| Abstract|| |
The significance of glycated albumin (GA) compared with casual plasma glucose (PG) and glycated hemoglobin (HbA1c) was evaluated as an indicator of the glycemic control state in hemodialysis (HD) patients with diabetes. In HD patients with diabetes (n = 25), the mean PG, GA and HbA1c levels were 192.9 + 23 mg/dL, 278.8 + 43 μmol/L and 5.9 + 0.5%, respectively, which were higher by 43.9%, 67.04% and 18%, respectively, compared with HD patients without diabetes (n = 25). HbA1c levels were significantly lower than simultaneous PG and GA values in those patients in comparison with the three parameters in patients who had diabetes without renal dysfunction (n = 25). A significant negative correlation was found between GA and serum albumin (r = 0.21, P <0.05) in HD patients with diabetes, whereas HbA1c correlated positively and negatively with hemoglobin (r = 0.11, P <0.01) and weekly dose of erythropoietin injection (r = -0.19, P < 0.01), respectively. Although PG and GA did not differ significantly between HD patients with diabetes and with and without erythropoietin injection, HbA1c levels were significantly higher in patients without erythropoietin. Categorization of glycemic control into arbitrary quartiles by GA level led to better glycemic control in a significantly higher proportion of HD patients with diabetes than those assessed by HA1c. Multiple regression analysis demonstrated that hemoglobin in addition to PG emerged as an independent factor associated with HbA1c in HD patients with diabetes, while PG, body mass index and albumin were an independent factor associated with GA. Conclusion: it is suggested that GA provides a significantly better measure to estimate glycemic control in HD patients with diabetes and that the assessment of glycemic control by HbA1c in these patients might lead to likely underestimation as a result of the increasing proportion of young erythrocyte by the use of erythropoietin.
|How to cite this article:|
Sany D, Elshahawy Y, Anwar W. Glycated albumin versus glycated hemoglobin as glycemic indicator in hemodialysis patients with diabetes mellitus: Variables that influence. Saudi J Kidney Dis Transpl 2013;24:260-73
|How to cite this URL:|
Sany D, Elshahawy Y, Anwar W. Glycated albumin versus glycated hemoglobin as glycemic indicator in hemodialysis patients with diabetes mellitus: Variables that influence. Saudi J Kidney Dis Transpl [serial online] 2013 [cited 2017 Dec 11];24:260-73. Available from: http://www.sjkdt.org/text.asp?2013/24/2/260/109568
| Introduction|| |
Strict glycemic control in patients with diabetes decreases the incidence of diabetic complications,  which can determine the quality of life and prognosis of such patients.
A reduction of the risk for the development of diabetic microangiopathy in patients with type-2 diabetes by strict glycemic control was demonstrated in the UK prospective diabetes study.  Recent clinical evidence has suggested the favorable effects of strict glycemic control on cardiovascular disease, a main cause of death in patients with diabetes.  Several clinical tests are useful for measuring long-term glycemic control in the general diabetic population. These same tests are routinely performed in diabetic subjects with chronic kidney disease (CKD) and end-stage renal disease (ESRD); however, their accuracy in these patients has not been rigorously tested.  Hemoglobin A1c (HbA1c), the most widely used assay, measures the percentage of circulating hemoglobin that has chemically reacted with glucose and reflects ambient blood glucose control over the prior 120 days, with the most profound effect in the preceding 30 days.  HbA1c values are influenced significantly in HD patients by either shortening of the life span of erythrocytes  or the changing proportion of young to old erythrocytes by erythropoietin use.  If this significantly impacts HbA1c, dialysis patients and clinicians would be falsely comforted by relatively low HbA1c values despite a high risk for subsequent cardiovascular disease and infectious complications. Previous studies attempting to address this concern were underpowered.  Recently, serum glycated albumin (GA) was hypothesized to be an alternative marker for glycemic control in patients with diabetes, which is not affected by changes in the survival time of erythrocytes in the case of type-2 diabetes with hemoglobinopathy.  Inaba et al  determined that HbA1c underestimated long-term glycemic control in dialysis patients with diabetes after comparing the mean of random blood glucose concentrations, HbA1c and percentage of glycated albumin (% GA). They found that the % GA assay provided a more accurate assessment of glycemic control among Japanese hemodialysis (HD) patients. The current study attempted to validate this clinically important result and extend its application to another ethnic group (Egyptian).
| Materials and Methods|| |
This study was composed of 25 HD patients with type-2 diabetes, 25 HD patients without diabetes, 25 patients with type-2 diabetes and chronic kidney disease and ten patients with type-2 diabetes and normal renal function, which was defined on the basis of serum creatinine levels of less than 1.2 mg/dL. The diagnosis of diabetes was based on a history of diabetes or on the ADA criteria. Information collected from participants included demographic data, height, weight (dry weight in HD patients), duration of diabetes and duration of HD. All patients provided written informed consent before participation in this study. Patients with diabetes were restricted to those with stable blood glucose and whose diabetes treatment had not been altered during the preceding six months before the determination of GA and HbA1c. Information on weekly doses of erythropoietin, which had not been changed during the three months before determination of GA and HbA1c, was also obtained.
Patients with hemoglobinopathy, anemia due to causes other than CKD as hemolytic anemia, patients who had a history of overt blood loss or who received a blood transfusion four months prior to the study, patients with evidence of hyporvitaminosis C (Scurvy), evidence of hepatic disorders, inflammatory disease or thyroid disease, patients with heavy proteinuria or taking steroid therapy were all excluded from the study
GA assay: Diazyme enzymatic assay for GA uses proteinase K to digest it into low molecular weight GA fragments and uses Diazyme's specific enzyme, a microorganism originated amadoriase to catalyze the oxidative degradation of amadori products of GA fragments to yield amino acids, glucosone and H 2 O 2 . The H 2 O 2 released is measured by a colorimetric Trinder end-point reaction. The absorbance at 550 nm is proportional to the concentration of GA. The following analytic concentrations were not found to affect the assay: ascorbic acid - no interference up to 10 mg/dL; bilirubin - no interference up to 14.6 mg/dL; glucose - no interference up to 1000 mg/d; uric acid - no interference up to 15 mg/dL; and triglycerides - no interference up to 500 mg/dL.
HbA1c assay: Stanbio glycohemoglobin assay is the quantitative colorimetric determination of glycohemoglobin in whole blood.
Blood was drawn without overnight fasting, immediately before the morning Monday/Tuesday session of HD, to measure serum parameters in HD patients. In patients with diabetes with and without CKD, blood samples were collected in the morning.
The mean values of the three-monthly measurements of casual plasma glucose (PG) that were obtained during the two months before determination of serum GA and HbA1c were used in the analysis. Serum GA and HbA1c were measured once, concomitant with the determination of red blood cells, hemoglobin (Hb), hematocrit, total protein, albumin, blood urea nitrogen and creatinine. Blood was drawn from the dialyzer circuit in subjects with ESRD prior to the initiation of dialysis or administration of anticoagulants. Blood samples were then divided with 5 mL sent for HbA1c and 5 mL centrifuged with the serum frozen at 80°C for measurement of GA, 8 mL was analyzed for total protein, albumin, blood urea nitrogen and creatinine and 2 mL on EDTA for CBC.
| Statistical Analysis|| |
Analysis of data was carried out using an IBM computer and SPSS (Statistical Program for Social Science, version 12). Data expressed
as means and SD. One-way analysis of variance was used to compare more than two groups as regards the quantitative variable. Unpaired t-test was used to compare two groups as regards the quantitative variable. The Mann Whitney test was used instead of the unpaired t-test in non-parametric data SD >50% mean. Correlation co-efficients were calculated by simple regression analysis and were used to rank different variables against each other positively or inversely. Chi-square test, Χ² test, was performed to compare the various distributions. Multiple logistic regression analysis assessed the independent contribution of PG, HbA1c and GA to the occurrence of diabetes. Multiple regression analyses were performed to explore the association of PG, hemoglobin, albumin, erythropoietin dose and other variables with HbA1c and GA.
| Results|| |
Demographic and clinical characteristics of the study population
Demographic characteristics of the study population is given in [Table 1]. Blood samples were collected from 60 diabetic patients, ten diabetic patients without CKD (group I), 25 diabetic patients with CKD (group II), 25 non-diabetic patients with ESRD receiving regular HD treatments (group III) and 25 diabetic patients with ESRD under regular HD (group IV); 22 patients were receiving erythropoietin with 4000 IU as the mean average weekly dose. Group II had the highest body mass index (BMI) and the most frequent obesity and morbid obesity compared with the other sub-groups; however, group III had the lowest weight and BMI (P <0.001) [Table 2] and [Table 3]. GA and BMI showed negative correlations in the studied groups (P <0.05) [Table 4]. A significant difference between groups was noted as regards different laboratory variables, except for total protein and serum albumin that did not differ between groups (P <0.001) [Table 5]. The prevalence of diabetic complications, viz. diabetic peripheral neuropathy, diabetic nephropathy, erectile dysfunction, diabetic retinopathy, cardiovascular disease and facial palsy were 80%, 55%, 33.3%, 28.3%, 26.7% and 1.7%, respectively.
|Table 4: Correlation between HbA1c and GA with BMI in the studied groups.|
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Variation of casual PG levels during the study period of 2 months
PG from patients with diabetes (n = 60) at two months before, one month before and at the time of measurement of GA and HbA1c were 198.8 ± 44, 197.6 ± 47.6 and 197.9 ± 45.8 mg/dL, respectively. The correlation co-efficients for PG between two and one months before, between two and zero months before and between one and zero months before were r = 0.988 (P >0.05), r = 0.986 (P >0.05) and r = 0.991 (P >0.05), respectively [Table 6]. These data suggested that glycemic control of our patients with diabetes was stable during the study period.
|Table 6: Variation of casual PG levels during the study period of 2 months in diabetic patients.|
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Glycemic control on the basis of PG, HbA1c and GA values in diabetic patients
The mean PG, GA and HbA1c levels in the HD patients with diabetes were 192.9 ± 23 mg/dL, 278.8 ± 43 μmol/L and 5.9 ± 0.5%, respectively, all of which were significantly higher than the corresponding values of 134 ± 14 mg/dL, 166.9 ± 25 μmol/L and 5 ± 0.6% in the HD patients without diabetes. Meanwhile, the mean PG and GA, levels in diabetic patients without CKD were 146.5 ± 35 mg/dL and 190 ± 67 umol/L, respectively, all of which were lower than the corresponding values of 208 ± 44 mg/ dL and 304 ± 81 μmol/L in diabetic patients with CKD. HbA1c levels in diabetic patients without CKD were 6.8 ± 0.8% and in diabetics with CKD patients were 6.7 ± 0.6%, which were higher than that in diabetic HD patients. A highly significant difference between diabetic groups as regard PG, HbA1c and GA values (P <0.001) was noted [Table 7].
|Table 7: Comparison of the degrees of glycemic control on the basis of PG HbA1c and GA values in diabetic patients.|
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Distribution of the degrees of glycemic control on the basis of the HbA1c and GA values
HD patients with diabetes were divided into four categories according to HbA 1 c values: excellent (HbA 1 c ≤5.5%), good (5.5% < HbA 1 c ≤6.8%), fair (6.8% < HbA 1 c ≤7.6%), and poor (HbA 1 c >7.6%). There were 13 (52%), seven (28%), three (12%) and two (8%) in each group, respectively. Glycemic control was also assessed according to the GA values: excellent (GA ≤122 μmol/L), good (122 μmol/L < GA ≤203 μmol/L), fair (203 μmol/L < GA ≤285 μmol/L) and poor (GA >285 μmol/L). There were seven (28%), six (24%), four (16%) and eight (32%) in each group, respectively [Table 8]. The proportions of glycemic control that were based on the HbA 1 c values were significantly different from those that were based on the GA values.
|Table 8. Distribution of the degrees of glycemic control on the basis of the HbA1c, GA and PG values in group IV.|
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Correlation between PG and GA or HbA1c in HD patients with and without diabetes, and diabetic patients with and without CKD
There was a significant and positive correlation between PG and serum GA (r = 0.54, P <0.01) in HD patients with diabetes, (r = 0.48, P <0.01) in diabetic patients without CKD, (r = 0.58, P <0.01) in diabetic patients with CKD and (r = 0.97, P <0.01) in non-diabetic HD patients. Also, with HbA1c (r = 0.51, P <0.01) in HD patients with diabetes, (r = 0.61, P <0.01) in diabetic patients without CKD and (r = 0.56, P <0.01) in diabetic patients with CKD. As shown, the relationship between PG and GA was identical between the HD patients with diabetes and patients with diabetes without and with CKD, although HbA1c values in comparison with those of PG seemed to be significantly lower in HD patients with diabetes than in patients with diabetes and without CKD [Table 5] and [Figure 1].
Correlation between GA and serum albumin and between HbA1c and hemoglobin levels in HD patients with and without diabetes, and diabetic patients with and without CKD.
The serum albumin and HbA1c in HD patients with diabetes ranged from 3 to 4.7 g/dL and from 5.3 to 7.1%, respectively. A significant and negative correlation was found between GA and serum albumin levels (r = -0.21, P <0.05) in diabetic HD patients, (r = -0.33, P <0.05) in diabetic patients without CKD, (r = -0.30, P <0.05) in diabetic patients with CKD and (r = -0.34, P <0.05) in non-diabetic HD patients, although HbA1c did not correlate with serum albumin levels (r = 0.13, P >0.05) in HD patients with diabetes, (r = 0.09, P >0.05) in diabetic patients without CKD, (r = 0.03, P >0.05) in diabetic with CKD and (r = 0.07, P >0.05) in non-diabetic HD patients. In contrast, there was a significant and positive correlation between HbA1c and hemoglobin levels (r = 0.11, P <0.001) in diabetic HD, (r = 0.87, P <0.01) in diabetics without CKD, (r = 0.57, P <0.01) in diabetics with CKD and (r = 0.77, P <0.01) in non-diabetic HD patients, although GA did not correlate with the serum hemoglobin levels (r = 0.07, P >0.05) in diabetic HD, (r = 0.09, P >0.05) in diabetics without CKD, (r = 0.14, P >0.05) in diabetics with CKD and (r = 0.13, P >0.05) in non-diabetic HD patients [Table 9] and [Table 10].
|Table 9: Correlation between HbA1c, GA and other variables in group I and group II.|
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|Table 10: Correlation between HbA1c, GA and other variables in group III and group IV.|
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Correlation between serum GA and HbA1c levels in HD patients with and without diabetes, patients with diabetes with and without CKD
There was a significant and positive correlation between serum GA and HbA1c levels in HD patients with diabetes (r = 0.70, P <0.01), patients with diabetes and without CKD (r = 0.67, P <0.01), diabetic patients with CKD (r = 0.65, P <0.01) and non-diabetic HD patients (r = 0.63, P <0.01) [Table 9] and [Table 10] and [Figure 2].
Correlation of the weekly erythropoietin dose with HbA1c, GA and PG in HD patients with diabetes and non-diabetic HD patients
There was a significant and negative correlation between HbA1c and the weekly dose of erythropoietin (r = -0.19, P <0.01) in both HD patients with diabetes and non-diabetic HD patients (r = -0.23, P <0.01), although GA did not correlate well (r = 0.08, P >0.05) in HD patients with diabetes, (r = 0.11, P >0.05) in non-diabetic HD patients. The average PG and GA levels in the HD patients with diabetes and without erythropoietin (n = 13) were 192.3 ± 23 mg/dL and 277.4 ± 43 μmol/L and in non-diabetic HD patients without erythropoietin (n = 15) these levels were (133.3 ± 27 mg/dL and 165 ± 21 umol/L), which were not significantly different from the respective values of 196.4 ± 19 mg/dL and 282.6 ± 45 μmol/L in those who received erythropoietin (n = 12) in diabetic HD patients and (137.2 ± 25 mg/dL, 169.8 ± 27 umol/L) in those who received erythropoietin (n = 10) in non-diabetic HD patients. However, the HbA1c values were significantly higher in those who were not treated with erythropoietin compared with those who were treated with erythropoietin (6.19 ± 1.49 versus 5.72 ± 1.3) (P <0.05) in the HD patients with diabetes and (5.01 ± 1.13 versus 4.61 ± 0.8) (P <0.05) in non-diabetic HD patients [Table 11] and [Table 12].
|Table 11: Correlation of the weekly erythropoietin dose with HbA1c, GA and PG in group III.|
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|Table 12: Correlation of the weekly erythropoietin dose with HbA1c, GA and PG group IV.|
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PG, GA and HbA1c levels as monitoring tests for diabetes in HD patients
At the cut-off value for PG, GA and HbA1c (160 mg/dL, 231 umol/L, 6.5%), the sensitivity was (83%, 80%, 40%) and specificity was (96%, 70%, 54%). Therefore, GA and PG were better than HbA1c as the follow-up method for diabetes in HD patients [Table 11] and [Figure 3].
Multiple regression analysis of factors for HbA1c and GA in HD patients with DM
In diabetic HD, multiple regression analysis of various clinical variables was used to evaluate their independent association with HbA1c and GA values in HD patients with diabetes. [Table 13] and [Figure 4]. Among various clinical variables that included average PG, serum albumin, GFR, erythropoietin treatment and hemoglobin, only average PG and hemoglobin were independent factors associated with HbA1c (P <0.01). Evaluation of the independent factors that were associated with GA showed that the average PG (P <0.01), serum albumin and BMI (P <0.05) exhibited a significant and independent association with GA [Table 14]. The same was true for all diabetic patients [Table 15].
|Figure 4: Receiver operator characteristics (ROC) curve between DM and non-DM as regards HBA1C and GA.|
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|Table 14: Multiple regression analysis of factors for HbA1c and GA in diabetic HD patients.|
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|Table 15: Multiple regression analysis of factors for HbA1c and GA in diabetic patients.|
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Logistic regression analysis of PG, GA and HbA1c with diabetes in HD patients
The independent contribution of PG, GA and HbA1c to the probability of diabetes in HD patients was assessed after adjustment for serum albumin and Hb by multiple logistic regression analysis. PG (per 10 mg/dL; odds ratio [OR] 1.5; P <0.01), GA (per 10 umol/L; OR 1.6; P <0.01) and HbA1c (per 1.0%; OR 2.1; P <0.01) were independent risk factors associated with diabetes in HD patients [Table 15].
| Discussion|| |
Accurate determination of glycemic control is of paramount importance in the diabetic population, as improved glycemic control reduces micro- and macro-vascular complications in patients with type-1 and type-2 diabetes mellitus.  HbA1c has been a cornerstone in the evaluation of dialyzed and non-dialyzed diabetic patients. This measurement relies on a relatively stable RBC survival, a characteristic typical of the general population, but not patients on HD. During HD, the uremic environment, blood loss during treatments and frequent phlebotomy contribute to decreased RBC life-span. Shortened RBC survival and red cell transfusions are likely to lower the HbA1c, potentially making it unreliable in assessing glycemic control. In this study, the measurement of GA was shown to provide a more relevant method to assess glycemic control in HD patients with diabetes, also demonstrating that HbA1c, relative to GA, significantly underestimates glycemic control in diabetic dialysis patients. In those with ESRD, lower HbA1c values were also associated with lower hemoglobin concentration and higher doses of erythropoietin. Although PG was measured without overnight fasting, a previous report showed that non-fasting, rather than fasting, PG was a better marker of glycemic control in type-2 diabetes.  Because the mean values of monthly determined PG essentially were the same throughout the study period, it was suggested that glycemic control had been stable during the two months before the determination of GA and HbA1c and that a single determination just before the Monday/Tuesday HD session might be representative of glycemic control in HD patients with diabetes. Although HbA1c and GA reflect glycemic control during the preceding four to six weeks and one to two weeks,  the stable glycemic control during the preceding months can negate the different impact of acute changes of glycemic control between HbA1c and GA in this study. Supportive of this notion is that the correlation coefficient between PG and HbA1c was similar with that between PG and GA [Table 16]. The correlation coefficients of PG at 2, 1 or 0 months before with HbA1c were very similar to those with GA. The degree with which serum GA correlated with PG was identical between the HD patients with and without diabetes and patients with diabetes with and without CKD [Table 10]. The significantly lower value of HbA1c relative to PG and GA in HD patients with diabetes compared with the patients with diabetes with and without CKD might suggest that the measurement of HbA1c would result in the underestimation of glycemic control in HD patients with diabetes [Figure 5].
|Table 16: Logistic regression analysis of PG, GA and HbA1c with diabetes.|
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As shown in [Table 8], in HD patients with diabetes, it was shown that a "good" category of GA of <203 μmol/L and HbA1c of <6.8% results in a PG of <140 and 222 mg/dL, respectively. Therefore, the GA value of <203 μmol/L was reasonably categorized into a good category, as reflected by the PG value of <140 mg/dL. However, categorization of the HbA1c value of <6.8% into a good category definitely was an underestimation, as reflected by PG values as high as 222 mg/dL. The mechanism for the significantly lower HbA1c value in those patients was explained by anemia and/or erythropoietin injection, as reflected by a significant positive correlation of HbA1c with hemoglobin and the negative correlation with weekly dose of erythropoietin [Table 9] and [Table 12].
Multiple regression analysis demonstrated that hemoglobin reduction was an independent factor that was associated significantly with the HbA1c values [Table 14]. In fact, the HbA1c values were significantly lower in HD patients who had diabetes and were treated with erythropoietin compared with those without erythropoietin, although PG and GA did not differ significantly between the two groups of patients [Table 12]. The differences of the mean HbA1c values between the HD patients with diabetes and HD patients without diabetes were smaller than those of PG and GA [Table 5], which is explained partly by a significantly greater erythropoietin dose in the HD patients with diabetes [Table 11] and [Table 12]. Importantly, although serum albumin correlated negatively with GA [Table 9], [Table 10] and [Table 14], it failed to be a significant factor between different groups of patients as the serum albumin levels were above 3.5 gm/dL [Table 5]. The factors that were associated independently with the GA value was the average PG, BMI and serum albumin, while those associated with HbA1c were the average PG and hemoglobin levels [Table 14]. In order to clarify the reasons for the negative association of BMI with GA, one could conclude that the obesity-related inflammation is connected to this negative association. Inflammation reduces the rate of albumin synthesis and increases its catabolic rate.  Also, hyperinsulinemia in obese diabetic patient leads to increased albumin turnover. Tessari et al  proved that albumin fractional synthesis rates and absolute synthesis rates increased by ~25% after hyperinsulinemia.
Multiple logistic regression analysis showed that PG, GA and HbA1c were independent risk factors associated with the prevalence of diabetes after adjustment for serum albumin and increase of Hb A 1% is indicative of a 2.1-fold increase to have diabetes, in contrast to a 1.6-fold increase per 10 μmol/L increase of GA value and 1.5-fold increase per 10 mg/dL of PG value [Table 15]. It was suggested that an increase of GA might be more highly indicative of diabetes than that of HbA1c.
The non-enzymatic glycation of various proteins is increased in patients with diabetes as a result of sustained higher PG.  The rate of production also depends on the half-life of each protein.  HbA1c provides an integrated measure of PG during the previous two to three months as a result of the long life span of erythrocytes (120 d),  whereas GA has been hypothesized to be a glycemic indicator during the immediately previous two weeks.  Although a rapid change in glycemic control may reflect a greater change of GA than HbA1c, this study examined the significance of GA compared with HbA1c under stationary state of diabetic control, without any change of antidiabetic drugs during the study period, and compared GA and HbA1c values in patients with diabetes and with and without CKD and non-diabetic HD patients. A previous report  showed that after erythropoietin treatment, the HbA1c levels decreased with the increase of hematocrit in 15 HD patients without diabetes, although PG did not change. Conversely, after stopping erythropoietin treatment, the HbA1c levels increased. Because erythropoietin accelerates the production of new erythrocytes, the proportion of young erythrocytes in peripheral blood must increase after erythropoietin administration. HbA1c is the product of the chemical condensation of hemoglobin and glucose, and the glycation rate of just-produced young erythrocytes is reported to be lower than that of old cells.  Therefore, it seems that the decrease of HbA1c levels relative to PG or GA in HD patients who have diabetes and are treated with erythropoietin might be due to the increasing proportion of young erythrocytes over old erythrocytes in peripheral blood of those patients.  Anemia that results from shorter life span of erythrocytes theoretically suppresses HbA1c values. Withdrawal of erythropoietin administration increases HbA1c values, although it suppresses Hb levels.  Therefore, a relationship between HbA1c and Hb could be controversial. These data may suggest that HbA1c is not an ideal index for glycemic control in HD patients who have diabetes and receive erythropoietin. Because approximately 70% of dialysis patients undergo erythropoietin treatment, HbA1c might be an unsuitable marker to reflect glycemic control in HD patients with diabetes because of the false reduction of HbA1c values as a result of the increasing proportion of young erythrocytes over old erythrocytes in peripheral blood of those who receive erythropoietin; however, this was not due to improvement of glycemic control, leading to the underestimation of integrated hyperglycemia when assessed by HbA1c value. GA acquires biologic properties that are linked to the pathogenesis of diabetic vascular complications,  suggesting that GA not only is significant as an indicator of hyperglycemia  but also contributes directly to vascular injury. As such, GA is better than HbA1c in predicting the development of vascular complications in HD patients with diabetes.
However, a limitation of the GA assay also exists. Albumin turnover should change in patients who are maintained on peritoneal dialysis and in patients who have CRF with massive proteinuria, in whom GA values theoretically should be reduced as a result of shorter exposure to plasma albumin. Also, in the present study, we found that HbA1c of 6.5% or greater provides sensitivity and specificity as a screening test for diabetes, at 40% and 54%, respectively, and positive predictive value test of 56% and negative predictive value test of 47% [Table 13]. Buell et al  recently completed a similar analysis based on the 1999-2004 NHANES data. The diagnosis of diabetes was considered established if FPG was 126 mg/dL or greater. Using an ROC analysis, they found that HbA1c of 5.8% or greater is the point that yielded the highest sum of sensitivity (86%) and specificity (92%). They concluded that HbA1c of 5.8% would be an appropriate cut-point above which to proceed to further evaluation. Also, we found that GA of >231 μmol/L or greater provides reasonable sensitivity and specificity as a screening test for diabetes, at 80% and 70%, respectively, and positive predictive value test of 75% and negative predictive value test of 85% [Table 13]. Cefalu et al  found that GA with a cut-off of 250 μmol/L for the second-generation assay, has the sensitivity to detect diabetes was 81%, specificity was 87% and positive predictive value was 43%.
Potential weaknesses in this study include use of random (not necessarily fasting) recent blood glucose measures in participants on dialysis and those with and without nephropathy, and the small number of patients. Fasting blood glucose measurements cannot be readily obtained as patients on the afternoon hemodialysis shift are unable to report fasting and those on the morning shift are typically away from home for periods exceeding 6 h and are not permitted to eat or drink during HD.
In conclusion, GA provides a significantly better measure to estimate glycemic control in HD patients with diabetes and the assessment of glycemic control by HbA1c in those patients might lead to underestimation due to EPO therapy and anemia.
| References|| |
|1.||The Diabetes Control and Complications Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993;329:977-86. |
|2.||UK Prospective Diabetes Study (UKPDS) Group: Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type-2 diabetes (UKPDS 33). Lancet 1998;352:837-53. |
|3.||Cao JJ, Hudson M, Jankowski M, Whitehouse F, Weaver WD. Relation of chronic and acute glycemic control on mortality in acute myocardial infarction with diabetes mellitus Am J Cardiol 2005;96:183-6. |
|4.||Calisti L, Tognetti S. Measure of glycosylated hemoglobin. Acta Biomed 2005;76(Suppl 3):59-62. |
|5.||Goldstein DE, Little RR, Lorenz RA, et al. Tests of glycemia in diabetes. Diabetes Care 2004;27:1761-73. |
|6.||Joy MS, Cefalu WT, Hogan SL, et al. Long-term glycemic control measurements in diabetic patients receiving hemodialysis. Am J Kidney Dis 2002;39:297-307. |
|7.||Nakao T, Matsumoto H, Okada T, et al. Influence of erythropoietin treatment on hemoglobin A1c levels in patients with chronic renal failure on hemodialysis. Intern Med 1998;38: 826-30. |
|8.||Tzamaloukas AH, Hsi KC, Quintana BJ, et al. Glycosylated hemoglobin measured by affinity chromatography in diabetic and nondiabetic patients on long-term dialysis therapy. West J Med 1989;150:415-9. |
|9.||Kosecki SM, Rodgers PT, Adams MB. Glycemic monitoring in diabetics with sickle cell plus beta-thalassemia hemoglobinopathy. Ann Pharmacother 2005;39:1557-60. |
|10.||Inaba M, Okuno S, Kumeda Y, et al. Glycated albumin is a better glycemic indicator than glycated hemoglobin values in hemodialysis patients with diabetes: effect of anemia and erythropoietin injection. J Am Soc Nephrol 2007;18:896-903. |
|11.||Adler AI, Stevens RJ, Manley SE, et al. Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64) Kidney Int 2003;63:225-32. |
|12.||Avignon A, Radauceanu A, Monnier L. Non-fasting plasma glucose is a better marker of diabetic control than fasting plasma glucose in type 2 diabetes. Diabetes Care 1997;20:1822-6. |
|13.||Koga M, Otsuki M, Matsumoto S, et al. Negative association of obesity and its related chronic inflammation with serum glycated albumin but not glycated hemoglobin levels. Clinica Chimica Acta. 2007;378:48-52. |
|14.||Tessari P, Kiwanuka E, Millioni R, et al. Albumin and fibrinogen synthesis and insulin effect in type 2 diabetic patients with normoalbuminuria. Diabetes Care 2006;29:323-8. |
|15.||Cohen MP. Nonenzymatic glycation: A central mechanism in diabetic microvasculopathy. J Diabet Comp1988;2:214-7. |
|16.||Schleicher ED, Olgemoller B, Wiedenmann E, et al. Specific glycation of albumin depends on its half-life. Clin Chem 1993;39:625-8. |
|17.||Bunn HF, Gabbay KH, Gallop PM. The glycosylation of hemoglobin: Relevance to diabetes mellitus. Science 1978;20:21-7. |
|18.||Fitzgibbons JF, Koler RD, Jones RT. Red cell age-related changes of hemoglobins A1a_b and A1c in normal and diabetic subjects. J Clin Invest 1976;58:820-4. |
|19.||Cohen MP, Ziyadeh FN, Chen S. Amadori-modified glycated serum proteins and acelerated atherosclerosis in diabetes: Pathogenic and therapeutic implications. J Lab Clin Med 2006;147:211-9. |
|20.||Amore A, Cirna P, Conti G, et al. Amadori-configurated albumin indices nitric oxide-dependent apoptosis of endothelial cells: A possible mechanism of diabetic vasculopathy. Nephrol Dial Transplant 2004;19:53-60. |
|21.||Buell C, Kermah D, Davidson MB. Utility of A1c for diabetes screening in the 1999-2004 NHANES population. Diabetes Care. 2007;30: 2233-5. |
|22.||Cefalu WT, Ettinger WH, Rushing JT. Serum glycated albumin as a screening test for diabetes in the elderly: a pilot study. Journal of the American Geriatrics Society. 1993;41: 1090-4. |
Nephrology Division, Faculty of Medicine, University of Ain-Shams, Cairo
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11], [Table 12], [Table 13], [Table 14], [Table 15], [Table 16]