glucose and self-monitoring of blood glucose shared comparable efficacy in glycaemic control, which appeared better than usual care without self monitoring, although no statistical significance was identified. BMI
and waist circumferences decreased significantly only in patients undertaking self-monitoring, which suggests a positive impact of self-monitoring on general metabolic status, probably by reinforcing moderations of lifestyle. On the other hand, triglyceride and total cholesterol levels showed no significant improvement either within or between groups at the end of the trial. This may be due to the instability of these markers which are influenced by diet, smoking, season or depression. Patients were more compliant with urine self monitoring than blood self monitoring, without affecting their quality of life or risk of hypoglycaemia. Among previous RCTs in non-insulin treated T2DM patients, only one study [21] was found comparing efficacy between urine self monitoring, blood self monitoring and usual care, in which mean decreases in HbA1c over six months were 0.1%, 0.4% and 0.5% respectively with no difference between each other, and Fontbonne et al. [21] concluded no definite advantage of regular self-monitoring. However, that trial had a drop-out rate of more than 20%, and patients were given no strategy for behavioral changes which indicates selfmonitoring not being fully utilized. Two early trials [13,14]
184
diabetes research and clinical practice 93 (2011) 179–186
Fig. 2 – Change in HbA1c over the 6-month study period. Urine glucose group (black); Blood glucose group (diagonal shade); Control group (white). Error bars indicate standard error (SE). * p < 0.05 compared to baseline within each group; y p < 0.05 compared to the 2nd month within each group.
Fig. 3 – Change in fasting plasma glucose over time. Urine glucose group (circles); Blood glucose group (squares); Control group (triangles). Error bars indicate standard error (SE).
demonstrated similar improvement in glycaemic control comparing urine to blood self monitoring, but one study had a small sample size of 54 participants, and the other was a crossover trial without an entirely clear mode of allocation. In 2006, Jansen et al. [15] performed a meta-analysis on 13 RCTs
which included the above three studies and showed that blood self monitoring was 88% more likely to be effective than urine self monitoring. Since included trials about urine monitoring were quite few and varied in internal validity quality, that conclusion still need further demonstration. The IN CONTOLtrial [22], designed to explore the effect of urine or blood self monitoring on diabetes related distress, is ongoing in the Netherlands. Difference in HbA1c is also one of the outcome measures which might provide new evidence for utilization of urine monitoring in glycaemic control, although the urine glucose was still tested semiquantitatively. There are relatively more studies assessing blood self monitoring in diabetes self-care, but these are varied in design and have shown conflicting results. The ASIA trial [23] reported a significant decrease in HbA1c level of 0.3% in blood glucose group compared with the control group, but the high drop-out rate of over 30% limits its validity. By contrast, the DiGEM trial [10] found no benefit of blood monitoring in glycaemic control; however, the baseline mean HbA1c was 7.5%, close to the target of ADA guidelines [4], which possibly have reduced its sensitivity to identify any positive result. The King-Drew Medical Center Trial [12] and the ESMON trial [11] are also recent large RCTs which reported improved HbA1c in all groups independent of blood self monitoring. But as some experts counterpointed [24–26], there was a lack of algorithms for patients’ or physicians’ response to self-monitoring readings in those RCTs; only if blood glucose monitoring is effectively translated into actions, could it be helpful in glycaemic control. The strengths of this study include the randomized controlled design, low drop-out rate (4.6%), identical diabetes self-management education to every participant, follow-up by consistent diabetes teams providing clinical feedback with equal attention, application of self-monitoring-guided diabetes management which included setting blood or urine glucose goals, interpreting self-monitoring readings for modification of lifestyle, and adjusting anti-diabetic drugs by physicians. Furthermore, urine glucose testing in our trial was quantitative which differed from previous studies. Compared to traditional urine dipsticks, this quantitative measurement appears more sensitive, objective and straightforward. Nevertheless, urine self monitoring should never be an alternative tool for blood self monitoring in patients with abnormal renal thresholds. As a result, renal threshold evaluation is necessary. Previous associated studies rarely took this into account when determining eligibity criteria [14,22], or simply made an estimation according to levels of urea nitrogen, creatinine [13], or occurrences of glucosuria, diabetic complications [20]. During the screening period of this trial, we further applied OGTT to the determination of renal thresholds, for insurance that urine self monitoring would be both reliable and safe to perform by patients. It is interesting to observe a significant reduction of HbA1c in the control group, probably owing to the systematic diabetes therapy in our trial, which consisted of structured education, lifestyle interventions, medications and regular clinical monitoring of HbA1c and fasting plasma glucose or simply the effect of being enrolled into a study. Despite such systematic therapy, we were unable to identify the advantage of self monitoring in further improvement of glycaemic
diabetes research and clinical practice 93 (2011) 179–186
control. Although the self monitoring groups showed lower HbA1c levels and a higher proportion of patients reaching HbA1c targets than the control group, no statistical significances were obtained. The weakness in detecting significance probably lies in the underestimated sample size. We enrolled 110 patients in order to detect a 1% difference in HbA1c between the groups, however, as the result suggests, only about 0.5% difference could be achieved. To significantly identify a difference, approximately 400 patients would have been required. Unlike some of the previous studies, physicians in this trial made prescriptions based on information available from an individual patient, rather than on a rigorous treatment algorithm. The distribution of oral anti-diabetic drugs in each group was uniform both at the entry and the last visit, which suggests that our strategy of treatment did not result in a pharmacological bias, especially bias of insulin secretagogues. Though significantly more patients in urine glucose group were prescribed with a three-drug combination therapy, it may suggest that, compared to no self-monitoring, urine self monitoring could provide more information for clinical diagnosis and optimum therapy. In conclusion, this pilot study evaluated self-monitoring policies for non-insulin treated type 2 diabetes. It showed that both self-monitoring of urine and blood glucose were effective though not statistically significant in glycaemic control, which suggests the need for further studies. Considering a greater numerical reduction in HbA1c, a higher percentage of patients reaching HbA1c targets, significantly better adherence than blood self monitoring, and low incidence of hypoglycaemia, self-monitoring of urine glucose will probably prove to be a specifically useful and effective tool in diabetes self-management in further large-scale and long term trials and can be considered a very practical tool especially in developing countries where the costs of blood glucose monitoring may be prohibitive.
Conflicts of interest The authors have a competing interest to declare. Rui-fang Bu and Zi-lin Sun have received funds for research from TANITA Corporation, Itabashi-ku, Tokyo, Japan.
Acknowledgements This study was sponsored by TANITA Corporation. We owe our sincere thanks to the local research teams and colleagues, especially C.H. Fan and Y.P. Wang from Wuxi People’s Hospital and X.H. Fang from Nanjing Zhongda Hospital. They contributed to the study organization, data collection and quality control. We would also like to thank Dr. Yang Lu from Shanghai Rundo Biotech Japan Co. Ltd. for providing document preparation and technology consultation during this trial. J.L. was responsible for collection and analysis of data and writing of the manuscript. R.F.B. and Z.L.S. were responsible for design of the experiment, analysis of data and writing of the manuscript, H.J., Y.W., Q.S.L., S.H.W., L.L., Z.L.X. and B.Q.Y. were responsible for collection of data and provision of advice.
185
references
[1] International Diabetes Federation (IDF), Diabetes atlas, fourth edition; 2009. [2] Song MJ. Diabetes mellitus and the importance of self-care. Cardiovasc Nurs 2010;25:93–8. [3] Khattab M, Khader YS, Al-Khawaldeh A, Ajlouni K. Factors associated with poor glycemic control among patients with type 2 diabetes. J Diabet Complications 2010;24:84–9. [4] American Diabetes Association. Standards of Medical Care in Diabetes. Diabetes Care 2010;33:S11–61. [5] International Diabetes Federation. Global guideline for type 2 diabetes. Brussels: International Diabetes Federation; 2005. [6] Karter AJ, Ackerson LM, Darbinian JA, D’Agostino Jr RB, Ferrara A, Liu J, et al. Self-monitoring of blood glucose levels and glycemic control: The Northern California Kaiser Permanente Diabetes Registry. Am J Med 2001;111:1–9. [7] Harris MI, National Health. Nutrition examination survey (NHANES III) frequency of blood glucose monitoring in relation to glycemic control in patients with type 2 diabetes. Diabetes Care 2001;24:979–82. [8] Davis WA, Bruce DG, Davis TM. Is self-monitoring of blood glucose appropriate for all type 2 diabetic patients? Diabetes Care 2006;29:1764–70. [9] Tengblad A, Grodzinsky E, Lindstro¨m K, Mo¨lstad S, Borgquist L, Ostgren CJ. Self-monitoring of blood glucose and glycaemic control in type 2 diabetes. Scand J Prim Health Care 2007;25:140–6. [10] Farmer A, Wade A, Goyder E, Yudkin P, French D, Craven A, et al. Impact of self monitoring of blood glucose in the management of patients with non-insulin treated diabetes: open parallel group randomized trial. BMJ 2007;335:132. [11] O’Kane MJ, Bunting B, Copeland M, Coates VE. Efficacy of self monitoring of blood glucose in patients with newly diagnosed type 2 diabetes (ESMON study): randomised controlled trial. BMJ 2008;336:1174–7. [12] Davidson MB, Castellanos M, Kain D, Duran P. The effect of self monitoring of blood glucose concentrations on glycated hemoglobin levels in diabetic patients not taking insulin: a blinded, randomized trial. Am J Med 2005;118:422–5. [13] Allen BT, DeLong ER, Feussner JR. Impact of glucose selfmonitoring on non-insulin-treated patients with type II diabetes mellitus. Randomized controlled trial comparing blood and urine testing. Diabetes Care 1990;13:1044–50. [14] Miles P, Everett J, Murphy J, Kerr D. Comparison of blood or urine testing by patients with newly diagnosed non-insulin dependent diabetes: patient survey after randomised crossover trial. BMJ 1997;315:348–9. [15] Jansen JP. Self-monitoring of glucose on type 2 diabetes mellitus: a Bayesian meta-analysis of direct and indirect comparisons. Curr Med Res Opin 2006;22:671–81. [16] Miyashita M, Ito N, Ikeda S, Murayama T, Oguma K, Kimura J. Development of urine glucose meter based on microplaner amperometric biosensor and its clinical application for self-monitoring of urine glucose. Biosens Bioelectron 2009;24:1336–40. [17] Ling Z. Practice of internal medicine, vol. 633. Beijing: People’s Medical Publishing House; 1990 (in Chinese). [18] Wang H. Laboratory diagnosis, vol.106. Beijing: People’s Medical Publishing House; 2001 (in Chinese). [19] The DCCT Research Group. Reliability and validity of a diabetes quality-of life measure for the diabetes control and complications trial (DCCT). Diabetes Care 1988;11:725–32. [20] Chinese Diabetes Society. Global guideline for type 2 diabetes. Chin J Endocrinol Metab 2008;24:2a-1-22.
186
diabetes research and clinical practice 93 (2011) 179–186
[21] Fontbonne A, Billault B, Acosta M, Percheron C, Varenne P, Besse A, et al. Is glucose self-monitoring beneficial in noninsulin-treated diabetic patients? Results of a randomized comparative trial. Diabet Metab 1989;15: 255–60. [22] Malanda UL, Bot SD, Kostense PJ, Snoek FJ, Dekker JM, Nijpels G. Effects of self-monitoring of glucose in noninsulin treated patients with type 2 diabetes: design of the IN CONTROL-trial. BMC Fam Pract 2009;10:26. [23] Guerci B, Drouin P, Grange V, Bougneres P, Fontaine P, Kerlan V, et al. Self-monitoring of blood glucose significantly improves metabolic control in patients with
type 2 diabetes mellitus: the auto-surveillance intervention active (ASIA) study. Diabet Metab 2003;29:587–94. [24] Ipp E, Aquino RL, Christenson P. Point: self-monitoring of blood glucose in type 2 diabetic patients not receiving insulin: the sanguine approach. Diabetes Care 2005;28:1528–30. [25] Kempf K, Neukirchen W, Martin S, Kolb H. Self-monitoring of blood glucose in type 2 diabetes: a new look at published trials. Diabetologia 2008;51:686–8. [26] Kolb H, Kempf K, Martin S, Stumvoll M, Landgraf R. On what evidence-base do we recommend self-monitoring of blood glucose? Diabetes Res Clin Pract 2010;87:150–6.