This article is quoted: He Gong, Zhao Songqing. Chinese General Medicine[J], 2022, 25(05): 589-594,602 doi:10.12114/j.issn.1007-9572.2021.01.409
HEYan, ZHAOSongqing. Influencing Factors of Glycemic Variability in Type 1 Diabetes Patients. Chinese General Practice[J], 2022, 25(05): 589-594,602 doi:10.12114/j.issn.1007-9572.2021.01.409
According to data released by the International Diabetes Federation, about 463 million people worldwide suffered from diabetes in 2019, of which about 116 million were in the mainland, ranking first in the world [1]. Patients with type 1 diabetes mellitus (T1DM) in mainland China account for 4.7% to 8.5% of the total number of diabetics, and are increasing at a rate of 3% to 5% per year [2]. T1DM is an autoimmune disorder that causes pancreatic β cell destruction and insulin deficiency, making it more difficult to meet blood glucose standards [3]. To control blood glucose in patients with T1DM and to avoid symptoms of hypoglycemia, it is essential to monitor the patient's blood glucose [4]. Flash glucose monitoring system (FGMS) is a new sensor-based scanning glucose monitoring system, which has the same principle as the traditional continuous glucose monitoring (CGM) system (all reflect the blood glucose changes of the person being measured by continuously detecting interstitial fluid glucose levels), but it is simpler than the traditional CGM operation method. Longer probe life and more affordable price make it more suitable for long-term, wide-ranging applications [5,6].
In the past, glycosylated hemoglobin (HbA1c) levels were considered critical to blood glucose management in patients with diabetes [7], but studies of the Diabetes Control and Complications Trial (DCCT) have shown that blood glucose fluctuations have a more profound effect on the disease than HbA1c levels [8,9]. Blood glucose fluctuations, also known as blood glucose variability, refer to the unstable state in which blood glucose levels fluctuate between the high and low values of their fluctuations [8]. Multiple studies have confirmed that blood glucose fluctuations are strongly associated with complications of large blood vessels and microvascular stenosis in diabetes mellitus [10,11,12,13]. Therefore, it is important to find the factors that lead to blood glucose fluctuations in patients and to give targeted care measures for these factors to prevent blood glucose fluctuations. Previous studies of factors influencing blood glucose fluctuations have been most common in patients with type 2 diabetes mellitus (T2DM), and T1DM influencing factors have been rarely reported [12,14]. Therefore, this study will apply FGMS to analyze the influencing factors of blood glucose fluctuations in patients with T1DM.
1 Objects and methods
1.1 Research Subjects
From May 2019 to April 2020, 85 patients with T1DM who were hospitalized in the Department of Endocrinology of the First Hospital of Huai'an Affiliated to Nanjing Medical University were selected by convenient sampling method. This study was approved by the Ethics Committee of the First Hospital of Huai'an Affiliated to Nanjing Medical University, and the patients signed an informed consent form.
1.2 Criteria for Inclusion and Exclusion
Inclusion criteria: (1) Meet the 1999 WHO diagnostic criteria for T1DM [15] ;(2) have normal comprehension and communication skills. Exclusion criteria: (1) patients who are using other types of CGM devices; (2) patients with severe diseases (severe heart, brain, liver, kidney dysfunction, etc.) ;(3) pregnant women; (4) reluctance to wear FGMS; (5) allergies to FGMS probe material.
1.3 Research Methodology
1.3.1 Data Collection
Collect baseline and clinical data from all patients. Baseline data includes: gender, age, course of diabetes, marital status, level of education, smoking (whether the patient currently smokes), and alcohol consumption (whether the patient is currently drinking). Clinical data include: body mass index (BMI), lumbar-to-hip ratio (WHR), systolic blood pressure (SBP), diastolic blood pressure (DBP), most recent HbA1c value, total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), and urine microalbumin/urine creatinine ratio (UACR).
1.3.2 Blood glucose data acquisition
Patients begin to wear the transient ambulatory glucose meter probe (Freestyle Libre H, produced by Abbott Trading (Shanghai) Co., Ltd.) on the outside of the upper arm 24 to 48 h after admission and continue to wear it for 10 to 14 days. The probe collects data on changes in the concentration of fluid in the patient's tissues and displays the data on the receiver via wireless transmission. If the patient is not scanned, the blood glucose data of the patient who was not collected in time for every 15 min is stored in the probe, and can be extracted by the receiver and supporting software in the later stage. The probe is calibrated at the factory and is no longer calibrated during wear. The blood glucose fluctuation indicators include: mean blood glucose concentration (MEAN), blood glucose within normal range (TIR), etc., and then calculate the standard deviation of blood glucose (SD) and the average blood glucose fluctuation range (MAGE) based on the FGMS recorded data, and the calculation method refers to the "China Ambulatory Blood Glucose Monitoring Clinical Application Guide (2012 Edition)"[16]. The MEDIA values of all patients were recorded, the average value was 0.82 mmol/L, and the patients were divided into the group with low blood glucose fluctuations (MAGE<0.82 mmol/L) and the group with high blood glucose fluctuations (MAGE≥0.82 mmol/L) according to whether the patients' AGE was higher than the overall mean of 0.82 mmol/L.
1.3.3 The Summary of Diabetes Self-Care Activities (SDSCA)
The SDSCA was developed by TOUR ET et al. [17] to assess the self-management behavior of diabetic patients, and then by Wan Qiaoqin et al. [18] Chinese, which evaluates the patient's self-management behavior activities by the number of days in the previous week when the patient performs the specified behavior, including the patient's diet control, regular exercise, blood glucose monitoring, foot care, and medication in accordance with the doctor's instructions. There are 11 entries in the scale, except for entry 4, which is a reverse score, and the rest is a positive score. The total score of the scale is 0 to 77 points, and the higher the score, the better the patient's self-management behavior. SDSCA's Cronbach's α coefficient is 0.620 and the retest reliability is 0.830.
1.3.4 Diabetes Empowerment Scale-Short Form (DES-SF)
DES-SF was developed by ANDERSON et al. [19] to assess the potential of self-management responsibilities in diabetics, and later chineseized by Hu Beibei et al. [20]. The scale has a total of 8 entries, using the Likert 5-level scoring method, from "very disagree" to "very agree" to give 1 to 5 points respectively, the total score is the sum of the 8 items, the higher the score, the higher the patient's self-management potential. DES-SF Chinese Edition cronbach's α coefficient of 0.848 and retest reliability of 0.817.
1.4 Statistical methods
SPSS 24.0 software was used for statistics and analysis. The measurement data that conform to the normal distribution are expressed as (x±s), and the comparison between the two groups is expressed by the independent sample t-test; the measurement data that do not conform to the normal distribution are expressed as M (P25, P75), and the comparison between the two groups is non-parametric test; the counting data is expressed as a relative number, and the comparison between the groups is χ2 test. Multiple linear regression analysis was used to explore the influencing factors affecting blood glucose fluctuations in patients with T1DM. The difference in P<0.05 is statistically significant.
2 Results
2.1 Comparison of baseline data from two patient groups
There were 40 patients in the low blood glucose fluctuation group and 45 patients in the high blood glucose fluctuation group. There was no statistical significance in the comparison of sex, marital status, educational level, smoking history ratio and drinking history between the two groups (P>0.05), and the difference was statistically significant in the age and diabetes course of the two groups (P<0.05). See Table 1.
Table 1 Basic clinical characteristics between two groups
2.2 Comparison of clinical data of two groups of patients
The differences were not statistically significant (P>0.05) in BMI, WHR, SBP, DBP, TC, HDL-C, LDL-C, and eGFR in the two groups( and HbA1c, TG, UACR, MEAN, SD, TIR, SDSCA scores, and DES-SF scores were statistically significant (P<0.05). See Table 2.
Table 2 Comparison of clinical data between two groups
2.3 Multiple linear regression analysis of factors influencing blood glucose fluctuations in patients with T1DM
MeAN (Assignment: Measured Value), SD (Assignment: Measured Value), TIR (Assignment: Measured Value), MAGE (Assignment: Measured Value), Age (Assignment: Measured Value), Diabetes Course (Assignment: Measured Value), BMI (Assignment: Measured Value), WHR (Assignment: Measured Value), HbA1c (Assignment: Measured Value), TC (Assignment: Measured Value), TG (Assignment: Measured Value), HDL-C (Assignment: Measured Value), HDL-C (Assignment: Measured Value), LDL-C (assignment: measured value), eGFR (assignment: measured value), UACR (assignment: measured value), SDSCA dimension score (assignment: measured value), SDSCA dimension total score (assignment: measured value), DES-SF entry score (assignment: measured value), DES-SF total score (assignment: measured value) for independent variables to conduct multiple linear regression analysis, the results show that the influencing factors of MEAN are age, diabetes course, HbA1c, diet control Total scores for blood glucose monitoring, self-management behavior, items 2, 4, 5, 7, and DES-SF (P<0.05); the influencing factors for SD are age, HbA1c, UACR, diet control, blood glucose monitoring, medication in accordance with doctor's instructions, and total scores for self-management behavior (P<0.05); the influencing factors for TIR are HbA1c, UACR, blood glucose monitoring, self-management behavior total scores, items 1, 2, 4, 7 and DES-SF total score (P<0.05); mage is age, HbA1c, UACR, diet control and blood glucose monitoring (P<0.05). See Table 3.
Table 3 Multiple linear regression analysis on blood glucose fluctuation in type 1 diabetes patients
3 Discussion
Blood glucose monitoring is essential for the health management of patients with T1DM. Previous studies have often used HbA1c as the "gold standard" for evaluating glycemic control, arguing that its biological variability is small and better predicts long-term blood glucose levels and chronic complication risk in patients [7]. However, recent studies have found that HbA1c alone does not fully explain the causes of chronic complications of diabetes in patients, and that blood glucose fluctuations may vary greatly from person to patient with diabetes, even if HbA1c levels are similar [21]. ZHANG et al. [22] found that HbA1c and MAGE were both important factors influencing cardiovascular complications in patients with T2DM, but MAGE was more predictive than HbA1c. The above studies show that blood glucose fluctuations are independent of HbA1c in the evaluation of blood glucose control, and FGMS can break through many restrictions in HbA1c measurement and self-glucose monitoring to more fully understand the overall blood glucose fluctuations of patients.
There are dozens of measures for evaluating blood glucose fluctuations [23], in this study, by extracting FGMS data and calculating, selecting the four evaluation indicators of MEAN, TIR, SD and MAGE with wide clinical application as the dependent variables, and conducting multiple linear regression analysis, it was found that HbA1c was the influencing factor of MEAN, SD, TIR and MAGE, indicating that HbA1c is an independent influencing factor of blood glucose fluctuations in patients with T1DM, which is inconsistent with the research results of Liu Wei et al. [24]. The study of Liu et al. [24] also used MEAN, TIR, SD and MAGE as blood glucose fluctuation evaluation indicators, but the results found that these four indicators were not significantly related to HbA1c, and the possible reasons for the difference between the results of the two studies were: (1) the sample size was different: Liu Et al. [24] The sample size was relatively small, with only 37 patients with T1DM; (2) the research methods were different: Liu Et al. [24] used correlation analysis to determine the closeness between the variables. The multivariate linear regression model used in this study can not only reveal the magnitude of the influence between variables, but also predict and control quantitatively by the regression equation, which is more scientific and reliable.
In previous studies, age [25], course of diabetes [25,26,27] and UACR [28] were the main risk factors for AGE and intraday average absolute blood glucose difference (MODD) in patients with T2DM, and this study demonstrated that they were also influencing blood glucose fluctuations in patients with T1DM. UACR is an early marker of renal impairment in patients with diabetes mellitus by providing easy and reliable estimation of microalbumin excretion in urine [29], and is closely related to the occurrence of cardiovascular disease and the event of death from cardiovascular disease, and has become a risk predictor. It is suggested that in clinical work, blood glucose variability can be reduced from the perspective of improving UACR, thereby preventing or delaying the occurrence of diabetic nephropathy in patients with T1DM.
Lou Qingqing, an expert in diabetes health education[30], pointed out that promoting patients to change behavior and maintain these behaviors is the key to improving various physiological indicators. A cross-sectional study [31] found that diabetic self-management behavior is an important influencing factor of SDBG, MAGE and MODD, and that intraday and daytime blood glucose fluctuations will be well controlled as self-management behavior of patients with T2DM improves. The results of this study confirm that self-management behaviors, particularly dietary control, blood glucose monitoring, and medication as prescribed, are also influencing blood glucose fluctuations in patients with T1DM. However, this study did not find that exercise plays an important role in blood glucose control in patients, which may be related to the fear of exercise hypoglycemia in patients with T1DM and the fear that blood glucose cannot be controlled. This suggests that in the process of diabetes education, when medical staff set personalized behavioral goals for patients, they need to pay special attention to the intervention of patients' self-management behavior, thereby helping patients obtain good blood sugar control; at the same time, self-management control cannot be too strict, so as not to increase the risk of hypoglycemia.
Patient behavior changes often occur externally, and self-management potential can be used to assess the degree of positivity of an individual's self-management knowledge and skills and the setting of individualized self-management goals [32]. Domestic studies on the effect of self-management potential on blood glucose fluctuations have not been reported in the literature. This study found that self-management potential is a factor influencing MEAN and TIR, and the stronger the patient's self-management potential, the lower the average blood glucose fluctuation, and the longer the blood glucose control within the normal range. In addition, when managing diabetes, "being able to turn goals into feasible plans", "maintaining an optimistic attitude" and "knowing the belief and motivation to manage diabetes well" have a greater impact on blood glucose fluctuations in patients with T1DM. In the self-management education of diabetes, it is necessary to help patients fully explore their inner potential, pay attention to their physical and mental health, and give emotional support if necessary, so as to enhance the self-management potential of patients and further reduce blood sugar fluctuations.
In summary, factors influencing blood glucose fluctuations in patients with T1DM include age, course of diabetes, HbA1c, UACR, self-management behavior, and self-management potential. By analyzing the factors affecting the patient's blood glucose fluctuations, it provides a scientific basis for medical staff to conduct targeted health education for patients, thereby improving the blood glucose fluctuations in T1DM patients and delaying the occurrence and development of complications.
There is no conflict of interest in this article.
The table in this article is omitted.
Bibliography