Prevalence of glycemic variability and factors associated with the glycemic arrays among end-stage kidney disease patients on chronic hemodialysis.

ABSTRACT: Glycemic variability (GV) confers a significantly higher risk of diabetic-related complications, especially cardiovascular. Despite extensive research in this area, data on end-stage kidney disease (ESKD) patients on chronic hemodialysis are scarce. This study aims to determine the magnitude of GV among ESKD (diabetic vs nondiabetic) patients and its associated factors on hemodialysis days (HDD) and non-hemodialysis days (NHDD) where postulation of a higher GV observed among diabetic on HDD.We recruited 150 patients on hemodialysis, 93 patients with type 2 diabetic (DM-ESKD), and 57 with nondiabetic (NDM-ESKD). The GV indices (standard deviation [SD] and percentage coefficient variant [%CV]) were obtained from 11-point and 7-point self-monitoring blood glucose (fasting to post-meal) (SMBG) profiles on HDD and NHDD. The GV indices and its associated factors of both DM-ESKD and NDM-ESKD were analyzed to compare HDD vs NHDD.Mean blood glucose on HDD was 9.33 [SD 2.7, %CV 30.6%] mmol/L in DM-ESKD compared with 6.07 [SD 0.85, %CV 21.3%] mmol/L in NDM-ESKD (P = <.01). The DM-ESKD group experienced significantly above target GV indices compared to NDM-ESKD on both HDD and NHDD, particularly in the subgroup with HbA1c 8-10% (P = <.01). Presence of diabetes, older age, hyperlipidemia, HbA1c, ferritin levels, and albumin were identified as factors associated with GV.DM-ESKD patients have above-target GV indices, especially on HDD, therefore increasing their risk of developing future complications. We identified high HbA1c, older age group, presence of hyperlipidemia, ferritin levels, and albumin as factors associated with GV indices that may be used as surrogate markers for GV. Since these groups of patients are vulnerable to CVD mortality, urgent attention is needed to rectify it.

Introduction

Type 2 diabetes mellitus (T2DM) is the primary cause of end-stage kidney disease (ESKD) worldwide.[ A similar trend is seen in Malaysia, where the prevalence of ESKD has doubled over the last 10 years, with almost two-thirds of patients having diabetes.[ Type 2 diabetic ESKD (DM-ESKD) is associated with higher morbidity and mortality, mainly related to cardiovascular complications, with poor glycemic control proven to be a predictor of mortality.[ Therefore, glycemic control had been a focus of extensive research, especially among DM-ESKD on hemodialysis, as these patients experience more marked fluctuations in blood glucose compared with non-hemodialysis diabetic populations.[

Glycemic variability (GV) had been coined to explain these glucose fluctuations among diabetic patients. GV is an independent risk factor for morbidity and mortality among the non-hemodialysis diabetic population as previous studies have demonstrated failure in improving cardiovascular outcomes by targeting HbA1c alone, a surrogate for chronic hyperglycemia.[

Data from the general population had prompted concerns regarding glycemic control among ESKD patients with higher cardiovascular risk. Large population studies among hemodialysis patients have shown an association between HbA1c levels of less than 6% and more than 8% with decreased overall survival.[ This U-shape association in hemodialysis patients might indicate that chronic hyperglycemia per se is not an indicator of morbidity and mortality and hypoglycemia with glucose fluctuations that were more evident in these malnourished and protein-energy-wasted patients.[

These findings indicate that reducing GV may be an important strategy in reducing cardiovascular complications in the hemodialysis population. However, although GV has been heavily investigated in non-ESKD diabetic patients, minimal data is available in ESKD patients, especially on the extent of GV on hemodialysis (HDD) and non-hemodialysis days (NHDD). Furthermore, data on GV still lacks among the Malaysian population. Hence, we aim to investigate the extent of GV among ESKD patients during HDD and NHDD as we hypothesize that larger GV will be detected, especially among DM-ESKD on HDD. We conducted a previous analysis of glycemic patterns during hemodialysis in our population, showing that DM-ESKD patients experience 4 times more post-hemodialysis hyperglycemia than their nondiabetic counterparts.[ Therefore, glycemic status needs to be closely monitored by frequent, careful measurement of glucose level, particularly during and after each hemodialysis session to prevent hemodialysis-induced glycemic disarray as a recent study had shown an increase in mortality among diabetic ESKD on hemodialysis with high GV.[ Hence, this study aims to determine the magnitude of GV and its associated factors among ESKD patients on HDD and NHDD to optimize management in this patient group.

Methods

Study design

In this cross-sectional study, we recruited 150 ESKD patients on maintenance hemodialysis with DM-ESKD (Type 2 diabetes mellitus) (n = 93) and NDM-ESKD (n = 57). This study was approved by the ethical committee of University Putra Malaysia (UPM) (UPM/TNCPI/RMC/1.4.18.2/JKEUPM) in August 2017 and was conducted according to the Declaration of Helsinki, where written consent was obtained prior to study participation. Patients were recruited from 5 private hemodialysis centers in Selangor, Malaysia, from December 2017 till June 2018. The sample size was calculated based on a study by Jin et al,[ which looked at blood glucose fluctuations among the hemodialysis population. Multiple logistic regression using G power software[ was used by considering a model with 1 binary covariate X with event rate under Ho, p1 = 0.13 and event rate under X = 1, p2 = 0.40, giving an odds ratio of ∼ 4.5. We further assumed R2 = 0.1, and an imbalanced design ratio of 2:1 between the 2 groups. The estimated sample size necessary to achieve a 2-sided test with an alpha of 0.05 and a power of at least 80% was 102. The final sample size was 146 rounded to 150, considering a 30% nonresponse rate.

Inclusion criteria were: adults over 18 years of age with or without diabetes (Type 2), patients on maintenance hemodialysis for at least 3 months, patients with stable hemoglobin levels over the last 3 months, and patients with no recent change in insulin or oral hypoglycemic agents. Exclusion criteria were: Type 1 diabetes mellitus, blood transfusion or hospitalization within the previous 3 months, hemoglobinopathy, presence of acute inflammatory state, and diagnosis of malignancy.

Sociodemographic and comorbidity data

A structured questionnaire was developed for sociodemographic information, medical information, comorbidities, and prescription lists. Baseline blood tests were taken from patients at the start of the study. All blood specimens were processed in the same laboratory for uniformity.

Hemodialysis day

Patients’ hemodialysis prescription was prescribed by their nephrologists and not altered for the study with a standard regime of; blood flow ≥ 300 mL/min, dialysis solution flow 500 mL/min, glucose concentration in dialysate fluid 00 mg/dL, and dialysis duration of 240 minutes. We only included patients in the morning shift in order that the timing of glucose sampling will be similar. SMBG was done on 2 consecutive days (HDD and NHDD). Patients’ hemodialysis regimens were as prescribed by their nephrologists and not altered for the study. Monitoring of clinical parameters was performed according to the standard of care. The administration of oral hypoglycemic agents (OHA) and insulin on HDD based on nephrologist advice.

Self-monitoring of blood glucose

Capillary glucose measurement was measured using standardized capillary glucometers (Bayer contour plus) in this study. Patients were trained to measure capillary glucose and to record glucose values. They were also taught to recognize symptoms of hypoglycemia and to record their blood glucose if they occurred.

On HDD, an 11-point capillary self-monitoring blood glucose (SMBG) profile was obtained: fasting, pre-hemodialysis, hourly during hemodialysis, followed by pre- and post-meal glucose readings at home. During hemodialysis, patients were assisted in the measurement of blood glucose levels. On NHDD, a 7-point SMBG profile was measured: fasting, pre- and post-meal for breakfast, lunch and dinner. Patients were advised to continue taking their usual medications, either on HDD or NHDD. Patients were not required to fast during hemodialysis. They were advised to eat as usual and log all meals and snacks.

Assessment of glycemic variability (GV)

We chose standard deviation (SD) and percentage coefficient (%CV) as GV indices, calculating these from SMBG reading on HDD and NHDD days. The SD was calculated as arithmetic SD, and %CV was obtained by ([SD of glucose]/[mean glucose]) × 100. Target GV was calculated as follows: SD × 3 < mean glucose and for %CV the value of < 36%.[

Statistical analysis

Statistical analysis was performed using RStudio. GV was calculated using 2 methods, that is SD and %CV. The data were checked for normality visually using a histogram and statistically using the Shapiro–Wilk test, and the data were normally distributed after fulfilling all the assumption. For univariate analysis, the chi-square test and independent-sample t test were used. The assumption for equal variance was met using Levene test. All tests were 2-sided, and the level of significance was set at 0.05. The association between GV with clinical and laboratory results were determined using simple logistic regression to derive crude odds ratios. Subsequently, variables that were significant at P <.15 were included in the final multivariable logistic regression analysis. All crude and adjusted odds ratios were presented with 95% confidence intervals. For missing data, the listwise deletion method was used.

Results

Baseline sociodemographic, clinical characteristics, and blood parameters of patients

The summary of baseline sociodemographic, clinical characteristics and patients’ blood parameters are shown in Table 1.[ A total of 148 patients were involved in the final analysis after excluding missing data. DM-ESKD accounted for 91 (61.5%) of patients with a mean age of 57.6 years and a mean duration of diabetes of 16.4 years. The mean duration of hemodialysis between DM-ESKD and NDM-ESKD was 3.8 and 4.5 years, respectively, and not statistically significant. A quarter of the patients reported a history of ischemic heart disease; however, the difference in the prevalence of cardiovascular disease in both groups was not statistically significant.

Table 1

Baseline sociodemographic, clinical characteristics, and blood parameters of patients (n = 148).

Characteristics	DM-ESKD	NDM-ESKD	P value
	Mean (SD)	Mean (SD)	P value
Age (yrs)	57.6 (11.1)	49.0 (11.2)	<.001‡
Duration of HD (yrs)	3.8 (3.1)	4.5 (3.3)	.198
BMI (kg/m2)	27.1 (4.4)	26.2 (6.6)	.391

Table 2 shows the types of medications prescribed in our study population. Antihypertensive agents were prescribed in 89 (60.1%) of patients; however, blood pressure control in both groups was suboptimal with only 16 (10.7%) of DM-ESKD and 47 (31.3%) of NDM-ESKD patients achieving pre- and post-hemodialysis target blood pressure of less than or equal to 130/80 mm Hg. Among DM-ESKD patients, 50 (54.9%) were on insulin therapy and a quarter not on any pharmacological treatment given normal HbA1c. The mean HbA1c among DM-ESKD patients was 7.4%, with around one-third having HbA1c less than 6.5% while another 30% had HbA1c in the range of 6.5% to 8%. Medication intake on HDD depended on the type of treatment: as per usual practice in the hemodialysis centers, that is patients on OHA alone would not take their OHA on HDD, while patients on basal-bolus insulin would omit the insulin dose before their hemodialysis session. However, the majority of patients, that is 56 (82.3%) patients, would not take their medications on HDD. Statins were prescribed in 66 (44.6%) of patients with a mean LDL of 2.97 mmol/L and 3.17 mmol/L in DM-ESKD and NDM-ESKD, respectively. Almost half of the patients were on iron supplementation, either oral or intermittent intravenous iron preparation, and almost all of the patients were on erythropoietin. The mean value of ferritin levels was 554.1 ug/L and 665.2 ug/L in DM-ESKD and NDM-ESKD, respectively.

Table 2

Type of medication prescribed to patients in the study (n = 148).

Type of medication	All	DM-ESKD	NDM-ESKD
Antidiabetics agents (DM-ESKD, n = 91)
• Insulin basal-bolus∗		27 (29.7)	–
• Insulin premixed∗		23 (25.3)	–
• Insulin basal and oral hypoglycemic agent (OHA)		2 (2.2)	–
• Oral hypoglycemic agent (OHA)∗		16 (17.6)	–
• No medication		23 (25.3)	–
Anti-hypertensive agent	88 (59.5)	58 (62.4)	30 (52.6)
Anti-platelet	41 (27.7)	28 (30.1)	13 (22.8)
Statin	66 (44.6)	46 (49.5)	20 (35.1)
Phosphate binders	146 (98.6)	90 (96.8)	56 (98.2)
Vitamin D	76 (51.4)	47 (50.9)	29 (50.9)
Diuretics	49 (33.1)	33 (35.5)	16 (28.1)
Iron supplements (oral or injectable)	141 (95.2)	84 (90.3)	57 (100)
Erythropoiesis stimulating agent (EPO)†
No	8 (5.4)	7 (7.5)	1 (1.8)
2000 units/wk	20 (13.5)	17 (18.3)	3 (5.3)
4000 units/wk	17 (11.5)	10 (10.8)	7 (12.3)
6000 units/wk	79 (53.4)	42 (45.2)	37 (64.9)
8000 units/wk	21 (14.2)	15 (16.1)	6 (10.5)
12,000 units/wk	5 (3.4)	2 (2.2)	3 (5.3)

In general, both groups had a statistically nonsignificant difference in terms of blood parameters (Table 1) apart from HbA1c, phosphate, and albumin. Albumin was lower in the DM-ESKD group, while phosphate is higher in NDM-ESKD. Highly sensitive C-reactive protein (hs-CRP) was used as a surrogate marker for cardiovascular risk, and both groups had a high hs-CRP level with a mean of 8.91 mg/L in and 7.03 mg/L in DM-ESKD and NDM-ESKD, respectively.

GV among ESKD patients

GV on HDD

Mean blood glucose ± SD during hemodialysis among DM-ESKD and NDM-ESKD in our study was 9.33 ± 2.7 mmol/L vs 6.07 ± 0.85 mmol/L, respectively. Figure 1 compares the GV indices between DM-ESKD and NDM-ESKD patients during HDD and NHDD. The majority of our patients achieved target GV, that is 111 (75.0%), based on SD and 121 (81.8%) based on %CV with higher mean GV for DM-ESKD [SD: 2.7; %CV: 30.6%] compared with NDM-ESKD [SD: 0.85, %CoV: 21.3%; P <.01]. DM-ESKD had higher prevalence of above-target GV compared with NDM-ESKD [SD: 33.0%, %CV: 25.3% vs SD: 12.2%, %CV:7.1%). More patients had above-target GV indices in the HbA1c group 8% to 10% and lowest in the < 6.5%. Figure 2 represents the glycemic patterns based on GV indices on HDD.

Figure 1

Comparison of GV indices between DM-ESKD and NDM-ESKD during hemodialysis (HDD) and non-hemodialysis day (NHDD). In our study, more DM-ESKD had above target GV during HDD vs NHDD. None of the NDM-ESKD experience above target GV during NHDD.

Figure 2

Glycemic pattern during hemodialysis day (HDD) based on GV indices, that is, SD and %CV. Both graphs show that glycemic fluctuations were more marked among patients with high GV indices and in DM-ESKD. Timing: D1 = fasting, D2 = prior hemodialysis, D3 = first hour hemodialysis, D4 = second hour hemodialysis, D5 = third hour hemodialysis, D6 = fourth hour hemodialysis, D7 = 2 hours post hemodialysis, before meal, D8 = 2 hours post-meal, D9 = before dinner, D10 = 2 hours post-dinner, D11 = before sleep.

GV on NHDD

On NHDD, the mean blood glucose ± SD among DM-ESKD and NDM-ESKD was 9.85 ± 3.1 mmol/L vs 6.0 ± 0.88 mmol, respectively. Mean GV for DM-ESKD was higher [SD: 3.1; %CV: 22.9%] compared with NDM-ESKD [SD: 0.88, %CV: 15.1%; P < .01] with 12.1% (SD) and 8.8% (%CV) of DM-ESKD experienced above-target GV indices. None of the NDM-ESKD patients demonstrated above-target GV indices during NHDD. More patients in the HbA1c group 8% to 10% had above target GV indices. Figure 3 represents the glycemic patterns based on GV indices on NHDD.

Figure 3

Glycemic pattern during non-hemodialysis day (NHDD) based on GV indices, that is, SD and %CV. Both graphs show that glycemic fluctuations were more marked among DM-ESKD with high GV indices. No NDM-ESKD had high GV during NHDD. Timing: ND1 = fasting – before breakfast, ND2 = 2 hours post breakfast, ND3 = before lunch, ND4 = 2 hours post-lunch, ND5 = before dinner, ND6 = 2 hours post-dinner, ND7 = before sleep.

GV HDD and NHDD

Significantly more patients achieved target GV on NHDD as compared with HDD (SD: 75.0% vs 92.6%; %CV: 82.0% vs 92.7%). However, when we factor in diabetes status, the results were not statistically significant.

Factors associated with above-target GV among ESKD patients

Tables 3 and 4 demonstrate the association between clinical characteristics and blood parameters with GV on HD and NHDD. In this study, the presence of diabetes and older age was associated with above-target GV indices. HbA1c, ferritin, LDL, and TG were associated with above-target GV during HDD. On NHDD, HbA1c, albumin, and ferritin were associated with above-target GV indices. Further multivariate analysis (Tables 5 and 6) showed that age and LDL were factors associated with GV on HDD while albumin was associated with above-target GV on NHDD. There was no association between type of medications with GV indices (Table 7). We found a weak and nonsignificant linear correlation between serum albumin and LDL level (r (148) = 0.08, P = .34) and between serum ferritin and HSCRP level (r (148) = −0.07, P = .41).

Table 3

Factors associated with above-target GV indices during hemodialysis day (HDD) using simple logistic regression.

	SD				% CV
	Target	Above-target	P value	Crude odds ratio (95% CI)	Target	Above-target	P value	Crude odds ratio (95% CI)
Diabetes			<.01∗				<.01∗
No	50 (45.0)	6 (17.1)		Reference	53 (43.8)	3 (12.0)		Reference
Yes	61 (55.0)	29 (82.9)		3.96 (1.62,11.25)	68 (56.2)	22 (88.0)		5.72 (1.85, 25.06)
Gender			.68				.84
Male	59 (53.2)	20 (57.1)		Reference	65 (53.7)	14 (56.0)		Reference
Female	52 (46.8)	15 (42.9)		0.85 (0.39,1.82)	56 (46.3)	11 (44.0)		0.91 (0.38,2.16)
Smoking			.22				.23
No	100 (90.1)	34 (97.1)		Reference	109 (90.1)	25 (100.0)		Reference
Yes	11 (9.9)	1 (2.9)		0.267 (0.01,1.45)	12 (9.9)	0 (0.0)		0.17 (0.01, 3.00)
BMI			.99				.99
Normal (18.5–22.9)	5 (4.5)	0 (0)		Reference	5 (4.1)	0 (0)		Reference
Underweight (<18.5)	19 (17.1)	7 (20.0)	.35	4.2 (0.2, 86.3)	20 (16.5)	6 (24.0)	.42	3.5 (0.17, 72.00)
Overweight (23.0– 24.9)	17 (15.3)	4 (11.4)	.51	2.8 (0.1, 61.2)	18 (14.9)	3 (12.0)	.64	2.1 (0.09, 46.80)
Obese 1 (25.0 – 29.9)	41 (36.9)	15 (42.9)	.35	4.1 (0.2, 78.8)	44 (36.4)	12 (48.0)	.46	3.1 (0.16, 60.00)
Obese 2 (>30.0)	29 (26.1)	9 (25.7)	.41	3.5 (0.18, 70.2)	34 (28.1)	4 (16.0)	.82	1.4 (0.07, 30.50)
IHD			.17				.18
No	86 (77.5)	23 (65.7)		Reference	93 (76.9)	16 (64.0)		Reference
Yes	25 (22.5)	12 (34.3)		1.79 (0.77,4.08)	28 (23.1)	9 (36.0)		1.87 (0.72,4.63)
Gout			.45				.86
No	100 (90.1)	33 (94.3)		Reference	110 (90.9)	23 (92.0)		Reference
Yes	11 (9.9)	2 (5.7)		0.55 (0.08,2.19)	11 (9.1)	2 (0.8)		0.87 (0.13,3.53)
Hyperlipidemia			.32				.95
No	52 (46.8)	13 (37.1)		Reference	54 (44.6)	11 (44.0)		Reference
Yes	59 (53.2)	22 (62.9)		1.49 (0.69,3.32)	67 (55.4)	14 (56.0)		1.02 (0.43, 2.49)
Stroke			.55				.41
No	105 (94.6)	34 (97.1)		Reference	114 (94.2)	25 (100)		Reference
Yes	6 (5.4)	1 (2.9)		0.51 (0.03,3.16)	7 (5.8)	0 (100)		0.30 (0.17, 5.40)
Hypertension			.47				.66
No	4 (3.6)	0 (0)		Reference	4 (3.3)	0 (0)		Reference
Yes	107 (96.4)	35 (100)		3.00 (0.16, 56.6)	117 (96.7)	25 (100)		2.0 (0.10, 37.40)
	Mean (SD)			Mean (SD)
Age	53.1 (11.8)	58.4 (11.8)	.03∗	1.04 (1.00,1.08)	53.1 (11.8)	58.4 (11.8)	<.01∗	1.07 (1.03,1.12)
Duration HD	4.1 (3.2)	4.0 (3.2)	.81	0.99 (0.87,1.11)	4.1 (3.2)	4.0 (3.2)	.83	0.99 (0.85,1.12)
BMI (kg/m2)	26.7 (5.4)	27.1 (5.2)	.72	1.01 (0.94,1.09)	26.7 (5.4)	27.1 (5.2)	.47	0.97 (0.89, 1.05)
HbA1c	6.4 (1.6)	7.4 (1.6)	<.01∗	1.42 (1.13,1.79)	6.4 (1.6)	7.4 (1.6)	.10∗	1.23 (0.96,1.57)
HSCRP	7.3 (7.0)	9.0 (9.8)	.27	1.02 (0.98, 1.07)	7.3 (7.0)	9.0 (4.8)	.53	1.02 (0.96, 1.07)
Ferritin	632.9 (424.8)	498.6 (372.1)	.11∗	0.99 (0.99,1.00)	632.9 (424.8)	498.6 (372.1)	.08∗	0.99 (0.99, 1.00)
Albumin	38.9 (3.9)	38.9 (3.4)	.90	1.01 (0.91,1.12)	38.9 (3.9)	38.9 (3.4)	.23	0.94 (0.84,1.05)
Clearance	69.2 (8.8)	70.1 (10.4)	.62	1.01 (0.97,1.06)	69.2 (8.8)	70.1 (10.5)	.52	1.01 (0.97, 1.07)
LDL	3.1 (1.1)	2.7 (1.2)	.14∗	0.76 (0.53,1.08)	3.1 (1.1)	2.7 (1.2)	.13∗	0.72 (0.47, 1.08)
HDL	1.01 (0.26)	1 (0.24)	.85	0.86 (0.18,3.83)	1.01 (0.3)	1 (0.2)	.81	0.81 (0.13, 4.35)
HB	10.4 (1.6)	10.6 (1.9)	.45	1.09 (0.87,1.36)	10.4 (1.6)	10.6 (1.9)	.99	0.99 (0.77, 1.28)
TG	2.1 (1.5)	2.6 (2.0)	.15∗	1.17 (0.94,1.45)	2.1 (1.5)	2.6 (2.0)	.38	1.11 (0.86,1.40)
Transferrin saturation	24.7 (10.4)	23.3 (9.9)	.48	0.99 (0.95,1.02)	24.7 (10.4)	23.3 (9.9)	.22	0.97 (0.92, 1.02)
Calcium	2.2 (0.2)	2.2 (0.21)	.85	0.85 (0.16,4.94)	2.2 (0.2)	2.2 (0.2)	.66	1.6 (0.23, 12.6)
Phosphate	2.0 (0.6)	2.0 (0.7)	.85	0.94 (0.51,1.70)	1.9 (0.6)	1.9 (0.7)	0.16	0.59 (0.27, 1.8)
ALP	151.8 (109.0)	176.1 (166.4)	.32	1.00 (0.99, 1.00)	151.8 (109.0)	176.1 (166.4)	0.17	1.00 (1.00,1.01)
iPTH	87.5 (82.3)	80.1 (79.0)	.66	0.99 (0.99,1.00)	87.5 (83.0)	80.0 (79.0)	0.77	1.00 (0.99,1.01)

Table 4

Factors associated with above-target GV indices during non-hemodialysis day (NHDD) using simple logistic regression.

	SD				% CV
	Target	Above-target	P value	Crude Odds ratio (95% CI)	Target	Above-target	P value	Crude odds ratio (95% CI)
Diabetes			.06∗				.11∗
No	57 (41.9)	0 (0)		Reference	57 (41.0)	0 (0)		Reference
Yes	79 (58.1)	10 (100)		15.2 (0.88, 264.5)	82 (59.0)	7 (100)		10.5 (0.59, 186.70)
Gender			.72				.93
Male	76 (55.9)	5 (50.0)		Reference	77 (55.4)	4 (57.1)		Reference
Female	60 (44.1)	5 (50.0)		1.27 (0.34, 4.75)	62 (44.6)	3 (42.9)		0.93 (0.18, 4.40)
Smoking			.61				.71
No	122 (89.7)	10 (100)		Reference	125 (89.9)	7 (100)		Reference
Yes	12 (10.3)	0 (0)		0.50 (0.02, 8.50)	14 (10.1)	0 (0)		0.58 (0.03, 10.60)
BMI			.992				.99
Normal (18.5–22.9)	5 (3.7)	0 (0)		Reference	5 (3.6)	0 (0)		Reference
Underweight (<18.5)	24 (17.7)	2 (20.0)	.94	1.1 (0.05, 26.80)	26 (18.7)	0 (0)	.44	0.2 (0.01, 11.6)
Overweight (23.0 – 24.9)	21 (15.4)	1 (10.0)	.88	0.77 (0.03, 21.50)	21 (15.1)	1 (14.3)	.88	0.7 (0.02, 21.5)
Obese 1 (25.0 – 29.9)	54 (39.7)	3 (30.0)	.83	0.71 (0.03, 15.50)	55 (39.6)	2 (28.6)	.66	0.5 (0.02, 11.7)
Obese 2 (>30.0)	32 (23.5)	4 (40.0)	.78	1.5 (0.07, 32.40)	32 (23.0)	4 (57.1)	.79	1.5 (0.07, 32.4)
IHD			.16				.25
No	99 (72.8)	10 (100.0)		Reference	102 (73.4)	7 (100)		Reference
Yes	37 (27.2)	0 (0)		0.13 (0.01, 2.20)	37 (26.6)	0 (0)		0.18 (0.01,3.30)
Gout			.57				.75
No	123 (90.4)	10 (100.0)		Reference	126 (90.6)	7 (100)		Reference
Yes	13 (9.6)	0 (0)		0.44 (0.02, 7.80)	13 (9.4)	0 (0)		0.62 (0.03, 11.60)
Hyperlipidemia			.15∗				.15∗
No	61 (44.9)	2 (20)		Reference	62 (44.6)	1 (14.3)		Reference
Yes	75 (55.1)	8 (80)		3.25 (0.78, 22.10)	77 (55.4)	6 (85.7)		4.8 (0.80, 92.50)
Stroke			.90				.840
No	129 (94.9)	10 (100)		Reference	132 (95.0)	7 (100)		Reference
Yes	7 (5.1)	0 (0)		0.82 (0.04, 15.40)	6 (5.0)	0 (0)		1.36 (0.07, 26.50)
Hypertension			.82				.650
No	4 (2.9)	0 (0)		Reference	4 (2.9)	0 (0)		Reference
Yes	132 (97.1)	10 (100)		0.71 (0.04, 14.20)	135 (97.1)	7 (100)		0.50 (0.02,10.10)
	Mean (SD)			Mean (SD)
Age	53.1 (11.8)	58.4 (11.8)	.03∗	1.07 (1.00, 1.40)	53.1 (11.8)	58.4 (11.8)	.06∗	1.07 (0.99,1.15)
Duration HD	4.1 (3.2)	4.0 (3.2)	.67	1.04 (0.84, 1.26)	4.1 (3.2)	4.0 (3.2)	.50	1.08 (0.84, 1.34)
BMI (kg/m2)	26.7 (5.4)	27.1 (5.2)	.62	1.03 (0.91, 1.16)	26.7 (5.4)	27.1 (5.2)	.17	1.11 (0.97, 1.28)
HbA1c	6.4 (1.6)	7.4 (1.6)	.02∗	1.49 (1.06, 2.10)	6.4 (1.6)	7.4 (1.6)	<.01∗	1.73 (1.17, 2.64)
HSCRP	7.3 (7.0)	9.0 (9.8)	.49	0.96 (0.85, 1.05)	7.3 (7.0)	9.0 (9.8)	.68	0.98 (0.84, 1.07)
Ferritin	632.9 (424.8)	498.6 (372.1)	.25	0.99 (0.99, 1.00)	632.9 (424.8)	498.6 (372.1)	.08∗	0.99 (0.99,0.99)
Albumin	38.9 (3.9)	38.9 (3.4)	.08∗	1.2 (0.99, 1.50)	38.9 (3.9)	38.9 (3.3)	.14∗	1.12 (0.96, 1.54)
Clearance	69.2 (8.8)	70.1 (10.4)	.30	1.08 (1.00, 1.18)	69.2 (8.8)	70.1 (10.4)	.55	1.03 (0.95, 1.12)
LDL	3.1 (1.1)	2.7 (1.2)	.82	1.07 (0.58, 1.88)	3.08 (1.1)	2.7 (1.2)	.60	1.19 (0.61, 2.23)
HDL	1.0 (0.3)	1 (0.2)	.96	1.07 (0.07, 11.8)	1.0 (0.3)	1 (0.2)	.72	1.7 (0.07, 26.2)
HB	10.4 (1.6)	10.6 (2.0)	.64	0.91 (0.61, 1.32)	10.4 (1.6)	10.6 (1.9)	.51	1.15 (0.7, 1.74)
TG	2.1 (1.5)	2.6 (2.0)	.31	1.17 (0.82, 1.55)	2.1 (1.5)	2.6 (2.0)	.87	0.96 (0.49, 1.42)
Transferrin saturation	24.7 (10.4)	23.3 (9.9)	.51	1.02 (0.96, 1.08)	24.7 (10.4)	23.3 (9.9)	.93	0.99 (0.92,1.07)
Calcium	2.2 (0.2)	2.2 (0.2)	.60	0.48 (0.03, 9.20)	2.2 (0.2)	2.2 (0.2)	.40	0.26 (0.01, 7.7)
Phosphate	2.0 (0.6)	2.0 (0.7)	.17	0.25 (0.06, 0.86)	2.0 (0.6)	2.0 (0.7)	.16	0.29 (0.05, 1.17)
ALP	151.8 (109.0)	176.1 (166.0)	.50	0.99 (0.99, 1.00)	151.8 (109.0)	176.1 (166.4)	.65	0.99 (0.99, 1.00)
iPTH	87.5 (83.0)	80.1 (79.0)	.76	0.99 (0.99,1.01)	87.5 (83.0)	80.1 (79.0)	.60	0.99 (0.98, 1.01)

Table 5

Multivariate analysis for factors associated with above-target GV indices during hemodialysis day (HDD) (n = 148).

SD variables	β	SE	Adjusted OR (95% CI)	P value
Diabetes	0.65	0.65	1.9 (0.5, 7.4)	.32
Age	0.027	0.02	1.0 (0.9, 1.1)	.20
HBA1C	0.26	0.16	1.3 (0.9, 1.8)	.11
Ferritin	−0.007	0.0006	0.9 (0.9, 1.00)	.29
LDL	−0.479	0.234	0.6 (0.4, 0.9)	.04∗
TG	0.248	0.157	1.3 (0.9, 1.7)	.11

Table 6

Multivariate analysis for factors associated with above-target GV indices during non-hemodialysis day (NHDD) (n = 148).

SD variable	β	SE	Adjusted OR (95% CI)	P value
Diabetes	18.23	2128.0	83.3 (0.001, Inf)	.99
Age	0.54	0.97	1.7 (0.3, 1.5)	.58
HBA1C	0.03	0.04	1.0 (0.9, 1.1)	.46
Ferritin	0.22	0.25	1.2 (0.8, 2.1)	.38
Albumin	0.26	0.15	1.3 (1.0, 1.8)	.08∗

Table 7

Glycemic variability indices comparing hemodialysis and non-hemodialysis day based on medications (diabetes patients) (n = 91).

	Mean glucose HDD (SD)
All	9.3 (2.7)
Medication
Insulin	10 (3.1)
OHA	9.1 (2.0)
No medication	7.9 (1.4)

Discussion

Dysglycemia in diabetes mellitus consists of 3 main components: sustained chronic hyperglycemia, GV, and hypoglycemic episodes, with each component appearing to be a link in a chain for the development and progression of diabetes-related complications.[ Previous studies have shown that besides HbA1c, short-term daily GV represents an independent risk factor for diabetes complications.[ Furthermore, hemodialysis is another independent risk factor for GV.[ Hence, it is paramount to evaluate the GV among ESKD patients as they are more vulnerable to cardiovascular complications.

GV denotes 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.[ Our previous study noted that DM-ESKD patients experienced greater fluctuations in blood glucose and had a 4-fold increase in post-prandial blood glucose compared with NDM-ESKD.[ We also noted episodes of intra-dialytic asymptomatic hypoglycemia in 12% of both DM-ESKD and NDM-ESKD patients, which demonstrated that blood glucose fluctuation occurs in all hemodialysis patients regardless of the presence of diabetes.[ The occurrence of in particular asymptomatic hypoglycemia is an important observation as ESKD patients are prone to hypoglycemia due to glucose-free dialysate, which is a standard in Malaysian practice, glucose loss during dialysis, decreased renal gluconeogenesis, and alteration in the metabolic pathway.[ In our study, a quarter of patients were not on medication due to the spontaneous resolution of hyperglycemia and treatment-independent normalization of HbA1c known as burnt-out diabetes that is more prominent in dialysis patients that further exacerbate the risk of hypoglycemia and the need for closer monitoring in particularly intradialytic.[

We aim to identify the magnitude of GV among ESKD patients, where we hypothesize greater GV will be observed among DM-ESKD on HDD. In our current study, GV among ESKD patients on hemodialysis was generally acceptable, where up to 80% and 90% of patients achieved the target GV on HDD and NHDD, respectively. We observed marked GV differences between our DM-ESKD patients with up to 33% experiencing above-target GV indices compared with NDM-ESKD (up to 12%) on HDD, which persists to NHDD (Figures 1 and 4). Interestingly, despite the absence of T2DM, a small percentage of NDM-ESKD experienced above-target GV only HDD with none during NHDD. This observation supports the notion that hemodialysis is an independent risk factor for GV, even among NDM-ESKD patients. Our findings correlate with other studies, showing worsened glycemic control among hemodialysis patients and larger GV among diabetic compared to nondiabetic patients (Table 8).[ Our findings of larger GV among DM-ESKD are an important observation as recent studies have shown that high GV was independently associated with all-cause mortality, hence emphasizing the prognostic value of GV.[

Figure 4

Flowchart of the patient population and study design. %CV = coefficient variant, DM-ESKD = diabetic-ESKD, ESKD = end-stage kidney disease, GV = glycemic variability, HDD = hemodialysis day, NDM-ESKD = nondiabetic-ESKD, NHDD = non-hemodialysis day, SD = standard deviation, SMBG = self-monitor blood glucose.

Table 8

Glycemic pattern and variability comparing hemodialysis and non-hemodialysis day.

Study	Population and methods	Glycemic profile pattern and GV	Conclusion
(Abe et al., 2007)	• n = 16 patients (DM-ESKD)• Mean HbA1c: 8.1 ± 1.2% (poor control) vs 5.8 ± 0.62% (good control)• Method: plasma blood glucose• GV indices: Mean	Glycemic profile and variability • Plasma blood glucose decreases significantly between poor and good control during initial hemodialysis period as compared to 2 hr and 4 hr in hemodialysis. • Poor control group: Hyperglycemia appeared post hemodialysis due to decrease in insulin. • Poor control group: significant changes in plasma glucose during hemodialysis and non-hemodialysis day.	• Plasma glucose decrease by hemodialysis.• Hyperglycemic observed post hemodialysis.• Fluctuations more pronounced during hemodialysis vs non-hemodialysis day (poor control)
(Kazempour-Ardebili et al., 2009)	• n = 17 patients (DM-ESKD)• Mean HbA1c: 6.9 ± 1.2%• Method: CGM• GV indices: mean, SD, AUC	Glycemic profile and variabilityHD vs NHD • Mean (SD) blood glucose: 9.8 ± 3.8 mmol/L vs 12.6 ± 5.6 mmol/L • 24 hr AUC: 4694 ± 1988 mmol.3 min−1 vs 5932.1 ± 2673.6 mmol.3 min−1 • Hypoglycemia: 3 (17%) had asymptomatic hypoglycemia during first 24 hr	• Glucose values are significantly lower on HD as compared to non-hemodialysis day.
(Mirani et al., 2010)	• n = 12 patients (DM-ESKD)• Mean HbA1c: 7.4 ± 1.1%• Method: CGM• GV indices: mean, SD and MAGE	Glycemic profile and variabilityHemodialysis vs non-hemodialysis day • Mean (SD) blood glucose: 10.32 ± 2.7 mmol/L vs 8.5 ± 1.4 mmol/L • SD: 3.16 ± 1.7 mmol/L vs 1.9 ± 0.6 mmol/L • MAGE: 4.16 ± 1.2 mmol/L vs 3.18 ± 0.6 mmol/L • Hypoglycemia: 2 (11%) had asymptomatic occurring 6 hr post dialysis	• Hemodialysis day had increase GV as compared with non-hemodialysis day
(Jung et al., 2010)	• n = 9 patients (DM-ESKD)• Mean HbA1c: 8.6 ± 1.2%• Method: CGM• GV indices: mean, SD, AUC, and MAGE.	Glycemic profile and variabilityhemodialysis vs non-hemodialysis day • No difference MAGE between hemodialysis and non-hemodialysis day. • More pronounced hypoglycemia on hemodialysis day.	• GV not affected by hemodialysis day.
(Gai et al., 2014)	• n = 12 patients (ESDN)• Mean HbA1c: 7.2 ± 1.0%• Method: CGM• GV indices: mean, SD, MAGE	Glycemic profile and variability • All patients showed a decrease in blood glucose during starting hemodialysis with nadir attained at around 200 min into hemodialysis. • Post dialysis hyperglycemia observed after 150 min. • Hypoglycemia: 2 (16%) experienced asymptomatic hypoglycemia • Mean blood glucose HD vs NHD: 7.88 mmol/L vs 7.27 mmol/L	• Hemodialysis associated with a significant intra-dialytic reduction of glycemia and post-dialytic hyperglycemia.
(Jin et al., 2015)	• n = 46 patients (DM-ESKD and NDM-ESKD)• Mean HbA1c: 7.3% ± 1.9• Method: CGM• GV indices: mean, SD and MAGE	Glycemic profile and variabilityDM-ESKD group (hemodialysis vs non-hemodialysis day) • Mean blood glucose: 11.05 ± 3.0 mmol/L vs 12.33 ± 4.09 mmol/L • SD: 2.97 ± 1.12 mmol/L vs 2.31 ± 1.24 mmol/L • MAGE: 7.54 ± 2.83 mmol/L vs 5.24 ± 2.64NDM-ESKD group (hemodialysis vs non-hemodialysis day) • Mean blood glucose: 7.34 ± 2.3 mmol/L vs 7.58 ± 2.14 mmol/L • SD: 1.39 ± 0.48 mmol/L vs 0.95 ± 0.71 mmol/L • MAGE: 4.10 ± 2.02 mmol/L vs 2.84 ± 2.89	• DM-ESKD had larger glycemic fluctuations as compared to NDM-ESKD.• GV more pronounced on hemodialysis vs non-hemodialysis day.

Although we observed more patients with above-target GV among DM-ESKD with HbA1c 8–10%, the sole use of HbA1c in ESKD is limited by several factors, for example, anemia, uremia, acidosis, and malnutrition.[ 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 glycemic control.[ In our study, the relationships between mean blood glucose and HbA1c were moderate, with R2 = 0.59.[ Our result was similar to bigger studies among hemodialysis patients, where the relationship (R2) is not more than 0.50.[ Therefore, knowledge of GV associated factors apart from HbA1c as a surrogate marker 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, these studies’ results varied among each other with small sample size and different indexes of measuring GV.[

We found that GV is higher in patients with older age, DM-ESKD, and hyperlipidemia. Blood parameters associated with above-target GV were HbA1c, ferritin level, lipid profile, and albumin. HbA1c and its association with GV and mean blood glucose among patients had been heavily investigated with inconsistent results. HbA1c, especially levels between 8% and 10%, was associated with above-target GV in our study. Current literature on the association of HbA1c with GV is heterogeneous, with results showing a weak correlation between HbA1c and GV but had a significant association with chronic hyperglycemia and average blood glucose.[ Conversely, recent studies among Asian populations showed similar findings with our study where HbA1c correlates well with GV indices.[ However, most of these studies include only patients with normal renal function in whom HbA1c is more reliable as a surrogate marker and would not be affected by the anaemia commonly seen in ESKD patients.

GV may be related to pancreatic beta-cell dysfunction and insulin resistance, which may occur part of the ageing process and duration of T2DM. In our study, older age was associated with above-target GV, which corroborates a previous Asian study that showed an association of GV with older age, longer duration of diabetes, and low c-peptide.[ 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 showed an association between the use of insulin and sulfonylureas (insulin secretagogues) with higher GV.[ In T2DM, beta-cell dysfunction plays a significant role in dysglycemia, where insufficient insulin secretion for accurate regulation may lead to glucose-related metabolic disorders, exposing patients to increased GV and sustained hyperglycemia.[ Furthermore, aging alone significantly affects pancreatic B cells due to deterioration in secretory and regenerative capacity.[

Hyperlipidemia is recognized as a risk factor for IHD and coronary mortality and was associated with high GV in our study.[ High-sensitive C-reactive protein (hs-CRP) was used to estimate cardiovascular risk in our population, and although it did not have a significant association with GV, we found that both DM-ESKD and NDM-ESKD patients had higher hs-CRP levels with a mean of 8.91 mg/L and 7.03 mg/L respectively.[ GV may further increase cardiovascular risk by propagating oxidative stress, leading to endothelial dysfunction and angiopathies.[ In our study, higher ferritin, although a nonspecific inflammatory marker, was seen more frequently in the target GV group than the above-target GV group. Nonetheless, patients with above-target GV also demonstrate high ferritin level with a mean value 554.1 ug/L. Although a significant number of our patients were on iron supplementation, we found the level of ferritin is independent of hs-CRP, a better marker for inflammation in cardiovascular disease. High ferritin and quantitative C-reactive protein levels have been associated with accelerated atherosclerosis in ESKD patients; however, it is unclear whether ferritin levels can be reliably interpreted in patients on iron supplementation.[ A study using a more specific marker for oxidative stress, N, N-diethyl paraphenylenediamine, showed an association between high GV and high oxidative stress.[ In our cohort, the albumin level was lower among DM-ESKD than NDM-ESKD and was associated with above-target GV indices. Although not a significant factor for GV, LDL was also lower in DM-ESKD, which may represent nutritional status among diabetic patients. Notably, it had a poor correlation with albumin in our study. A 10-year cohort study evaluated serum albumin, C-reactive protein, and carotid atherosclerosis as predictors of 10-year mortality in hemodialysis patients showed that serum albumin concentration was a better predictor of mortality.[ Hence, targeting chronic inflammation and improving nutrition, and observing the effect on GV could be a subject for future research.

Our study's limitations are the cross-sectional design, utilization of SMBG instead of continuous glucose monitoring, which is more accurate in assessing GV, and possibly the lack of standardized dietary restriction in our patients. We elected for SMBG due to ease of availability and lower cost, making it the preferred method for glucose monitoring in our population. We did not limit or measure the patients’ dietary intake during the study period, which may make it a confounding factor in the patients’ glycemic profile. Some previous studies restricted dietary intake or mandated fasting during HD; however, the readings would not represent normal day-to-day glucose fluctuations. Therefore by allowing usual dietary intake, it would be more practical, reflective of real-life data, and may subsequently allow alterations in management. Future study in this field is vast, especially among the Malaysian population, where current data still lacks in using CGMS, dietary and nutritional impact, and effect of chronic inflammation among hemodialysis patients on the GV magnitude.

Conclusion

ESKD patients experienced significant GV on HDD and NHDD with a more pronounced effect seen among DM-ESKD patients. Above-target GV indices were associated with older age, DM-ESKD, hyperlipidemia, high HbA1c, ferritin, and albumin. These factors correlate to the illness's progression, beta-cells dysfunctions, and chronic malnutrition-inflammatory state seen among ESKD patients. In particular, regular glucose monitoring may be beneficial in these groups of patients to optimize management and reduce diabetic-related complications.

Acknowledgments

The authors thank all the staff and patients from Pusat Perubatan Dialysis Bangi, Dengkil, Semenyih, Farah Mahami Dialysis Center, and Bangi Dialysis Centre.

Author contributions

Conceptualization: NFZ, NAK, AHKYK; Data Curation: NFZ, AHKYK, AHK, Funding: NFZ, AHKYK; Formal Analysis: AHKYK, MAZA; Investigation and Methodology: NFZ, AHKYK, MAZA; Sofware: MAZA; Supervision: NFZ, NAK,; Writing original draft: NFZ, AHKYK, MAZA; Writing – review & editing: NFZ, AHKYK, MAZA,.

All authors have read and approved the manuscript.

Conceptualization: Abdul Hanif Khan Yusof Khan, Nor Fadhlina Zakaria, Nor Azmi Kamaruddin.

Data curation: Abdul Hanif Khan Yusof Khan, Nor Fadhlina Zakaria.

Formal analysis: Abdul Hanif Khan Yusof Khan, Muhammad Adil Zainal Abidin.

Funding acquisition: Abdul Hanif Khan Yusof Khan, Nor Fadhlina Zakaria.

Investigation: Abdul Hanif Khan Yusof Khan.

Methodology: Abdul Hanif Khan Yusof Khan, Nor Fadhlina Zakaria, Muhammad Adil Zainal Abidin, Nor Azmi Kamaruddin.

Project administration: Abdul Hanif Khan Yusof Khan, Nor Fadhlina Zakaria, Muhammad Adil Zainal Abidin, Nor Azmi Kamaruddin.

Resources: Abdul Hanif Khan Yusof Khan, Muhammad Adil Zainal Abidin.

Software: Muhammad Adil Zainal Abidin.

Supervision: Nor Fadhlina Zakaria, Nor Azmi Kamaruddin.

Validation: Nor Fadhlina Zakaria.

Writing – original draft: Abdul Hanif Khan Yusof Khan, Nor Fadhlina Zakaria.

Writing – review & editing: Abdul Hanif Khan Yusof Khan, Nor Fadhlina Zakaria, Nor Azmi Kamaruddin.