Dysglycemia in diabetes mellitus can be depicted as the glycemic triumvirate with its three main components, i.e., sustained chronic hyperglycemia, GV, and hypoglycemic episodes where each component appears as a link in a chain for the development and progression of diabetes-related complication more importantly cardiovascular outcomes [17]. Previous studies had shown that not only the average HbA1c represents independent risk factors for diabetes complications, but also the short-term daily glycemic variation [18, 19]. Furthermore, haemodialysis per se is another independent risk factor for GV [20]. Hence, it is paramount important to evaluate the GV among ESKD patients as they are more vulnerable to cardiovascular complications.
GV defined as by swings in blood glucose level that occur throughout the day, including hypoglycemic periods, post-prandial increases, and other blood glucose fluctuations that occur at the same time on a different day [21]. In our study, GV among ESKD on haemodialysis was generally acceptable, where up to 80% and 90% of patients achieved the desired target GV during haemodialysis and non-haemodialysis day, respectively. We observed marked GV differences between our DM-ESKD with up to 33% experienced high GV compared to NDM-ESKD (up to 12%) during haemodialysis day, which persists to non-haemodialysis day (Figs. 1 & 2). Interestingly, despite not being diabetic, a small percentage of NDM-ESKD experienced high GV during haemodialysis day with none of them showed high GV during non-haemodialysis day. This observation supports the notion that haemodialysis is an independent risk factor for GV even among the NDM-ESKD. Our study is in accordance with other studies (Table 8), which shows haemodialysis worsened glycemic control, and diabetic patients had larger GV as compared to non-diabetic patients [13, 20, 22–25].
Table 8
Factors associated with glycaemic variability in diabetic population (DM: diabetes mellitus, GV: glycaemic variability, SMBG: Self-monitoring blood glucose, CGM: continuous glucose monitoring, SD: standard deviation, CoV: co-efficient variant, MAGE: mean amplitude glucose excursion, MAG: mean absolute glucose, CONGA: continuous overall net glycaemic action, MODD: mean of daily difference, OHA: oral hypoglycaemic agent, GA: glycated albumin, DPP4-i: dipeptidyl-peptidase-4-inhibitor.
Study | Population and objectives | Methods of assessing GV | Conclusion |
Kohnert et al. (2007)[29] | • Type 2 DM: 63 pts. • Objective: Investigate the association with HbA1c and GV• | • Cross-sectional study • GV indices: SD, MAGE • Method: CGM | • HbA1c was related to chronic hyperglycaemia and not GV. |
Borg et al. (2010)[30] A1c-derived Average Blood Glucose (ADAG) study | • Type 1 DM: 268 pts. • Type 2 DM: 159 pts. • Objective: Examine the relationship between indices of post-prandial hyperglycaemia, overall glycemia and GV with HbA1c | • Cross-sectional study • GV indices: SD, MAGE • Method: SMBG and CGM | • GV weak correlation with HbA1c • HbA1c related to hyperglycaemia indicator (pre-prandial > than postprandial) |
Greven et al. (2010)[31] | • Type 1 DM: 166 pts. • Type 2 DM: 58 pts. • Objective: Investigate different GV parameters in inadequately controlled T1DM and T2DM on insulin | • Cross-sectional study • GV indices: SD, CONGA, CoV, MDD • Method: CGM and SMBG | • T1DM > T2DM in terms of GV • Higher GV in longer insulin therapy |
Kuenen et al. (2011)[32] | • Type 1 DM: 268 pts • Type 2 DM: 159 pts • Objective: assessing GV indices with mean-blood glucose/HbA1c | • Cross-sectional study • GV indices: SD and CONGA • Method: CGM and SMBG | • GV indices correlate with higher HbA1c in T1DM • No association with T2DM |
Sartore et al. (2012)[33] | • Type 1 DM: 35 pts. • Type 2 DM: 17 pts. On BB and 16 pts. On OHA/basal insulin • Objective: Investigate the association with HbA1c and GV | • Cross-sectional study • GV indices: SD and CONGA • Method: CGM | • Higher GV in T1DM and long-standing DM • No correlation with insulin treatment and HbA1c |
Fang et al. (2012)[34] | • Type 2 DM: 291 pts (elderly male) • Objective: Influence of GV on HbA1c | • Cross-sectional study • GV indices: SD and MAGE • Method: CGM | • GV correlate with HbA1c i.e. higher GV in higher HbA1c |
Tanaka et al. (2014)[35] | • Type 1 DM: 20 pts • Type 2 DM: 88 pts • Objective: Assessing the determinants of GV in Japanese patients with diabetes | • Cross-sectional study • GV indices: SD and MAGE • Method: CGM | • T1DM > T2DM in terms of GV • GV association: older age, longer duration diabetes, GA/HbA1c and beta-cell function (c-peptide level) |
Juarez et al. (2012)[36] | • Type 2 DM: 2970 pts. • Objective: Identify factors associated with sustained poor glycaemic control and glycaemic variability | • Retrospective • GV indices: HbA1c • Method: HbA1c serial assessment | • Factors associated with poor glycaemic control and wide glycaemic variability: Age, longer duration of DM, multi-pharmacy |
Yoo et al. (2015)[37] | • Type 2 DM: 209 pts. • Objective: Identify factors associated with glycaemic variability: focus on OHA | • Cross-sectional study • GV indices: SD and MAG • Method: SMBG | • High GV associated with sulfonylureas used • Lower in DPP4-i |
Mori et al. (2017)[38] | • Type 2 DM: 45 pts. • Objective: Identify factors influencing GV in diabetic patients receiving insulin therapy | • Cross-sectional study • GV indices: MODD • Method: CGM | • High GV associated with HbA1c, GA level, female, high insulin dose. |
Although we observed a higher GV among DM-ESKD with HbA1c 8–10%, the sole use of HbA1c in ESKD is limited by several factors, e.g., anaemia, uremia, acidosis, and malnutrition [26]. In the general population, there is a linear relationship between HbA1c and mean blood glucose with R2 more than 0.80, which makes HbA1c as an excellent surrogate marker for glycaemic control [27]. In our study, the relationships between mean blood glucose and HbA1c were moderate, with R2 = 0.59 [12]. Our result was similar to other bigger studies among haemodialysis patients, where the relationship (R2) is not more than 0.50 [28]. Therefore, knowledge of factors associated with GV is essential as it allows health professionals to provide targeted interventions to patients with a higher risk of diabetic complications. Currently, many studies that investigate factors affecting GV were done amongst diabetic patients with normal renal function. Moreover, results from these studies varied among each other with small sample size and different indexes of measuring GV (Table 9) [29–38].
In our study, we report that GV, as reported by SMBG, is higher in the older age group, DM-ESKD and patients with hyperlipidemia. Blood parameters associated with high GV was HbA1c, ferritin level, lipid profile and albumin. HbA1c level and its association with GV and mean blood glucose among patients had been heavily investigated previously with inconsistent results. In our study, HbA1c was associated with higher GV, especially in the group with HbA1c between 8–10%. Currently, the literature on the association of HbA1c with GV has varied results with studies showed that HbA1c had a weak correlation with GV but had a significant relation with chronic hyperglycemia and average blood glucose [29, 30, 32, 33] Conversely, recent studies among the Asian population, showed similar findings with our study where HbA1c correlates well with GV indices [34, 35]. However, most of these studies include only normal renal function patients where HbA1c were more reliable as a surrogate marker and would not be affected with anaemia which is commonly seen among ESKD patients.
GV may be related to pancreatic beta cells dysfunction and insulin resistance, which may occur part of the aging process and duration of illness. In our study, older age group was associated with higher GV which coincide with a previous study done among the Asian population that shown association among the older age group, longer duration of diabetes and low c-peptide [34, 35]. Types of medication also may reflect the process of pancreatic beta-cell dysfunction. Although no association between GV with types of medication was found in this study, previous studies did show an association between insulin and sulfonylureas (insulin secretagogues) treatment with higher GV [31, 37, 38]. In T2DM, beta-cell dysfunction plays a significant role in explaining dysglycaemia, where insufficient insulin secretion for accurate regulation may lead to glucose-related metabolic disorders, which might expose patients to increase GV and sustained hyperglycemia [39–41]. Furthermore, aging per-say has a significant influence on pancreatic B cells function as their functions decline with age with limited capability to regenerate [42, 43].
Hyperlipidaemia is recognized as a risk factor for IHD and coronary mortality and was associated with high GV in our study [44, 45]. High-sensitive C-reactive protein (hs-CRP) were used to prognosticate cardiovascular risk in our population, and although it is not a significant association with GV, we found that both DM-ESKD and NDM-ESKD has a higher level of hs-CRP with mean of 8.91 mg/L and 7.03 mg/L respectively [12]. GV may further increase cardiovascular risk by propagating oxidative stress, which leads to endothelial dysfunction and angiopathies [46]. In our study, higher ferritin, although non-specific inflammatory markers, were seen higher in the target GV group compared to the high GV group. Nonetheless, patients with high GV also demonstrate high ferritin level with a mean value 554.1 ug/L. A study has shown that chronic inflammation is a risk factor for accelerated atherosclerosis in ESKD patients with markers such as ferritin and quantitative C-reactive protein level can be used to predict the cardiovascular outcome [47]. A study done by using a more specific marker for oxidative stress, i.e., N, N-diethyl paraphenylenediamine, showed that high GV associated with high oxidative stress [48]. In our cohort, the albumin level was lower among DM-ESKD as compared to NDM-ESKD and was associated with high GV. A 10-year cohort study evaluated serum albumin, C-reactive protein, and carotid atherosclerosis as predictors of 10-year mortality in haemodialysis patients shows that serum albumin concentration was a better predictor of mortality [50]. Hence, targeting chronic inflammatory state and improving nutrition could be emerging future research in haemodialysis patients to minimize the associated GV.
Our study has limitations; firstly, the cross-sectional design of the study, and one-off blood sugar monitoring during haemodialysis and non-haemodialysis day SMBG instead of the continuous glucose monitoring system (CGMS) in assessing glycaemic variability. CGMS may be the preferable systems in assessing GV as SMBG can miss specific peaks and nadir in glucose values where the mean amplitude of glycaemic excursion (MAGE) remains the preferable index for GV analysis [51, 52]. However, it is challenging to perform CGM in daily practice, given discomfort, costly, and the need for calibration compared to the SMBG. The practical aspect of SMBG in terms of easy availability, monitoring, and interpretation coupled with relatively cheaper cost makes SMBG be a preferred method in our population. Furthermore, studies had shown that SMBG correlates strongly with GV indices obtained from CGMS [52, 53]. We did not limit or measure the dietary intake of the patients during the study period, which makes dietary intake as a cofounding factor in the glycaemic profile of the patients. Some previous studies will restrict dietary intake or will ask the patient to fast during HD to avoid dietary intake to influence the reading. However, by doing that, it might not represent the normal day-to-day glucose fluctuations of the patients. Moreover, by allowing usual dietary intake, it would be more practical, reflect real-life data, and subsequently may allow alteration in management.